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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Maik Heistermann, Daniel Altdorff, Till Francke, Martin Schrön, Peter M. Grosse, Arvid Markert, Albrecht Bauriegel, Peter Biró, Sabine Attinger, Frank Beyrich, Peter Dietrich, Rebekka Eichstädt, Jakob Terschlüsen, Ariane Walz, Steffen Zacharias, and Sascha E. Oswald
Nat. Hazards Earth Syst. Sci., 26, 465–486, https://doi.org/10.5194/nhess-26-465-2026, https://doi.org/10.5194/nhess-26-465-2026, 2026
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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 monitoring network based on cosmic-ray neutron sensing technology. The resulting observations can be particularly helpful to evaluate and improve soil moisture products from modelling or remote sensing.
Robert Reinecke, Tanjila Akhter, Annemarie Bäthge, Ricarda Dietrich, Sebastian Gnann, Simon N. Gosling, Danielle Grogan, Andreas Hartmann, Stefan Kollet, Rohini Kumar, Richard Lammers, Sida Liu, Yan Liu, Nils Moosdorf, Bibi Naz, Sara Nazari, Chibuike Orazulike, Yadu Pokhrel, Jacob Schewe, Mikhail Smilovic, Maryna Strokal, Wim Thiery, Yoshihide Wada, Shan Zuidema, and Inge de Graaf
Geosci. Model Dev., 19, 523–542, https://doi.org/10.5194/gmd-19-523-2026, https://doi.org/10.5194/gmd-19-523-2026, 2026
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Here we describe a collaborative effort to improve predictions of how climate change will affect groundwater. The ISIMIP (The Inter-Sectoral Impact Model Intercomparison Project) groundwater sector combines multiple global groundwater models to capture a range of possible outcomes and reduce uncertainty. Initial comparisons reveal significant differences between models in key metrics like water table depth and recharge rates, highlighting the need for structured model intercomparisons.
Malve Heinz, Annelie Holzkämper, Rohini Kumar, Sélène Ledain, Pascal Horton, and Bettina Schaefli
EGUsphere, https://doi.org/10.5194/egusphere-2025-5447, https://doi.org/10.5194/egusphere-2025-5447, 2025
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Droughts increasingly threaten agriculture. Improving soils to store more water, for example by increasing soil organic carbon, can help. We simulated this in a Swiss catchment and found that more soil carbon slightly increased soil water storage and evapotranspiration, modestly reduced floods, and shortened periods with very little streamflow. However in warmer, drier areas, these periods with little streamflow could sometimes last longer.
Timo Houben, Christian Siebert, Thomas Kalbacher, Mariaines Di Dato, Thomas Fischer, and Sabine Attinger
EGUsphere, https://doi.org/10.5194/egusphere-2025-5666, https://doi.org/10.5194/egusphere-2025-5666, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
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Groundwater is Germany’s main source of drinking water but is under stress from climate change and growing use. We used a frequency domain approach to study how quickly groundwater reacts to changes in precipitation (recharge) by analyzing groundwater levels from almost 200 wells in southern Germany. Most systems responded within 50 to 300 days. Areas with slower response times tend to handle dry periods better. These results can help manage groundwater more safely in the future.
Pia Ebeling, Alexander Hubig, Alexander Wachholz, Ulrike Scharfenberger, Sarah Haug, Tam Nguyen, Fanny Sarrazin, Masooma Batool, Andreas Musolff, and Rohini Kumar
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-450, https://doi.org/10.5194/essd-2025-450, 2025
Revised manuscript accepted for ESSD
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This update of the water quality dataset QUADICA for 1386 German catchments adds new data on water quality, flow, and drivers. Additions include water temperature and concentrations of oxygen and others, as well as catchment-wise time series on pollution sources and improved integration with other relevant datasets. This expanded dataset will help researchers and practitioners better understand how human activities affect water quality and ecosystems, and make more informed decisions.
Maria Staudinger, Anna Herzog, Ralf Loritz, Tobias Houska, Sandra Pool, Diana Spieler, Paul D. Wagner, Juliane Mai, Jens Kiesel, Stephan Thober, Björn Guse, and Uwe Ehret
Hydrol. Earth Syst. Sci., 29, 5005–5029, https://doi.org/10.5194/hess-29-5005-2025, https://doi.org/10.5194/hess-29-5005-2025, 2025
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Three process-based and four data-driven hydrological models are compared using different training data. We found that process-based models perform better with small datasets but stop learning soon, while data-driven models learn longer. The study highlights the importance of memory in data and the impact of different data sampling methods on model performance. The direct comparison of these models is novel and provides a clear understanding of their performance under various data conditions.
Katoria Lekarkar, Oldrich Rakovec, Rohini Kumar, Stefaan Dondeyne, and Ann van Griensven
EGUsphere, https://doi.org/10.5194/egusphere-2025-4526, https://doi.org/10.5194/egusphere-2025-4526, 2025
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Belgium has faced intense droughts in recent years, causing major losses across sectors. To assess their rarity, we used a hydrological model to reconstruct fifty years of soil moisture in the country. We show that 2011–2020 experienced the most severe droughts since 1971, with nearly 30 % of the decade under drought. We also show that rainfall-based indicators underestimate soil moisture droughts, so including soil moisture monitoring can give decision-makers a clearer picture of drought risks.
Vishal Thakur, Yannis Markonis, Rohini Kumar, Johanna Ruth Thomson, Mijael Rodrigo Vargas Godoy, Martin Hanel, and Oldrich Rakovec
Hydrol. Earth Syst. Sci., 29, 4395–4416, https://doi.org/10.5194/hess-29-4395-2025, https://doi.org/10.5194/hess-29-4395-2025, 2025
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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.
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ivan Alsina-Ferrer, Llorenç Lledó, Ehsan Sharifi, and Francisco Doblas-Reyes
Geosci. Model Dev., 18, 5873–5890, https://doi.org/10.5194/gmd-18-5873-2025, https://doi.org/10.5194/gmd-18-5873-2025, 2025
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We present One_Pass (v0.8.0), a Python package enabling computation of statistics from streamed global climate model output using one-pass algorithms. Users often need statistics covering periods longer than the stream duration, requiring algorithms that do not store full time series. One-pass methods address this need while avoiding full data archiving, offering memory-efficient, accurate results for high-performance computing (HPC) workflows and downstream applications like bias adjustment.
Francisco J. Doblas-Reyes, Jenni Kontkanen, Irina Sandu, Mario Acosta, Mohammed Hussam Al Turjmam, Ivan Alsina-Ferrer, Miguel Andrés-Martínez, Leo Arriola, Marvin Axness, Marc Batlle Martín, Peter Bauer, Tobias Becker, Daniel Beltrán, Sebastian Beyer, Hendryk Bockelmann, Pierre-Antoine Bretonnière, Sebastien Cabaniols, Silvia Caprioli, Miguel Castrillo, Aparna Chandrasekar, Suvarchal Cheedela, Victor Correal, Emanuele Danovaro, Paolo Davini, Jussi Enkovaara, Claudia Frauen, Barbara Früh, Aina Gaya Àvila, Paolo Ghinassi, Rohit Ghosh, Supriyo Ghosh, Iker González, Katherine Grayson, Matthew Griffith, Ioan Hadade, Christopher Haine, Carl Hartick, Utz-Uwe Haus, Shane Hearne, Heikki Järvinen, Bernat Jiménez, Amal John, Marlin Juchem, Thomas Jung, Jessica Kegel, Matthias Kelbling, Kai Keller, Bruno Kinoshita, Theresa Kiszler, Daniel Klocke, Lukas Kluft, Nikolay Koldunov, Tobias Kölling, Joonas Kolstela, Luis Kornblueh, Sergey Kosukhin, Aleksander Lacima-Nadolnik, Jeisson Javier Leal Rojas, Jonni Lehtiranta, Tuomas Lunttila, Anna Luoma, Pekka Manninen, Alexey Medvedev, Sebastian Milinski, Ali Omar Abdelazim Mohammed, Sebastian Müller, Devaraju Naryanappa, Natalia Nazarova, Sami Niemelä, Bimochan Niraula, Henrik Nortamo, Aleksi Nummelin, Matteo Nurisso, Pablo Ortega, Stella Paronuzzi, Xabier Pedruzo Bagazgoitia, Charles Pelletier, Carlos Peña, Suraj Polade, Himansu Pradhan, Rommel Quintanilla, Tiago Quintino, Thomas Rackow, Jouni Räisänen, Maqsood Mubarak Rajput, René Redler, Balthasar Reuter, Nuno Rocha Monteiro, Francesc Roura-Adserias, Silva Ruppert, Susan Sayed, Reiner Schnur, Tanvi Sharma, Dmitry Sidorenko, Outi Sievi-Korte, Albert Soret, Christian Steger, Bjorn Stevens, Jan Streffing, Jaleena Sunny, Luiggi Tenorio, Stephan Thober, Ulf Tigerstedt, Oriol Tinto, Juha Tonttila, Heikki Tuomenvirta, Lauri Tuppi, Ginka Van Thielen, Emanuele Vitali, Jost von Hardenberg, Ingo Wagner, Nils Wedi, Jan Wehner, Sven Willner, Xavier Yepes-Arbós, Florian Ziemen, and Janos Zimmermann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2198, https://doi.org/10.5194/egusphere-2025-2198, 2025
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The Climate Change Adaptation Digital Twin (Climate DT) pioneers the operationalisation of climate projections. The system produces global simulations with local granularity for adaptation decision-making. Applications are embedded to generate tailored indicators. A unified workflow orchestrates all components in several supercomputers. Data management ensures consistency and streaming enables real-time use. It is a complementary innovation to initiatives like CMIP, CORDEX, and climate services.
Jan Řehoř, Rudolf Brázdil, Oldřich Rakovec, Martin Hanel, Milan Fischer, Rohini Kumar, Jan Balek, Markéta Poděbradská, Vojtěch Moravec, Luis Samaniego, Yannis Markonis, and Miroslav Trnka
Hydrol. Earth Syst. Sci., 29, 3341–3358, https://doi.org/10.5194/hess-29-3341-2025, https://doi.org/10.5194/hess-29-3341-2025, 2025
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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, with clusters of 775 and 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.
Sebastian Müller, Martin Lange, Thomas Fischer, Sara König, Matthias Kelbling, Jeisson Javier Leal Rojas, and Stephan Thober
Geosci. Model Dev., 18, 4483–4498, https://doi.org/10.5194/gmd-18-4483-2025, https://doi.org/10.5194/gmd-18-4483-2025, 2025
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This study presents FINAM (
FINAM is not a model), a new coupling framework written in Python to dynamically connect 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.
Pia Ebeling, Andreas Musolff, Rohini Kumar, Andreas Hartmann, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 29, 2925–2950, https://doi.org/10.5194/hess-29-2925-2025, https://doi.org/10.5194/hess-29-2925-2025, 2025
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Groundwater is a crucial resource at risk due to droughts. To understand drought effects on groundwater levels in Germany, we grouped 6626 wells into six regional and two national patterns. Weather explained half of the level variations with varied response times. Shallow groundwater responds fast and is more vulnerable to short droughts (a 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
Nat. Hazards Earth Syst. Sci., 25, 2115–2135, https://doi.org/10.5194/nhess-25-2115-2025, https://doi.org/10.5194/nhess-25-2115-2025, 2025
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This study measures the direct effects of droughts in association with other extreme weather events on agriculture in Germany at the district level. Using a statistical yield model, we quantify the direct damage of extremes on crop yields and farm revenue. Extreme events during drought cause an average annual damage of EUR 781 million, accounting for 45 % of reported revenue losses. The insights herein can help develop better strategies for managing and mitigating the effects of future climate extremes.
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., 25, 2007–2029, https://doi.org/10.5194/nhess-25-2007-2025, https://doi.org/10.5194/nhess-25-2007-2025, 2025
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The July 2021 flood in central Europe was one of the deadliest floods in Europe in the recent 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 had been only slightly different. The presented methodology of spatial counterfactuals generates plausible unprecedented events and helps to better prepare for future extreme floods.
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, Lukas Gudmundsson, 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
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
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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 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.
Masooma Batool, Fanny J. Sarrazin, and Rohini Kumar
Earth Syst. Sci. Data, 17, 881–916, https://doi.org/10.5194/essd-17-881-2025, https://doi.org/10.5194/essd-17-881-2025, 2025
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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 the EU 27 tripling over 170 years. Our dataset enables flexible aggregation at various spatial scales, providing critical insights for land and water management strategies.
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.
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.
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.
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.
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...
