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Bringing Statistical Learning Machines Together For Hydro-Climatological Predictions - Case Study For Sacramento San Joaquin River Basin, California, Balbhadra Thakur, Ajay Kalra, Sajjad Ahmad, Kenneth W. Lamb, Venkat Lakshmi
Bringing Statistical Learning Machines Together For Hydro-Climatological Predictions - Case Study For Sacramento San Joaquin River Basin, California, Balbhadra Thakur, Ajay Kalra, Sajjad Ahmad, Kenneth W. Lamb, Venkat Lakshmi
Civil and Environmental Engineering and Construction Faculty Research
Study region: Sacramento San Joaquin River Basin, California Study focus: The study forecasts the streamflow at a regional scale within SSJ river basin with largescale climate variables. The proposed approach eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962–2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors …
Changes In Snow Phenology From 1979 To 2016 Over The Tianshan Mountains, Central Asia, Tao Yang, Qian Li, Sajjad Ahmad, Hongfei Zhou, Lanhai Li
Changes In Snow Phenology From 1979 To 2016 Over The Tianshan Mountains, Central Asia, Tao Yang, Qian Li, Sajjad Ahmad, Hongfei Zhou, Lanhai Li
Civil and Environmental Engineering and Construction Faculty Research
Snowmelt from the Tianshan Mountains (TS) is a major contributor to the water resources of the Central Asian region. Thus, changes in snow phenology over the TS have significant implications for regional water supplies and ecosystem services. However, the characteristics of changes in snow phenology and their influences on the climate are poorly understood throughout the entire TS due to the lack of in situ observations, limitations of optical remote sensing due to clouds, and decentralized political landscapes. Using passive microwave remote sensing snow data from 1979 to 2016 across the TS, this study investigates the spatiotemporal variations of snow …