Sarfaraz Alam is a Postdoctoral Scholar at Stanford University, where he is modeling nitrate transport in groundwater and surface water to improve approaches to enforcement. His research integrates hydrologic modeling, contaminant transport, remote sensing, and data science to understand how climate and human-induced changes affect water resources and the environment. Sarfaraz earned his Ph.D. in Civil Engineering from UCLA in 2021.

Sarfaraz received an Outstanding Ph.D. student award, Dissertation Year Fellowship, and Graduate Division Fellowship at UCLA. He authored nine peer-reviewed journal articles and presented his research in over ten international conferences.

Honors & Awards

  • Civil & Environmental Engineering Outstanding Ph.D. Student Award, UCLA (2021)
  • Dissertation Year Fellowship, UCLA (2020)

Professional Education

  • Ph.D., University of California, Los Angeles, Civil Engineering
  • M.S., Bangladesh University of Engineering and Technology, Water Resources Engineering
  • B.S., Bangladesh University of Engineering and Technology, Water Resources Engineering

Stanford Advisors

All Publications

  • Subsurface Water Flux in California's Central Valley and Its Source Watershed From Space Geodesy GEOPHYSICAL RESEARCH LETTERS Argus, D. F., Martens, H. R., Borsa, A. A., Knappe, E., Wiese, D. N., Alam, S., Anderson, M., Khatiwada, A., Lau, N., Peidou, A., Swarr, M., White, A. M., Bos, M. S., Ellmer, M., Landerer, F. W., Gardiner, W. 2022; 49 (22)
  • Corrigendum to "Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage" [Sci. Total Environ. 807 (2022) 150635]. The Science of the total environment Ahamed, A., Knight, R., Alam, S., Pauloo, R., Melton, F. 2022; 847: 157678

    View details for DOI 10.1016/j.scitotenv.2022.157678

    View details for PubMedID 35914382

  • The evolving roles of intensity and wet season timing in rainfall regimes surrounding the Red Sea ENVIRONMENTAL RESEARCH LETTERS Haleakala, K., Yue, H., Alam, S., Mitra, R., Bushara, A., Gebremichael, M. 2022; 17 (4)
  • Post-Drought Groundwater Storage Recovery in California's Central Valley WATER RESOURCES RESEARCH Alam, S., Gebremichael, M., Ban, Z., Scanlon, B. R., Senay, G., Lettenmaier, D. P. 2021; 57 (10)
  • Managed aquifer recharge implementation criteria to achieve water sustainability SCIENCE OF THE TOTAL ENVIRONMENT Alam, S., Borthakur, A., Ravi, S., Gebremichael, M., Mohanty, S. K. 2021; 768: 144992


    Depletion of groundwater is accelerated due to an increase in water demand for applications in urbanized areas, agriculture sectors, and energy extraction, and dwindling surface water during changing climate. Managed aquifer recharge (MAR) is one of the several methods that can help achieve long-term water sustainability by increasing the natural recharge of groundwater reservoirs with water from non-traditional supplies such as excess surface water, stormwater, and treated wastewater. Despite the multiple benefits of MAR, the wide-scale implementation of MAR is lacking, partly because of challenges to select the location for MAR implementation and identify the MAR type based on site conditions and needs. In this review, we provide an overview of MAR types with a basic framework to select and implement specific MAR at a site based on water availability and quality, land use, source type, soil, and aquifer properties. Our analysis of 1127 MAR projects shows that MAR has been predominantly implemented in sites with sandy clay loam soil (soil group C) and with access to river water for recharge. Spatial analysis reveals that many regions with depleting water storage have opportunities to implement MAR projects. Analyzing data from 34 studies where stormwater was used for recharge, we show that MAR can remove dissolved organic carbon, most metals, E. coli but not efficient at removing most trace organics, and enterococci. Removal efficiency depends on the type of MAR. In the end, we highlight potential challenges for implementing MAR at a site and additional benefits such as minimizing land subsidence, flood risk, augmenting low dry-season flow, and minimizing salt-water intrusion. These results could help identify locations in the water-stressed regions to implement specific MAR for water sustainability.

    View details for DOI 10.1016/j.scitotenv.2021.144992

    View details for Web of Science ID 000625384700123

    View details for PubMedID 33736333

  • Budyko-Based Long-Term Water and Energy Balance Closure in Global Watersheds From Earth Observations WATER RESOURCES RESEARCH Koppa, A., Alam, S., Miralles, D. G., Gebremichael, M. 2021; 57 (5): e2020WR028658


    Earth observations offer potential pathways for accurately closing the water and energy balance of watersheds, a fundamental challenge in hydrology. However, previous attempts based on purely satellite-based estimates have focused on closing the water and energy balances separately. They are hindered by the lack of estimates of key components, such as runoff. Here, we posit a novel approach based on Budyko's water and energy balance constraints. The approach is applied to quantify the degree of long-term closure at the watershed scale, as well as its associated uncertainties, using an ensemble of global satellite data sets. We find large spatial variability across aridity, elevation, and other environmental gradients. Specifically, we find a positive correlation between elevation and closure uncertainty, as derived from the Budyko approach. In mountainous watersheds the uncertainty in closure is 3.9 ± 0.7 (dimensionless). Our results show that uncertainties in terrestrial evaporation contribute twice as much as precipitation uncertainties to errors in the closure of water and energy balance. Moreover, our results highlight the need for improving satellite-based precipitation and evaporation data in humid temperate forests, where the closure error in the Budyko space is as high as 1.1 ± 0.3, compared to only 0.2 ± 0.03 in tropical forests. Comparing the results with land surface model-based data sets driven by in situ precipitation, we find that Earth observation-based data sets perform better in regions where precipitation gauges are sparse. These findings have implications for improving the understanding of global hydrology and regional water management and can guide the development of satellite remote sensing-based data sets and Earth system models.

    View details for DOI 10.1029/2020WR028658

    View details for Web of Science ID 000654464300036

    View details for PubMedID 34219820

    View details for PubMedCentralID PMC8244049

  • Multi-model ensemble projection of mean and extreme streamflow of Brahmaputra River Basin under the impact of climate change JOURNAL OF WATER AND CLIMATE CHANGE Alam, S., Ali, M., Rahaman, A., Islam, Z. 2021
  • What Drives Crop Land Use Change during Multi-Year Droughts in California's Central Valley? Prices or Concern for Water? REMOTE SENSING Gebremichael, M., Krishnamurthy, P., Ghebremichael, L. T., Alam, S. 2021; 13 (4)

    View details for DOI 10.3390/rs13040650

    View details for Web of Science ID 000624424200001

  • Assessing the utility of remote sensing data to accurately estimate changes in groundwater storage. The Science of the total environment Ahamed, A., Knight, R., Alam, S., Pauloo, R., Melton, F. 2021: 150635


    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

  • Can Managed Aquifer Recharge Mitigate the Groundwater Overdraft in California's Central Valley? WATER RESOURCES RESEARCH Alam, S., Gebremichael, M., Li, R., Dozier, J., Lettenmaier, D. P. 2020; 56 (8)
  • Climate change impacts on groundwater storage in the Central Valley, California CLIMATIC CHANGE Alam, S., Gebremichael, M., Li, R., Dozier, J., Lettenmaier, D. P. 2019; 157 (3-4): 387-406
  • Remote Sensing-Based Assessment of the Crop, Energy and Water Nexus in the Central Valley, California REMOTE SENSING Alam, S., Gebremichael, M., Li, R. 2019; 11 (14)

    View details for DOI 10.3390/rs11141701

    View details for Web of Science ID 000480527800071

  • Climate Elasticity of Low Flows in the Maritime Western US Mountains WATER RESOURCES RESEARCH Cooper, M. G., Schaperow, J. R., Cooley, S. W., Alam, S., Smith, L. C., Lettenmaier, D. P. 2018; 54 (8): 5602-5619
  • Future Streamflow of Brahmaputra River Basin under Synthetic Climate Change Scenarios JOURNAL OF HYDROLOGIC ENGINEERING Alam, S., Ali, M., Islam, Z. 2016; 21 (11)