Articles | Volume 17, issue 17
https://doi.org/10.5194/gmd-17-6819-2024
© Author(s) 2024. 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-17-6819-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
12 Sep 2024
Development and technical paper |
| 12 Sep 2024
| 12 Sep 2024
Regionalization in global hydrological models and its impact on runoff simulations: a case study using WaterGAP3 (v 1.0.0)
Institute of Engineering Hydrology and Water Resources Management, Ruhr University Bochum, 44801, Bochum, Germany
Nina Kupzig
Faculty of Management and Economics, Ruhr University Bochum, 44780, Bochum, Germany
Martina Flörke
Institute of Engineering Hydrology and Water Resources Management, Ruhr University Bochum, 44801, Bochum, Germany
Related authors
No articles found.
Hannes Müller Schmied, Tim Trautmann, Sebastian Ackermann, Denise Cáceres, Martina Flörke, Helena Gerdener, Ellen Kynast, Thedini Asali Peiris, Leonie Schiebener, Maike Schumacher, and Petra Döll
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-213, https://doi.org/10.5194/gmd-2023-213, 2023
Preprint under review for GMD
Short summary
Short summary
Assessing water availability and water use at the global scale is challenging but essential for a range of purposes. We describe the newest version of the global hydrological model WaterGAP which has been used for numerous water resources assessments since 1996. We show the effects of new model features and model evaluations against observed streamflow and water storage anomalies as well as water abstractions statistics. The publically available model output for several variants is described.
Vili Virkki, Elina Alanärä, Miina Porkka, Lauri Ahopelto, Tom Gleeson, Chinchu Mohan, Lan Wang-Erlandsson, Martina Flörke, Dieter Gerten, Simon N. Gosling, Naota Hanasaki, Hannes Müller Schmied, Niko Wanders, and Matti Kummu
Hydrol. Earth Syst. Sci., 26, 3315–3336, https://doi.org/10.5194/hess-26-3315-2022, https://doi.org/10.5194/hess-26-3315-2022, 2022
Short summary
Short summary
Direct and indirect human actions have altered streamflow across the world since pre-industrial times. Here, we apply a method of environmental flow envelopes (EFEs) that develops the existing global environmental flow assessments by methodological advances and better consideration of uncertainty. By assessing the violations of the EFE, we comprehensively quantify the frequency, severity, and trends of flow alteration during the past decades, illustrating anthropogenic effects on streamflow.
Hannes Müller Schmied, Denise Cáceres, Stephanie Eisner, Martina Flörke, Claudia Herbert, Christoph Niemann, Thedini Asali Peiris, Eklavyya Popat, Felix Theodor Portmann, Robert Reinecke, Maike Schumacher, Somayeh Shadkam, Camelia-Eliza Telteu, Tim Trautmann, and Petra Döll
Geosci. Model Dev., 14, 1037–1079, https://doi.org/10.5194/gmd-14-1037-2021, https://doi.org/10.5194/gmd-14-1037-2021, 2021
Short summary
Short summary
In a globalized world with large flows of virtual water between river basins and international responsibilities for the sustainable development of the Earth system and its inhabitants, quantitative estimates of water flows and storages and of water demand by humans are required. Global hydrological models such as WaterGAP are developed to provide this information. Here we present a thorough description, evaluation and application examples of the most recent model version, WaterGAP v2.2d.
Robert Reinecke, Hannes Müller Schmied, Tim Trautmann, Lauren Seaby Andersen, Peter Burek, Martina Flörke, Simon N. Gosling, Manolis Grillakis, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Wim Thiery, Yoshihide Wada, Satoh Yusuke, and Petra Döll
Hydrol. Earth Syst. Sci., 25, 787–810, https://doi.org/10.5194/hess-25-787-2021, https://doi.org/10.5194/hess-25-787-2021, 2021
Short summary
Short summary
Billions of people rely on groundwater as an accessible source of drinking water and for irrigation, especially in times of drought. Groundwater recharge is the primary process of regenerating groundwater resources. We find that groundwater recharge will increase in northern Europe by about 19 % and decrease by 10 % in the Amazon with 3 °C global warming. In the Mediterranean, a 2 °C warming has already lead to a reduction in recharge by 38 %. However, these model predictions are uncertain.
Xingcai Liu, Wenfeng Liu, Hong Yang, Qiuhong Tang, Martina Flörke, Yoshimitsu Masaki, Hannes Müller Schmied, Sebastian Ostberg, Yadu Pokhrel, Yusuke Satoh, and Yoshihide Wada
Hydrol. Earth Syst. Sci., 23, 1245–1261, https://doi.org/10.5194/hess-23-1245-2019, https://doi.org/10.5194/hess-23-1245-2019, 2019
Short summary
Short summary
Human activities associated with water resource management have significantly increased in China during the past decades. This assessment helps us understand how streamflow has been affected by climate and human activities in China. Our analyses indicate that the climate impact has dominated streamflow changes in most areas, and human activities (in terms of water withdrawals) have increasingly decreased streamflow in the northern basins of China which are vulnerable to future climate change.
Jaap Schellekens, Emanuel Dutra, Alberto Martínez-de la Torre, Gianpaolo Balsamo, Albert van Dijk, Frederiek Sperna Weiland, Marie Minvielle, Jean-Christophe Calvet, Bertrand Decharme, Stephanie Eisner, Gabriel Fink, Martina Flörke, Stefanie Peßenteiner, Rens van Beek, Jan Polcher, Hylke Beck, René Orth, Ben Calton, Sophia Burke, Wouter Dorigo, and Graham P. Weedon
Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, https://doi.org/10.5194/essd-9-389-2017, 2017
Short summary
Short summary
The dataset combines the results of 10 global models that describe the global continental water cycle. The data can be used as input for water resources studies, flood frequency studies etc. at different scales from continental to medium-scale catchments. We compared the results with earth observation data and conclude that most uncertainties are found in snow-dominated regions and tropical rainforest and monsoon regions.
Christof Schneider, Martina Flörke, Lucia De Stefano, and Jacob D. Petersen-Perlman
Hydrol. Earth Syst. Sci., 21, 2799–2815, https://doi.org/10.5194/hess-21-2799-2017, https://doi.org/10.5194/hess-21-2799-2017, 2017
Short summary
Short summary
Riparian wetlands are disappearing worldwide due to altered river flow regimes. The WaterGAP3 modeling framework is used to compare modified to natural flow regimes at 93 Ramsar sites. Results indicate that water resource management seriously impairs inundation patterns at 29 % of the sites. New dam initiatives are likely to affect especially wetlands located in South America, Asia, and the Balkan Peninsula. Hotspots for climate change impacts could be eastern Europe and South America.
Hannes Müller Schmied, Linda Adam, Stephanie Eisner, Gabriel Fink, Martina Flörke, Hyungjun Kim, Taikan Oki, Felix Theodor Portmann, Robert Reinecke, Claudia Riedel, Qi Song, Jing Zhang, and Petra Döll
Proc. IAHS, 374, 53–62, https://doi.org/10.5194/piahs-374-53-2016, https://doi.org/10.5194/piahs-374-53-2016, 2016
Short summary
Short summary
We analyzed simulated water balance components on global and continental scale as impacted by the uncertainty of climate forcing datasets. On average, around 62 % of precipitation on global land area evapotranspires and 38 % is discharge to oceans and inland sinks. Human water use increased during the 20th century by a factor of 5. Uncertainty of precipitation variable has most impact on model results, followed by shortwave downward radiation. Model calibration reduces this uncertainty.
Hannes Müller Schmied, Linda Adam, Stephanie Eisner, Gabriel Fink, Martina Flörke, Hyungjun Kim, Taikan Oki, Felix Theodor Portmann, Robert Reinecke, Claudia Riedel, Qi Song, Jing Zhang, and Petra Döll
Hydrol. Earth Syst. Sci., 20, 2877–2898, https://doi.org/10.5194/hess-20-2877-2016, https://doi.org/10.5194/hess-20-2877-2016, 2016
Short summary
Short summary
The assessment of water balance components of the global land surface by means of hydrological models is affected by large uncertainties, in particular related to meteorological forcing. We analyze the effect of five state-of-the-art forcings on water balance components at different spatial and temporal scales modeled with WaterGAP. Furthermore, the dominant effect (precipitation/human alteration) for long-term changes in river discharge is assessed.
Y. Wada, M. Flörke, N. Hanasaki, S. Eisner, G. Fischer, S. Tramberend, Y. Satoh, M. T. H. van Vliet, P. Yillia, C. Ringler, P. Burek, and D. Wiberg
Geosci. Model Dev., 9, 175–222, https://doi.org/10.5194/gmd-9-175-2016, https://doi.org/10.5194/gmd-9-175-2016, 2016
Short summary
Short summary
The Water Futures and Solutions (WFaS) initiative coordinates its work with other ongoing scenario efforts for the sake of establishing a consistent set of new global water scenarios based on the shared socio-economic pathways (SSPs) and the representative concentration pathways (RCPs). The WFaS "fast-track" assessment uses three global water models, H08, PCR-GLOBWB, and WaterGAP, to provide the first multi-model analysis of global water use for the 21st century based on the water scenarios.
H. Müller Schmied, S. Eisner, D. Franz, M. Wattenbach, F. T. Portmann, M. Flörke, and P. Döll
Hydrol. Earth Syst. Sci., 18, 3511–3538, https://doi.org/10.5194/hess-18-3511-2014, https://doi.org/10.5194/hess-18-3511-2014, 2014
G. Forzieri, L. Feyen, R. Rojas, M. Flörke, F. Wimmer, and A. Bianchi
Hydrol. Earth Syst. Sci., 18, 85–108, https://doi.org/10.5194/hess-18-85-2014, https://doi.org/10.5194/hess-18-85-2014, 2014
P. J. Ward, S. Eisner, M. Flörke, M. D. Dettinger, and M. Kummu
Hydrol. Earth Syst. Sci., 18, 47–66, https://doi.org/10.5194/hess-18-47-2014, https://doi.org/10.5194/hess-18-47-2014, 2014
C. Schneider, C. L. R. Laizé, M. C. Acreman, and M. Flörke
Hydrol. Earth Syst. Sci., 17, 325–339, https://doi.org/10.5194/hess-17-325-2013, https://doi.org/10.5194/hess-17-325-2013, 2013
Related subject area
Hydrology
STORM v.2: A simple, stochastic rainfall model for exploring the impacts of climate and climate change at and near the land surface in gauged watersheds
Fluvial flood inundation and socio-economic impact model based on open data
RoGeR v3.0.5 – a process-based hydrological toolbox model in Python
Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments
An open-source refactoring of the Canadian Small Lakes Model for estimates of evaporation from medium-sized reservoirs
EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions
Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software
HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications
Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community
HydroFATE (v1): a high-resolution contaminant fate model for the global river system
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
Prediction of Hysteretic Matric Potential Dynamics Using Artificial Intelligence: Application of Autoencoder Neural Networks
Generalized drought index: A novel multi-scale daily approach for drought assessment
GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications
GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers
mesas.py v1.0: a flexible Python package for modeling solute transport and transit times using StorAge Selection functions
rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment
GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model
Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Development and performance of a high-resolution surface wave and storm surge forecast model (COASTLINES-LO): Application to a large lake
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
Deep Dive into Global Hydrologic Simulations: Harnessing the Power of Deep Learning and Physics-informed Differentiable Models (δHBV-globe1.0-hydroDL)
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
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)
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
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
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
Short summary
Short summary
STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
Short summary
Short summary
River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
Short summary
Short summary
The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
Short summary
Short summary
This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
Short summary
Short summary
This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
Short summary
Short summary
The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
Short summary
Short summary
Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
Short summary
Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
Short summary
Short summary
We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
Short summary
Short summary
The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
Short summary
Short summary
Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Matevž Vremec, Raoul Collenteur, and Steffen Birk
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-63, https://doi.org/10.5194/gmd-2024-63, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Geoscientists commonly use various Potential EvapoTranpiration (PET) formulas for environmental studies, which can be prone to errors and sensitive to climate change. PyEt, a tested and open-source Python package, simplifies the application of 20 PET methods for both time series and gridded data, ensuring accurate and consistent PET estimations suitable for a wide range of environmental applications.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
Short summary
Short summary
We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-407, https://doi.org/10.5194/egusphere-2024-407, 2024
Short summary
Short summary
The soil water potential (SWP) determines various soil water processes. Because it cannot be measured directly by remote sensing techniques, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of SWP.
João Careto, Rita Cardoso, Ana Russo, Daniela Lima, and Pedro Soares
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-9, https://doi.org/10.5194/gmd-2024-9, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
In this study, a new drought index is proposed, which not only is able to identify the same events but also can improve the results obtained from other established drought indices. The index is empirically based and is extremely straightforward to compute. It is as well, a daily drought index with the ability to not only assess flash droughts but also events at longer aggregation scales, such as the traditional monthly indices.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
Short summary
Short summary
Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
Short summary
Short summary
We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
Short summary
Short summary
Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in 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 codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
Short summary
Short summary
This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a 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.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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.
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.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-151, https://doi.org/10.5194/gmd-2023-151, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We develop an operational forecast system, COATLINES-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model requires a relatively small computational demand and results compare well with near real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and waves predictions can improve in accuracy.
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.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-190, https://doi.org/10.5194/gmd-2023-190, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Accurate hydrological modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall-runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
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.
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.
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.
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.
Cited articles
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.
Arsenault, R. and Brissette, F. P.: Continuous streamflow prediction in ungauged basins: The effects of equifinality and parameter set selection on uncertainty in regionalization approaches, Water Resour. Res., 50, 6135–6153, https://doi.org/10.1002/2013WR014898, 2014.
Ayzel, G. V., Gusev, E. M., and Nasonova, O. N.: River runoff evaluation for ungauged watersheds by SWAP model. 2. Application of methods of physiographic similarity and spatial geostatistics, Water Resour., 4, 547–558, https://doi.org/10.1134/S0097807817040029, 2017.
Barbarossa, V., Bosmans, J., Wanders, N., King, H., Bierkens, M. F. P., Huijbregts, M. A. J., and Schipper, A. M.: Threats of global warming to the world's freshwater fishes, Nat. Commun., 12, 1701, https://doi.org/10.1038/s41467-021-21655-w, 2021.
Batjes, N. H.: ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global grid (ver. 1.2), ISRIC [data set], https://data.isric.org/geonetwork/srv/eng/catalog.search#/metadata/82f3d6b0-a045-4fe2-b960-6d05bc1f37c0 (last access: 22 June 2020), 2012.
Beck, H. E., van Dijk, A. I. J. M., Roo, A. de, 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.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R., and Schellekens, J.: Global evaluation of runoff from 10 state-of-the-art hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903, https://doi.org/10.5194/hess-21-2881-2017, 2017.
Beck, H. E., Pan, M., Lin, P., Seibert, J., van Dijk, A. I. J. M., and Wood, E. F: Global Fully Distributed Parameter Regionalization Based on Observed Streamflow From 4,229 Headwater Catchments, J. Geophys. Res.-Atmos., 125, e2019JD031485, https://doi.org/10.1029/2019JD031485, 2020.
Benjamini, Y. and Hochberg, Y.: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing, J. Roy. Stat. Soc. B, 57, 289–300, 1995.
Boulange, J, Hanasaki, N, Yamazaki, D., and Pokhrel, Y.: Role of dams in reducing global flood exposure under climate change, Nat. Commun., 12, 417, https://doi.org/10.1038/s41467-020-20704-0, 2021.
Box, G. E. P. and Cox, D. R.: An analysis of transformations, J. Roy. Stat. Soc. B, 26, 211–252, 1964.
Breimann, L.: Random Forests, Mach. Learn., 45, 1–32, https://doi.org/10.1023/A:1010933404324, 2001.
Chaney, N. W., Herman, J. D., Ek, M. B., and Wood, E. F.: Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning, J. Geophys. Res.-Atmos., 121, 13218–13235, https://doi.org/10.1002/2016JD024821, 2016.
Charrad, M., Ghazzali, N., Boiteau, V., and Niknafs, A.: NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set, J. Stat. Softw., 61, 1–36, https://doi.org/10.18637/jss.v061.i06, 2014.
Clark, M. P., Vogel, R. M., Lamontagne, J. R., Mizukami, N., Knoben, W. J. M., Tang, G., Gharari, S., Freer, J. E., Whitfield, P. H., Shook, K., and Papalexiou, S. M.: The abuse of popular performance metrics in hydrologic modelling, Water Resour. Res., 57, e2020WR029001, https://doi.org/10.1029/2020WR029001, 2021.
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.
Döll, P. and Fiedler, K.: Global-scale modeling of groundwater recharge, Hydrol. Earth Syst. Sci., 12, 863–885, https://doi.org/10.5194/hess-12-863-2008, 2008.
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, https://doi.org/10.1016/S0022-1694(02)00283-4, 2003.
Döll, P., Hasan, H. M. M., Schulze, K., Gerdener, H., Börger, L., Shadkam, S., Ackermann, S., Hosseini-Moghari, S.-M., Müller Schmied, H., Güntner, A., and Kusche, J.: Leveraging multi-variable observations to reduce and quantify the output uncertainty of a global hydrological model: evaluation of three ensemble-based approaches for the Mississippi River basin, Hydrol. Earth Syst. Sci., 28, 2259–2295, https://doi.org/10.5194/hess-28-2259-2024, 2024.
Draper, C. S., Walker, J. P., Steinle, P. J., de Jeu, R. A. M., and Holmes, T. R. H.: An evaluation of AMSR–E derived soil moisture over Australia, Remote Sens. Environ., 113, 703–710, https://doi.org/10.1016/j.rse.2008.11.011, 2008.
Earth Resources Observation and Science Center, U.S. Geological Survey, U.S. Department of the Interior: USGS 30 ARC-second Global Elevation Data, GTOPO30. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/A1Z4-EE71, 1997.
Eisner, S.: Comprehensive Evaluation of the WaterGAP3 Model across Climatic, Physiographic, and Anthropogenic Gradients, Ph.D. thesis, University of Kassel, Kassel, Germany, 128 pp., 2016.
Feigl, M., Thober, S., Schweppe, R., Herrnegger, M., Samaniego, L., and Schulz, K.: Automatic Regionalization of Model Parameters for Hydrological Models, Water Resour. Res., 58, e2022WR031966, https://doi.org/10.1029/2022WR031966, 2022.
Flörke, M., Kynast, E., Eisner, S., Verzano, K., Kupzig, J., Voß, F., Lehner, B., Rivera, J., aus der Beek, T., aus der Beek, M., Malsy, M., and Alcamo, J.: WaterGAP3 (v1.0.0), Zenodo [software], https://doi.org/10.5281/zenodo.10940380, 2024.
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land, Cover Type Yearly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Golian, S., Murphy, C., and Meresa, H.: Regionalization of hydrological models for flow estimation in ungauged catchments in Ireland, J. Hydrol.-Regional Studies, 36, 100859, https://doi.org/10.1016/j.ejrh.2021.100859, 2021.
GRDC: The Global Runoff Data Centre, 56068 Koblenz, Germany, 2020.
Gudmundsson, L., Tallaksen, L. M., Stahl, K., Clark, D. B., Dumont, E., Hagemann, S., Bertrand, N., Gerten, D., Heinke, J., Hanasaki, N., Voss, F., & Koirala, S.: Comparing Large-Scale Hydrological Model Simulations to Observed Runoff Percentiles in Europe, J. Hydrometeorol., 13, 604–620, https://doi.org/10.1175/JHM-D-11-083.1, 2012.
Guo, Y., Zhang, Y., Zhang, L., and Wang, Z.: Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review, WIREs Water, 8, e1487, https://doi.org/10.1002/wat2.1487, 2020.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34, 751–763, https://doi.org/10.1029/97WR03495, 1998.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
He, Y., Bárdossy, A., and Zehe, E.: A review of regionalisation for continuous streamflow simulation, Hydrol. Earth Syst. Sci., 15, 3539–3553, https://doi.org/10.5194/hess-15-3539-2011, 2011.
Herbert, C. and Döll, P.: Analyzing the informative value of alternative hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale, Nat. Hazards Earth Syst. Sci., 23, 2111–2131, https://doi.org/10.5194/nhess-23-2111-2023, 2023.
Jansen, K. F., Teuling, A. J., Craig, J. R., Dal Molin, M., Knoben, W. J. M., Parajka, J., Vis, M., and Melsen, L. A.: Mimicry of a conceptual hydrological model (HBV): What's in a name?, Water Resour. Res., 57, e2020WR029143, https://doi.org/10.1029/2020WR029143, 2022.
Janssen, P. H. M. and Heuberger, P. S. C.: Calibration of process-oriented models, Ecol. Model., 83, 55–66, https://doi.org/10.1016/0304-3800(95)00084-9, 1995.
Jones, E. R., Bierkens, M. F. P., and van Vliet, M. T. H.: Current and future global water scarcity intensifies when accounting for surface water quality, Nat. Clim. Change, 14, 629–635, https://doi.org/10.1038/s41558-024-02007-0, 2024.
Kaspar, F.: Entwicklung und Unsicherheitsanalyse eines globalen hydrologischen Modells, Ph.D. thesis, University of Kassel, Kassel, Germany, 129 pp., 2004.
Khosa, F. V., Mateyisi, M. J., van der Merwe, M. R., Feig, G. T., Engelbrecht, F. A., and Savage, M. J.: Evaluation of soil moisture from CCAM-CABLE simulation, satellite-based models estimates and satellite observations: a case study of Skukuza and Malopeni flux towers, Hydrol. Earth Syst. Sci., 24, 1587–1609, https://doi.org/10.5194/hess-24-1587-2020, 2020.
Kiers, H. A. L. and Smilde, A. K.: A comparison of various methods for multivariate regression with highly collinear variables, Stat. Meth. Appl., 16, 193–228, https://doi.org/10.1007/s10260-006-0025-5, 2007.
Krabbenhoft, C. A., Allen, G. H., Lin, P., Godsey, S. E., Allen, D. C., Burrows, R. M., DelVecchia, A. G., Fritz, K. M., Shanafield, M., Burgin, A. J., Zimmer, M. A., Datry, T., Dodds, W. K., Jones, C. N., Mims, M. C., Franklin, C., Hammond, J. C., Zipper, S., Ward, A. S., and Olden, J. D.: Assessing placement bias of the global river gauge network, Nat. Sustain., 5, 586–592, https://doi.org/10.1038/s41893-022-00873-0, 2022.
Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, https://doi.org/10.5194/adgeo-5-89-2005, 2005.
Kroll, C., Lutz, J., Allen, B., and Vogel, R. M.: Developing a Watershed Characteristics Database to Improve Low Streamflow Prediction, J. Hydrol. Eng., 9, 116–125, https://doi.org/10.1061/(ASCE)1084-0699(2004)9:2(116), 2004.
Kroll, C. N. and Song P.: Impact of Multicollinearity on Small Sample Hydrologic Regression Models, Water Resour. Res., 49, 3756–3769, https://doi.org/10.1002/wrcr.20315, 2013.
Kupzig, J.: JKupzig/regionalization_watergap3: Revised Manuscript (v1.1) (v.1.1.2), Zenodo [code and data set], https://doi.org/10.5281/zenodo.13122859, 2024.
Kupzig, J., Reinecke, R., Pianosi, F., Flörke, M., and Wagener, T.: Towards parameter estimation in global hydrological models, Environ. Res. Lett., 18, 74023, https://doi.org/10.1088/1748-9326/acdae8, 2023.
Lange, S.: EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI), V. 1.1, GFZ Data Services [data set], https://doi.org/10.5880/pik.2019.004, 2019.
Lebecherel, L., Andréassian, V., and Perrin, C.: On evaluating the robustness of spatial-proximity-based regionalization methods, J. Hydrol., 539, 196–203, https://doi.org/10.1016/j.jhydrol.2016.05.031, 2016.
Lehner, B. and Döll, P: Development and validation of a global database of lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22, https://doi.org/10.1016/j.jhydrol.2004.03.028, 2004.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from spaceborne elevation data, Eos, Transactions, AGU, 89, 93–94, https://doi.org/10.1029/2008EO100001, 2008.
Liam, A. and Wiener, M.: Classification and Regression by randomForest, R News, 2, 18–22, 2002.
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and Bergström, S.: Development and test of the distributed HBV-96 hydrological model, J. Hydrol., 201, 272–288, https://doi.org/10.1016/S0022-1694(97)00041-3, 1997.
McIntyre, N, Lee, H., Wheater, H., Young, A., and Wagener, T.: Ensemble predictions of runoff in ungauged catchments, Water Resour. Res., 41, W12434, https://doi.org/10.1029/2005WR004289, 2005.
Merz, R. and Blöschl, G.: Regionalisation of catchment model parameters, J. Hydrol., 287, 95–123, https://doi.org/10.1016/j.jhydrol.2003.09.028, 2004.
Müller Schmied, H., Cáceres, D., Eisner, S., Flörke, M., Herbert, C., Niemann, C., Peiris, T. A., Popat, E., Portmann, F. T., Reinecke, R., Schumacher, M., Shadkam, S., Telteu, C.-E., Trautmann, T., and Döll, P.: The global water resources and use model WaterGAP v2.2d: model description and evaluation, Geosci. Model Dev., 14, 1037–1079, https://doi.org/10.5194/gmd-14-1037-2021, 2021.
Müller Schmied, H., Trautmann, T., Ackermann, S., Cáceres, D., Flörke, M., Gerdener, H., Kynast, E., Peiris, T. A., Schiebener, L., Schumacher, M., and Döll, P.: The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-213, in review, 2023.
Nash, J. E. and Sutcliff, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Nijssen, B., O'Donnell, G. M., Lettenmeier, D. P., Lohmann, D., and Wood, E. F.: Predicting the Discharge of Global Rivers, Am. Meteorol. Soc., 3307–3323, https://doi.org/10.1175/1520-0442(2001)014<3307:PTDOGR>2.0.CO;2, 2000.
Oloruntoba, B. J., Kollet, S., Montzka, C., Vereecken, H., and Hendricks Franssen, H.-J.: High Resolution Land Surface Modelling over Africa: the role of uncertain soil properties in combination with temporal model resolution, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-3132, 2024.
Onyutha, C.: Pros and cons of various efficiency criteria for hydrological model performance evaluation, Proc. IAHS, 385, 181–187, https://doi.org/10.5194/piahs-385-181-2024, 2024.
Oudin, L., Andréassian, V., Perrin, C., Michel, C., and Le Moine, N.: 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.
Oudin, L., Kay, A., Andréassian, V., and Perrin, C.: Are seemingly physically similar catchments truly hydrologically similar?, Water Resour. Res., 46, W11558, https://doi.org/10.1029/2009WR008887, 2010.
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.
Parajka, J., Merz, R., and Blöschl, G.: A comparison of regionalisation methods for catchment model parameters, Hydrol. Earth Syst. Sci., 9, 157–171, https://doi.org/10.5194/hess-9-157-2005, 2005.
Poissant, D., Arsenault, R., and Brissette, F.: Impact of parameter set dimensionality and calibration procedures on streamflow prediction at ungauged catchments, J. Hydrol.-Regional Studies, 12, 220–237, https://doi.org/10.1016/j.ejrh.2017.05.005, 2017.
Pool, S., Vis, M., and Seibert, J.: Regionalization for ungauged catchments – Lessons learned from a comparative large-sample study, Water Resour. Res., 57, e2021WR030437, https://doi.org/10.1029/2021WR030437, 2021.
Qi, W., Chen, J., Li, L., Xu, C., Li, J., Xiang, Y., and Zhang, S.: A framework to regionalize conceptual model parameters for global hydrological modeling, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-127, 2020.
Razavi, T. and Coulibaly, P.: Streamflow Prediction in Ungauged Basins: Review of Regionalization Methods, J. Hydrol. Eng., 18, 958–975, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000690, 2013.
R Core Team.: R: A language and environment for statistical computing R Foundation for Statistical Computing, Vienna, Austria, https://www.r-project.org/ (last access: 9 September 2023), 2020.
Reichl, J. P. C., Western, A. W., McIntyre, N. R., and Chiew, F. H. S.: Optimization of a Similarity Measure for Estimating Ungauged Streamflow, Water Resour. Res., 45, W10423, https://doi.org/10.1029/2008WR007248, 2009.
Ritter, A. and Muñoz-Carpena, R.: Performance evaluation of hydrological models: Statistical significance for reducing subjectivity in goodness-of-fit assessments, J. Hydrol., 480, 33–45, https://doi.org/10.1016/j.jhydrol.2012.12.004, 2013.
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523, https://doi.org/10.1029/2008WR007327, 2010.
Schaefli, B. and Gupta, H. V.: Do Nash values have value?, Hydrol. Process., 21, 2075–2080, https://doi.org/10.1002/hyp.6825, 2007.
Schweppe, R., Thober, S., Müller, S., Kelbling, M., Kumar, R., Attinger, S., and Samaniego, L.: MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models, Geosci. Model Dev., 15, 859–882, https://doi.org/10.5194/gmd-15-859-2022, 2022.
Seibert, J.: On the need for benchmarks in hydrological modelling, Hydrol. Process., 15, 1063–1064, https://doi.org/10.1002/hyp.446, 2001.
Seibert, J., Staudinger, M., and van Meerveld, H. J. I.: Validation and Over-Parameterization – Experiences from Hydrological Modeling, in: Computer Simulation Validation, edited by: Breisbart, C. and Saam, J. S., Springer Nature Switzerland, Cham, Switzerland, 811–834, https://doi.org/10.1007/978-3-319-70766-2, 2019.
Shannon, C. E.: A Mathematical Theory of Communication, The Bell System Technical Journal, 3, 379–423, https://doi.org/10.1002/j.1538-7305.1948.tb01338.x, 1948.
Stacke, T. and Hagemann, S.: HydroPy (v1.0): a new global hydrology model written in Python, Geosci. Model Dev., 14, 7795–7816, https://doi.org/10.5194/gmd-14-7795-2021, 2021.
Tang, Y., Marshall, L., Sharma, A., and Smith, T.: Tools for investigating the prior distribution in Bayesian hydrology, J. Hydrol., 538, 551–562, https://doi.org/10.1016/j.jhydrol.2016.04.032, 2016.
Tilahun, A. B., Dürr, H. H., Schweden, K., and Flörke, M.: Perspectives on total phosphorus response in rivers: Examining the influence of rainfall extremes and post-dry rainfall, Sci. Total Environ., 940, 173677, https://doi.org/10.1016/j.scitotenv.2024.173677, 2024.
Tongal, H. and Sivakumar, B.: Cross-entropy clustering framework for catchment classification, J. Hydrol., 552, 433–446, https://doi.org/10.1016/j.jhydrol.2017.07.005, 2017.
Venables, W. N. and Ripley, B. D.: Modern Applied Statistics with S (Fourth Edition). Springer Science+Business Media New York, USA, 501 pp., ISBN 978-1-4419-3008-8, 2002
Wagener, T., Wheater, H. S., and Gupta, H. V.: Rainfall – Runoff Modelling in Gauged and Ungauged Catchments, Imperial College Press, London, UK, 332 pp., https://doi.org/10.1142/p335, 2004.
Wagener, T. and Wheater, H. S.: Parameter estimation and regionalization for continuous rainfall-runoff models including uncertainty, J. Hydrol., 320, 132–154, https://doi.org/10.1016/j.jhydrol.2005.07.015, 2006.
Ward, P. J., Jongman, B., Sperna Weiland, F., Bouwman, A., Van Beek, R., Bierkens, M. F. P., Ligtvoet, W., and Winsemius, H. C.: Assessing flood risk at the global scale: model setup, results, and sensitivity, Environ. Res. Lett., 8, 044019, https://doi.org/10.1088/1748-9326/8/4/044019, 2013.
Widén-Nilsson, E., Halldin, S., and Xu, C.: Global water-balance modelling with WASMOD-M: Parameter estimation and regionalisation, J. Hydrol., 340, 105–118, https://doi.org/10.1016/j.jhydrol.2007.04.002, 2007.
Wu, H., Zhang, J., Bao, Z., Wang, G., Wang, W., Yang, Y., and Wang, J.: Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology, Engineering, 28, 93–104, https://doi.org/10.1016/j.eng.2021.12.014, 2023.
Yang, X., Magnusson, J., Huang, S., Beldring, S., and Xu, C.: Dependence of regionalization methods on the complexity of hydrological models in multiple climatic regions, J. Hydrol., 582, 124357, https://doi.org/10.1016/j.jhydrol.2019.124357, 2020.
Yoshida, T., Hanasaki, N, Nishina, K., Boulange, J, Okada, M., and Troch, P. A.: Inference of Parameters for a Global Hydrological Model: Identifiability and Predictive Uncertainties of Climate-Based Parameters, Water Resour. Res., 58, e2021WR03066, https://doi.org/10.1029/2021WR030660, 2022.
Short summary
Valid simulation results from global hydrological models (GHMs) are essential, e.g., to studying climate change impacts. Adapting GHMs to ungauged basins requires regionalization, enabling valid simulations. In this study, we highlight the impact of regionalization of GHMs on runoff simulations using an ensemble of regionalization methods for WaterGAP3. We have found that regionalization leads to temporally and spatially varying uncertainty, potentially reaching up to inter-model differences.
Valid simulation results from global hydrological models (GHMs) are essential, e.g., to studying...
