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1 Podgorski, J. E.; Eqani, S. A. M. A. S.; Khanam, T.; Ullah, R.; Shen, H.; Berg, M. 2017. Extensive arsenic contamination in high-pH unconfined aquifers in the Indus Valley. Science Advances, 3(8):1-10. [doi: https://doi.org/10.1126/sciadv.1700935]

Arsenic ; Contamination ; Groundwater ; Aquifers ; pH ; Water quality ; Drinking water ; Public health ; Health hazards ; Soils ; Probability analysis ; Regression analysis ; Models ; Forecasting / Pakistan / Indus Valley

(Location: IWMI HQ Call no: e-copy only Record No: H048293)

http://advances.sciencemag.org/content/3/8/e1700935.full.pdf
https://vlibrary.iwmi.org/pdf/H048293.pdf

(0.96 MB) (980 KB)

Arsenic-contaminated aquifers are currently estimated to affect ~150 million people around the world. However, the full extent of the problem remains elusive. This is also the case in Pakistan, where previous studies focused on isolated areas. Using a new data set of nearly 1200 groundwater quality samples throughout Pakistan, we have created state-of-the-art hazard and risk maps of arsenic-contaminated groundwater for thresholds of 10 and 50 mg/liter. Logistic regression analysis was used with 1000 iterations, where surface slope, geology, and soil parameters were major predictor variables. The hazard model indicates that much of the Indus Plain is likely to have elevated arsenic concentrations, although the rest of the country is mostly safe. Unlike other arsenic-contaminated areas of Asia, the arsenic release process in the arid Indus Plain appears to be dominated by elevated-pH dissolution, resulting from alkaline topsoil and extensive irrigation of unconfined aquifers, although pockets of reductive dissolution are also present. We estimate that approximately 50 million to 60 million people use groundwater within the area at risk, with hot spots around Lahore and Hyderabad. This number is alarmingly high and demonstrates the urgent need for verification and testing of all drinking water wells in the Indus Plain, followed by appropriate mitigation measures.

2 Kim, S.; Shen, H.; Noh, S.; Seo, D.-J.; Welles, E.; Pelgrim, E.; Weerts, A.; Lyons, E.; Philips, B. 2021. High-resolution modeling and prediction of urban floods using WRF-hydro and data assimilation. Journal of Hydrology, 598:126236. (Online first) [doi: https://doi.org/10.1016/j.jhydrol.2021.126236]

Flooding ; Forecasting ; Urban areas ; Hydrology ; Models ; Precipitation ; Rainfall-runoff relationships ; Stream flow ; Observation ; Catchment areas ; Land cover / USA / Texas / Dallas-Fort Worth Area / Arlington / Grand Prairie / Fish Creek Catchment / Johnson Creek Catchment / Cottonwood Creek Catchment

(Location: IWMI HQ Call no: e-copy only Record No: H050332)

https://vlibrary.iwmi.org/pdf/H050332.pdf

(6.43 MB)

We assess the impact of increasing the resolution of hydrologic modeling, calibration of selected model parameters and assimilation of streamflow observation toward event-based urban flood modeling and prediction using WRF-Hydro in the Dallas-Fort Worth area (DFW). We use quantitative precipitation estimates at 500-m 1-min resolution from the Collaborative Adaptive Sensing of the Atmosphere radar network for observed rainfall, Stepwise Line Search for calibration, and fixed-lag smoothing for data assimilation (DA). The model domain is a 144.6 km2 area comprising 3 urban catchments in Arlington and Grand Prairie in the middle of DFW. It is shown that event-specific calibration of 6 WRF-Hydro parameters is largely successful in simulating hydrographs at the catchment outlets particularly for the most important rising limbs, but less so for attenuated peaks or fast-receding falling limbs. A spatial resolution of at least 250 m was necessary for the land surface model (LSM) to delineate small catchments and hence to capture catchment-wide rainfall with acceptable accuracy. Simulations at selected combinations of resolutions, 250 and 125 m for the LSM and 250, 125, 50 m for the routing models, showed mixed results. The overall results indicate that, in the absence of resolution-specific prescription and calibration of channel routing parameters, a resolution of 250 m for both the LSM and routing models is a good choice in terms of performance and computational requirements, and that, in the absence of high-quality calibration and continuous simulation of streamflow, DA is necessary to initialize WRF-Hydro for event-based high-resolution urban flood prediction.

3 Yue, L.; Li, B.; Zhu, S.; Yuan, Q.; Shen, H.. 2023. A fully automatic and high-accuracy surface water mapping framework on google earth engine using landsat time-series. International Journal of Digital Earth, 16(1):210-233. [doi: https://doi.org/10.1080/17538947.2023.2166606]

Surface water ; Mapping ; Frameworks ; Landsat ; Remote sensing ; Satellite imagery ; Models ; Water extraction / China / Wuhan

(Location: IWMI HQ Call no: e-copy only Record No: H051708)

https://www.tandfonline.com/doi/epdf/10.1080/17538947.2023.2166606?needAccess=true&role=button
https://vlibrary.iwmi.org/pdf/H051708.pdf

(7.71 MB) (7.71 MB)

Efficient and continuous monitoring of surface water is essential for water resource management. Much effort has been devoted to the task of water mapping based on remote sensing images. However, few studies have fully considered the diverse spectral properties of water for the collection of reference samples in an automatic manner. Moreover, water area statistics are sensitive to the satellite image observation quality. This study aims to develop a fully automatic surface water mapping framework based on Google Earth Engine (GEE) with a supervised random forest classifier. A robust scheme was built to automatically construct training samples by merging the information from multi-source water occurrence products. The samples for permanent and seasonal water were mapped and collected separately, so that the supplement of seasonal samples can increase the spectral diversity of the sample space. To reduce the uncertainty of the derived water occurrences, temporal correction was applied to repair the classification maps with invalid observations. Extensive experiments showed that the proposed method can generate reliable samples and produce good-quality water mapping results. Comparative tests indicated that the proposed method produced water maps with a higher quality than the index-based detection methods, as well as the GSWD and GLAD datasets.