Articles | Volume 16, issue 11
https://doi.org/10.5194/gmd-16-3137-2023
© Author(s) 2023. 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-16-3137-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
05 Jun 2023
Methods for assessment of models |
| 05 Jun 2023
| 05 Jun 2023
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
INRAE, RiverLy, Lyon-Villeurbanne, 69625 Villeurbanne CEDEX, France
Claire Lauvernet
INRAE, RiverLy, Lyon-Villeurbanne, 69625 Villeurbanne CEDEX, France
Bruno Sudret
Chair of Risk, Safety and Uncertainty Quantification, ETH Zurich, Stefano-Franscini-Platz 5, 8093 Zurich, Switzerland
Arthur Vidard
Inria, CNRS, Univ. Grenoble-Alpes, Grenoble-INP, LJK, 38000 Grenoble, France
Related authors
Emilie Rouzies, Claire Lauvernet, and Arthur Vidard
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-219, https://doi.org/10.5194/hess-2024-219, 2024
Preprint under review for HESS
Short summary
Short summary
Hydrological models are useful for assessing the impact of landscape organization for effective mitigation strategies. However, using these models requires reducing uncertainties in their results, which can be achieved through model-data fusion. We integrate satellite surface moisture images into a water and pesticide transfer model. We compare 3 methods, studying their performance, and exploring various scenarios. This study helps improving decision support in water quality management.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-272, https://doi.org/10.5194/hess-2024-272, 2024
Preprint under review for HESS
Short summary
Short summary
Our study projects how climate change will affect drying of river segments and stream networks in Europe, using advanced modeling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent, intense and start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists in evaluating the ecological health of river ecosystem.
Emilie Rouzies, Claire Lauvernet, and Arthur Vidard
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-219, https://doi.org/10.5194/hess-2024-219, 2024
Preprint under review for HESS
Short summary
Short summary
Hydrological models are useful for assessing the impact of landscape organization for effective mitigation strategies. However, using these models requires reducing uncertainties in their results, which can be achieved through model-data fusion. We integrate satellite surface moisture images into a water and pesticide transfer model. We compare 3 methods, studying their performance, and exploring various scenarios. This study helps improving decision support in water quality management.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, Olivier Vannier, and Laurie Caillouet
Hydrol. Earth Syst. Sci., 28, 3457–3474, https://doi.org/10.5194/hess-28-3457-2024, https://doi.org/10.5194/hess-28-3457-2024, 2024
Short summary
Short summary
Daily streamflow series for 661 near-natural French catchments are reconstructed over 1871–2012 using two ensemble datasets: HydRE and HydREM. They include uncertainties coming from climate forcings, streamflow measurement, and hydrological model error (for HydrREM). Comparisons with other hydrological reconstructions and independent/dependent observations show the added value of the two reconstructions in terms of quality, uncertainty estimation, and representation of extremes.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, Olivier Vannier, and Laurie Caillouet
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-78, https://doi.org/10.5194/hess-2023-78, 2023
Publication in HESS not foreseen
Short summary
Short summary
The recent development of the a new meteorological dataset providing precipitation and temperature over France – FYRE Climate – has been transformed to streamflow time series over 1871–2012 through the used of a hydrological model. This led to the creation of the daily hydrological reconstructions called HyDRE and HyDRE. These two reconstructions are evaluated allow to better understand the variability of past hydrology over France.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, and Olivier Vannier
Clim. Past, 17, 1857–1879, https://doi.org/10.5194/cp-17-1857-2021, https://doi.org/10.5194/cp-17-1857-2021, 2021
Short summary
Short summary
This article presents FYRE Climate, a dataset providing daily precipitation and temperature spanning the 1871–2012 period at 8 km resolution over France. FYRE Climate has been obtained through the combination of daily and yearly observations and a gridded reconstruction already available through a statistical technique called data assimilation. Results highlight the quality of FYRE Climate in terms of both long-term variations and reproduction of extreme events.
Rafael Muñoz-Carpena, Claire Lauvernet, and Nadia Carluer
Hydrol. Earth Syst. Sci., 22, 53–70, https://doi.org/10.5194/hess-22-53-2018, https://doi.org/10.5194/hess-22-53-2018, 2018
Short summary
Short summary
Seasonal shallow water tables (WTs) in lowlands limit vegetation-buffer efficiency to control runoff pollution. Mechanistic models are needed to quantify true field efficiency. A new simplified algorithm for soil infiltration over WTs is tested against reference models and lab data showing WT effects depend on local settings but are negligible after 2 m depth. The algorithm is coupled to a complete vegetation buffer model in a companion paper to analyze pesticide and sediment control in situ.
Claire Lauvernet and Rafael Muñoz-Carpena
Hydrol. Earth Syst. Sci., 22, 71–87, https://doi.org/10.5194/hess-22-71-2018, https://doi.org/10.5194/hess-22-71-2018, 2018
Short summary
Short summary
Vegetation buffers, often placed in lowlands to control runoff pollution, can exhibit limited efficiency due to seasonal shallow water tables (WTs). A new shallow water table infiltration algorithm developed in a companion paper is coupled to a complete vegetation buffer model to quantify pesticide and sediment control in the field. We evaluated the model on two field experiments in France with and without WT conditions and show WTs can control efficiency depending on land and climate settings.
Related subject area
Hydrology
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
Regionalization in global hydrological models and its impact on runoff simulations: a case study using WaterGAP3 (v 1.0.0)
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)
The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features
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)
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
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.
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.
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
Short summary
Short summary
STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
Short summary
Short summary
River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
Short summary
Short summary
The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
Short summary
Short summary
This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
Short summary
Short summary
This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
Short summary
Short summary
The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
Short summary
Short summary
Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
Short summary
Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
Short summary
Short summary
We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
Short summary
Short summary
The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
Short summary
Short summary
Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
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.
Hannes Müller Schmied, Tim Trautmann, Sebastian Ackermann, Denise Cáceres, Martina Flörke, Helena Gerdener, Ellen Kynast, Thedini Asali Peiris, Leonie Schiebener, Maike Schumacher, and Petra Döll
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-213, https://doi.org/10.5194/gmd-2023-213, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Assessing water availability and water use at the global scale is challenging but essential for a range of purposes. We describe the newest version of the global hydrological model WaterGAP which has been used for numerous water resources assessments since 1996. We show the effects of new model features and model evaluations against observed streamflow and water storage anomalies as well as water abstractions statistics. The publically available model output for several variants is described.
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.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
Short summary
Short summary
In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
Short summary
Short summary
We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
Short summary
Short summary
During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
Short summary
Short summary
It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
Short summary
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
Short summary
Short summary
This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
Short summary
Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
Short summary
Short summary
Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
Short summary
Short summary
Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
Short summary
Short summary
A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
Short summary
Short summary
Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
Short summary
Short summary
The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
Short summary
Short summary
A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
Short summary
Short summary
A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
Short summary
Short summary
We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Cited articles
Alipour, A., Jafarzadegan, K., and Moradkhani, H.: Global sensitivity analysis
in hydrodynamic modeling and flood inundation mapping, 152, 105398,
https://doi.org/10.1016/j.envsoft.2022.105398, 2022. a
Alletto, L., Pot, V., Giuliano, S., Costes, M., Perdrieux, F., and Justes, E.:
Temporal variation in soil physical properties improves the water dynamics
modeling in a conventionally-tilled soil, Geoderma, 243–244, 18–28,
https://doi.org/10.1016/j.geoderma.2014.12.006, 2015. a, b
Antoniadis, A., Lambert-Lacroix, S., and Poggi, J.-M.: Random forests for
global sensitivity analysis: A selective review,
Reliab. Eng. Syst. Safe., 206, 107312, https://doi.org/10.1016/j.ress.2020.107312, 2021. a
Archer, G. E. B., Saltelli, A., and Sobol, I. M.: Sensitivity measures,
ANOVA-like Techniques and the use of bootstrap,
J. Stat. Comput. Sim., 58, 99–120, https://doi.org/10.1080/00949659708811825,
1997. a
Aulia, A., Jeong, D., Mohd Saaid, I., Kania, D., Taleb Shuker, M., and
El-Khatib, N. A.: A Random Forests-based sensitivity analysis framework for
assisted history matching, J. Petrol. Sci. Eng., 181,
106237, https://doi.org/10.1016/j.petrol.2019.106237, 2019. a
Balasubramanian, K., Sriperumbudur, B. K., and Lebanon, G.:
Ultrahigh Dimensional Feature Screening via RKHS Embeddings,
International Conference on Artificial Intelligence and Statistics,
2013. a
Becker, W. E., Tarantola, S., and Deman, G.: Sensitivity analysis approaches to
high-dimensional screening problems at low sample size, J. Stat. Comput. Sim., 88, 2089–2110,
https://doi.org/10.1080/00949655.2018.1450876, 2018. a
Bénard, C., Da Veiga, S., and Scornet, E.: Mean decrease accuracy for random forests: inconsistency, and a practical solution via the Sobol-MDA, Biometrika, 109, 881–900, https://doi.org/10.1093/biomet/asac017, 2022. a, b
Blatman, G. and Sudret, B.: Adaptive sparse polynomial chaos expansion based on
least angle regression, J. Comput. Phys., 230, 2345–2367,
https://doi.org/10.1016/j.jcp.2010.12.021, 2011. a, b
Branger, F. and McMillan, H. K.: Deriving hydrological signatures from soil
moisture data, Hydrol. Process., 34, 1410–1427,
https://doi.org/10.1002/hyp.13645, 2020. a
Branger, F., Braud, I., Debionne, S., Viallet, P., Dehotin, J., Henine, H.,
Nedelec, Y., and Anquetin, S.: Towards multi-scale integrated hydrological
models using the LIQUID® framework. Overview of the concepts and first
application examples, Environ. Modell. Softw., 25, 1672–1681,
https://doi.org/10.1016/j.envsoft.2010.06.005, 2010. a
Breiman, L.: Bagging predictors, Mach. Learn., 24, 123–140, 1996. a
Brown, C., Alix, A., Alonso-Prados, J.-L., Auteri, D., Gril, J.-J., Hiederer,
R., Holmes, C., Huber, A., de Jong, F., M. Liess, S., Loutseti, Mackay, N.,
Maier, W.-M., Maund, S., Pais, C., Reinert, W., Russell, M., Schad, T.,
Stadler, R., Streloke, M., Styczen, M., and van de Zande, J.: Landscape and
mitigation factors in aquatic risk assessment. Volume 2: detailed technic,
Tech. rep., European Commission, SANCO/10422/2005 v2.0, 2007. a, b, c
Buis, S., Piacentini, A., and Déclat, D.: PALM: a computational framework
for assembling high-performance computing applications,
Concurr. Comp.-Pract. E., 18, 231–245, https://doi.org/10.1002/cpe.914,
2006. a
Caisson, A.: Prise en main et application d’un modèle spatialisé
à base physique (CATHY) sur un versant expérimental pour la mise en
place d'un système d’assimilation de données, Master's thesis,
ENGEES, 2019. a
Catalogne, C., Lauvernet, C., and Carluer, N.: Guide d’utilisation de
l’outil BUVARD pour le dimensionnement des bandes tampons
végétalisées destinées à limiter les transferts de
pesticides par ruissellement, Tech. rep., Agence française pour la
biodiversité, 2018. a
Coutadeur, C., Coquet, Y., and Roger-Estrade, J.: Variation of hydraulic
conductivity in a tilled soil, Eur. J. Soil Sci., 53,
619–628, https://doi.org/10.1046/j.1365-2389.2002.00473.x, 2002. a
Da Veiga, S.: Global sensitivity analysis with dependence measures,
J. Stat. Comput. Sim., 85, 1283–1305,
https://doi.org/10.1080/00949655.2014.945932, 2015. a, b, c, d
Da Veiga, S., Gamboa, F., Iooss, B., and Prieur, C.: Basics and Trends in
Sensitivity Analysis, Society for Industrial and Applied Mathematics, https://doi.org/10.1137/1.9781611976694, 2021. a, b
D'Andrea, M. F., Letourneau, G., Rousseau, A. N., and Brodeur, J. C.:
Sensitivity analysis of the Pesticide in Water Calculator model for
applications in the Pampa region of Argentina, Sci. Total Environ., 698, 134232,
https://doi.org/10.1016/j.scitotenv.2019.134232, 2020. a, b
Darcy, H.: Recherches expérimentales relatives au mouvement de l'eau dans
les tuyaux, Impr. Impériale, 1857. a
De Lozzo, M. and Marrel, A.: New improvements in the use of dependence measures
for sensitivity analysis and screening, J. Stat. Comput. Sim.,
86, 3038–3058, https://doi.org/10.1080/00949655.2016.1149854, 2014. a, b
Dehotin, J., Braud, I., Vazquez, R., Debionne, S., and Viallet, P.: Prise en
compte de l'hétérogénéité des surfaces continentales dans la
modélisation couplées zone non saturé-zone saturée, Bulletin du GFHN, 54,
57–62, 2008. a
Dosskey, M. G., Helmers, M. J., and Eisenhauer, D. E.: A design aid for sizing
filter strips using buffer area ratio,
J. Soil Water Conserv., 66, 29–39, https://doi.org/10.2489/jswc.66.1.29, 2011. a
Dubus, I. G. and Brown, C. D.: Sensitivity and First-Step Uncertainty Analyses
for the Preferential Flow Model MACRO, J. Environ. Qual., 31,
227–240, https://doi.org/10.2134/jeq2002.2270, 2002. a
Dubus, I. G., Brown, C. D., and Beulke, S.: Sensitivity analyses for four
pesticide leaching models, Pest Manag. Sci., 59, 962–982,
https://doi.org/10.1002/ps.723, 2003. a, b, c
Durand, C.: Modélisation du transfert de pesticides à l'échelle de
la parcelle. Application au bassin versant de la Morcille (Nord Beaujolais,
69) et analyse de sensibilité du modèle, Master's thesis, ENGEES,
2014. a
Fajraoui, N., Ramasomanana, F., Younes, A., Mara, T., Ackerer, P., and
Guadagnini, A.: Use of global sensitivity analysis and polynomial chaos
expansion for interpretation of nonreactive transport experiments in
laboratory-scale porous media, Water Resour. Res., 47, W02521,
https://doi.org/10.1029/2010WR009639, 2011. a
Faúndez Urbina, C. A., van den Berg, F., van Dam, J. C., Tang, D. W. S., and
Ritsema, C. J.: Parameter sensitivity of SWAP-PEARL models for
pesticide leaching in macroporous soils, Vadose Zone J., 19, e20075,
https://doi.org/10.1002/vzj2.20075, 2020. a, b
FOCUS: FOCUS surface water scenarios in the EU evaluation process under
91/414/EEC, European commission, report of the FOCUS Working Group on Surface
Water Scenarios, EC Document Reference SANCO/4802/2001, 2001. a
Fouilloux, A. and Piacentini, A.: The PALM Project: MPMD paradigm for an
oceanic data assimilation software, in: Euro-Par'99 Parallel Processing:
5th International Euro-Par Conference Toulouse, France, 31August–3 September 1999, Proceedings, 1423–1430, Springer Berlin Heidelberg, Berlin,
Heidelberg, https://doi.org/10.1007/3-540-48311-X_200, 1999 (data available at: http://www.cerfacs.fr/globc/PALM_WEB/user.html#download, last access: 15 June 2020). a, b
Fox, G. A., Muñoz-Carpena, R., and Sabbagh, G. J.: Influence of flow
concentration on parameter importance and prediction uncertainty of pesticide
trapping by vegetative filter strips, J. Hydrol., 384, 164–173,
https://doi.org/10.1016/j.jhydrol.2010.01.020, 2010. a, b, c
Frésard, F.: Cartographie des sols d’un petit bassin versant en
Beaujolais viticole, en appui à l’évaluation du risque de
contamination des eaux par les pesticides, Master's thesis, Université de
Franche Comté, 2010. a
Fukumizu, K., Gretton, A., Xiaohai, S., and Schölkopf, B.:
Kernel Measures of Conditional Dependence,
in: Advances in Neural Information Processing Systems 20,
edited by: Platt, J. C., Koller, D., Singer, Y., and Roweis, S. T.,
Curran Associates, Inc.,
489–496,
2008. a
Gamboa, F., Janon, A., Klein, T., and Lagnoux, T.: Sensitivity indices for
multivariate outputs, C. R. Math., 351, 307–310,
https://doi.org/10.1016/j.crma.2013.04.016, 2013. a, b, c, d
Gao, B., Walter, M., Steenhuis, T., Hogarth, W., and Parlange, J.: Rainfall
induced chemical transport from soil to runoff: theory and experiments,
J. Hydrol., 295, 291–304, https://doi.org/10.1016/j.jhydrol.2004.03.026,
2004. a
Garcia, D., Arostegui, I., and Prellezo, R.: Robust combination of the Morris
and Sobol methods in complex multidimensional models, Environ. Modell.
Softw., 122, 104517, https://doi.org/10.1016/j.envsoft.2019.104517, 2019. a, b
Gouy, V., Liger, L., Carluer, N., and Margoum, C.: Site Atelier Ardières
Morcille, Irstea, BDOH, https://doi.org/10.17180/obs.saam, 2015. a, b
Gregorutti, B., Michel, B., and Saint-Pierre, P.: Correlation and variable
importance in random forests, Stat. Comput., 27, 659–678,
https://doi.org/10.1007/s11222-016-9646-1, 2017. a, b
Gretton, A., Bousquet, O., Smola, A., and Schölkopf, B.: Measuring
statistical dependence with Hilbert-Schmidt norms, in: International
conference on algorithmic learning theory, 63–77, Springer, https://doi.org/10.1007/11564089_7,
2005a. a
Gretton, A., Herbrich, R., Smola, A., Bousquet, O., and Schölkopf, B.:
Kernel Methods for Measuring Independence, J. Mach. Learn. Res., 6,
2075–2129, 2005b. a
Gupta, H. V., Wagener, T., and Liu, Y.: Reconciling theory with observations:
elements of a diagnostic approach to model evaluation, Hydrol.
Process., 22, 3802–3813, https://doi.org/10.1002/hyp.6989, 2008. a
Hamby, D. M.: A review of techniques for parameter sensitivity analysis of
environmental models, Environ. Monit. Assess., 32, 135–154, https://doi.org/10.1007/BF00547132, 1994. a
Harper, E. B., Stella, J. C., and Fremier, A. K.: Global sensitivity analysis
for complex ecological models: a case study of riparian cottonwood population
dynamics, Ecol. Appl., 21, 1225–1240, https://doi.org/10.1890/10-0506.1,
2011. a
Holvoet, K., van Griensven, A., Seuntjens, P., and Vanrolleghem, P. A.:
Sensitivity analysis for hydrology and pesticide supply towards the river in
SWAT, Phys. Chem. Earth, 30, 518–526, https://doi.org/10.1016/j.pce.2005.07.006, 2005. a, b
Hong, T. and Purucker, S. T.: Spatiotemporal sensitivity analysis of vertical
transport of pesticides in soil, Environ. Modell. Softw., 105,
24–38, https://doi.org/10.1016/j.envsoft.2018.03.018, 2018. a, b
Horner, I.: Design and evaluation of hydrological signatures for the diagnostic
and improvement of a process-based distributed hydrological model, PhD
thesis, Université Grenoble Alpes,
thèse de doctorat
dirigée par Branger, Flora Océan, Atmosphère, Hydrologie Université
Grenoble Alpes 2020, http://www.theses.fr/2020GRALU014 (last access: 15 March 2022), 2020. a
Ishwaran, H. and Kogalur, U.: Fast Unified Random Forests for Survival,
Regression, and Classification (RF-SRC), R package version 2.9.3., 2020. a
Ishwaran, H. and Lu, M.: Standard errors and confidence intervals for variable
importance in random forest regression, classification, and survival,
Stat. Med., 38, 558–582, https://doi.org/10.1002/sim.7803, 2019. a
Lauvernet, C. and Muñoz-Carpena, R.: Shallow water table effects on water, sediment, and pesticide transport in vegetative filter strips – Part 2: model coupling, application, factor importance, and uncertainty, Hydrol. Earth Syst. Sci., 22, 71–87, https://doi.org/10.5194/hess-22-71-2018, 2018. a, b, c, d, e, f
Lewis, K.-A., Tzilivakis, J., Warner, D., and Green, A.: An international
database for pesticide risk assessments and management,
Hum. Ecol.
Risk Assess., 22, 1050–1064,
https://doi.org/10.1080/10807039.2015.1133242, 2016. a, b
Li, K., De Jong, R., and Boisvert, J.: An exponential root-water-uptake model
with water stress compensation, J. Hydrol., 252, 189–204,
https://doi.org/10.1016/S0022-1694(01)00456-5, 2001. a
Lighthill, M. J. and Whitham, G. B.: On kinematic waves I. Flood movement in
long rivers, P. Roy. Soc. Lond. A, 229, 281–316,
https://doi.org/10.1098/rspa.1955.0088, 1955. a
Marelli, S. and Sudret, B.: UQLab: A framework for uncertainty quantification
in Matlab, in: Proc. 2nd Int. Conf. on Vulnerability, Risk Analysis and
Management (ICVRAM2014), https://doi.org/10.1061/9780784413609.257, 2014 (data available at: https://www.uqlab.com, last access: 11 January 2022). a, b
Marelli, S. and Sudret, B.: An active-learning algorithm that combines sparse
polynomial chaos expansions and bootstrap for structural reliability
analysis, Struct. Saf., 75, 67–74,
https://doi.org/10.1016/j.strusafe.2018.06.003, 2018. a, b
Marrel, A., Marie, N., and De Lozzo, M.: Advanced surrogate model and
sensitivity analysis methods for sodium fast reactor accident assessment,
Reliab. Eng. Syst. Safe., 138, 232–241,
https://doi.org/10.1016/j.ress.2015.01.019, 2015. a
McKay, M. D., Beckman, R. J., and Conover, W. J.: A comparison of three methods
for selecting values of input variables in the analysis of output from a
computer code, Technometrics, 21, 239–245, https://doi.org/10.2307/1268522, 1979. a
MeteoFrance: Evapotranspiration potentielle MONTHEIH, [data set], 2008. a
Meynaoui, A., Marrel, A., and Laurent-Bonneau, B.: Méthodologie basée
sur les mesures de dépendance HSIC pour l'analyse de sensibilité de
second niveau, in: 50èmes Journées de Statistique (JdS2018),
Palaiseau, France, cea-02339273, 2018. a
Nossent, J. and Bauwens, W.: Multi-variable sensitivity and identifiability
analysis for a complex environmental model in view of integrated water
quantity and water quality modeling, Water Sci. Technol., 65,
539–549, https://doi.org/10.2166/wst.2012.884, 2012. a
Nossent, J., Elsen, P., and Bauwens, W.: Sobol’ sensitivity analysis of a
complex environmental model, Environ. Modell. Softw., 26, 1515–1525,
https://doi.org/10.1016/j.envsoft.2011.08.010, 2011. a, b
Peyrard, X., Liger, L., Guillemain, C., and Gouy, V.: A trench study to assess
transfer of pesticides in subsurface lateral flow for a soil with contrasting
texture on a sloping vineyard in Beaujolais, Environ. Sci.
Pollut. Res., 13, https://doi.org/10.1007/s11356-015-4917-5, 2016. a
Pianosi, F., Beven, K., Freer, J., Hall, J., Rougier, J., Stephenson, D., and
Wagener, T.: Sensitivity analysis of environmental models: A systematic
review with practical workflow, Environ. Modell. Softw., 79, 214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 2016. 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: 1 November 2022),
2017. a
Reichenberger, S., Bach, M., Skitschak, A., and Frede, H.-G.: Mitigation
strategies to reduce pesticide inputs into ground- and surface water and
their effectiveness; A review, Sci. Total Environ., 384,
1–35, https://doi.org/10.1016/j.scitotenv.2007.04.046, 2007. a
Rodriguez-Galiano, V., Mendes, M., Garcia-Soldado, M., Chica-Olmo, M., and
Ribeiro, L.: Predictive modeling of groundwater nitrate pollution using
Random Forest and multisource variables related to intrinsic and specific
vulnerability: A case study in an agricultural setting (Southern Spain),
Sci. Total Environ., 476-477, 189–206,
https://doi.org/10.1016/j.scitotenv.2014.01.001, 2014. a, b
Ross, P. J.: Modeling soil water and solute transport – fast, simplified
numerical solutions, Agron. J., 95, 1352–1361,
https://doi.org/10.2134/agronj2003.1352, 2003. a
Ross, P. J.: Fast solution of Richards’ equation for flexible soil hydraulic
property descriptions, Tech. rep., CSIRO, https://doi.org/10.4225/08/5859741868a90, 2006. a
Roux, S., Buis, S., Lafolie, F., and Lamboni, M.: Cluster-based GSA: Global
sensitivity analysis of models with temporal or spatial outputs using
clustering, Environ. Modell. Softw., 140, 105046,
https://doi.org/10.1016/j.envsoft.2021.105046, 2021. a
Rouzies, E., Lauvernet, C., Barachet, C., Morel, T., Branger, F., Braud, I.,
and Carluer, N.: From agricultural catchment to management scenarios: A
modular tool to assess effects of landscape features on water and pesticide
behavior, Sci. Total Environ., 671, 1144–1160,
https://doi.org/10.1016/j.scitotenv.2019.03.060, 2019. a, b, c, d
Rouzies, E., Lauvernet, C., Sudret, B., and Vidard, A.: Software for: How to perform global sensitivity analysis of a catchment-scale, distributed pesticide transfer model? Application to the PESHMELBA model, Zenodo [software], https://doi.org/10.15454/2HAU8R, 2022a. a
Rouzies, E., Lauvernet, C., Sudret, B., and Vidard, A.: Code availability and data for: How to perform global sensitivity analysis of a catchment-scale, distributed pesticide transfer model? Application to the PESHMELBA model, Zenodo [code], https://doi.org/10.15454/2YVY4O, 2022b. a
Saint-Geours, N.: Analyse de sensibilité de modèles spatialisés :
application à l'analyse coût-bénéfice de projets de
prévention du risque d'inondation, PhD thesis, Université de
Montpellier 2, tel-00761032, 2012. a
Saltelli, A.: Sensitivity analysis for importance assessment, Risk Analysis, 22, 579–590,
https://doi.org/10.1111/0272-4332.00040, 2002. a
Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M.: Sensitivity
Analysis in Practice: A Guide to Assessing Scientific Models, Wiley,
https://doi.org/10.1002/0470870958, 2004. a
Saltelli, A., Jakeman, A., Razavi, S., and Wu, Q.: Sensitivity analysis: A
discipline coming of age, Environ. Modell. Softw., 146,
105226, https://doi.org/10.1016/j.envsoft.2021.105226, 2021. a
Sarrazin, F., Pianosi, F., and Wagener, T.: Global sensitivity analysis of
environmental models: convergence and validation, Environ. Modell.
Softw., 79, 135–152, https://doi.org/10.1016/j.envsoft.2016.02.005, 2016. a, b
Schwen, A., Bodner, G., Scholl, P., Buchan, G., and Loiskandl, W.: Temporal
dynamics of soil hydraulic properties and the water-conducting porosity under
different tillage, Soil Till. Res., 113, 89–98,
https://doi.org/10.1016/j.still.2011.02.005, 2011. a, b, c
Seki, K.: SWRC fit – a nonlinear fitting program with a water retention curve for soils having unimodal and bimodal pore structure, Hydrol. Earth Syst. Sci. Discuss., 4, 407–437, https://doi.org/10.5194/hessd-4-407-2007, 2007. a
Sheikholeslami, R., Razavi, S., Gupta, H. V., Becker, W., and Haghnegahdar, A.:
Global sensitivity analysis for high-dimensional problems: How to objectively
group factors and measure robustness and convergence while reducing
computational cost, Environ. Modell. Softw., 111, 282–299,
https://doi.org/10.1016/j.envsoft.2018.09.002, 2019. a
Sheikholeslami, R., Gharari, S., Papalexiou, S. M., and Clark, M. P.: VISCOUS:
A Variance-Based Sensitivity Analysis Using Copulas for Efficient
Identification of Dominant Hydrological Processes, Water Resour. Res.,
57, e2020WR028435, https://doi.org/10.1029/2020WR028435, 2021. a
Smart, D., Schwass, E., Lakso, A., and Morano, L.: Grapevine rooting patterns:
A comprehensive analysis and a review,
Am. J. Enol. Viticult., 57, 89–104, 2006. a
Soleimani, F.: Analytical seismic performance and sensitivity evaluation of
bridges based on random decision forest framework, Structures, 32, 329–341,
https://doi.org/10.1016/j.istruc.2021.02.049, 2021. a
Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., and Xu, C.: Global sensitivity
analysis in hydrological modeling: Review of concepts, methods, theoretical
framework, and applications, J. Hydrol., 523, 739–757,
https://doi.org/10.1016/j.jhydrol.2015.02.013, 2015. a
Sudret, B.: Global sensitivity analysis using polynomial chaos expansions,
Reliab. Eng. Syst. Safe., 93, 964–979,
https://doi.org/10.1016/j.ress.2007.04.002, 2008. a, b, c
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. a, b
Tarantola, S., Giglioli, N., Jesinghaus, J., and Saltelli, A.: Can global
sensitivity analysis steer the implementation of models for environmental
assessments and decision-making?,
Stoch. Env. Res. Risk A., 16, 63–76, https://doi.org/10.1007/s00477-001-0085-x, 2002. a, b
Tissot, J.-Y. and Prieur, C.: A randomized orthogonal array-based procedure for
the estimation of first- and second-order Sobol' indices, J. Stat. Comput. Sim., 85, 1358–1381,
https://doi.org/10.1080/00949655.2014.971799, 2015. a
Touzani, S. and Busby, D.: Screening Method Using the Derivative-based Global
Sensitivity Indices with Application to Reservoir Simulator,
Oil Gas Sci. Technol., 69, 619–632, https://doi.org/10.2516/ogst/2013195, 2014. a
van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., and
Srinivasan, R.: A global sensitivity analysis tool for the parameters of
multi-variable catchment models, J. Hydrol., 324, 10–23,
https://doi.org/10.1016/j.jhydrol.2005.09.008, 2006. a
Varado, N., Braud, I., and Ross, P.: Development and assessment of an efficient
vadose zone module solving the 1D Richards' equation and including root
extraction by plants, J. Hydrol., 323, 258–275,
https://doi.org/10.1016/j.jhydrol.2005.09.015, 2006. a, b
Walter, M., Gao, B., and Parlange, J.-Y.: Modeling soil solute release into
runoff with infiltration, J. Hydrol., 347, 430–437,
https://doi.org/10.1016/j.jhydrol.2007.09.033, 2007. a
Wang, S., Huang, G., Baetz, B., and Huang, W.: A polynomial chaos ensemble
hydrologic prediction system for efficient parameter inference and robust
uncertainty assessment, J. Hydrol., 530, 716–733,
https://doi.org/10.1016/j.jhydrol.2015.10.021, 2015. a
Yang, J.: Convergence and uncertainty analyses in Monte-Carlo based sensitivity
analysis, Environ. Modell. Softw., 26, 444–457,
https://doi.org/10.1016/j.envsoft.2010.10.007, 2011. a
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.
Water and pesticide transfer models are complex and should be simplified to be used in decision...
