Articles | Volume 11, issue 6
https://doi.org/10.5194/gmd-11-2189-2018
© Author(s) 2018. 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-11-2189-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
13 Jun 2018
Development and technical paper |
| 13 Jun 2018
| 13 Jun 2018
The design, deployment, and testing of kriging models in GEOframe with SIK-0.9.8
Marialaura Bancheri
CORRESPONDING AUTHOR
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
Francesco Serafin
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
Michele Bottazzi
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
Wuletawu Abera
International Center for Tropical Agriculture (CIAT), P.O. Box 5689, Addis Ababa, Ethiopia
Giuseppe Formetta
Centre for Ecology & Hydrology, Crowmarsh Gifford, Wallingford, UK
Riccardo Rigon
Department of Civil, Environmental and Mechanical Engineering, University of Trento, Trento, Italy
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Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Niccolò Tubini, Concetta D'Amato, Olaf David, and Christian Massari
Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
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Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.
Riccardo Rigon, Marialaura Bancheri, and Timothy R. Green
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The goal of the paper is to analyze the theory of water age inside a catchment while accounting for multiple outflows. It tries to propose the material under a new perspective where it lines up concepts, cleans the notation, discusses some classical results, and offers some examples that help to relate the modern achievements to the theory of the IUH, clarifying assets of both of them. In doing all of this, it also produces various new results, and some regarding solute transport.
Giuseppe Formetta, Marialaura Bancheri, Olaf David, and Riccardo Rigon
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Ten algorithms for estimating DL and one for UL are integrated in a new model (LWRB) and connected to hydrological model JGrass-NewAge. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. We evaluated the performances of simplified models (SMs) of DL, as presented in literature formulations, and determined by automatic calibration the site-specific parameter sets for SMs of DL to improve model predictions.
Shima Azimi, Christian Massari, Giuseppe Formetta, Silvia Barbetta, Alberto Tazioli, Davide Fronzi, Sara Modanesi, Angelica Tarpanelli, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 27, 4485–4503, https://doi.org/10.5194/hess-27-4485-2023, https://doi.org/10.5194/hess-27-4485-2023, 2023
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We analyzed the water budget of nested karst catchments using simple methods and modeling. By utilizing the available data on precipitation and discharge, we were able to determine the response lag-time by adopting new techniques. Additionally, we modeled snow cover dynamics and evapotranspiration with the use of Earth observations, providing a concise overview of the water budget for the basin and its subbasins. We have made the data, models, and workflows accessible for further study.
Martin Morlot, Simone Russo, Luc Feyen, and Giuseppe Formetta
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We analyzed recent trends in heat and cold wave (HW and CW) risk in a European alpine region, defined by a time and spatially explicit framework to quantify hazard, vulnerability, exposure, and risk. We find a statistically significant increase in HW hazard and exposure. A decrease in vulnerability is observed except in the larger cities. HW risk increased in 40 % of the region, especially in highly populated areas. Stagnant CW hazard and declining vulnerability result in reduced CW risk.
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
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Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Niccolò Tubini, Concetta D'Amato, Olaf David, and Christian Massari
Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
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Barry Hankin, Ian Hewitt, Graham Sander, Federico Danieli, Giuseppe Formetta, Alissa Kamilova, Ann Kretzschmar, Kris Kiradjiev, Clint Wong, Sam Pegler, and Rob Lamb
Nat. Hazards Earth Syst. Sci., 20, 2567–2584, https://doi.org/10.5194/nhess-20-2567-2020, https://doi.org/10.5194/nhess-20-2567-2020, 2020
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With growing support for nature-based solutions to reduce flooding by local communities, government authorities and international organisations, it is still important to improve how we assess risk reduction. We demonstrate an efficient, simplified 1D network model that allows us to explore the
whole-systemresponse of numerous leaky barriers placed in different stream networks, whilst considering utilisation, synchronisation effects and cascade failure, and we provide advice on their siting.
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.
Riccardo Rigon, Marialaura Bancheri, and Timothy R. Green
Hydrol. Earth Syst. Sci., 20, 4929–4947, https://doi.org/10.5194/hess-20-4929-2016, https://doi.org/10.5194/hess-20-4929-2016, 2016
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The goal of the paper is to analyze the theory of water age inside a catchment while accounting for multiple outflows. It tries to propose the material under a new perspective where it lines up concepts, cleans the notation, discusses some classical results, and offers some examples that help to relate the modern achievements to the theory of the IUH, clarifying assets of both of them. In doing all of this, it also produces various new results, and some regarding solute transport.
Giuseppe Formetta, Marialaura Bancheri, Olaf David, and Riccardo Rigon
Hydrol. Earth Syst. Sci., 20, 4641–4654, https://doi.org/10.5194/hess-20-4641-2016, https://doi.org/10.5194/hess-20-4641-2016, 2016
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Ten algorithms for estimating DL and one for UL are integrated in a new model (LWRB) and connected to hydrological model JGrass-NewAge. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. We evaluated the performances of simplified models (SMs) of DL, as presented in literature formulations, and determined by automatic calibration the site-specific parameter sets for SMs of DL to improve model predictions.
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M. Graham Clark and Sean K. Carey
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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.
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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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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
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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.
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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.
Matevž Vremec, Raoul Collenteur, and Steffen Birk
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-63, https://doi.org/10.5194/gmd-2024-63, 2024
Revised manuscript accepted for GMD
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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.
Jenny Kupzig, Nina Kupzig, and Martina Floerke
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-47, https://doi.org/10.5194/gmd-2024-47, 2024
Revised manuscript accepted for GMD
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Valid simulation results from global hydrological models (GHM) are essential, e.g., to studying climate change impacts. Regionalization is a necessary step, to adapt GHM to ungauged basins to enable such valid simulations. In this study, we highlight the impact of regionalization on global simulations by using different regionalization methods. Applying two valid regionalization strategies globally we’ve found that the “outflow to the ocean” changed in the range of inter-model differences.
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.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-407, https://doi.org/10.5194/egusphere-2024-407, 2024
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The soil water potential (SWP) determines various soil water processes. Because it cannot be measured directly by remote sensing techniques, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of SWP.
João Careto, Rita Cardoso, Ana Russo, Daniela Lima, and Pedro Soares
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-9, https://doi.org/10.5194/gmd-2024-9, 2024
Revised manuscript accepted for GMD
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In this study, a new drought index is proposed, which not only is able to identify the same events but also can improve the results obtained from other established drought indices. The index is empirically based and is extremely straightforward to compute. It is as well, a daily drought index with the ability to not only assess flash droughts but also events at longer aggregation scales, such as the traditional monthly indices.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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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.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-151, https://doi.org/10.5194/gmd-2023-151, 2023
Revised manuscript accepted for GMD
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We develop an operational forecast system, COATLINES-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model requires a relatively small computational demand and results compare well with near real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and waves predictions can improve in accuracy.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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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.
Cited articles
Abera, W., Formetta, G., Borga, M., and Rigon, R.: Estimating the water budget components and their variability in a pre-alpine basin with JGrass-NewAGE, Adv. Water Resour., 104, 37–54, 2017.
Adams, B. M., Bohnhoff, W., Dalbey, K., Eddy, J., Eldred, M., Gay, D., Haskell, K., Hough, P. D., and Swiler, L. P.: Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis: Version 5.0 user's manual, Sandia National Laboratories, Tech. Rep. SAND2010-2183, 2009.
Adhikary, S. K., Muttil, N., and Yilmaz, A. G.: Genetic programming-based ordinary kriging for spatial interpolation of rainfall, J. Hydrol. Eng., 21, 04015062, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001300, 2015.
Aidoo, E. N., Mueller, U., Goovaerts, P., and Hyndes, G. A.: Evaluation of geostatistical estimators and their applicability to characterise the spatial patterns of recreational fishing catch rates, Fish. Res., 168, 20–32, 2015.
Argent, R. M.: An overview of model integration for environmental applications–components, frameworks and semantics, Environ. Modell. Softw., 19, 219–234, 2004.
Attorre, F., Alfo, M., De Sanctis, M., Francesconi, F., and Bruno, F.: Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale, Int. J. Climatol., 27, 1825–1843, 2007.
Balme, M., Vischel, T., Lebel, T., Peugeot, C., and Galle, S.: Assessing the water balance in the Sahel: Impact of small scale rainfall variability on runoff: Part 1: Rainfall variability analysis, J. Hydrol., 331, 336–348, 2006.
Bancheri, M.: A flexible approach to the estimation of water budgets and its connection to the travel time theory, PhD thesis, University of Trento, 2017.
Bancheri, M., Serafin, F., and Bottazzi, M.: OMS project for Kriging interpolation of precipitation and temperature in Isarco River Valley (Version 1.0) [Data set], Zenodo, https://doi.org/10.5281/zenodo.1244034, 2017.
Basistha, A., Arya, D., and Goel, N.: Spatial distribution of rainfall in Indian himalayas – a case study of Uttarakhand region, Water Resour. Manag., 22, 1325–1346, 2008.
Beck, K.: Test-driven development: by example, Addison-Wesley Professional, 2003.
Boer, E. P., de Beurs, K. M., and Hartkamp, A. D.: Kriging and thin plate splines for mapping climate variables, Int. J. Appl. Earth Obs., 3, 146–154, 2001.
Buytaert, W., Celleri, R., Willems, P., Bièvre, B. D., and Wyseure, G.: Spatial and temporal rainfall variability in mountainous areas: A case study from the south Ecuadorian Andes, J. Hydrol., 329, 413–421, 2006.
Carrera-Hernández, J. and Gaskin, S.: Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico, J. Hydrol., 336, 231–249, 2007.
Cressie, N. A. and Cassie, N. A.: Statistics for spatial data, vol. 900, Wiley New York, 1993.
Creutin, J. and Obled, C.: Objective analyses and mapping techniques for rainfall fields: an objective comparison, Water Resour. Res., 18, 413–431, 1982.
David, O., Ascough Ii, J., Lloyd, W., Green, T., Rojas, K., Leavesley, G., and Ahuja, L.: A software engineering perspective on environmental modeling framework design: The Object Modeling System, Environ. Modell. Softw., 39, 201–213, 2013.
Deutsch, C. V. and Journel, A. G.: Geostatistical software library and user's guide, vol. 1996, Oxford university press New York, 1992.
Di Piazza, A., Conti, F. L., Noto, L., Viola, F., and La Loggia, G.: Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy, Int. J. Appl. Earth Obs., 13, 396–408, 2011.
Donatelli, M. and Rizzoli, A.-E.: A design for framework-independent model components of biophysical systems, in: Proceedings of the iEMSs Fourth Biennial Meeting, Barcelona, Catalonia, International Congress on Environmental Modelling and Software iEMSs 2008, 727–734, 2008.
Eberhart, R. and Kennedy, J.: A new optimizer using particle swarm theory, in: Micro Machine and Human Science, 1995, MHS'95, Proceedings of the Sixth International Symposium on IEEE, 39–43, 1995.
Eckel, B.: Thinking in JAVA, Prentice Hall Professional, 2003.
Efron, B.: The Jackknife, the Bootstrap and Other Resampling Plans, Society for Industrial and Applied Mathematics, https://doi.org/10.1137/1.9781611970319, 1982.
Eischeid, J. K., Pasteris, P. A., Diaz, H. F., Plantico, M. S., and Lott, N. J.: Creating a serially complete, national daily time series of temperature and precipitation for the western United States, J. Appl. Meteorol., 39, 1580–1591, 2000.
Ellis, B., Stylos, J., and Myers, B.: The factory pattern in API design: A usability evaluation, in: Proceedings of the 29th international conference on Software Engineering, IEEE Computer Society, 302–312, 2007.
Formetta, G., Rigon, R., Chávez, J. L., and David, O.: Modeling shortwave solar radiation using the JGrass-NewAge system, Geosci. Model Dev., 6, 915–928, https://doi.org/10.5194/gmd-6-915-2013, 2013.
Formetta, G., Antonello, A., Franceschi, S., David, O., and Rigon, R.: Hydrological modelling with components: A GIS-based open-source framework, Environ. Modell. Softw., 55, 190–200, 2014.
Formetta, G., Bancheri, M., David, O., and Rigon, R.: Performance of site-specific parameterizations of longwave radiation, Hydrol. Earth Syst. Sci., 20, 4641–4654, https://doi.org/10.5194/hess-20-4641-2016, 2016.
Freeman, E., Robson, E., Bates, B., and Sierra, K.: Head First Design Patterns: A Brain-Friendly Guide, O'Reilly Media, Inc., 2004.
Gamma, E.: Design patterns: elements of reusable object-oriented software, Pearson Education India, 1994.
Gardner, H. and Manduchi, G.: Design Patterns for e-Science, Springer edition, 2002.
Goovaerts, P.: Geostatistics for natural resources evaluation, Oxford university press, 1997.
Goovaerts, P.: Geostatistics in soil science: state-of-the-art and perspectives, Geoderma, 89, 1–45, 1999.
Goovaerts, P.: Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall, J. Hydrol., 228, 113–129, 2000.
Gräler, B., Pebesma, E., and Heuvelink, G.: Spatio-Temporal Interpolation using gstat, R J., 8, 204–218, 2016.
Haberlandt, U.: Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event, J. Hydrol., 332, 144–157, 2007.
Hall, D. K., Riggs, G. A., and Salomonson, V. V.: MODIS snow and sea ice products, in: Earth science satellite remote sensing, Springer, 154–181, 2006.
Hevesi, J. A., Istok, J. D., and Flint, A. L.: Precipitation estimation in mountainous terrain using multivariate geostatistics. Part I: structural analysis, J. Appl. Meteorol., 31, 661–676, 1992.
Hutchinson, M.: Interpolating mean rainfall using thin plate smoothing splines, Int. J. Geogr. Inf. Syst., 9, 385–403, 1995.
Isaaks, E. H. and Srivastava, R. M.: An introduction to applied geostatistics, vol. 561, Oxford University Press New York, 1989.
Jarvis, C. H. and Stuart, N.: A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part I: The selection of “guiding” topographic and land cover variables, J. Appl. Meteorol., 40, 1060–1074, 2001.
Kitanidis, P. K.: Introduction to geostatistics: applications in hydrogeology, Cambridge University Press, 1997.
Krige, D. G.: A statistical approach to some basic mine valuation problems on the Witwatersrand, J. S. Afr. I. Min. Metall., 52, 119–139, 1951.
Li, J. and Heap, A. D.: A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors, Ecol. Inform., 6, 228–241, 2011.
Lloyd, C.: Assessing the effect of integrating elevation data into the estimation of monthly precipitation in Great Britain, J. Hydrol., 308, 128–150, 2005.
Ly, S., Charles, C., and Degré, A.: Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium, Hydrol. Earth Syst. Sci., 15, 2259–2274, https://doi.org/10.5194/hess-15-2259-2011, 2011.
Ly, S., Charles, C., and Degré, A.: Different methods for spatial interpolation of rainfall data for operational hydrology and hydrological modeling at watershed scale, A review, Biotechnologie, Agronomie, Société et Environnement, 17, 392, 2013.
Martin, J. D. and Simpson, T. W.: A study on the use of kriging models to approximate deterministic computer models, in: Proceedings of DETC, vol. 3, 2–6, 2003.
Martin, R. C.: Agile software development: principles, patterns, and practices, Prentice Hall, 2002.
Matheron, G.: Splines and kriging: their formal equivalence, Down-to-earth statistics: solutions looking for geological problems, 8, 77–95, 1981.
Meyer, M.: Continuous integration and its tools, IEEE Software, 31, 14–16, 2014.
Mitášová, H. and Mitáš, L.: Interpolation by regularized spline with tension: I. Theory and implementation, Math. Geol., 25, 641–655, 1993.
Murphy, B.: PyKrige: development of a kriging toolkit for Python, in: AGU fall meeting abstracts, 2014.
Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Pebesma, E. J.: Multivariable geostatistics in S: the gstat package, Comput. Geosci., 30, 683–691, 2004.
Phillips, D. L., Dolph, J., and Marks, D.: A comparison of geostatistical procedures for spatial analysis of precipitation in mountainous terrain, Agr. Forest Meteorol., 58, 119–141, 1992.
Rigon, R: krigings paper, available at: https://osf.io/24rgv, last access: 7 June 2018.
Robeson, S. M.: Spatial interpolation, network bias, and terrestrial air temperature variability, 1992.
Rouson, D., Xia, J., and Xu, X.: Scientific software design: the object-oriented way, Cambridge University Press, 2011.
Saghafian, B. and Bondarabadi, S. R.: Validity of regional rainfall spatial distribution methods in mountainous areas, J. Hydrol. Eng., 13, 531–540, 2008.
Schymanski, S. J. and Or, D.: Leaf-scale experiments reveal an important omission in the Penman–Monteith equation, Hydrol. Earth Syst. Sci., 21, 685–706, https://doi.org/10.5194/hess-21-685-2017, 2017.
Serafin, F.: krigings, available at: https://github.com/geoframecomponents/Krigings/tree/v0.9.8, last access: 7 June 2018.
Stahl, K., Moore, R., Floyer, J., Asplin, M., and McKendry, I.: Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density, Agr. Forest Meteorol., 139, 224–236, 2006.
Stooksbury, D. E., Idso, C. D., and Hubbard, K. G.: The effects of data gaps on the calculated monthly mean maximum and minimum temperatures in the continental United States: A spatial and temporal study, J. Climate, 12, 1524–1533, 1999.
Tabios, G. Q. and Salas, J. D.: A comparative analysis of techniques for spatial interpolation of precipitation, JAWRA J. Am. Water Resour. As., 21.3, 365–380, 1985.
Thiessen, A. H.: Precipitation averages for large areas, Mon. Weather Rev., 39, 1082–1089, 1911.
Todini, E.: Influence of parameter estimation uncertainty in Kriging: Part 1 – Theoretical Development, Hydrol. Earth Syst. Sci., 5, 215–223, https://doi.org/10.5194/hess-5-215-2001, 2001.
Turk, F. J. and Miller, S. D.: Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques, IEEE T. Geosci. Remote, 43, 1059–1069, 2005.
Van Ittersum, M. K., Ewert, F., Heckelei, T., Wery, J., Olsson, J. A., Andersen, E., Bezlepkina, I., Brouwer, F., Donatelli, M., Flichman, G., Olsson, L., Rizzoli, A. E., van der Wal, T., Wien, J. E., and Wolf, J.: Integrated assessment of agricultural systems – A component-based framework for the European Union (SEAMLESS), Agr. Syst., 96, 150–165, 2008.
Verfaillie, E., Van Lancker, V., and Van Meirvenne, M.: Multivariate geostatistics for the predictive modelling of the surficial sand distribution in shelf seas, Cont. Shelf Res., 26, 2454–2468, 2006.
WMO: WMO. 1994: Guide to hydrological practices: Data acquisition and processing, analysis, forecasting and other applications, WMO Publication No. 168, 1994.
Xu, C.-Y. and Singh, V. P.: A review on monthly water balance models for water resources investigations, Water Resour. Manag., 12, 20–50, 1998.
Short summary
This paper presents a new modeling package for the spatial interpolation of environmental variables. It includes 11 theoretical semivariogram models and four types of Kriging interpolations. To test the performances of the package, two applications are performed: the interpolation of 1 year of temperatures
and a rainfall event. Both interpolations gave good results. In comparison with gstat, the SIK package proved to be a good alternative, regarding both the easiness of use and the accuracy.
This paper presents a new modeling package for the spatial interpolation of environmental...
