Articles | Volume 19, issue 12
https://doi.org/10.5194/hess-19-4831-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-4831-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
18 Dec 2015
Research article |
| 18 Dec 2015
The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales
Universities Space Research Association, Columbia, MD, USA
Global Modeling and Assimilation Office, NASA GSFC, Greenbelt, MD, USA
R. Reichle
Global Modeling and Assimilation Office, NASA GSFC, Greenbelt, MD, USA
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Clément Albergel, Yongjun Zheng, Bertrand Bonan, Emanuel Dutra, Nemesio Rodríguez-Fernández, Simon Munier, Clara Draper, Patricia de Rosnay, Joaquin Muñoz-Sabater, Gianpaolo Balsamo, David Fairbairn, Catherine Meurey, and Jean-Christophe Calvet
Hydrol. Earth Syst. Sci., 24, 4291–4316, https://doi.org/10.5194/hess-24-4291-2020, https://doi.org/10.5194/hess-24-4291-2020, 2020
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LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states.
Xin Huang, Qing He, Naota Hanasaki, Rolf H. Reichle, and Taikan Oki
EGUsphere, https://doi.org/10.5194/egusphere-2025-2004, https://doi.org/10.5194/egusphere-2025-2004, 2025
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This study demonstrates a new method using SMAP soil moisture products to identify irrigation effects, tested to be valid in an example region in California's Central Valley and showed great potential for application in arid/ semi-arid regions. The approach offers a simple, straightforward approach to monitoring irrigation signals without additional in-situ data or model tuning, providing a useful tool to extract irrigation water use data in observation-scarce regions.
Elias C. Massoud, Lauren Andrews, Rolf Reichle, Andrea Molod, Jongmin Park, Sophie Ruehr, and Manuela Girotto
Earth Syst. Dynam., 14, 147–171, https://doi.org/10.5194/esd-14-147-2023, https://doi.org/10.5194/esd-14-147-2023, 2023
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In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.
Jianxiu Qiu, Jianzhi Dong, Wade T. Crow, Xiaohu Zhang, Rolf H. Reichle, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 1569–1586, https://doi.org/10.5194/hess-25-1569-2021, https://doi.org/10.5194/hess-25-1569-2021, 2021
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The SMAP L4 dataset has been extensively used in hydrological applications. We innovatively use a machine learning method to analyze how the efficiency of the L4 data assimilation (DA) system is determined. It shows that DA efficiency is mainly related to Tb innovation, followed by error in precipitation forcing and microwave soil roughness. Since the L4 system can effectively filter out precipitation error, future development should focus on correctly specifying the SSM–RZSM coupling strength.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
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We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Clément Albergel, Yongjun Zheng, Bertrand Bonan, Emanuel Dutra, Nemesio Rodríguez-Fernández, Simon Munier, Clara Draper, Patricia de Rosnay, Joaquin Muñoz-Sabater, Gianpaolo Balsamo, David Fairbairn, Catherine Meurey, and Jean-Christophe Calvet
Hydrol. Earth Syst. Sci., 24, 4291–4316, https://doi.org/10.5194/hess-24-4291-2020, https://doi.org/10.5194/hess-24-4291-2020, 2020
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LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states.
Jing Tao, Randal D. Koster, Rolf H. Reichle, Barton A. Forman, Yuan Xue, Richard H. Chen, and Mahta Moghaddam
The Cryosphere, 13, 2087–2110, https://doi.org/10.5194/tc-13-2087-2019, https://doi.org/10.5194/tc-13-2087-2019, 2019
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The active layer thickness (ALT) in middle-to-high northern latitudes from 1980 to 2017 was produced at 81 km2 resolution by a global land surface model (NASA's CLSM) with forcing fields from a reanalysis data set, MERRA-2. The simulated permafrost distribution and ALTs agree reasonably well with an observation-based map and in situ measurements, respectively. The accumulated above-freezing air temperature and maximum snow water equivalent explain most of the year-to-year variability of ALT.
Yonghong Yi, John S. Kimball, Richard H. Chen, Mahta Moghaddam, Rolf H. Reichle, Umakant Mishra, Donatella Zona, and Walter C. Oechel
The Cryosphere, 12, 145–161, https://doi.org/10.5194/tc-12-145-2018, https://doi.org/10.5194/tc-12-145-2018, 2018
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An important feature of the Arctic is large spatial heterogeneity in active layer conditions. We developed a modeling framework integrating airborne longwave radar and satellite data to investigate active layer thickness (ALT) sensitivity to landscape heterogeneity in Alaska. We find uncertainty in spatial and vertical distribution of soil organic carbon is the largest factor affecting ALT accuracy. Advances in remote sensing of soil conditions will enable more accurate ALT predictions.
Gabriëlle J. M. De Lannoy and Rolf H. Reichle
Hydrol. Earth Syst. Sci., 20, 4895–4911, https://doi.org/10.5194/hess-20-4895-2016, https://doi.org/10.5194/hess-20-4895-2016, 2016
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The SMOS mission provides various various products to estimate soil moisture. This paper evaluates the performance of assimilating either Level-1-based multi-angle brightness temperature (Tb) observations, Level-1-based single-angle Tb observations, or Level 2 soil moisture retrievals, into the NASA Catchment land surface model.
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
Related subject area
Subject: Global hydrology | Techniques and Approaches: Modelling approaches
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Rajesh R. Shrestha, Alex J. Cannon, Sydney Hoffman, Marie Whibley, and Aranildo Lima
Hydrol. Earth Syst. Sci., 29, 2881–2900, https://doi.org/10.5194/hess-29-2881-2025, https://doi.org/10.5194/hess-29-2881-2025, 2025
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We evaluate the historical performance and future projections from a large-scale hydrologic model, the Community Water Model, against a watershed hydrologic model, Variable Infiltration Capacity, for the Liard River basin in Canada. Results from the two models are generally consistent at annual and monthly timescales, suggesting that a calibrated global hydrologic model can provide robust projections. We explain the differences in projections in terms of model uncertainties.
Alessia Matanó, Raed Hamed, Manuela I. Brunner, Marlies H. Barendrecht, and Anne F. Van Loon
Hydrol. Earth Syst. Sci., 29, 2749–2764, https://doi.org/10.5194/hess-29-2749-2025, https://doi.org/10.5194/hess-29-2749-2025, 2025
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Persistent droughts change how rivers respond to rainfall. Our study of over 5000 catchments worldwide found that hydrological and soil moisture droughts decrease river-flow response to rain, especially in arid regions, while vegetation decline slightly increases it. Snow-covered areas are more resilient due to stored water buffering changes. Droughts can also cause long-lasting changes, with short and intense droughts reducing river response to rainfall and prolonged droughts increasing it.
Nicole Gyakowah Otoo, Edwin H. Sutanudjaja, Michelle T. H. van Vliet, Aafke M. Schipper, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 29, 2153–2165, https://doi.org/10.5194/hess-29-2153-2025, https://doi.org/10.5194/hess-29-2153-2025, 2025
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The contribution of groundwater to groundwater-dependent ecosystems (GDEs) is declining as a result of an increase in groundwater abstractions and climate change. This may lead to loss of habitat and biodiversity. This proposed framework enables the mapping and understanding of the temporal and spatial dynamics of GDEs on a large scale. The next step is to assess the global impacts of climate change and water use on GDE extent and health.
Freek Engel, Anne J. Hoek van Dijke, Caspar T. J. Roebroek, and Imme Benedict
Hydrol. Earth Syst. Sci., 29, 1895–1918, https://doi.org/10.5194/hess-29-1895-2025, https://doi.org/10.5194/hess-29-1895-2025, 2025
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A warming climate alters the freshwater availability over land, and, due to related tree cover change and potential forestation, this availability can be further enhanced or negated. We find that large-scale change in tree cover may counteract climate-driven changes on a global scale, whereas, regionally, the climate and tree cover impacts can differ extensively. Current ecosystem restoration projects should account for the effects of (re-)forestation on (non-)local water availability.
Mohamed Hamitouche, Giorgia Fosser, Alessandro Anav, Cenlin He, and Tzu-Shun Lin
Hydrol. Earth Syst. Sci., 29, 1221–1240, https://doi.org/10.5194/hess-29-1221-2025, https://doi.org/10.5194/hess-29-1221-2025, 2025
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This study evaluates how different methods of simulating runoff impact river flow predictions globally. By comparing seven approaches within the Noah-Multi-parameterisation (Noah-MP) land surface model, we found significant differences in accuracy, with some methods underestimating or overestimating runoff. The results are crucial for improving water resource management and flood prediction. Our work highlights the need for precise modelling to better prepare for climate-related challenges.
Howlader Mohammad Mehedi Hasan, Petra Döll, Seyed-Mohammad Hosseini-Moghari, Fabrice Papa, and Andreas Güntner
Hydrol. Earth Syst. Sci., 29, 567–596, https://doi.org/10.5194/hess-29-567-2025, https://doi.org/10.5194/hess-29-567-2025, 2025
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We calibrate a global hydrological model using multiple observations to analyse the benefits and trade-offs of multi-variable calibration. We found such an approach to be very important for understanding the real-world system. However, some observations are very essential to the system, in particular, streamflow. We also showed uncertainties in the calibration results, which are often useful for making informed decisions. We emphasize considering observation uncertainty in model calibration.
Shulei Zhang, Hongbin Liang, Fang Li, Xingjie Lu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2024-4093, https://doi.org/10.5194/egusphere-2024-4093, 2025
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This study enhances irrigation modeling in the Common Land Model by capturing the full irrigation process, detailing water supplies from various sources, and enabling bidirectional coupling between water demand and supply. The proposed model accurately simulates irrigation water withdrawals, energy fluxes, river flow, and crop yields. It offers insights into irrigation-related climate impacts and water scarcity, contributing to sustainable water management and improved Earth system modeling.
Vivek K. Arora, Aranildo Lima, and Rajesh Shrestha
Hydrol. Earth Syst. Sci., 29, 291–312, https://doi.org/10.5194/hess-29-291-2025, https://doi.org/10.5194/hess-29-291-2025, 2025
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This study presents a Canada-wide assessment of climate change impacts on the hydro-climatology of the region's major river basins. We find that precipitation, runoff, and temperature are all expected to increase over Canada in the future. The northerly Mackenzie and Yukon rivers are relatively less affected by climate change compared to the southerly Fraser and Columbia rivers, which are located in the milder northwestern Pacific region.
Gabriel Narváez-Campo and Constantin Ardilouze
EGUsphere, https://doi.org/10.5194/egusphere-2024-2962, https://doi.org/10.5194/egusphere-2024-2962, 2024
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We demonstrate the capability of a global operational system to predict seasonal river discharges by accounting for interactions between the atmosphere, ocean, land, and rivers. The fully coupled approach introduces a convenient single-step workflow, allowing the simultaneous production of atmospheric and streamflow forecasts. Overall, the approach outperforms the classical Ensemble Streamflow Prediction approach, providing insight into the next-generation hydrological forecasting systems.
Sophie Wagner, Fabian Stenzel, Tobias Krueger, and Jana de Wiljes
Hydrol. Earth Syst. Sci., 28, 5049–5068, https://doi.org/10.5194/hess-28-5049-2024, https://doi.org/10.5194/hess-28-5049-2024, 2024
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Statistical models that explain global irrigation rely on location-referenced data. Traditionally, a system based on longitude and latitude lines is chosen. However, this introduces bias to the analysis due to the Earth's curvature. We propose using a system based on hexagonal grid cells that allows for distortion-free representation of the data. We show that this increases the model's accuracy by 28 % and identify biophysical and socioeconomic drivers of historical global irrigation expansion.
Admin Husic, John Hammond, Adam N. Price, and Joshua K. Roundy
EGUsphere, https://doi.org/10.5194/egusphere-2024-3235, https://doi.org/10.5194/egusphere-2024-3235, 2024
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We used explainable machine learning to evaluate the accuracy of two continental-scale hydrologic models. We analyzed a suite of catchment attributes and found that soil water content had the biggest impact on model performance, especially in dry areas. Key thresholds for variables like precipitation and road density were identified, which could guide future improvements in these models. Our findings highlight the potential of data-driven methods to inform process-based models.
Seyed-Mohammad Hosseini-Moghari and Petra Döll
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-291, https://doi.org/10.5194/hess-2024-291, 2024
Revised manuscript accepted for HESS
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Modeling reservoir outflow and storage is challenging due to limited publicly available data and human decision-making. For 100 reservoirs in the USA, we examined how calibrating reservoir algorithms against outflow and storage-related variables affects performance. We found that calibration notably improves storage simulations, while outflow simulations are more influenced by the quality of inflow data. We recommend using remotely sensed storage anomalies to calibrate reservoir algorithms.
Tuvia Turkeltaub and Golan Bel
Hydrol. Earth Syst. Sci., 28, 4263–4274, https://doi.org/10.5194/hess-28-4263-2024, https://doi.org/10.5194/hess-28-4263-2024, 2024
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Future climate projections suggest that climate change will impact groundwater recharge, with its exact effects being uncertain due to incomplete understanding of rainfall, evapotranspiration, and recharge relations. We studied the effects of changes in the average, spread, and frequency of extreme events of rainfall and evapotranspiration on groundwater recharge. We found that increasing or decreasing the potential evaporation has the most dominant effect on groundwater recharge.
Dominik Paprotny, Belinda Rhein, Michalis I. Vousdoukas, Paweł Terefenko, Francesco Dottori, Simon Treu, Jakub Śledziowski, Luc Feyen, and Heidi Kreibich
Hydrol. Earth Syst. Sci., 28, 3983–4010, https://doi.org/10.5194/hess-28-3983-2024, https://doi.org/10.5194/hess-28-3983-2024, 2024
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Long-term trends in flood losses are regulated by multiple factors, including climate variation, population and economic growth, land-use transitions, reservoir construction, and flood risk reduction measures. Here, we reconstruct the factual circumstances in which almost 15 000 potential riverine, coastal and compound floods in Europe occurred between 1950 and 2020. About 10 % of those events are reported to have caused significant socioeconomic impacts.
Anna M. Ukkola, Steven Thomas, Elisabeth Vogel, Ulrike Bende-Michl, Steven Siems, Vjekoslav Matic, and Wendy Sharples
EGUsphere, https://doi.org/10.31223/X56110, https://doi.org/10.31223/X56110, 2024
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Future drought changes in Australia –the driest inhabited continent on Earth– have remained stubbornly uncertain. We assess future drought changes in Australia using projections from climate and hydrological models. We show an increasing probability of drought over highly-populated and agricultural regions of Australia in coming decades, suggesting potential impacts on agricultural activities, ecosystems and urban water supply.
Solomon H. Gebrechorkos, Julian Leyland, Simon J. Dadson, Sagy Cohen, Louise Slater, Michel Wortmann, Philip J. Ashworth, Georgina L. Bennett, Richard Boothroyd, Hannah Cloke, Pauline Delorme, Helen Griffith, Richard Hardy, Laurence Hawker, Stuart McLelland, Jeffrey Neal, Andrew Nicholas, Andrew J. Tatem, Ellie Vahidi, Yinxue Liu, Justin Sheffield, Daniel R. Parsons, and Stephen E. Darby
Hydrol. Earth Syst. Sci., 28, 3099–3118, https://doi.org/10.5194/hess-28-3099-2024, https://doi.org/10.5194/hess-28-3099-2024, 2024
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This study evaluated six high-resolution global precipitation datasets for hydrological modelling. MSWEP and ERA5 showed better performance, but spatial variability was high. The findings highlight the importance of careful dataset selection for river discharge modelling due to the lack of a universally superior dataset. Further improvements in global precipitation data products are needed.
Mauricio Lima, Katherine Deck, Oliver R. A. Dunbar, and Tapio Schneider
EGUsphere, https://doi.org/10.48550/arXiv.2404.14212, https://doi.org/10.48550/arXiv.2404.14212, 2024
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Machine learning is playing an increasingly important role in hydrological modeling. In this paper, we introduce an adaptation of existing machine learning models forecasting streamflow in river basins, redesigning them with the goal of integrating them into climate models. We demonstrate the effectiveness of our adapted model by showing that it outperforms a physics-based river model. These results motivate further studies of the use of machine learning based river models inside climate models.
Fransje van Oorschot, Ruud J. van der Ent, Andrea Alessandri, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 2313–2328, https://doi.org/10.5194/hess-28-2313-2024, https://doi.org/10.5194/hess-28-2313-2024, 2024
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Vegetation plays a crucial role in regulating the water cycle by transporting water from the subsurface to the atmosphere via roots; this transport depends on the extent of the root system. In this study, we quantified the effect of irrigation on roots at a global scale. Our results emphasize the importance of accounting for irrigation in estimating the vegetation root extent, which is essential to adequately represent the water cycle in hydrological and climate models.
Omar V. Müller, Patrick C. McGuire, Pier Luigi Vidale, and Ed Hawkins
Hydrol. Earth Syst. Sci., 28, 2179–2201, https://doi.org/10.5194/hess-28-2179-2024, https://doi.org/10.5194/hess-28-2179-2024, 2024
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This work evaluates how rivers are projected to change in the near future compared to the recent past in the context of a warming world. We show that important rivers of the world will notably change their flows, mainly during peaks, exceeding the variations that rivers used to exhibit. Such large changes may produce more frequent floods, alter hydropower generation, and potentially affect the ocean's circulation.
Mugni Hadi Hariadi, Gerard van der Schrier, Gert-Jan Steeneveld, Samuel J. Sutanto, Edwin Sutanudjaja, Dian Nur Ratri, Ardhasena Sopaheluwakan, and Albert Klein Tank
Hydrol. Earth Syst. Sci., 28, 1935–1956, https://doi.org/10.5194/hess-28-1935-2024, https://doi.org/10.5194/hess-28-1935-2024, 2024
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We utilize the high-resolution CMIP6 for extreme rainfall and streamflow projection over Southeast Asia. This region will experience an increase in both dry and wet extremes in the near future. We found a more extreme low flow and high flow, along with an increasing probability of low-flow and high-flow events. We reveal that the changes in low-flow events and their probabilities are not only influenced by extremely dry climates but also by the catchment characteristics.
Ayenew D. Ayalew, Paul D. Wagner, Dejene Sahlu, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 28, 1853–1872, https://doi.org/10.5194/hess-28-1853-2024, https://doi.org/10.5194/hess-28-1853-2024, 2024
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The study presents a pioneering comprehensive integrated approach to unravel hydrological complexities in data-scarce regions. By integrating diverse data sources and advanced analytics, we offer a holistic understanding of water systems, unveiling hidden patterns and driving factors. This innovative method holds immense promise for informed decision-making and sustainable water resource management, addressing a critical need in hydrological science.
Juliette Godet, Eric Gaume, Pierre Javelle, Pierre Nicolle, and Olivier Payrastre
Hydrol. Earth Syst. Sci., 28, 1403–1413, https://doi.org/10.5194/hess-28-1403-2024, https://doi.org/10.5194/hess-28-1403-2024, 2024
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This work was performed in order to precisely address a point that is often neglected by hydrologists: the allocation of points located on a river network to grid cells, which is often a mandatory step for hydrological modelling.
Sneha Chevuru, Rens L. P. H. van Beek, Michelle T. H. van Vliet, Jerom P. M. Aerts, and Marc F. P. Bierkens
EGUsphere, https://doi.org/10.5194/egusphere-2024-465, https://doi.org/10.5194/egusphere-2024-465, 2024
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This paper integrates PCR-GLOBWB 2 hydrological model with WOFOST crop growth model to analyze mutual feedbacks between hydrology and crop growth. It quantifies one-way and two-way feedbacks between hydrology and crop growth, revealing patterns in crop yield and irrigation water use. Dynamic interactions enhance understanding of climate variability impacts on food production, highlighting the importance of two-way model coupling for accurate assessments.
Jim Yoon, Nathalie Voisin, Christian Klassert, Travis Thurber, and Wenwei Xu
Hydrol. Earth Syst. Sci., 28, 899–916, https://doi.org/10.5194/hess-28-899-2024, https://doi.org/10.5194/hess-28-899-2024, 2024
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Global and regional models used to evaluate water shortages typically neglect the possibility that irrigated crop areas may change in response to future hydrological conditions, such as the fallowing of crops in response to drought. Here, we enhance a model used for water shortage analysis with farmer agents that dynamically adapt their irrigated crop areas based on simulated hydrological conditions. Results indicate that such cropping adaptation can strongly alter simulated water shortages.
Celray James Chawanda, Albert Nkwasa, Wim Thiery, and Ann van Griensven
Hydrol. Earth Syst. Sci., 28, 117–138, https://doi.org/10.5194/hess-28-117-2024, https://doi.org/10.5194/hess-28-117-2024, 2024
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Africa's water resources are being negatively impacted by climate change and land-use change. The SWAT+ hydrological model was used to simulate the hydrological cycle in Africa, and results show likely decreases in river flows in the Zambezi and Congo rivers and highest flows in the Niger River basins due to climate change. Land cover change had the biggest impact in the Congo River basin, emphasizing the importance of including land-use change in studies.
Tom Kimpson, Margarita Choulga, Matthew Chantry, Gianpaolo Balsamo, Souhail Boussetta, Peter Dueben, and Tim Palmer
Hydrol. Earth Syst. Sci., 27, 4661–4685, https://doi.org/10.5194/hess-27-4661-2023, https://doi.org/10.5194/hess-27-4661-2023, 2023
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Lakes play an important role when we try to explain and predict the weather. More accurate and up-to-date description of lakes all around the world for numerical models is a continuous task. However, it is difficult to assess the impact of updated lake description within a weather prediction system. In this work, we develop a method to quickly and automatically define how, where, and when updated lake description affects weather prediction.
Haiyang Shi, Geping Luo, Olaf Hellwich, Xiufeng He, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Hydrol. Earth Syst. Sci., 27, 4551–4562, https://doi.org/10.5194/hess-27-4551-2023, https://doi.org/10.5194/hess-27-4551-2023, 2023
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Using evidence from meteorological stations, this study assessed the climatic, hydrological, and ecological aridity changes in global drylands and their associated mechanisms. A decoupling between atmospheric, hydrological, and vegetation aridity was found. This highlights the added value of using station-scale data to assess dryland change as a complement to results based on coarse-resolution reanalysis data and land surface models.
En Ning Lai, Lan Wang-Erlandsson, Vili Virkki, Miina Porkka, and Ruud J. van der Ent
Hydrol. Earth Syst. Sci., 27, 3999–4018, https://doi.org/10.5194/hess-27-3999-2023, https://doi.org/10.5194/hess-27-3999-2023, 2023
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This research scrutinized predicted changes in root zone soil moisture dynamics across different climate scenarios and different climate regions globally between 2021 and 2100. The Mediterranean and most of South America stood out as regions that will likely experience permanently drier conditions, with greater severity observed in the no-climate-policy scenarios. These findings underscore the impact that possible future climates can have on green water resources.
Julia Pfeffer, Anny Cazenave, Alejandro Blazquez, Bertrand Decharme, Simon Munier, and Anne Barnoud
Hydrol. Earth Syst. Sci., 27, 3743–3768, https://doi.org/10.5194/hess-27-3743-2023, https://doi.org/10.5194/hess-27-3743-2023, 2023
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The GRACE (Gravity Recovery And Climate Experiment) satellite mission enabled the quantification of water mass redistributions from 2002 to 2017. The analysis of GRACE satellite data shows here that slow changes in terrestrial water storage occurring over a few years to a decade are severely underestimated by global hydrological models. Several sources of errors may explain such biases, likely including the inaccurate representation of groundwater storage changes.
Thedini Asali Peiris and Petra Döll
Hydrol. Earth Syst. Sci., 27, 3663–3686, https://doi.org/10.5194/hess-27-3663-2023, https://doi.org/10.5194/hess-27-3663-2023, 2023
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Hydrological models often overlook vegetation's response to CO2 and climate, impairing their ability to forecast impacts on evapotranspiration and water resources. To address this, we suggest involving two model variants: (1) the standard method and (2) a modified approach (proposed here) based on the Priestley–Taylor equation (PT-MA). While not universally applicable, a dual approach helps consider uncertainties related to vegetation responses to climate change, enhancing model representation.
Samah Larabi, Juliane Mai, Markus Schnorbus, Bryan A. Tolson, and Francis Zwiers
Hydrol. Earth Syst. Sci., 27, 3241–3263, https://doi.org/10.5194/hess-27-3241-2023, https://doi.org/10.5194/hess-27-3241-2023, 2023
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The computational cost of sensitivity analysis (SA) becomes prohibitive for large hydrologic modeling domains. Here, using a large-scale Variable Infiltration Capacity (VIC) deployment, we show that watershed classification helps identify the spatial pattern of parameter sensitivity within the domain at a reduced cost. Findings reveal the opportunity to leverage climate and land cover attributes to reduce the cost of SA and facilitate more rapid deployment of large-scale land surface models.
Tanja Denager, Torben O. Sonnenborg, Majken C. Looms, Heye Bogena, and Karsten H. Jensen
Hydrol. Earth Syst. Sci., 27, 2827–2845, https://doi.org/10.5194/hess-27-2827-2023, https://doi.org/10.5194/hess-27-2827-2023, 2023
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This study contributes to improvements in the model characterization of water and energy fluxes. The results show that multi-objective autocalibration in combination with mathematical regularization is a powerful tool to improve land surface models. Using the direct measurement of turbulent fluxes as the target variable, parameter optimization matches simulations and observations of latent heat, whereas sensible heat is clearly biased.
Yuki Kimura, Yukiko Hirabayashi, Yuki Kita, Xudong Zhou, and Dai Yamazaki
Hydrol. Earth Syst. Sci., 27, 1627–1644, https://doi.org/10.5194/hess-27-1627-2023, https://doi.org/10.5194/hess-27-1627-2023, 2023
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Since both the frequency and magnitude of flood will increase by climate change, information on spatial distributions of potential inundation depths (i.e., flood-hazard map) is required. We developed a method for constructing realistic future flood-hazard maps which addresses issues due to biases in climate models. A larger population is estimated to face risk in the future flood-hazard map, suggesting that only focusing on flood-frequency change could cause underestimation of future risk.
Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala
Hydrol. Earth Syst. Sci., 27, 1531–1563, https://doi.org/10.5194/hess-27-1531-2023, https://doi.org/10.5194/hess-27-1531-2023, 2023
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We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
Jannis M. Hoch, Edwin H. Sutanudjaja, Niko Wanders, Rens L. P. H. van Beek, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci., 27, 1383–1401, https://doi.org/10.5194/hess-27-1383-2023, https://doi.org/10.5194/hess-27-1383-2023, 2023
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To facilitate locally relevant simulations over large areas, global hydrological models (GHMs) have moved towards ever finer spatial resolutions. After a decade-long quest for hyper-resolution (i.e. equal to or smaller than 1 km), the presented work is a first application of a GHM at 1 km resolution over Europe. This not only shows that hyper-resolution can be achieved but also allows for a thorough evaluation of model results at unprecedented detail and the formulation of future research.
Chinchu Mohan, Tom Gleeson, James S. Famiglietti, Vili Virkki, Matti Kummu, Miina Porkka, Lan Wang-Erlandsson, Xander Huggins, Dieter Gerten, and Sonja C. Jähnig
Hydrol. Earth Syst. Sci., 26, 6247–6262, https://doi.org/10.5194/hess-26-6247-2022, https://doi.org/10.5194/hess-26-6247-2022, 2022
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The relationship between environmental flow violations and freshwater biodiversity at a large scale is not well explored. This study intended to carry out an exploratory evaluation of this relationship at a large scale. While our results suggest that streamflow and EF may not be the only determinants of freshwater biodiversity at large scales, they do not preclude the existence of relationships at smaller scales or with more holistic EF methods or with other biodiversity data or metrics.
Zhaofei Liu
Hydrol. Earth Syst. Sci., 26, 6207–6226, https://doi.org/10.5194/hess-26-6207-2022, https://doi.org/10.5194/hess-26-6207-2022, 2022
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Ground heat flux (G) accounts for a significant fraction of the surface energy balance (SEB), but there is insufficient research on these models compared with other flux. The accuracy of G simulation methods in the SEB-based remote sensing evapotranspiration models is evaluated. Results show that the accuracy of each method varied significantly at different sites and at half-hour intervals. Further improvement of G simulations is recommended for the remote sensing evapotranspiration modelers.
Pau Wiersma, Jerom Aerts, Harry Zekollari, Markus Hrachowitz, Niels Drost, Matthias Huss, Edwin H. Sutanudjaja, and Rolf Hut
Hydrol. Earth Syst. Sci., 26, 5971–5986, https://doi.org/10.5194/hess-26-5971-2022, https://doi.org/10.5194/hess-26-5971-2022, 2022
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We test whether coupling a global glacier model (GloGEM) with a global hydrological model (PCR-GLOBWB 2) leads to a more realistic glacier representation and to improved basin runoff simulations across 25 large-scale basins. The coupling does lead to improved glacier representation, mainly by accounting for glacier flow and net glacier mass loss, and to improved basin runoff simulations, mostly in strongly glacier-influenced basins, which is where the coupling has the most impact.
Feng Zhong, Shanhu Jiang, Albert I. J. M. van Dijk, Liliang Ren, Jaap Schellekens, and Diego G. Miralles
Hydrol. Earth Syst. Sci., 26, 5647–5667, https://doi.org/10.5194/hess-26-5647-2022, https://doi.org/10.5194/hess-26-5647-2022, 2022
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A synthesis of rainfall interception data from past field campaigns is performed, including 166 forests and 17 agricultural plots distributed worldwide. These site data are used to constrain and validate an interception model that considers sub-grid heterogeneity and vegetation dynamics. A global, 40-year (1980–2019) interception dataset is generated at a daily temporal and 0.1° spatial resolution. This dataset will serve as a benchmark for future investigations of the global hydrological cycle.
Dongyu Feng, Zeli Tan, Darren Engwirda, Chang Liao, Donghui Xu, Gautam Bisht, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Hydrol. Earth Syst. Sci., 26, 5473–5491, https://doi.org/10.5194/hess-26-5473-2022, https://doi.org/10.5194/hess-26-5473-2022, 2022
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Sea level rise, storm surge and river discharge can cause coastal backwater effects in downstream sections of rivers, creating critical flood risks. This study simulates the backwater effects using a large-scale river model on a coastal-refined computational mesh. By decomposing the backwater drivers, we revealed their relative importance and long-term variations. Our analysis highlights the increasing strength of backwater effects due to sea level rise and more frequent storm surge.
Kieran M. R. Hunt, Gwyneth R. Matthews, Florian Pappenberger, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 5449–5472, https://doi.org/10.5194/hess-26-5449-2022, https://doi.org/10.5194/hess-26-5449-2022, 2022
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In this study, we use three models to forecast river streamflow operationally for 13 months (September 2020 to October 2021) at 10 gauges in the western US. The first model is a state-of-the-art physics-based streamflow model (GloFAS). The second applies a bias-correction technique to GloFAS. The third is a type of neural network (an LSTM). We find that all three are capable of producing skilful forecasts but that the LSTM performs the best, with skilful 5 d forecasts at nine stations.
Tongtiegang Zhao, Haoling Chen, Yu Tian, Denghua Yan, Weixin Xu, Huayang Cai, Jiabiao Wang, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 26, 4233–4249, https://doi.org/10.5194/hess-26-4233-2022, https://doi.org/10.5194/hess-26-4233-2022, 2022
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This paper develops a novel set operations of coefficients of determination (SOCD) method to explicitly quantify the overlapping and differing information for GCM forecasts and ENSO teleconnection. Specifically, the intersection operation of the coefficient of determination derives the overlapping information for GCM forecasts and the Niño3.4 index, and then the difference operation determines the differing information in GCM forecasts (Niño3.4 index) from the Niño3.4 index (GCM forecasts).
Vili Virkki, Elina Alanärä, Miina Porkka, Lauri Ahopelto, Tom Gleeson, Chinchu Mohan, Lan Wang-Erlandsson, Martina Flörke, Dieter Gerten, Simon N. Gosling, Naota Hanasaki, Hannes Müller Schmied, Niko Wanders, and Matti Kummu
Hydrol. Earth Syst. Sci., 26, 3315–3336, https://doi.org/10.5194/hess-26-3315-2022, https://doi.org/10.5194/hess-26-3315-2022, 2022
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Direct and indirect human actions have altered streamflow across the world since pre-industrial times. Here, we apply a method of environmental flow envelopes (EFEs) that develops the existing global environmental flow assessments by methodological advances and better consideration of uncertainty. By assessing the violations of the EFE, we comprehensively quantify the frequency, severity, and trends of flow alteration during the past decades, illustrating anthropogenic effects on streamflow.
Toby R. Marthews, Simon J. Dadson, Douglas B. Clark, Eleanor M. Blyth, Garry D. Hayman, Dai Yamazaki, Olivia R. E. Becher, Alberto Martínez-de la Torre, Catherine Prigent, and Carlos Jiménez
Hydrol. Earth Syst. Sci., 26, 3151–3175, https://doi.org/10.5194/hess-26-3151-2022, https://doi.org/10.5194/hess-26-3151-2022, 2022
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Reliable data on global inundated areas remain uncertain. By matching a leading global data product on inundation extents (GIEMS) against predictions from a global hydrodynamic model (CaMa-Flood), we found small but consistent and non-random biases in well-known tropical wetlands (Sudd, Pantanal, Amazon and Congo). These result from known limitations in the data and the models used, which shows us how to improve our ability to make critical predictions of inundation events in the future.
Jawairia A. Ahmad, Barton A. Forman, and Sujay V. Kumar
Hydrol. Earth Syst. Sci., 26, 2221–2243, https://doi.org/10.5194/hess-26-2221-2022, https://doi.org/10.5194/hess-26-2221-2022, 2022
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Assimilation of remotely sensed data into a land surface model to improve the spatiotemporal estimation of soil moisture across South Asia exhibits potential. Satellite retrieval assimilation corrects biases that are generated due to an unmodeled hydrologic phenomenon, i.e., irrigation. The improvements in fine-scale, modeled soil moisture estimates by assimilating coarse-scale retrievals indicates the utility of the described methodology for data-scarce regions.
Naota Hanasaki, Hikari Matsuda, Masashi Fujiwara, Yukiko Hirabayashi, Shinta Seto, Shinjiro Kanae, and Taikan Oki
Hydrol. Earth Syst. Sci., 26, 1953–1975, https://doi.org/10.5194/hess-26-1953-2022, https://doi.org/10.5194/hess-26-1953-2022, 2022
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Global hydrological models (GHMs) are usually applied with a spatial resolution of about 50 km, but this time we applied the H08 model, one of the most advanced GHMs, with a high resolution of 2 km to Kyushu island, Japan. Since the model was not accurate as it was, we incorporated local information and improved the model, which revealed detailed water stress in subregions that were not visible with the previous resolution.
Basil Kraft, Martin Jung, Marco Körner, Sujan Koirala, and Markus Reichstein
Hydrol. Earth Syst. Sci., 26, 1579–1614, https://doi.org/10.5194/hess-26-1579-2022, https://doi.org/10.5194/hess-26-1579-2022, 2022
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We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective.
Tina Trautmann, Sujan Koirala, Nuno Carvalhais, Andreas Güntner, and Martin Jung
Hydrol. Earth Syst. Sci., 26, 1089–1109, https://doi.org/10.5194/hess-26-1089-2022, https://doi.org/10.5194/hess-26-1089-2022, 2022
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We assess the effect of how vegetation is defined in a global hydrological model on the composition of total water storage (TWS). We compare two experiments, one with globally uniform and one with vegetation parameters that vary in space and time. While both experiments are constrained against observational data, we found a drastic change in the partitioning of TWS, highlighting the important role of the interaction between groundwater–soil moisture–vegetation in understanding TWS variations.
Marc F. P. Bierkens, Edwin H. Sutanudjaja, and Niko Wanders
Hydrol. Earth Syst. Sci., 25, 5859–5878, https://doi.org/10.5194/hess-25-5859-2021, https://doi.org/10.5194/hess-25-5859-2021, 2021
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We introduce a simple analytical framework that allows us to estimate to what extent large-scale groundwater withdrawal affects groundwater levels and streamflow. It also calculates which part of the groundwater withdrawal comes out of groundwater storage and which part from a reduction in streamflow. Global depletion rates obtained with the framework are compared with estimates from satellites, from global- and continental-scale groundwater models, and from in situ datasets.
Dirk Eilander, Willem van Verseveld, Dai Yamazaki, Albrecht Weerts, Hessel C. Winsemius, and Philip J. Ward
Hydrol. Earth Syst. Sci., 25, 5287–5313, https://doi.org/10.5194/hess-25-5287-2021, https://doi.org/10.5194/hess-25-5287-2021, 2021
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Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
Cited articles
Bolten, J., Crow, W., Zhan, X., Jackson, T., and Reynolds, C.: Evaluating the utility of remotely-sensed soil moisture retrievals for operational agricultural drought monitoring, IEEE J. Sel. Top. Appl., 3, 57–66, https://doi.org/10.1109/JSTARS.2009.2037163, 2010.
Bosch, D., Lakshmi, V., Jackson, T., Jacobs, J.: Large scale measurements of soil moisture for validation of remotely sensed data: Georgia soil moisture experiments of 2003, J. Hydrol., 323, 120–137, https://doi.org/10.1016/j.jhydrol.2005.08.024, 2006.
Cosh, M., Jackson, T., Starks, P., and Heathman, G.: Temporal stability of surface soil moisture in the Little Washita River watershed and its applications in satellite soil moisture product validation, J. Hydrol. 323, 168–177, https://doi.org/10.1016/j.jhydrol.2005.08.020, 2006.
Cosh, M., Jackson, T., Moran, M., and Bindlish, R.: Temporal persistence and stability of surface soil moisture in a semi-arid watershed, Remote Sens. Environ. 112, 304–313, https://doi.org/10.1016/j.rse.2007.07.001, 2008.
De Lannoy, G. and Reichle, R.: Global assimilation of multi-angle and multi-polarization SMOS brightness temperature observations into the GEOS-5 Catchment land surface model for soil moisture estimation, J. Hydrometeorol., https://doi.org/10.1175/JHM-D-15-0037.1, in press, 2015.
De Lannoy, G., Koster, R., Reichle, R., Mahanama, S., and Liu, Q.: An updated treatment of soil texture and associated hydraulic properties in a global land modeling system, J. Adv. Model. Earth Syst., 6, 957–979, https://doi.org/10.1002/2014MS000330, 2014.
Dorigo, W., Gruber, A., de Jeu, R., Wagner, W., Stacke, T., Loew, A., Albergel, C., Brocca, L., Chung, D., Parinussa, R., and Kidd, R.: Evaluation of the ESA CCI soil moisture product using ground-based observations, Remote Sens. Environ., 162, 380–395, https://doi.org/10.1016/j.rse.2014.07.023, 2015.
Draper, C., Reichle, R., De Lannoy, G., and Liu, Q.: Assimilation of passive and active microwave soil moisture retrievals, Geophys. Res. Lett., 39, L04401, https://doi.org/10.1029/2011GL050655, 2012.
Drusch, M., Wood, E., and Gao, H.: Observation operators for the direct assimilation of TRMM microwave imager retrieved soil moisture, Geophys. Res. Lett., 32, L15403, https://doi.org/10.1029/2005GL023623, 2005.
Entekhabi, D., Njoku, E., O'Neill, P., Kellogg, K., Crow, W., Edelstein, W., Entin, J., Goodman, S., Jackson, T., Johnson, J., Kimball, J., Piepmeier, J., Koster, R., Martin, N., McDonald, K., Moghaddam, M., Moran, S., Reichle, R., Shi, J., Spencer, M., Thurman, S., Tsang, L., and Van Zyl, J.: The Soil Moisture Active Passive (SMAP) mission, Proc. IEEE, 98, 704–716, https://doi.org/10.1109/jproc.2010.2043918, 2010a.
Entekhabi, D., Reichle, R., Koster, R., and Crow, W.: Performance metrics for soil moisture retrievals and application requirements, J. Hydrometeorol., 11, 832–840, https://doi.org/10.1175/2010JHM1223.1, 2010b.
Entin, J., Robock, A., Vinnikov, K., Hollinger, S., Liu, S., and Namkhai, A.: Temporal and spatial scales of observed soil moisture variations in the extratropics, J. Geophys. Res., 105, 11865–11877, https://doi.org/10.1029/2000JD900051, 2000.
Jackson, T., Cosh, M., Bindlish, R., Starks, P., Bosch, D., Seyfried, M., Goodrich, D., Moran, M., and Du, J.: Validation of advanced microwave scanning radiometer soil moisture products, IEEE T. Geosci. Remote, 48, 4256–4272, https://doi.org/10.1109/TGRS.2010.2051035, 2010.
Koster, R., Suarez, M., Ducharne, A., Stieglitz, M., and Kumar, P.: A catchment-based approach to modeling land surface processes in a general circulation model: 1. Model structure, J. Geophys. Res., 105, 24809–24822, https://doi.org/10.1029/2000JD900327, 2000.
Liu, Q., Reichle, R., Bindlish, R., Cosh, M., Crow, W., de Jeu, R., De Lannoy, G., Huffman, G., and Jackson, T.: The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates in a land data assimilation system, J. Hydrometeorol., 12, 750–765, https://doi.org/10.1175/JHM-D-10-05000.1, 2011.
Liu, Y., Dorigo, W., Parinussa, R., de Jeu, R., Wagner W., McCabe, M., Evans, J., and van Dijk, A.: Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sens. Environ., 123, 280–297, https://doi.org/10.1016/j.rse.2012.03.014, 2012.
Narapusetty, B., DelSole, T., and Tippett, M.: Optimal estimation of the climatological mean, J. Climate, 22, 4845–4859, https://doi.org/10.1175/2009JCLI2944.1, 2009.
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.
Reichle, R. and Koster, R.: Bias reduction in short records of satellite soil moisture, Geophys. Res. Lett., 31, L19501, https://doi.org/10.1029/2004GL020938, 2004.
Reichle, R., Koster, R., Dong, J., and Berg, A.: Global soil moisture from satellite observations, land surface models, and ground data: implications for data assimilation, J. Hydrometeorol., 5, 430–442, https://doi.org/10.1175/1525-7541(2004)005<0430:GSMFSO>2.0.CO;2, 2004.
Reichle, R., Koster, R., Liu, P., Mahanama, S., Njoku, E., and Owe, M.: Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and the Scanning Multichannel Microwave Radiometer (SMMR), J. Geophys. Res., 112, D09108, https://doi.org/10.1029/2006JD008033, 2007.
Rienecker, M., Suarez, M., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M., Schubert, S., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P., Redder, C., Reichle, R., Robertson, F., Ruddick, A., Sienkiewicz, M., and Woollen, J.: MERRA – NASA's Modern-Era Retrospective Analysis for Research and Applications, J. Climate, 24, 3624–3648, https://doi.org/10.1175/JCLI-D-11-00015.1, 2011.
Schaefer, G., Cosh, M., and Jackson, T.: The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN), J. Atmos. Ocean. Tech., 24, 2073–2077, https://doi.org/10.1175/2007JTECHA930.1, 2007.
Scharlemann, J., Benz, D., Hay, S., Purse, B., Tatem, A., Wint, G. W., and Rogers, D.: Global data for ecology and epidemiology: a novel algorithm for temporal fourier processing MODIS data, PLOS ONE, 1, e1408, https://doi.org/10.1371/journal.pone.0001408, 2008.
Scipal, K., Drusch, M., and Wagner, W.: Assimilation of a ERS scatterometer derived soil moisture index in the ECMWF numerical weather prediction system, Adv. Water Resour., 31, 1101–1112, https://doi.org/10.1016/j.advwatres.2008.04.013, 2008.
Su, C.-H. and Ryu, D.: Multi-scale analysis of bias correction of soil moisture, Hydrol. Earth Syst. Sci., 19, 17–31, https://doi.org/10.5194/hess-19-17-2015, 2015.
Su, Z., Fernàndez-Prieto, D., Timmermans, J., Chen, X., Hungershoefer, K., Roebeling, R., Schr\:oder, M., Schulz, J., Stammes, P., Wang, P., and Wolters, E.: First results of the earth observation Water Cycle Multi-mission Observation Strategy (WACMOS), Int. J. Appl. Earth Obs. Geoinf., 26, 270–285, https://doi.org/10.1016/j.jag.2013.08.002, 2014.
Vinnikov, K., Yu, Y., Rama Varma Raja, M., Tarpley, D., and Goldberg, M.: Diurnal-seasonal and weather-related variations of land surface temperature observed from geostationary satellites, Geophys. Res. Lett., 35, L22708, https://doi.org/10.1029/2008gl035759, 2008.
Short summary
A soil moisture assimilation experiment is evaluated separately at sub-seasonal, seasonal, and inter-annual timescales. In addition to previously reported improvements in sub-seasonal scale soil moisture, it is show that such an assimilation can also improve the model soil moisture at seasonal and inter-annual timescales, demonstrating the potential for near-surface soil moisture assimilation to improve model representation of important long-term events, such as droughts.
A soil moisture assimilation experiment is evaluated separately at sub-seasonal, seasonal, and...
