Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-6935-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6935-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Sep 2022
Model description paper |
| 15 Sep 2022
| 15 Sep 2022
Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation
Stefania Camici
CORRESPONDING AUTHOR
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Gabriele Giuliani
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Luca Brocca
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Christian Massari
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Angelica Tarpanelli
National Research Council, Research Institute for Geo-Hydrological Protection, Perugia, Italy
Hassan Hashemi Farahani
Institute of Geodesy, University of Stuttgart, Geschwister-Scholl-Straße 24D, 70174 Stuttgart, Germany
Nico Sneeuw
Institute of Geodesy, University of Stuttgart, Geschwister-Scholl-Straße 24D, 70174 Stuttgart, Germany
Marco Restano
SERCO c/o ESA-ESRIN, Largo Galileo Galilei, Frascati, 00044, Italy
Jérôme Benveniste
European Space Agency, ESA-ESRIN, Largo Galileo Galilei, Frascati, 00044, Italy
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Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, and Gabrielle J. M. De Lannoy
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Hydrol. Earth Syst. Sci., 25, 5589–5601, https://doi.org/10.5194/hess-25-5589-2021, https://doi.org/10.5194/hess-25-5589-2021, 2021
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Denise Dettmering, Felix L. Müller, Julius Oelsmann, Marcello Passaro, Christian Schwatke, Marco Restano, Jérôme Benveniste, and Florian Seitz
Earth Syst. Sci. Data, 13, 3733–3753, https://doi.org/10.5194/essd-13-3733-2021, https://doi.org/10.5194/essd-13-3733-2021, 2021
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Maria Teresa Brunetti, Massimo Melillo, Stefano Luigi Gariano, Luca Ciabatta, Luca Brocca, Giriraj Amarnath, and Silvia Peruccacci
Hydrol. Earth Syst. Sci., 25, 3267–3279, https://doi.org/10.5194/hess-25-3267-2021, https://doi.org/10.5194/hess-25-3267-2021, 2021
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Satellite and rain gauge data are tested to predict landslides in India, where the annual toll of human lives and loss of property urgently demands the implementation of strategies to prevent geo-hydrological instability. For this purpose, we calculated empirical rainfall thresholds for landslide initiation. The validation of thresholds showed that satellite-based rainfall data perform better than ground-based data, and the best performance is obtained with an hourly temporal resolution.
Louise Mimeau, Yves Tramblay, Luca Brocca, Christian Massari, Stefania Camici, and Pascal Finaud-Guyot
Hydrol. Earth Syst. Sci., 25, 653–669, https://doi.org/10.5194/hess-25-653-2021, https://doi.org/10.5194/hess-25-653-2021, 2021
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Stefania Camici, Christian Massari, Luca Ciabatta, Ivan Marchesini, and Luca Brocca
Hydrol. Earth Syst. Sci., 24, 4869–4885, https://doi.org/10.5194/hess-24-4869-2020, https://doi.org/10.5194/hess-24-4869-2020, 2020
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Yvan Gouzenes, Fabien Léger, Anny Cazenave, Florence Birol, Pascal Bonnefond, Marcello Passaro, Fernando Nino, Rafael Almar, Olivier Laurain, Christian Schwatke, Jean-François Legeais, and Jérôme Benveniste
Ocean Sci., 16, 1165–1182, https://doi.org/10.5194/os-16-1165-2020, https://doi.org/10.5194/os-16-1165-2020, 2020
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El Mahdi El Khalki, Yves Tramblay, Christian Massari, Luca Brocca, Vincent Simonneaux, Simon Gascoin, and Mohamed El Mehdi Saidi
Nat. Hazards Earth Syst. Sci., 20, 2591–2607, https://doi.org/10.5194/nhess-20-2591-2020, https://doi.org/10.5194/nhess-20-2591-2020, 2020
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Christian Massari, Luca Brocca, Thierry Pellarin, Gab Abramowitz, Paolo Filippucci, Luca Ciabatta, Viviana Maggioni, Yann Kerr, and Diego Fernandez Prieto
Hydrol. Earth Syst. Sci., 24, 2687–2710, https://doi.org/10.5194/hess-24-2687-2020, https://doi.org/10.5194/hess-24-2687-2020, 2020
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Luca Brocca, Paolo Filippucci, Sebastian Hahn, Luca Ciabatta, Christian Massari, Stefania Camici, Lothar Schüller, Bojan Bojkov, and Wolfgang Wagner
Earth Syst. Sci. Data, 11, 1583–1601, https://doi.org/10.5194/essd-11-1583-2019, https://doi.org/10.5194/essd-11-1583-2019, 2019
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Michaël Ablain, Benoît Meyssignac, Lionel Zawadzki, Rémi Jugier, Aurélien Ribes, Giorgio Spada, Jerôme Benveniste, Anny Cazenave, and Nicolas Picot
Earth Syst. Sci. Data, 11, 1189–1202, https://doi.org/10.5194/essd-11-1189-2019, https://doi.org/10.5194/essd-11-1189-2019, 2019
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A description of the uncertainties in the Global Mean Sea Level (GMSL) record has been performed; 25 years of satellite altimetry data were used to estimate the error variance–covariance matrix for the GMSL record to derive its confidence envelope. Then a least square approach was used to estimate the GMSL trend and acceleration uncertainties over any time periods. A GMSL trend of 3.35 ± 0.4 mm/yr and a GMSL acceleration of 0.12 ± 0.07 mm/yr² have been found within a 90 % confidence level.
Malcolm McMillan, Alan Muir, Andrew Shepherd, Roger Escolà, Mònica Roca, Jérémie Aublanc, Pierre Thibaut, Marco Restano, Américo Ambrozio, and Jérôme Benveniste
The Cryosphere, 13, 709–722, https://doi.org/10.5194/tc-13-709-2019, https://doi.org/10.5194/tc-13-709-2019, 2019
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Melting of the Greenland and Antarctic ice sheets is one of the main causes of current sea level rise. Understanding ice sheet change requires large-scale systematic satellite monitoring programmes. This study provides the first assessment of a new long-term source of measurements, from Sentinel-3 satellite altimetry. We estimate the accuracy of Sentinel-3 across Antarctica, show that the satellite can detect regions that are rapidly losing ice, and identify signs of subglacial lake activity.
Felix Zaussinger, Wouter Dorigo, Alexander Gruber, Angelica Tarpanelli, Paolo Filippucci, and Luca Brocca
Hydrol. Earth Syst. Sci., 23, 897–923, https://doi.org/10.5194/hess-23-897-2019, https://doi.org/10.5194/hess-23-897-2019, 2019
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About 70 % of global freshwater is consumed by irrigation. Yet, policy-relevant estimates of irrigation water use (IWU) are virtually lacking at regional to global scales. To bridge this gap, we develop a method for quantifying IWU from a combination of state-of-the-art remotely sensed and modeled soil moisture products and apply it over the United States for the period 2013–2016. Overall, our estimates agree well with reference data on irrigated area and irrigation water withdrawals.
Victor Pellet, Filipe Aires, Simon Munier, Diego Fernández Prieto, Gabriel Jordá, Wouter Arnoud Dorigo, Jan Polcher, and Luca Brocca
Hydrol. Earth Syst. Sci., 23, 465–491, https://doi.org/10.5194/hess-23-465-2019, https://doi.org/10.5194/hess-23-465-2019, 2019
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This study is an effort for a better understanding and quantification of the water cycle and related processes in the Mediterranean region, by dealing with satellite products and their uncertainties. The aims of the paper are 3-fold: (1) developing methods with hydrological constraints to integrate all the datasets, (2) giving the full picture of the Mediterranean WC, and (3) building a model-independent database that can evaluate the numerous regional climate models (RCMs) for this region.
Jean-François Legeais, Michaël Ablain, Lionel Zawadzki, Hao Zuo, Johnny A. Johannessen, Martin G. Scharffenberg, Luciana Fenoglio-Marc, M. Joana Fernandes, Ole Baltazar Andersen, Sergei Rudenko, Paolo Cipollini, Graham D. Quartly, Marcello Passaro, Anny Cazenave, and Jérôme Benveniste
Earth Syst. Sci. Data, 10, 281–301, https://doi.org/10.5194/essd-10-281-2018, https://doi.org/10.5194/essd-10-281-2018, 2018
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Sea level is one of the best indicators of climate change and has been listed as one of the essential climate variables. Sea level measurements have been provided by satellite altimetry for 25 years, and the Climate Change Initiative (CCI) program of the European Space Agency has given the opportunity to provide a long-term, homogeneous and accurate sea level record. It will help scientists to better understand climate change and its variability.
Luca Ciabatta, Christian Massari, Luca Brocca, Alexander Gruber, Christoph Reimer, Sebastian Hahn, Christoph Paulik, Wouter Dorigo, Richard Kidd, and Wolfgang Wagner
Earth Syst. Sci. Data, 10, 267–280, https://doi.org/10.5194/essd-10-267-2018, https://doi.org/10.5194/essd-10-267-2018, 2018
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In this study, rainfall is estimated starting from satellite soil moisture observation on a global scale, using the ESA CCI soil moisture datasets. The new obtained rainfall product has proven to correctly identify rainfall events, showing performance sometimes higher than those obtained by using classical rainfall estimation approaches.
Hylke E. Beck, Noemi Vergopolan, Ming Pan, Vincenzo Levizzani, Albert I. J. M. van Dijk, Graham P. Weedon, Luca Brocca, Florian Pappenberger, George J. Huffman, and Eric F. Wood
Hydrol. Earth Syst. Sci., 21, 6201–6217, https://doi.org/10.5194/hess-21-6201-2017, https://doi.org/10.5194/hess-21-6201-2017, 2017
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This study represents the most comprehensive global-scale precipitation dataset evaluation to date. We evaluated 13 uncorrected precipitation datasets using precipitation observations from 76 086 gauges, and 9 gauge-corrected ones using hydrological modeling for 9053 catchments. Our results highlight large differences in estimation accuracy, and hence, the importance of precipitation dataset selection in both research and operational applications.
Christian Massari, Wade Crow, and Luca Brocca
Hydrol. Earth Syst. Sci., 21, 4347–4361, https://doi.org/10.5194/hess-21-4347-2017, https://doi.org/10.5194/hess-21-4347-2017, 2017
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The paper explores a method for the assessment of the performance of global rainfall estimates without relying on ground-based observations. Thanks to this method, different global correlation maps are obtained (for the first time without relying on a benchmark dataset) for some of the most used globally available rainfall products. This is central for hydroclimatic studies within data-scarce regions, where ground observations are scarce to evaluate the relative quality of a rainfall product
Graham D. Quartly, Jean-François Legeais, Michaël Ablain, Lionel Zawadzki, M. Joana Fernandes, Sergei Rudenko, Loren Carrère, Pablo Nilo García, Paolo Cipollini, Ole B. Andersen, Jean-Christophe Poisson, Sabrina Mbajon Njiche, Anny Cazenave, and Jérôme Benveniste
Earth Syst. Sci. Data, 9, 557–572, https://doi.org/10.5194/essd-9-557-2017, https://doi.org/10.5194/essd-9-557-2017, 2017
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We have produced an improved monthly record of mean sea level for 1993–2015. It is developed by careful processing of the records from nine satellite altimeter missions, making use of the best available orbits, instrumental corrections and geophysical corrections. This paper details the selection process and the processing method. The data are suitable for investigation of sea level changes at scales from seasonal to long-term sea level rise, including interannual variations due to El Niño.
Xiaodong Gao, Xining Zhao, Luca Brocca, Gaopeng Huo, Ting Lv, and Pute Wu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-292, https://doi.org/10.5194/hess-2017-292, 2017
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Profile soil moisture is key state variable in the Critical Zone ecology and hydrology. This paper sucessfully used a simple statistical method, the cumulative distribution frequency (CDF) matching method for the first time, to predict profile soil moisture (0–100 cm) from surface measurement (5 cm). The findings here can provide insights into profile soil moisture estimation from remote sensing moisture products.
Wuletawu Abera, Giuseppe Formetta, Luca Brocca, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 21, 3145–3165, https://doi.org/10.5194/hess-21-3145-2017, https://doi.org/10.5194/hess-21-3145-2017, 2017
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This study documents a state-of-the-art estimation of the water budget (rainfall, evapotranspiration, discharge, and soil and groundwater storage) components for the Upper Blue Nile river. The budget uses various JGrass-NewAGE components, satellite data and all ground measurements available. The analysis shows that precipitation of the basin is 1360 ± 230 mm per year. Evapotranspiration accounts for 56 %, runoff is 33 %, and storage varies from minus 10 % to plus 17 % of the annual water budget.
Xiaodong Gao, Xining Zhao, Luca Brocca, Ting Lv, Gaopeng Huo, and Pute Wu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2016-617, https://doi.org/10.5194/hess-2016-617, 2016
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We built observation operators by the CDF matching method. Two-year duration was identified as the optimal data length in prediction accuracy. Application in different climates in USA showed these operators are a robust statistical tool for upscaling soil moisture from surface to profile by using exponential filter as a reference method. The findings here may be applied in the prediction of profile soil moisture from surface measurements via remote sensing techniques.
F. Todisco, L. Brocca, L. F. Termite, and W. Wagner
Hydrol. Earth Syst. Sci., 19, 3845–3856, https://doi.org/10.5194/hess-19-3845-2015, https://doi.org/10.5194/hess-19-3845-2015, 2015
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We developed a new formulation of USLE, named Soil Moisture for Erosion (SM4E), that directly incorporates soil moisture information. SM4E is applied here by using modeled data and satellite observations obtained from the Advanced SCATterometer (ASCAT). SM4E is found to outperform USLE and USLE-MM models in silty–clay soil in central Italy. Through satellite data, there is the potential of applying SM4E for large-scale monitoring and quantification of the soil erosion process.
M. Ablain, A. Cazenave, G. Larnicol, M. Balmaseda, P. Cipollini, Y. Faugère, M. J. Fernandes, O. Henry, J. A. Johannessen, P. Knudsen, O. Andersen, J. Legeais, B. Meyssignac, N. Picot, M. Roca, S. Rudenko, M. G. Scharffenberg, D. Stammer, G. Timms, and J. Benveniste
Ocean Sci., 11, 67–82, https://doi.org/10.5194/os-11-67-2015, https://doi.org/10.5194/os-11-67-2015, 2015
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This paper presents various respective data improvements achieved within the European Space Agency (ESA) Climate Change Initiative (ESA CCI) project on sea level during its first phase (2010-2013), using multi-mission satellite altimetry data over the 1993-2010 time span.
S. Manfreda, L. Brocca, T. Moramarco, F. Melone, and J. Sheffield
Hydrol. Earth Syst. Sci., 18, 1199–1212, https://doi.org/10.5194/hess-18-1199-2014, https://doi.org/10.5194/hess-18-1199-2014, 2014
C. Massari, L. Brocca, S. Barbetta, C. Papathanasiou, M. Mimikou, and T. Moramarco
Hydrol. Earth Syst. Sci., 18, 839–853, https://doi.org/10.5194/hess-18-839-2014, https://doi.org/10.5194/hess-18-839-2014, 2014
L. Brocca, S. Liersch, F. Melone, T. Moramarco, and M. Volk
Hydrol. Earth Syst. Sci., 17, 3159–3169, https://doi.org/10.5194/hess-17-3159-2013, https://doi.org/10.5194/hess-17-3159-2013, 2013
Related subject area
Hydrology
STORM v.2: A simple, stochastic rainfall model for exploring the impacts of climate and climate change at and near the land surface in gauged watersheds
Fluvial flood inundation and socio-economic impact model based on open data
RoGeR v3.0.5 – a process-based hydrological toolbox model in Python
Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments
An open-source refactoring of the Canadian Small Lakes Model for estimates of evaporation from medium-sized reservoirs
EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions
Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software
HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications
Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community
HydroFATE (v1): a high-resolution contaminant fate model for the global river system
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)
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
Deep Dive into Global Hydrologic Simulations: Harnessing the Power of Deep Learning and Physics-informed Differentiable Models (δHBV-globe1.0-hydroDL)
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Customized deep learning for precipitation bias correction and downscaling
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality
Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale
RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling
Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest
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.
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.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-190, https://doi.org/10.5194/gmd-2023-190, 2023
Revised manuscript accepted for GMD
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Accurate hydrological modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall-runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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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
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Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
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A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
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Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
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The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
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A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
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A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
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This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
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We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
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We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
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Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Cited articles
Albergel, C., Rüdiger, C., Carrer, D., Calvet, J.-C., Fritz, N., Naeimi, V., Bartalis, Z., and Hasenauer, S.: An evaluation of ASCAT surface soil moisture products with in-situ observations in Southwestern France, Hydrol. Earth Syst. Sci., 13, 115–124, https://doi.org/10.5194/hess-13-115-2009, 2009.
Alcamo, J., Döll, P., Henrichs, T., Kaspar, F., Lehner, B., Rösch, T., and Siebert, S.: Development and testing of the WaterGAP 2 global model of water use and availability, Hydrolog. Sci. J., 48, 317–337, https://doi.org/10.1623/hysj.48.3.317.45290, 2003.
Alexander, J. S., Wilson, R. C., and Green, W. R.: A brief history and summary of the effects of river engineering and dams on the Mississippi River system and delta, US Department of the Interior, US Geological Survey, 53, https://doi.org/10.3133/cir1375, 2012.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration – guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper 56, FAO, Rome, 300, D05109, ISBN 92-5-104219-5, 1988.
Arabzadeh, A. and Behrangi, A.: Investigating Various Products of IMERG for Precipitation Retrieval Over Surfaces With and Without Snow and Ice Cover, Remote Sens.-Basel, 13, 2726, https://doi.org/10.3390/rs13142726, 2021.
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., vanden Hurk, B., Hirschi, M., and Betts, A. K.: A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the integrated forecast system, J. Hydrometeorol., 10, 623–643, https://doi.org/10.1175/2008JHM1068.1, 2009.
Barbarossa, V., Huijbregts, M. A., Beusen, A. H., Beck, H. E., King, H., and Schipper, A. M.: FLO1K, global maps of mean, maximum and minimum annual streamflow at 1 km resolution from 1960 through 2015, Sci. Data, 55, 180052, https://doi.org/10.1038/sdata.2018.52, 2018.
Beck, H. E., van Dijk, A. I. J. M., de Roo, A., Dutra, E., Fink, G., Orth, R., and Schellekens, J.: Global evaluation of runoff from 10 state-of-the-art hydrological models, Hydrol. Earth Syst. Sci., 21, 2881–2903, https://doi.org/10.5194/hess-21-2881-2017, 2017.
Berghuijs, W. R., Woods, R. A., Hutton, C. J., and Sivapalan, M.: Dominant flood generating mechanisms across the United States, Geophys. Res. Lett., 43, 4382–4390, https://doi.org/10.1002/2016GL068070, 2016.
Bergström, S.: The HBV model, in: Computer models of watershed hydrology. Water Resources Publications, edited by: Singh, V. P., Highlands Ranch, CO, 443–476, ISBN 978-1-887201-74-2, 1995.
Berthet, L., Andréassian, V., Perrin, C., and Javelle, P.: How crucial is it to account for the antecedent moisture conditions in flood forecasting? Comparison of event-based and continuous approaches on 178 catchments, Hydrol. Earth Syst. Sci., 13, 819–831, https://doi.org/10.5194/hess-13-819-2009, 2009.
Blöschl, G., Sivapalan, M., Wagener, T., Viglione, A., and Savenije, H. H. G. (Eds.): Runoff predictions in ungauged basins: A synthesis across processes, places and scales, Cambridge University Press, Cambridge, ISBN 9781107028180, 2013.
Bober, W.: Introduction to Numerical and Analytical Methods with MATLAB for Engineers and Scientists, CRC Press, Inc., Boca Raton, FL, USA, https://doi.org/10.1201/b16030, 2013.
Botter, G., Peratoner, F., Porporato, A., Rodriguez-Iturbe, I., and Rinaldo, A.: Signatures of large-scale soil moisture dynamics on streamflow statistics across U. S. Climate regimes, Water Resour. Res., 43, W11413, https://doi.org/10.1029/2007WR006162, 2007a.
Botter, G., Porporato, A., Daly, E., Rodriguez-Iturbe, I., and Rinaldo, A.: Probabilistic characterization of base flows in river basins: Roles of soil, vegetation, and geomorphology, Water Resour. Res., 43, W06404, https://doi.org/10.1029/2006WR005397, 2007b.
Brocca, L., Melone, F., and Moramarco, T.: On the estimation of antecedent wetness conditions in rainfall-runoff modelling, Hydrol. Process., 22, 629–642, https://doi.org/10.1002/hyp.6629, 2008.
Brocca, L., Melone, F., Moramarco, T., and Morbidelli, R.: Antecedent wetness conditions based on ERS scatterometer data, J. Hydrol., 364, 73–87, https://doi.org/10.1016/j.jhydrol.2008.10.007, 2009.
Brocca, L., Melone, F., and Moramarco, T.: Distributed rainfall-runoff modelling for flood frequency estimation and flood forecasting, Hydrol. Process., 25, 2801–2813, https://doi.org/10.1002/hyp.8042, 2011.
Brocca, L., Ciabatta, L., Massari, C., Camici, S., and Tarpanelli, A.: Soil moisture for hydrological applications: open questions and new opportunities, Water, 9, 140, https://doi.org/10.3390/w9020140, 2017.
Cai, X., Yang, Z. L., David, C. H., Niu, G. Y., and Rodell, M.: Hydrological evaluation of the Noah-MP land surface model for the Mississippi River Basin, J. Geophys. Res.-Atmos., 119, 23–38, https://doi.org/10.1002/2013JD020792, 2014.
Camici, S.: STREAM (SaTellite based Runoff Evaluation And Mapping) code (1.3), Zenodo [code], https://doi.org/10.5281/zenodo.4744984, 2021.
Cislaghi, A., Masseroni, D., Massari, C., Camici, S., and Brocca, L.: Combining a rainfall–runoff model and a regionalization approach for flood and water resource assessment in the western Po Valley, Italy, Hydrolog. Sci. J., 65, 348–370, https://doi.org/10.1080/02626667.2019.1690656, 2020.
Corradini, C., Morbidelli, R., Saltalippi, C., and Melone, F.: An adaptive model for flood forecasting on medium size basins, in: Applied Simulation and Modelling, edited by: Ubertini, L., IASTED Acta Press, Anaheim, CA, 555–559, ISBN 0-88986334-2, 2002.
Crochemore, L., Isberg, K., Pimentel, R., Pineda, L., Hasan, A., and Arheimer, B.: Lessons learnt from checking the quality of openly accessible river flow data worldwide, Hydrolog. Sci. J., 65, 699–711, https://doi.org/10.1080/02626667.2019.1659509, 2020.
Crow, W. T., Bindlish, R., and Jackson, T. J.: The added value of spaceborne passive microwave soil moisture retrievals for forecasting rainfall-runoff partitioning, Geophys. Res. Lett., 32, L18401, https://doi.org/10.1029/2005GL023543, 2005.
Döll, P., Kaspar, F., and Lehner, B.: A global hydrological model for deriving water availability indicators: Model tuning and validation, J. Hydrol., 270, 105–134, https://doi.org/10.1016/S0022-1694(02)00283-4, 2003.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, D., Hamer, P., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: state-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
Dyer, J.: Snow depth and streamflow relationships in large North American watersheds, J. Geophys. Res., 113, D18113, https://doi.org/10.1029/2008JD010031, 2008.
Eaton, J. W., Bateman, D., Hauberg, S., and Wehbring, R.: GNU Octave version 7.2.0 manual: a high-level interactive language for numerical computations, https://docs.octave.org/octave.pdf (last access: 26 August 2022), 2020.
Entekhabi, D., Njoku, E. G., O’Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J., Goodman, S., Jackson, T., Johnson, J. T., Kimball, J., Piepmeier, J., Koster, R., Martin, N., McDonald, K., Moghaddam, M., Moran, M. S., Reichle, R., Shi, J., Spencer, M., Thurman, S., Tsang, L., and Van Zyl, J.: The soil moisture active passive (SMAP) mission, P. IEEE, 98, 704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010.
ESA CCI SM: Soil moisture data, http://www.esa-soilmoisture-cci.org/, last access: 26 August 2022.
Famiglietti, J. S. and Rodell, M.: Water in the balance, Science, 340, 1300–1301, https://doi.org/10.1126/science.1236460, 2013.
Famiglietti, J. S. and Wood, E. F.: Multiscale modeling of spatially variable water and energy balance processes, Water Resour. Res., 30, 3061–3078, https://doi.org/10.1029/94WR01498, 1994.
Fan, Y. and Van den Dool, H. A.: Global monthly land surface air temperature analysis for 1948–present, J. Geophys. Res.-Atmos., 113, D01103, https://doi.org/10.1029/2007JD008470, 2008.
Fekete, B. M., Looser, U., Pietroniro, A., and Robarts, R. D.: Rationale for monitoring discharge on the ground, J. Hydrometeorol., 13, 1977–1986, https://doi.org/10.1175/JHM-D-11-0126.1, 2012.
Feldman, A. D.: Hydrologic modeling system HEC-HMS, Technical reference manual, US Army Corps of Engineers, Hydrologic Engineering Center, 2000.
Georgakakos, K. P. and Baumer, O. W.: Measurement and utilization of onsite soil moisture data, J. Hydrol., 184, 131–152, https://doi.org/10.1016/0022-1694(95)02971-0, 1996.
Ghiggi, G., Humphrey, V., Seneviratne, S. I., and Gudmundsson, L.: GRUN: an observation-based global gridded runoff dataset from 1902 to 2014, Earth Syst. Sci. Data, 11, 1655–1674, https://doi.org/10.5194/essd-11-1655-2019, 2019a.
Ghiggi, G., Seneviratne, S. I., Humphrey, V., and Gudmundsson, L.: GRUN: Global Runoff Reconstruction (GRUN_v1), https://doi.org/10.3929/ethz-b-000324386, 2019b.
Ghotbi, S., Wang, D., Singh, A., Blöschl, G., and Sivapalan, M.: A New Framework for Exploring Process Controls of Flow Duration Curves, Water Resour. Res., 56, e2019WR026083, https://doi.org/10.1029/2019WR026083, 2020.
Global Runoff Data Centre (GRDC): In situ river discharge data, https://www.bafg.de/GRDC/EN/Home/homepage_node.html, last access: 26 August 2022.
Gochis, D. J., Barlage, M., Dugger, A., FitzGerald, K., Karsten, L., McAllister, M., McCreight, J., Mills, J., RafieeiNasab, A., Read, L., Sampson, K., Yates, D., and Yu, W.: The WRF-Hydro modeling system technical description (Version 5.0), NCAR Technical Note, https://ral.ucar.edu/sites/default/files/public/WRF-HydroV5TechnicalDescription.pdf (last access: 26 August 2022), 2018.
Gudmundsson, L. and Seneviratne, S. I.: Observation-based gridded runoff estimates for Europe (E-RUN version 1.1), Earth Syst. Sci. Data, 8, 279–295, https://doi.org/10.5194/essd-8-279-2016, 2016.
Gudmundsson, L., Tallaksen, L. M., Stahl, K., Clark, D. B., Dumont, E., Hagemann, S., Bertrand, N., Gerten, D., Heinke, J., Hanasaki, N., Voss, F., and Koirala, S.: Comparing Large-Scale Hydrological Model Simulations to Observed Runoff Percentiles in Europe, J. Hydrometeorol., 13, 604–662, https://doi.org/10.1175/JHM-D-11-083.1, 2012a.
Gudmundsson, L., Wagener, T., Tallaksen, L. M., and Engeland, K.: Evaluation of nine large-scale hydrological models with respect to the seasonal runoff climatology in Europe, Water Resour. Res., 48, W11504, https://doi.org/10.1029/2011WR010911, 2012b.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Gupta, V. K., Waymire, E., and Wang, C. T.: A representation of an instantaneous unit hydrograph from geomorphology, Water Resour. Res., 16, 855–862, https://doi.org/10.1029/WR016i005p00855, 1980.
Haddeland, I., Heinke, J., Voß, F., Eisner, S., Chen, C., Hagemann, S., and Ludwig, F.: Effects of climate model radiation, humidity and wind estimates on hydrological simulations, Hydrol. Earth Syst. Sci., 16, 305–318, https://doi.org/10.5194/hess-16-305-2012, 2012.
Hanasaki, N., Kanae, S., Oki, T., Masuda, K., Motoya, K., Shirakawa, N., Shen, Y., and Tanaka, K.: An integrated model for the assessment of global water resources – Part 1: Model description and input meteorological forcing, Hydrol. Earth Syst. Sci., 12, 1007–1025, https://doi.org/10.5194/hess-12-1007-2008, 2008.
Hastie, T., Tibshirani, R., and Friedman, J. H.: The Elements of Statistical Learning – Data Mining, Inference, and Prediction, 2nd edn., Springer Series in Statistics, Springer, New York, http://www-stat.stanford.edu/~tibs/ElemStatLearn/ (last access: 5 July 2016), 2009.
Hong, Y., Adler, R. F., Hossain, F., Curtis, S., and Huffman, G. J.: A first approach to global runoff simulation using satellite rainfall estimation, Water Resour. Res., 43, W08502, https://doi.org/10.1029/2006WR005739, 2007.
Horton, R. E.: Hydrological approach to quantitative morphology, Geol. Soc. Am. Bull., 56, 275–370, 1945.
Houborg, R., Rodell, M., Li, B., Reichle, R., and Zaitchik, B. F.: Drought indicators based on model-assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations, Water Resour. Res., 48, W07525, https://doi.org/10.1029/2011WR011291, 2012.
Hu, G. R. and Li, X. Y.: Subsurface Flow, in: Observation and Measurement. Ecohydrology, edited by: Li, X. and Vereecken, H., Springer, Berlin, Heidelberg, 2018.
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G. J., Nelkin, E. J., Bowman, K. P., Hong, Y., Stocker, E. F., and Wolff, D. B.: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/jhm560.1, 2007.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Adler, R. F.: TRMM Version 7 3B42 and 3B43 Data Sets, NASA/GSFC, Greenbelt, MD, https://gpm.nasa.gov/sites/default/files/document_files/3B42_3B43_doc_V7.pdf (last access: 26 August 2022), 2014.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG), https://docserver.gesdisc.eosdis.nasa.gov/public/project/GPM/IMERG_ATBD_V06.pdf (last access: 26 August 2022), 2019.
Jäggi, A., Weigelt, M., Flechtner, F., Güntner, A., Mayer-Gürr, T., Martinis, S., Bruinsma, S., Flury, J., Bourgogne, S., Steffen, H., Meyer, U., Jean, Y., Sušnik, A., Grahsl, A., Arnold, D., Cann-Guthauser, K., Dach, R., Li, Z., Chen, Q., van Dam, T., Gruber, C., Poropat, L., Gouweleeuw, B., Kvas, A., Klinger, B., Lemoine, J-M., Biancale, R., Zwenzner, H., Bandikova, T., and Shabanloui, A.: European gravity service for improved emergency management (EGSIEM) – from concept to implementation, Geophys. J. Int., 218, 1572–1590, https://doi.org/10.1093/gji/ggz238, 2019.
Kauffeldt, A., Wetterhall, F., Pappenberger, F., Salamon, P., and Thielen, J.: Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level, Environ. Modell. Softw., 75, 68–76, https://doi.org/10.1016/j.envsoft.2015.09.009, 2016.
Kim, H., Watanabe, S., Chang, E. C., Yoshimura, K., Hirabayashi, J., Famiglietti, J., and Oki, T.: Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1), Data Integration and Analysis System (DIAS) [data set], https://doi.org/10.20783/DIAS.501, 2017.
Kirchner, J. W.: Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology, Water Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006.
Klees, R., Revtova, E. A., Gunter, B. C., Ditmar, P., Oudman, E., Winsemius, H. C., and Savenije, H. H. G.: The design of an optimal filter for monthly GRACE gravity models, Geophys. J. Int., 175, 417–432, https://doi.org/10.1111/j.1365-246X.2008.03922.x, 2008.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012.
Landerer, F. W. and Swenson, S. C.: Accuracy of scaled GRACE terrestrial water storage estimates, Water Resour. Res., 48, W04531, https://doi.org/10.1029/2011WR011453, 2012.
Landerer, F. W., Flechtner, F. M., Save, H., Webb, F. H., Bandikova, T., Bertiger, W. I., Bettadpur, S. V., Byun, S. H., Dahle, C., Dobslaw, H., Fahnestock, E., Harvey, N., Kang, Z., Kruizinga, G. L. H., Loomis, B. D., McCullough, C., Murböck, M., Nagel, P., Paik, M., Pie, N., Poole, S., Strekalov, D., Tamisiea, M. E., Wang, F. Watkins, M. M., Wen, H.-Y., Wiese, D. N., and Yuan, D.-N.: Extending the global mass change data record: GRACE Follow-On instrument and science data performance, Geophys. Res. Lett., 47, e2020GL088306, https://doi.org/10.1029/2020GL088306, 2020.
Lehner, B., Reidy Liermann, C., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rodel, R., Sindorf, N., and Wisser, D.: High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011.
Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., and Arheimer, B.: Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales, Hydrol. Res., 41, 295–319, https://doi.org/10.2166/nh.2010.007, 2010.
Long, D., Longuevergne, L., and Scanlon, B. R.: Uncertainty in evapotranspiration from land surface modeling, remote sensing, and GRACE satellites, Water Resour. Res., 50, 1131–1151, https://doi.org/10.1002/2013WR014581, 2014.
Lorenz, C., Kunstmann, H., Devaraju, B., Tourian, M. J., Sneeuw, N., and Riegger, J.: Large-Scale Runoff from Landmasses: A Global Assessment of the Closure of the Hydrological and Atmospheric Water Balances, J. Hydrometeorol., 15, 2111–2139, https://doi.org/10.1175/JHM-D-13-0157.1, 2014.
Luthcke, S. B., Sabaka, T. J., Loomis, B. D., Arendt, A. A., McCarthy, J. J., and Camp, J.: Antarctica, Greenland and Gulf of Alaska land-ice evolution from an iterated GRACE global mascon solution, J. Glaciol., 59, 613–631, https://doi.org/10.3189/2013JoG12J147, 2013.
Markstrom, S. L., Regan, R. S., Hay, L. E., Viger, R. J., Webb, R. M. T., Payn, R. A., and LaFontaine, J. H.: PRMS-IV, the precipitation-runoff modeling system, version 4, Techniques and Methods 6-B7, U. S. Geological Survey, 158 pp., https://doi.org/10.3133/tm6B7, 2015.
Massari, C., Brocca, L., Barbetta, S., Papathanasiou, C., Mimikou, M., and Moramarco, T.: Using globally available soil moisture indicators for flood modelling in Mediterranean catchments, Hydrol. Earth Syst. Sci., 18, 839–853, https://doi.org/10.5194/hess-18-839-2014, 2014.
Massari, C., Brocca, L., Tarpanelli, A., Hong, Y., Crow, W., Ciabatta, L., Camici, S., Barbetta, S., and Moramarco, T.: Global surface runoff estimation in near real time by using SMAP and GPM, poster at SMAP conference, 3rd Satellite Soil Moisture Validation and Application Workshop, New York, 210-22 September 2016, https://doi.org/10.13140/RG.2.2.34725.09448/2, 2016.
Massotti, L., Siemes, C., March, G., Haagmans, R., and Silvestrin, P.: Next generation gravity mission elements of the mass change and geoscience international constellation: From orbit selection to instrument and mission design, Remote Sens.-Basel, 13, 3935, https://doi.org/10.3390/rs13193935, 2021.
Maxwell, R. M., Condon, L. E., and Kollet, S. J.: A high-resolution simulation of groundwater and surface water over most of the continental US with the integrated hydrologic model ParFlow v3, Geosci. Model Dev., 8, 923–937, https://doi.org/10.5194/gmd-8-923-2015, 2015.
Merz, R. and Blöschl, G.: Regionalisation of catchment model parameters, J. Hydrol., 287, 95–123, https://doi.org/10.1016/j.jhydrol.2003.09.028, 2004.
Merz, R. and Blöschl, G.: A regional analysis of event runoff coefficients with respect to climate and catchment characteristics in Austria, Water Resour. Res., 45, W01405, https://doi.org/10.1029/2008WR007163, 2009.
Müller Schmied, H., Adam, L., Eisner, S., Fink, G., Flörke, M., Kim, H., Oki, T., Portmann, F. T., Reinecke, R., Riedel, C., Song, Q., Zhang, J., and Döll, P.: Variations of global and continental water balance components as impacted by climate forcing uncertainty and human water use, Hydrol. Earth Syst. Sci., 20, 2877–2898, https://doi.org/10.5194/hess-20-2877-2016, 2016.
Muneepeerakul, R., Azaele, S., Botter, G., Rinaldo, A., and Rodriguez-Iturbe, I.: Daily streamflow analysis based on a two-scaled gamma pulse model, Water Resour. Res., 46, W11546, https://doi.org/10.1029/2010WR009286, 2010.
Nash, J. E.: The form of the instantaneous unit hydrograph, IASH publication, 45, 114–121, 1957.
Natural Resources Conservation Service (NRCS): Urban hydrology for small watersheds, Tech. Release 55, 2nd Edn., U. S. Dep. of Agric., Washington, D. C., https://nationalstormwater.com/wp/wp-content/uploads/2020/07/Urban-Hydrology-for-Small-Watersheds-TR-55.pdf (last access: 26 August 2022), 1986.
NOAA PSL: Air temperature data, https://psl.noaa.gov/data/gridded/data.cpc.globaltemp.html, last access: 26 August 2022.
Noacco, V., Sarrazin, F., Pianosi, F., and Wagener, T.: Matlab/R workflows to assess critical choices in Global Sensitivity Analysis using the SAFE toolbox, MethodsX, 6, 2258–2280, https://doi.org/10.1016/j.mex.2019.09.033, 2019.
Oleson, K., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven, C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S., Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, J. F., Lawrence, P. J., Leung, L. R., Lipscomb,W., Muszala, S. P., Ricciuto, D. M., Sacks, W. J., Sun, Y., Tang, J., and Yang, Z.-L.: Technical description of version 4.5 of the Community Land Model (CLM), no. NCAR/TN-503+STR, https://doi.org/10.5065/D6RR1W7M, 2013.
O'Neill, M. M. F., Tijerina, D. T., Condon, L. E., and Maxwell, R. M.: Assessment of the ParFlow–CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States, Geosci. Model Dev., 14, 7223–7254, https://doi.org/10.5194/gmd-14-7223-2021, 2021.
Orth, R. and Seneviratne, S. I.: Introduction of a simple-model-based land surface dataset for Europe, Environ. Res. Lett., 10, 044012, https://doi.org/10.1088/1748-9326/10/4/044012, 2015.
Pellet, V., Aires, F., Munier, S., Fernández Prieto, D., Jordá, G., Dorigo, W. A., Polcher, J., and Brocca, L.: Integrating multiple satellite observations into a coherent dataset to monitor the full water cycle – application to the Mediterranean region, Hydrol. Earth Syst. Sci., 23, 465–491, https://doi.org/10.5194/hess-23-465-2019, 2019.
Pianosi, F., Sarrazin, F., and Wagener, T.: A Matlab toolbox for Global Sensitivity Analysis, Environ. Modell. Softw., 70, 80–85, https://doi.org/10.1016/j.envsoft.2015.04.009, 2015.
Prudhomme, C., Giuntoli, I., Robinson, E. L., Clark, D. B., Arnell, N. W., Dankers, R., Fekete, B. M., Franssen, W., Dieter Gerten, Gosling, S. N., Hagemann, S., Hannah, D. M., Kim, H., Masaki, Y., Satoh, Y., Stacke, T., Wada, Y., and Wisser, D.: Hydrological droughts in the 21st century, hotspots and uncertainties from a global multimodel ensemble experiment, P. Natl. Acad. Sci. USA, 111, 3262–3267, 2014.
Rakovec, O., Kumar, R., Attinger, S., and Samaniego, L.: Improving the realism of hydrologic model functioning through multivariate parameter estimation, Water Resour. Res., 52, 7779–7792, https://doi.org/10.1002/2016WR019430, 2016.
Riegger, J. and Tourian, M. J.: Characterization of runoff-storage relationships by satellite gravimetry and remote sensing, Water Resour. Res., 50, 3444–3466, https://doi.org/10.1002/2013WR013847, 2014.
Rodell, M., Beaudoing, H. K., L'Ecuyer, T. S., Olson, W. S., Famiglietti, J. S., Houser, P. R., Adler, R., Bosilovich, M. G., Clayson, C. A., Chambers, D., Clark, E., Fetzer, E. J., Gao, X., Gu, G., Hilburn, K., Huffman, G. J., Lettenmaier, D. P., Liu, W. T., Robertson, F. R., Schlosser, C. A., Sheffield, J., and Wood, E. F.: The observed state of the water cycle in the early 15twenty-first century, J. Climate, 28, 8289–8318, https://doi.org/10.1175/JCLI-D-14-00555.1, 2015.
Schellekens, J., Dutra, E., Martínez-de la Torre, A., Balsamo, G., van Dijk, A., Sperna Weiland, F., Minvielle, M., Calvet, J.-C., Decharme, B., Eisner, S., Fink, G., Flörke, M., Peßenteiner, S., van Beek, R., Polcher, J., Beck, H., Orth, R., Calton, B., Burke, S., Dorigo, W., and Weedon, G. P.: A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, 2017.
Schwanghart, W. and Kuhn, N. J.: TopoToolbox: A set of Matlab functions for topographic analysis, Environ. Modell. Softw., 25, 770–781, 2010.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture–climate interactions in a changing climate: A review, Earth-Sci. Rev., 99, 125–161, https://doi.org/10.1016/j.earscirev.2010.02.004, 2010.
Sneeuw, N., Lorenz, C., Devaraju, B., Tourian, M. J., Riegger, J., Kunstmann, H., and Bárdossy, A.: Estimating runoff using hydro-geodetic approaches, Surv. Geophys., 35, 1333–1359, https://doi.org/10.1007/s10712-014-9300-4, 2014.
Sobol, I. M.: Sensitivity analysis for non-linear mathematical models, Mathematical Modelling and Computational Experiments, 2, 407–414, 1993.
Solomatine, D. P. and Ostfeld, A.: Data-driven modelling: some past experiences and new approaches, J. Hydroinform., 10, 3–22, https://doi.org/10.2166/hydro.2008.015, 2008.
Sood, A. and Smakhtin, V.: Global hydrological models: a review, Hydrolog. Sci. J., 60, 549–565, https://doi.org/10.1080/02626667.2014.950580, 2015.
Strahler, A. N.: Hypsometric (area-altitude) analysis of erosional topography, Geol. Soc. Am. Bull., 63, 1117–1142, https://doi.org/10.1130/0016-7606(1952)63[1117:HAAOET]2.0.CO;2, 1952.
Tang, Y., Reed, P., Wagener, T., and van Werkhoven, K.: Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation, Hydrol. Earth Syst. Sci., 11, 793–817, https://doi.org/10.5194/hess-11-793-2007, 2007.
Tapley, B. D., Watkins, M. M., Flechtner, F., Reigber, C., Bettadpur, S., Rodell, M., Sasgen, I., Famiglietti, J. S., Landerer, F. W., Chambers, D. P., Reager, J. T., Gardner, A. S., Save, H., Ivins, E. R., Swenson, S. C., Boening, C., Dahle, C., Wiese, D. N., Dobslaw, H., Tamisiea, M. E., and Velicogna, I.: Contributions of GRACE to understanding climate change, Nat. Clim. Change, 9, 358–369, https://doi.org/10.1038/s41558-019-0456-2, 2019.
Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., and De Roo, A.: Hydrological evaluation of satellite rainfall estimates over the Volta and Baro-Akobo Basin, J. Hydrol., 499, 324–338, https://doi.org/10.1016/j.jhydrol.2013.07.012, 2013.
Tijerina, D., Condon, L., FitzGerald, K., Dugger, A., O'Neill, M. M., Sampson, K., Gochis, D., and Maxwell, R.: Continental hydrologic intercomparison project, phase 1: A large-scale hydrologic model comparison over the continental United States, Water Resour. Res., 57, e2020WR028931, https://doi.org/10.1029/2020WR028931, 2021.
Tourian, M. J., Reager, J. T., and Sneeuw, N.: The total drainable water storage of the Amazon river basin: A first estimate using GRACE, Water Resour. Res., 54, 3290–3312, https://doi.org/10.1029/2017WR021674, 2018.
Tramblay, Y., Bouvier, C., Martin, C., Didon-Lescot, J. F., Todorovik, D., and Domergue, J. M.: Assessment of initial soil moisture conditions for event-based rainfall–runoff modelling, J. Hydrol., 387, 176–187, https://doi.org/10.1016/j.jhydrol.2010.04.006, 2010.
Tropical Rainfall Measuring Mission (TRMM): TRMM (TMPA) Rainfall Estimate L3 3 hour 0.25 degree x 0.25 degree V7, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/TRMM/TMPA/3H/7, 2011.
Troutman, B. M. and Karlinger, M. B.: Unit hydrograph approximation assuming linear flow through topologically random channel networks, Water Resour. Res., 21, 743–754, https://doi.org/10.1029/WR021i005p00743, 1985.
Van Beek, L. P. H. and Bierkens, M. F. P.: The global hydrological model PCR-GLOBWB: conceptualization, parameterization and verification, Utrecht University, Utrecht, the Netherlands, 1, 25–26, http://vanbeek.geo.uu.nl/suppinfo/vanbeekbierkens2009.pdf (last access: 26 August 2022), 2009.
Vishwakarma, B. D., Devaraju, B., and Sneeuw, N.: What is the spatial resolution of GRACE satellite products for hydrology?, Remote Sens.-Basel, 10, 852, https://doi.org/10.3390/rs10060852, 2018.
Vörösmarty, C., Askew, A., Grabs, W., Barry, R. G., Birkett, C., Döll, P., Goodison, B., Hall, A., Jenne, R., Kitaev, L., Landwehr, J., Keeler, M., Leavesley, G., Schaake, J., Strzepek, K., Sundarvel, S. S., Takeuchi, K., and Webster, F.: Global water data: A newly endangered species, Eos Trans. Am. Geophys. Union, 82, 54–58, https://doi.org/10.1029/01EO00031, 2001.
Vose, R. S., Applequist, S., Durre, I., Menne, M. J., Williams, C. N., Fenimore, C., Gleason, K., and Arndt, D.: Improved Historical Temperature and Precipitation on Time Series For U. S. Climate Divisions, J. Appl. Meteorol. Clim., 53, 1232–1251, https://doi.org/10.1175/JAMC-D-13-0248.1, 2014.
Wagner, W., Lemoine, G., and Rott, H.: A method for estimating soil moisture from ERS scatterometer and soil data, Remote Sens. Environ., 70, 191–207, https://doi.org/10.1016/S0034-4257(99)00036-X, 1999.
Wagner, W., Blöschl, G., Pampaloni, P., Calvet, J. C., Bizzarri, B., Wigneron, J. P., and Kerr, Y.: Operational readiness of microwave remote sensing of soil moisture for hydrologic applications, Hydrol. Res., 38, 1–20, https://doi.org/10.2166/nh.2007.029, 2007.
Wang, Y. H., Broxton, P., Fang, Y., Behrangi, A., Barlage, M., Zeng, X., and Niu, G. Y.: A wet-bulb temperature-based rain-snow partitioning scheme improves snowpack prediction over the drier western United States, Geophys. Res. Lett., 46, 13825–13835, https://doi.org/10.1029/2019GL085722, 2019.
Wisser, D., Fekete, B. M., Vörösmarty, C. J., and Schumann, A. H.: Reconstructing 20th century global hydrography: a contribution to the Global Terrestrial Network- Hydrology (GTN-H), Hydrol. Earth Syst. Sci., 14, 1–24, https://doi.org/10.5194/hess-14-1-2010, 2010.
Yi, S. and Sneeuw, N.: Filling the data gaps within GRACE missions using Singular Spectrum Analysis, J. Geophys. Res.-Sol. Ea., 126, e2020JB021227, https://doi.org/10.1029/2020JB021227, 2021.
Yokoo, Y. and Sivapalan, M.: Towards reconstruction of the flow duration curve: development of a conceptual framework with a physical basis, Hydrol. Earth Syst. Sci., 15, 2805–2819, https://doi.org/10.5194/hess-15-2805-2011, 2011.
Zhang, Y., Pan, M., Sheffield, J., Siemann, A. L., Fisher, C. K., Liang, M., Beck, H. E., Wanders, N., MacCracken, R. F., Houser, P. R., Zhou, T., Lettenmaier, D. P., Pinker, R. T., Bytheway, J., Kummerow, C. D., and Wood, E. F.: A Climate Data Record (CDR) for the global terrestrial water budget: 1984–2010, Hydrol. Earth Syst. Sci., 22, 241–263, https://doi.org/10.5194/hess-22-241-2018, 2018.
Short summary
This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And...
