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NASA NTRS2021

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods toforecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

Machine Learning↗

NASA NTRS2013

The Utility of the Real-Time NASA Land Information System Data for Drought Monitoring Applications

Measurements of soil moisture are a crucial component for the proper monitoring of drought conditions. The large spatial variability of soil moisture complicates the problem. Unfortunately, in situ soil moisture observing networks typically consist of sparse point observations, and conventional numerical model analyses of soil moisture used to diagnose drought are of coarse spatial resolution. Decision support systems such as the U.S. Drought Monitor contain drought impact resolution on sub-county scales, which may not be supported by the existing soil moisture networks or analyses. The NASA Land Information System, which is run with 3 km grid spacing over the eastern United States, has demonstrated utility for monitoring soil moisture. Some of the more useful output fields from the Land Information System are volumetric soil moisture in the 0-10 cm and 40-100 cm layers, column-integrated relative soil moisture, and the real-time green vegetation fraction derived from MODIS (Moderate Resolution Imaging Spectroradiometer) swath data that are run within the Land Information System in place of the monthly climatological vegetation fraction. While these and other variables have primarily been used in local weather models and other operational forecasting applications at National Weather Service offices, the use of the Land Information System for drought monitoring has demonstrated utility for feedback to the Drought Monitor. Output from the Land Information System is currently being used at NWS Huntsville to assess soil moisture, and to provide input to the Drought Monitor. Since feedback to the Drought Monitor takes place on a weekly basis, weekly difference plots of column-integrated relative soil moisture are being produced by the NASA Short-term Prediction Research and Transition Center and analyzed to facilitate the process. In addition to the Drought Monitor, these data are used to assess drought conditions for monthly feedback to the Alabama Drought Monitoring and Impact Group and the Tennessee Drought Task Force, which are comprised of federal, state, and local agencies and other water resources professionals.

White, Kristopher D.↗

NASA NTRS2007

Solutions Network Formulation Report. Visible/Infrared Imager/Radiometer Suite and Advanced Microwave Scanning Radiometer Data Products for National Drought Monitor Decision Support

Drought effects are either direct or indirect depending on location, population, and regional economic vitality. Common direct effects of drought are reduced crop, rangeland, and forest productivity; increased fire hazard; reduced water levels; increased livestock and wildlife mortality rates; and damage to wildlife and fish habitat. Indirect impacts follow on the heels of direct impacts. For example, a reduction in crop, rangeland, and forest productivity may result in reduced income for farmers and agribusiness, increased prices for food and timber, unemployment, reduced tax revenues, increased crime, foreclosures on bank loans to farmers and businesses, migration, and disaster relief programs. In the United States alone, drought is estimated to result in annual losses of between $6 - 8 billion. Recent sustained drought in the United States has made decision-makers aware of the impacts of climate change on society and environment. The eight major droughts that occurred in the United States between 1980 and 1999 accounted for the largest percentage of weather-related monetary losses. Monitoring drought and its impact that occurs at a variety of scales is an important government activity -- not only nationally but internationally as well. The NDMC (National Drought Mitigation Center) and the USDA (U.S. Department of Agriculture) RMA (Risk Management Agency) have partnered together to develop a DM-DSS (Drought Monitoring Decision Support System). This monitoring system will be an interactive portal that will provide users the ability to visualize and assess drought at all levels. This candidate solution incorporates atmospherically corrected VIIRS data products, such as NDVI (Normalized Difference Vegetation Index) and Ocean SST (sea surface temperature), and AMSR-E soil moisture data products into two NDMC vegetation indices -- VegDRI (Vegetation Drought Response Index) and VegOUT (Vegetation Outlook) -- which are then input into the DM-DSS.

Estep, Leland↗

DOE OSTI2024

Emergent Trends Complicate the Interpretation of the United States Drought Monitor (USDM)

Abstract Effective drought management must be informed by an understanding of whether and how current drought monitoring and assessment practices represent underlying nonstationary climate conditions, either naturally occurring or forced by climate change. Here we investigate the emerging climatology and associated trends in drought classes defined by the United States Drought Monitor (USDM), a weekly product that, since 2000, has been used to inform drought management in the United States. The USDM classifies drought intensity based in part on threshold percentiles in key hydroclimate quantities. Here we assess how those USDM‐defined drought threshold percentiles have changed over the last 23 years, examining precipitation, runoff, soil moisture (SM), terrestrial water storage (TWS), vapor pressure deficit (VPD), and near‐surface air temperature. We also assess underlying trends in the frequency of drought classifications across the U.S. Our analysis suggests that the frequency of drought class occurrence is exceeding the threshold percentiles defined by the USDM in a number of regions in the United States, particularly in the American West, where the last 23 years have emerged as a prolonged dry period. These trends are also reflected in percentile‐based thresholds in precipitation, runoff, SM, TWS, VPD, and temperature. Our results emphasize that while the USDM appears to be accurately reflecting observed nonstationarity in the physical climate, such trends raise critical questions about whether and how drought diagnosis, classification, and monitoring should address long‐term intervals of wet and dry periods or trends.

Geology↗

NASA NTRS2018

Developing a Remotely Sensed Drought Monitoring Indicator for Morocco

Drought is one of the most serious climatic and natural disasters inflicting serious impacts on the socio-economy of Morocco, which is characterized both by low-average annual rainfall and high irregularity in the spatial distribution and timing of precipitation across the country. This work aims to develop a comprehensive and integrated method for drought monitoring based on remote sensing techniques. The main input parameters are derived monthly from satellite data at the national scale and are then combined to generate a composite drought index presenting different severity classes of drought. The input parameters are: Standardized Precipitation Index calculated from satellite-based precipitation data since 1981 (CHIRPS), anomalies in the day-night difference of Land Surface Temperature as a proxy for soil moisture, Normalized Difference Vegetation Index anomalies from Moderate Resolution Imaging Spectroradiometer (MODIS) data and Evapotranspiration anomalies from surface energy balance modeling. All of these satellite-based indices are being used to monitor vegetation condition, rainfall and land surface temperature. The weighted combination of these input parameters into one composite indicator takes into account the importance of the rainfall-based parameter (SPI). The composite drought index maps were generated during the growing seasons going back to 2003. These maps have been compared to both the historical, in situ precipitation data across Morocco and with the historical yield data across different provinces with information being available since 2000. The maps are disseminated monthly to several main stakeholders' groups including the Ministry of Agriculture and Department of Water in Morocco.

Drought monitoring↗

NASA NTRS2019

Evaluating the Operational Application of SMAP for Global Agricultural Drought Monitoring

Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regionalagricultural drought monitoring is one of the most logicalapplication areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimetersof the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entireroot zone. The assimilation of remotely sensed soil moisture productsinto continuous soil water balance models provides a way ofaddressing this shortcoming. Here, we describe the assimilationof NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impactof SMAP on USDA FAS drought monitoring capabilities. Theassimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for randomerrors inherent in its rainfall forcing data. The performanceof this SMAP-based assimilation system is evaluated using two approaches.At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moistureand the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ-based soil moisture estimatesis carried out at seven of the SMAP core Cal/Val sites located in theUSA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.

Mladenova, Iliana E.↗

NASA NTRS2010

Agricultural Productivity Forecasts for Improved Drought Monitoring

Water stresses on agricultural crops during critical phases of crop phenology (such as grain filling) has higher impact on the eventual yield than at other times of crop growth. Therefore farmers are more concerned about water stresses in the context of crop phenology than the meteorological droughts. However the drought estimates currently produced do not account for the crop phenology. US Department of Agriculture (USDA) and National Oceanic and Atmospheric Administration (NOAA) have developed a drought monitoring decision support tool: The U.S. Drought Monitor, which currently uses meteorological droughts to delineate and categorize drought severity. Output from the Drought Monitor is used by the States to make disaster declarations. More importantly, USDA uses the Drought Monitor to make estimates of crop yield to help the commodities market. Accurate estimation of corn yield is especially critical given the recent trend towards diversion of corn to produce ethanol. Ethanol is fast becoming a standard 10% ethanol additive to petroleum products, the largest traded commodity. Thus the impact of large-scale drought will have dramatic impact on the petroleum prices as well as on food prices. USDA's World Agricultural Outlook Board (WAOB) serves as a focal point for economic intelligence and the commodity outlook for U.S. WAOB depends on Drought Monitor and has emphatically stated that accurate and timely data are needed in operational agrometeorological services to generate reliable projections for agricultural decision makers. Thus, improvements in the prediction of drought will reflect in early and accurate assessment of crop yields, which in turn will improve commodity projections. We have developed a drought assessment tool, which accounts for the water stress in the context of crop phenology. The crop modeling component is done using various crop modules within Decision Support System for Agrotechnology Transfer (DSSAT). DSSAT is an agricultural crop simulation system, which integrates the effects of soil, crop phenotype, weather, and management options. It has been in use for more than 15 years by researchers, growers and has become a de-facto standard in crop modeling communities spanning over 100 countries. The meteorological forcings to DSSAT are provided by NASA s National Land Data Assimilation System (NLDAS) datasets. NLDAS is a framework that incorporates atmospheric forcing and land parameter values along with land surface models to diagnose and predict the state of the land surface.

Limaye, Ashutosh↗

NASA NTRS2010

Integrating Enhanced Grace Terrestrial Water Storage Data Into the U.S. and North American Drought Monitors

NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations nf the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including ground water. The U.S. and North American Drought Monitors are two of the premier drought monitoring products available to decision-makers for assessing and minimizing drought impacts, but they rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors hy filling this observational gap. Horizontal, vertical and temporal disaggregation of the coarse-resolution GRACE TWS data has been accomplished by assimilating GRACE TWS anomalies into the Catchment Land Surface Model using ensemble Kalman smoother. The Drought Monitors combine several short-term and long-term drought indices and indicators expressed in percentiles as a reference to their historical frequency of occurrence for the location and time of year in question. To be consistent, we are in the process of generating a climatology of estimated soil moisture and ground water based on m 60-year Catchment model simulation which will subsequently be used to convert seven years of GRACE assimilated fields into soil moisture and groundwater percentiles. for systematic incorporation into the objective blends that constitute Drought Monitor baselines. At this stage we provide a preliminary evaluation of GRACE assimilated Catchment model output against independent datasets including soil moisture observations from Aqua AMSR-E and groundwater level observations from the U.S. Geological Survey's Groundwater Climate Response Network.

Housborg, Rasmus↗

NASA NTRS2021

Illinois Disasters: Utilizing NASA Earth Observations to Enhance Drought Monitoring in Illinois

Drought and flooding in Illinois have severe impacts on the communities and ecosystems of the state. Soil moisture is a valuable indicator of drought and flood vulnerability but can be difficult to measure since in situ monitoring is limited to discrete stations throughout the state. The team created a framework to compare in situ, modeled, and NASA satellite soil moisture measurements to increase the spatial coverage of soil moisture monitoring. The team partnered with the Illinois State Weather Survey, USDA Midwest Climate Hub, NOAA Regional Climate Services of the Central Region, NOAA National Integrated Drought Information System’s Midwest Drought Early Warning System, and the NOAA North Central River ForecastCenter. The team standardized and compared soil moisture data from NASA’s Soil Moisture Active Passive (SMAP) mission, modeled soil moisture outputs from NASA’s SPoRT Land Information System (SPoRT-LIS), and in situ measurements from the Illinois State Weather Survey’s Water and Atmospheric Resources Monitoring (WARM) program. Compared to the WARM data, the satellite and modeled data showed seasonally variable differences and bias. The difference was highest in the winter months and lowest in the late summer and early fall months for the SMAP and SPoRT-LIS data products. SPoRT-LIS produced lower seasonal variability and SMAP demonstrated higher correlation values and lower differences. These analyses suggest that both SMAP and SPoRT-LIS products offer unique strengths and limitations when used for soil moisture monitoring

Joshua Green↗

NASA NTRS2011

Using Enhanced Grace Water Storage Data to Improve Drought Detection by the U.S. and North American Drought Monitors

NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations of the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including groundwater. The U.S. and North American Drought Monitors rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors by filling this observational gap. GRACE TWS data were assimilating into the Catchment Land Surface Model using an ensemble Kalman smoother enabling spatial and temporal downscaling and vertical decomposition into soil moisture and groundwater components. The Drought Monitors combine several short- and long-term drought indicators expressed in percentiles as a reference to their historical frequency of occurrence. To be consistent, we generated a climatology of estimated soil moisture and ground water based on a 60-year Catchment model simulation, which was used to convert seven years of GRACE assimilated fields into drought indicator percentiles. At this stage we provide a preliminary evaluation of the GRACE assimilated moisture and indicator fields.

Houborg, Rasmus↗

NASA NTRS2023

Quantifying the Importance of Selected Drought Indicators for the United States Drought Monitor

Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically-based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., indicating the need of another remotely sensed soil moisture source); and [3] new insights into the importance of variables such as Snow Water Equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to: [1] hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); [2] hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures respectively); and [3] predictability (high for the California 2012-2017 event, with longer-timescale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.

Drought↗

NASA NTRSDate not supplied

Improving Drought Monitoring for the Middle East and North Africa Region via Data Assimilation Using the NASA Land Information System (LIS)

Food and water security become an increasingly pressing concern for countries within the Middle East and North Africa (MENA) region under climate change. Droughts are among the most important issues facing this region in both economic and social terms, which have become much more severe in recent decades. An effective drought monitoring and early warning system is, therefore, critical to support drought impact assessment and risk management. This presentation describes the effort to improve the simulation of hydrological conditions for such semi-arid environment by Noah Multi-parameterization Land Surface Model within the NASA Land Information System (LIS) via assimilating remotely sensed leaf area index and soil moisture in the presence of irrigation. Multi-source satellite evapotranspiration products are used to evaluate the model performance under the impact of the choice of met-forcings, the presence of irrigation and data assimilation. The improved model configuration is aimed to deliver a better soil moisture estimation for the composite drought indicator (CDI) for drought monitoring and to provide a better initial condition in support for hydrological drought forecasting.

Wanshu Nie↗

NASA NTRS2016

Monitoring Drought Conditions in the Navajo Nation Using NASA Earth Observations

The Navajo Nation, a 65,700 sq km Native American territory located in the southwestern United States, has been increasingly impacted by severe drought events and changes in climate. These events are coupled with a lack of domestic water infrastructure and economic resources, leaving approximately one-third of the population without access to potable water in their homes. Current methods of monitoring drought are dependent on state-based monthly Standardized Precipitation Index value maps calculated by the Western Regional Climate Center. However, these maps do not provide the spatial resolution needed to illustrate differences in drought severity across the vast Nation. To better understand and monitor drought events and drought regime changes in the Navajo Nation, this project created a geodatabase of historical climate information specific to the area, and a decision support tool to calculate average Standardized Precipitation Index values for user-specified areas. The tool and geodatabase use Tropical Rainfall Monitoring Mission (TRMM) and Global Precipitation Monitor (GPM) observed precipitation data and Parameter-elevation Relationships on Independent Slopes Model modeled historical precipitation data, as well as NASA's modeled Land Data Assimilation Systems deep soil moisture, evaporation, and transpiration data products. The geodatabase and decision support tool will allow resource managers in the Navajo Nation to utilize current and future NASA Earth observation data for increased decision-making capacity regarding future climate change impact on water resources.

Ly, Vickie↗

NASA NTRS2012

Drought Monitoring for 3 North American Case Studies Based on the North American Land Data Assimilation System (NLDAS)

Both NLDAS Phase 1 (1996-2007) and Phase 2 (1979-present) datasets have been evaluated against in situ observational datasets, and NLDAS forcings and outputs are used by a wide variety of users. Drought indices and drought monitoring from NLDAS were recently examined by Mo et al. (2010) and Sheffield et al. (2010). In this poster, we will present results analyzing NLDAS Phase 2 forcings and outputs for 3 North American Case studies being analyzed as part of the NOAA MAPP Drought Task Force: (1) Western US drought (1998- 2004); (2) plains/southeast US drought (2006-2007); and (3) Current Texas-Mexico drought (2011-). We will examine percentiles of soil moisture consistent with the NLDAS drought monitor.

Peters-Lidard, Christa D.↗

DOE OSTI2023

Multivariate Drought Monitoring, Propagation, and Projection Using Bias-Corrected General Circulation Models

Understanding how droughts are characterized, propagated, and projected, particularly multivariate droughts, is necessary to explain the variability and changes in drought characteristics. This study aims to understand multimodel global drought monitoring, propagation, and projection by utilizing a multivariate standardized drought index (MSDI) during the historical (1959–2014) and future (2045–2100) periods under two socioeconomic pathways SSPs (370 and 585), derived from the bias-corrected Coupled Model Intercomparison Project Phase 6 (CMIP6). Based on the energy metrics, the multivariate bias correction method outperformed other techniques in correcting the biases in the CMIP6 drought representation. The drought indicators demonstrate distinct categories for meteorological, hydrological, and multivariate droughts. There were significant high cross correlations between Heatwave Total Length (HWTL) and MSDI in Africa and South America for all lagged times. Europe and North America generally saw the maximum MSDI drought duration (228 months) during the historical period. For future projections, Africa recorded the maximum drought duration (197 months), while Europe witnessed the minimum drought duration for SSP 370 (171 months), and North America (149 months) for SSP 585. Furthermore, during the historical period in tropical Africa, the propagation of meteorological to hydrological drought was slower during the wet months than during the dry months. Under the SSP 370 future projection, there was a shift in the long period of meteorological-hydrological propagation from the middle and late wet months to the beginning of the wet months in tropical Africa. Therefore, tracking and projecting drought characteristics is vital for understanding the risk of drought-related consequences.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Assessing the Utility of 3-km Land Information System Soil Moisture Data for Drought Monitoring and Hydrologic Applications

The NASA Short term Prediction Research and Transition (SPoRT) Center in Huntsville, AL has been running a real-time configuration of the Noah land surface model within the NASA Land Information System (LIS) since June 2010. The SPoRT LIS version is run as a stand-alone land surface model over a Southeast Continental U.S. domain with 3-km grid spacing. The LIS contains output variables including soil moisture and temperature at various depths, skin temperature, surface heat fluxes, storm surface runoff, and green vegetation fraction (GVF). The GVF represents another real-time SPoRT product, which is derived from the Moderate Resolution Imaging Spectroradiometer instrument aboard NASA's Aqua and Terra satellites. These data have demonstrated operational utility for drought monitoring and hydrologic applications at the National Weather Service (NWS) office in Huntsville, AL since early 2011. The most relevant data for these applications have proven to be the moisture availability (%) in the 0-10 cm and 0-200 cm layers, and the volumetric soil moisture (%) in the 0-10 cm layer. In an effort to better understand their applicability among locations with different terrain, soil and vegetation types, SPoRT is conducting the first formal assessment of these data at NWS offices in Houston, TX, Huntsville, AL and Raleigh, NC during summer 2014. The goal of this assessment is to evaluate the LIS output in the context of assessing flood risk and determining drought designations for the U.S. Drought Monitor. Forecasters will provide formal feedback via a survey question web portal, in addition to the NASA SPoRT blog. In this presentation, the SPoRT LIS and its applications at NWS offices will be presented, along with information about the summer assessment, including training module development and preliminary results.

White, Kristopher D.↗

NASA NTRS1989

Specification of parameters for development of a spatial database for drought monitoring and famine early warning in the African Sahel

Parameters were described for spatial database to facilitate drought monitoring and famine early warning in the African Sahel. The proposed system, referred to as the African Drought and Famine Information System (ADFIS) is ultimately recommended for implementation with the NASA/FEMA Spatial Analysis and Modeling System (SAMS), a GIS/Dymanic Modeling software package, currently under development. SAMS is derived from FEMA'S Integration Emergency Management Information System (IEMIS) and the Pacific Northwest Laborotory's/Engineering Topographic Laboratory's Airland Battlefield Environment (ALBE) GIS. SAMS is primarily intended for disaster planning and resource management applications with the developing countries. Sources of data for the system would include the Developing Economics Branch of the U.S. Dept. of Agriculture, the World Bank, Tulane University School of Public Health and Tropical Medicine's Famine Early Warning Systems (FEWS) Project, the USAID's Foreign Disaster Assistance Section, the World Resources Institute, the World Meterological Institute, the USGS, the UNFAO, UNICEF, and the United Nations Disaster Relief Organization (UNDRO). Satellite imagery would include decadal AVHRR imagery and Normalized Difference Vegetation Index (NDVI) values from 1981 to the present for the African continent and selected Landsat scenes for the Sudan pilot study. The system is initially conceived for the MicroVAX 2/GPX, running VMS. To facilitate comparative analysis, a global time-series database (1950 to 1987) is included for a basic set of 125 socio-economic variables per country per year. A more detailed database for the Sahelian countries includes soil type, water resources, agricultural production, agricultural import and export, food aid, and consumption. A pilot dataset for the Sudan with over 2,500 variables from the World Bank's ANDREX system, also includes epidemiological data on incidence of kwashiorkor, marasmus, other nutritional deficiencies, and synergistically-related infectious diseases.

Rochon, Gilbert L.↗

NASA NTRS2012

Assimilation of GRACE Terrestrial Water Storage into a Land Surface Model: Evaluation 1 and Potential Value for Drought Monitoring in Western and Central Europe

A land surface model s ability to simulate states (e.g., soil moisture) and fluxes (e.g., runoff) is limited by uncertainties in meteorological forcing and parameter inputs as well as inadequacies in model physics. In this study, anomalies of terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission were assimilated into the NASA Catchment land surface model in western and central Europe for a 7-year period, using a previously developed ensemble Kalman smoother. GRACE data assimilation led to improved runoff correlations with gauge data in 17 out of 18 hydrological basins, even in basins smaller than the effective resolution of GRACE. Improvements in root zone soil moisture were less conclusive, partly due to the shortness of the in situ data record. In addition to improving temporal correlations, GRACE data assimilation also reduced increasing trends in simulated monthly TWS and runoff associated with increasing rates of precipitation. GRACE assimilated root zone soil moisture and TWS fields exhibited significant changes in their dryness rankings relative to those without data assimilation, suggesting that GRACE data assimilation could have a substantial impact on drought monitoring. Signals of drought in GRACE TWS correlated well with MODIS Normalized Difference Vegetation Index (NDVI) data in most areas. Although they detected the same droughts during warm seasons, drought signatures in GRACE derived TWS exhibited greater persistence than those in NDVI throughout all seasons, in part due to limitations associated with the seasonality of vegetation.

Li, Bailing↗