Articles | Volume 10, issue 8
https://doi.org/10.5194/gmd-10-3001-2017
© Author(s) 2017. 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-10-3001-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
10 Aug 2017
Model description paper |
| 10 Aug 2017
lumpR 2.0.0: an R package facilitating landscape discretisation for hillslope-based hydrological models
Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany
Till Francke
Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany
Axel Bronstert
Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany
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This work investigates different model types for drought prediction in a dryland region. Consequently, the performances of seasonal reservoir volume forecasts derived by a process-based and a statistical hydrological model were evaluated. The process-based approach obtained lower accuracy while resolution and reliability of drought prediction were comparable. Initialisation of the process-based model is worthwhile for more in-depth analyses, provided adequate rainfall forecasts are available.
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This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
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The German federal state of Brandenburg is particularly prone to soil moisture droughts. To support the management of related risks, we introduce a novel soil moisture and drought monitoring network based on cosmic-ray neutron sensing technology. This initiative is driven by a collaboration of research institutions and federal state agencies, and it is the first of its kind in Germany to have started operation. In this brief communication, we outline the network design and share first results.
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Multiple methods for measuring soil moisture beyond the point scale exist. Their validation generally hindered by lack of knowing the truth. We propose a virtual framework, in which this truth is fully known and the sensor observations for Cosmic Ray Neutron Sensing, Remote Sensing, and Hydrogravimetry are simulated. This allows the rigourous testing of these virtual sensors to understand their effectiveness and limitations.
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Cosmic-ray neutron sensing (CRNS) is a non-invasive technique used to obtain estimates of soil water content (SWC) at a horizontal footprint of around 150 m and a vertical penetration depth of up to 30 cm. However, typical CRNS applications require the local calibration of a function which converts neutron counts to SWC. As an alternative, we propose a generalized function as a way to avoid the use of local reference measurements of SWC and hence a major source of uncertainty.
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Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, Christoph Mayer, and Axel Bronstert
Hydrol. Earth Syst. Sci., 27, 1841–1863, https://doi.org/10.5194/hess-27-1841-2023, https://doi.org/10.5194/hess-27-1841-2023, 2023
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We present a suitable method to reconstruct sediment export from decadal records of hydroclimatic predictors (discharge, precipitation, temperature) and shorter suspended sediment measurements. This lets us fill the knowledge gap on how sediment export from glacierized high-alpine areas has responded to climate change. We find positive trends in sediment export from the two investigated nested catchments with step-like increases around 1981 which are linked to crucial changes in glacier melt.
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Nat. Hazards Earth Syst. Sci., 23, 809–822, https://doi.org/10.5194/nhess-23-809-2023, https://doi.org/10.5194/nhess-23-809-2023, 2023
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Data-driven models are becoming more of a surrogate that overcomes the limitations of the computationally expensive 2D hydrodynamic models to map urban flood hazards. However, the model's ability to generalize outside the training domain is still a major challenge. We evaluate the performance of random forest and convolutional neural networks to predict urban floodwater depth and investigate their transferability outside the training domain.
Lena Katharina Schmidt, Till Francke, Erwin Rottler, Theresa Blume, Johannes Schöber, and Axel Bronstert
Earth Surf. Dynam., 10, 653–669, https://doi.org/10.5194/esurf-10-653-2022, https://doi.org/10.5194/esurf-10-653-2022, 2022
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Climate change fundamentally alters glaciated high-alpine areas, but it is unclear how this affects riverine sediment transport. As a first step, we aimed to identify the most important processes and source areas in three nested catchments in the Ötztal, Austria, in the past 15 years. We found that areas above 2500 m were crucial and that summer rainstorms were less influential than glacier melt. These findings provide a baseline for studies on future changes in high-alpine sediment dynamics.
Maik Heistermann, Heye Bogena, Till Francke, Andreas Güntner, Jannis Jakobi, Daniel Rasche, Martin Schrön, Veronika Döpper, Benjamin Fersch, Jannis Groh, Amol Patil, Thomas Pütz, Marvin Reich, Steffen Zacharias, Carmen Zengerle, and Sascha Oswald
Earth Syst. Sci. Data, 14, 2501–2519, https://doi.org/10.5194/essd-14-2501-2022, https://doi.org/10.5194/essd-14-2501-2022, 2022
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This paper presents a dense network of cosmic-ray neutron sensing (CRNS) to measure spatio-temporal soil moisture patterns during a 2-month campaign in the Wüstebach headwater catchment in Germany. Stationary, mobile, and airborne CRNS technology monitored the root-zone water dynamics as well as spatial heterogeneity in the 0.4 km2 area. The 15 CRNS stations were supported by a hydrogravimeter, biomass sampling, and a wireless soil sensor network to facilitate holistic hydrological analysis.
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Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
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Geosci. Instrum. Method. Data Syst., 11, 75–92, https://doi.org/10.5194/gi-11-75-2022, https://doi.org/10.5194/gi-11-75-2022, 2022
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Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools like soil moisture, snow, or vegetation. This study presents a directional shielding approach, aiming to measure in specific directions only. The results show that non-directional neutron transport blurs the signal of the targeted direction. For typical instruments, this does not allow acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates is feasible.
Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
Hydrol. Earth Syst. Sci., 25, 4807–4824, https://doi.org/10.5194/hess-25-4807-2021, https://doi.org/10.5194/hess-25-4807-2021, 2021
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Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil moisture in footprints extending over hectometres in the horizontal and decimetres in the vertical. This study, however, demonstrates the potential of CRNS to obtain spatio-temporal patterns of soil moisture beyond isolated footprints. To that end, we analyse data from a unique observational campaign that featured a dense network of more than 20 neutron detectors in an area of just 1 km2.
Erwin Rottler, Axel Bronstert, Gerd Bürger, and Oldrich Rakovec
Hydrol. Earth Syst. Sci., 25, 2353–2371, https://doi.org/10.5194/hess-25-2353-2021, https://doi.org/10.5194/hess-25-2353-2021, 2021
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The mesoscale hydrological model (mHM) forced with an ensemble of climate projection scenarios was used to assess potential future changes in flood seasonality in the Rhine River basin. Results indicate that future changes in flood characteristics are controlled by increases in precipitation sums and diminishing snowpacks. The decreases in snowmelt can counterbalance increasing precipitation, resulting in only small and transient changes in streamflow maxima.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Erwin Rottler, Till Francke, Gerd Bürger, and Axel Bronstert
Hydrol. Earth Syst. Sci., 24, 1721–1740, https://doi.org/10.5194/hess-24-1721-2020, https://doi.org/10.5194/hess-24-1721-2020, 2020
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In the attempt to identify and disentangle long-term impacts of changes in snow cover and precipitation along with reservoir constructions, we employ a set of analytical tools on hydro-climatic time series. We identify storage reservoirs as an important factor redistributing runoff from summer to winter. Furthermore, our results hint at more (intense) rainfall in recent decades. Detected increases in high discharge can be traced back to corresponding changes in precipitation.
Tobias Pilz, José Miguel Delgado, Sebastian Voss, Klaus Vormoor, Till Francke, Alexandre Cunha Costa, Eduardo Martins, and Axel Bronstert
Hydrol. Earth Syst. Sci., 23, 1951–1971, https://doi.org/10.5194/hess-23-1951-2019, https://doi.org/10.5194/hess-23-1951-2019, 2019
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This work investigates different model types for drought prediction in a dryland region. Consequently, the performances of seasonal reservoir volume forecasts derived by a process-based and a statistical hydrological model were evaluated. The process-based approach obtained lower accuracy while resolution and reliability of drought prediction were comparable. Initialisation of the process-based model is worthwhile for more in-depth analyses, provided adequate rainfall forecasts are available.
José Miguel Delgado, Sebastian Voss, Gerd Bürger, Klaus Vormoor, Aline Murawski, José Marcelo Rodrigues Pereira, Eduardo Martins, Francisco Vasconcelos Júnior, and Till Francke
Hydrol. Earth Syst. Sci., 22, 5041–5056, https://doi.org/10.5194/hess-22-5041-2018, https://doi.org/10.5194/hess-22-5041-2018, 2018
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The feasibility of drought prediction is assessed in the Brazilian northeast. The models were provided by a regional agency and a European meteorological agency and downscaling was done using three empirical models. This work showed that the combination of different forecast and downscaling models can provide skillful predictions of drought events on timescales relevant to water managers. But the models also showed little to no skill for quantitative predictions of monthly precipitation.
Till Francke, Saskia Foerster, Arlena Brosinsky, Erik Sommerer, Jose A. Lopez-Tarazon, Andreas Güntner, Ramon J. Batalla, and Axel Bronstert
Earth Syst. Sci. Data, 10, 1063–1075, https://doi.org/10.5194/essd-10-1063-2018, https://doi.org/10.5194/essd-10-1063-2018, 2018
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This paper presents a hydro-sedimentological dataset for the Isábena catchment, northeastern Spain, for the period 2010–2018. It contains the results of several years of monitoring rainfall, discharge and sediment flux and analysing soil spectroscopic properties. The dataset features data in high spatial and temporal resolution suitable for the advanced process understanding of water and sediment fluxes, their origin and connectivity and sediment budgeting and for model development.
C. Kormann, T. Francke, M. Renner, and A. Bronstert
Hydrol. Earth Syst. Sci., 19, 1225–1245, https://doi.org/10.5194/hess-19-1225-2015, https://doi.org/10.5194/hess-19-1225-2015, 2015
K. Vormoor, D. Lawrence, M. Heistermann, and A. Bronstert
Hydrol. Earth Syst. Sci., 19, 913–931, https://doi.org/10.5194/hess-19-913-2015, https://doi.org/10.5194/hess-19-913-2015, 2015
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Projected shifts towards more dominant autumn/winter events during a future climate correspond to an increasing relevance of rainfall as a flood generating process in six Norwegian catchments. The relative role of hydrological model parameter uncertainty, compared to other uncertainty sources from our applied ensemble, is highest in those catchments showing the largest shifts in flood seasonality which indicates a lack in parameter robustness under non-stationary hydroclimatological conditions.
C. H. Mohr, A. Zimmermann, O. Korup, A. Iroumé, T. Francke, and A. Bronstert
Earth Surf. Dynam., 2, 117–125, https://doi.org/10.5194/esurf-2-117-2014, https://doi.org/10.5194/esurf-2-117-2014, 2014
T. Conradt, F. Wechsung, and A. Bronstert
Hydrol. Earth Syst. Sci., 17, 2947–2966, https://doi.org/10.5194/hess-17-2947-2013, https://doi.org/10.5194/hess-17-2947-2013, 2013
M. Heistermann, I. Crisologo, C. C. Abon, B. A. Racoma, S. Jacobi, N. T. Servando, C. P. C. David, and A. Bronstert
Nat. Hazards Earth Syst. Sci., 13, 653–657, https://doi.org/10.5194/nhess-13-653-2013, https://doi.org/10.5194/nhess-13-653-2013, 2013
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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.
Zhi Li, Gregor Rickert, Na Zheng, Zhibo Zhang, Ilhan Özgen-Xian, and Daniel Caviedes-Voullième
EGUsphere, https://doi.org/10.5194/egusphere-2024-2588, https://doi.org/10.5194/egusphere-2024-2588, 2024
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We introduce SERGHEI-RE, a 3D subsurface flow simulator with performance-portable parallel computing capabilities. SERGHEI-RE performs effectively on various computational devices, from personal computers to advanced clusters. It allows users to solve flow equations with multiple numerical schemes, making it adaptable to various hydrological scenarios. Testing results show its accuracy and performance, confirming that SERGHEI-RE is a powerful tool for hydrological research.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-106, https://doi.org/10.5194/gmd-2024-106, 2024
Revised manuscript accepted for GMD
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Multiple methods for measuring soil moisture beyond the point scale exist. Their validation generally hindered by lack of knowing the truth. We propose a virtual framework, in which this truth is fully known and the sensor observations for Cosmic Ray Neutron Sensing, Remote Sensing, and Hydrogravimetry are simulated. This allows the rigourous testing of these virtual sensors to understand their effectiveness and limitations.
Chi-Ling Wei, Pei-Chun Chen, Chien-Yu Tseng, Ting-Yu Dai, Yun-Ting Ho, Ching-Chun Chou, Christian Onof, and Li-Pen Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-1918, https://doi.org/10.5194/egusphere-2024-1918, 2024
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pyBL is an open-source package for generating realistic rainfall time series based on the Bartlett-Lewis (BL) model. It can preserve not only standard but also extreme rainfall statistics across various timescales. Notably, compared to traditional frequency analysis methods, the BL model requires only half the record length (or even shorter) to achieve similar consistency in estimating sub-hourly rainfall extremes. This makes it a valuable tool for modelling rainfall extremes with short records.
Kerry L. Callaghan, Andrew D. Wickert, Richard Barnes, and Jacqueline Austermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-131, https://doi.org/10.5194/gmd-2024-131, 2024
Revised manuscript accepted for GMD
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We present the Water Table Model (WTM), which simulates groundwater and lake levels at continental scales over millennia. Our simulations show that North America held more ground- and lake-water at the Last Glacial Maximum than in the present day – enough to lower sea level by 6 cm. We also simulate the changing water table from 21,000 to 16,000 years ago, finding that groundwater storage decreased following reduced precipitation in the model inputs. Open-source WTM code is available on Github.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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.
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
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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
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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
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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
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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
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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
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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.
Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale
EGUsphere, https://doi.org/10.5194/egusphere-2023-2865, https://doi.org/10.5194/egusphere-2023-2865, 2024
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Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. The Bayes’ factor is a tool that can be used to compare models, however it is very difficult to compute the Bayes’ factor numerically. In our paper we explore and develop highly efficient algorithms for computing the Bayes’ factor of hydrological systems, which will bring this useful tool for selecting models to everyday hydrological practice.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Cited articles
Ajami, H., Khan, U., Tuteja, N. K., and Sharma, A.: Development of a computationally efficient semi-distributed hydrologic modeling application for soil moisture, lateral flow and runoff simulation, Environ. Modell. Softw., 85, 319–331, https://doi.org/10.1016/j.envsoft.2016.09.002, 2016.
Band, L. E., Tague, C. L., Brun, S. E., Tenenbaum, D. E., and Fernandes, R. A.: Modelling Watersheds as Spatial Object Hierarchies: Structure and Dynamics, Trans. GIS, 4, 181–196, https://doi.org/10.1111/1467-9671.00048, 2000.
Beven, K.: Linking parameters across scales: Subgrid parameterizations and scale dependent hydrological models, Hydrol. Process., 9, 507–525, https://doi.org/10.1002/hyp.3360090504, 1995.
Beven, K.: Searching for the Holy Grail of scientific hydrology: Qt = (S, R, Δt)A as closure, Hydrol. Earth Syst. Sci., 10, 609–618, https://doi.org/10.5194/hess-10-609-2006, 2006.
Beven, K., Calver, A., and Morris, E. M.: The Institute of Hydrology distributed model, IH Report 98, Institute of Hydrology, Wallingford, UK, 1987.
Bogaart, P. W. and Troch, P. A.: Curvature distribution within hillslopes and catchments and its effect on the hydrological response, Hydrol. Earth Syst. Sci., 10, 925–936, https://doi.org/10.5194/hess-10-925-2006, 2006.
Bronstert, A.: Modellierung der Abflussbildung und der Bodenwasserdynamik von Hängen, in: Mitteilungen des Instituts für Hydrologie und Wasserwirtschaft, 46, Universität Karlsruhe, 1994.
Bronstert, A.: Capabilities and limitations of detailed hillslope hydrological modelling, Hydrol. Process., 13, 21–48, https://doi.org/10.1002/(SICI)1099-1085(199901)13:1<21::AID-HYP702>3.0.CO;2-4, 1999.
Bronstert, A. and Bárdossy, A.: The role of spatial variability of soil moisture for modelling surface runoff generation at the small catchment scale, Hydrol. Earth Syst. Sci., 3, 505–516, https://doi.org/10.5194/hess-3-505-1999, 1999.
Bronstert, A., de Araújo, J.-C., Batalla, R. J., Costa, A. C., Delgado, J. M., Francke, T., Foerster, S., Guentner, A., López-Tarazón, J. A., Mamede, G. L., Medeiros, P. H., Mueller, E., and Vericat, D.: Process-based modelling of erosion, sediment transport and reservoir siltation in mesoscale semi-arid catchments, J. Soils Sediments, 14, 2001–2018, https://doi.org/10.1007/s11368-014-0994-1, 2014.
Cochrane, T. and Flanagan, D.: Representative hillslope methods for applying the WEPP model with DEMs and GIS, T. ASAE, 46, 1041–1049, 2003.
Costa-Cabral, M. C. and Burges, S. J.: Digital Elevation Model Networks (DEMON): A model of flow over hillslopes for computation of contributing and dispersal areas, Water Resour. Res., 30, 1681–1692, https://doi.org/10.1029/93WR03512, 1994.
Dawes, W. R. and Short, D.: The significance of topology for modeling the surface hydrology of fluvial landscapes, Water Resour. Res., 30, 1045–1055, https://doi.org/10.1029/93WR02479, 1994.
de Araújo, J. C. and Medeiros, P. H. A.: Impact of Dense Reservoir Networks on Water Resources in Semiarid Environments, Aust. J. Water Resour., 17, 87–100, 2013.
de Figueiredo, J. V., de Araújo, J. C., Medeiros, P. H. A., and Costa, A. C.: Runoff initiation in a preserved semiarid Caatinga small watershed, Northeastern Brazil, Hydrol. Process., 30, 2390–2400, https://doi.org/10.1002/hyp.10801, 2016.
DeVantier, B. A. and Feldman, A. D.: Review of GIS Applications in Hydrologic Modeling, J. Water Res. Pl.-ASCE, 119, 246–261, https://doi.org/10.1061/(ASCE)0733-9496(1993)119:2(246), 1993.
Di Luzio, M., Srinivasan, R., and Arnold, J. G.: A GIS-Coupled Hydrological Model System for the Watershed Assessment of Agricultural Nonpoint and Point Sources of Pollution, Trans. GIS, 8, 113–136, https://doi.org/10.1111/j.1467-9671.2004.00170.x, 2004.
Euser, T., Hrachowitz, M., Winsemius, H. C., and Savenije, H. H.: The effect of forcing and landscape distribution on performance and consistency of model structures, Hydrol. Process., 29, 3727–3743, https://doi.org/10.1002/hyp.10445, 2015.
Fenicia, F., Kavetski, D., Savenije, H. H. G., and Pfister, L.: From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions, Water Resour. Res., 52, 954–989, https://doi.org/10.1002/2015WR017398, 2016.
Flanagan, D. C. and Nearing, M. A.: USDA Water Erosion Prediction Project – Hillslope Profile and Watershed Model Documentation, NSERL Report 10, USDA-ARS National Soil Erosion Research Laboratory, West Lafayette, Indiana, USA, 1995.
Flanagan, D. C., Frankenberger, J. R., Cochrane, T. A., Renschler, C. S., and Elliot, W. J.: Geospatial Application of the Water Erosion Prediction Project (WEPP) Model, T. ASABE, 56, 591–601, https://doi.org/10.13031/2013.42681, 2013.
Flügel, W.-A.: Delineating hydrological response units by geographical information system analyses for regional hydrological modelling using PRMS/MMS in the drainage basin of the River Brül, Germany, Hydrol. Process., 9, 423–436, https://doi.org/10.1002/hyp.3360090313, 1995.
Francke, T., Güntner, A., Mamede, G., Müller, E. N., and Bronstert, A.: Automated catena-based discretization of landscapes for the derivation of hydrological modelling units, Int. J. Geogr. Inf. Sci., 22, 111–132, https://doi.org/10.1080/13658810701300873, 2008.
Freitas, H. R. d. A., Freitas, C. d. C., Rosim, S., and Oliveira, J. R. d. F.: Drainage networks and watersheds delineation derived from TIN-based digital elevation models, Comput. Geosci., 92, 21–37, https://doi.org/10.1016/j.cageo.2016.04.003, 2016.
Gallant, J. C. and Wilson, J. P.: TAPES-G: A grid-based terrain analysis program for the environmental sciences, Comput. Geosci., 22, 713–722, 1996.
Garbrecht, J. and Martz, L. W.: An Overview of TOPAZ: An Automated Digital Landscape Analysis Tool for Topographic Evaluation, Drainage Identification, Watershed Segmentation, and Subcatchment Parameterization, Grazinglands Research Laboratory, USDA, Agricultural Research Service, El Reno, Oklahoma, USA, report no. GRL 99-1, 1999.
Gerstengarbe, F.-W. and Werner, P. C.: Estimation of the beginning and end of recurrent events within a climate regime, Clim. Res., 11, 97–107, 1999.
González, V. I., Carkovic, A. B., Lobo, G. P., Flanagan, D. C., and Bonilla, C. A.: Spatial discretization of large watersheds and its influence on the estimation of hillslope sediment yield, Hydrol. Process., 30, 30–39, https://doi.org/10.1002/hyp.10559, 2016.
Grayson, R. B., Moore, I. D., and McMahon, T. A.: Physically based hydrologic modeling: 1. A terrain-based model for investigative purposes, Water Resour. Res., 28, 2639–2658, https://doi.org/10.1029/92WR01258, 1992.
Güntner, A.: Large-scale hydrological modelling in the semi-arid North-East of Brazil, PIK Report 77, Potsdam Institute for Climate Impact Research, Potsdam, Germany, 2002.
Güntner, A. and Bronstert, A.: Representation of landscape variability and lateral redistribution processes for large-scale hydrological modelling in semi-arid areas, J. Hydrol., 297, 136–161, https://doi.org/10.1016/j.jhydrol.2004.04.008, 2004.
Haghnegahdar, A., Tolson, B. A., Craig, J. R., and Paya, K. T.: Assessing the performance of a semi-distributed hydrological model under various watershed discretization schemes, Hydrol. Process., 29, 4018–4031, https://doi.org/10.1002/hyp.10550, 2015.
Han, J.-C., Huang, G.-H., Zhang, H., Li, Z., and Li, Y.-P.: Effects of watershed subdivision level on semi-distributed hydrological simulations: case study of the SLURP model applied to the Xiangxi River watershed, China, Hydrolog. Sci. J., 59, 108–125, https://doi.org/10.1080/02626667.2013.854368, 2014.
Haverkamp, S., Fohrer, N., and Frede, H.-G.: Assessment of the effect of land use patterns on hydrologic landscape functions: a comprehensive GIS-based tool to minimize model uncertainty resulting from spatial aggregation, Hydrol. Process., 19, 715–727, https://doi.org/10.1002/hyp.5626, 2005.
Hazenberg, P., Fang, Y., Broxton, P., Gochis, D., Niu, G.-Y., Pelletier, J. D., Troch, P. A., and Zeng, X.: A hybrid-3D hillslope hydrological model for use in Earth system models, Water Resour. Res., 51, 8218–8239, 2015.
Ivanov, V. Y., Vivoni, E. R., Bras, R. L., and Entekhabi, D.: Catchment hydrologic response with a fully distributed triangulated irregular network model, Water Resour. Res., 40, W11102, https://doi.org/10.1029/2004WR003218, 2004.
Jackisch, C., Zehe, E., Samaniego, L., and Singh, A. K.: An experiment to gauge an ungauged catchment: rapid data assessment and eco-hydrological modelling in a data-scarce rural catchment, Hydrolog. Sci. J., 59, 2103–2125, https://doi.org/10.1080/02626667.2013.870662, 2014.
Jacomine, P. K. T., Almeida, J. C., and Medeiros, L. A. R.: Levantamento exploratorio – Reconhecimento de solos do Estado do Ceará, vol. 1, DNPEA, DRN-SUDENE, Recife, Brazil, 1973.
Khan, U., Tuteja, N. K., Ajami, H., and Sharma, A.: An equivalent cross-sectional basis for semidistributed hydrological modeling, Water Resour. Res., 50, 4395–4415, https://doi.org/10.1002/2013WR014741, 2014.
Kinner, D., Mitasova, H., Stallard, R., Harmon, R. S., and Toma, L.: GIS-Based Stream Network Analysis for the Upper Río Chagres Basin, Panama, in: The Río Chagres, Panama: A Multidisciplinary Profile of a Tropical Watershed, edited by: Harmon, R. S., chap. 6, Springer Netherlands, 83–95, https://doi.org/10.1007/1-4020-3297-8_6, 2005.
Kite, G. W.: Scaling of Input Data for Macroscale Hydrologic Modeling, Water Resour. Res., 31, 2769–2781, https://doi.org/10.1029/95WR02102, 1995.
Kneis, D.: A lightweight framework for rapid development of object-based hydrological model engines, Environ. Modell. Softw., 68, 110–121, https://doi.org/10.1016/j.envsoft.2015.02.009, 2015.
Kouwen, N.: WATFLOOD / CHARM Canadian Hydrological And Routing Model, Department of Civil Engineering, University of Waterloo, Waterloo, Ontario, Canada, 2016.
Kouwen, N., Soulis, E. D., Pietroniro, A., Donald, J., and Harrington, R. A.: Grouped Response Units for Distributed Hydrologic Modeling, J. Water Res. Pl.-ASCE, 119, 289–305, https://doi.org/10.1061/(ASCE)0733-9496(1993)119:3(289), 1993.
Krol, M. S., de Vries, M. J., Oel, P. R., and de Araújo, J. C.: Sustainability of Small Reservoirs and Large Scale Water Availability Under Current Conditions and Climate Change, Water Resour. Manage., 25, 3017–3026, https://doi.org/10.1007/s11269-011-9787-0, 2011.
Krysanova, V., Müller-Wohlfeil, D.-I., and Becker, A.: Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds, Ecol. Model., 106, 261–289, https://doi.org/10.1016/S0304-3800(97)00204-4, 1998.
Kumar, R., Samaniego, L., and Attinger, S.: The effects of spatial discretization and model parameterization on the prediction of extreme runoff characteristics, J. Hydrol., 392, 54–69, https://doi.org/10.1016/j.jhydrol.2010.07.047, 2010.
Lacroix, M. P., Martz, L. W., Kite, G. W., and Garbrecht, J.: Using digital terrain analysis modeling techniques for the parameterization of a hydrologic model, Environ. Modell. Softw., 17, 125–134, https://doi.org/10.1016/S1364-8152(01)00042-1, 2002.
Lagacherie, P., Rabotin, M., Colin, F., Moussa, R., and Voltz, M.: Geo-MHYDAS: A landscape discretization tool for distributed hydrological modeling of cultivated areas, Comput. Geosci., 36, 1021–1032, https://doi.org/10.1016/j.cageo.2009.12.005, 2010.
Leavesley, G. H., Lichty, R. W., Troutman, B. M., and Saindon, L. G.: Precipitation-Runoff Modeling System: User's manual, Water-resources investigations report 83-4238, United States Department of the Interior, Denver, Colorado, USA, 1983.
Manguerra, H. B. and Engel, B. A.: Hydrologic parameterization of watersheds for runoff prediction using SWAT, J. Am. Water Resour. As., 34, 1149–1162, https://doi.org/10.1111/j.1752-1688.1998.tb04161.x, 1998.
Markstrom, S., Niswonger, R., Regan, R., Prudic, D., and Barlow, P.: GSFLOW–Coupled ground-water and surface-water flow model based on the integration of the Precipitation-Runoff Modeling System (PRMS) and the Modular Ground-Water Flow Model (MODFLOW-2005), U.S. Geological Survey Techniques and Methods 6-D1, 240 pp., 2008.
Maunder, C. J.: An automated method for constructing contour-based digital elevation models, Water Resour. Res., 35, 3931–3940, https://doi.org/10.1029/1999WR900166, 1999.
Maurer, T.: Physikalisch begründete, zeitkontinuierliche Modellierung des Wassertransports in kleinen ländlichen Einzugsgebieten, Dissertation, Mitteilungen Inst. f. Hydrologie u. Wasserwirtschaft, 61, Universität Karlsruhe, 1997.
Medeiros, P. H. A. and de Araújo, J. C.: Temporal variability of rainfall in a semiarid environment in Brazil and its effect on sediment transport processes, J. Soils Sediments, 14, 1216–1223, https://doi.org/10.1007/s11368-013-0809-9, 2014.
Medeiros, P. H. A., Güntner, A., Francke, T., Mamede, G. L., and de Araújo, J. C.: Modelling spatio-temporal patterns of sediment yield and connectivity in a semi-arid catchment with the WASA-SED model, Hydrolog. Sci. J., 55, 636–648, https://doi.org/10.1080/02626661003780409, 2010.
Medeiros, P. H. A., de Araújo, J. C., Mamede, G. L., Creutzfeldt, B., Güntner, A., and Bronstert, A.: Connectivity of sediment transport in a semiarid environment: a synthesis for the Upper Jaguaribe Basin, Brazil, J. Soils Sediments, 14, 1938–1948, https://doi.org/10.1007/s11368-014-0988-z, 2014.
Metz, M., Mitasova, H., and Harmon, R. S.: Efficient extraction of drainage networks from massive, radar-based elevation models with least cost path search, Hydrol. Earth Syst. Sci., 15, 667–678, https://doi.org/10.5194/hess-15-667-2011, 2011.
Miller, S., Semmens, D., Miller, R., Hernandez, M., Goodrich, D., Miller, W., Kepner, W., and Ebert, D.: GIS-based Hydrologic Modeling: The Automated Geospatial Watershed Assessment Tool, in: Second Federal Interagency Hydrologic Modeling Conference, Las Vegas, Nevada, USA, 2002.
Miller, S. N., Semmens, D. J., Goodrich, D. C., Hernandez, M., Miller, R. C., Kepner, W. G., and Guertin, D. P.: The Automated Geospatial Watershed Assessment tool, Environ. Modell. Softw., 22, 365–377, https://doi.org/10.1016/j.envsoft.2005.12.004, 2007.
Molnár, D. K. and Julien, P. Y.: Grid-Size Effects on Surface Runoff Modeling, J. Hydrol. Eng., 5, 8–16, https://doi.org/10.1061/(ASCE)1084-0699(2000)5:1(8), 2000.
Moore, I. D. and Grayson, R. B.: Terrain-based catchment partitioning and runoff prediction using vector elevation data, Water Resour. Res., 27, 1177–1191, https://doi.org/10.1029/91WR00090, 1991.
Moore, I. D., O'Loughlin, E. M., and Burch, G. J.: A contour-based topographic model for hydrological and ecological applications, Earth Surf. Proc. Land., 13, 305–320, https://doi.org/10.1002/esp.3290130404, 1988.
Moore, I. D., Grayson, R. B., and Ladson, A. R.: Digital terrain modelling: A review of hydrological, geomorphological, and biological applications, Hydrol. Process., 5, 3–30, https://doi.org/10.1002/hyp.3360050103, 1991.
Moretti, G. and Orlandini, S.: Automatic delineation of drainage basins from contour elevation data using skeleton construction techniques, Water Resour. Res., 44, W05403, https://doi.org/10.1029/2007WR006309, 2008.
Moussa, R., Voltz, M., and Andrieux, P.: Effects of the spatial organization of agricultural management on the hydrological behaviour of a farmed catchment during flood events, Hydrol. Process., 16, 393–412, https://doi.org/10.1002/hyp.333, 2002.
Mueller, E. N., Francke, T., Batalla, R. J., and Bronstert, A.: Modelling the effects of land-use change on runoff and sediment yield for a meso-scale catchment in the Southern Pyrenees, CATENA, 79, 288–296, https://doi.org/10.1016/j.catena.2009.06.007, 2009.
Mueller, E. N., Güntner, A., Francke, T., and Mamede, G.: Modelling sediment export, retention and reservoir sedimentation in drylands with the WASA-SED model, Geosci. Model Dev., 3, 275–291, https://doi.org/10.5194/gmd-3-275-2010, 2010.
Neteler, M., Bowman, M. H., Landa, M., and Metz, M.: GRASS GIS: A multi-purpose open source GIS, Environ. Modell. Softw., 31, 124–130, https://doi.org/10.1016/j.envsoft.2011.11.014, 2012.
Nijzink, R. C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H. H. G., and Hrachowitz, M.: The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models, Hydrol. Earth Syst. Sci., 20, 1151–1176, https://doi.org/10.5194/hess-20-1151-2016, 2016.
Noël, P., Rousseau, A. N., Paniconi, C., and Nadeau, D. F.: Algorithm for Delineating and Extracting Hillslopes and Hillslope Width Functions from Gridded Elevation Data, J. Hydrol. Eng., 19, 366–374, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000783, 2014.
O'Callaghan, J. F. and Mark, D. M.: The extraction of drainage networks from digital elevation data, Lect. Notes Comput. Sc., 28, 323–344, https://doi.org/10.1016/S0734-189X(84)80011-0, 1984.
Passalacqua, P., Do Trung, T., Foufoula-Georgiou, E., Sapiro, G., and Dietrich, W. E.: A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths, J. Geophys. Res.-Earth, 115, F01002, https://doi.org/10.1029/2009JF001254, 2010.
Pianosi, F. and Wagener, T.: A simple and efficient method for global sensitivity analysis based on cumulative distribution functions, Environ. Modell. Softw., 67, 1–11, https://doi.org/10.1016/j.envsoft.2015.01.004, 2015.
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., and Wagener, T.: Sensitivity analysis of environmental models: A systematic review with practical workflow, Environ. Modell. Softw., 79, 214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 2016.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, available at: https://www.R-project.org/ (last access: 5 July 2016), 2015.
Refsgaard, J. C.: Parameterisation, calibration and validation of distributed hydrological models, J. Hydrol., 198, 69–97, https://doi.org/10.1016/S0022-1694(96)03329-X, 1997.
Reggiani, P., Sivapalan, M., and Hassanizadeh, S. M.: A unifying framework for watershed thermodynamics: balance equations for mass, momentum, energy and entropy, and the second law of thermodynamics, Adv. Water Resour., 22, 367–398, https://doi.org/10.1016/S0309-1708(98)00012-8, 1998.
Renschler, C. S.: Designing geo-spatial interfaces to scale process models: the GeoWEPP approach, Hydrol. Process., 17, 1005–1017, https://doi.org/10.1002/hyp.1177, 2003.
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.
Sangireddy, H., Stark, C. P., Kladzyk, A., and Passalacqua, P.: GeoNet: An open source software for the automatic and objective extraction of channel heads, channel network, and channel morphology from high resolution topography data, Environ. Modell. Softw., 83, 58–73, https://doi.org/10.1016/j.envsoft.2016.04.026, 2016.
Sanzana, P., Jankowfsky, S., Branger, F., Braud, I., Vargas, X., Hitschfeld, N., and Gironás, J.: Computer-assisted mesh generation based on hydrological response units for distributed hydrological modeling, Comput. Geosci., 57, 32–43, https://doi.org/10.1016/j.cageo.2013.02.006, 2013.
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15, 2895–2911, https://doi.org/10.5194/hess-15-2895-2011, 2011.
Schulla, J. and Jasper, K.: Model Description WaSiM-ETH, Technical report 2007, 181 pp., ETH Zürich, 2007.
Schwartze, C.: Deriving Hydrological Response Units (HRUs) using a Web Processing Service implementation based on GRASS GIS, Geoinformatics FCE CTU, 3, 67–78, 2008.
Smith, T., Marshall, L., McGlynn, B., and Jencso, K.: Using field data to inform and evaluate a new model of catchment hydrologic connectivity, Water Resour. Res., 49, 6834–6846, https://doi.org/10.1002/wrcr.20546, 2013.
Sulis, M., Paniconi, C., and Camporese, M.: Impact of grid resolution on the integrated and distributed response of a coupled surface–subsurface hydrological model for the des Anglais catchment, Quebec, Hydrol. Process., 25, 1853–1865, https://doi.org/10.1002/hyp.7941, 2011.
Tague, C. and Band, L.: RHESSys: Regional Hydro-ecologic simulation system: An object-oriented approach to spatially distributed modeling of carbon, water and nutrient cycling, Earth Interact., 8, 1–42, 2004.
Tarboton, D.: Terrain Analysis Using Digital Elevation Models in Hydrology, in: 23rd ESRI International Users Conference, San Diego, California, USA, 2003.
Tarboton, D. G., Bras, R. L., and Rodriguez-Iturbe, I.: On the extraction of channel networks from digital elevation data, Hydrol. Process., 5, 81–100, https://doi.org/10.1002/hyp.3360050107, 1991.
Troch, P. A., Paniconi, C., and Emiel van Loon, E.: Hillslope-storage Boussinesq model for subsurface flow and variable source areas along complex hillslopes: 1. Formulation and characteristic response, Water Resour. Res., 39, 1316, https://doi.org/10.1029/2002WR001728, 2003.
Tucker, G. E., Lancaster, S. T., Gasparini, N. M., Bras, R. L., and Rybarczyk, S. M.: An object-oriented framework for distributed hydrologic and geomorphic modeling using triangulated irregular networks, Comput. Geosci., 27, 959–973, https://doi.org/10.1016/S0098-3004(00)00134-5, 2001.
Uhlenbrook, S. and Leibundgut, C.: Process-oriented catchment modelling and multiple-response validation, Hydrol. Process., 16, 423–440, https://doi.org/10.1002/hyp.330, 2002.
Viviroli, D., Zappa, M., Gurtz, J., and Weingartner, R.: An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools, Environ. Modell. Softw., 24, 1209–1222, https://doi.org/10.1016/j.envsoft.2009.04.001, 2009.
Vivoni, E. R., Ivanov, V. Y., Bras, R. L., and Entekhabi, D.: Generation of Triangulated Irregular Networks Based on Hydrological Similarity, J. Hydrol. Eng., 9, 288–302, 2004.
Weiler, M. and Beven, K.: Do we need a Community Hydrological Model?, Water Resour. Res., 51, 7777–7784, https://doi.org/10.1002/2014WR016731, 2015.
Winter, T. C.: The concept of hydrologic landscapes, J. Am. Water Resour. As., 37, 335–349, 2001.
Wood, E. F., Sivapalan, M., Beven, K., and Band, L.: Effects of spatial variability and scale with implications to hydrologic modeling, J. Hydrol., 102, 29–47, https://doi.org/10.1016/0022-1694(88)90090-X, 1988.
Woolhiser, D. A., Smith, R. E., and Goodrich, D. C.: KINEROS, A Kinematic Runoff and Erosion Model: Documentation and User Manual, USDA, Agricultural Research Service, ARS-77, 130 pp., 1990.
Xavier, A. C., King, C. W., and Scanlon, B. R.: Daily gridded meteorological variables in Brazil (1980–2013), Int. J. Climatol., 36, 2644–2659, https://doi.org/10.1002/joc.4518, 2016.
Yang, D., Herath, S., and Musiake, K.: Comparison of different distributed hydrological models for characterization of catchment spatial variability, Hydrol. Process., 14, 403–416, https://doi.org/10.1002/(SICI)1099-1085(20000228)14:3<403::AID-HYP945>3.0.CO;2-3, 2000.
Yang, D., Herath, S., and Musiake, K.: A hillslope-based hydrological model using catchment area and width functions, Hydrol. Sci. J., 47, 49–65, https://doi.org/10.1080/02626660209492907, 2002.
Zehe, E., Ehret, U., Pfister, L., Blume, T., Schröder, B., Westhoff, M., Jackisch, C., Schymanski, S. J., Weiler, M., Schulz, K., Allroggen, N., Tronicke, J., van Schaik, L., Dietrich, P., Scherer, U., Eccard, J., Wulfmeyer, V., and Kleidon, A.: HESS Opinions: From response units to functional units: a thermodynamic reinterpretation of the HRU concept to link spatial organization and functioning of intermediate scale catchments, Hydrol. Earth Syst. Sci., 18, 4635–4655, https://doi.org/10.5194/hess-18-4635-2014, 2014.
Zhang, L., Dawes, W. R., Hatton, T. J., Reece, P. H., Beale, G. T. H., and Packer, I.: Estimation of soil moisture and groundwater recharge using the TOPOG_IRM Model, Water Resour. Res., 35, 149–161, https://doi.org/10.1029/98WR01616, 1999.
Zhang, W. and Montgomery, D. R.: Digital elevation model grid size, landscape representation, and hydrologic simulations, Water Resour. Res., 30, 1019–1028, https://doi.org/10.1029/93WR03553, 1994.
Short summary
To discretise and transfer a landscape into a hydrological model, many different algorithms and software implementations exist. These are, however, often model specific, commercial, and allow for only a limited workflow automation. Overcoming these limitations, the software package lumpR was developed. It employs an hillslope-based discretisation algorithm directed at large-scale application. The software is demonstrated in a case study and crucial discretisation parameters are investigated.
To discretise and transfer a landscape into a hydrological model, many different algorithms and...
