Aakash Ahamed (BS, with honors, Franklin and Marshall College; MSc, Boston College; PhD Candidate, Stanford University) is a hydrologist developing scientific methods for satellite and airborne remote sensing measurements with applications to water resources, natural hazards, and agricultural systems. As a PhD Candidate in the Department of Geophysics, his current doctoral project focuses on modeling, monitoring, and forecasting key hydrologic components of the Central Valley Aquifer System in California using techniques in data assimilation and machine learning. Aakash previously worked as a support scientist in the Hydrological Sciences Lab at NASA Goddard Space Flight Center, where he constructed satellite-based models of flood and landslide hazards. He has also developed remote sensing analyses and software at Ceres Imaging, a successful precision agriculture start up based in Silicon Valley, and interned as a GIS analyst at the World Wildlife Fund for Nature in Washington, DC.
Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage.
The Science of the total environment
Accurate and timely estimates of groundwater storage changes are critical to the sustainable management of aquifers worldwide, but are hindered by the lack of in-situ groundwater measurements in most regions. Hydrologic remote sensing measurements provide a potential pathway to quantify groundwater storage changes by closing the water balance, but the degree to which remote sensing data can accurately estimate groundwater storage changes is unclear. In this study, we quantified groundwater storage changes in California's Central Valley at two spatial scales for the period 2002 through 2020 using remote sensing data and an ensemble water balance method. To evaluate performance, we compared estimates of groundwater storage changes to three independent estimates: GRACE satellite data, groundwater wells and a groundwater flow model. Results suggest evapotranspiration has the highest uncertainty among water balance components, while precipitation has the lowest. We found that remote sensing-based groundwater storage estimates correlated well with independent estimates; annual trends during droughts fall within 15% of trends calculated using wells and groundwater models within the Central Valley. Remote sensing-based estimates also reliably estimated the long-term trend, seasonality, and rate of groundwater depletion during major drought events. Additionally, our study suggests that the proposed method estimate changes in groundwater at sub-annual latencies, which is not currently possible using other methods. The findings have implications for improving the understanding of aquifer dynamics and can inform regional water managers about the status of groundwater systems during droughts.
View details for DOI 10.1016/j.scitotenv.2021.150635
View details for PubMedID 34606871
- Automated Satellite-Based Landslide Identification Product for Nepal EARTH INTERACTIONS 2019; 23 (3)
- Socioeconomic Impact Evaluation for Near Real-Time Flood Detection in the Lower Mekong River Basin HYDROLOGY 2018; 5 (2)
A MODIS-based automated flood monitoring system for southeast asia
International Journal of Applied Earth Observation and Geoinformation
2017; 61: 104 - 117
View details for DOI 10.1016/j.jag.2017.05.006
- Near Real Time Flood Monitoring and Impact Assessment Systems Remote Sensing of Hydrological Extremes Springer. 2017: 105–118
- Optical and Physical Methods for Mapping Flooding with Satellite Imagery In Remote Sensing of Hydrological Extremes Springer. 2017: 83–103