Your search found 3 records
1 Arsenault, K. R.; Shukla, S.; Hazra, A.; Getirana, A.; McNally, A.; Kumar, S. V.; Koster, R. D.; Peters-Lidard, C. D.; Zaitchik, B. F.; Badr, H.; Jung, H. C.; Narapusetty, B.; Navari, M.; Wang, S.; Mocko, D. M.; Funk, C.; Harrison, L.; Husak, G. J.; Adoum, A.; Galu, G.; Magadzire, T.; Roningen, J.; Shaw, M.; Eylander, J.; Bergaoui, K.; McDonnell, Rachael A.; Verdin, J. P. 2020. The NASA hydrological forecast system for food and water security applications. Bulletin of the American Meteorological Society (BAMS), 101(7):E1007-E1025. [doi: https://doi.org/10.1175/BAMS-D-18-0264.1]
Hydrology ; Forecasting ; Early warning systems ; Food security ; Water security ; Drought ; Flooding ; Precipitation ; Groundwater ; Water storage ; Soil water content ; Stream flow ; Monitoring ; Land area ; Meteorological factors ; Satellite observation ; Modelling / Africa / Middle East
(Location: IWMI HQ Call no: e-copy only Record No: H049803)
https://journals.ametsoc.org/bams/article-pdf/101/7/E1007/4981535/bamsd180264.pdf
https://vlibrary.iwmi.org/pdf/H049803.pdf
https://vlibrary.iwmi.org/pdf/H049803.pdf
(8.47 MB) (8.47 MB)
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.
2 Zhou, Y.; Zaitchik, B. F.; Kumar, S. V.; Arsenault, K. R.; Matin, M. A.; Qamer, F. M.; Zamora, R. A.; Shakya, K. 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(1):41-61. [doi: https://doi.org/10.5194/hess-25-41-2021]
Hydrology ; Monitoring ; Forecasting ; River basins ; Precipitation ; Drought ; Indicators ; Soil moisture ; Estimation ; Meteorological factors ; Satellite observation ; Models / South Asia / Southeast Asia / Helmand Basin / Indus Basin / Ganges Basin / Brahmaputra Basin / Mekong Basin
(Location: IWMI HQ Call no: e-copy only Record No: H050187)
https://hess.copernicus.org/articles/25/41/2021/hess-25-41-2021.pdf
https://vlibrary.iwmi.org/pdf/H050187.pdf
https://vlibrary.iwmi.org/pdf/H050187.pdf
(4.23 MB) (4.23 MB)
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.
3 Zeleke, T. T.; Giorgi, F.; Diro, G. T.; Zaitchik, B. F.; Giuliani, G.; Ayal, D.; Kassahun, T.; Sintayehu, W. D.; Demissie, T. 2023. Effect of urbanization on East African climate as simulated by coupled urban-climate model. Climate Services, 31:100398. (Online first) [doi: https://doi.org/10.1016/j.cliser.2023.100398]
Climate models ; Climate variability ; Climate change ; Urbanization ; Land cover change ; Land use ; Surface temperature ; Precipitation ; Evapotranspiration / Africa
(Location: IWMI HQ Call no: e-copy only Record No: H052208)
https://www.sciencedirect.com/science/article/pii/S2405880723000596/pdfft?md5=05c8ce7dc28a558868d0846c9810d2b2&pid=1-s2.0-S2405880723000596-main.pdf
https://vlibrary.iwmi.org/pdf/H052208.pdf
https://vlibrary.iwmi.org/pdf/H052208.pdf
(17.10 MB) (17.1 MB)
This study examines the effect of urbanization on climate variability over East Africa. Seasonal trend of rainfall and temperature was analyzed using Mann-Kendall trend test and statistically significant rainfall trend is observed during spring (February-May) and summer (June-September) over northeast and spring/“bega”(October-January) seasons in southeastern regions of Ethiopia, thereby suggesting a seasonal shift of rainfall distribution. The temperature trend showed significant warming in the simulated field, except in central East Sudan, where there has been a significant decline. A numbers of idealized sensitivity experiments have been conducted with the Regional Climate Model (RegCM4.6) to investigate the contribution of urbanization to the East African region climate variability and trend. Model assessment against observed climate variables showed good performance in the simulation of spatial and temporal variability of regional climate variables. The results of the sensitivity experiment by prescribing different urban environments (tall building district (TBD), high density (HD), medium density (MD) and original land use) for the surface scheme (CLM4.5) reveal statistically significant impacts of urbanized surfaces on surface temperatures and precipitation due to variations in energy budget, local circulation and disturbance of hydro meteorological variables. It is noted that TBD urban environment has a higher impact on the local climate than other urban environments. Patterns of seasonal rainfall variability simulated using artificially urbanized land cover suggests involvement of complex interactions and is less similar to the observed rainfall trend, while surface temperature variability is significantly affected by local land-cover change and is very similar to the observed surface temperature trend.
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