Google Earth Engine as Multi-Sensor Open-Source Tool for Monitoring Stream Flow in the Transboundary River Basin: Doosti River Dam

Abstract

Understanding the effects of global change and human activities on water supplies depends greatly on surface water dynamics. A comprehensive examination of the hydroclimatic variations at the transboundary level is essential for the development of any adaptation or mitigation plans to deal with the negative effects of climate change. This research paper examines the hydroclimatic factors that contribute to the desiccation of the Doosti Dam’s basin in the transboundary area using multisensor satellite data from the Google Earth Engine (GEE) platform. The Mann–Kendall and Sens slope estimator test was applied to the satellite datasets to analyse the spatial and temporal variation of the hydroclimate variables and their trend over the transboundary area for 18 years from 2004 to 2021 (as the dam began operating in 2005). Statistical analysis results showed decreasing trends in temperature and an increase in rainfall with respect to station-observed available data. Evapotranspiration and irrigated area development followed the increasing pattern and a slight decrease in snow cover. The results confirmed a large expansion of the irrigated area, especially during the winter growing season. The increase in irrigated cultivated areas during both winter and summer seasons is possibly the main reason for the diversion of water to meet the irrigation requirements of the developed agriculture areas. The approach followed in this study could be applied to any location around the globe to evaluate the hydrological conditions and spatiotemporal changes in response to climate change, trend analysis and human activities.

A Multi-Framework of Google Earth Engine and GEV for Spatial Analysis of Extremes in Non-Stationary Condition in Southeast Queensland, Australia

“…The alternative hypothesis (Ha) in this test shows a monotonic trend over time. More details about Mann-Kendall are documented in [31]. The Bayesian approach employs a generic inference methodology.…”

“…We believe that the physical disturbance within a catchment alters the underlying process and results in temporal fluctuation in parameter values. As hydroclimatic variables can significantly vary over time, space, and climate zones, it is necessary to employ accurate data with higher spatial resolution for variability analysis [31]. So, we provided the geeSE-BAL results by incorporating high quality datasets and considering the physical system drivers and their relationship.…”

Variability of Extreme Climate Events and Prediction of Land Cover Change and Future Climate Change Effects on the Streamflow in Southeast Queensland, Australia

“…The Landsat satellite images (from TM, ETM+, and OLI 1&2 sensors) and ESA global land cover dataset were accessed and used through GEE [40] for conducting the classifications and modelling of changes in land cover and urban growth. Geospatial datasets of road networks, population density, and the Hydrologically Enforced Digital Elevation Model (DEM-H) product with a 30 m spatial resolution [41] dataset were used as supplementary data inputs during the landcover projection analysis.…”