Articles | Volume 11, issue 9
https://doi.org/10.5194/gmd-11-3605-2018
© 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-11-3605-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
05 Sep 2018
Model description paper |
| 05 Sep 2018
| 05 Sep 2018
The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems
Kristi R. Arsenault
CORRESPONDING AUTHOR
Science Applications International Corporation, McLean, VA, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Sujay V. Kumar
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
James V. Geiger
Science Data Processing Branch, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Shugong Wang
Science Applications International Corporation, McLean, VA, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Eric Kemp
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Science Systems and Applications, Inc., Lanham, MD, USA
David M. Mocko
Science Applications International Corporation, McLean, VA, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Hiroko Kato Beaudoing
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
ESSIC, University of Maryland, College Park, MD, USA
Augusto Getirana
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
ESSIC, University of Maryland, College Park, MD, USA
Mahdi Navari
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
ESSIC, University of Maryland, College Park, MD, USA
Bailing Li
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
ESSIC, University of Maryland, College Park, MD, USA
Jossy Jacob
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Science Systems and Applications, Inc., Lanham, MD, USA
Jerry Wegiel
Science Applications International Corporation, McLean, VA, USA
Headquarters 557th Weather Wing, Offutt Air Force Base, NE, USA
Christa D. Peters-Lidard
Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA
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Hydrol. Earth Syst. Sci., 22, 5735–5739, https://doi.org/10.5194/hess-22-5735-2018, https://doi.org/10.5194/hess-22-5735-2018, 2018
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Ricardo Zubieta, Augusto Getirana, Jhan Carlo Espinoza, Waldo Lavado-Casimiro, and Luis Aragon
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Sujay V. Kumar, Jiarui Dong, Christa D. Peters-Lidard, David Mocko, and Breogán Gómez
Hydrol. Earth Syst. Sci., 21, 2637–2647, https://doi.org/10.5194/hess-21-2637-2017, https://doi.org/10.5194/hess-21-2637-2017, 2017
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S. V. Kumar, C. D. Peters-Lidard, J. A. Santanello, R. H. Reichle, C. S. Draper, R. D. Koster, G. Nearing, and M. F. Jasinski
Hydrol. Earth Syst. Sci., 19, 4463–4478, https://doi.org/10.5194/hess-19-4463-2015, https://doi.org/10.5194/hess-19-4463-2015, 2015
Z. Tao, J. A. Santanello, M. Chin, S. Zhou, Q. Tan, E. M. Kemp, and C. D. Peters-Lidard
Atmos. Chem. Phys., 13, 6207–6226, https://doi.org/10.5194/acp-13-6207-2013, https://doi.org/10.5194/acp-13-6207-2013, 2013
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Hydrol. Earth Syst. Sci., 17, 923–933, https://doi.org/10.5194/hess-17-923-2013, https://doi.org/10.5194/hess-17-923-2013, 2013
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GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers
mesas.py v1.0: a flexible Python package for modeling solute transport and transit times using StorAge Selection functions
rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment
GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model
Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Deep Dive into Global Hydrologic Simulations: Harnessing the Power of Deep Learning and Physics-informed Differentiable Models (δHBV-globe1.0-hydroDL)
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Customized deep learning for precipitation bias correction and downscaling
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality
Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale
RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling
Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
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STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
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River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
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The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
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This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
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This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
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The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
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Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
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We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
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The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
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Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
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We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-190, https://doi.org/10.5194/gmd-2023-190, 2023
Revised manuscript accepted for GMD
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Accurate hydrological modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall-runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
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Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
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This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
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Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
Short summary
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Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
Short summary
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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
Short summary
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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
Short summary
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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
Short summary
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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
Short summary
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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
Short summary
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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
Short summary
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We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
Short summary
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
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This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
Short summary
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We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
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We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
Short summary
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Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Cited articles
Arsenault, K. R., Kumar, S., Geiger, J., Wang, S., Kemp, E.,
Beaudoing, H., and Li, B: The Land surface Data Toolkit
(LDT) (Version version 7.2), Zenodo, https://doi.org/10.5281/zenodo.1322613, 2017.
Avissar, R. and Pielke, R.: A parameterization of heterogeneous land
surfaces for atmospheric numerical models and its impact on regional
meteorology, Mon. Weather Rev., 117, 2113–2136, 1989.
Bartalis, Z., Naeimi, V., Hasenauer, S., and Wagner, W.: ASCAT Soil Moisture
Product Handbook, Report No. ASCAT Soil Moisture Report Series, No. 15, 30 pp., 2008.
Bengio, Y.: Learning Deep Architectures for AI,
Found. Trends in
Mach. Learn., 2, 1–127, https://doi.org/10.1561/2200000006, 2009.
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H.,
Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N.,
Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C.
S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES),
model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4,
677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011.
Bounoua, L., Masek, J., and Tourre, Y. M.: Sensitivity of surface climate to
land surface parameters: A case study using the simple biosphere model SiB2,
J. Geophys. Res., 111, D22S09, https://doi.org/10.1029/2006JD007309, 2006.
Case, J., Crosson, W., Kumar, S., Lapenta, W., and Peters-Lidard, C.:
Impacts of High-Resolution Land Surface Initialization on Regional Sensible
Weather Forecasts from the WRF Model, J. Hydrometeorol., 9, 1249–1266, https://doi.org/10.1175/2008JHM990.1, 2008.
Chaney, N.W., Metcalfe, P., and Wood, E. F.: HydroBlocks: a field-scale
resolving land surface model for application over continental extents,
Hydrol. Process., 30, 3543–3559, https://doi.org/10.1002/hyp.10891, 2016.
Chen, F., Manning, K. W., LeMone, M. A., Trier, S. B., Alfieri, J.G.,
Roberts, R., Tewari, M., Niyogi, D., Horst, T. W., Oncley, S. P., Basara, J.
B., and Blanken, P. D.: Description and Evaluation of the Characteristics of
the NCAR High-Resolution Land Data Assimilation System, J. Appl. Meteor.
Climatol., 46, 694–713, https://doi.org/10.1175/JAM2463.1, 2007.
Chen, F., Mitchell, K., Schaake, J., Xue, Y., Pan,
H., Koren, V., Duan, Y.,
Ek, M., and Betts, A.: Modeling of land-surface
evaporation by four schemes
and comparison with FIFE observations, J. Geophys.
Res., 101, 7251–7268, 1996.
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser,
P. R., Wood, E. F.,
Schaake, J. C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., Luo, L.,
Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Meng, J.: Real-time and
retrospective forcing in the North American Land Data Assimilation System
(NLDAS) project, J. Geophys. Res., 108, 8842,
https://doi.org/10.1029/2002JD003118, 2003.
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F.,
Schaake, J. C., Robock, A., Sheffield, J., Duan, Q., Luo, L., Higgins, R. W.,
Pinker, R. T., Tarpley, J. D.: Land surface model spin-up behavior in the North
American Land Data Assimilation System (NLDAS), J. Geophys. Res., 108,
8845, https://doi.org/10.1029/2002JD003316, 2004.
Daly, C., Taylor, G., and Gibson, W.: The PRISM approach to mapping
precipitation and temperature, 10th AMS Conf. on Applied Climatology, Reno,
NV, 10–12, 1997.
Dee, D.: Bias and data assimilation, Q. J. Roy. Meteorol. Soc., 131, 3323–3343,
2005.
de Vrese, P., Schulz, J.-P., and Hagemann, S.: On the representation of
heterogeneity in land-surface-atmosphere coupling, Bound.-Layer Meteorol.,
160, 157–183, https://doi.org/10.1007/s10546-016-0133-1, 2016.
Duan, Q., Schaake, J., Andreassian, V., Franks, S., Goteti, G., Gupta, H.V.,
Gusev, Y. M., Habets, F., Hall, A., Hay, L., Hogue, T., Huang, M., Leavesley,
G., Liang, X., Nasonova, O.N., Noilhan, J., Oudin, L., Sorooshian, S.,
Wagener, T., and Wood, E. F.: Model Parameter Estimation Experiment (MOPEX):
An overview of science strategy and major results from the second and third
workshops, J. Hydrol., 320, 3–17, https://doi.org/10.1016/j.jhydrol.2005.07.031,
2006.
Dunderdale, M., Muller, J. P., and Cox, P. M.: “Sensitivity of the Hadley
Centre climate model to different earth observation and cartographically
derived land surface data-sets”, in: The Contribution of POLDER and New
Generation Spaceborne Sensors to Global Change Studies, 1–6, Meribel,
France, 1999.
Entekhabi, D. and Eagleson, P.: Land surface hydrology parameterization for
atmospheric General Circulation models including subgrid scale spatial
variability, J. Climate, 2, 816–831,
https://doi.org/10.1175/1520-0442(1989)002<0816:LSHPFA>2.0.CO;2, 1989.
Entekhabi, D., Yueh, S., O'Neill, P., et al.: SMAP Handbook,
JPL Publication JPL 400-1567, Jet Propulsion Laboratory, Pasadena,
California, 182 pp., 2014.
Esri, ArcGIS Desktop: Release 10.5, Redlands, CA: Environmental Systems
Research Institute, 2016.
Essery, R. L. H., Best, M. J., Betts, R. A., and Cox, P. M.: Explicit
representation of subgrid heterogeneity in a GCM land surface scheme, J.
Hydrometeorol., 4, 530–543, 2003.
European Centre for Medium-Range Weather Forecasts (ECMWF): GRIB API version
1.10.0 and onwards, available at:
https://software.ecmwf.int/wiki/display/GRIB/Home (last access: 25 July 2018), 2015.
Fick, S. E. and Hijmans, S. E.: WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas, Int. J.
Climatol., 37, 4302–4315, https://doi.org/10.1002/joc.5086, 2017.
Fiddes, J. and Gruber, S.: TopoSCALE v.1.0: downscaling gridded climate data
in complex terrain, Geosci. Model Dev., 7, 387–405, https://doi.org/10.5194/gmd-7-387-2014, 2014.
Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D.,
Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A.,
Gao, F., and Schaaf, C.: Global land cover mapping from MODIS: algorithms and
early results, Remote Sens. Environ., 83, 287–302,
https://doi.org/10.1016/S0034-4257(02)00078-0, 2002.
Getirana, A. C., Boone, A., Yamazaki, D., Decharme, B., Papa,
F., and Mognard,
N.: The Hydrological Modelling and Analysis Platform (HyMAP): Evaluation in
the Amazon Basin, J. Hydrometeorol., 13, 1641–1665,
https://doi.org/10.1175/jhm-d-12-021.1, 2012.
Getirana, A., Peters-Lidard, C., Rodell, M., and Bates, P. D.: Trade-off
between cost and accuracy in large-scale surface water dynamic modelling,
Water Resour. Res., 53, 4942–4955, https://doi.org/10.1002/2017WR020519, 2017.
Gesch, D. B., Verdin, K. L., and Greenlee, S. K.: New land surface digital
elevation model covers the Earth, Eos Trans. AGU, 80, 69–70,
https://doi.org/10.1029/99EO00050, 1999.
Giorgi, F. and Avissar, R.: Representation of heterogeneity effects in
Earth system modelling: Experience from land surface modelling, Rev.
Geophys., 35, 413–437, https://doi.org/10.1029/97RG01754, 1997.
GitHub: available at: https://github.com, last access: 25 July 2018.
Gochis, D. J., Yu, W., and Yates, D. N.: The WRF-Hydro model technical
description and user's guide, version 2.0. NCAR Technical Document, 120
pages, 2014, available at: WRF-Hydro 2.0 User Guide; WRF-Hydro Preprocesser
Guide and Information: https://www.ral.ucar.edu/projects/wrf_hydro
(last access: 25 July 2018), 2014.
Hansen, M., DeFries, R., Townshend, J. R. G., and Sohlberg, R.: Global land
cover classification at 1km resolution using a decision tree classifier,
Int. J. Remote Sens., 21, 1331–1365.
https://doi.org/10.1080/014311600210209, 2000.
Harrison, K. W., Kumar, S. V, Peters-Lidard, C. D., and Santanello, J. A.:
Quantifying the change in soil moisture modelling uncertainty from remote
sensing observations using Bayesian inference techniques, Water Resour.
Res., 48, W11514, https://doi.org/10.1029/2012WR012337, 2012.
Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G. B. M.,
Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh, M. G.,
and Gonzalez, M. R.: SoilGrids1km – Global Soil Information Based on
Automated Mapping, PLoS ONE, 9, e105992, https://doi.org/10.1371/journal.pone.0105992,
2014.
Hill, C., DeLuca, C., Balaji, V., Suarez, M., and da Silva, A.: The
Architecture of the Earth System Modelling Framework, Comput. Sci. Eng., 6,
18–28, 2004.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled SRTM for the
globe Version 4, available from the CGIAR-CSI SRTM 90 m Database, available
at: http://srtm.csi.cgiar.org
(last access: 30 January 2018), 2008.
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Martinuzzi, J.-M., Font, J.,
and Berger, M.: Soil Moisture Retrieval from Space: The Soil Moisture and
Ocean Salinity (SMOS) Mission, IEEE T. Geosci. Remote Sens.,
39, 1729–1735, 2001.
Kirillin, G., Hochschild, J., Mironov, D., Terzhevik, A.,
Golosov, S., and
Nützmann, G.: Software, Data and Modelling News: FLake-Global:
Online lake
model with worldwide coverage, Environ. Modell. Softw., 26,
683–684, https://doi.org/10.1016/j.envsoft.2010.12.004, 2011.
Koren, V., Smith, V., Cui, Z., Cosgrove, B., Werner, K., and Zamora, R.:
Modification of Sacramento Moisture Accounting Heat Transfer Component
(SAC-HT) for Enhanced Evapotranspiration, NOAA Technical Report, NWS 53, U.S.
Department of Commerce, NOAA National Weather Service, 2010.
Koster, R. D. and Suarez, M. J.: Modelling the land surface boundary in
climate models as a composite of independent vegetation stands, J. Geophys.
Res., 97, 2697–2715, 1992.
Koster, R. and Suarez, M.: Energy and Water Balance Calculations in the
Mosaic LSM, NASA Tech. Memo. 104606, Vol. 9, 1996.
Koster, R. D., Suarez, M. J., Ducharne, A., Stieglitz, M., and Kumar, P.: A
catchment-based approach to modelling land surface processes in a general
circulation model: 1. Model structure, J. Geophys. Res., 105, 24809–24822,
doi.10.1029/2000JD900327, 2000.
Koster, R., Sud, Y., Guo, Z., Dirmeyer, P., Bonan, G., Oleson, K., Chan, E.,
Verseghy, D., Cox, P., Davies, H., Kowalczyk, E., Gordon, C., Kanae, S.,
Lawrence, D., Liu, P., Mocko, D., Lu, C., Mitchell, K., Malyshev, S.,
McAvaney, B., Oki, T., Yamada, T., Pitman, A., Taylor, C., Vasic, R., and
Xue, Y.: GLACE: The Global Land-Atmosphere Coupling Experiment. Part I:
Overview, J. Hydrometeor., 7, 590–610, https://doi.org/10.1175/JHM510.1, 2006.
Kowalczyk, E., Stevens, L., Law, R., Dix, M., Wang, Y., Harman, I., Haynes,
K., Srbinovsky, J., Pak, B., and Ziehn, T.: The land surface model component
of ACCESS: Description and impact on the simulated surface climatology,
Aust. Meteorol. Oceanogr. J., 663, 65–82, 2013.
Kumar, S., Peters-Lidard, C., Tian, T., Houser, P., Geiger, J., Olden, S.,
Lighty, L., Eastman, J., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K.,
Wood, E., and Sheffield, J.: Land information system: An interoperable
framework for high resolution land surface modelling, Environ. Model.
Softw., 21, 1402–1415, 2006.
Kumar, S., Peters-Lidard, C., Eastman, J. L., and Tao, W.-K.: An integrated
high resolution hydrometeorological modelling testbed using LIS and WRF,
Environ. Model. Softw., 23, 169–181, 2008a.
Kumar, S., Peters-Lidard, C., Tian, Y., Reichle, R. H., Alonge, C., Geiger,
J., Eylander, J., and Houser, P.: An integrated hydrologic modelling and
data assimilation framework enabled by the Land Information System (LIS),
IEEE Comput., 41, 52–59, https://doi.org/10.1109/MC.2008.511, 2008b.
Kumar, S., Reichle, R., Peters-Lidard, C., Koster, R., Zhan, X., Crow, W.,
Eylander, J., and Houser, P.: A land surface data assimilation framework
using the Land Information System: Description and Applications, Adv. Water
Resour., 31, 1419–1432, https://doi.org/10.1016/j.advwatres.2008.01.013, 2008c.
Kumar, S. V., Peters-Lidard, C. D., Santanello, J., Harrison, K., Liu, Y.,
and Shaw, M.: Land surface Verification Toolkit (LVT) – a generalized
framework for land surface model evaluation, Geosci. Model Dev., 5, 869–886,
https://doi.org/10.5194/gmd-5-869-2012, 2012.
Kumar, S. V., Peters-Lidard, C. D., Mocko, D., and Tian, Y.: Multiscale
Evaluation of the Improvements in Surface Snow Simulation through Terrain
Adjustments to Radiation, J. Hydrometeor., 14, 220–232,
https://doi.org/10.1175/JHM-D-12-046.1, 2013.
Kumar, S. V., Peters-Lidard, C. D., Santanello, J. A., Reichle, R. H.,
Draper, C. S., Koster, R. D., Nearing, G., and Jasinski, M. F.: Evaluating
the utility of satellite soil moisture retrievals over irrigated areas and
the ability of land data assimilation methods to correct for unmodeled
processes, Hydrol. Earth Syst. Sci., 19, 4463–4478,
https://doi.org/10.5194/hess-19-4463-2015, 2015.
Kumar, S. V., Zaitchik, B. F., Peters-Lidard, C. D., Rodell, M., Reichle, R.,
Li, B., Jasinski, M., Mocko, D., Getirana, A., De Lannoy, G., Cosh, M. H.,
Hain, C. R., Anderson, M., Arsenault, K. R., Xia, Y., and Ek, M.: Assimilation
of Gridded GRACE Terrestrial Water Storage Estimates in the North American
Land Data Assimilation System, J. Hydrometeorol., 17, 1951–1972,
https://doi.org/10.1175/JHM-D-15-0157.1, 2016.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from
spaceborne elevation data. Eos, Transactions, AGU, 89, 93–94, 2008.
Leung, R. L. and Ghan, S. J.: A subgrid parameterization of orographic
precipitation, Theor. Appl. Climatol., 52, 95–118, 1995.
Li, L., Gaiser, P. W., Gao, B.-C., Bevilacqua, R. M., Jackson, T. J., Njoku,
E. G., Rudiger, C., Calvet, J.-C., and Bindlish, R.: WindSat Global Soil
Moisture Retrieval and Validation, IEEE T. Geosci.
Remote Sens., 48, 2224–2241, https://doi.org/10.1109/TGRS.2009.2037749, 2010.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for
general circulation models, J. Geophys. Res., 99, 14415–14428,
https://doi.org/10.1029/94JD00483, 1994.
Liu, J., Zhan, X., Hain, C., Yin, J., Fang, L., Li, Z., and Zhao, L.: ”NOAA
Soil Moisture Operational Product System (SMOPS) and its validations,” 2016
IEEE International Geoscience and Remote Sensing Symposium (IGARSS),
Beijing, 3477–3480, https://doi.org/10.1109/IGARSS.2016.7729899, 2016.
Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W.,
van Dijk, A. I. J. M., McCabe, M. F., and Evans, J. P.: Developing an
improved soil moisture dataset by blending passive and active microwave
satellite-based retrievals, Hydrol. Earth Syst. Sci., 15, 425–436,
https://doi.org/10.5194/hess-15-425-2011, 2011.
Manabe, S.: Climate and the ocean circulation, Mon. Weather Rev., 97, 739–774,
1969.
Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J.,
Widmann, M., Brienen, S., Rust, H. W., Sauter, T., ThemeBl, M., Venema, V. K.,
Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich,
I.: Precipitation downscaling under climate change: Recent developments to
bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003,
https://doi.org/10.1029/2009RG000314, 2010.
Masson, V., Champeaux, J., Chauvin, F., Meriguet, C., and Lacaze, R.: A
Global Database of Land Surface Parameters at 1-km Resolution in
Meteorological and Climate Models, J. Climate, 16, 1261–1282,
https://doi.org/10.1175/1520-0442(2003)16<1261:AGDOLS>2.0.CO;2, 2003.
McNally, A., Arsenault, K., Kumar, S. Shukla, S., Peterson, P., Wang, S.,
Funk, C., Peters-Lidard, C. D., and Verdin, J. P.: A land data assimilation
system for sub-Saharan Africa food and water security applications,
Sci. Data, 4, 170012, https://doi.org/10.1038/sdata.2017.12, 2017.
Miller, D. A. and White, R. A.: A conterminous United States multilayer soil
characteristics dataset for regional climate and hydrology modelling, Earth
Interact., 2, doi:https://doi.org/10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2, 1998.
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J. C.,
Robock, A., Cosgrove, B. A., Sheffield, J., Duan, Q., Luo, L., Higgins, R. W.,
Pinker, R. T., Tarpley, J. D., Lettenmaier, D. P., Marshall, C. H., Entin, J. K.,
Pan, M., Shi, W., Koren, V., Meng, J., Ramsay, B. H., and Bailey, A. A.: The
multi-institution North American Land Data Assimilation System (NLDAS):
Utilizing multiple GCIP products and partners in a continental distributed
hydrological modelling system, J. Geophys. Res., 109, D07S90,
https://doi.org/10.1029/2003JD003823, 2004.
Monfreda, C., Ramankutty, N., and Foley, J. A.: Farming the planet: 2.
Geographic distribution of crop areas, yields, physiological types, and net
primary production in the year 2000, Global Biogeochem. Cy., 22, GB1022,
https://doi.org/10.1029/2007GB002947, 2008.
Nearing, G. S., Mocko, D. M., Peters-Lidard, C. D., Kumar, S. V., and Xia, Y.:
Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate
Uncertainty Contributions, J. Hydrometeor., 17, 745–759,
https://doi.org/10.1175/JHM-D-15-0063.1, 2016.
Newman, A. J., Clark, M. P., Winstral, A., Marks, D., and Seyfried, M.: The
use of similarity concepts to represent subgrid variability in Land Surface
Models: Case study in a snowmelt-dominated watershed, J. Hydrometeor., 15,
1717–1738, https://doi.org/10.1175/JHM-D-13-038.1, 2014.
Nijssen, B., Schnur, R., and Lettenmaier, D. P.: Global Retrospective
Estimation of Soil Moisture Using the Variable Infiltration Capacity Land
Surface Model, 1980–93, J. Climate, 14, 1790–1808,
https://doi.org/10.1175/1520-0442(2001)014<1790:GREOSM>2.0.CO;2, 2001.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale
measurements, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E.,
Lawrence, P. J., Levis, S., Swenson, S. C., and Thornton P. E.: Technical
Description of version 4.0 of the Community Land Model (CLM),
NCAR/TN-478+STR, National Center for Atmospheric Research, Boulder, 2010.
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of
satellite-derived global land surface moisture, J. Geophys.
Res., 113, F01002, https://doi.org/10.1029/2007JF000769, 2008.
Ozdogan, M. and Gutman, G.: A new methodology to map irrigated areas using
multi-temporal MODIS and ancillary data: An application example in the
continental U.S., Remote Sens. Environ., 112, 3520–3537, 2008.
Ozdogan, M., Rodell, M., Beaudoing, H. K., and Toll, D.: Simulating the
effects of irrigation over the United States in a Land Surface Model based
on satellite-derived agricultural data., J. Hydrometeorol., 11, 171–184,
https://doi.org/10.1175/2009JHM1116.1, 2010.
Peters-Lidard, C. D., Houser, P. R., Tian, Y., Kumar, S. V., Geiger, J., Olden,
S., Lighty, L., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E. F.,
and Sheffield, J.: High-performance Earth system modeling with NASA/GSFC's
Land Information System, Innov. Syst. Softw. Eng., 3,
157–165, https://doi.org/10.1007/s11334-007-0028-x, 2007.
Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., Tewari,
M., Barlage, M., Dudhia, J., Yu, W., Miller, K., Grubisic, V., Thompson, G.,
and Gutmann, E.: High-resolution coupled climate runoff simulations of seasonal
snowfall over Colorado: a process study of current and warmer climate, J.
Climate, 24, 3015–3048, 2011.
Raupach, M., Rayner, P., Barrett, D., DeFries, R., Heimann, M., Ojima, D.,
Quegan, S., and Schmullius, C.: Model-data synthesis in terrestrial carbon
observation: methods, data requirements and data uncertainty specifications,
Global Change Biol., 11, 378–397, 2005.
Reichle, R. H. and Koster, R. D.: Bias reduction in short records of
satellite soil moisture, Geophys. Res. Lett., 31, L19501,
https://doi.org/10.1029/2004GL020938, 2004.
Reichle, R. H., McLaughlin, D. B., and Entekhabi, D.: Hydrologic data
assimilation with the ensemble Kalman filter, Mon. Weather Rev., 130, 103–114,
2002.
Reichle, R. H., Koster, R. D., De Lannoy, G. J., Forman, B. A., Liu, Q.,
Mahanama, S. P., and Touré, A.: Assessment and Enhancement of MERRA Land
Surface Hydrology Estimates, J. Climate, 24, 6322–6338,
https://doi.org/10.1175/JCLI-D-10-05033.1, 2011.
Reynolds, C. A., Jackson, T. J., and Rawls, W. J.: Estimating soil
water-holding capacities by linking the Food and Agriculture Organization
Soil map of the world with global pedon databases and continuous
pedotransfer functions, Water Resour. Res., 36, 3653–3662,
https://doi.org/10.1029/2000WR900130, 2000.
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, Bull. Am. Meteorol. Soc., 85, 381–394, 2004.
Rodell, M., Houser, P., Berg, A., and Famiglietti, J.: Evaluation of 10
Methods for Initializing a Land Surface Model, J. Hydrometeor., 6, 146–155,
https://doi.org/10.1175/JHM414.1, 2005.
Ryo, M., Saavedra Valeriano, O. C., Kanae, S., and Ngoc, T. D.: Temporal
Downscaling of Daily Gauged Precipitation by Application of a Satellite
Product for Flood Simulation in a Poorly Gauged Basin and Its Evaluation
with Multiple Regression Analysis, J. Hydrometeor., 15, 563–580,
https://doi.org/10.1175/JHM-D-13-052.1, 2014.
Salmon, J. M., Friedl, M. A., Frolking, S., Wisser, D., and Douglas, E. M.:
Global rain-fed, irrigated, and paddy croplands: A new high resolution map
derived from remote sensing, crop inventories and climate data,
Int. J. Appl. Earth Observ. Geoinform., 38,
321–334, 2015.
Sampson, K. and Gochis, D.: WRF Hydro GIS Pre-Processing Tools, version
2.2, National Center for Atmospheric Research, Boulder, CO, Reference
site, available at:
https://www.ral.ucar.edu/sites/default/files/public/projects/wrf_hydro/v3_0/Standalone_Tool_v2_2.zip
(last access: 25 July 2018), 2015.
Save, H., Bettadpur, S., and Tapley, B. D.: High resolution CSR GRACE RL05
mascons, J. Geophys. Res.-Solid Earth, 121, 7547–7569, https://doi.org/10.1002/2016JB013007, 2016.
Sellers, P. J., Randall, D. A., Collatz, G. J., Berry, J. A., Field, C. B.,
Dazlich, D. A., Zhang, C., Collelo, G. D., and Bounoua, L.: A revised land
surface parameterization (SiB2) for Atmospheric GCMs, Part I: Model
formulation, J. Climate, 9, 676–704, 1996.
Sen Gupta, A. and Tarboton, D. G.: A tool for downscaling weather
data from large-grid reanalysis products to finer spatial scales for
distributed hydrological applications, Environ. Modell.
Softw., 84, 50–69, https://doi.org/10.1016/j.envsoft.2016.06.014, 2016.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the advanced
research WRF Version 3. Tech. Rep. TN-475, National Center for Atmospheric
Research, 2008.
Smirnova, T. G., Brown, J. M., Benjamin, S. G., and Kenyon, J. S.: Modifications
to the Rapid Update Cycle Land Surface Model (RUC LSM) Available in the
Weather Research and Forecasting (WRF) Model, Mon. Weather Rev., 144, 1851–1865,
https://doi.org/10.1175/MWR-D-15-0198.1, 2016.
Sun, W.-Y. and Bosilovich, M. G.: Planetary boundary layer and surface
layer sensitivity to land surface parameters, Bound.-Layer Meteorol.,
77, 353–378, https://doi.org/10.1007/BF00123532, 1996.
Tapley, B., Bettadpur, S., Watkins, M., and Reigber, C.: The Gravity
Recovery and Climate Experiment: Mission overview and early results,
Geophys, Res. Lett., 31, L09607, https://doi.org/10.1029/2004GL019920,
2004.
The HDF Group: Hierarchical data format version 5,
available at: http://www.hdfgroup.org/HDF5 (last access: 25 July 2018), 2015.
Unidata: Network Command Data Form (NetCDF), version 4.3.3.1[software].
Boulder, CO: UCAR/Unidata Program Center, available at:
http://www.unidata.ucar.edu/blogs/news/entry/netcdf_4_3_3_1 (last access: 25 July 2018), 2015.
USGS: Global Land Cover Characterization (GLCC) Version 2 Dataset,
available at: https://lta.cr.usgs.gov/GLCC, last access: 30 January 2018.
Verdin, J. and Klaver, R.: Gridcell-based Crop Water Accounting for the
Famine Early Warning System, Hydrol. Process., 16, 1617–1630, https://doi.org/10.1002/hyp.1025, 2002.
Wentz, F. J., Meissner, T., Gentemann, C., Hilburn, K. A., and Scott, J.: Remote
Sensing Systems GCOM-W1 AMSR2 Daily Environmental Suite on 0.25 deg grid,
Version V.2. Remote Sensing Systems, Santa Rosa, CA, 2014.
Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P.,
Verbeeck, H., Carvalhais, N., Jun, M., Hollinger, D. Y., Kattge, J., Leuning,
R., Luo, Y., Tomelleri, E., Trudinger, C. M., and Wang, Y.-P.: Improving
land surface models with FLUXNET data, Biogeosci., 6, 1341–1359, 2009.
Xia, Y., Youlong, X., Mitchell, K., Ek, M., Sheffield, J.,
Cosgrove, B., Wood, E., Luo, L., Alonge, C.,
Wei, H., Meng, J., Livneh, B., Lettenmaier, D.,
Koren, V., Duan, Q., Mo, K., Fan, Y., and Mocko, D.: Continental-scale water and energy flux analysis and
validation for the North American Land Data Assimilation System project
phase 2 (NLDAS-2): 1. Intercomparison and application of model products, J.
Geophys. Res., 117, D03109, https://doi.org/10.1029/2011JD016048, 2012.
Zalasiewicz, J., Williams, M., Haywood, A., and Ellis, M.: The Anthropocene: a
new epoch of geological time?, Phil. Trans. R. Soc. A, 369, 835–841,
https://doi.org/10.1098/rsta.2010.0339, 2011.
Short summary
The Earth’s land surface hydrology and physics can be represented in highly sophisticated models known as land surface models. The Land surface Data Toolkit (LDT) software was developed to meet these models’ input processing needs. LDT supports a variety of land surface and hydrology models and prepares the inputs (e.g., meteorological data, satellite observations to be assimilated into a model), which can be used for inter-model studies and to initialize weather and climate forecasts.
The Earth’s land surface hydrology and physics can be represented in highly sophisticated...
