This page has only limited features, please log in for full access.
The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area index (LAI) and soil moisture observations to improve representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric processes for this region, but rather it degrades simulation of interannual variation of evapotranspiration (ET) and carbon fluxes, mainly due to model weaknesses in representing dynamic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the dynamic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region.
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; Mahdi Navari. 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. 2021, 2021, 1 -36.
AMA StyleWanshu 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, Mahdi Navari. 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. . 2021; 2021 ():1-36.
Chicago/Turabian StyleWanshu 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; Mahdi Navari. 2021. "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." 2021, no. : 1-36.
Climate variability is an important driver of irrigation water use in many regions. Efforts to anticipate climate change impacts on future water availability can benefit from understanding how irrigation water demand has responded to these drivers to date. Here we apply satellite‐derived data, meteorological reanalysis, an advanced land surface model, and available state‐level reports to quantify irrigation demand sensitivities to temperature and precipitation across the Contiguous United States, for the period of 2002‐2017. As expected, strong negative correlations are found between precipitation and irrigation withdrawals, both simulated and reported. Temperature sensitivities, however, vary by region and season, as do the interactive effects of temperature and precipitation on irrigation. Climate‐induced irrigation variability is largest in transitional climate zones. These transitional zones are generally separate from the regions where rates of irrigation withdrawals are greatest, such that climate‐induced variability in irrigation demand represents a water resource consideration that is distinct from chronic over‐pumping.This article is protected by copyright. All rights reserved.
Wanshu Nie; Benjamin F. Zaitchik; Matthew Rodell; Sujay V. Kumar; Kristi R. Arsenault; Hamada S. Badr. Irrigation Water Demand Sensitivity to Climate Variability Across the Contiguous United States. Water Resources Research 2021, 57, 1 .
AMA StyleWanshu Nie, Benjamin F. Zaitchik, Matthew Rodell, Sujay V. Kumar, Kristi R. Arsenault, Hamada S. Badr. Irrigation Water Demand Sensitivity to Climate Variability Across the Contiguous United States. Water Resources Research. 2021; 57 (3):1.
Chicago/Turabian StyleWanshu Nie; Benjamin F. Zaitchik; Matthew Rodell; Sujay V. Kumar; Kristi R. Arsenault; Hamada S. Badr. 2021. "Irrigation Water Demand Sensitivity to Climate Variability Across the Contiguous United States." Water Resources Research 57, no. 3: 1.
Though coarse in spatial resolution, the nearly all weather measurements from passive microwave sensors can help in improving the spatio‐temporal coverage of optical and thermal infrared sensors for monitoring vegetation changes on the land surface. This study demonstrates the use of vegetation optical depth retrievals from the Soil Moisture Active Passive mission for capturing the vegetation alterations from the recent 2019‐2020 Australian bushfires and drought. The impact of vegetation disturbances on terrestrial water budget is examined by assimilating the vegetation optical depth retrievals into a dynamic phenology model. The results demonstrate that assimilating vegetation optical depth observations lead to improved simulation of evapotranspiration, runoff, and soil moisture states. The study also demonstrates that the vegetation changes from the 2019‐2020 Australian drought and fires led to significant modifications in the partitioning of evaporative and runoff fluxes, resulting in increased bare soil evaporation, reduced transpiration, and higher runoff.This article is protected by copyright. All rights reserved.
Sujay V. Kumar; Thomas Holmes; Niels Andela; Imtiaz Dharssi; Vinodkumar; Christopher Hain; Christa Peters‐Lidard; Sarith P. Mahanama; Kristi R. Arsenault; Wanshu Nie; Augusto Getirana. The 2019–2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes. Geophysical Research Letters 2021, 48, 1 .
AMA StyleSujay V. Kumar, Thomas Holmes, Niels Andela, Imtiaz Dharssi, Vinodkumar, Christopher Hain, Christa Peters‐Lidard, Sarith P. Mahanama, Kristi R. Arsenault, Wanshu Nie, Augusto Getirana. The 2019–2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes. Geophysical Research Letters. 2021; 48 (1):1.
Chicago/Turabian StyleSujay V. Kumar; Thomas Holmes; Niels Andela; Imtiaz Dharssi; Vinodkumar; Christopher Hain; Christa Peters‐Lidard; Sarith P. Mahanama; Kristi R. Arsenault; Wanshu Nie; Augusto Getirana. 2021. "The 2019–2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes." Geophysical Research Letters 48, no. 1: 1.
South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end users across the region. This system applies the Noah-Multiparameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5 km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made 1 to 2 months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.
Yifan Zhou; Benjamin F. Zaitchik; Sujay V. Kumar; Kristi R. Arsenault; Mir A. Matin; Faisal M. Qamer; Ryan A. Zamora; Kiran Shakya. Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins. Hydrology and Earth System Sciences 2021, 25, 41 -61.
AMA StyleYifan Zhou, Benjamin F. Zaitchik, Sujay V. Kumar, Kristi R. Arsenault, Mir A. Matin, Faisal M. Qamer, Ryan A. Zamora, Kiran Shakya. Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins. Hydrology and Earth System Sciences. 2021; 25 (1):41-61.
Chicago/Turabian StyleYifan Zhou; Benjamin F. Zaitchik; Sujay V. Kumar; Kristi R. Arsenault; Mir A. Matin; Faisal M. Qamer; Ryan A. Zamora; Kiran Shakya. 2021. "Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins." Hydrology and Earth System Sciences 25, no. 1: 41-61.
Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001–2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4 mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12–35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.
Augusto Getirana; Dalia Kirschbaum; Felipe Mandarino; Marta Ottoni; Sana Khan; Kristi Arsenault. Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil. Remote Sensing 2020, 12, 4095 .
AMA StyleAugusto Getirana, Dalia Kirschbaum, Felipe Mandarino, Marta Ottoni, Sana Khan, Kristi Arsenault. Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil. Remote Sensing. 2020; 12 (24):4095.
Chicago/Turabian StyleAugusto Getirana; Dalia Kirschbaum; Felipe Mandarino; Marta Ottoni; Sana Khan; Kristi Arsenault. 2020. "Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil." Remote Sensing 12, no. 24: 4095.
South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth Observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end-users across the region. This system applies the Noah-MultiParameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model - sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5-km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made one to two months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.
Yifan Zhou; Benjamin F. Zaitchik; Sujay V. Kumar; Kristi R. Arsenault; Mir A. Matin; Faisal M. Qamer; Ryan A. Zamora; Kiran Shakya. Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins. 2020, 2020, 1 -36.
AMA StyleYifan Zhou, Benjamin F. Zaitchik, Sujay V. Kumar, Kristi R. Arsenault, Mir A. Matin, Faisal M. Qamer, Ryan A. Zamora, Kiran Shakya. Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins. . 2020; 2020 ():1-36.
Chicago/Turabian StyleYifan Zhou; Benjamin F. Zaitchik; Sujay V. Kumar; Kristi R. Arsenault; Mir A. Matin; Faisal M. Qamer; Ryan A. Zamora; Kiran Shakya. 2020. "Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins." 2020, no. : 1-36.
Yifan Zhou; Benjamin F. Zaitchik; Sujay V. Kumar; Kristi R. Arsenault; Mir A. Matin; Faisal M. Qamer; Ryan A. Zamora; Kiran Shakya. Supplementary material to "Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins". 2020, 1 .
AMA StyleYifan Zhou, Benjamin F. Zaitchik, Sujay V. Kumar, Kristi R. Arsenault, Mir A. Matin, Faisal M. Qamer, Ryan A. Zamora, Kiran Shakya. Supplementary material to "Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins". . 2020; ():1.
Chicago/Turabian StyleYifan Zhou; Benjamin F. Zaitchik; Sujay V. Kumar; Kristi R. Arsenault; Mir A. Matin; Faisal M. Qamer; Ryan A. Zamora; Kiran Shakya. 2020. "Supplementary material to "Developing a hydrological monitoring and sub-seasonal to seasonal forecasting system for South and Southeast Asian river basins"." , no. : 1.
Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing–based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET’s current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean–land–atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1–5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.
Kristi R. Arsenault; Shraddhanand Shukla; Abheera Hazra; Augusto Getirana; Amy McNally; Sujay V. Kumar; Randal D. Koster; Christa D. Peters-Lidard; Benjamin F. Zaitchik; Hamada Badr; Hahn Chul Jung; Bala Narapusetty; Mahdi Navari; Shugong Wang; David M. Mocko; Chris Funk; Laura Harrison; Gregory J. Husak; Alkhalil Adoum; Gideon Galu; Tamuka Magadzire; Jeanne Roningen; Michael Shaw; John Eylander; Karim Bergaoui; Rachael A. McDonnell; James P. Verdin. The NASA Hydrological Forecast System for Food and Water Security Applications. Bulletin of the American Meteorological Society 2020, 101, E1007 -E1025.
AMA StyleKristi R. Arsenault, Shraddhanand Shukla, Abheera Hazra, Augusto Getirana, Amy McNally, Sujay V. Kumar, Randal D. Koster, Christa D. Peters-Lidard, Benjamin F. Zaitchik, Hamada Badr, Hahn Chul Jung, Bala Narapusetty, Mahdi Navari, Shugong Wang, David M. Mocko, Chris Funk, Laura Harrison, Gregory J. Husak, Alkhalil Adoum, Gideon Galu, Tamuka Magadzire, Jeanne Roningen, Michael Shaw, John Eylander, Karim Bergaoui, Rachael A. McDonnell, James P. Verdin. The NASA Hydrological Forecast System for Food and Water Security Applications. Bulletin of the American Meteorological Society. 2020; 101 (7):E1007-E1025.
Chicago/Turabian StyleKristi R. Arsenault; Shraddhanand Shukla; Abheera Hazra; Augusto Getirana; Amy McNally; Sujay V. Kumar; Randal D. Koster; Christa D. Peters-Lidard; Benjamin F. Zaitchik; Hamada Badr; Hahn Chul Jung; Bala Narapusetty; Mahdi Navari; Shugong Wang; David M. Mocko; Chris Funk; Laura Harrison; Gregory J. Husak; Alkhalil Adoum; Gideon Galu; Tamuka Magadzire; Jeanne Roningen; Michael Shaw; John Eylander; Karim Bergaoui; Rachael A. McDonnell; James P. Verdin. 2020. "The NASA Hydrological Forecast System for Food and Water Security Applications." Bulletin of the American Meteorological Society 101, no. 7: E1007-E1025.
The region of southern Africa (SA) has a fragile food economy and is vulnerable to frequent droughts. Interventions to mitigate food insecurity impacts require early warning of droughts – preferably as early as possible before the harvest season (typically starting in April) and lean season (typically starting in November). Hydrologic monitoring and forecasting systems provide a unique opportunity to support early warning efforts, since they can provide regular updates on available root-zone soil moisture (RZSM), a critical variable for crop yield, and provide forecasts of RZSM by combining the estimates of antecedent soil moisture conditions with climate forecasts. For SA, this study documents the predictive capabilities of RZSM products from the recently developed NASA Hydrological Forecasting and Analysis System (NHyFAS). Results show that the NHyFAS products would have identified the regional severe drought event – which peaked during December–February of 2015–2016 – at least as early as 1 November 2015. Next, it is shown that during 1982–2016, February RZSM (Feb-RZSM) forecasts (monitoring product) available in early November (early March) have a correlation of 0.49 (0.79) with the detrended regional crop yield. It is also found that when the February RZSM forecast (monitoring product) available in early November (early March) is indicated to be in the lowest tercile, the detrended regional crop yield is below normal about two-thirds of the time (always), at least over the sample years considered. Additionally, it is shown that the February RZSM forecast (monitoring product) can provide “out-of-sample” crop yield forecasts with comparable (substantially better with 40 % reduction in mean error) skill to December–February ENSO. These results indicate that the NHyFAS products can effectively support food insecurity early warning in the SA region. Finally, since a framework similar to NHyFAS can be used to provide RZSM monitoring and forecasting products over other regions of the globe, this case study also demonstrates potential for supporting food insecurity early warning globally.
Shraddhanand Shukla; Kristi R. Arsenault; Abheera Hazra; Christa Peters-Lidard; Randal D. Koster; Frank Davenport; Tamuka Magadzire; Chris Funk; Sujay Kumar; Amy McNally; Augusto Getirana; Greg Husak; Ben Zaitchik; Jim Verdin; Faka Dieudonne Nsadisa; Inbal Becker-Reshef. Improving early warning of drought-driven food insecurity in southern Africa using operational hydrological monitoring and forecasting products. Natural Hazards and Earth System Sciences 2020, 20, 1187 -1201.
AMA StyleShraddhanand Shukla, Kristi R. Arsenault, Abheera Hazra, Christa Peters-Lidard, Randal D. Koster, Frank Davenport, Tamuka Magadzire, Chris Funk, Sujay Kumar, Amy McNally, Augusto Getirana, Greg Husak, Ben Zaitchik, Jim Verdin, Faka Dieudonne Nsadisa, Inbal Becker-Reshef. Improving early warning of drought-driven food insecurity in southern Africa using operational hydrological monitoring and forecasting products. Natural Hazards and Earth System Sciences. 2020; 20 (4):1187-1201.
Chicago/Turabian StyleShraddhanand Shukla; Kristi R. Arsenault; Abheera Hazra; Christa Peters-Lidard; Randal D. Koster; Frank Davenport; Tamuka Magadzire; Chris Funk; Sujay Kumar; Amy McNally; Augusto Getirana; Greg Husak; Ben Zaitchik; Jim Verdin; Faka Dieudonne Nsadisa; Inbal Becker-Reshef. 2020. "Improving early warning of drought-driven food insecurity in southern Africa using operational hydrological monitoring and forecasting products." Natural Hazards and Earth System Sciences 20, no. 4: 1187-1201.
This work provides an envisioned overview of scientific collaboration among multiple United States agencies including the National Aeronautics and Space Administration (NASA), U.S. Army Engineer Research and Development Center (ERDC), Oak Ridge National Laboratory (ORNL), and National Geospatial-Intelligence Agency (NGA) for the integration of existing data and model capabilities to support global scale water security applications. The primary objective is to develop a high-resolution, operational streamflow and flood forecasting system at the global scale, leveraging multiple process-based models, remote sensing data assimilation, and high-performance computing techniques. We present a preliminary case study that demonstrates the integration of the modeling framework using NASA’s Land Information System (LIS), ERDC’s Streamflow Prediction Tool (SPT), and ORNL’s GPU-accelerated 2D flood model (TRITON). Using the high-resolution terrain data from NGA, a historic flood event that occurred in March 2019 at Offutt Air Force Base in Nebraska, USA, was simulated on ORNL’s supercomputer, Summit. This benchmark test case is used to validate the modeling framework and to help establish a roadmap for the expanded modeling efforts at the global scale. In a broader sense, the proposed infrastructure will enable decision-makers to address issues such as transboundary water conflicts, flood and drought monitoring, and sustainable water resources management and to study their impacts on human, water-energy and natural systems in the short, medium and long term.
Sudershan Gangrade; Mario Morales-Hernandez; Ahmad A. Tavakoly; Kristi R. Arsenault; Jerry Wegiel; Kimberly McCormack; Mark Wahl; Sujay V. Kumar; Christa D. Peters-Lidard; Shih-Chieh Kao; Katherine J. Evans. Towards the Development of a High-resolution, Global Streamflow and Flood Forecasting System – An U.S. Interagency Collaboration Effort. 2020, 1 .
AMA StyleSudershan Gangrade, Mario Morales-Hernandez, Ahmad A. Tavakoly, Kristi R. Arsenault, Jerry Wegiel, Kimberly McCormack, Mark Wahl, Sujay V. Kumar, Christa D. Peters-Lidard, Shih-Chieh Kao, Katherine J. Evans. Towards the Development of a High-resolution, Global Streamflow and Flood Forecasting System – An U.S. Interagency Collaboration Effort. . 2020; ():1.
Chicago/Turabian StyleSudershan Gangrade; Mario Morales-Hernandez; Ahmad A. Tavakoly; Kristi R. Arsenault; Jerry Wegiel; Kimberly McCormack; Mark Wahl; Sujay V. Kumar; Christa D. Peters-Lidard; Shih-Chieh Kao; Katherine J. Evans. 2020. "Towards the Development of a High-resolution, Global Streamflow and Flood Forecasting System – An U.S. Interagency Collaboration Effort." , no. : 1.
Acute and chronic water scarcity impacts four billion people, a number likely to climb with population growth and increasing demand for food and energy production. Chronic water insecurity and long-term trends are well studied at the global and regional level; however, there have not been adequate systems in place for routinely monitoring acute water scarcity. To address this gap, we developed a monthly monitoring system that computes annual water availability per capita based on hydrologic data from the Famine Early Warning System Network (FEWS NET) Land Data Assimilation System (FLDAS) and gridded population data from WorldPop. The monitoring system yields maps of acute water scarcity using monthly Falkenmark classifications and departures from the long-term mean classification. These maps are designed to serve FEWS NET monitoring objectives; however, the underlying data are publicly available and can support research on the roles of population and hydrologic change on water scarcity at sub-annual and sub-national scales.
Amy McNally; Kristine Verdin; Laura Harrison; Augusto Getirana; Jossy Jacob; Shraddhanand Shukla; Kristi Arsenault; Christa Peters-Lidard; James P. Verdin. Acute Water-Scarcity Monitoring for Africa. Water 2019, 11, 1968 .
AMA StyleAmy McNally, Kristine Verdin, Laura Harrison, Augusto Getirana, Jossy Jacob, Shraddhanand Shukla, Kristi Arsenault, Christa Peters-Lidard, James P. Verdin. Acute Water-Scarcity Monitoring for Africa. Water. 2019; 11 (10):1968.
Chicago/Turabian StyleAmy McNally; Kristine Verdin; Laura Harrison; Augusto Getirana; Jossy Jacob; Shraddhanand Shukla; Kristi Arsenault; Christa Peters-Lidard; James P. Verdin. 2019. "Acute Water-Scarcity Monitoring for Africa." Water 11, no. 10: 1968.
The region of southern Africa (SA) has a fragile food economy and is vulnerable to frequent droughts. In 2015–2016, an El Niño-driven drought resulted in major maize production shortfalls, food price increases, and livelihood disruptions that pushed 29 million people into severe food insecurity. Interventions to mitigate food insecurity impacts require early warning of droughts – preferably as early as possible before the harvest season (typically, starting in April) and lean season (typically, starting in November). Hydrologic monitoring and forecasting systems provide a unique opportunity to support early warning efforts, since they can provide regular updates on available rootzone soil moisture (RZSM), a critical variable for crop yield, and provide forecasts of RZSM by combining the estimates of antecedent soil moisture conditions with climate forecasts. For SA, this study documents the predictive capabilities of a recently developed NASA Hydrological Forecasting and Analysis System (NHyFAS). The NHyFAS system's ability to forecast and monitor the 2015/2016 drought event is evaluated. The system's capacity to explain interannual variations in regional crop yield and identify below-normal crop yield events is also evaluated. Results show that the NHyFAS products would have identified the regional severe drought event, which peaked during December–February of 2015/2016, at least as early as 1 November 2015. Next, it is shown that February RZSM forecasts produced as early as 1 November (4–5 months before the start of harvest and about a year before the start of the next lean season) correlate fairly well with regional crop yields (r = 0.49). The February RZSM monitoring product, available in early March, correlates with the regional crop yield with higher skill (r = 0.79). It is also found that when the February RZSM forecast produced on November 1 is indicated to be in the lowest tercile, the detrended regional crop yield is below normal about two-thirds (significance level ~ 86 %) of the time. Furthermore, when the February RZSM monitoring product (available in early March) indicates a lowest tercile value, the crop yield is always below normal, at least over the sample years considered. These results indicate that the NHyFAS products can effectively support food insecurity early warning in the SA region.
Shraddhanand Shukla; Kristi R. Arsenault; Abheera Hazra; Christa Peters-Lidard; Randal D. Koster; Frank Davenport; Tamuka Magadzire; Chris Funk; Sujay Kumar; Amy McNally; Augusto Getirana; Greg Husak; Ben Zaitchik; Jim Verdin; Faka Dieudonne Nsadisa; Inbal Becker-Reshef. Improving early warning of drought-driven food insecurity in Southern Africa using operational hydrological monitoring and forecasting products. 2019, 2019, 1 -29.
AMA StyleShraddhanand Shukla, Kristi R. Arsenault, Abheera Hazra, Christa Peters-Lidard, Randal D. Koster, Frank Davenport, Tamuka Magadzire, Chris Funk, Sujay Kumar, Amy McNally, Augusto Getirana, Greg Husak, Ben Zaitchik, Jim Verdin, Faka Dieudonne Nsadisa, Inbal Becker-Reshef. Improving early warning of drought-driven food insecurity in Southern Africa using operational hydrological monitoring and forecasting products. . 2019; 2019 ():1-29.
Chicago/Turabian StyleShraddhanand Shukla; Kristi R. Arsenault; Abheera Hazra; Christa Peters-Lidard; Randal D. Koster; Frank Davenport; Tamuka Magadzire; Chris Funk; Sujay Kumar; Amy McNally; Augusto Getirana; Greg Husak; Ben Zaitchik; Jim Verdin; Faka Dieudonne Nsadisa; Inbal Becker-Reshef. 2019. "Improving early warning of drought-driven food insecurity in Southern Africa using operational hydrological monitoring and forecasting products." 2019, no. : 1-29.
An evapotranspiration (ET) ensemble composed of 36 land surface model (LSM) experiments and four diagnostic datasets (GLEAM, ALEXI, MOD16, and FLUXNET) is used to investigate uncertainties in ET estimate over five climate regions in West Africa. Diagnostic ET datasets show lower uncertainty estimates and smaller seasonal variations than the LSM-based ET values, particularly in the humid climate regions. Overall, the impact of the choice of LSMs and meteorological forcing datasets on the modeled ET rates increases from north to south. The LSM formulations and parameters have the largest impact on ET in humid regions, contributing to 90% of the ET uncertainty estimates. Precipitation contributes to the ET uncertainty primarily in arid regions. The LSM-based ET estimates are sensitive to the uncertainty of net radiation in arid region and precipitation in humid region. This study serves as support for better determining water availability for agriculture and livelihoods in Africa with earth observations and land surface models.
Hahn Chul Jung; Augusto Getirana; Kristi R. Arsenault; Thomas R.H. Holmes; Amy McNally. Uncertainties in Evapotranspiration Estimates over West Africa. Remote Sensing 2019, 11, 892 .
AMA StyleHahn Chul Jung, Augusto Getirana, Kristi R. Arsenault, Thomas R.H. Holmes, Amy McNally. Uncertainties in Evapotranspiration Estimates over West Africa. Remote Sensing. 2019; 11 (8):892.
Chicago/Turabian StyleHahn Chul Jung; Augusto Getirana; Kristi R. Arsenault; Thomas R.H. Holmes; Amy McNally. 2019. "Uncertainties in Evapotranspiration Estimates over West Africa." Remote Sensing 11, no. 8: 892.
Snow depletion curves (SDC) are functions that are used to show the relationship between snow covered area and snow depth or water equivalent. Previous snow cover data assimilation (DA) studies have used theoretical SDC models as observation operators to map snow depth to snow cover fraction (SCF). In this study, a new approach is introduced that uses snow water equivalent (SWE) observations and satellite-based SCF retrievals to derive SDC relationships for use in an Ensemble Kalman filter (EnKF) to assimilate snow cover estimates. A histogram analysis is used to bin the SWE observations, which the corresponding SCF observations are then averaged within, helping to constrain the amount of data dispersion across different temporal and regional conditions. Logarithmic functions are linearly regressed with the binned average values, for two U.S. mountainous states: Colorado and Washington. The SDC-based logarithmic functions are used as EnKF observation operators, and the satellite-based SCF estimates are assimilated into a land surface model. Assimilating satellite-based SCF estimates with the observation-based SDC shows a reduction in SWE-related RMSE values compared to the model-based SDC functions. In addition, observation-based SDC functions were derived for different intra-annual and physiographic conditions, and landcover and elevation bands. Lower SWE-based RMSE values are also found with many of these categorical observation-based SDC EnKF experiments. All assimilation experiments perform better than the open-loop runs, except for the Washington region’s 2004–2005 snow season, which was a major drought year that was difficult to capture with the ensembles and observations.
Kristi R. Arsenault; Paul R. Houser. Generating Observation-Based Snow Depletion Curves for Use in Snow Cover Data Assimilation. Geosciences 2018, 8, 484 .
AMA StyleKristi R. Arsenault, Paul R. Houser. Generating Observation-Based Snow Depletion Curves for Use in Snow Cover Data Assimilation. Geosciences. 2018; 8 (12):484.
Chicago/Turabian StyleKristi R. Arsenault; Paul R. Houser. 2018. "Generating Observation-Based Snow Depletion Curves for Use in Snow Cover Data Assimilation." Geosciences 8, no. 12: 484.
The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.
Kristi R. Arsenault; Sujay V. Kumar; James V. Geiger; Shugong Wang; Eric Kemp; David M. Mocko; Hiroko Kato Beaudoing; Augusto Getirana; Mahdi Navari; Bailing Li; Jossy Jacob; Jerry Wegiel; Christa D. Peters-Lidard. The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems. Geoscientific Model Development 2018, 11, 3605 -3621.
AMA StyleKristi R. Arsenault, Sujay V. Kumar, James V. Geiger, Shugong Wang, Eric Kemp, David M. Mocko, Hiroko Kato Beaudoing, Augusto Getirana, Mahdi Navari, Bailing Li, Jossy Jacob, Jerry Wegiel, Christa D. Peters-Lidard. The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems. Geoscientific Model Development. 2018; 11 (9):3605-3621.
Chicago/Turabian StyleKristi R. Arsenault; Sujay V. Kumar; James V. Geiger; Shugong Wang; Eric Kemp; David M. Mocko; Hiroko Kato Beaudoing; Augusto Getirana; Mahdi Navari; Bailing Li; Jossy Jacob; Jerry Wegiel; Christa D. Peters-Lidard. 2018. "The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems." Geoscientific Model Development 11, no. 9: 3605-3621.
Kristi R. Arsenault. Response to Reviewer Comment. 2018, 1 .
AMA StyleKristi R. Arsenault. Response to Reviewer Comment. . 2018; ():1.
Chicago/Turabian StyleKristi R. Arsenault. 2018. "Response to Reviewer Comment." , no. : 1.
Kristi R. Arsenault. Response to Short Comment. 2018, 1 .
AMA StyleKristi R. Arsenault. Response to Short Comment. . 2018; ():1.
Chicago/Turabian StyleKristi R. Arsenault. 2018. "Response to Short Comment." , no. : 1.
Kristi R. Arsenault. Response to Reviewer Comment. 2018, 1 .
AMA StyleKristi R. Arsenault. Response to Reviewer Comment. . 2018; ():1.
Chicago/Turabian StyleKristi R. Arsenault. 2018. "Response to Reviewer Comment." , no. : 1.
The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for the dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decomposition to assess the sensitivity of estimates of sensible heat, latent heat, soil moisture, and net ecosystem exchange made by certain standard Noah-MP configurations that include the dynamic simulation of vegetation and carbon to 43 primary user-specified parameters. This is done using 32 years’ worth of data from 10 international FluxNet sites. Findings indicate that there are five soil parameters and six (or more) vegetation parameters (depending on the model configuration) that act as primary controls on these states and fluxes.
Kristi R. Arsenault; Grey S. Nearing; Shugong Wang; Soni Yatheendradas; Christa D. Peters-Lidard. Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation. Journal of Hydrometeorology 2018, 19, 815 -830.
AMA StyleKristi R. Arsenault, Grey S. Nearing, Shugong Wang, Soni Yatheendradas, Christa D. Peters-Lidard. Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation. Journal of Hydrometeorology. 2018; 19 (5):815-830.
Chicago/Turabian StyleKristi R. Arsenault; Grey S. Nearing; Shugong Wang; Soni Yatheendradas; Christa D. Peters-Lidard. 2018. "Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation." Journal of Hydrometeorology 19, no. 5: 815-830.
The effective applications of land surface model (LSM) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a pre-processor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions, meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational data sets and data analytics algorithms to aid land surface modelling and data assimilation.
Kristi R. Arsenault; Sujay V. Kumar; James V. Geiger; Shugong Wang; Eric Kemp; David M. Mocko; Hiroko Kato Beaudoing; Augusto Getirana; Mahdi Navari; Bailing Li; Jossy Jacob; Jerry Wegiel; Christa Peters-Lidard. The Land surface Data Toolkit (LDTv7.2) – a data fusion environment for land data assimilation systems. 2018, 2018, 1 -38.
AMA StyleKristi R. Arsenault, Sujay V. Kumar, James V. Geiger, Shugong Wang, Eric Kemp, David M. Mocko, Hiroko Kato Beaudoing, Augusto Getirana, Mahdi Navari, Bailing Li, Jossy Jacob, Jerry Wegiel, Christa Peters-Lidard. The Land surface Data Toolkit (LDTv7.2) – a data fusion environment for land data assimilation systems. . 2018; 2018 ():1-38.
Chicago/Turabian StyleKristi R. Arsenault; Sujay V. Kumar; James V. Geiger; Shugong Wang; Eric Kemp; David M. Mocko; Hiroko Kato Beaudoing; Augusto Getirana; Mahdi Navari; Bailing Li; Jossy Jacob; Jerry Wegiel; Christa Peters-Lidard. 2018. "The Land surface Data Toolkit (LDTv7.2) – a data fusion environment for land data assimilation systems." 2018, no. : 1-38.