Articles | Volume 25, issue 7
https://doi.org/10.5194/hess-25-4099-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-4099-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jul 2021
Research article |
| 14 Jul 2021
| 14 Jul 2021
Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters
Michiel Maertens
CORRESPONDING AUTHOR
KU Leuven, Department of Earth and Environmental Sciences, Leuven, Belgium
Gabriëlle J. M. De Lannoy
KU Leuven, Department of Earth and Environmental Sciences, Leuven, Belgium
Sebastian Apers
KU Leuven, Department of Earth and Environmental Sciences, Leuven, Belgium
Sujay V. Kumar
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Sarith P. P. Mahanama
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
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Anne Springer, Gabriëlle De Lannoy, Matthew Rodell, Yorck Ewerdwalbesloh, Helena Gerdener, Mehdi Khaki, Bailing Li, Fupeng Li, Maike Schumacher, Natthachet Tangdamrongsub, Mohammad J. Tourian, Wanshu Nie, and Jürgen Kusche
Hydrol. Earth Syst. Sci., 30, 985–1022, https://doi.org/10.5194/hess-30-985-2026, https://doi.org/10.5194/hess-30-985-2026, 2026
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The GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On satellites monitor changes in Earth's water storage by observing gravity variations. By integrating these observations into hydrological models through data assimilation, estimates of groundwater, soil moisture, and hydrological trends are improved, helping to monitor droughts, floods, and human water use. This review highlights recent advances in GRACE data assimilation, identifies key challenges, and discusses future directions with upcoming satellite missions.
Rémi Madelon, K. Arthur Endsley, John S. Kimball, Gabriëlle J. M. De Lannoy, Oliver Sonnentag, Haley Alcock, Alex Mavrovic, Scott N. Williamson, Vincent Maire, Arnaud Mialon, and Alexandre Roy
EGUsphere, https://doi.org/10.5194/egusphere-2026-720, https://doi.org/10.5194/egusphere-2026-720, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
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This study aims to improve estimates of carbon dioxide release and uptake in the North American Arctic and subarctic regions. Several modeling approaches were tested, showing that a better representation of sunlight and temperature effects on ecosystems leads to improved estimates. This work provides new perspectives to better assess whether these regions act as sources or sinks of greenhouse gases and how they may influence the climate system by amplifying or slowing global warming.
Pierre Laluet, Jacopo Dari, Louise Busschaert, Zdenko Heyvaert, Gabrielle De Lannoy, Pia Langhans, Sara Modanesi, Christian Massari, Luca Brocca, Carla Saltalippi, Renato Morbidelli, Clément Albergel, and Wouter Dorigo
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-737, https://doi.org/10.5194/essd-2025-737, 2026
Preprint under review for ESSD
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We developed a long-term dataset collection of irrigation water use based on about two decades of satellite observations, three distinct approaches, and many input datasets. The collection provides monthly estimates for major agricultural regions and helps describe how irrigation varies across locations, seasons, and years. It offers a foundation for improving how irrigation is quantified, compared across methods, and integrated into large-scale hydrological and climate studies.
Zanpin Xing, Xiaojun Li, Frédéric Frappart, Gabrielle De Lannoy, Thomas Jagdhuber, Jian Peng, Lei Fan, Hongliang Ma, Karthikeyan Lanka, Xiangzhuo Liu, Mengjia Wang, Lin Zhao, Yongqin Liu, and Jean-Pierre Wigneron
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-728, https://doi.org/10.5194/essd-2025-728, 2026
Preprint under review for ESSD
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Satellite observations of Earth's land surface are important for tracking soil and vegetation water. We use data from the Soil Moisture and Ocean Salinity satellite to build a new product that cleans the raw microwave signal and yields more reliable estimates of soil moisture and vegetation water content. Tests against ground stations and other satellites show that the new record exceeds existing products and can support applications such as drought, freeze–thaw, and carbon monitoring.
Devon Dunmire, Michel Bechtold, Lucas Boeykens, and Gabriëlle J. M. De Lannoy
The Cryosphere, 20, 609–628, https://doi.org/10.5194/tc-20-609-2026, https://doi.org/10.5194/tc-20-609-2026, 2026
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Snow is vital for society and the climate, yet estimates of snowpack remain uncertain due to observational and modeling limitations. Data assimilation (DA) helps by integrating observations with models. Here, we integrate snow depth retrievals into a physically-based snow model across the European Alps. This work offers advancements for snow data assimilation, such as incorporating a dynamic observational uncertainty, which is essential for forecasting and water resource management.
Cenlin He, Tzu-Shun Lin, David M. Mocko, Ronnie Abolafia-Rosenzweig, Jerry W. Wegiel, and Sujay V. Kumar
Geosci. Model Dev., 18, 8439–8460, https://doi.org/10.5194/gmd-18-8439-2025, https://doi.org/10.5194/gmd-18-8439-2025, 2025
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This study integrates the refactored community Noah-MP version 5.0 model with the NASA Land Information System (LIS) version 7.5.2 to streamline the synchronization, development, and maintenance of Noah-MP within LIS and to enhance their interoperability and applicability. The model benchmarking and evaluation results reveal key model strengths and weaknesses in simulating land surface quantities and show implications for future model improvements.
Peyman Abbaszadeh, Fadji Zaouna Maina, Chen Yang, Dan Rosen, Sujay Kumar, Matthew Rodell, and Reed Maxwell
Hydrol. Earth Syst. Sci., 29, 5429–5452, https://doi.org/10.5194/hess-29-5429-2025, https://doi.org/10.5194/hess-29-5429-2025, 2025
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To manage Earth's water resources effectively amid climate change, it is crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS (Land Information System)/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.
Gabriëlle J. M. De Lannoy, Louise Busschaert, Michel Bechtold, Niccolò Lanfranco, Shannon de Roos, Zdenko Heyvaert, Jonas Mortelmans, Samuel A. Scherrer, Maxime Van den Bossche, Sujay Kumar, David M. Mocko, Eric Kemp, Lee Heng, Pasquale Steduto, and Dirk Raes
EGUsphere, https://doi.org/10.5194/egusphere-2025-4417, https://doi.org/10.5194/egusphere-2025-4417, 2025
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To facilitate regional crop growth simulations at any spatial resolution, with a range of different input sources for meteorology, soil and crop parameters, we have incorporated the AquaCrop model into the NASA Land Information System. This system also facilitates the assimilation of satellite data to update the crop and water conditions during model simulations. We present three exploratory applications to highlight the possibilities and pathways for future research on crop estimation.
Lucas Boeykens, Devon Dunmire, Jonas-Frederik Jans, Willem Waegeman, Gabriëlle De Lannoy, Ezra Beernaert, Niko E. C. Verhoest, and Hans Lievens
EGUsphere, https://doi.org/10.5194/egusphere-2025-3327, https://doi.org/10.5194/egusphere-2025-3327, 2025
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We used AI to better estimate the height of the snowpack present on the ground across the European Alps, by using novel satellite data, complemented by weather information or snow depth estimates from a computer model. We found that both combinations improve the accuracy of our AI-based snow depth estimates, performing almost equally well. This helps us better monitor how much water is stored as snow, which is vital for drinking water, farming, and clean energy production in Europe.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabriëlle J. M. De Lannoy
EGUsphere, https://doi.org/10.5194/egusphere-2025-2550, https://doi.org/10.5194/egusphere-2025-2550, 2025
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Min Huang, Gregory R. Carmichael, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
Atmos. Chem. Phys., 25, 1449–1476, https://doi.org/10.5194/acp-25-1449-2025, https://doi.org/10.5194/acp-25-1449-2025, 2025
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We use model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutant budgets in this area as local anthropogenic emissions drop.
Paolo Nasta, Günter Blöschl, Heye R. Bogena, Steffen Zacharias, Roland Baatz, Gabriëlle De Lannoy, Karsten H. Jensen, Salvatore Manfreda, Laurent Pfister, Ana M. Tarquis, Ilja van Meerveld, Marc Voltz, Yijian Zeng, William Kustas, Xin Li, Harry Vereecken, and Nunzio Romano
Hydrol. Earth Syst. Sci., 29, 465–483, https://doi.org/10.5194/hess-29-465-2025, https://doi.org/10.5194/hess-29-465-2025, 2025
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The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two endmembers of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented supersites.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabrielle J. M. De Lannoy
EGUsphere, https://doi.org/10.2139/ssrn.4974019, https://doi.org/10.2139/ssrn.4974019, 2024
Preprint archived
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This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Isis Brangers, Hans-Peter Marshall, Gabrielle De Lannoy, Devon Dunmire, Christian Mätzler, and Hans Lievens
The Cryosphere, 18, 3177–3193, https://doi.org/10.5194/tc-18-3177-2024, https://doi.org/10.5194/tc-18-3177-2024, 2024
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To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains. The reflections were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The results demonstrate that C-band radar is sensitive to seasonal patterns in snow accumulation but that changes in microstructure, stratigraphy and snow wetness may complicate satellite-based snow depth retrievals.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024, https://doi.org/10.5194/hess-28-631-2024, 2024
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Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
Jonas Mortelmans, Anne Felsberg, Gabriëlle J. M. De Lannoy, Sander Veraverbeke, Robert D. Field, Niels Andela, and Michel Bechtold
Nat. Hazards Earth Syst. Sci., 24, 445–464, https://doi.org/10.5194/nhess-24-445-2024, https://doi.org/10.5194/nhess-24-445-2024, 2024
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With global warming increasing the frequency and intensity of wildfires in the boreal region, accurate risk assessments are becoming more crucial than ever before. The Canadian Fire Weather Index (FWI) is a renowned system, yet its effectiveness in peatlands, where hydrology plays a key role, is limited. By incorporating groundwater data from numerical models and satellite observations, our modified FWI improves the accuracy of fire danger predictions, especially over summer.
Anne Felsberg, Zdenko Heyvaert, Jean Poesen, Thomas Stanley, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 23, 3805–3821, https://doi.org/10.5194/nhess-23-3805-2023, https://doi.org/10.5194/nhess-23-3805-2023, 2023
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The Probabilistic Hydrological Estimation of LandSlides (PHELS) model combines ensembles of landslide susceptibility and of hydrological predictor variables to provide daily, global ensembles of hazard for hydrologically triggered landslides. Testing different hydrological predictors showed that the combination of rainfall and soil moisture performed best, with the lowest number of missed and false alarms. The ensemble approach allowed the estimation of the associated prediction uncertainty.
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023, https://doi.org/10.5194/hess-27-4087-2023, 2023
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We explored different options for data assimilation (DA) of the remotely sensed leaf area index (LAI). We found strong biases between LAI predicted by Noah-MP and observations. LAI DA that does not take these biases into account can induce unphysical patterns in the resulting LAI and flux estimates and leads to large changes in the climatology of root zone soil moisture. We tested two bias-correction approaches and explored alternative solutions to treating bias in LAI DA.
Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 27, 4039–4056, https://doi.org/10.5194/hess-27-4039-2023, https://doi.org/10.5194/hess-27-4039-2023, 2023
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An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
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As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
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While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Sara Modanesi, Christian Massari, Michel Bechtold, Hans Lievens, Angelica Tarpanelli, Luca Brocca, Luca Zappa, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 4685–4706, https://doi.org/10.5194/hess-26-4685-2022, https://doi.org/10.5194/hess-26-4685-2022, 2022
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Given the crucial impact of irrigation practices on the water cycle, this study aims at estimating irrigation through the development of an innovative data assimilation system able to ingest high-resolution Sentinel-1 radar observations into the Noah-MP land surface model. The developed methodology has important implications for global water resource management and the comprehension of human impacts on the water cycle and identifies main challenges and outlooks for future research.
Anne Felsberg, Jean Poesen, Michel Bechtold, Matthias Vanmaercke, and Gabriëlle J. M. De Lannoy
Nat. Hazards Earth Syst. Sci., 22, 3063–3082, https://doi.org/10.5194/nhess-22-3063-2022, https://doi.org/10.5194/nhess-22-3063-2022, 2022
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In this study we assessed global landslide susceptibility at the coarse 36 km spatial resolution of global satellite soil moisture observations to prepare for a subsequent combination of the two. Specifically, we focus therefore on the susceptibility of hydrologically triggered landslides. We introduce ensemble techniques, common in, for example, meteorology but not yet in the landslide community, to retrieve reliable estimates of the total prediction uncertainty.
Louise Busschaert, Shannon de Roos, Wim Thiery, Dirk Raes, and Gabriëlle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 26, 3731–3752, https://doi.org/10.5194/hess-26-3731-2022, https://doi.org/10.5194/hess-26-3731-2022, 2022
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Increasing amounts of water are used for agriculture. Therefore, we looked into how irrigation requirements will evolve under a changing climate over Europe. Our results show that, by the end of the century and under high emissions, irrigation water will increase by 30 % on average compared to the year 2000. Also, the irrigation requirement is likely to vary more from 1 year to another. However, if emissions are mitigated, these effects are reduced.
Amy McNally, Jossy Jacob, Kristi Arsenault, Kimberly Slinski, Daniel P. Sarmiento, Andrew Hoell, Shahriar Pervez, James Rowland, Mike Budde, Sujay Kumar, Christa Peters-Lidard, and James P. Verdin
Earth Syst. Sci. Data, 14, 3115–3135, https://doi.org/10.5194/essd-14-3115-2022, https://doi.org/10.5194/essd-14-3115-2022, 2022
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The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams described here generate routine estimates of snow, soil moisture, runoff, and other variables useful for tracking water availability. These data are hosted by NASA and USGS data portals for public use.
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022, https://doi.org/10.5194/acp-22-7461-2022, 2022
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This study demonstrates that ozone dry-deposition modeling can be improved by revising the model's dry-deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
Wanshu Nie, Sujay V. Kumar, Kristi R. Arsenault, Christa D. Peters-Lidard, Iliana E. Mladenova, Karim Bergaoui, Abheera Hazra, Benjamin F. Zaitchik, Sarith P. Mahanama, Rachael McDonnell, David M. Mocko, and Mahdi Navari
Hydrol. Earth Syst. Sci., 26, 2365–2386, https://doi.org/10.5194/hess-26-2365-2022, https://doi.org/10.5194/hess-26-2365-2022, 2022
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The MENA (Middle East and North Africa) region faces significant food and water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing data sets into an Earth system model to help build an effective drought monitoring system and support risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation conditions into the model as being one of the key processes for a successful application for the region.
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.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
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Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Sara Modanesi, Christian Massari, Alexander Gruber, Hans Lievens, Angelica Tarpanelli, Renato Morbidelli, and Gabrielle J. M. De Lannoy
Hydrol. Earth Syst. Sci., 25, 6283–6307, https://doi.org/10.5194/hess-25-6283-2021, https://doi.org/10.5194/hess-25-6283-2021, 2021
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Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Land surface models are not able to correctly simulate irrigation. Remote sensing observations offer an opportunity to fill this gap as they are directly affected by irrigation. We equipped a land surface model with an observation operator able to transform Sentinel-1 backscatter observations into realistic vegetation and soil states via data assimilation.
Shannon de Roos, Gabriëlle J. M. De Lannoy, and Dirk Raes
Geosci. Model Dev., 14, 7309–7328, https://doi.org/10.5194/gmd-14-7309-2021, https://doi.org/10.5194/gmd-14-7309-2021, 2021
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A spatially distributed version of the field-scale crop model AquaCrop v6.1 was developed for applications at various spatial scales. Multi-year 1 km simulations over central Europe were evaluated against biomass and surface soil moisture products derived from optical and microwave satellite missions, as well as in situ observations of soil moisture. The regional version of the AquaCrop model provides a suitable setup for subsequent satellite-based data assimilation.
Huisheng Bian, Eunjee Lee, Randal D. Koster, Donifan Barahona, Mian Chin, Peter R. Colarco, Anton Darmenov, Sarith Mahanama, Michael Manyin, Peter Norris, John Shilling, Hongbin Yu, and Fanwei Zeng
Atmos. Chem. Phys., 21, 14177–14197, https://doi.org/10.5194/acp-21-14177-2021, https://doi.org/10.5194/acp-21-14177-2021, 2021
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The study using the NASA Earth system model shows ~2.6 % increase in burning season gross primary production and ~1.5 % increase in annual net primary production across the Amazon Basin during 2010–2016 due to the change in surface downward direct and diffuse photosynthetically active radiation by biomass burning aerosols. Such an aerosol effect is strongly dependent on the presence of clouds. The cloud fraction at which aerosols switch from stimulating to inhibiting plant growth occurs at ~0.8.
Min Huang, James H. Crawford, Joshua P. DiGangi, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Xiwu Zhan
Atmos. Chem. Phys., 21, 11013–11040, https://doi.org/10.5194/acp-21-11013-2021, https://doi.org/10.5194/acp-21-11013-2021, 2021
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This study evaluates the impact of satellite soil moisture data assimilation on modeled weather and ozone fields at various altitudes above the southeastern US during the summer. It emphasizes the importance of soil moisture in the understanding of surface ozone pollution and upper tropospheric chemistry, as well as air pollutants’ source–receptor relationships between the US and its downwind areas.
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.
Cited articles
Albergel, C., Balsamo, G., de Rosnay, P., Muñoz-Sabater, J., and Boussetta, S.: A bare ground evaporation revision in the ECMWF land-surface scheme: evaluation of its impact using ground soil moisture and satellite microwave data, Hydrol. Earth Syst. Sci., 16, 3607–3620, https://doi.org/10.5194/hess-16-3607-2012, 2012. a, b
Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu, A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C., Le Moigne, P., Decharme, B., Mahfouf, J.-F., and Calvet, J.-C.: Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area, Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, 2017. a
Alton, P.: A simple retrieval of ground albedo and vegetation absorptance from MODIS satellite data for parameterisation of global land-surface models,
Agr. Forest Meteorol., 149, 1769–1775, 2009. a
Amdan, M., Aragón, R., Jobbágy, E., Volante, J., and Paruelo, J.: Onset of deep drainage and salt mobilization following forest clearing and
cultivation in the Chaco plains (Argentina), Water Resour. Res., 49, 6601–6612, 2013. a
Balsamo, G., Beljaars, A., Scipal, K., Viterbo, P., van den Hurk, B., Hirschi, M., and Betts, A. K.: A revised hydrology for the ECMWF model: Verification from field site to terrestrial water storage and impact in the Integrated Forecast System, J. Hydrometeorol., 10, 623–643, 2009. a
Barbu, A. L., Calvet, J.-C., Mahfouf, J.-F., Albergel, C., and Lafont, S.:
Assimilation of Soil Wetness Index and Leaf Area Index into the ISBA-A-gs
land surface model: grassland case study, Biogeosciences, 8, 1971–1986,
https://doi.org/10.5194/bg-8-1971-2011, 2011. a
Barbu, A. L., Calvet, J.-C., Mahfouf, J.-F., and Lafont, S.: Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France, Hydrol. Earth Syst. Sci., 18, 173–192, https://doi.org/10.5194/hess-18-173-2014, 2014. a
Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P.,
and Smets, B.: GEOV1: LAI and FAPAR essential climate variables and FCOVER
global time series capitalizing over existing products. Part 1: Principles of
development and production, Remote Sens. Environ., 137, 299–309, 2013. a
Bonan, G. B., Levis, S., Kergoat, L., and Oleson, K. W.: Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models, Global Biogeochem. Cy., 16, 5–1–5-23, 2002a. a
Bonan, G. B., Oleson, K. W., Vertenstein, M., Levis, S., Zeng, X., Dai, Y.,
Dickinson, R. E., and Yang, Z.-L.: The land surface climatology of the
Community Land Model coupled to the NCAR Community Climate Model, J. Climate, 15, 3123–3149, 2002b. a
Bontemps, S., Herold, M., Kooistra, L., van Groenestijn, A., Hartley, A., Arino, O., Moreau, I., and Defourny, P.: Revisiting land cover observation to address the needs of the climate modeling community, Biogeosciences, 9, 2145–2157, https://doi.org/10.5194/bg-9-2145-2012, 2012. a
Boussetta, S., Balsamo, G., Beljaars, A., Kral, T., and Jarlan, L.: Impact of a satellite-derived leaf area index monthly climatology in a global numerical
weather prediction model, Int. J. Remote Sens., 34, 3520–3542, 2013. a
Boussetta, S., Balsamo, G., Dutra, E., Beljaars, A., and Albergel, C.:
Assimilation of surface albedo and vegetation states from satellite
observations and their impact on numerical weather prediction, Remote Sens.
Environ., 163, 111–126, 2015. a
Brocca, L., Tarpanelli, A., Filippucci, P., Dorigo, W., Zaussinger, F., Gruber, A., and Fernández-Prieto, D.: How much water is used for irrigation? A new approach exploiting coarse resolution satellite soil moisture products, Int. J. Appl. Earth Obs. Geoinf., 73, 752–766, 2018. a
Cao, Q., Yu, D., Georgescu, M., Han, Z., and Wu, J.: Impacts of land use and
land cover change on regional climate: A case study in the agro-pastoral
transitional zone of China, Environ. Res. Lett., 10, 124025, https://doi.org/10.1088/1748-9326/10/12/124025, 2015. a
Chadburn, S. E., Burke, E. J., Essery, R. L. H., Boike, J., Langer, M., Heikenfeld, M., Cox, P. M., and Friedlingstein, P.: Impact of model developments on present and future simulations of permafrost in a global land-surface model, The Cryosphere, 9, 1505–1521, https://doi.org/10.5194/tc-9-1505-2015, 2015. a
Chen, S., Su, H., and Zhan, J.: Estimating the impact of land use change on
surface energy partition based on the Noah model, Front. Earth Sci., 8, 18–31, 2014. a
Chen, X., Zhang, Z., Chen, X., and Shi, P.: The impact of land use and land
cover changes on soil moisture and hydraulic conductivity along the karst
hillslopes of southwest China, Environ. Earth Sci., 59, 811–820, 2009. a
Clark, M. L., Aide, T. M., Grau, H. R., and Riner, G.: A scalable approach to
mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America, Remote Sens. Environ., 114, 2816–2832, 2010. a
Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis, D. J., Hooper, R. P., Kumar, M., Leung, L. R., Mackay, D. S., Maxwell, R. M., Shen, C., Swenson, S. C., and Zeng, X.: Improving the representation of hydrologic processes in Earth System Models, Water Resour. Res., 51, 5929–5956, 2015. a, b, c
Crawford, T. M., Stensrud, D. J., Mora, F., Merchant, J. W., and Wetzel, P. J.: Value of incorporating satellite-derived land cover data in MM5/PLACE for
simulating surface temperatures, J. Hydrometeorol., 2, 453–468, 2001. a
Csiszar, I. and Gutman, G.: Mapping global land surface albedo from NOAA AVHRR, J. Geophys. Res.-Atmos., 104, 6215–6228, 1999. a
De Lannoy, G. J., Reichle, R. H., and Pauwels, V. R.: Global calibration of the GEOS-5 L-band microwave radiative transfer model over nonfrozen land using SMOS observations, J. Hydrometeorol., 14, 765–785, 2013. a
De Lannoy, G. J., Reichle, R. H., Peng, J., Kerr, Y., Castro, R., Kim, E. J.,
and Liu, Q.: Converting between SMOS and SMAP level-1 brightness temperature
observations over nonfrozen land, IEEE Geosci. Remote Sens. Lett., 12, 1908–1912, 2015. a
de Queiroz, M. G., da Silva, T. G. F., Zolnier, S., Jardim, A. M. D. R. F.,
de Souza, C. A. A., Júnior, G. D. N. A., de Morais, J. E. F., and
de Souza, L. S. B.: Spatial and temporal dynamics of soil moisture for
surfaces with a change in land use in the semi-arid region of Brazil, Catena,
188, 104457, https://doi.org/10.1016/j.catena.2020.104457, 2020. a
Dickinson, R. E.: Land processes in climate models, Remote Sens. Environ., 51, 27–38, 1995. a
Ek, M., Mitchell, K., Lin, Y., Rogers, E., Grunmann, P., Koren, V., and Gayno, G.: Implementation of Noah Land Surface Model Advances in the National
Centers for Environmental Prediction Operational Mesoscale Eta Model, J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003. a, b
Entekhabi, D., Reichle, R. H., Koster, R. D., and Crow, W. T.: Performance
metrics for soil moisture retrievals and application requirements, J.
Hydrometeorol., 11, 832–840, 2010. a
Feldman, A. F., Akbar, R., and Entekhabi, D.: Characterization of higher-order scattering from vegetation with SMAP measurements, Remote Sens. Environ., 219, 324–338, 2018. a
Ge, Q., Zhang, X., and Zheng, J.: Simulated effects of vegetation
increase/decrease on temperature changes from 1982 to 2000 across the Eastern
China, Int. J. Climatol., 34, 187–196, 2014. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), J. Climate, 30, 5419–5454, 2017. a
Girotto, M., De Lannoy, G. J., Reichle, R. H., Rodell, M., Draper, C., Bhanja, S. N., and Mukherjee, A.: Benefits and pitfalls of GRACE data assimilation: A case study of terrestrial water storage depletion in India, Geophys. Res. Lett., 44, 4107–4115, 2017. a
Grossman-Clarke, S., Zehnder, J. A., Loridan, T., and Grimmond, C. S. B.:
Contribution of land use changes to near-surface air temperatures during
recent summer extreme heat events in the Phoenix metropolitan area, J. Appl. Meteorol. Clim., 49, 1649–1664, 2010. a
Han, X., Franssen, H.-J. H., Montzka, C., and Vereecken, H.: Soil moisture and soil properties estimation in the Community Land Model with synthetic
brightness temperature observations, Water Resour. Res., 50, 6081–6105, 2014. a
Hansen, M. C., DeFries, R. S., Townshend, J. R., and Sohlberg, R.: Global land cover classification at 1 km spatial resolution using a classification tree approach, Int. J. Remote Sens., 21, 1331–1364, 2000. a
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C., and Townshend, J.: High-resolution global maps of 21st-century forest cover change, Science, 342, 850–853, 2013. a, b
Hengl, T., de Jesus, J. M., Heuvelink, G. B., Gonzalez, M. R., Kilibarda, M., Blagotic, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A., Batjes, N., Leenaars, J., Carvalho Ribeiro, E., Wheeler, I., Mantel, S., and Kempen, B.: SoilGrids250m: Global gridded soil information based on machine learning, PLoS One, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017. a
Houspanossian, J., Giménez, R., Jobbágy, E., and Nosetto, M.: Surface
albedo raise in the South American Chaco: Combined effects of deforestation
and agricultural changes, Agr. Forest Meteorol., 232, 118–127, 2017. a
INTA: Sistema De Información y Gestión Agrometeorológica, Instituto Nacional de Tecnologíca Agropecuaria, available at: http://siga2.inta.gov.ar/#/, last access: 15 January 2020. a
James, K. A., Stensrud, D. J., and Yussouf, N.: Value of real-time vegetation
fraction to forecasts of severe convection in high-resolution models, Weather
Forecast., 24, 187–210, 2009. a
Jarlan, L., Balsamo, G., Lafont, S., Beljaars, A., Calvet, J.-C., and Mougin,
E.: Analysis of leaf area index in the ECMWF land surface model and impact on
latent heat and carbon fluxes: Application to West Africa, J. Geophys. Res.-Atmos., 113, D24117, https://doi.org/10.1029/2007JD009370, 2008. a
Jiang, L., Kogan, F. N., Guo, W., Tarpley, J. D., Mitchell, K. E., Ek, M. B.,
Tian, Y., Zheng, W., Zou, C.-Z., and Ramsay, B. H.: Real-time weekly global
green vegetation fraction derived from advanced very high resolution
radiometer-based NOAA operational global vegetation index (GVI) system,
J. Geophys. Res.-Atmos., 115, D11114, https://doi.org/10.1029/2009JD013204, 2010. a, b
Jobbágy, E. G., Giménez, R., Marchesini, V., Diaz, Y., Jayawickreme,
D. H., and Nosetto, M. D.: Salt Accumulation and Redistribution in the Dry
Plains of Southern South America: Lessons from Land Use Changes, in: Saline
and Alkaline Soils in Latin America, Springer Nature, Cham, Switzerland, 51–70, 2020. a
Kaufmann, R. K., Zhou, L., Knyazikhin, Y., Shabanov, V., Myneni, R. B., and
Tucker, C. J.: Effect of orbital drift and sensor changes on the time series
of AVHRR vegetation index data, IEEE T. Geosci. Remote, 38, 2584–2597, 2000. a
Kirches, G., Brockmann, C., Boettcher, M., Peters, M., Bontemps, S., Lamarche, C., Schlerf, M., Santoro, M., and Defourny, P.: Land Cover CCI-Product User Guide-Version 2, available at: http://maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-PUG-v2.5.pdf (last access: 15 September 2020), 2014. a, b, c
Knyazikhin, Y., Martonchik, J., Myneni, R. B., Diner, D., and Running, S. W.:
Synergistic algorithm for estimating vegetation canopy leaf area index and
fraction of absorbed photosynthetically active radiation from MODIS and MISR
data, J. Geophys. Res.-Atmos., 103, 32257–32275, 1998. a
Koster, R.: “Efficiency space”: A framework for evaluating joint evaporation and runoff behavior, B. Am. Meteorol. Soc., 96, 393–396, 2015. a
Koster, R. D., Suarez, M. J., 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.-Atmos., 105, 24809–24822, 2000. a
Koster, R. D., Guo, Z., Yang, R., Dirmeyer, P. A., Mitchell, K., and Puma,
M. J.: On the nature of soil moisture in land surface models, J. Climate, 22, 4322–4335, 2009. a
Koster, R. D. and Mahanama, P. P. S.: Land surface controls on hydroclimatic means and variability, J. Hydrometeorol., 13, 1604–1620, 2012. a
Kumar, S.: LIS Framework, available at: https://lis.gsfc.nasa.gov/, last access: 15 July 2021. a
Kumar, A., Chen, F., Barlage, M., Ek, M. B., and Niyogi, D.: Assessing impacts of integrating MODIS vegetation data in the weather research and forecasting (WRF) model coupled to two different canopy-resistance approaches, J. Appl. Meteorol. Clim., 53, 1362–1380, 2014. a
Kumar, S., Holmes, T., Mocko, D., Wang, S., and Peters-Lidard, C.: Attribution of flux partitioning variations between land surface models over the continental us, Remote Sens., 10, 751, https://doi.org/10.3390/rs10050751, 2018. a
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., Lighty, L., Eastman, J. L., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E. F., and Sheffield, J.: Land information system: An interoperable framework for high resolution land surface modeling, Environ. Model. Softw., 21, 1402–1415, 2006. a
Kumar, S. V., Peters-Lidard, C. D., Eastman, J. L., and Tao, W.-K.: An
integrated high-resolution hydrometeorological modeling testbed using LIS and
WRF, Environ. Model. Softw., 23, 169–181, 2008. a
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. a
Kumar, S. V., M. Mocko, D., Wang, S., Peters-Lidard, C. D., and Borak, J.:
Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface
Model: Impacts on Water and Carbon Fluxes and States over the Continental
United States, J. Hydrometeorol., 20, 1359–1377, 2019. a
Lawrence, D. M., Thornton, P. E., Oleson, K. W., and Bonan, G. B.: The
partitioning of evapotranspiration into transpiration, soil evaporation, and
canopy evaporation in a GCM: Impacts on land–atmosphere interaction, J.
Hydrometeorol., 8, 862–880, 2007. a
Maertens, M., De Lannoy, G. J. M., Apers, S., Kumar, S. V., and Mahanama, S. P. P.: Land Surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters, Zenodo, https://doi.org/10.5281/zenodo.5091408, 2021. a
Marchesini, V. A., Nosetto, M. D., Houspanossian, J., and Jobbágy, E. G.:
Contrasting hydrological seasonality with latitude in the South American
Chaco: The roles of climate and vegetation activity, J. Hydrol., 587, 124933, https://doi.org/10.1016/j.jhydrol.2020.124933, 2020. a, b
Martens, B., Miralles, D. G., Lievens, H., van der Schalie, R., de Jeu, R. A. M., Fernández-Prieto, D., Beck, H. E., Dorigo, W. A., and Verhoest, N. E. C.: GLEAM v3: satellite-based land evaporation and root-zone soil moisture, Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, 2017. a
Miller, J., Barlage, M., Zeng, X., Wei, H., Mitchell, K., and Tarpley, D.:
Sensitivity of the NCEP/Noah land surface model to the MODIS green vegetation
fraction data set, Geophys. Res. Lett., 33, L13404, https://doi.org/10.1029/2006GL026636, 2006. a, b
Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A., and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15, 453–469, https://doi.org/10.5194/hess-15-453-2011, 2011. a
Morgan, C. L., Yimam, Y. T., Barlage, M., Gochis, D., and Dornblaser, B.:
Valuing of soil capability in land surface modeling, in: Global Soil
Security, Springer, , Cham, Switzerland, 53–61, 2017. a
Oleson, K., Dai, Y., Bonan, G., Bosilovich, M., Dickinson, R., Dirmeyer, P., Hoffman, F., Houser, P., Levis, S., Niu, G., and Thornton, P.: Technical description of the community land model (CLM), NCAR Technical Note, NCAR, Boulder, USA, 2004. a
Oleson, K. W., Bonan, G. B., Schaaf, C., Gao, F., Jin, Y., and Strahler, A.:
Assessment of global climate model land surface albedo using MODIS data,
Geophys. Res. Lett., 30, 1443, https://doi.org/10.1029/2002GL016749, 2003. a
O'Neill, P., Njoku, E., Jackson, T., Chan, S., and Bindlish, R.: SMAP
algorithm theoretical basis document: Level 2 & 3 soil moisture (passive)
data products, JPL D-66480, Jet Propulsion Lab., California Inst. Technol., Pasadena, CA, USA, 2015. a
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, 2007. a
Pitman, A. J.: The evolution of, and revolution in, land surface schemes
designed for climate models, Int. J. Climatol., 23, 479–510, 2003. a
Pitman, A. J., de Noblet-Ducoudré, N., Cruz, F., Davin, E. L., Bonan, G., Brovkin, V., Claussen, M., Delire, C., Ganzeveld, L., Gayler, V., van den Hurk, B. J. J. M., Lawrence, P.J., van der Molen, M. K., Müller, C., Reick, C. H., Seneviratne, S. I., Strengers, B. J., and Voldoire, A.: Uncertainties in climate responses to past land cover change: First results from the LUCID intercomparison study, Geophys. Res. Lett., 36, L14814, https://doi.org/10.1029/2009GL039076, 2009. a
Priestley, C. H. B. and Taylor, R.: On the assessment of surface heat flux and evaporation using large-scale parameters, Mon. Weather Rev., 100, 81–92, 1972. a
Quets, J., De Lannoy, G. J., Al Yaari, A., Chan, S., Cosh, M. H., Gruber, A.,
Reichle, R. H., Van der Schalie, R., and Wigneron, J.-P.: Uncertainty in soil
moisture retrievals: An ensemble approach using SMOS L-band microwave data,
Remote Sens. Environ., 229, 133–147, 2019. a
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, 2011. a
Reichle, R. H., Draper, C. S., Liu, Q., Girotto, M., Mahanama, S. P., Koster,
R. D., and De Lannoy, G. J.: Assessment of MERRA-2 land surface hydrology
estimates, J. Climate, 30, 2937–2960, 2017. a
Reichle, R. H., Liu, Q., Koster, R. D., Crow, W. T., De Lannoy, G. J., Kimball, J. S., Ardizzone, J. V., Bosch, D., Colliander, A., Cosh, M., Kolassa, J., Mahanama, S. P. P., Prueger, J., Starks, P., and Walker, J. P.: Version 4 of the SMAP Level-4 Soil Moisture Algorithm and Data Product, J. Adv. Model. Earth Syst., 11, 3106–3130, 2019. a
Rigden, A. J., Salvucci, G. D., Entekhabi, D., and Short Gianotti, D. J.:
Partitioning evapotranspiration over the continental United States using
weather station data, Geophys. Res. Lett., 45, 9605–9613, 2018. a
Ruiz-Vasquez, M., Arias, P. A., Martínez, J. A., and Espinoza, J. C.:
Effects of Amazon basin deforestation on regional atmospheric circulation and
water vapor transport towards tropical South America, Clim. Dynam., 54, 4169–4189, 2020. a
Sabater, J. M., Rüdiger, C., Calvet, J.-C., Fritz, N., Jarlan, L., and
Kerr, Y.: Joint assimilation of surface soil moisture and LAI observations
into a land surface model, Agr. Forest Meteorol., 148, 1362–1373, 2008. a
Sellers, P., Mintz, Y., Sud, Y. E. A., and Dalcher, A.: A simple biosphere
model (SiB) for use within general circulation models, J. Atmos. Sci., 43, 505–531, 1986. a
Thibeault, M., Cáceres, J. M., Dadamia, D., Soldano, A. G., Quirno, M. U., Guerrieri, J. M., Edrosa, R., Palomeque, M., Romaldi, L., Pucheta, J., Mogadouro, J., De Luca, E., Bustos, S., Agüero, S., Pascual, I., and Mariotti, M.: Spatial and temporal analysis of the Monte Buey SAOCOM and SMAP core site, in: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 26 July 2015, Milan, Italy, 969–971, 2015. a
Villegas, J. C., Breshears, D. D., Zou, C. B., and Law, D. J.: Ecohydrological controls of soil evaporation in deciduous drylands: how the hierarchical effects of litter, patch and vegetation mosaic cover interact with phenology and season, J. Arid Environ., 74, 595–602, 2010. a
Wang, J. R. and Schmugge, T. J.: An Empirical Model for the Complex Dielectric Permittivity of Soils as a Function of Water Content, IEEE T. Geosci. Remote, GE-18, 288–295, 1980. a
Wang, K. and Dickinson, R. E.: A review of global terrestrial evapotranspiration: Observation, modeling, climatology, and climatic
variability, Rev. Geophys., 50, RG2005, https://doi.org/10.1029/2011RG000373, 2012. a
Wei, H., Xia, Y., Mitchell, K. E., and Ek, M. B.: Improvement of the Noah land surface model for warm season processes: Evaluation of water and energy flux simulation, Hydrol. Process., 27, 297–303, 2013. a
Wiekenkamp, I., Huisman, J. A., Bogena, H. R., and Vereecken, H.: Effects of
Deforestation on Water Flow in the Vadose Zone, Water, 12, 35, https://doi.org/10.3390/w12010035, 2020. a
Wood, E. F., Roundy, J. K., Troy, T. J., Van Beek, L., Bierkens, M. F., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis, D., van de giesen, N., Houser, P., Jaffé, P., Kollet, S., Lehner, B., Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., and Whitehead, P.: Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011. a
Wösten, J., Lilly, A., Nemes, A., and Le Bas, C.: Development and use of a database of hydraulic properties of European soils, Geoderma, 90, 169–185,
1999. a
Wösten, J., Pachepsky, Y. A., and Rawls, W.: Pedotransfer functions:
bridging the gap between available basic soil data and missing soil hydraulic
characteristics, J. Hydrol., 251, 123–150, 2001. a
Zeng, X.: Global vegetation root distribution for land modeling, J. Hydrometeorol., 2, 525–530, 2001. a
Zhang, B., Xia, Y., Long, B., Hobbins, M., Zhao, X., Hain, C., Li, Y., and
Anderson, M. C.: Evaluation and comparison of multiple evapotranspiration data models over the contiguous United States: Implications for the next
phase of NLDAS (NLDAS-Testbed) development, Agr. Forest Meteorol., 280, 107810, https://doi.org/10.1016/j.agrformet.2019.107810, 2020. a
Zhao, H., Zeng, Y., Lv, S., and Su, Z.: Analysis of soil hydraulic and thermal properties for land surface modeling over the Tibetan Plateau, Earth Syst. Sci. Data, 10, 1031–1061, https://doi.org/10.5194/essd-10-1031-2018, 2018. a
Zhu, Z., Bi, J., Pan, Y., Ganguly, S., Anav, A., Xu, L., Samanta, A., Piao, S., Nemani, R., and Myneni, R.: Global data sets of vegetation leaf area index (LAI) 3g and fraction of photosynthetically active radiation (FPAR) 3g
derived from global inventory modeling and mapping studies (GIMMS) normalized
difference vegetation index (NDVI3g) for the period 1981 to 2011, Remote Sens., 5, 927–948, 2013. a
Zwieback, S., Colliander, A., Cosh, M. H., Martínez-Fernández, J., McNairn, H., Starks, P. J., Thibeault, M., and Berg, A.: Estimating time-dependent vegetation biases in the SMAP soil moisture product, Hydrol. Earth Syst. Sci., 22, 4473–4489, https://doi.org/10.5194/hess-22-4473-2018, 2018. a
Short summary
In this study, we simulated the water balance over the South American Dry Chaco and assessed the impact of land cover changes thereon using three different land surface models. Our simulations indicated that different models result in a different partitioning of the total water budget, but all showed an increase in soil moisture and percolation over the deforested areas. We also found that, relative to independent data, no specific land surface model is significantly better than another.
In this study, we simulated the water balance over the South American Dry Chaco and assessed the...
