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Browsing Research Data - Other by Author "Ukasha, Muhammad"

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    ItemOpen Access
    Dataset associated with "An Improved Rescaling Algorithm for Estimating Groundwater Depletion Rates using the GRACE Satellite"
    (Colorado State University. Libraries, 2022) Ukasha, Muhammad; Ramirez, Jorge; Niemann, Jeffrey
    The Gravity Recovery And Climate Experiments (GRACE) satellite mission has been instrumental in estimating large-scale groundwater storage changes across the globe. GRACE observations include significant errors, so pre-processing is normally required before the data are used. In particular, the regional observations of terrestrial water storage anomalies (TWSA) are usually filtered to reduce the effects of measurement errors and then rescaled to reduce the unintended impacts of the filtering. The rescaling is typically selected to maximize the Nash-Sutcliffe Efficiency (NSE) between the rescaled filtered TWSA and original TWSA from large-scale hydrologic models that represent an incomplete water budget. The objectives of this study are (1) to evaluate the use of NSE in the current GRACE rescaling methodology, (2) develop an improved methodology that incorporates a complete regional water budget, and (3) examine the impacts of the rescaling methodology on regional assessments of groundwater depletion. To evaluate the use of NSE as a performance metric, we implement an analytical solution to improve the relative variability between the filtered and original TWSA series. Improved relative variability produces more reliable estimates when comparing the results to TWSA estimates from global positioning systems (GPS) for the Sacramento and San Joaquin River basins (containing Central Valley) in California. Rescaling with the complete regional water budget based on observed hydrological fluxes results in a larger scale factor (3.18) than the scale factor from the large-scale hydrologic model outputs (1.97), and the new TWSA results are more consistent with those from GPS. The larger scale factor also suggests that regional groundwater depletion is more severe than previously estimated.
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    ItemOpen Access
    Dataset associated with "Temporal Variations of NDVI and LAI and Interactions with Hydroclimatic Variables in a Large and Agro-Ecologically Diverse Region"
    (Colorado State University. Libraries, 2021) Ukasha, Muhammad; Ramirez, Jorge A.; Niemann, Jeffrey D.
    Satellite based vegetation indices are increasingly used to characterize seasonal and interannual variations in vegetation as well as vegetation’s response to hydroclimatic variability. However, differences in the behavior of vegetation indices are not well understood over large spatial extents (e.g., 0.5° or larger). We hypothesize that normalized difference vegetation index (NDVI) and leaf area index (LAI) can exhibit different behaviors due to different relationships with hydroclimatic variables. To test this hypothesis, observations of monthly precipitation, discharge, temperature, vapor pressure deficit, evapotranspiration, and total water storage anomalies (TWSA) are processed for the combined Sacramento and San Joaquin river basins in California for 13 water years (October 2002-September 2015). Estimates of NDVI and LAI are obtained for the same period from MODerate resolution Imaging Spectroradiometer (MODIS). The seasonal cycle of NDVI peaks 2-3 months earlier than LAI. The seasonal variation in NDVI follows the seasonality of TWSA (i.e. water availability) whereas the seasonal cycle of LAI follows the seasonality in mean temperature and vapor pressure deficit (i.e. atmospheric water demand). Cross-correlation analyses of NDVI and LAI with the hydroclimatic variables show that LAI is more strongly correlated with most of the hydroclimatic variables considered.