Articles | Volume 8, issue 4
https://doi.org/10.5194/gmd-8-1233-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-1233-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
29 Apr 2015
Methods for assessment of models |
| 29 Apr 2015
Reduction of predictive uncertainty in estimating irrigation water requirement through multi-model ensembles and ensemble averaging
S. Multsch
CORRESPONDING AUTHOR
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (IFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
J.-F. Exbrayat
School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, New South Wales, Australia
M. Kirby
CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia
N. R. Viney
CSIRO Land and Water, GPO Box 1666, Canberra, ACT 2601, Australia
H.-G. Frede
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (IFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
L. Breuer
Institute for Landscape Ecology and Resources Management (ILR), Research Centre for BioSystems, Land Use and Nutrition (IFZ), Justus Liebig University Giessen, Heinrich-Buff-Ring 26, 35390 Giessen, Germany
Related authors
T. Houska, S. Multsch, P. Kraft, H.-G. Frede, and L. Breuer
Biogeosciences, 11, 2069–2082, https://doi.org/10.5194/bg-11-2069-2014, https://doi.org/10.5194/bg-11-2069-2014, 2014
S. Multsch, Y. A. Al-Rumaikhani, H.-G. Frede, and L. Breuer
Geosci. Model Dev., 6, 1043–1059, https://doi.org/10.5194/gmd-6-1043-2013, https://doi.org/10.5194/gmd-6-1043-2013, 2013
Elizabeth Gachibu Wangari, Ricky Mwangada Mwanake, Tobias Houska, David Kraus, Gretchen Maria Gettel, Ralf Kiese, Lutz Breuer, and Klaus Butterbach-Bahl
Biogeosciences, 20, 5029–5067, https://doi.org/10.5194/bg-20-5029-2023, https://doi.org/10.5194/bg-20-5029-2023, 2023
Short summary
Short summary
Agricultural landscapes act as sinks or sources of the greenhouse gases (GHGs) CO2, CH4, or N2O. Various physicochemical and biological processes control the fluxes of these GHGs between ecosystems and the atmosphere. Therefore, fluxes depend on environmental conditions such as soil moisture, soil temperature, or soil parameters, which result in large spatial and temporal variations of GHG fluxes. Here, we describe an example of how this variation may be studied and analyzed.
Ricky Mwangada Mwanake, Gretchen Maria Gettel, Elizabeth Gachibu Wangari, Clarissa Glaser, Tobias Houska, Lutz Breuer, Klaus Butterbach-Bahl, and Ralf Kiese
Biogeosciences, 20, 3395–3422, https://doi.org/10.5194/bg-20-3395-2023, https://doi.org/10.5194/bg-20-3395-2023, 2023
Short summary
Short summary
Despite occupying <1 %; of the globe, streams are significant sources of greenhouse gas (GHG) emissions. In this study, we determined anthropogenic effects on GHG emissions from streams. We found that anthropogenic-influenced streams had up to 20 times more annual GHG emissions than natural ones and were also responsible for seasonal peaks. Anthropogenic influences also altered declining GHG flux trends with stream size, with potential impacts on stream-size-based spatial upscaling techniques.
Jaqueline Stenfert Kroese, John N. Quinton, Suzanne R. Jacobs, Lutz Breuer, and Mariana C. Rufino
SOIL, 7, 53–70, https://doi.org/10.5194/soil-7-53-2021, https://doi.org/10.5194/soil-7-53-2021, 2021
Short summary
Short summary
Particulate macronutrient concentrations were up to 3-fold higher in a natural forest catchment compared to fertilized agricultural catchments. Although the particulate macronutrient concentrations were lower in the smallholder agriculture catchment, because of higher sediment loads from that catchment, the total particulate macronutrient loads were higher. Land management practices should be focused on agricultural land to reduce the loss of soil carbon and nutrients to the stream.
Amani Mahindawansha, Christoph Külls, Philipp Kraft, and Lutz Breuer
Hydrol. Earth Syst. Sci., 24, 3627–3642, https://doi.org/10.5194/hess-24-3627-2020, https://doi.org/10.5194/hess-24-3627-2020, 2020
Short summary
Short summary
Stable isotopes of soil water are an effective tool to reveal soil hydrological processes in irrigated agricultural fields. Flow mechanisms and isotopic patterns of soil water in the soil matrix differ, depending on the crop and irrigation practices. Isotope data supported the fact that unproductive water losses via evaporation can be reduced by introducing dry seasonal crops to the crop rotation system.
Michael C. Thrun, Alfred Ultsch, and Lutz Breuer
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-87, https://doi.org/10.5194/gmd-2020-87, 2020
Revised manuscript not accepted
Short summary
Short summary
We propose an explainable AI (XAI) framework for times series describing water quality & environmental parameters. The relationship between parameters is investigated by swarm based cluster analysis designed to find similar days within & dissimilar days between clusters. Resulting clusters define three states of water bodies & are visualized by a topographic map of high-dimensional structures. Rules generated by the XAI system explain clusters & improve the understanding of aquatic environments.
Florian U. Jehn, Konrad Bestian, Lutz Breuer, Philipp Kraft, and Tobias Houska
Hydrol. Earth Syst. Sci., 24, 1081–1100, https://doi.org/10.5194/hess-24-1081-2020, https://doi.org/10.5194/hess-24-1081-2020, 2020
Short summary
Short summary
We grouped 643 rivers from the United States into 10 behavioral groups based on their hydrological behavior (e.g., how much water they transport overall). Those groups are aligned with the ecoregions in the United States. Depending on the groups’ location and other characteristics, either snow, aridity or seasonality is most important for the behavior of the rivers in a group. We also find that very similar river behavior can be found in rivers far apart and with different characteristics.
Sebastian Multsch, Maarten S. Krol, Markus Pahlow, André L. C. Assunção, Alberto G. O. P. Barretto, Quirijn de Jong van Lier, and Lutz Breuer
Hydrol. Earth Syst. Sci., 24, 307–324, https://doi.org/10.5194/hess-24-307-2020, https://doi.org/10.5194/hess-24-307-2020, 2020
Short summary
Short summary
Expanding irrigation in agriculture is one of Brazil's strategies to increase production. In this study the amount of water required to grow the main crops has been calculated and compared to the water that is available in rivers at least 95 % of the time. Future decisions regarding expanding irrigated cropping areas must, while intensifying production practices, consider the likely regional effects on water scarcity levels, in order to reach sustainable agricultural production.
Suzanne R. Jacobs, Edison Timbe, Björn Weeser, Mariana C. Rufino, Klaus Butterbach-Bahl, and Lutz Breuer
Hydrol. Earth Syst. Sci., 22, 4981–5000, https://doi.org/10.5194/hess-22-4981-2018, https://doi.org/10.5194/hess-22-4981-2018, 2018
Short summary
Short summary
This study investigated how land use affects stream water sources and flow paths in an East African tropical montane area. Rainfall was identified as an important stream water source in the forest and smallholder agriculture sub-catchments, while springs were more important in the commercial tea plantation sub-catchment. However, 15 % or less of the stream water consisted of water with an age of less than 3 months, indicating that groundwater plays an important role in all land use types.
Florian U. Jehn, Lutz Breuer, Tobias Houska, Konrad Bestian, and Philipp Kraft
Hydrol. Earth Syst. Sci., 22, 4565–4581, https://doi.org/10.5194/hess-22-4565-2018, https://doi.org/10.5194/hess-22-4565-2018, 2018
Short summary
Short summary
By realizing that hydrological models are not one single hypothesis, but an assemblage of many hypotheses, new ways to scrutinize hydrological models are needed. Up until now, studies concentrate on comparing existing models or built models incrementally. This approach here tries to tackle the problem the other way around. We construct a complex model, containing all processes important for the catchment, and deconstruct it step by step to understand the influence of single processes.
Natalie Orlowski, Lutz Breuer, Nicolas Angeli, Pascal Boeckx, Christophe Brumbt, Craig S. Cook, Maren Dubbert, Jens Dyckmans, Barbora Gallagher, Benjamin Gralher, Barbara Herbstritt, Pedro Hervé-Fernández, Christophe Hissler, Paul Koeniger, Arnaud Legout, Chandelle Joan Macdonald, Carlos Oyarzún, Regine Redelstein, Christof Seidler, Rolf Siegwolf, Christine Stumpp, Simon Thomsen, Markus Weiler, Christiane Werner, and Jeffrey J. McDonnell
Hydrol. Earth Syst. Sci., 22, 3619–3637, https://doi.org/10.5194/hess-22-3619-2018, https://doi.org/10.5194/hess-22-3619-2018, 2018
Short summary
Short summary
To extract water from soils for isotopic analysis, cryogenic water extraction is the most widely used removal technique. This work presents results from a worldwide laboratory intercomparison test of cryogenic extraction systems. Our results showed large differences in retrieved isotopic signatures among participating laboratories linked to interactions between soil type and properties, system setup, extraction efficiency, extraction system leaks, and each lab’s internal accuracy.
Tobias Houska, David Kraus, Ralf Kiese, and Lutz Breuer
Biogeosciences, 14, 3487–3508, https://doi.org/10.5194/bg-14-3487-2017, https://doi.org/10.5194/bg-14-3487-2017, 2017
Short summary
Short summary
CO2 and N2O are two prominent GHGs contributing to global warming. We combined measurement and modelling to quantify GHG emissions from adjacent arable, forest and grassland sites in Germany. Measured emissions reveal seasonal patterns and management effects like fertilizer application, tillage, harvest and grazing. Modelling helps to estimate the magnitude and uncertainty of not measurable C and N fluxes and indicates missing input source, e.g. nitrate uptake from groundwater.
Natalie Orlowski, Philipp Kraft, Jakob Pferdmenges, and Lutz Breuer
Hydrol. Earth Syst. Sci., 20, 3873–3894, https://doi.org/10.5194/hess-20-3873-2016, https://doi.org/10.5194/hess-20-3873-2016, 2016
Short summary
Short summary
The 2-year measurements of δ2H and δ18O in rainfall, stream, soil, and groundwater revealed that surface and groundwater are isotopically disconnected from the annual precipitation cycle but showed bidirectional interactions in the Schwingbach catchment. We established a hydrological model to estimate spatially distributed groundwater ages and flow directions. Our model revealed complex age dynamics and showed that runoff must have been stored in the catchment for much longer than event water.
Giovanny M. Mosquera, Catalina Segura, Kellie B. Vaché, David Windhorst, Lutz Breuer, and Patricio Crespo
Hydrol. Earth Syst. Sci., 20, 2987–3004, https://doi.org/10.5194/hess-20-2987-2016, https://doi.org/10.5194/hess-20-2987-2016, 2016
Short summary
Short summary
This study focuses on the investigation of baseflow mean transit times (MTTs) in a high-elevation tropical ecosystem (páramo) using stable water isotopes. Results showed short MTTs (< 9 months) and topographic controls on their spatial variability. We conclude that (1) the hydrology of the ecosystem is dominated by shallow subsurface flow and (2) the interplay between the high storage capacity of the páramo soils and the catchments' slopes provides the ecosystem with high regulation capacity.
A. H. Aubert, O. Schnepel, P. Kraft, T. Houska, I. Plesca, N. Orlowski, and L. Breuer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-11591-2015, https://doi.org/10.5194/hessd-12-11591-2015, 2015
Revised manuscript not accepted
Short summary
Short summary
Studienlandschaft Schwingbachtal is an out-door full-scale study site since 2008. It deals with hydrology in an interdisciplinary approach and enhances active learning by various means (field monitoring, education trails and geocache). In order to adapt to the change in students habits and to suit better as a communication tool for the locals, it is newly equipped with augmented reality which adds virtual objects on the real landscape, making learning pleasant.
E. Timbe, D. Windhorst, R. Celleri, L. Timbe, P. Crespo, H.-G. Frede, J. Feyen, and L. Breuer
Hydrol. Earth Syst. Sci., 19, 1153–1168, https://doi.org/10.5194/hess-19-1153-2015, https://doi.org/10.5194/hess-19-1153-2015, 2015
Short summary
Short summary
Stream, soil and precipitation waters were collected in a tropical montane cloud forest catchment for 2 years and analyzed for stable water isotopes in order to infer transit time distribution functions and mean transit times for semi-steady-state conditions. Samples were aggregated to diverse sampling resolutions for checking the sensitivity of sampling frequency on lumped-model predictions. Results provide valuable information for the planning of future fieldwork in similar catchments.
J.-F. Exbrayat, A. J. Pitman, and G. Abramowitz
Biogeosciences, 11, 6999–7008, https://doi.org/10.5194/bg-11-6999-2014, https://doi.org/10.5194/bg-11-6999-2014, 2014
Short summary
Short summary
We use a reduced complexity soil organic carbon (SOC) model to address the influence of two parameters on the response of SOC stocks to climate change: baseline turnover time (k) and temperature sensitivity of decomposition (Q10). In our model, k determines SOC stocks and the magnitude of the response to climate change (from 1850 to 2100 under RCP 8.5) while Q10 drives its sign. We dismiss unlikely simulations using global SOC data to reduce the uncertainty in projections and parameter values.
J.-F. Exbrayat, A. J. Pitman, and G. Abramowitz
Geosci. Model Dev., 7, 2683–2692, https://doi.org/10.5194/gmd-7-2683-2014, https://doi.org/10.5194/gmd-7-2683-2014, 2014
Short summary
Short summary
Pre-industrial soil organic carbon (SOC) stocks vary 6-fold in models used in the 5th IPCC Assessment Report. This paper shows that this range is largely determined by model-specific responses of microbal decomposition during the equilibration procedure. As SOC stocks are maintained through the present and to 2100 almost unchanged, we propose that current SOC observations could be used to constrain this equilibration procedure and thereby reduce the uncertainty in climate change projections.
D. Windhorst, P. Kraft, E. Timbe, H.-G. Frede, and L. Breuer
Hydrol. Earth Syst. Sci., 18, 4113–4127, https://doi.org/10.5194/hess-18-4113-2014, https://doi.org/10.5194/hess-18-4113-2014, 2014
E. Timbe, D. Windhorst, P. Crespo, H.-G. Frede, J. Feyen, and L. Breuer
Hydrol. Earth Syst. Sci., 18, 1503–1523, https://doi.org/10.5194/hess-18-1503-2014, https://doi.org/10.5194/hess-18-1503-2014, 2014
T. Houska, S. Multsch, P. Kraft, H.-G. Frede, and L. Breuer
Biogeosciences, 11, 2069–2082, https://doi.org/10.5194/bg-11-2069-2014, https://doi.org/10.5194/bg-11-2069-2014, 2014
N. Orlowski, H.-G. Frede, N. Brüggemann, and L. Breuer
J. Sens. Sens. Syst., 2, 179–193, https://doi.org/10.5194/jsss-2-179-2013, https://doi.org/10.5194/jsss-2-179-2013, 2013
S. Multsch, Y. A. Al-Rumaikhani, H.-G. Frede, and L. Breuer
Geosci. Model Dev., 6, 1043–1059, https://doi.org/10.5194/gmd-6-1043-2013, https://doi.org/10.5194/gmd-6-1043-2013, 2013
D. Windhorst, T. Waltz, E. Timbe, H.-G. Frede, and L. Breuer
Hydrol. Earth Syst. Sci., 17, 409–419, https://doi.org/10.5194/hess-17-409-2013, https://doi.org/10.5194/hess-17-409-2013, 2013
J.-F. Exbrayat, N. R. Viney, H.-G. Frede, and L. Breuer
Geosci. Model Dev., 6, 117–125, https://doi.org/10.5194/gmd-6-117-2013, https://doi.org/10.5194/gmd-6-117-2013, 2013
Related subject area
Hydrology
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
rSHUD v2.0: Advancing Unstructured Hydrological Modeling in the R Environment
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Development of Inter-Grid Cell Lateral Unsaturated and Saturated Flow Model in the E3SM Land Model (v2.0)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Customized deep learning for precipitation bias correction and downscaling
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
mesas.py v1.0: A flexible Python package for modeling solute transport and transit times using StorAge Selection functions
GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality
Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale
RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling
Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest
Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation
A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability
CREST-VEC: a framework towards more accurate and realistic flood simulation across scales
Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains
The eWaterCycle platform for open and FAIR hydrological collaboration
Evaluating the Atibaia River hydrology using JULES6.1
A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations
Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model
Evaluating a reservoir parametrization in the vector-based global routing model mizuRoute (v2.0.1) for Earth system model coupling
Improved runoff simulations for a highly varying soil depth and complex terrain watershed in the Loess Plateau with the Community Land Model version 5
GSTools v1.3: a toolbox for geostatistical modelling in Python
AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
Modeling of streamflow in a 30 km long reach spanning 5 years using OpenFOAM 5.x
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
Short summary
Short summary
We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
Short summary
Short summary
Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
Short summary
Short summary
Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
Short summary
Short summary
Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
Short summary
Short summary
NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
Short summary
Short summary
We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
Short summary
Short summary
Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
Short summary
Short summary
DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
Short summary
Short summary
Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Lele Shu, Paul Ullrich, Xianghong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-128, https://doi.org/10.5194/gmd-2023-128, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
Short summary
Short summary
Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
Short summary
Short summary
Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
Short summary
Short summary
In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
Short summary
Short summary
We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
Short summary
Short summary
During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
EGUsphere, https://doi.org/10.5194/egusphere-2023-375, https://doi.org/10.5194/egusphere-2023-375, 2023
Short summary
Short summary
We developed and validated an inter-grid cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
Short summary
Short summary
It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
Short summary
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
Short summary
Short summary
This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
Short summary
Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
Short summary
Short summary
Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
Short summary
Short summary
Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
Short summary
Short summary
A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
Short summary
Short summary
Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
Short summary
Short summary
The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
Short summary
Short summary
A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Ciaran Harman and Esther Xu Fei
EGUsphere, https://doi.org/10.5194/egusphere-2022-1262, https://doi.org/10.5194/egusphere-2022-1262, 2022
Short summary
Short summary
Over the last 10 years scientists have developed a new way of modeling how material is transported through complex systems, called StorAge Selection. Here we present some new code implementing this method that is easy to use, but also flexible and very accurate. We show that for cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other people's code to the right answer in an important way: it conserves mass.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-226, https://doi.org/10.5194/gmd-2022-226, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1km at the equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for higher resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
Short summary
Short summary
A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
Short summary
Short summary
We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
Short summary
Short summary
Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
Short summary
Short summary
This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
Short summary
Short summary
We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
Short summary
Short summary
We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
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
Short summary
Short summary
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.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
Short summary
Short summary
This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
Ji Li, Daoxian Yuan, Fuxi Zhang, Jiao Liu, and Mingguo Ma
Geosci. Model Dev., 15, 6581–6600, https://doi.org/10.5194/gmd-15-6581-2022, https://doi.org/10.5194/gmd-15-6581-2022, 2022
Short summary
Short summary
A new karst hydrological model (the QMG model) is developed to simulate and predict the floods in karst trough valley basins. Unlike the complex structure and parameters of current karst groundwater models, this model has a simple double-layered structure with few parameters and decreases the demand for modeling data in karst areas. The flood simulation results based on the QMG model of the Qingmuguan karst trough valley basin are satisfactory, indicating the suitability of the model simulation.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
Short summary
Short summary
MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Zhi Li, Shang Gao, Mengye Chen, Jonathan Gourley, Naoki Mizukami, and Yang Hong
Geosci. Model Dev., 15, 6181–6196, https://doi.org/10.5194/gmd-15-6181-2022, https://doi.org/10.5194/gmd-15-6181-2022, 2022
Short summary
Short summary
Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes, with streamflow routing being one of the most time-consuming parts. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme with a lake module that markedly speeds up streamflow prediction. Moreover, accuracy is improved and flood false alarms are mitigated.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
Short summary
Short summary
Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
Short summary
Short summary
With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev., 15, 5233–5240, https://doi.org/10.5194/gmd-15-5233-2022, https://doi.org/10.5194/gmd-15-5233-2022, 2022
Short summary
Short summary
Land surface models allow us to understand and investigate the cause and effect of environmental process changes. Therefore, this type of model is increasingly used for hydrological assessments. Here we explore the possibility of this approach using a case study in the Atibaia River basin, which serves as a major water supply for the metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to simulate the basin hydrology.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
Short summary
Short summary
Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson
Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, https://doi.org/10.5194/gmd-15-4569-2022, 2022
Short summary
Short summary
Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, https://doi.org/10.5194/gmd-15-4275-2022, 2022
Short summary
Short summary
The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
Short summary
Short summary
Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Jiming Jin, Lei Wang, Jie Yang, Bingcheng Si, and Guo-Yue Niu
Geosci. Model Dev., 15, 3405–3416, https://doi.org/10.5194/gmd-15-3405-2022, https://doi.org/10.5194/gmd-15-3405-2022, 2022
Short summary
Short summary
This study aimed to improve runoff simulations and explore deep soil hydrological processes for a highly varying soil depth and complex terrain watershed in the Loess Plateau, China. The actual soil depths and river channels were incorporated into the model to better simulate the runoff in this watershed. The soil evaporation scheme was modified to better describe the evaporation processes. Our results showed that the model significantly improved the runoff simulations.
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
Short summary
Short summary
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.
Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho
Geosci. Model Dev., 15, 3021–3039, https://doi.org/10.5194/gmd-15-3021-2022, https://doi.org/10.5194/gmd-15-3021-2022, 2022
Short summary
Short summary
The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
Yunxiang Chen, Jie Bao, Yilin Fang, William A. Perkins, Huiying Ren, Xuehang Song, Zhuoran Duan, Zhangshuan Hou, Xiaoliang He, and Timothy D. Scheibe
Geosci. Model Dev., 15, 2917–2947, https://doi.org/10.5194/gmd-15-2917-2022, https://doi.org/10.5194/gmd-15-2917-2022, 2022
Short summary
Short summary
Climate change affects river discharge variations that alter streamflow. By integrating multi-type survey data with a computational fluid dynamics tool, OpenFOAM, we show a workflow that enables accurate and efficient streamflow modeling at 30 km and 5-year scales. The model accuracy for water stage and depth average velocity is −16–9 cm and 0.71–0.83 in terms of mean error and correlation coefficients. This accuracy indicates the model's reliability for evaluating climate impact on rivers.
Cited articles
ABARES: Land Use of Australia Version 4 2005/2006, Department of Agriculture Fisheries and Forestry, Australian Bureau of Agricultural and Resource Economics, 2010.
ABS: Water Use on Australian Farms Murray-Darling basin 2005-06, 46180DO012, 2006.
Allen, R. G.: REF-ET user's guide, University of Idaho Kimberly Research Stations, Kimberly, 2003.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56, FAO, Rome, 300, 6541, 1998.
Balkovič, J., van der Velde, M., Schmid, E., Skalský, R., Khabarov, N., Obersteiner, M., Stürmer, B., and Xiong, W.: Pan-European crop modelling with EPIC: Implementation, up-scaling and regional crop yield validation, Agr. Syst., 120, 61–75, 2013.
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, 2006.
Bocchiola, D., Nana, E., and Soncini, A.: Impact of climate change scenarios on crop yield and water footprint of maize in the Po valley of Italy, Agr. Water Manage., 116, 50–61, 2013.
Bormann, H.: Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations, Climatic Change, 104, 729–753, 2011.
Chiew, F., Wang, Q. J., McConachy, F., James, R., Wright, W., and deHoedt, G.: Evapotranspiration maps for Australia, in Hydrology and Water Resources Symposium 2002: the Water Challenge, Balancing the Risks, p. 167, Institution of Engineers, Australia, 2002.
Chiew, F. H. S. and Leahy, C.: Comparison of Evapotranspiration Variables in Evapotranspiration Maps for Australia with Commonly Used Evapotranspiration Variables, Austr. J. Water Resour., 7, 1–11, 2003.
Da Silva, V. de P., da Silva, B. B., Albuquerque, W. G., Borges, C. J., de Sousa, I. F., and Neto, J. D.: Crop coefficient, water requirements, yield and water use efficiency of sugarcane growth in Brazil, Agr. Water Manage., 128, 102–109, 2013.
De Fraiture, C. and Wichelns, D.: Satisfying future water demands for agriculture, Agr. Water Manage., 97, 502–511, 2010.
Döll, P., Kaspar, F., and Lehner, B.: A global hydrological model for deriving water availability indicators: model tuning and validation, J. Hydrol., 270, 105–134, 2003.
Donohue, R. J., Roderick, M. L., and McVicar, T. R.: Deriving consistent long-term vegetation information from AVHRR reflectance data using a cover-triangle-based framework, Remote Sens. Environ., 112, 2938–2949, 2008.
Donohue, R. J., McVicar, T. R., and Roderick, M. L.: Assessing the ability of potential evaporation formulations to capture the dynamics in evaporative demand within a changing climate, J. Hydrol., 386, 186–197, 2010.
Exbrayat, J.-F., Viney, N. R., Frede, H.-G., and Breuer, L.: Using multi-model averaging to improve the reliability of catchment scale nitrogen predictions, Geosci. Model Dev., 6, 117–125, https://doi.org/10.5194/gmd-6-117-2013, 2013.
Exbrayat, J.-F., Buytaert, Timbe, E., Windhorst, D., and Breuer, L.: Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes, Climatic Change, 125, 221–235, 2014.
Fader, M., Rost, S., Müller, C., Bondeau, A., and Gerten, D.: Virtual water content of temperate cereals and maize: Present and potential future patterns, J. Hydrol., 384, 218–231, 2010.
Fisher, J. B., Whittaker, R. J., and Malhi, Y.: ET come home: potential evapotranspiration in geographical ecology, Global Ecol. Biogeogr., 20, 1–18, 2011.
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O'Connell, C., Ray, D. K., West, P. C., Balzer, C., Bennett, E. M., Carpenter, S. R., Hill, J., Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., Tilman, D., and Zaks, D. P. M.: Solutions for a cultivated planet, Nature, 478, 337–342, https://doi.org/10.1038/nature10452, 2011.
George, B. A., Shende, S. A., and Raghuwanshi, N. S.: Development and testing of an irrigation scheduling model, Agr. Water Manage., 46, 121–136, 2000.
Giorgi, F. and Mearns, L. O.: Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the "reliability ensemble averaging" (REA) method, J. Climate, 15, 1141–1158, 2002.
Hanjra, M. A. and Qureshi, M. E.: Global water crisis and future food security in an era of climate change, Food Policy, 35, 365–377, https://doi.org/10.1016/j.foodpol.2010.05.006, 2010.
Hargreaves, G. H. and Samani, Z. A.: Reference crop evapotranspiration from temperature, Appl. Eng. Agric., 1, 96–99, 1985.
Harris, G. A.: Irrigation: water balance scheduling, Queensland Department of Primary Industries and Fisheries (DPI Note FSO546), 2002.
Hoekstra, A. Y. and Mekonnen, M. M.: The water footprint of humanity, P. Natl. Acad. Sci. USA, 109, 3232–3237, 2012.
Hoekstra, A. Y., Mekonnen, M. M., Chapagain, A. K., Mathews, R. E., and Richter, B. D.: Global monthly water scarcity: blue water footprints versus blue water availability, PlOS One, 7, e32688, https://doi.org/10.1371/journal.pone.0032688, 2012.
Howell, T. A., Evett, S. R., Tolk, J. A., and Schneider, A. D.: Evapotranspiration of full-, deficit-irrigated, and dryland cotton on the Northern Texas High Plains, J. Irrig. Drain. E., 130, 277–285, 2004.
Hughes, J. D.: Southern Irrigation SOILpak. For irrigated broad area agriculture on the Riverine Plain in the Murray and Murrumbidgee valleys, NSW Agriculture, Orange, 1999.
Huisman, J. A., Breuer, L., Bormann, H., Bronstert, A., Croke, B. F. W., Frede, H.-G., Gräff, T., Hubrechts, L., Jakeman, A. J., and Kite, G.: Assessing the impact of land use change on hydrology by ensemble modeling (LUCHEM) III: Scenario analysis, Adv. Water Resour., 32, 159–170, 2009.
Jeffrey, S. J., Carter, J. O., Moodie, K. B., and Beswick, A. R.: Using spatial interpolation to construct a comprehensive archive of Australian climate data, Environ. Modell. Softw., 16, 309–330, 2001.
Jensen, M. E.: Water consumption by agricultural plants (Chapter 1), in: Water Deficits and Plant Growth. Vol. II. Plant Water Consumption and Response, edited by: Kozlowski, T. T., Academic Press, New York & London, 1–22, 1968.
Jensen, M. E., Burman, R. D., and Allen, R. G.: Evapotranspiration and irrigation water requirements, Amer. Soc. Civil Eng. Manuals and Reports on Engineering Practice No. 70, ASCE, New York, USA, 332 pp., 1990.
Kirby, M., Mainuddin, M., Gao, L., Connor, J. D., and Ahmad, M. D.: Integrated, dynamic economic–hydrology model of the Murray-Darling Basin, in 2012 Conference (56th), 7–10 February 2012, No. 124487, Freemantle, Australia, Australian Agricultural and Resource Economics Society., 2012.
Kite, G. W. and Droogers, P.: Comparing evapotranspiration estimates from satellites, hydrological models and field data, J. Hydrol., 229, 3–18, 2000.
Ko, J., Piccinni, G., Marek, T., and Howell, T.: Determination of growth-stage-specific crop coefficients (Kc) of cotton and wheat, Agr. Water Manage., 96, 1691–1697, 2009.
Lascano, R. J.: A general system to measure and calculate daily crop water use, Agron. J., 92, 821–832, 2000.
Lascano, R. J. and Van Bavel, C. H.: Explicit and recursive calculation of potential and actual evapotranspiration, Agron. J., 99, 585–590, 2007.
Lascano, R. J., Van Bavel, C. H. M., and Evett, S. R.: A field test of recursive calculation of crop evapotranspiration, T. ASABE, 53, 1117–1126, 2010.
Lathuillière, M. J., Johnson, M. S., and Donner, S. D.: Water use by terrestrial ecosystems: temporal variability in rainforest and agricultural contributions to evapotranspiration in Mato Grosso, Brazil, Environ. Res. Lett., 7, 024024, https://doi.org/10.1088/1748-9326/7/2/024024, 2012.
Liu, J., Wiberg, D., Zehnder, A. J., and Yang, H.: Modeling the role of irrigation in winter wheat yield, crop water productivity, and production in China, Irrigation Sci., 26, 21–33, 2007.
McMahon, T. A., Peel, M. C., Lowe, L., Srikanthan, R., and McVicar, T. R.: Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis, Hydrol. Earth Syst. Sci., 17, 1331–1363, https://doi.org/10.5194/hess-17-1331-2013, 2013.
Mekonnen, M. M. and Hoekstra, A. Y.: The green, blue and grey water footprint of crops and derived crop products, Hydrol. Earth Syst. Sci., 15, 1577–1600, https://doi.org/10.5194/hess-15-1577-2011, 2011.
Meyer, W. S.: Standard reference evaporation calculation for inland, south eastern Australia, CSIRO Land and Water., 1999.
Monteith, J. L.: Evaporation and environment, in: Symp. Soc. Exp. Biol, Vol. 19, p. 4, 1965.
Morton, F. I.: Operational estimates of areal evapotranspiration and their significance to the science and practice of hydrology, J. Hydrol., 66, 1–76, 1983.
Multsch, S., Al-Rumaikhani, Y. A., Frede, H.-G., and Breuer, L.: A Site-sPecific Agricultural water Requirement and footprint Estimator (SPARE:WATER 1.0), Geosci. Model Dev., 6, 1043–1059, https://doi.org/10.5194/gmd-6-1043-2013, 2013.
Oki, T. and Kanae, S.: Global hydrological cycles and world water resources, Science, 313, 1068–1072, 2006.
Penman, H. L.: Natural evaporation from open water, bare soil and grass, P. Roy. Soc. Lond. A. Mat., 193, 120–145, 1948.
Pereira, L. S., Paredes, P., Cholpankulov, E. D., Inchenkova, O. P., Teodoro, P. R., and Horst, M. G.: Irrigation scheduling strategies for cotton to cope with water scarcity in the Fergana Valley, Central Asia, Agr. Water Manage., 96, 723–735, 2009.
Priestley, C. H. B. and Taylor, R. J.: On the assessment of surface heat flux and evaporation using large-scale parameters, Mon. Weather Rev., 100, 81–92, 1972.
Refsgaard, J. C., van der Sluijs, J. P., Højberg, A. L., and Vanrolleghem, P. A.: Uncertainty in the environmental modelling process–a framework and guidance, Environ. Modell. Softw., 22, 1543–1556, 2007.
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.: Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors, Water Resour. Res., 46, W05521, https://doi.org/10.1029/2009WR008328, 2010.
Sheffield, J., Wood, E. F., and Roderick, M. L.: Little change in global drought over the past 60 years, Nature, 491, 435–438, 2012.
Siebert, S. and Döll, P.: Quantifying blue and green virtual water contents in global crop production as well as potential production losses without irrigation, J. Hydrol., 384, 198–217, 2010.
Smith, M.: CROPWAT: A computer program for irrigation planning and management, Food and Agriculture Organization of the United Nations, Rome, Rome, Italy, 1992.
Steduto, P., Hsiao, T. C., Raes, D., and Fereres, E.: AquaCrop – The FAO crop model to simulate yield response to water: I. Concepts and underlying principles, Agron. J., 101, 426–437, 2009.
Sun, S., Wu, P., Wang, Y., Zhao, X., Liu, J., and Zhang, X.: The impacts of interannual climate variability and agricultural inputs on water footprint of crop production in an irrigation district of China, Sci. Total Environ., 444, 498–507, 2013.
Tabari, H.: Evaluation of reference crop evapotranspiration equations in various climates, Water Resour. Manag., 24, 2311–2337, 2010.
Tabari, H., Grismer, M. E., and Trajkovic, S.: Comparative analysis of 31 reference evapotranspiration methods under humid conditions, Irrigation Sci., 31, 107–117, 2013.
Toews, M. W. and Allen, D. M.: Simulated response of groundwater to predicted recharge in a semi-arid region using a scenario of modelled climate change, Environ. Res. Lett., 4, 035003, https://doi.org/10.1088/1748-9326/4/3/035003, 2009.
Turc, L.: Estimation of irrigation water requirements, potential evapotranspiration: a simple climatic formula evolved up to date, Ann. Agron., 12, 13–49, 1961.
Viney, N. R., Bormann, H., Breuer, L., Bronstert, A., Croke, B. F. W., Frede, H., Gräff, T., Hubrechts, L., Huisman, J. A., and Jakeman, A. J.: Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions, Adv. Water Resour., 32, 147–158, 2009.
Vrugt, J. A., Ter Braak, C. J., Gupta, H. V., and Robinson, B. A.: Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?, Stoch. Env. Res. Risk A, 23, 1011–1026, 2009.
Wada, Y. and Bierkens, M. F. P.: Sustainability of global water use: past reconstruction and future projections, Environ. Res. Lett., 9, 104003, https://doi.org/10.1088/1748-9326/9/10/104003, 2014.
Wada, Y., van Beek, L. P., van Kempen, C. M., Reckman, J. W., Vasak, S., and Bierkens, M. F.: Global depletion of groundwater resources, Geophys. Res. Lett., 37, L20402, https://doi.org/10.1029/2010GL044571, 2010.
Wada, Y., Wisser, D., Eisner, S., Flörke, M., Gerten, D., Haddeland, I., Hanasaki, N., Masaki, Y., Portmann, F. T., and Stacke, T.: Multimodel projections and uncertainties of irrigation water demand under climate change, Geophys. Res. Lett., 40, 4626–4632, 2013.
Wang, H., Zhang, L., Dawes, W. R., and Liu, C.: Improving water use efficiency of irrigated crops in the North China Plain – measurements and modelling, Agr. Water Manage., 48, 151–167, 2001.
Williams, J. R., Jones, C. A., Kiniry, J. R., and Spanel, D. A.: The EPIC crop growth model, T. ASAE, 32, 497–511, 1989.
Ye, B., Yang, D., and Ma, L.: Effect of precipitation bias correction on water budget calculation in Upper Yellow River, China, Environ. Res. Lett., 7, 025201, https://doi.org/10.1088/1748-9326/7/2/025201, 2012.
Short summary
Irrigation agriculture is required to sustain yields that allow feeding the world population. A robust assessment of irrigation requirement (IRR) relies on a sound quantification of evapotranspiration (ET). We prepared a multi-model ensemble considering several ET methods and investigate uncertainties in simulating IRR. More generally, we provide an example of the value of investigating the uncertainty in models that may be used to inform policy-making and to elaborate best management practices.
Irrigation agriculture is required to sustain yields that allow feeding the world population. A...
