Articles | Volume 26, issue 9
https://doi.org/10.5194/hess-26-2365-2022
© Author(s) 2022. 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-26-2365-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 May 2022
Research article |
| 06 May 2022
| 06 May 2022
Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco
Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
NASA Goddard Earth and Sciences Technology and Research (GESTAR), Greenbelt, Maryland, USA
Sujay V. Kumar
Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Kristi R. Arsenault
Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Science Applications International Corporation, McLean, Virginia, USA
Christa D. Peters-Lidard
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Iliana E. Mladenova
USDA Foreign Agricultural Service, Washington, DC, USA
Karim Bergaoui
ACQUATEC Solutions, Dubai Technology Entrepreneur Campus (DTEC), Dubai Silicon Oasis, Dubai, UAE
Abheera Hazra
Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
Benjamin F. Zaitchik
Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
Sarith P. Mahanama
Science Systems and Applications Inc., Lanham, Maryland, USA
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Rachael McDonnell
Water, Climate Change and Resilience Program, International Water Management Institute – Rome office, Rome, Italy
David M. Mocko
Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Science Applications International Corporation, McLean, Virginia, USA
Mahdi Navari
Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
Related authors
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2058, https://doi.org/10.5194/egusphere-2025-2058, 2025
Short summary
Short summary
The GRACE 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.
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
Short summary
Short summary
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
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.
Bailing Li, Abheera Hazra, Amy McNally, Kimberly Slinski, Shraddhanand Shukla, and Weston Anderson
EGUsphere, https://doi.org/10.5194/egusphere-2025-4198, https://doi.org/10.5194/egusphere-2025-4198, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Accurately predicting total water storage in soils and aquifers is vital for managing droughts, floods and food insecurity. This study evaluates subseasonal to seasonal forecasts of terrestrial water storage (TWS) across Africa using satellite gravity data. Results show that models with better groundwater representation provide more reliable TWS forecasts. The findings highlight the importance of satellite observations for improving water resource prediction.
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025, https://doi.org/10.5194/gmd-18-4293-2025, 2025
Short summary
Short summary
This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.
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
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2058, https://doi.org/10.5194/egusphere-2025-2058, 2025
Short summary
Short summary
The GRACE 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.
Cenlin He, Tzu-Shun Lin, David M. Mocko, Ronnie Abolafia-Rosenzweig, Jerry W. Wegiel, and Sujay V. Kumar
EGUsphere, https://doi.org/10.5194/egusphere-2024-4176, https://doi.org/10.5194/egusphere-2024-4176, 2025
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Justin Schulte, Frederick Policelli, and Benjamin Zaitchik
Nonlin. Processes Geophys., 29, 1–15, https://doi.org/10.5194/npg-29-1-2022, https://doi.org/10.5194/npg-29-1-2022, 2022
Short summary
Short summary
The skewness of a time series is commonly used to quantify the extent to which positive (negative) deviations from the mean are larger than negative (positive) ones. However, in some cases, traditional skewness may not provide reliable information about time series skewness, motivating the development of a waveform skewness index in this paper. The waveform skewness index is used to show that changes in the relationship strength between climate time series could arise from changes in skewness.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Michiel Maertens, Gabriëlle J. M. De Lannoy, Sebastian Apers, Sujay V. Kumar, and Sarith P. P. Mahanama
Hydrol. Earth Syst. Sci., 25, 4099–4125, https://doi.org/10.5194/hess-25-4099-2021, https://doi.org/10.5194/hess-25-4099-2021, 2021
Short summary
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.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
Short summary
Short summary
High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
Mahmoud Osman, Benjamin F. Zaitchik, Hamada S. Badr, Jordan I. Christian, Tsegaye Tadesse, Jason A. Otkin, and Martha C. Anderson
Hydrol. Earth Syst. Sci., 25, 565–581, https://doi.org/10.5194/hess-25-565-2021, https://doi.org/10.5194/hess-25-565-2021, 2021
Short summary
Short summary
Our study of flash droughts' definitions over the United States shows that published definitions yield markedly different inventories of flash drought geography and frequency. Results suggest there are several pathways that can lead to events that are characterized as flash droughts. Lack of consensus across definitions helps to explain apparent contradictions in the literature on trends and indicates the selection of a definition is important for accurate monitoring of different mechanisms.
Yifan Zhou, Benjamin F. Zaitchik, Sujay V. Kumar, Kristi R. Arsenault, Mir A. Matin, Faisal M. Qamer, Ryan A. Zamora, and Kiran Shakya
Hydrol. Earth Syst. Sci., 25, 41–61, https://doi.org/10.5194/hess-25-41-2021, https://doi.org/10.5194/hess-25-41-2021, 2021
Short summary
Short summary
South and Southeast Asia face significant food insecurity and hydrological hazards. Here we introduce a South and Southeast Asia hydrological monitoring and sub-seasonal to seasonal forecasting system (SAHFS-S2S) to help local governments and decision-makers prepare for extreme hydroclimatic events. The monitoring system captures soil moisture variability well in most regions, and the forecasting system offers skillful prediction of soil moisture variability 2–3 months in advance, on average.
Justin Schulte, Frederick Policielli, and Benjamin Zaitchik
Hydrol. Earth Syst. Sci., 24, 5473–5489, https://doi.org/10.5194/hess-24-5473-2020, https://doi.org/10.5194/hess-24-5473-2020, 2020
Short summary
Short summary
Wavelet coherence is now a commonly used method for detecting scale-dependent relationships between time series. In this study, the concept of wavelet coherence is generalized to higher-order wavelet coherence methods that quantify the relationship between higher-order statistical moments associated with two time series. The methods are applied to the El Niño–Southern Oscillation (ENSO) and the Indian monsoon to show that the ENSO–Indian monsoon relationship is impacted by ENSO nonlinearity.
Cited articles
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.
Arsenault, K. R., Kumar, S. V., Geiger, J. V., Wang, S., Kemp, E., Mocko, D. M., Beaudoing, H. K., Getirana, A., Navari, M., Li, B., Jacob, J., Wegiel, J., and Peters-Lidard, C. D.: The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems, Geosci. Model Dev., 11, 3605–3621, https://doi.org/10.5194/gmd-11-3605-2018, 2018.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal
conductance and its contribution to the control of photosynthesis under
different environmental conditions, Prog. Photosynthes. Res., 221–224, 1987.
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.
Bastiaanssen, W., Cheema, M., Immerzeel, W. W., Miltenburg, I. J., and Pelgrum, H.: Surface energy balance and actual evapotranspiration of the
transboundary Indus Basin estimated from satellite measurements and the
ETLook model, Water Resour. Res., 48, W11512, https://doi.org/10.1029/2011WR010482, 2012.
Bergaoui, K., Mitchell, D., Otto, F., Allen, M., Zaaboul, R., and McDonnell,
R.: The contribution of human-induced climate change to the drought of 2014
in the southern Levant region, B. Am. Meteorol. Soc., 96, S66–S70, 2015.
Bhaga, T. D., Dube, T., Shekede, M. D., and Shoko, C.: Impacts of Climate
Variability and Drought on Surface Water Resources in Sub-Saharan Africa
Using Remote Sensing: A Review, Remote Sens., 12, 4184, https://doi.org/10.3390/rs12244184, 2020.
Bijaber, N., El Hadani, D., Saidi, M., Svoboda, M., Wardlow, B., Hain, C.,
Poulsen, C., Yessef, M., and Rochdi, A.: Developing a Remotely Sensed Drought
Monitoring Indicator for Morocco, Geosciences, 8, 55, https://doi.org/10.3390/geosciences8020055, 2018.
Blatchford, M. L., Mannaerts, C. M., Njuki, S. M., Nouri, H., Zeng, Y.,
Pelgrum, H., Wonink, S., and Karimi, P.: Evaluation of WaPOR V2 evapotranspiration products across Africa, Hydrol. Process., 3, 1–22, https://doi.org/10.1002/hyp.13791, 2020.
Bolten, J. D., Crow, W. T., Zhan, X., Jackson, T. J., and Reynolds, C. A.:
Evaluating the utility of remotely sensed soil moisture retrievals for
operational agricultural drought monitoring, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 3, 57–66, 2009.
Bonan, G. B.: Land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: Technical description and users
guide, Technical note No. PB-97-131494/XAB; NCAR/TN-417-STR, National
Center for Atmospheric Research, Climate and Global Dynamics Div., Boulder, CO, USA, http://n2t.net/ark:/85065/d78c9vm7 (last access: 14 July 2020), 1996.
Brocca, L., Crow, W. T., Ciabatta, L., Massari, C., De Rosnay, P., Enenkel,
M., Hahn, S., Amarnath, G., Camici, S., and Tarpanelli, A.: A review of the
applications of ASCAT soil moisture products, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 10, 2285–2306, 2017.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and
environmental regulation of stomatal conductance, photosynthesis and
transpiration: a model that includes a laminar boundary layer, Agr. Forest Meteorol., 54, 107–136, 1991.
Cook, B. I., Mankin, J. S., and Anchukaitis, K. J.: Climate change and
drought: From past to future, Curr. Clim. Change Rep., 4, 164–179, 2018.
Crow, W. T., Gomez, C. A., Sabater, J. M., Holmes, T., Hain, C. R., Lei, F.,
Dong, J., Alfieri, J. G., and Anderson, M. C.: Soil Moisture–Evapotranspiration Overcoupling and L-Band Brightness Temperature
Assimilation: Sources and Forecast Implications, J. Hydrometeorol., 21, 2359–2374, https://doi.org/10.1175/JHM-D-20-0088.1, 2020.
Cui, C., Xu, J., Zeng, J., Chen, K.-S., Bai, X., Lu, H., Chen, Q., and Zhao,
T.: Soil moisture mapping from satellites: An intercomparison of SMAP, SMOS,
FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial
scales, Remote Sens., 10, 33, https://doi.org/10.3390/rs10010033, 2018.
Das, N. N., Entekhabi, D., Dunbar, R. S., Chaubell, M. J., Colliander, A.,
Yueh, S., Jagdhuber, T., Chen, F., Crow, W., and O'Neill, P. E.: The SMAP and
Copernicus Sentinel 1A/B microwave active-passive high resolution surface
soil moisture product, Remote Sens. Environ., 233, 111380, https://doi.org/10.1016/j.rse.2019.111380, 2019.
De Lannoy, G. J. M. and Reichle, R. H.: Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model, Hydrol. Earth Syst. Sci., 20, 4895–4911, https://doi.org/10.5194/hess-20-4895-2016, 2016.
Derber, J. C., Parrish, D. F., and Lord, S. J.: The new global operational
analysis system at the National Meteorological Center, Weather Forecast., 6, 538–547, 1991.
Dickinson, R. E., Shaikh, M., and Climate, R. B. O.: Interactive canopies for a climate model, J. Climate, 11, 2823–2836, 1998.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: 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.
Eller, C. B., Rowland, L., Mencuccini, M., Rosas, T., Williams, K., Harper,
A., Medlyn, B. E., Wagner, Y., Klein, T., Teodoro, G. S., Oliveira, R. S.,
Matos, I. S., Rosado, B. H. P., Fuchs, K., Wohlfahrt, G., Montagnani, L.,
Meir, P., Sitch, S., and Cox, P. M.: Stomatal optimization based on xylem
hydraulics (SOX) improves land surface model simulation of vegetation
responses to climate, New Phytol., 226, 1622–1637, https://doi.org/10.1111/nph.16419,
2020.
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T.,
Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., and Johnson,
J.: The soil moisture active passive (SMAP) mission, Proc. IEEE, 98, 704–716, 2010.
FAO: WaPOR Database Methodology: Version 2 release, April 2020, FAO, Rome,
1–91, https://doi.org/10.4060/ca9894en, 2020.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., and Roth, L.: The shuttle radar
topography mission, Rev. Geophys., 45, 1–33, https://doi.org/10.1029/2005RG000183, 2007.
Felfelani, F., Pokhrel, Y., Guan, K., and Lawrence, D. M.: Utilizing SMAP
soil moisture data to constrain irrigation in the Community Land Model,
Geophys. Res. Lett., 45, 12–892–12–902, 2018.
Fisher, R. A.: On the Probable Error of a Coefficient of Correlation Deduced
from a Small Sample, Metron, 1, 1–32, 1921.
Fragaszy, S. R., Jedd, T., Wall, N., Knutson, C., Belhaj Fraj, M., Bergaoui,
K., Svoboda, M., Hayes, M., and McDonnell, R.: Drought Monitoring in the
Middle East and North Africa (MENA) Region: Participatory Engagement to
Inform Early Warning Systems, B. Am. Meteorol. Soc., 101, E1148–E1173, https://doi.org/10.1175/BAMS-D-18-0084.1, 2020.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N.,
Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm
refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, 2010.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., and Hoell, A.: The climate hazards
infrared precipitation with stations – a new environmental record for monitoring extremes, Scient. Data, 2, 1–21, 2015.
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.
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A.,
Frankenberg, C., Huete, A. R., Zarco-Tejada, P., and Lee, J.-E.: Global and
time-resolved monitoring of crop photosynthesis with chlorophyll
fluorescence, P. Natl. Acad. Sci. USA, 111, E1327–E1333, 2014.
Hayes, M. J., Svoboda, M. D., Wardlow, B. D., Anderson, M. C., and Kogan, F.:
Drought monitoring: Historical and current perspectives, Digital Commons @ University of Nebraska, Lincoln, https://digitalcommons.unl.edu/droughtfacpub/94 (last access: 14 July 2020), 2012.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., and
Bauer-Marschallinger, B.: SoilGrids250m: Global gridded soil information
based on machine learning, PLoS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Hssaisoune, M., Bouchaou, L., Sifeddine, A., Bouimetarhan, I., and Chehbouni,
A.: Moroccan Groundwater Resources and Evolution with Global Climate Changes, Geosciences, 12, 81, https://doi.org/10.3390/geosciences10020081, 2020.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P.,
and Yoo, S.-H.: NASA global precipitation measurement (GPM) integrated
multi-satellite retrievals for GPM (IMERG), Algorithm Theoretical Basis
Document (ATBD) Version 4, NASA/GSFC, 26 pp., 2015.
Ines, A. V. M., Das, N. N., Hansen, J. W., and Njoku, E. G.: Assimilation of
remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction, Remote Sens. Environ., 138, 149–164,
https://doi.org/10.1016/j.rse.2013.07.018, 2013.
Jalilvand, E., Abolafia-Rosenzweig, R., Tajrishy, M., and Das, N. N.: Evaluation of SMAP/Sentinel 1 High-resolution soil moisture data to detect
irrigation over agricultural domain, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 14, 10733–10747, 2021.
Jedd, T., Fragaszy, S. R., Knutson, C., Hayes, M. J., Fraj, M. B., Wall, N.,
Svoboda, M., and McDonnell, R.: Drought Management Norms: Is the Middle East
and North Africa Region Managing Risks or Crises?, J. Environ. Dev., 116, 107049652096020-38, https://doi.org/10.1177/1070496520960204, 2020.
Joiner, J., Guanter, L., Lindstrot, R., Voigt, M., Vasilkov, A. P., Middleton, E. M., Huemmrich, K. F., Yoshida, Y., and Frankenberg, C.: Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2, Atmos. Meas. Tech., 6, 2803–2823, https://doi.org/10.5194/amt-6-2803-2013, 2013.
Joiner, J., Yoshida, Y., Zhang, Y., Duveiller, G., Jung, M., Lyapustin, A.,
Wang, Y., and Tucker, C.: Estimation of Terrestrial Global Gross Primary
Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux
Data, Remote Sens., 10, 1346, https://doi.org/10.3390/rs10091346, 2018.
Kandasamy, S., Baret, F., Verger, A., Neveux, P., and Weiss, M.: A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products, Biogeosciences, 10, 4055–4071, https://doi.org/10.5194/bg-10-4055-2013, 2013.
Kerr, Y. H., Wigneron, J. P., Al Bitar, A., Mialon, A., and Srivastava, P. K.: Soil moisture from space: Techniques and limitations, Satellite Soil
Moisture Retrieval, 3–27, https://doi.org/10.1016/B978-0-12-803388-3.00001-2, 2016.
Kharrou, M. H., Er-Raki, S., Chehbouni, A., Duchemin, B., Simonneaux, V.,
LePage, M., Ouzine, L., and Jarlan, L.: Water use efficiency and yield of
winter wheat under different irrigation regimes in a semi-arid region, Agricult. Sci., 2, 273–282, https://doi.org/10.4236/as.2011.23036, 2011.
Kolassa, J., Reichle, R., Liu, Q., Cosh, M., Bosch, D., Caldwell, T., Colliander, A., Holifield Collins, C., Jackson, T., Livingston, S., Moghaddam, M., and Starks, P.: Data Assimilation to Extract Soil Moisture
Information from SMAP Observations, Remote Sens., 9, 1179, https://doi.org/10.3390/rs9111179, 2017.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., Lighty, L., Eastman, J. L., Doty, B., and Dirmeyer, P.: Land
information system: An interoperable framework for high resolution land
surface modeling, Environ. Model. Softw., 21, 1402–1415, 2006.
Kumar, S. V., Reichle, R. H., Harrison, K. W., Peters-Lidard, C. D.,
Yatheendradas, S., and Santanello, J. A.: A comparison of methods for a priori bias correction in soil moisture data assimilation, Water Resour. Res., 48, W03515, https://doi.org/10.1029/2010WR010261, 2012.
Kumar, S. V., Peters-Lidard, C. D., Mocko, D., Reichle, R., Liu, Y., Arsenault, K. R., Xia, Y., Ek, M., Riggs, G., and Livneh, B.: Assimilation of
remotely sensed soil moisture and snow depth retrievals for drought estimation, J. Hydrometeorol., 15, 2446–2469, 2014.
Kumar, S. V., Zaitchik, B. F., Peters-Lidard, C. D., Rodell, M., Reichle,
R., Li, B., Jasinski, M., Mocko, D., Getirana, A., and De Lannoy, G.:
Assimilation of gridded GRACE terrestrial water storage estimates in the
North American Land Data Assimilation System, J. Hydrometeorol., 17, 1951–1972, 2016.
Kumar, S. V., Dirmeyer, P. A., Peters-Lidard, C. D., Bindlish, R., and
Bolten, J.: Information theoretic evaluation of satellite soil moisture
retrievals, Remote Sens. Environ., 204, 392–400, 2018.
Kumar, S. V., Jasinski, M., Mocko, D. M., Rodell, M., Borak, J., Li, B.,
Beaudoing, H. K., and Peters-Lidard, C. D.: NCA-LDAS land analysis: Development and performance of a multisensor, multivariate land data
assimilation system for the National Climate Assessment, J. Hydrometeorol., 20, 1571–1593, 2019a.
Kumar, S. V., 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, https://doi.org/10.1175/JHM-D-18-0237.1, 2019b.
Kumar, S. V., Holmes, T. R., Bindlish, R., de Jeu, R., and Peters-Lidard, C.: Assimilation of vegetation optical depth retrievals from passive microwave radiometry, Hydrol. Earth Syst. Sci., 24, 3431–3450, https://doi.org/10.5194/hess-24-3431-2020, 2020.
Lawston, P. M., Santanello Jr., J. A., and Kumar, S. V.: Irrigation signals
detected from SMAP soil moisture retrievals, Geophys. Res. Lett., 44, 11-860–11-867, 2017.
Lei, F., Crow, W. T., Kustas, W. P., Dong, J., Yang, Y., Knipper, K. R.,
Anderson, M. C., Gao, F., Notarnicola, C., Greifeneder, F., McKee, L. M., Alfieri, J. G., Hain, C., and Dokoozlian, N.: Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard, Remote Sens. Environ., 239, 111622, https://doi.org/10.1016/j.rse.2019.111622, 2020.
Li, C., Lu, H., Yang, K., Han, M., Wright, J. S., Chen, Y., Yu, L., Xu, S.,
Huang, X., and Gong, W.: The evaluation of SMAP enhanced soil moisture
products using high-resolution model simulations and in-situ observations on
the Tibetan Plateau, Remote Sens., 10, 535, https://doi.org/10.3390/rs10040535, 2018.
Lievens, H., Reichle, R. H., Liu, Q., De Lannoy, G. J. M., Dunbar, R. S.,
Kim, S. B., Das, N. N., Cosh, M., Walker, J. P., and Wagner, W.: Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates,
Geophys. Res. Lett., 44, 6145–6153, https://doi.org/10.1002/2017GL073904, 2017.
Liu, Q., Reichle, R. H., Bindlish, R., Cosh, M. H., Crow, W. T., de Jeu, R.,
De Lannoy, G. J., Huffman, G. J., and Jackson, T. J.: 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, 2011.
Liu, X., Chen, F., Barlage, M., Zhou, G., and Niyogi, D.: Noah-MP-Crop:
Introducing dynamic crop growth in the Noah-MP land surface model, J. Geophys. Res.-Atmos., 121, 13953–13972, https://doi.org/10.1002/2016JD025597, 2016.
Liu, Y., Kumar, M., Katul, G. G., Feng, X., and Konings, A. G.: Plant
hydraulics accentuates the effect of atmospheric moisture stress on
transpiration, Nat. Clim. Change, 10, 691–695, https://doi.org/10.1038/s41558-020-0781-5, 2020.
Meier, J., Zabel, F., and Mauser, W.: A global approach to estimate irrigated areas – a comparison between different data and statistics, Hydrol. Earth Syst. Sci., 22, 1119–1133, https://doi.org/10.5194/hess-22-1119-2018, 2018.
Mocko, D. M., Kumar, S. V., Peters-Lidard, C. D., and Wang, S.: Assimilation
of vegetation conditions improves the representation of drought over
agricultural areas, J. Hydrometeorol., 22, 1085–1098, https://doi.org/10.1175/JHM-D-20-0065.1, 2021.
MODIS15: MODIS Collectio 6 (C6) LAI/FPAR Product User's Guide, p. 13,
https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf, last access: 20 July 2020.
Molle, F. and Sanchis-Ibor, C.: Irrigation policies in the Mediterranean:
Trends and challenges, Irrigation in the Mediterranean, Springer, Cham,
279–313, ISBN 13 978-3030036966, 2019.
Müller, M. F., Müller-Itten, M. C., and Gorelick, S. M.: How Jordan and Saudi Arabia are avoiding a tragedy of the commons over shared groundwater, Water Resour. Res., 53, 5451–5468, 2017.
Myneni, R., Knyazikhin, Y., and Park, T.: MCD15A2H MODIS/Terra+Aqua Leaf
Area Index/FPAR 8-day L4 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD15A2H.061, 2015.
Nearing, G., Yatheendradas, S., Crow, W., Zhan, X., Liu, J., and Chen, F.:
The efficiency of data assimilation, Water Resour. Res., 54, 6374–6392, 2018.
Nie, W., Zaitchik, B. F., Rodell, M., Kumar, S. V., Anderson, M. C., and
Hain, C.: Groundwater withdrawals under drought: Reconciling GRACE and land
surface models in the United States High Plains Aquifer, Water Resour. Res.,
54, 5282–5299, 2018.
Nie, W., Kumar, S. V., Arsenault, K. R., Peters-Lidard, C. D., Mladenova, I. E., Bergaoui, K., Hazra, A., Zaitchik, B. F., Mahanama, S. P., McDonnell, R., Mocko, D. M., and Navari, M.: Data associated with the publication: Towards effective drought monitoring in the Middle East and North Africa (MENA) region: Implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco, V1, Johns Hopkins University Data Archive [data set], https://doi.org/10.7281/T1/X4MXHC, 2022.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, 1381–1419,
https://doi.org/10.1029/2010JD015139, 2011.
Niu, G. Y., Fang, Y. H., Chang, L. L., Jin, J., Yuan, H., and Zeng, X.:
Enhancing the Noah-MP Ecosystem Response to Droughts with an Explicit
Representation of Plant Water Storage Supplied by Dynamic Root Water Uptake,
J. Adv. Model. Earth Syst., 12, 1–29, https://doi.org/10.1029/2020MS002062, 2020.
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., and
Chaubell, J.: SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil
Moisture, Version 4, NASA National Snow and Ice Data Center Distributed
Active Archive Center, Boulder, Colorado, USA, https://doi.org/10.5067/T90W6VRLCBHI, 2020.
Ozdogan, M., Rodell, M., Beaudoing, H. K., and Toll, D. L.: Simulating the
effects of irrigation over the United States in a land surface model based on satellite-derived agricultural data, J. Hydrometeorol., 11, 171–184, 2010.
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ, R., Church, J. A., Clarke, L., Dahe, Q., and Dasgupta, P.: Climate change 2014: synthesis report, in: Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change, IPCC, 2014.
Pulwarty, R. S. and Sivakumar, M. V. K.: Information systems in a changing
climate_Early warnings and drought risk management, Weather Clim. Extrem., 3, 14–21, https://doi.org/10.1016/j.wace.2014.03.005, 2014.
Rajsekhar, D. and Gorelick, S. M.: Increasing drought in Jordan: Climate
change and cascading Syrian land-use impacts on reducing transboundary flow,
Sci. Adv., 3, e1700581, https://doi.org/10.1126/sciadv.1700581, 2017.
Reichle, R. H.: Data assimilation methods in the Earth sciences, Adv. Water Resour., 31, 1411–1418, 2008.
Reichle, R. H. and Koster, R. D.: Bias reduction in short records of satellite soil moisture, Geophys. Res. Lett., 31, L19501, https://doi.org/10.1029/2004GL020938, 2004.
Reichle, R. H., McLaughlin, D. B., and Entekhabi, D.: Hydrologic data
assimilation with the ensemble Kalman filter, Mon. Weather Rev., 130, 103–114, 2002.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., and Kim, G.-K.:
MERRA: NASA's modern-era retrospective analysis for research and applications, J. Climate, 24, 3624–3648, 2011.
Rossini, M., Nedbal, L., Guanter, L., Ač, A., Alonso, L., Burkart, A.,
Cogliati, S., Colombo, R., Damm, A., and Drusch, M.: Red and far red Sun-induced chlorophyll fluorescence as a measure of plant photosynthesis,
Geophys. Res. Lett., 42, 1632–1639, 2015.
Ryu, D., Crow, W. T., Zhan, X., and Jackson, T. J.: Correcting unintended
perturbation biases in hydrologic data assimilation, J. Hydrometeorol., 10, 734–750, 2009.
Salmon, J. M., Friedl, M. A., Frolking, S., Wisser, D., and Douglas, E. M.:
Global rain-fed, irrigated, and paddy croplands: A new high resolution map
derived from remote sensing, crop inventories and climate data, Int. J. Appl. Earth Obs. Geoinf., 38, 321–334, 2015.
Sheffield, J., Xia, Y., Luo, L., Wood, E. F., Ek, M., and Mitchell, K. E.:
Drought monitoring with the North American Land Data Assimilation System (NLDAS): A framework for merging model and satellite data for improved
drought monitoring, Remote Sensing of Drought: Innovative Monitoring
Approaches, Taylor & Friends Group, 227–259, https://doi.org/10.1201/b11863, 2012.
Stanke, C., Kerac, M., Prudhomme, C., Medlock, J., and Murray, V.: Health
effects of drought: a systematic review of the evidence, PLoS Currents, 5,
https://doi.org/10.1371/currents.dis.7a2cee9e980f91ad7697b570, 2013.
Tavakol, A., Rahmani, V., Quiring, S. M., and Kumar, S. V.: Evaluation
analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States, Remote Sen. Environ., 229, 234–246, 2019.
Thenkabail, P. S., Biradar, C. M., Noojipady, P., Dheeravath, V., Li, Y.,
Velpuri, M., Gumma, M., Gangalakunta, O. R. P., Turral, H., and Cai, X.:
Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium, Int. J. Remote Sens., 30, 3679–3733, 2009.
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016.
Verner, D., Treguer, D., Redwood, J., Christensen, J., McDonnell, R.,
Elbert, C., Konishi, Y., and Belghazi, S.: Climate Variability, Drought, and
Drought Management in Morocco's Agricultural Sector, World Bank, https://doi.org/10.1596/30603, 2018.
Weerasinghe, I., Bastiaanssen, W., Mul, M., Jia, L., and van Griensven, A.:
Can we trust remote sensing evapotranspiration products over Africa?, Hydrol.
Earth Syst. Sci., 24, 1565–1586, https://doi.org/10.5194/hess-24-1565-2020, 2020.
Weinthal, E., Zawahri, N., and Sowers, J.: Securitizing water, climate, and
migration in Israel, Jordan, and Syria, Intern. Environ. Agreements, 15, 293–307, 2015.
Xie, Y., Wang, P., Bai, X., Khan, J., Zhang, S., Li, L., and Wang, L.:
Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the
CERES-Wheat model, Agr.d Forest Meteorol., 246, 194–206, 2017.
Yan, K., Park, T., Yan, G., Chen, C., Yang, B., Liu, Z., Nemani, R. R.,
Knyazikhin, Y., and Myneni, R. B.: Evaluation of MODIS LAI/FPAR product
collection 6. Part 1: Consistency and improvements, Remote Sens., 8, 359, https://doi.org/10.3390/rs8050359, 2016a.
Yan, K., Park, T., Yan, G., Liu, Z., Yang, B., Chen, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B.: Evaluation of MODIS LAI/FPAR product
collection 6. Part 2: Validation and intercomparison, Remote Sens., 8, 460, https://doi.org/10.3390/rs8060460, 2016b.
Yang, Z. L., Niu, G. Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Longuevergne, L., Manning, K., Niyogi, D., and Tewari, M.: The community Noah
land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Zhang, R., Kim, S., and Sharma, A.: A comprehensive validation of the SMAP
Enhanced Level-3 Soil Moisture product using ground measurements over varied
climates and landscapes, Remote Sens. Environ., 223, 82–94, 2019.
Short summary
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.
The MENA (Middle East and North Africa) region faces significant food and water insecurity and...
