Articles | Volume 17, issue 13
https://doi.org/10.5194/gmd-17-5387-2024
© 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-17-5387-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
12 Jul 2024
Model description paper |
| 12 Jul 2024
| 12 Jul 2024
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
Manuel F. Rios Gaona
CORRESPONDING AUTHOR
School of Earth and Environmental Sciences, Cardiff University, Cardiff, CF10 3AT, UK
Katerina Michaelides
School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
Cabot Institute for the Environment, University of Bristol, Bristol, BS8 1QU, UK
Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
Michael Bliss Singer
School of Earth and Environmental Sciences, Cardiff University, Cardiff, CF10 3AT, UK
Water Research Institute, Cardiff University, Cardiff, CF10 3AX, UK
Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
Related authors
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
Short summary
Short summary
stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
Manuel F. Rios Gaona, Aart Overeem, Timothy H. Raupach, Hidde Leijnse, and Remko Uijlenhoet
Atmos. Meas. Tech., 11, 4465–4476, https://doi.org/10.5194/amt-11-4465-2018, https://doi.org/10.5194/amt-11-4465-2018, 2018
Short summary
Short summary
Rainfall estimates from commercial microwave links were obtained for the city of Sao Paulo (Brazil). The results show the potential of such networks as complementary rainfall measurements for more robust networks (e.g. radars, gauges, satellites).
M. F. Rios Gaona, A. Overeem, H. Leijnse, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 19, 3571–3584, https://doi.org/10.5194/hess-19-3571-2015, https://doi.org/10.5194/hess-19-3571-2015, 2015
Short summary
Short summary
Commercial cellular networks are built for telecommunication purposes. These kinds of networks have lately been used to obtain rainfall maps at country-wide scales. From previous studies, we now quantify the uncertainties associated with such maps. To do so, we divided the sources or error into two categories: from microwave link measurements and from mapping. It was found that the former is the source that contributes the most to the overall error in rainfall maps from microwave link network.
Saskia Salwey, Gemma Coxon, Francesca Pianosi, Rosanna Lane, Chris Hutton, Michael Bliss Singer, Hilary McMillan, and Jim Freer
Hydrol. Earth Syst. Sci., 28, 4203–4218, https://doi.org/10.5194/hess-28-4203-2024, https://doi.org/10.5194/hess-28-4203-2024, 2024
Short summary
Short summary
Reservoirs are essential for water resource management and can significantly impact downstream flow. However, representing reservoirs in hydrological models can be challenging, particularly across large scales. We design a new and simple method for simulating river flow downstream of water supply reservoirs using only open-access data. We demonstrate the approach in 264 reservoir catchments across Great Britain, where we can significantly improve the simulation of reservoir-impacted flow.
Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, https://doi.org/10.5194/essd-15-5449-2023, 2023
Short summary
Short summary
Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
Short summary
Short summary
stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
Shiuan-An Chen, Katerina Michaelides, David A. Richards, and Michael Bliss Singer
Earth Surf. Dynam., 10, 1055–1078, https://doi.org/10.5194/esurf-10-1055-2022, https://doi.org/10.5194/esurf-10-1055-2022, 2022
Short summary
Short summary
Drainage basin erosion rates influence landscape evolution through controlling land surface lowering and sediment flux, but gaps remain in understanding their large-scale patterns and drivers between timescales. We analysed global erosion rates and show that long-term erosion rates are controlled by rainfall, former glacial processes, and basin landform, whilst human activities enhance short-term erosion rates. The results highlight the complex interplay of controls on land surface processes.
E. Andrés Quichimbo, Michael Bliss Singer, Katerina Michaelides, Daniel E. J. Hobley, Rafael Rosolem, and Mark O. Cuthbert
Geosci. Model Dev., 14, 6893–6917, https://doi.org/10.5194/gmd-14-6893-2021, https://doi.org/10.5194/gmd-14-6893-2021, 2021
Short summary
Short summary
Understanding and quantifying water partitioning in dryland regions are of key importance to anticipate the future impacts of climate change in water resources and dryland ecosystems. Here, we have developed a simple hydrological model (DRYP) that incorporates the key processes of water partitioning in drylands. DRYP is a modular, versatile, and parsimonious model that can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions.
Maria Magdalena Warter, Michael Bliss Singer, Mark O. Cuthbert, Dar Roberts, Kelly K. Caylor, Romy Sabathier, and John Stella
Hydrol. Earth Syst. Sci., 25, 3713–3729, https://doi.org/10.5194/hess-25-3713-2021, https://doi.org/10.5194/hess-25-3713-2021, 2021
Short summary
Short summary
Intensified drying of soil and grassland vegetation is raising the impact of fire severity and extent in Southern California. While browned grassland is a common sight during the dry season, this study has shown that there is a pronounced shift in the timing of senescence, due to changing climate conditions favoring milder winter temperatures and increased precipitation variability. Vegetation may be limited in its ability to adapt to these shifts, as drought periods become more frequent.
Isaac Kipkemoi, Katerina Michaelides, Rafael Rosolem, and Michael Bliss Singer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-48, https://doi.org/10.5194/hess-2021-48, 2021
Manuscript not accepted for further review
Short summary
Short summary
The work is a novel investigation of the role of temporal rainfall resolution and intensity in affecting the water balance of soil in a dryland environment. This research has implications for what rainfall data are used to assess the impact of climate and climate change on the regional water balance. This information is critical for anticipating the impact of a changing climate on dryland communities globally who need it to know when to plant their seeds or where livestock pasture is available.
Thomas Turpin-Jelfs, Katerina Michaelides, Joel A. Biederman, and Alexandre M. Anesio
Biogeosciences, 16, 369–381, https://doi.org/10.5194/bg-16-369-2019, https://doi.org/10.5194/bg-16-369-2019, 2019
Short summary
Short summary
Increasing shrub cover promotes land degradation in semi-arid grasslands and has the potential to impact the soil nitrogen pool, which is essential to primary production. Our study showed that increasing shrub cover concentrates soil nitrogen into localised patches beneath shrub canopies. Further, we determined that increasing shrub cover inhibits inputs of nitrogen by the soil microbial community. Thus, we conclude this phenomenon can perturb nitrogen cycling in these ecosystems.
Michael Bliss Singer, Katerina Michaelides, and Daniel E. J. Hobley
Geosci. Model Dev., 11, 3713–3726, https://doi.org/10.5194/gmd-11-3713-2018, https://doi.org/10.5194/gmd-11-3713-2018, 2018
Short summary
Short summary
For various applications, a regional or local characterization of rainfall is required, particularly at the watershed scale, where there is spatial heterogeneity. Furthermore, simple models are needed that can simulate various scenarios of climate change including changes in seasonal wetness and rainstorm intensity. To this end, we have developed the STOchastic Rainstorm Model (STORM). We explain its developments and data requirements, and illustrate how it simulates rainstorms over a basin.
Manuel F. Rios Gaona, Aart Overeem, Timothy H. Raupach, Hidde Leijnse, and Remko Uijlenhoet
Atmos. Meas. Tech., 11, 4465–4476, https://doi.org/10.5194/amt-11-4465-2018, https://doi.org/10.5194/amt-11-4465-2018, 2018
Short summary
Short summary
Rainfall estimates from commercial microwave links were obtained for the city of Sao Paulo (Brazil). The results show the potential of such networks as complementary rainfall measurements for more robust networks (e.g. radars, gauges, satellites).
C. E. M. Lloyd, K. Michaelides, D. R. Chadwick, J. A. J. Dungait, and R. P. Evershed
Biogeosciences, 13, 551–566, https://doi.org/10.5194/bg-13-551-2016, https://doi.org/10.5194/bg-13-551-2016, 2016
Short summary
Short summary
Our interdisciplinary research brings together methodologies from hydrology, soil science and biogeochemistry to address key questions about the transport of cattle slurry in the environment. The paper provides a novel approach to trace dissolved and particulate components of cattle slurry through an experimental hillslope system. This work provides one of the first examples of using biomarkers to assess the effects of slope gradient and rainfall intensity on the movement of slurry derived-OM.
A. F. Charteris, T. D. J. Knowles, K. Michaelides, and R. P. Evershed
SOIL Discuss., https://doi.org/10.5194/soild-2-1135-2015, https://doi.org/10.5194/soild-2-1135-2015, 2015
Manuscript not accepted for further review
M. F. Rios Gaona, A. Overeem, H. Leijnse, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 19, 3571–3584, https://doi.org/10.5194/hess-19-3571-2015, https://doi.org/10.5194/hess-19-3571-2015, 2015
Short summary
Short summary
Commercial cellular networks are built for telecommunication purposes. These kinds of networks have lately been used to obtain rainfall maps at country-wide scales. From previous studies, we now quantify the uncertainties associated with such maps. To do so, we divided the sources or error into two categories: from microwave link measurements and from mapping. It was found that the former is the source that contributes the most to the overall error in rainfall maps from microwave link network.
Related subject area
Hydrology
Virtual Joint Field Campaign: a framework of synthetic landscapes to assess multiscale measurement methods of water storage
SERGHEI v2.0: introducing a performance-portable, high-performance, three-dimensional variably saturated subsurface flow solver (SERGHEI-RE)
The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features
Generalised drought index: a novel multi-scale daily approach for drought assessment
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Prediction of hysteretic matric potential dynamics using artificial intelligence: application of autoencoder neural networks
LM4-SHARC v1.0: Resolving the Catchment-scale Soil-Hillslope Aquifer-River Continuum for the GFDL Earth System Modeling Framework
Regionalization in global hydrological models and its impact on runoff simulations: a case study using WaterGAP3 (v 1.0.0)
Modelling rainfall with a Bartlett-Lewis process: pyBL (v1.0.0), a Python software package and an application with short records
The Water Table Model (WTM) v2.0.1: Coupled groundwater and dynamic lake modelling
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
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
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)
Selecting a conceptual hydrological model using Bayes' factors computed with Replica Exchange Hamiltonian Monte Carlo and Thermodynamic Integration
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
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
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., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not 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 for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Zhi Li, Gregor Rickert, Na Zheng, Zhibo Zhang, Ilhan Özgen-Xian, and Daniel Caviedes-Voullième
Geosci. Model Dev., 18, 547–562, https://doi.org/10.5194/gmd-18-547-2025, https://doi.org/10.5194/gmd-18-547-2025, 2025
Short summary
Short summary
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.
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., 17, 8817–8852, https://doi.org/10.5194/gmd-17-8817-2024, https://doi.org/10.5194/gmd-17-8817-2024, 2024
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 resource assessments since 1996. We show the effects of new model features, as well as model evaluations, against water abstraction statistics and observed streamflow and water storage anomalies. The publicly available model output for several variants is described.
João António Martins Careto, Rita Margarida Cardoso, Ana Russo, Daniela Catarina André Lima, and Pedro Miguel Matos Soares
Geosci. Model Dev., 17, 8115–8139, https://doi.org/10.5194/gmd-17-8115-2024, https://doi.org/10.5194/gmd-17-8115-2024, 2024
Short summary
Short summary
This study proposes a new daily drought index, the generalised drought index (GDI). The GDI not only identifies the same events as established indices but is also capable of improving their results. The index is empirically based and easy to compute, not requiring fitting the data to a probability distribution. The GDI can detect flash droughts and longer-term events, making it a versatile tool for drought monitoring.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024, https://doi.org/10.5194/gmd-17-7751-2024, 2024
Short summary
Short summary
We develop an operational forecast system, Coastlines-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 has relatively low computational requirements, 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 wave predictions can improve in accuracy.
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., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
Short summary
Accurate hydrologic 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 modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Geosci. Model Dev., 17, 7083–7103, https://doi.org/10.5194/gmd-17-7083-2024, https://doi.org/10.5194/gmd-17-7083-2024, 2024
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.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024, https://doi.org/10.5194/gmd-17-6949-2024, 2024
Short summary
Short summary
The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, 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 the dynamic SWP.
Minki Hong, Nathaniel Chaney, Sergey Malyshev, Enrico Zorzetto, Anthony Preucil, and Elena Shevliakova
EGUsphere, https://doi.org/10.5194/egusphere-2024-2005, https://doi.org/10.5194/egusphere-2024-2005, 2024
Short summary
Short summary
This study aims to understand the significance of groundwater in resolving water-energy budgets in the context of Earth system processes. LM4-SHARC describes the hillslope groundwater using its emergent properties derived from streamflow observations and yields noticeable improvements in soil moisture/temperature and groundwater discharge predictions. The implication of the groundwater-mediated hydrologic interactions between hillslope and stream needs further exploration in the ESM community.
Jenny Kupzig, Nina Kupzig, and Martina Flörke
Geosci. Model Dev., 17, 6819–6846, https://doi.org/10.5194/gmd-17-6819-2024, https://doi.org/10.5194/gmd-17-6819-2024, 2024
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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.
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.
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.
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
Short summary
Short summary
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
Short summary
Short summary
We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
Short summary
Short summary
Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
Short summary
Short summary
Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
Short summary
Short summary
Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
Short summary
Short summary
NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
Short summary
Short summary
We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
Short summary
Short summary
Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
Short summary
Short summary
DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
Short summary
Short summary
Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
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.
Cited articles
Anaconda Software Distribution: Conda, https://anaconda.com (last access: 7 July 2024), 2023. a
Asfaw, D. T., Singer, M. B., Rosolem, R., MacLeod, D., Cuthbert, M., Miguitama, E. Q., Rios Gaona, M. F., and Michaelides, K.: stoPET v1.0: a stochastic potential evapotranspiration generator for simulation of climate change impacts, Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, 2023. a
Benoit, L., Allard, D., and Mariethoz, G.: Stochastic Rainfall Modeling at Sub-kilometer Scale, Water Resour. Res., 54, 4108–4130, https://doi.org/10.1029/2018WR022817, 2018. a, b
Berger, D.: Kendall's Rank Correlation vs Pearson's Linear Correlation: A Proof Of Greiner's Relation, SSRN, https://doi.org/10.2139/ssrn.2837712, 2016. a
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Bonan, G. B.: A Land Surface Model (LSM Version 1.0) for Ecological, Hydrological, and Atmospheric Studies: Technical Description and User's Guide (No. NCAR/TN-417+STR), Tech. Rep. NCAR/TN-417+STR, University Corporation for Atmospheric Research, https://doi.org/10.5065/D6DF6P5X, 1996. a
Branch, M. A., Coleman, T. F., and Li, Y.: A Subspace, Interior, and Conjugate Gradient Method for Large-Scale Bound-Constrained Minimization Problems, SIAM J. Sci. Comput., 21, 1–23, https://doi.org/10.1137/S1064827595289108, 1999. a
Breitenberger, E.: Analogues of the normal distribution on the circle and the sphere, Biometrika, 50, 81–88, https://doi.org/10.1093/biomet/50.1-2.81, 1963. a
Burton, A., Kilsby, C., Fowler, H., Cowpertwait, P., and O'Connell, P.: RainSim: A spatial-temporal stochastic rainfall modelling system, Environ. Modell. Softw., 23, 1356–1369, https://doi.org/10.1016/j.envsoft.2008.04.003, 2008. a
Caylor, K. K., D'Odorico, P., and Rodriguez-Iturbe, I.: On the ecohydrology of structurally heterogeneous semiarid landscapes, Water Resour. Res., 42, W07424, https://doi.org/10.1029/2005WR004683, 2006. a
Chen, L. and Guo, S.: Copulas and Its Application in Hydrology and Water Resources, no. 2364-8198 in Springer Water, Springer Singapore, 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore, 1st edn., https://doi.org/10.1007/978-981-13-0574-0, 2019. a, b
Cokelaer, T., Kravchenko, A., lahdjirayhan, msat59, Varma, A., L, B., Stringari, C. E., Brueffer, C., Broda, E., Pruesse, E., Singaravelan, K., Li, Z., mark padgham, and negodfre: cokelaer/fitter: v1.6.0, Zenodo [code], https://doi.org/10.5281/zenodo.8226571, 2023. a
Cuthbert, M. O., Acworth, R. I., Andersen, M. S., Larsen, J. R., McCallum, A. M., Rau, G. C., and Tellam, J. H.: Understanding and quantifying focused, indirect groundwater recharge from ephemeral streams using water table fluctuations, Water Resour. Res., 52, 827–840, https://doi.org/10.1002/2015WR017503, 2016. a
Dai, Q., Han, D., Rico-Ramirez, M. A., and Islam, T.: Modelling radar-rainfall estimation uncertainties using elliptical and Archimedean copulas with different marginal distributions, Hydrol. Sci. J., 59, 1992–2008, https://doi.org/10.1080/02626667.2013.865841, 2014. a, b
Dawkins, L. C., Osborne, J. M., Economou, T., Darch, G. J., and Stoner, O. R.: The Advanced Meteorology Explorer: a novel stochastic, gridded daily rainfall generator, J. Hydrol., 607, 127478, https://doi.org/10.1016/j.jhydrol.2022.127478, 2022. a, b
Dawson, T. E. and Ehleringer, J. R.: Streamside trees that do not use stream water, Nature, 350, 335–337, https://doi.org/10.1038/350335a0, 1991. a
De Luca, D. L. and Petroselli, A.: STORAGE (STOchastic RAinfall GEnerator): A User-Friendly Software for Generating Long and High-Resolution Rainfall Time Series, Hydrology, 8, 76, https://doi.org/10.3390/hydrology8020076, 2021. a, b
Dhillon, I. and Sra, S.: Modeling Data using Directional Distributions, Tech. Rep. TR-03-06, University of Texas: Department of Computer Science, Austin, TX, USA, https://www.cs.utexas.edu/users/inderjit/public_papers/tr03-06.pdf (last access: 7 July 2024), 2003. a
Diaz, H. F. and Markgraf, V. (Eds.): El Niño and the Southern Oscillation: Multiscale Variability and Global and Regional Impacts, Cambridge University Press, https://doi.org/10.1017/CBO9780511573125, 2000. a
Diez-Sierra, J., Navas, S., and del Jesus, M.: NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process, Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, 2023. a
D'Odorico, P., Caylor, K., Okin, G. S., and Scanlon, T. M.: On soil moisture-vegetation feedbacks and their possible effects on the dynamics of dryland ecosystems, J. Geophys. Res.-Biogeo., 112, G04010, https://doi.org/10.1029/2006JG000379, 2007. a
Evans, C. M., Dritschel, D. G., and Singer, M. B.: Modeling Subsurface Hydrology in Floodplains, Water Resour. Res., 54, 1428–1459, https://doi.org/10.1002/2017WR020827, 2018. a
Fang, K.-T., Kotz, S., and Ng, K. W.: Symmetric Multivariate and Related Distributions, no. 36 in Monographs on Statistics and Applied Probability, Chapman & Hall/CRC, 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742, 1st edn., https://doi.org/10.1201/9781351077040, 1990. a
Genest, C., Rémillard, B., and Beaudoin, D.: Goodness-of-fit tests for copulas: A review and a power study, Insurance Math. Econom., 44, 199–213, https://doi.org/10.1016/j.insmatheco.2007.10.005, 2009. a
Goodrich, D. C., Keefer, T. O., Unkrich, C. L., Nichols, M. H., Osborn, H. B., Stone, J. J., and Smith, J. R.: Long-term precipitation database, Walnut Gulch Experimental Watershed, Arizona, United States, Water Resour. Res., 44, W05S04, https://doi.org/10.1029/2006WR005782, 2008. a, b, c
Hobley, D. E. J., Adams, J. M., Nudurupati, S. S., Hutton, E. W. H., Gasparini, N. M., Istanbulluoglu, E., and Tucker, G. E.: Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, 2017. a
Hofert, M., Kojadinovic, I., Mächler, M., and Yan, J.: Elements of Copula Modeling with R, no. 2197-5744 in Use R!, Springer Cham, Gewerbestrasse 11, 6330 Cham, Switzerland, 1st edn., https://doi.org/10.1007/978-3-319-89635-9, 2018. a, b
Hornik, K. and Grün, B.: On maximum likelihood estimation of the concentration parameter of von Mises–Fisher distributions, Comput. Statist., 29, 945–957, https://doi.org/10.1007/s00180-013-0471-0, 2013. a
Hornik, K. and Grün, B.: movMF: An R Package for Fitting Mixtures of von Mises-Fisher Distributions, J. Stat. Softw., 58, 1–31, https://doi.org/10.18637/jss.v058.i10, 2014. a
Jammalamadaka, S. R. and SenGupta, A.: Topics in Circular Statistics, no. 5 in Series on Multivariate Analysis, World Scientific, P.O. Box 128, Farrer Road, Singapore 912805, https://doi.org/10.1142/4031, 2001. a, b
Joe, H.: Dependence Modeling with Copulas, no. 134 in Monographs on Statistics and Applied Probability, Chapman & Hall/CRC, 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742, 1st edn., https://doi.org/10.1201/b17116, 2014. a, b, c
Keefer, T. O., et al.: Southwest Watershed Research Center and Walnut Gulch Experimental Watershed, Tech. Rep. SWRC Publ. Reference No. 1588, Southwest Watershed Research Center, 2000 East Allen Road, Tucson, AZ 85719, http://www.tucson.ars.ag.gov/unit/publications/PDFfiles/1588.pdf (last access: 7 July 2024), 2007. a, b
Kendall, M. G.: The Treatment of Ties in Ranking Problems, Biometrika, 33, 239–251, https://doi.org/10.1093/biomet/33.3.239, 1945. a
Khedun, C. P., Mishra, A. K., Singh, V. P., and Giardino, J. R.: A copula-based precipitation forecasting model: Investigating the interdecadal modulation of ENSO's impacts on monthly precipitation, Water Resour. Res., 50, 580–600, https://doi.org/10.1002/2013WR013763, 2014. a
Kim, D., Cho, H., Onof, C., and Choi, M.: Let-It-Rain: a web application for stochastic point rainfall generation at ungaged basins and its applicability in runoff and flood modeling, Stoch. Env. Res. Risk A., 31, 1023–1043, https://doi.org/10.1007/s00477-016-1234-6, 2017. a, b, c
Kleiber, W., Katz, R. W., and Rajagopalan, B.: Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes, Water Resour. Res., 48, W01523, https://doi.org/10.1029/2011WR011105, 2012. a
Laio, F., D'Odorico, P., and Ridolfi, L.: An analytical model to relate the vertical root distribution to climate and soil properties, Geophys. Res. Lett., 33, L18401, https://doi.org/10.1029/2006GL027331, 2006. a
Langworthy, B. W., Stephens, R. L., Gilmore, J. H., and Fine, J. P.: Canonical correlation analysis for elliptical copulas, J. Multivariate Anal., 183, 104715, https://doi.org/10.1016/j.jmva.2020.104715, 2021. a
Lark, R. M., Clifford, D., and Waters, C. N.: Modelling complex geological circular data with the projected normal distribution and mixtures of von Mises distributions, Solid Earth, 5, 631–639, https://doi.org/10.5194/se-5-631-2014, 2014. a
Mardia, K. and Jupp, P.: Directional Statistics, Wiley Series in Probability and Statistics, John Wiley & Sons, Ltd., West Sussex, PO19 1UD England, https://doi.org/10.1002/9780470316979, 1999. a, b, c
Meles, M. B., Demaria, E. M. C., Heilman, P., Goodrich, D. C., Kautz, M. A., Armendariz, G., Unkrich, C., Wei, H., and Perumal, A. T.: Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets, Water, 14, 2198, https://doi.org/10.3390/w14142198, 2022. a, b, c
Michaelides, K. and Martin, G. J.: Sediment transport by runoff on debris-mantled dryland hillslopes, J. Geophys. Res.-Earth Surf., 117, F03014, https://doi.org/10.1029/2012JF002415, 2012. a
Michaelides, K. and Singer, M. B.: Impact of coarse sediment supply from hillslopes to the channel in runoff-dominated, dryland fluvial systems, J. Geophys. Res.-Earth Surf., 119, 1205–1221, https://doi.org/10.1002/2013JF002959, 2014. a
Michaelides, K. and Wainwright, J.: Modelling the effects of hillslope-channel coupling on catchment hydrological response, Earth Surf. Proc. Land., 27, 1441–1457, https://doi.org/10.1002/esp.440, 2002. a
Michaelides, K. and Wilson, M. D.: Uncertainty in predicted runoff due to patterns of spatially variable infiltration, Water Resour. Res., 43, W02415, https://doi.org/10.1029/2006WR005039, 2007. a
Michaelides, K., Hollings, R., Singer, M. B., Nichols, M. H., and Nearing, M. A.: Spatial and temporal analysis of hillslope-channel coupling and implications for the longitudinal profile in a dryland basin, Earth Surf. Proc. Land., 43, 1608–1621, https://doi.org/10.1002/esp.4340, 2018. a
Moran, M. S., Holifield Collins, C. D., Goodrich, D. C., Qi, J., Shannon, D. T., and Olsson, A.: Long-term remote sensing database, Walnut Gulch Experimental Watershed, Arizona, United States, Water Resour. Res., 44, W05S10, https://doi.org/10.1029/2006WR005689, 2008. a, b
Moré, J. J., Garbow, B. S., and Hillstrom, K. E.: User Guide for MINPACK-1, Tech. Rep. ANL-80-74, Argonne National Laboratory, Argonne, IL, USA, https://www.math.utah.edu/software/minpack/ (last access: 7 July 2024), 1980. a
Morin, E., Goodrich, D. C., Maddox, R. A., Gao, X., Gupta, H. V., and Sorooshian, S.: Rainfall modeling for integrating radar information into hydrological model, Atmos. Sci. Lett., 6, 23–30, https://doi.org/10.1002/asl.86, 2005. a, b, c
Nelsen, R. B.: An Introduction to Copulas, no. 2197-568X in Springer Series in Statistics, Springer New York, New York, NY 10013, USA, 2nd edn., https://doi.org/10.1007/0-387-28678-0, 2006. a, b
Nicholson, S. E.: Dryland Climatology, Cambridge University Press, The Edinburgh Building, Cambridge CB2 8RU, UK, https://doi.org/10.1017/CBO9780511973840, 2011. a, b
Osborn, H. B.: Timing and duration of high rainfall rates in the southwestern United States, Water Resour. Res., 19, 1036–1042, https://doi.org/10.1029/WR019i004p01036, 1983. a
Osborn, H. B. and Lane, L.: Precipitation-runoff relations for very small semiarid rangeland watersheds, Water Resour. Res., 5, 419–425, https://doi.org/10.1029/WR005i002p00419, 1969. a
Papalexiou, S. M., Serinaldi, F., and Porcu, E.: Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond, Water Resour. Res., 57, e2020WR029466, https://doi.org/10.1029/2020WR029466, 2021. a
Paschalis, A., Molnar, P., Fatichi, S., and Burlando, P.: A stochastic model for high-resolution space-time precipitation simulation, Water Resour. Res., 49, 8400–8417, https://doi.org/10.1002/2013WR014437, 2013. a, b
Peleg, N., Fatichi, S., Paschalis, A., Molnar, P., and Burlando, P.: An advanced stochastic weather generator for simulating 2-D high-resolution climate variables, J. Adv. Model. Earth Syst., 9, 1595–1627, https://doi.org/10.1002/2016MS000854, 2017. a
Philander, S. G.: El Niño, La Niña, and the Southern Oscillation., no. 46 in International Geophysics Series, Academic Press, San Diego, California 92101, US, 1990. a
Powell, M. J. D.: A Hybrid Method for Nonlinear Equations, in: Numerical Methods for Nonlinear Algebraic Equations, edited by Rabinowitz, P., chap. 6, 87–114, Gordon and Breach Science Publishers, 150 Fifth Avenue, New York, N.Y. 10011, U.S., 1970. a
Powell, M. J. D.: On nonlinear optimization since 1959, in: The Birth of Numerical Analysis, edited by Bulthee, A. and Cools, R., 141–160, World Scientific, 5 Toh Tuck Link, Singapore 596224, https://doi.org/10.1142/9789812836267_0009, 2009. a
Quichimbo, E. A., Singer, M. B., and Cuthbert, M. O.: Characterising groundwater-surface water interactions in idealised ephemeral stream systems, Hydrol. Process., 34, 3792–3806, https://doi.org/10.1002/hyp.13847, 2020. a
Quichimbo, E. A., Singer, M. B., Michaelides, K., Hobley, D. E. J., Rosolem, R., and Cuthbert, M. O.: DRYP 1.0: a parsimonious hydrological model of DRYland Partitioning of the water balance, Geosci. Model Dev., 14, 6893–6917, https://doi.org/10.5194/gmd-14-6893-2021, 2021. a
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: 7 July 2024), 2023. a
Renard, K. G. and Keppel, R. V.: Hydrographs of Ephemeral Streams in the Southwest, J. Hydr. Eng. Div-asce., 92, 33–52, https://doi.org/10.1061/JYCEAJ.0001419, 1966. a
Renard, K. G. and Laursen, E. M.: Dynamic Behavior Model of Ephemeral Stream, J. Hydr. Eng. Div-asce., 101, 511–528, https://doi.org/10.1061/JYCEAJ.0004340, 1975. a
Rios Gaona, M. F.: feliperiosg/STORM2: v2.2.2, Zenodo [code and data set], https://doi.org/10.5281/zenodo.8071820, 2023. a
Rios Gaona, M. F. and Villarini, G.: Characterization of the diurnal cycle of maximum rainfall in tropical cyclones, J. Hydrol., 564, 997–1007, https://doi.org/10.1016/j.jhydrol.2018.07.062, 2018. a
Ross, S. M.: Simulation, Academic Press, Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands, 5th edn., https://doi.org/10.1016/C2011-0-04574-X, 2013. a
Sabathier, R., Singer, M. B., Stella, J. C., Roberts, D. A., and Caylor, K. K.: Vegetation responses to climatic and geologic controls on water availability in southeastern Arizona, Environ. Res. Lett., 16, 064029, https://doi.org/10.1088/1748-9326/abfe8c, 2021. a
Salvadori, G. and De Michele, C.: Statistical characterization of temporal structure of storms, Adv. Water Resour., 29, 827–842, https://doi.org/10.1016/j.advwatres.2005.07.013, 2006. a
Sarachik, E. S. and Cane, M. A.: The El Niño-Southern Oscillation Phenomenon, Cambridge University Press, The Edinburgh Building, Cambridge CB2 8RU, UK, https://doi.org/10.1017/CBO9780511817496, 2010. a
Sargeant, C. I. and Singer, M. B.: Sub-annual variability in historical water source use by Mediterranean riparian trees, Ecohydrology, 9, 1328–1345, https://doi.org/10.1002/eco.1730, 2016. a
Scanlon, B. R., Keese, K. E., Flint, A. L., Flint, L. E., Gaye, C. B., Edmunds, W. M., and Simmers, I.: Global synthesis of groundwater recharge in semiarid and arid regions, Hydrol. Process., 20, 3335–3370, https://doi.org/10.1002/hyp.6335, 2006. a
Seabold, S. and Perktold, J.: Statsmodels: Econometric and Statistical Modeling with Python, in: Proceedings of the 9th Python in Science Conference, edited by: van der Walt, S. and Millman, J., 92–96, https://doi.org/10.25080/Majora-92bf1922-011, 2010. a, b
Shmaliy, Y. S.: Von Mises/Tikhonov-based distributions for systems with differential phase measurement, Signal Process., 85, 693–703, https://doi.org/10.1016/j.sigpro.2004.11.008, 2005. a
Singer, M. B. and Michaelides, K.: How is topographic simplicity maintained in ephemeral dryland channels?, Geology, 42, 1091–1094, https://doi.org/10.1130/G36267.1, 2014. a
Singer, M. B. and Michaelides, K.: Deciphering the expression of climate change within the Lower Colorado River basin by stochastic simulation of convective rainfall, Environ. Res. Lett., 12, 104011, https://doi.org/10.1088/1748-9326/aa8e50, 2017. a
Singer, M. B., Sargeant, C. I., Piégay, H., Riquier, J., Wilson, R. J. S., and Evans, C. M.: Floodplain ecohydrology: Climatic, anthropogenic, and local physical controls on partitioning of water sources to riparian trees, Water Resour. Res., 50, 4490–4513, https://doi.org/10.1002/2014WR015581, 2014. a
Singer, M. B., Michaelides, K., and Hobley, D. E. J.: STORM 1.0: a simple, flexible, and parsimonious stochastic rainfall generator for simulating climate and climate change, Geosci. Model Dev., 11, 3713–3726, https://doi.org/10.5194/gmd-11-3713-2018, 2018. a, b, c
Stillman, S., Zeng, X., Shuttleworth, W. J., Goodrich, D. C., Unkrich, C. L., and Zreda, M.: Spatiotemporal Variability of Summer Precipitation in Southeastern Arizona, J. Hydrometeorol., 14, 1944–1951, https://doi.org/10.1175/JHM-D-13-017.1, 2013. a
Temme, N.: On the numerical evaluation of the modified bessel function of the third kind, J. Comput. Phys., 19, 324–337, https://doi.org/10.1016/0021-9991(75)90082-0, 1975. a
The Economist: In defense of the Gaussian copula, electronic periodical, https://www.economist.com/free-exchange/2009/04/29/in-defense-of-the-gaussian-copula (last access: 5 October 2022), 2009. a
Tjøstheim, D., Otneim, H., and Støve, B.: Statistical Modeling Using Local Gaussian Approximation, Academic Press, 125 London Wall, London EC2Y 5AS, United Kingdom, https://doi.org/10.1016/C2017-0-02646-0, 2022. a, b, c
Tucker, G. E. and Bras, R. L.: A stochastic approach to modeling the role of rainfall variability in drainage basin evolution, Water Resour. Res., 36, 1953–1964, https://doi.org/10.1029/2000WR900065, 2000. a
Tucker, G. E. and Hancock, G. R.: Modelling landscape evolution, Earth Surf. Proc. Land., 35, 28–50, https://doi.org/10.1002/esp.1952, 2010. a
Tucker, G. E. and Slingerland, R.: Drainage basin responses to climate change, Water Resour. Res., 33, 2031–2047, https://doi.org/10.1029/97WR00409, 1997. a
UniData: Network Common Data Form (NetCDF), UniData [software], https://doi.org/10.5065/D6H70CW6, 2023. a
Vandenberghe, S., Verhoest, N. E. C., Onof, C., and De Baets, B.: A comparative copula-based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett-Lewis modeled rainfall, Water Resour. Res., 47, W07529, https://doi.org/10.1029/2009WR008388, 2011. a, b
Van Rossum, G. and Drake, F. L.: Python 3 Reference Manual, CreateSpace, Scotts Valley, CA, ISBN 978-1-4414-1269-0, 2009. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a, b, c, d
Vu, T. M., Mishra, A. K., Konapala, G., and Liu, D.: Evaluation of multiple stochastic rainfall generators in diverse climatic regions, Stoch. Env. Res. Risk A., 32, 1337–1353, https://doi.org/10.1007/s00477-017-1458-0, 2018. a, b
Warter, M. M., Singer, M. B., Cuthbert, M. O., Roberts, D., Caylor, K. K., Sabathier, R., and Stella, J.: Drought onset and propagation into soil moisture and grassland vegetation responses during the 2012–2019 major drought in Southern California, Hydrol. Earth Syst. Sci., 25, 3713–3729, https://doi.org/10.5194/hess-25-3713-2021, 2021. a
Warter, M. M., Singer, M. B., Cuthbert, M. O., Roberts, D., Caylor, K. K., Sabathier, R., and Stella, J.: Modeling seasonal vegetation phenology from hydroclimatic drivers for contrasting plant functional groups within drylands of the Southwestern USA, Environ. Res.-Ecology, 2, 025001, https://doi.org/10.1088/2752-664X/acb9a0, 2023. a
Wheater, H. S., Mathias, S. A., and Li, X.: Groundwater Modelling in Arid and Semi-Arid Areas, Cambridge University Press, https://doi.org/10.1017/CBO9780511760280, 2010. a
Wilcox, C., Aly, C., Vischel, T., Panthou, G., Blanchet, J., Quantin, G., and Lebel, T.: Stochastorm: A Stochastic Rainfall Simulator for Convective Storms, J. Hydrometeorol., 22, 387–404, https://doi.org/10.1175/JHM-D-20-0017.1, 2021. a
Zhang, L. and Singh, V. P.: Copulas and their Applications in Water Resources Engineering, Cambridge University Press, University Printing House, Cambridge CB2 8BS, United Kingdom, https://doi.org/10.1017/9781108565103, 2019. a, b, c
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.
STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly...
