Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-859-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-859-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
31 Jan 2022
Model description paper |
| 31 Jan 2022
| 31 Jan 2022
MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models
Robert Schweppe
Helmholtz-Centre for Environmental Research – UFZ, Permoserstraße 15, 04315 Leipzig, Germany
Institute of Earth and Environmental Science, University of Potsdam, Karl-Liebknecht-Str. 24–25,
14476 Potsdam-Golm, Germany
Helmholtz-Centre for Environmental Research – UFZ, Permoserstraße 15, 04315 Leipzig, Germany
Sebastian Müller
Helmholtz-Centre for Environmental Research – UFZ, Permoserstraße 15, 04315 Leipzig, Germany
Matthias Kelbling
Helmholtz-Centre for Environmental Research – UFZ, Permoserstraße 15, 04315 Leipzig, Germany
Rohini Kumar
Helmholtz-Centre for Environmental Research – UFZ, Permoserstraße 15, 04315 Leipzig, Germany
Sabine Attinger
Helmholtz-Centre for Environmental Research – UFZ, Permoserstraße 15, 04315 Leipzig, Germany
Institute of Earth and Environmental Science, University of Potsdam, Karl-Liebknecht-Str. 24–25,
14476 Potsdam-Golm, Germany
Luis Samaniego
Helmholtz-Centre for Environmental Research – UFZ, Permoserstraße 15, 04315 Leipzig, Germany
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EGUsphere, https://doi.org/10.5194/egusphere-2025-28, https://doi.org/10.5194/egusphere-2025-28, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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To provide the most accurate climate adaptation information, climate models are being run with finer grid resolution, resulting in larger data output. This paper presents intelligent data reduction algorithms that act on streamed data, a novel way of processing climate data as soon as it is produced. Using these algorithms to calculate statistics, we show that the accuracy provided is well within acceptable bounds while still providing memory savings that bypass unfeasible storage requirements.
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
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This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Daniel Altdorff, Maik Heistermann, Till Francke, Martin Schrön, Sabine Attinger, Albrecht Bauriegel, Frank Beyrich, Peter Biró, Peter Dietrich, Rebekka Eichstädt, Peter Martin Grosse, Arvid Markert, Jakob Terschlüsen, Ariane Walz, Steffen Zacharias, and Sascha E. Oswald
EGUsphere, https://doi.org/10.5194/egusphere-2024-3848, https://doi.org/10.5194/egusphere-2024-3848, 2024
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The German federal state of Brandenburg is particularly prone to soil moisture droughts. To support the management of related risks, we introduce a novel soil moisture and drought monitoring network based on cosmic-ray neutron sensing technology. This initiative is driven by a collaboration of research institutions and federal state agencies, and it is the first of its kind in Germany to have started operation. In this brief communication, we outline the network design and share first results.
Sven Armin Westermann, Anke Hildebrandt, Souhail Bousetta, and Stephan Thober
Biogeosciences, 21, 5277–5303, https://doi.org/10.5194/bg-21-5277-2024, https://doi.org/10.5194/bg-21-5277-2024, 2024
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Plants at the land surface mediate between soil and the atmosphere regarding water and carbon transport. Since plant growth is a dynamic process, models need to consider these dynamics. Two models that predict water and carbon fluxes by considering plant temporal evolution were tested against observational data. Currently, dynamizing plants in these models did not enhance their representativeness, which is caused by a mismatch between implemented physical relations and observable connections.
Vishal Thakur, Yannis Markonis, Rohini Kumar, Johanna Ruth Thomson, Mijael Rodrigo Vargas Godoy, Martin Hanel, and Oldrich Rakovec
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-341, https://doi.org/10.5194/hess-2024-341, 2024
Preprint under review for HESS
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Understanding the changes in water movement in earth is crucial for everyone. To quantify this water movement there are several techniques. We examined how different methods of estimating evaporation impact predictions of various types of water movement across Europe. We found that, while these methods generally agree on whether changes are increasing or decreasing, they differ in magnitude. This means selecting the right evaporation method is crucial for accurate predictions of water movement.
Fanny J. Sarrazin, Sabine Attinger, and Rohini Kumar
Earth Syst. Sci. Data, 16, 4673–4708, https://doi.org/10.5194/essd-16-4673-2024, https://doi.org/10.5194/essd-16-4673-2024, 2024
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Nitrogen (N) and phosphorus (P) contamination of water bodies is a long-term issue due to the long history of N and P inputs to the environment and their persistence. Here, we introduce a long-term and high-resolution dataset of N and P inputs from wastewater (point sources) for Germany, combining data from different sources and conceptual understanding. We also account for uncertainties in modelling choices, thus facilitating robust long-term and large-scale water quality studies.
Pia Ebeling, Andreas Musolff, Rohini Kumar, Andreas Hartmann, and Jan H. Fleckenstein
EGUsphere, https://doi.org/10.5194/egusphere-2024-2761, https://doi.org/10.5194/egusphere-2024-2761, 2024
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Groundwater is a crucial resource at risk by droughts. To understand drought effects on groundwater in Germany, we grouped 6626 wells into six regional and two nationwide head patterns. Weather explained half of the head variations with varied response times. Shallow groundwater responds fast and is more vulnerable to short droughts (few months). Dampened deep heads buffer short droughts but suffer from long droughts and recoveries. Two nationwide trend patterns were linked to human water use.
Mansi Nagpal, Jasmin Heilemann, Luis Samaniego, Bernd Klauer, Erik Gawel, and Christian Klassert
EGUsphere, https://doi.org/10.5194/egusphere-2024-2585, https://doi.org/10.5194/egusphere-2024-2585, 2024
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This study measures the direct effects of droughts in association with other extreme weather events on agriculture in Germany at district level. Using a statistical yield model, we quantify the direct impact of extremes on crop yields and farm revenues. Extreme events during drought cause an average annual damage of €781 million, accounting for 45 % of reported revenue losses. The insights can help develop better strategies for managing and mitigating the effects of future climate extremes.
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-144, https://doi.org/10.5194/gmd-2024-144, 2024
Preprint under review for GMD
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This study presents FINAM ("FINAM Is Not A Model"), a new coupling framework written in Python to dynamically link independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
Masooma Batool, Fanny J. Sarrazin, and Rohini Kumar
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-294, https://doi.org/10.5194/essd-2024-294, 2024
Revised manuscript accepted for ESSD
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Our paper presents a reconstruction and analysis of the gridded P surplus in European landscapes from 1850 to 2019 at a 5 arcmin resolution. By utilizing 48 different estimates, we account for uncertainties in major components of the P surplus. Our findings highlight substantial historical changes, with the total P surplus in EU-27 tripling over 170 years. Our dataset enables flexible aggregation at various spatial scales, providing critical insights for land and water management strategies.
Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-97, https://doi.org/10.5194/nhess-2024-97, 2024
Revised manuscript under review for NHESS
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The July 2021 flood in Central Europe was one of the deadliest floods in Europe in the past decades and the most expensive flood in Germany. In this paper we show that the hydrological impact of this event in the Ahr valley could have been even worse if the rainfall footprint trajectory was only slightly different. The presented methodology of spatial counterfactuals generates plausible unprecedented events and helps better prepare for future extreme floods.
Jan Řehoř, Rudolf Brázdil, Oldřich Rakovec, Martin Hanel, Milan Fischer, Rohini Kumar, Jan Balek, Markéta Poděbradská, Vojtěch Moravec, Luis Samaniego, and Miroslav Trnka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1434, https://doi.org/10.5194/egusphere-2024-1434, 2024
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We present a robust method for identification and classification of global land drought events (GLDEs) based on soil moisture. Two models were used to calculate soil moisture and delimit soil drought over global land from 1980–2022, which was clustered into 775/630 GLDEs. Using four spatiotemporal and three motion-related characteristics, we categorized GLDEs into seven severity and seven dynamic categories. The frequency of GLDEs has generally increased in recent decades.
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
EGUsphere, https://doi.org/10.5194/egusphere-2024-1303, https://doi.org/10.5194/egusphere-2024-1303, 2024
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Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of the reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers and data users.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Kingsley Nnaemeka Ogbu, Oldrich Rakovec, Luis Samaniego, Gloria Chinwendu Okafor, Bernhard Tischbein, and Hadush Meresa
Proc. IAHS, 385, 211–218, https://doi.org/10.5194/piahs-385-211-2024, https://doi.org/10.5194/piahs-385-211-2024, 2024
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In this study, the MPR-mHM technique was applied in four data-scarce basins in Nigeria. Remotely sensed rainfall datasets were used as model forcings to evaluate the mHM capability in reproducing observed stream discharge under single and multivariable model calibration frameworks. Overall, model calibration performances displayed satisfactory outputs as evident in the Kling-Gupta Efficiency (KGE) scores across most basins.
Falk Heße, Sebastian Müller, and Sabine Attinger
Hydrol. Earth Syst. Sci., 28, 357–374, https://doi.org/10.5194/hess-28-357-2024, https://doi.org/10.5194/hess-28-357-2024, 2024
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In this study, we have presented two different advances for the field of subsurface geostatistics. First, we present data of variogram functions from a variety of different locations around the world. Second, we present a series of geostatistical analyses aimed at examining some of the statistical properties of such variogram functions and their relationship to a number of widely used variogram model functions.
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci., 27, 2989–3004, https://doi.org/10.5194/hess-27-2989-2023, https://doi.org/10.5194/hess-27-2989-2023, 2023
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We analyzed the uncertainty of the water transit time distribution (TTD) arising from model input (interpolated tracer data) and structure (StorAge Selection, SAS, functions). We found that uncertainty was mainly associated with temporal interpolation, choice of SAS function, nonspatial interpolation, and low-flow conditions. It is important to characterize the specific uncertainty sources and their combined effects on TTD, as this has relevant implications for both water quantity and quality.
Carolin Winter, Tam V. Nguyen, Andreas Musolff, Stefanie R. Lutz, Michael Rode, Rohini Kumar, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 27, 303–318, https://doi.org/10.5194/hess-27-303-2023, https://doi.org/10.5194/hess-27-303-2023, 2023
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The increasing frequency of severe and prolonged droughts threatens our freshwater resources. While we understand drought impacts on water quantity, its effects on water quality remain largely unknown. Here, we studied the impact of the unprecedented 2018–2019 drought in Central Europe on nitrate export in a heterogeneous mesoscale catchment in Germany. We show that severe drought can reduce a catchment's capacity to retain nitrogen, intensifying the internal pollution and export of nitrate.
Nikolai Knapp, Sabine Attinger, and Andreas Huth
Biogeosciences, 19, 4929–4944, https://doi.org/10.5194/bg-19-4929-2022, https://doi.org/10.5194/bg-19-4929-2022, 2022
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The biomass of forests is determined by forest growth and mortality. These quantities can be estimated with different methods such as inventories, remote sensing and modeling. These methods are usually being applied at different spatial scales. The scales influence the obtained frequency distributions of biomass, growth and mortality. This study suggests how to transfer between scales, when using forest models of different complexity for a tropical forest.
Friedrich Boeing, Oldrich Rakovec, Rohini Kumar, Luis Samaniego, Martin Schrön, Anke Hildebrandt, Corinna Rebmann, Stephan Thober, Sebastian Müller, Steffen Zacharias, Heye Bogena, Katrin Schneider, Ralf Kiese, Sabine Attinger, and Andreas Marx
Hydrol. Earth Syst. Sci., 26, 5137–5161, https://doi.org/10.5194/hess-26-5137-2022, https://doi.org/10.5194/hess-26-5137-2022, 2022
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In this paper, we deliver an evaluation of the second generation operational German drought monitor (https://www.ufz.de/duerremonitor) with a state-of-the-art compilation of observed soil moisture data from 40 locations and four different measurement methods in Germany. We show that the expressed stakeholder needs for higher resolution drought information at the one-kilometer scale can be met and that the agreement of simulated and observed soil moisture dynamics can be moderately improved.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
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Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Sadaf Nasreen, Markéta Součková, Mijael Rodrigo Vargas Godoy, Ujjwal Singh, Yannis Markonis, Rohini Kumar, Oldrich Rakovec, and Martin Hanel
Earth Syst. Sci. Data, 14, 4035–4056, https://doi.org/10.5194/essd-14-4035-2022, https://doi.org/10.5194/essd-14-4035-2022, 2022
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This article presents a 500-year reconstructed annual runoff dataset for several European catchments. Several data-driven and hydrological models were used to derive the runoff series using reconstructed precipitation and temperature and a set of proxy data. The simulated runoff was validated using independent observed runoff data and documentary evidence. The validation revealed a good fit between the observed and reconstructed series for 14 catchments, which are available for further analysis.
Pia Ebeling, Rohini Kumar, Stefanie R. Lutz, Tam Nguyen, Fanny Sarrazin, Michael Weber, Olaf Büttner, Sabine Attinger, and Andreas Musolff
Earth Syst. Sci. Data, 14, 3715–3741, https://doi.org/10.5194/essd-14-3715-2022, https://doi.org/10.5194/essd-14-3715-2022, 2022
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Environmental data are critical for understanding and managing ecosystems, including the mitigation of water quality degradation. To increase data availability, we present the first large-sample water quality data set (QUADICA) of riverine macronutrient concentrations combined with water quantity, meteorological, and nutrient forcing data as well as catchment attributes. QUADICA covers 1386 German catchments to facilitate large-sample data-driven and modeling water quality assessments.
Sebastian Müller, Lennart Schüler, Alraune Zech, and Falk Heße
Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022, https://doi.org/10.5194/gmd-15-3161-2022, 2022
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The GSTools package provides a Python-based platform for geoostatistical applications. Salient features of GSTools are its random field generation, its kriging capabilities and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige, ogs5py or scikit-gstat, and provides interfaces to meshio and PyVista. Four presented workflows showcase the abilities of GSTools.
Michael Peichl, Stephan Thober, Luis Samaniego, Bernd Hansjürgens, and Andreas Marx
Hydrol. Earth Syst. Sci., 25, 6523–6545, https://doi.org/10.5194/hess-25-6523-2021, https://doi.org/10.5194/hess-25-6523-2021, 2021
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Using a statistical model that can also take complex systems into account, the most important factors affecting wheat yield in Germany are determined. Different spatial damage potentials are taken into account. In many parts of Germany, yield losses are caused by too much soil water in spring. Negative heat effects as well as damaging soil drought are identified especially for north-eastern Germany. The model is able to explain years with exceptionally high yields (2014) and losses (2003, 2018).
Joni Dehaspe, Fanny Sarrazin, Rohini Kumar, Jan H. Fleckenstein, and Andreas Musolff
Hydrol. Earth Syst. Sci., 25, 6437–6463, https://doi.org/10.5194/hess-25-6437-2021, https://doi.org/10.5194/hess-25-6437-2021, 2021
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Increased nitrate concentrations in surface waters can compromise river ecosystem health. As riverine nitrate uptake is hard to measure, we explore how low-frequency nitrate concentration and discharge observations (that are widely available) can help to identify (in)efficient uptake in river networks. We find that channel geometry and water velocity rather than the biological uptake capacity dominate the nitrate-discharge pattern at the outlet. The former can be used to predict uptake.
Bernd Schalge, Gabriele Baroni, Barbara Haese, Daniel Erdal, Gernot Geppert, Pablo Saavedra, Vincent Haefliger, Harry Vereecken, Sabine Attinger, Harald Kunstmann, Olaf A. Cirpka, Felix Ament, Stefan Kollet, Insa Neuweiler, Harrie-Jan Hendricks Franssen, and Clemens Simmer
Earth Syst. Sci. Data, 13, 4437–4464, https://doi.org/10.5194/essd-13-4437-2021, https://doi.org/10.5194/essd-13-4437-2021, 2021
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In this study, a 9-year simulation of complete model output of a coupled atmosphere–land-surface–subsurface model on the catchment scale is discussed. We used the Neckar catchment in SW Germany as the basis of this simulation. Since the dataset includes the full model output, it is not only possible to investigate model behavior and interactions between the component models but also use it as a virtual truth for comparison of, for example, data assimilation experiments.
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, https://doi.org/10.5194/gmd-14-3843-2021, 2021
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We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
Alraune Zech, Peter Dietrich, Sabine Attinger, and Georg Teutsch
Hydrol. Earth Syst. Sci., 25, 1–15, https://doi.org/10.5194/hess-25-1-2021, https://doi.org/10.5194/hess-25-1-2021, 2021
Miao Jing, Rohini Kumar, Falk Heße, Stephan Thober, Oldrich Rakovec, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 24, 1511–1526, https://doi.org/10.5194/hess-24-1511-2020, https://doi.org/10.5194/hess-24-1511-2020, 2020
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This study investigates the response of regional groundwater system to the climate change under three global warming levels (1.5, 2, and 3 °C) in a central German basin. A comprehensive uncertainty analysis is also presented. This study indicates that the variability of responses increases with the amount of global warming, which might affect the cost of managing the groundwater system.
Sophie Ehrhardt, Rohini Kumar, Jan H. Fleckenstein, Sabine Attinger, and Andreas Musolff
Hydrol. Earth Syst. Sci., 23, 3503–3524, https://doi.org/10.5194/hess-23-3503-2019, https://doi.org/10.5194/hess-23-3503-2019, 2019
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This study shows quantitative and temporal offsets between nitrogen input and riverine output, using time series of three nested catchments in central Germany. The riverine concentrations show lagged reactions to the input, but at the same time exhibit strong inter-annual changes in the relationship between riverine discharge and concentration. The study found a strong retention of nitrogen that is dominantly assigned to a hydrological N legacy, which will affect future stream concentrations.
Stephan Thober, Matthias Cuntz, Matthias Kelbling, Rohini Kumar, Juliane Mai, and Luis Samaniego
Geosci. Model Dev., 12, 2501–2521, https://doi.org/10.5194/gmd-12-2501-2019, https://doi.org/10.5194/gmd-12-2501-2019, 2019
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We present a model that aggregates simulated runoff along a river
(i.e. a routing model). The unique feature of the model is that it
can be run at multiple resolutions without any modifications to the
input data. The model internally (dis-)aggregates all input data to
the resolution given by the user. The model performance does not
depend on the chosen resolution. This allows efficient model
calibration at coarse resolution and subsequent model application at
fine resolution.
Naoki Mizukami, Oldrich Rakovec, Andrew J. Newman, Martyn P. Clark, Andrew W. Wood, Hoshin V. Gupta, and Rohini Kumar
Hydrol. Earth Syst. Sci., 23, 2601–2614, https://doi.org/10.5194/hess-23-2601-2019, https://doi.org/10.5194/hess-23-2601-2019, 2019
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We find that Nash–Sutcliffe (NSE)-based model calibrations result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. The use of Kling–Gupta efficiency (KGE) results in annual peak flow estimates that are better than from NSE, with only a slight degradation in performance with respect to other related metrics.
Miao Jing, Falk Heße, Rohini Kumar, Olaf Kolditz, Thomas Kalbacher, and Sabine Attinger
Hydrol. Earth Syst. Sci., 23, 171–190, https://doi.org/10.5194/hess-23-171-2019, https://doi.org/10.5194/hess-23-171-2019, 2019
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We evaluated the uncertainty propagation from the inputs (forcings) and parameters to the predictions of groundwater travel time distributions (TTDs) using a fully distributed numerical model (mHM-OGS) and the StorAge Selection (SAS) function. Through detailed numerical and analytical investigations, we emphasize the key role of recharge estimation in the reliable predictions of TTDs and the good interpretability of the SAS function.
Miao Jing, Falk Heße, Rohini Kumar, Wenqing Wang, Thomas Fischer, Marc Walther, Matthias Zink, Alraune Zech, Luis Samaniego, Olaf Kolditz, and Sabine Attinger
Geosci. Model Dev., 11, 1989–2007, https://doi.org/10.5194/gmd-11-1989-2018, https://doi.org/10.5194/gmd-11-1989-2018, 2018
Vimal Mishra, Reepal Shah, Syed Azhar, Harsh Shah, Parth Modi, and Rohini Kumar
Hydrol. Earth Syst. Sci., 22, 2269–2284, https://doi.org/10.5194/hess-22-2269-2018, https://doi.org/10.5194/hess-22-2269-2018, 2018
Michael Peichl, Stephan Thober, Volker Meyer, and Luis Samaniego
Nat. Hazards Earth Syst. Sci., 18, 889–906, https://doi.org/10.5194/nhess-18-889-2018, https://doi.org/10.5194/nhess-18-889-2018, 2018
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Crop yields are routinely derived from meteorological variables, especially temperature. However, the primary water source for plant growth (soil moisture) is neglected. In this study, the predictability of maize yield is investigated using soil moisture or meteorological variables in Germany. The effects of soil moisture dominate those of temperature and are time-dependent. For example, comparatively moist soil conditions in June reduce crop yields, while in August they increase yields.
Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, and Simon Stisen
Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, https://doi.org/10.5194/hess-22-1299-2018, 2018
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Satellite data offer great opportunities to improve spatial model predictions by means of spatially oriented model evaluations. In this study, satellite images are used to observe spatial patterns of evapotranspiration at the land surface. These spatial patterns are utilized in combination with streamflow observations in a model calibration framework including a novel spatial performance metric tailored to target the spatial pattern performance of a catchment-scale hydrological model.
Andreas Marx, Rohini Kumar, Stephan Thober, Oldrich Rakovec, Niko Wanders, Matthias Zink, Eric F. Wood, Ming Pan, Justin Sheffield, and Luis Samaniego
Hydrol. Earth Syst. Sci., 22, 1017–1032, https://doi.org/10.5194/hess-22-1017-2018, https://doi.org/10.5194/hess-22-1017-2018, 2018
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Hydrological low flows are affected under different levels of future global warming (i.e. 1.5, 2, and 3 K). The multi-model ensemble results show that the change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean, while they increase in the Alpine and Northern regions. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. Adaptation should make use of change and uncertainty information.
Simon Höllering, Jan Wienhöfer, Jürgen Ihringer, Luis Samaniego, and Erwin Zehe
Hydrol. Earth Syst. Sci., 22, 203–220, https://doi.org/10.5194/hess-22-203-2018, https://doi.org/10.5194/hess-22-203-2018, 2018
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Hydrological fingerprints are introduced as response targets for sensitivity analysis and combined with a conventional approach using streamflow data for a temporally resolved sensitivity analysis. The joint benefit of both approaches is presented for several headwater catchments. The approach allows discerning a clarified pattern for parameter influences pinpointed to diverse response characteristics and detecting even slight regional differences.
Luis Samaniego, Rohini Kumar, Stephan Thober, Oldrich Rakovec, Matthias Zink, Niko Wanders, Stephanie Eisner, Hannes Müller Schmied, Edwin H. Sutanudjaja, Kirsten Warrach-Sagi, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 4323–4346, https://doi.org/10.5194/hess-21-4323-2017, https://doi.org/10.5194/hess-21-4323-2017, 2017
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We inspect the state-of-the-art of several land surface (LSMs) and hydrologic models (HMs) and show that most do not have consistent and realistic parameter fields for land surface geophysical properties. We propose to use the multiscale parameter regionalization (MPR) technique to solve, at least partly, the scaling problem in LSMs/HMs. A general model protocol is presented to describe how MPR can be applied to a specific model.
Christa D. Peters-Lidard, Martyn Clark, Luis Samaniego, Niko E. C. Verhoest, Tim van Emmerik, Remko Uijlenhoet, Kevin Achieng, Trenton E. Franz, and Ross Woods
Hydrol. Earth Syst. Sci., 21, 3701–3713, https://doi.org/10.5194/hess-21-3701-2017, https://doi.org/10.5194/hess-21-3701-2017, 2017
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In this synthesis of hydrologic scaling and similarity, we assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological hypotheses. We call upon the community to develop a focused effort towards a fourth paradigm for hydrology.
Martyn P. Clark, Marc F. P. Bierkens, Luis Samaniego, Ross A. Woods, Remko Uijlenhoet, Katrina E. Bennett, Valentijn R. N. Pauwels, Xitian Cai, Andrew W. Wood, and Christa D. Peters-Lidard
Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, https://doi.org/10.5194/hess-21-3427-2017, 2017
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The diversity in hydrologic models has led to controversy surrounding the “correct” approach to hydrologic modeling. In this paper we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, summarize modeling advances that address these challenges, and define outstanding research needs.
Gabriele Baroni, Matthias Zink, Rohini Kumar, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 2301–2320, https://doi.org/10.5194/hess-21-2301-2017, https://doi.org/10.5194/hess-21-2301-2017, 2017
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Three methods are used to characterize the uncertainty in soil properties. The effect on simulated states and fluxes is quantified using a distributed hydrological model. Different impacts are identified as function of the perturbation method, of the model outputs and of the spatio-temporal resolution. The study underlines the importance of a proper characterization of the uncertainty in soil properties for a correct assessment of their role and further improvements in the model application.
Anne F. Van Loon, Rohini Kumar, and Vimal Mishra
Hydrol. Earth Syst. Sci., 21, 1947–1971, https://doi.org/10.5194/hess-21-1947-2017, https://doi.org/10.5194/hess-21-1947-2017, 2017
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Summer 2015 was extremely dry in Europe, hampering groundwater supply to irrigation and drinking water. For effective management, the groundwater situation should be monitored in real time, but data are not available. We tested two methods to estimate groundwater in near-real time, based on satellite data and using the relationship between rainfall and historic groundwater levels. The second method gave a good spatially variable representation of the 2015 groundwater drought in Europe.
Matthias Zink, Rohini Kumar, Matthias Cuntz, and Luis Samaniego
Hydrol. Earth Syst. Sci., 21, 1769–1790, https://doi.org/10.5194/hess-21-1769-2017, https://doi.org/10.5194/hess-21-1769-2017, 2017
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We discuss the estimation of a long-term, high-resolution, continuous and consistent dataset of hydro-meteorological variables for Germany. Here we describe the derivation of national-scale parameter sets and analyze the uncertainty of the estimated hydrologic variables (focusing on the parametric uncertainty). Our study highlights the role of accounting for the parametric uncertainty in model-derived hydrological datasets.
Falk Heße, Matthias Zink, Rohini Kumar, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 549–570, https://doi.org/10.5194/hess-21-549-2017, https://doi.org/10.5194/hess-21-549-2017, 2017
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Travel-time distributions are a comprehensive tool for the characterization of hydrological systems. In our study, we used data that were simulated by virtue of a well-established hydrological model. This gave us a very large yet realistic dataset, both in time and space, from which we could infer the relative impact of different factors on travel-time behavior. These were, in particular, meteorological (precipitation), land surface (land cover, leaf-area index) and subsurface (soil) properties.
Bernd Schalge, Jehan Rihani, Gabriele Baroni, Daniel Erdal, Gernot Geppert, Vincent Haefliger, Barbara Haese, Pablo Saavedra, Insa Neuweiler, Harrie-Jan Hendricks Franssen, Felix Ament, Sabine Attinger, Olaf A. Cirpka, Stefan Kollet, Harald Kunstmann, Harry Vereecken, and Clemens Simmer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-557, https://doi.org/10.5194/hess-2016-557, 2016
Manuscript not accepted for further review
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In this work we show how we used a coupled atmosphere-land surface-subsurface model at highest possible resolution to create a testbed for data assimilation. The model was able to capture all important processes and interactions between the compartments as well as showing realistic statistical behavior. This proves that using a model as a virtual truth is possible and it will enable us to develop data assimilation methods where states and parameters are updated across compartment.
Simon Höllering, Jürgen Ihringer, Luis Samaniego, and Erwin Zehe
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-249, https://doi.org/10.5194/hess-2016-249, 2016
Preprint withdrawn
Alraune Zech and Sabine Attinger
Hydrol. Earth Syst. Sci., 20, 1655–1667, https://doi.org/10.5194/hess-20-1655-2016, https://doi.org/10.5194/hess-20-1655-2016, 2016
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A new method is presented which allows interpreting pumping test in heterogeneous transmissivity fields. Based on radially dependent transmissivity, the effective well flow solution is derived for two cases: the ensemble mean of pumping tests and the drawdown at an individual heterogeneous transmissivity field. The analytical form of the solution allows inversely estimating the parameters of aquifer heterogeneity (mean, variance, and correlation length) from steady-state pumping test data.
Remko C. Nijzink, Luis Samaniego, Juliane Mai, Rohini Kumar, Stephan Thober, Matthias Zink, David Schäfer, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 20, 1151–1176, https://doi.org/10.5194/hess-20-1151-2016, https://doi.org/10.5194/hess-20-1151-2016, 2016
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The heterogeneity of landscapes in river basins strongly affects the hydrological response. In this study, the distributed mesoscale Hydrologic Model (mHM) was equipped with additional processes identified by landscapes within one modelling cell. Seven study catchments across Europe were selected to test the value of this additional sub-grid heterogeneity. In addition, the models were constrained based on expert knowledge. Generally, the modifications improved the representation of low flows.
Rohini Kumar, Jude L. Musuuza, Anne F. Van Loon, Adriaan J. Teuling, Roland Barthel, Jurriaan Ten Broek, Juliane Mai, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 20, 1117–1131, https://doi.org/10.5194/hess-20-1117-2016, https://doi.org/10.5194/hess-20-1117-2016, 2016
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In a maiden attempt, we performed a multiscale evaluation of the widely used SPI to characterize local- and regional-scale groundwater (GW) droughts using observations at 2040 groundwater wells in Germany and the Netherlands. From this data-based exploratory analysis, we provide sufficient evidence regarding the inability of the SPI to characterize GW drought events, and stress the need for more GW observations and accounting for regional hydrogeological characteristics in GW drought monitoring.
S. Gharari, M. Shafiei, M. Hrachowitz, R. Kumar, F. Fenicia, H. V. Gupta, and H. H. G. Savenije
Hydrol. Earth Syst. Sci., 18, 4861–4870, https://doi.org/10.5194/hess-18-4861-2014, https://doi.org/10.5194/hess-18-4861-2014, 2014
M. Bechmann, C. Schneider, A. Carminati, D. Vetterlein, S. Attinger, and A. Hildebrandt
Hydrol. Earth Syst. Sci., 18, 4189–4206, https://doi.org/10.5194/hess-18-4189-2014, https://doi.org/10.5194/hess-18-4189-2014, 2014
H. V. Gupta, C. Perrin, G. Blöschl, A. Montanari, R. Kumar, M. Clark, and V. Andréassian
Hydrol. Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/hess-18-463-2014, https://doi.org/10.5194/hess-18-463-2014, 2014
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Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
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Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
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The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
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We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
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We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
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In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
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We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
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The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
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The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
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Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
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We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
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Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Giovanni Seijo-Ellis, Donata Giglio, Gustavo Marques, and Frank Bryan
Geosci. Model Dev., 17, 8989–9021, https://doi.org/10.5194/gmd-17-8989-2024, https://doi.org/10.5194/gmd-17-8989-2024, 2024
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A CESM–MOM6 regional configuration of the Caribbean Sea was developed in response to the rising need for high-resolution models for climate impact studies. The configuration is validated for the period 2000–2020 and improves significant errors in a low-resolution model. Oceanic properties are well represented. Patterns of freshwater associated with the Amazon River are well captured, and the mean flows of ocean waters across multiple passages in the Caribbean Sea agree with observations.
Deifilia To, Julian Quinting, Gholam Ali Hoshyaripour, Markus Götz, Achim Streit, and Charlotte Debus
Geosci. Model Dev., 17, 8873–8884, https://doi.org/10.5194/gmd-17-8873-2024, https://doi.org/10.5194/gmd-17-8873-2024, 2024
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Pangu-Weather is a breakthrough machine learning model in medium-range weather forecasting that considers 3D atmospheric information. We show that using a simpler 2D framework improves robustness, speeds up training, and reduces computational needs by 20 %–30 %. We introduce a training procedure that varies the importance of atmospheric variables over time to speed up training convergence. Decreasing computational demand increases the accessibility of training and working with the model.
Fang Li, Xiang Song, Sandy P. Harrison, Jennifer R. Marlon, Zhongda Lin, L. Ruby Leung, Jörg Schwinger, Virginie Marécal, Shiyu Wang, Daniel S. Ward, Xiao Dong, Hanna Lee, Lars Nieradzik, Sam S. Rabin, and Roland Séférian
Geosci. Model Dev., 17, 8751–8771, https://doi.org/10.5194/gmd-17-8751-2024, https://doi.org/10.5194/gmd-17-8751-2024, 2024
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This study provides the first comprehensive assessment of historical fire simulations from 19 Earth system models in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Most models reproduce global totals, spatial patterns, seasonality, and regional historical changes well but fail to simulate the recent decline in global burned area and underestimate the fire response to climate variability. CMIP6 simulations address three critical issues of phase-5 models.
Seung H. Baek, Paul A. Ullrich, Bo Dong, and Jiwoo Lee
Geosci. Model Dev., 17, 8665–8681, https://doi.org/10.5194/gmd-17-8665-2024, https://doi.org/10.5194/gmd-17-8665-2024, 2024
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We evaluate downscaled products by examining locally relevant co-variances during precipitation events. Common statistical downscaling techniques preserve expected co-variances during convective precipitation (a stationary phenomenon). However, they dampen future intensification of frontal precipitation (a non-stationary phenomenon) captured in global climate models and dynamical downscaling. Our study quantifies a ramification of the stationarity assumption underlying statistical downscaling.
Emmanuel Nyenah, Petra Döll, Daniel S. Katz, and Robert Reinecke
Geosci. Model Dev., 17, 8593–8611, https://doi.org/10.5194/gmd-17-8593-2024, https://doi.org/10.5194/gmd-17-8593-2024, 2024
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Research software is vital for scientific progress but is often developed by scientists with limited skills, time, and funding, leading to challenges in usability and maintenance. Our study across 10 sectors shows strengths in version control, open-source licensing, and documentation while emphasizing the need for containerization and code quality. We recommend workshops; code quality metrics; funding; and following the findable, accessible, interoperable, and reusable (FAIR) standards.
Chris Smith, Donald P. Cummins, Hege-Beate Fredriksen, Zebedee Nicholls, Malte Meinshausen, Myles Allen, Stuart Jenkins, Nicholas Leach, Camilla Mathison, and Antti-Ilari Partanen
Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, https://doi.org/10.5194/gmd-17-8569-2024, 2024
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Climate projections are only useful if the underlying models that produce them are well calibrated and can reproduce observed climate change. We formalise a software package that calibrates the open-source FaIR simple climate model to full-complexity Earth system models. Observations, including historical warming, and assessments of key climate variables such as that of climate sensitivity are used to constrain the model output.
Jingwei Xie, Xi Wang, Hailong Liu, Pengfei Lin, Jiangfeng Yu, Zipeng Yu, Junlin Wei, and Xiang Han
Geosci. Model Dev., 17, 8469–8493, https://doi.org/10.5194/gmd-17-8469-2024, https://doi.org/10.5194/gmd-17-8469-2024, 2024
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We propose the concept of mesoscale ocean direct numerical simulation (MODNS), which should resolve the first baroclinic deformation radius and ensure the numerical dissipative effects do not directly contaminate the mesoscale motions. It can be a benchmark for testing mesoscale ocean large eddy simulation (MOLES) methods in ocean models. We build an idealized Southern Ocean model using MITgcm to generate a type of MODNS. We also illustrate the diversity of multiscale eddy interactions.
Emily Black, John Ellis, and Ross I. Maidment
Geosci. Model Dev., 17, 8353–8372, https://doi.org/10.5194/gmd-17-8353-2024, https://doi.org/10.5194/gmd-17-8353-2024, 2024
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We present General TAMSAT-ALERT, a computationally lightweight and versatile tool for generating ensemble forecasts from time series data. General TAMSAT-ALERT is capable of combining multiple streams of monitoring and meteorological forecasting data into probabilistic hazard assessments. In this way, it complements existing systems and enhances their utility for actionable hazard assessment.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleussner
Geosci. Model Dev., 17, 8283–8320, https://doi.org/10.5194/gmd-17-8283-2024, https://doi.org/10.5194/gmd-17-8283-2024, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Yet, projecting future precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows us to generate monthly means of local precipitation and temperature at low computational costs. Our modelling framework is particularly useful for all downstream applications of climate model data.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3522, https://doi.org/10.5194/egusphere-2024-3522, 2024
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We analyzed carbon and nitrogen mass conservation in data from CMIP6 Earth System Models. Our findings reveal significant discrepancies between flux and pool size data, particularly in nitrogen, where cumulative imbalances can reach hundreds of gigatons. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Benjamin M. Sanderson, Ben B. B. Booth, John Dunne, Veronika Eyring, Rosie A. Fisher, Pierre Friedlingstein, Matthew J. Gidden, Tomohiro Hajima, Chris D. Jones, Colin G. Jones, Andrew King, Charles D. Koven, David M. Lawrence, Jason Lowe, Nadine Mengis, Glen P. Peters, Joeri Rogelj, Chris Smith, Abigail C. Snyder, Isla R. Simpson, Abigail L. S. Swann, Claudia Tebaldi, Tatiana Ilyina, Carl-Friedrich Schleussner, Roland Séférian, Bjørn H. Samset, Detlef van Vuuren, and Sönke Zaehle
Geosci. Model Dev., 17, 8141–8172, https://doi.org/10.5194/gmd-17-8141-2024, https://doi.org/10.5194/gmd-17-8141-2024, 2024
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We discuss how, in order to provide more relevant guidance for climate policy, coordinated climate experiments should adopt a greater focus on simulations where Earth system models are provided with carbon emissions from fossil fuels together with land use change instructions, rather than past approaches that have largely focused on experiments with prescribed atmospheric carbon dioxide concentrations. We discuss how these goals might be achieved in coordinated climate modeling experiments.
Peter Berg, Thomas Bosshard, Denica Bozhinova, Lars Bärring, Joakim Löw, Carolina Nilsson, Gustav Strandberg, Johan Södling, Johan Thuresson, Renate Wilcke, and Wei Yang
Geosci. Model Dev., 17, 8173–8179, https://doi.org/10.5194/gmd-17-8173-2024, https://doi.org/10.5194/gmd-17-8173-2024, 2024
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Philip J. Rasch, Haruki Hirasawa, Mingxuan Wu, Sarah J. Doherty, Robert Wood, Hailong Wang, Andy Jones, James Haywood, and Hansi Singh
Geosci. Model Dev., 17, 7963–7994, https://doi.org/10.5194/gmd-17-7963-2024, https://doi.org/10.5194/gmd-17-7963-2024, 2024
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We introduce a protocol to compare computer climate simulations to better understand a proposed strategy intended to counter warming and climate impacts from greenhouse gas increases. This slightly changes clouds in six ocean regions to reflect more sunlight and cool the Earth. Example changes in clouds and climate are shown for three climate models. Cloud changes differ between the models, but precipitation and surface temperature changes are similar when their cooling effects are made similar.
Trude Eidhammer, Andrew Gettelman, Katherine Thayer-Calder, Duncan Watson-Parris, Gregory Elsaesser, Hugh Morrison, Marcus van Lier-Walqui, Ci Song, and Daniel McCoy
Geosci. Model Dev., 17, 7835–7853, https://doi.org/10.5194/gmd-17-7835-2024, https://doi.org/10.5194/gmd-17-7835-2024, 2024
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We describe a dataset where 45 parameters related to cloud processes in the Community Earth System Model version 2 (CESM2) Community Atmosphere Model version 6 (CAM6) are perturbed. Three sets of perturbed parameter ensembles (263 members) were created: current climate, preindustrial aerosol loading and future climate with sea surface temperature increased by 4 K.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
Geosci. Model Dev., 17, 7815–7834, https://doi.org/10.5194/gmd-17-7815-2024, https://doi.org/10.5194/gmd-17-7815-2024, 2024
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The regional Earth system model GCOAST-AHOI v2.0 that includes the regional climate model ICON-CLM coupled to the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in ICON-CLM makes it more flexible for coupling to an external ocean model and an external hydrological discharge model.
Sandro Vattioni, Rahel Weber, Aryeh Feinberg, Andrea Stenke, John A. Dykema, Beiping Luo, Georgios A. Kelesidis, Christian A. Bruun, Timofei Sukhodolov, Frank N. Keutsch, Thomas Peter, and Gabriel Chiodo
Geosci. Model Dev., 17, 7767–7793, https://doi.org/10.5194/gmd-17-7767-2024, https://doi.org/10.5194/gmd-17-7767-2024, 2024
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We quantified impacts and efficiency of stratospheric solar climate intervention via solid particle injection. Microphysical interactions of solid particles with the sulfur cycle were interactively coupled to the heterogeneous chemistry scheme and the radiative transfer code of an aerosol–chemistry–climate model. Compared to injection of SO2 we only find a stronger cooling efficiency for solid particles when normalizing to the aerosol load but not when normalizing to the injection rate.
Ingo Richter, Ping Chang, Gokhan Danabasoglu, Dietmar Dommenget, Guillaume Gastineau, Aixue Hu, Takahito Kataoka, Noel Keenlyside, Fred Kucharski, Yuko Okumura, Wonsun Park, Malte Stuecker, Andrea Taschetto, Chunzai Wang, Stephen Yeager, and Sang-Wook Yeh
EGUsphere, https://doi.org/10.5194/egusphere-2024-3110, https://doi.org/10.5194/egusphere-2024-3110, 2024
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The tropical ocean basins influence each other through multiple pathways and mechanisms, here referred to as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models, but have obtained conflicting results. This may be partly due to differences in experiment protocols, and partly due to systematic model errors. TBIMIP aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Samuel Rémy, Swen Metzger, Vincent Huijnen, Jason E. Williams, and Johannes Flemming
Geosci. Model Dev., 17, 7539–7567, https://doi.org/10.5194/gmd-17-7539-2024, https://doi.org/10.5194/gmd-17-7539-2024, 2024
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In this paper we describe the development of the future operational cycle 49R1 of the IFS-COMPO system, used for operational forecasts of atmospheric composition in the CAMS project, and focus on the implementation of the thermodynamical model EQSAM4Clim version 12. The implementation of EQSAM4Clim significantly improves the simulated secondary inorganic aerosol surface concentration. The new aerosol and precipitation acidity diagnostics showed good agreement against observational datasets.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev., 17, 7629–7643, https://doi.org/10.5194/gmd-17-7629-2024, https://doi.org/10.5194/gmd-17-7629-2024, 2024
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Sofia Allende, Anne Marie Treguier, Camille Lique, Clément de Boyer Montégut, François Massonnet, Thierry Fichefet, and Antoine Barthélemy
Geosci. Model Dev., 17, 7445–7466, https://doi.org/10.5194/gmd-17-7445-2024, https://doi.org/10.5194/gmd-17-7445-2024, 2024
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We study the parameters of the turbulent-kinetic-energy mixed-layer-penetration scheme in the NEMO model with regard to sea-ice-covered regions of the Arctic Ocean. This evaluation reveals the impact of these parameters on mixed-layer depth, sea surface temperature and salinity, and ocean stratification. Our findings demonstrate significant impacts on sea ice thickness and sea ice concentration, emphasizing the need for accurately representing ocean mixing to understand Arctic climate dynamics.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
Geosci. Model Dev., 17, 7365–7399, https://doi.org/10.5194/gmd-17-7365-2024, https://doi.org/10.5194/gmd-17-7365-2024, 2024
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In this study, we improved a climate model by adding the representation of water use sectors such as domestic, industry, and agriculture. This new feature helps us understand how water is used and supplied in various areas. We tested our model from 1971 to 2010 and found that it accurately identifies areas with water scarcity. By modelling the competition between sectors when water availability is limited, the model helps estimate the intensity and extent of individual sectors' water shortages.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
EGUsphere, https://doi.org/10.5194/egusphere-2024-2685, https://doi.org/10.5194/egusphere-2024-2685, 2024
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Forecasting river runoff, crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using Convolutional Long Short-Term Memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Thi Nhu Ngoc Do, Kengo Sudo, Akihiko Ito, Louisa Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
EGUsphere, https://doi.org/10.5194/egusphere-2024-2313, https://doi.org/10.5194/egusphere-2024-2313, 2024
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Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth System Models mainly due to partially incorporating CO2 effects and land cover changes rather than climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant-climate interactions.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-162, https://doi.org/10.5194/gmd-2024-162, 2024
Revised manuscript accepted for GMD
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Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most dangerous effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a sub-sea CO2 injection.
Cynthia Whaley, Montana Etten-Bohm, Courtney Schumacher, Ayodeji Akingunola, Vivek Arora, Jason Cole, Michael Lazare, David Plummer, Knut von Salzen, and Barbara Winter
Geosci. Model Dev., 17, 7141–7155, https://doi.org/10.5194/gmd-17-7141-2024, https://doi.org/10.5194/gmd-17-7141-2024, 2024
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This paper describes how lightning was added as a process in the Canadian Earth System Model in order to interactively respond to climate changes. As lightning is an important cause of global wildfires, this new model development allows for more realistic projections of how wildfires may change in the future, responding to a changing climate.
Erik Gustafsson, Bo G. Gustafsson, Martijn Hermans, Christoph Humborg, and Christian Stranne
Geosci. Model Dev., 17, 7157–7179, https://doi.org/10.5194/gmd-17-7157-2024, https://doi.org/10.5194/gmd-17-7157-2024, 2024
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Methane (CH4) cycling in the Baltic Proper is studied through model simulations, enabling a first estimate of key CH4 fluxes. A preliminary budget identifies benthic CH4 release as the dominant source and two main sinks: CH4 oxidation in the water (92 % of sinks) and outgassing to the atmosphere (8 % of sinks). This study addresses CH4 emissions from coastal seas and is a first step toward understanding the relative importance of open-water outgassing compared with local coastal hotspots.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-133, https://doi.org/10.5194/gmd-2024-133, 2024
Revised manuscript accepted for GMD
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Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
EGUsphere, https://doi.org/10.5194/egusphere-2024-2526, https://doi.org/10.5194/egusphere-2024-2526, 2024
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CropSuite is a fuzzy-logic based high resolution open-source crop suitability model considering the impact of climate variability. We apply CropSuite for 48 important staple and cash crops at 1 km spatial resolution for Africa. We find that climate variability significantly impacts on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The results provide information that can be used for climate impact assessments, adaptation and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-135, https://doi.org/10.5194/gmd-2024-135, 2024
Revised manuscript accepted for GMD
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The Icosahedral Nonhydrostatic (ICON) Model Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++ and Python) and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Tridib Banerjee, Patrick Scholz, Sergey Danilov, Knut Klingbeil, and Dmitry Sidorenko
Geosci. Model Dev., 17, 7051–7065, https://doi.org/10.5194/gmd-17-7051-2024, https://doi.org/10.5194/gmd-17-7051-2024, 2024
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In this paper we propose a new alternative to one of the functionalities of the sea ice model FESOM2. The alternative we propose allows the model to capture and simulate fast changes in quantities like sea surface elevation more accurately. We also demonstrate that the new alternative is faster and more adept at taking advantages of highly parallelized computing infrastructure. We therefore show that this new alternative is a great addition to the sea ice model FESOM2.
Yuwen Fan, Zhao Yang, Min-Hui Lo, Jina Hur, and Eun-Soon Im
Geosci. Model Dev., 17, 6929–6947, https://doi.org/10.5194/gmd-17-6929-2024, https://doi.org/10.5194/gmd-17-6929-2024, 2024
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Irrigated agriculture in the North China Plain (NCP) has a significant impact on the local climate. To better understand this impact, we developed a specialized model specifically for the NCP region. This model allows us to simulate the double-cropping vegetation and the dynamic irrigation practices that are commonly employed in the NCP. This model shows improved performance in capturing the general crop growth, such as crop stages, biomass, crop yield, and vegetation greenness.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
EGUsphere, https://doi.org/10.5194/egusphere-2024-1941, https://doi.org/10.5194/egusphere-2024-1941, 2024
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We studied the coupled carbon-nitrogen cycle effect in Earth System Models by developing a carbon-nitrogen coupling in a reduced complexity model, MAGICC. Our model successfully emulated the global carbon-nitrogen cycle dynamics seen in CMIP6 complex models. Results indicate consistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100. Our findings suggest that nitrogen deficiency could reduce future land carbon sequestration.
Ed Blockley, Emma Fiedler, Jeff Ridley, Luke Roberts, Alex West, Dan Copsey, Daniel Feltham, Tim Graham, David Livings, Clement Rousset, David Schroeder, and Martin Vancoppenolle
Geosci. Model Dev., 17, 6799–6817, https://doi.org/10.5194/gmd-17-6799-2024, https://doi.org/10.5194/gmd-17-6799-2024, 2024
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This paper documents the sea ice model component of the latest Met Office coupled model configuration, which will be used as the physical basis for UK contributions to CMIP7. Documentation of science options used in the configuration are given along with a brief model evaluation. This is the first UK configuration to use NEMO’s new SI3 sea ice model. We provide details on how SI3 was adapted to work with Met Office coupling methodology and documentation of coupling processes in the model.
Jean-François Lemieux, William H. Lipscomb, Anthony Craig, David A. Bailey, Elizabeth C. Hunke, Philippe Blain, Till A. S. Rasmussen, Mats Bentsen, Frédéric Dupont, David Hebert, and Richard Allard
Geosci. Model Dev., 17, 6703–6724, https://doi.org/10.5194/gmd-17-6703-2024, https://doi.org/10.5194/gmd-17-6703-2024, 2024
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We present the latest version of the CICE model. It solves equations that describe the dynamics and the growth and melt of sea ice. To do so, the domain is divided into grid cells and variables are positioned at specific locations in the cells. A new implementation (C-grid) is presented, with the velocity located on cell edges. Compared to the previous B-grid, the C-grid allows for a natural coupling with some oceanic and atmospheric models. It also allows for ice transport in narrow channels.
Rachid El Montassir, Olivier Pannekoucke, and Corentin Lapeyre
Geosci. Model Dev., 17, 6657–6681, https://doi.org/10.5194/gmd-17-6657-2024, https://doi.org/10.5194/gmd-17-6657-2024, 2024
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This study introduces a novel approach that combines physics and artificial intelligence (AI) for improved cloud cover forecasting. This approach outperforms traditional deep learning (DL) methods in producing realistic and physically consistent results while requiring less training data. This architecture provides a promising solution to overcome the limitations of classical AI methods and contributes to open up new possibilities for combining physical knowledge with deep learning models.
Marit Sandstad, Borgar Aamaas, Ane Nordlie Johansen, Marianne Tronstad Lund, Glen Philip Peters, Bjørn Hallvard Samset, Benjamin Mark Sanderson, and Ragnhild Bieltvedt Skeie
Geosci. Model Dev., 17, 6589–6625, https://doi.org/10.5194/gmd-17-6589-2024, https://doi.org/10.5194/gmd-17-6589-2024, 2024
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The CICERO-SCM has existed as a Fortran model since 1999 that calculates the radiative forcing and concentrations from emissions and is an upwelling diffusion energy balance model of the ocean that calculates temperature change. In this paper, we describe an updated version ported to Python and publicly available at https://github.com/ciceroOslo/ciceroscm (https://doi.org/10.5281/zenodo.10548720). This version contains functionality for parallel runs and automatic calibration.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-140, https://doi.org/10.5194/gmd-2024-140, 2024
Revised manuscript accepted for GMD
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This article details a new feature we implemented in the most popular regional atmospheric model (WRF). This feature allows data to be exchanged between WRF and any other model (e.g. an ocean model) using the coupling library Ocean-Atmosphere-Sea-Ice-Soil – Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
EGUsphere, https://doi.org/10.5194/egusphere-2024-2072, https://doi.org/10.5194/egusphere-2024-2072, 2024
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We outline and validate developments to the pre-existing process-based model T&C to better represent cropland processes. Foreseen applications of T&C-CROP include hydrological and carbon storage implications of land-use transitions involving crop, forest, and pasture conversion, as well as studies on optimal irrigation and fertilization under a changing climate.
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, https://doi.org/10.5194/gmd-17-6437-2024, 2024
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A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
Cited articles
Adams, S. V., Ford, R. W., Hambley, M., Hobson, J. M., Kavčič, I., Maynard,
C. M., Melvin, T., Müller, E. H., Mullerworth, S., Porter, A. R., Rezny, M.,
Shipway, B. J., and Wong, R.: LFRic: Meeting the challenges of
scalability and performance portability in Weather and Climate models,
J. Parallel Distr. Com., 132, 383–396,
https://doi.org/10.1016/j.jpdc.2019.02.007, 2019. a
Arheimer, B., Pimentel, R., Isberg, K., Crochemore, L., Andersson, J. C. M., Hasan, A., and Pineda, L.: Global catchment modelling using World-Wide HYPE (WWH), open data, and stepwise parameter estimation, Hydrol. Earth Syst. Sci., 24, 535–559, https://doi.org/10.5194/hess-24-535-2020, 2020. a
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B., Hirschi,
M., and Betts, A. K.: A Revised Hydrology for the ECMWF Model:
Verification from Field Site to Terrestrial Water Storage and
Impact in the Integrated Forecast System, J.
Hydrometeorol., 10, 623–643, https://doi.org/10.1175/2008JHM1068.1, 2009. a, b
Beck, H. E., Dijk, A. I. J. M. v., Roo, A. d., Miralles, D. G., McVicar, T. R.,
Schellekens, J., and Bruijnzeel, L. A.: Global-scale regionalization of
hydrologic model parameters, Water Resour. Res., 52, 3599–3622,
https://doi.org/10.1002/2015WR018247, 2016. a, b
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Blöschl, G., Bierkens, M. F. P., Chambel, A., Cudennec, C., Destouni, G.,
Fiori, A., Kirchner, J. W., McDonnell, J. J., Savenije, H. H. G., Sivapalan,
M., Stumpp, C., Toth, E., Volpi, E., Carr, G., Lupton, C., Salinas, J.,
Széles, B., Viglione, A., Aksoy, H., Allen, S. T., Amin, A., Andréassian,
V., Arheimer, B., Aryal, S. K., Baker, V., Bardsley, E., Barendrecht, M. H.,
Bartosova, A., Batelaan, O., Berghuijs, W. R., Beven, K., Blume, T., Bogaard,
T., Amorim, P. B. d., Böttcher, M. E., Boulet, G., Breinl, K., Brilly, M.,
Brocca, L., Buytaert, W., Castellarin, A., Castelletti, A., Chen, X., Chen,
Y., Chen, Y., Chifflard, P., Claps, P., Clark, M. P., Collins, A. L., Croke,
B., Dathe, A., David, P. C., Barros, F. P. J. d., Rooij, G. d., Baldassarre,
G. D., Driscoll, J. M., Duethmann, D., Dwivedi, R., Eris, E., Farmer, W. H.,
Feiccabrino, J., Ferguson, G., Ferrari, E., Ferraris, S., Fersch, B., Finger,
D., Foglia, L., Fowler, K., Gartsman, B., Gascoin, S., Gaume, E., Gelfan, A.,
Geris, J., Gharari, S., Gleeson, T., Glendell, M., Bevacqua, A. G.,
González-Dugo, M. P., Grimaldi, S., Gupta, A. B., Guse, B., Han, D., Hannah,
D., Harpold, A., Haun, S., Heal, K., Helfricht, K., Herrnegger, M., Hipsey,
M., Hlaváčiková, H., Hohmann, C., Holko, L., Hopkinson, C., Hrachowitz,
M., Illangasekare, T. H., Inam, A., Innocente, C., Istanbulluoglu, E.,
Jarihani, B., Kalantari, Z., Kalvans, A., Khanal, S., Khatami, S., Kiesel,
J., Kirkby, M., Knoben, W., Kochanek, K., Kohnová, S., Kolechkina, A.,
Krause, S., Kreamer, D., Kreibich, H., Kunstmann, H., Lange, H., Liberato, M.
L. R., Lindquist, E., Link, T., Liu, J., Loucks, D. P., Luce, C., Mahé, G.,
Makarieva, O., Malard, J., Mashtayeva, S., Maskey, S., Mas-Pla, J.,
Mavrova-Guirguinova, M., Mazzoleni, M., Mernild, S., Misstear, B. D.,
Montanari, A., Müller-Thomy, H., Nabizadeh, A., Nardi, F., Neale, C.,
Nesterova, N., Nurtaev, B., Odongo, V. O., Panda, S., Pande, S., Pang, Z.,
Papacharalampous, G., Perrin, C., Pfister, L., Pimentel, R., Polo, M. J.,
Post, D., Sierra, C. P., Ramos, M.-H., Renner, M., Reynolds, J. E., Ridolfi,
E., Rigon, R., Riva, M., Robertson, D. E., Rosso, R., Roy, T., Sá, J. H. M.,
Salvadori, G., Sandells, M., Schaefli, B., Schumann, A., Scolobig, A.,
Seibert, J., Servat, E., Shafiei, M., Sharma, A., Sidibe, M., Sidle, R. C.,
Skaugen, T., Smith, H., Spiessl, S. M., Stein, L., Steinsland, I., Strasser,
U., Su, B., Szolgay, J., Tarboton, D., Tauro, F., Thirel, G., Tian, F., Tong,
R., Tussupova, K., Tyralis, H., Uijlenhoet, R., Beek, R. v., Ent, R. J.
v. d., Ploeg, M. v. d., Loon, A. F. V., Meerveld, I. v., Nooijen, R. v., Oel,
P. R. v., Vidal, J.-P., Freyberg, J. v., Vorogushyn, S., Wachniew, P., Wade,
A. J., Ward, P., Westerberg, I. K., White, C., Wood, E. F., Woods, R., Xu,
Z., Yilmaz, K. K., and Zhang, Y.: Twenty-three unsolved problems in hydrology
(UPH) – a community perspective, Hydrol. Sci. J., 64,
1141–1158, https://doi.org/10.1080/02626667.2019.1620507, 2019. a
Burnash, R.: The NWS River Forecast System – Catchment Modeling,
in: Computer models of watershed hydrology. revised edition, p. 1144, edited by: Singh, V. P., Highlands Ranch, Colo,
available at: https://www.wrpllc.com/books/cmwhn.html (last access: 16 January 2022), 1995. a
Campbell, G.: A simple method for determining unsaturated conductivity from
moisture retention data, Soil Sci., 117, 311–314, 1974. a
Carrera, J., Alcolea, A., Medina, A., Hidalgo, J., and Slooten, L. J.: Inverse
problem in hydrogeology, Hydrogeol. J., 13, 206–222,
https://doi.org/10.1007/s10040-004-0404-7, 2005. a
Clark, M. P., Schaefli, B., Schymanski, S. J., Samaniego, L., Luce, C. H.,
Jackson, B. M., Freer, J. E., Arnold, J. R., Moore, R. D., Istanbulluoglu,
E., and Ceola, S.: Improving the theoretical underpinnings of process-based
hydrologic models, Water Resour. Res., 52, 2350–2365,
https://doi.org/10.1002/2015WR017910, 2016. a
Collins, N., Theurich, G., DeLuca, C., Suarez, M., Trayanov, A., Balaji, V.,
Li, P., Yang, W., Hill, C., and da Silva, A.: Design and Implementation of
Components in the Earth System Modeling Framework,
Int. J. High Perform. C., 19,
341–350, https://doi.org/10.1177/1094342005056120, 2005. a
GDAL/OGR contributors: GDAL/OGR Geospatial Data Abstraction software
Library, available at: https://gdal.org (last access: 16 January 2022), 2019. a
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10, 3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017. a
Cuntz, M., Mai, J., Samaniego, L., Clark, M., Wulfmeyer, V., Branch, O.,
Attinger, S., and Thober, S.: The impact of standard and hard-coded
parameters on the hydrologic fluxes in the Noah-MP land surface model,
J. Geophys. Res.-Atmos., 121, 10676–10700,
https://doi.org/10.1002/2016JD025097, 2016. a, b
Deconinck, W., Bauer, P., Diamantakis, M., Hamrud, M., Kühnlein, C., Maciel,
P., Mengaldo, G., Quintino, T., Raoult, B., Smolarkiewicz, P. K., and Wedi,
N. P.: Atlas : A library for numerical weather prediction and climate
modelling, Comput. Phys. Commun., 220, 188–204,
https://doi.org/10.1016/j.cpc.2017.07.006, 2017. a
Dembélé, M., Hrachowitz, M., Savenije, H. H. G., Mariéthoz, G., and
Schaefli, B.: Improving the Predictive Skill of a Distributed
Hydrological Model by Calibration on Spatial Patterns With
Multiple Satellite Data Sets, Water Resour. Res., 56,
e2019WR026085, https://doi.org/10.1029/2019WR026085, 2020. a
Duan, Q. Y., Gupta, V. K., and Sorooshian, S.: Shuffled complex evolution
approach for effective and efficient global minimization, Journal of
Optimization Theory and Applications, 76, 501–521, https://doi.org/10.1007/BF00939380,
1993. a
Eaton, B., Gregory, J., Drach, B., Taylor, K., Hankin, S., Blower, J., Caron,
J., Signell, R., Bentley, P., Rappa, G., Höck, H., Pamment, A., Juckes, M.,
and Raspaud, M.: NetCDF Climate and Forecast (CF) Metadata
Conventions, available at:
http://cfconventions.org/Data/cf-conventions/ (last access: 16 January 2022),
2017. a, b
ECMWF: IFS Documentation CY46R1, IFS Documentation, ECMWF, https://doi.org/10.24381/cds.bd0915c6,
available at: https://www.ecmwf.int/en/elibrary/19308-part-iv-physical-processes (last access: 16 January 2022),
2019. a, b, c, d
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V.,
Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model
advances in the National Centers for Environmental Prediction
operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003. a
ESRI: ArcGIS,
available at: https://www.esri.com/en-us/arcgis/about-arcgis/overview (last access: 16 January 2022),
2020. a
Feigl, M., Herrnegger, M., Klotz, D., and Schulz, K.: Function Space
Optimization: A symbolic regression method for estimating parameter
transfer functions for hydrological models, Earth Space Sci. Open Arch., 30 pp., https://doi.org/10.1002/essoar.10502385.1, 2020. a
Flügel, W.-A.: Delineating hydrological response units by geographical
information system analyses for regional hydrological modelling using
PRMS/MMS in the drainage basin of the River Bröl, Germany,
Hydrol. Process., 9, 423–436, https://doi.org/10.1002/hyp.3360090313, 1995. a
Friedl, M., Strahler, A., and Hodges, J.: ISLSCP II MODIS (Collection
4) IGBP Land Cover, 2000–2001, ORNL Distributed Active Archive Center,
https://doi.org/10.3334/ORNLDAAC/968, 2010. a, b
Gassman, P. W., Reyes, M. R., Green, C. H., and Arnold, J. G.: The Soil
and Water Assessment Tool: Historical Development, Applications,
and Future Research Directions, Transact. ASABE, 50,
1211–1250, https://doi.org/10.13031/2013.23637, 2007. a
Genuchten, M. T. v.: A Closed-form Equation for Predicting the
Hydraulic Conductivity of Unsaturated Soils, Soil Sci. Soc.
Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x,
1980. a, b
González-García, R., Rico-Martínez, R., and Kevrekidis, I. G.:
Identification of distributed parameter systems: A neural net based
approach, Comput. Chem. Eng.g, 22, S965–S968,
https://doi.org/10.1016/S0098-1354(98)00191-4, 1998. a
Gupta, H. V., Perrin, C., Blöschl, G., Montanari, A., Kumar, R., Clark, M.,
and Andréassian, V.: Large-sample hydrology: a need to balance depth with
breadth, Hydrol. Earth Syst. Sci., 18, 463–477,
https://doi.org/10.5194/hess-18-463-2014,
2014. a
Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y.: The
Variable Infiltration Capacity model version 5 (VIC-5):
infrastructure improvements for new applications and reproducibility,
Geosci. Model Dev., 11, 3481–3496,
https://doi.org/10.5194/gmd-11-3481-2018,
2018. a
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G. B. M., Ribeiro, E., Samuel-Rosa, A.,
Kempen, B., Leenaars, J. G. B., Walsh, M. G., Ruiperez Gonzalez, M.:
SoilGrids1km – Global Soil Information Based on Automated Mapping, PLoS
ONE, 9, e105992, https://doi.org/10.1371/journal.pone.0105992, 2014. a
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on
machine learning, PLOS ONE, 12, e0169748,
https://doi.org/10.1371/journal.pone.0169748, 2017. a, b, c, d, e, f, g, h, i, j, k, l
Hundecha, Y., Arheimer, B., Donnelly, C., and Pechlivanidis, I.: A regional
parameter estimation scheme for a pan-European multi-basin model, J.
Hydrol., 6, 90–111, https://doi.org/10.1016/j.ejrh.2016.04.002,
2016. a
HYPRES: European soil map texture classes, The James Hutton Institute,
available at: https://www.hutton.ac.uk/learning/ (last access: 16 January 2022),
1997. a, b, c
Imhoff, R. O., van Verseveld, W. J., van Osnabrugge, B., and Weerts, A. H.:
Scaling Point-Scale (Pedo)transfer Functions to Seamless
Large-Domain Parameter Estimates for High-Resolution
Distributed Hydrologic Modeling: An Example for the Rhine
River, Water Resour. Res., 56, e2019WR026807,
https://doi.org/10.1029/2019WR026807, 2020. a
Kirchner, J. W.: Getting the right answers for the right reasons: Linking
measurements, analyses, and models to advance the science of hydrology, Water
Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006. a
Klotz, D., Herrnegger, M., and Schulz, K.: Symbolic Regression for the
Estimation of Transfer Functions of Hydrological Models, Water
Resour. Res., 53, 9402–9423, https://doi.org/10.1002/2017WR021253, 2017. a
Krinner, G., Viovy, N., Noblet‐Ducoudré, N. d., Ogée, J., Polcher, J.,
Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic
global vegetation model for studies of the coupled atmosphere-biosphere
system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005. a
Kumar, R., Livneh, B., and Samaniego, L.: Toward computationally efficient
large-scale hydrologic predictions with a multiscale regionalization scheme,
Water Resour. Res., 49, 5700–5714, https://doi.org/10.1002/wrcr.20431,
2013a. a
Li, Y., Grimaldi, S., Pauwels, V. R. N., and Walker, J. P.: Hydrologic model
calibration using remotely sensed soil moisture and discharge measurements:
The impact on predictions at gauged and ungauged locations, J.
Hydrol., 557, 897–909, https://doi.org/10.1016/j.jhydrol.2018.01.013, 2018. a
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res.-Atmos., 99,
14415–14428, https://doi.org/10.1029/94JD00483, 1994. a
Livneh, B., Kumar, R., and Samaniego, L.: Influence of soil textural properties
on hydrologic fluxes in the Mississippi river basin, Hydrol.
Process., 29, 4638–4655, https://doi.org/10.1002/hyp.10601, 2015. a
MDAL contributors: The MDAL Mesh Data Abstraction software Library, available at: https://www.mdal.xyz/ (last access: 16 January 2022), 2020. a
Ma, N., Niu, G.-Y., Xia, Y., Cai, X., Zhang, Y., Ma, Y., and Fang, Y.: A
Systematic Evaluation of Noah-MP in Simulating Land-Atmosphere
Energy, Water, and Carbon Exchanges Over the Continental United
States, J. Geophys. Res.-Atmos., 122, 12245–12268,
https://doi.org/10.1002/2017JD027597, 2017. a, b
MacMackin, C.: FORD, Zenodo [code], https://doi.org/10.5281/zenodo.1422473, 2018. a
Markstrom, S. L., Regan, R. S., Hay, L. E., Viger, R. J.,
Webb, R. M., Payn, R. A., and LaFontaine, J. H.: PRMS-IV, the
Precipitation-Runoff Modeling System, Version 4, USGS Numbered Series, PRMS-IV,
the Precipitation-Runoff Modeling System, Version 4, Vol. 6-B7, Techniques and
Methods, Reston, VA: U.S. Geological Survey, https://doi.org/10.3133/tm6B7, 2015. a
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann,
S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T.,
Jimenéz‐de‐la‐Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S.,
Kracher, D., Kinne, S., Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke,
J., Matei, D., Meraner, K., Mikolajewicz, U., Modali, K., Möbis, B.,
Müller, W. A., Nabel, J. E. M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S.,
Paulsen, H., Peters, K., Pincus, R., Pohlmann, H., Pongratz, J., Popp, M.,
Raddatz, T. J., Rast, S., Redler, R., Reick, C. H., Rohrschneider, T.,
Schemann, V., Schmidt, H., Schnur, R., Schulzweida, U., Six, K. D., Stein,
L., Stemmler, I., Stevens, B., Storch, J.-S. v., Tian, F., Voigt, A., Vrese,
P., Wieners, K.-H., Wilkenskjeld, S., Winkler, A., and Roeckner, E.:
Developments in the MPI-M Earth System Model version 1.2
(MPI-ESM1.2) and Its Response to Increasing CO2, J.
Adv. Model. Earth Syst., 11, 998–1038,
https://doi.org/10.1029/2018MS001400, 2019. a
Merz, R., Tarasova, L., and Basso, S.: Parameter's Controls of Distributed
Catchment Models—How Much Information is in Conventional
Catchment Descriptors?, Water Resour. Res., 56, e2019WR026008,
https://doi.org/10.1029/2019WR026008, 2020. a
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J. C.,
Robock, A., Cosgrove, B. A., Sheffield, J., Duan, Q., Luo, L., Higgins,
R. W., Pinker, R. T., Tarpley, J. D., Lettenmaier, D. P., Marshall, C. H.,
Entin, J. K., Pan, M., Shi, W., Koren, V., Meng, J., Ramsay, B. H., and
Bailey, A. A.: The multi‐institution North American Land Data
Assimilation System (NLDAS): Utilizing multiple GCIP products and
partners in a continental distributed hydrological modeling system, J. Geophys. Res.-Atmos., 109, D07S90, https://doi.org/10.1029/2003JD003823, 2004. a
Mizukami, N., Clark, M. P., Newman, A. J., Wood, A. W., Gutmann, E. D.,
Nijssen, B., Rakovec, O., and Samaniego, L.: Towards seamless large-domain
parameter estimation for hydrologic models, Water Resour. Res., 53,
8020–8040, https://doi.org/10.1002/2017WR020401, 2017. a, b, c, d
Montzka, C., Herbst, M., Weihermüller, L., Verhoef, A., and Vereecken, H.: A
global data set of soil hydraulic properties and sub-grid variability of soil
water retention and hydraulic conductivity curves, Earth Syst. Sci. Data,
9, 529–543, https://doi.org/10.5194/essd-9-529-2017, 2017. a
NCAR: Noah-MP Version 1.6 (as implemented in WRFv3.6),
available at: https://ral.ucar.edu/sites/default/files/public/ (last access: 16 January 2022),
2020. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale
measurements, J. Geophys. Res., 116, D12109,
https://doi.org/10.1029/2010JD015139, 2011. a
Oudin, L., Andréassian, V., Perrin, C., Michel, C., and Moine, N. L.: Spatial
proximity, physical similarity, regression and ungaged catchments: A
comparison of regionalization approaches based on 913 French catchments,
Water Resour. Res., 44, W03413, https://doi.org/10.1029/2007WR006240, 2008. a
Pagliero, L., Bouraoui, F., Diels, J., Willems, P., and McIntyre, N.:
Investigating regionalization techniques for large-scale hydrological
modelling, J. Hydrol., 570, 220–235,
https://doi.org/10.1016/j.jhydrol.2018.12.071, 2019. a
Patil, N. G. and Singh, S. K.: Pedotransfer Functions for Estimating Soil
Hydraulic Properties: A Review, Pedosphere, 26, 417–430,
https://doi.org/10.1016/S1002-0160(15)60054-6, 2016. a
Pokhrel, P. and Gupta, H. V.: On the use of spatial regularization strategies
to improve calibration of distributed watershed models, Water Resour.
Res., 46, W01505, https://doi.org/10.1029/2009wr008066, 2010. a
Rakovec, O., Kumar, R., Attinger, S., and Samaniego, L.: Improving the realism
of hydrologic model functioning through multivariate parameter estimation,
Water Resour. Res., 52, 7779–7792,
https://doi.org/10.1002/2016WR019430, 2016. a
Richards, L. A.: Capillary conduction of liquids through porous mediums,
Physics, 1, 318–333, https://doi.org/10.1063/1.1745010, 1931. a
Richardson, L. F.: Weather prediction by numerical process, Cambridge, The
University press,
available at: http://archive.org/details/weatherpredictio00richrich (last access: 16 January 2022), 1922. a
Rojas‐Serna, C., Lebecherel, L., Perrin, C., Andréassian, V., and Oudin, L.:
How should a rainfall-runoff model be parameterized in an almost ungauged
catchment? A methodology tested on 609 catchments, Water Resour.
Res., 52, 4765–4784, https://doi.org/10.1002/2015WR018549, 2016. a
Samaniego, L., Kumar, R., Thober, S., Rakovec, O., Zink, M., Wanders, N.,
Eisner, S., Müller Schmied, H., Sutanudjaja, E. H., Warrach-Sagi, K., and
Attinger, S.: Toward seamless hydrologic predictions across spatial scales,
Hydrol. Earth Syst. Sci., 21, 4323–4346, https://doi.org/10.5194/hess-21-4323-2017,
2017. a, b, c, d, e, f, g
Samaniego, L., Kaluza, M., Kumar, R., Rakovec, O., Schüler, L., Schweppe, R.,
Kumar Shreshta, P., Thober, S., and Attinger, S.: mesoscale Hydrologic
Model, Zenodo, https://doi.org/10.5281/zenodo.3239055, 2019. a, b, c
Saxe, S., Farmer, W., Driscoll, J., and Hogue, T. S.: Implications of model selection: a comparison of publicly available, conterminous US-extent hydrologic component estimates, Hydrol. Earth Syst. Sci., 25, 1529–1568, https://doi.org/10.5194/hess-25-1529-2021, 2021. a
Saxton, K. E. and Rawls, W. J.: Soil Water Characteristic Estimates by
Texture and Organic Matter for Hydrologic Solutions, Soil Sci.
Soc. Am. J., 70, 1569, https://doi.org/10.2136/sssaj2005.0117, 2006. a, b, c
Schaake, J. C.: Introduction, in: Calibration of Watershed Models, American Geophysical Union (AGU), 1–7,
https://doi.org/10.1029/WS006p0001, 2003. a
Schulzweida, U.: CDO User Guide, Zenodo,
https://doi.org/10.5281/zenodo.3539275, 2019. a
Schweppe, R., Thober, S.,
Müller, S.,
Kelbling, M.,
Kumar, R.,
Attinger, S., and
Samaniego, L.: Multiscale Parameter Regionalization too – MPR v. 1.0, Zenodo [code], https://doi.org/10.5281/zenodo.4650513, 2021. a, b
Shoarinezhad, V., Wieprecht, S., and Haun, S.: Comparison of Local and
Global Optimization Methods for Calibration of a 3D Morphodynamic
Model of a Curved Channel, Water, 12, 1333, https://doi.org/10.3390/w12051333, 2020. a
Skamarock, W. C., Klemp, J. B., Duda, M. G., Fowler, L. D., Park, S.-H., and
Ringler, T. D.: A Multiscale Nonhydrostatic Atmospheric Model Using
Centroidal Voronoi Tesselations and C-Grid Staggering, Mon.
Weather Rev., 140, 3090–3105, https://doi.org/10.1175/MWR-D-11-00215.1, 2012. a
Staff, S. S. D.: Soil Survey Manual, no. 18 in United States Department
of Agriculture Handbook, U.S. Government Printing Office, Washington,
D.C, 1993. a
Sykora, S.: Mathematical Means and Averages: Basic Properties, Stan's
Library, https://doi.org/10.3247/sl3math09.001, 2009. a
QGIS development team: QGIS, GitHub, available at: https://github.com/qgis/QGIS (last access: 16 January 2022), 2020. a
Thiemann, M., Trosset, M., Gupta, H., and Sorooshian, S.: Bayesian recursive
parameter estimation for hydrologic models, Water Resour. Res.h, 37,
2521–2535, https://doi.org/10.1029/2000WR900405, 2001. a
Tolson, B. A. and Shoemaker, C. A.: Dynamically dimensioned search algorithm
for computationally efficient watershed model calibration, Water Resour.
Res., 43, W01413, https://doi.org/10.1029/2005WR004723, 2007. a
Troy, T. J., Wood, E. F., and Sheffield, J.: An efficient calibration method
for continental-scale land surface modeling, Water Resour. Res., 44, W09411,
https://doi.org/10.1029/2007WR006513, 2008. a
Ullrich, P. A. and Taylor, M. A.: Arbitrary-Order Conservative and
Consistent Remapping and a Theory of Linear Maps: Part I,
Mon. Weather Rev., 143, 2419–2440, https://doi.org/10.1175/MWR-D-14-00343.1,
2015. a
Unidata UCAR: NetCDF User's Guide, UCAR/Unidata Program Center,
https://doi.org/10.5065/D6H70CW6,
2020. a
Service Center Agencies: Watershed Boundary Dataset,
available at: http://datagateway.nrcs.usda.gov (last access: 16 January 2022), 2019. a
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U.,
Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G., Tóth,
B., Verhoef, A., Vanderborght, J., van der Ploeg, M. J., Weihermüller, L.,
Zacharias, S., Zhang, Y., and Vereecken, H.: Pedotransfer Functions in
Earth System Science: Challenges and Perspectives: PTFs in
Earth system science perspective, Rev. Geophys., 55, 1199–1256,
https://doi.org/10.1002/2017RG000581, 2017. a, b, c, d, e, f
Vereecken, H., Maes, J., Feyen, J., and Darius, P.: Estimating the soil
moisture retention characteristic from texture, bulk density,
and carbon content, Soil Sci., 148, 389–403,
https://doi.org/10.1097/00010694-198912000-00001, 1989. a, b, c, d
Vereecken, H., Maes, J., and Feyen, J.: Estimating unsaturated hydraulic
conductivity from easily measured soil properties, Soil Sci.,
149, 1–12, https://doi.org/10.1097/00010694-199001000-00001, 1990. a, b, c
Vereecken, H., Weihermüller, L., Assouline, S., Šimůnek, J., Verhoef, A.,
Herbst, M., Archer, N., Mohanty, B., Montzka, C., Vanderborght, J., Balsamo,
G., Bechtold, M., Boone, A., Chadburn, S., Cuntz, M., Decharme, B., Ducharne,
A., Ek, M., Garrigues, S., Goergen, K., Ingwersen, J., Kollet, S., Lawrence,
D. M., Li, Q., Or, D., Swenson, S., Vrese, P. d., Walko, R., Wu, Y., and Xue,
Y.: Infiltration from the Pedon to Global Grid Scales: An
Overview and Outlook for Land Surface Modeling, Vadose Zone
J., 18, 180191, https://doi.org/10.2136/vzj2018.10.0191, 2019. a, b, c
Ward, M., HoWol76,
Reno,
Penn, J.,
Katz, D. S.,
jenssss,
Oleksandr, H.,
Dix, M.,
Work, D.,
naught101,
Kiss, A.,
barpaum,
Riechert, M.,
Lamparski, M.,
Hebbeker, P.,
Ball, W.: marshallward/f90nml, Zenodo [code], https://doi.org/10.5281/zenodo.4768748, 2021. a
Wellen, C., Kamran-Disfani, A.-R., and Arhonditsis, G. B.: Evaluation of the
Current State of Distributed Watershed Nutrient Water Quality
Modeling, Environ. Sci. Technol., 49, 3278–3290,
https://doi.org/10.1021/es5049557, 2015. a
Weynants, M. and Tóth, B.: The euptf package,
available at: http://eusoils.jrc.ec.europa.eu/ (last access: 16 January 2022), 2014. a
Weynants, M., Vereecken, H., and Javaux, M.: Revisiting Vereecken
Pedotransfer Functions: Introducing a Closed – Form Hydraulic
Model, Vadose Zone J., 8, 86–95, https://doi.org/10.2136/vzj2008.0062, 2009. a, b, c
Wösten, J. H. M., Lilly, A., Nemes, A., and Le Bas, C.: Development and use of
a database of hydraulic properties of European soils, Geoderma, 90,
169–185, https://doi.org/10.1016/S0016-7061(98)00132-3, 1999. a
Xia, Y. and NCEP/EMC: NLDAS Primary Forcing Data L4 Hourly 0.125×0.125∘, edited by: Mocko, D., NASA/GSFC/HSL, Greenbelt, Maryland, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/6j5lhhohzhn4, 2009. a, b, c
Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., Luo, L.,
Alonge, C., Wei, H., Meng, J., Livneh, B., Lettenmaier, D., Koren, V., Duan,
Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy flux
analysis and validation for the North American Land Data
Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and
application of model products, J. Geophys. Res.-Atmos.,
117, D03109, https://doi.org/10.1029/2011JD016048, 2012. a
Xu, X., Frey, S. K., Boluwade, A., Erler, A. R., Khader, O., Lapen, D. R., and
Sudicky, E.: Evaluation of variability among different precipitation products
in the Northern Great Plains, J. Hydrol.,
24, 100608, https://doi.org/10.1016/j.ejrh.2019.100608, 2019. a
Zacharias, S. and Wessolek, G.: Excluding Organic Matter Content from
Pedotransfer Predictors of Soil Water Retention, Soil Sci.
Soc. Am. J., 71, 43–50, https://doi.org/10.2136/sssaj2006.0098, 2007. a, b, c, d
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON
(ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M:
Description of the non-hydrostatic dynamical core, Q. J.
Roy. Meteorol. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a, b, c
Zender, C. S.: Analysis of self-describing gridded geoscience data with
netCDF Operators (NCO), Environ. Model. Softw., 23,
1338–1342, https://doi.org/10.1016/j.envsoft.2008.03.004, 2008. a
Zhu, J. and Mohanty, B. P.: Spatial Averaging of van Genuchten Hydraulic
Parameters for Steady – State Flow in Heterogeneous Soils: A
Numerical Study, Vadose Zone J., 1, 261–272,
https://doi.org/10.2136/vzj2002.2610, 2002. a
Zink, M., Kumar, R., Cuntz, M., and Samaniego, L.: A high-resolution dataset of
water fluxes and states for Germany accounting for parametric uncertainty,
Hydrol. Earth Syst. Sci., 21, 1769–1790,
https://doi.org/10.5194/hess-21-1769-2017,
2017. a, b
Short summary
The recently released multiscale parameter regionalization (MPR) tool enables
environmental modelers to efficiently use extensive datasets for model setups.
It flexibly ingests the datasets using user-defined data–parameter relationships
and rescales parameter fields to given model resolutions. Modern
land surface models especially benefit from MPR through increased transparency and
flexibility in modeling decisions. Thus, MPR empowers more sound and robust
simulations of the Earth system.
The recently released multiscale parameter regionalization (MPR) tool enables
environmental...
