Abstract
Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely distributed number of rain gauges, which reduces the reliability of the spatio-temporal fields generated. The current research proposes a series of measures to address the problem of data scarcity, in particular regarding in-situ measurements of precipitation. Once the issue of improving the network of ground precipitation measurements is settled, this may pave the way for much-needed hydrological research on topics such as the spatiotemporal distribution of precipitation, flash flood prevention, and soil erosion reduction. In this study, a k-means cluster analysis is used to determine new locations for the rain gauge network at the Eastern side of the Gulf of Suez in Sinai. The clustering procedure adopted is based on integrating a digital elevation model obtained from The Shuttle Radar Topography Mission (SRTM 90 × 90 m) and Integrated Multi-Satellite Retrievals for GPM (IMERG) for four rainy events. This procedure enabled the determination of the potential centroids for three different cluster sizes (3, 6, and 9). Subsequently, each number was tested using the Empirical Cumulative Distribution Function (ECDF) in an effort to determine the optimal one. However, all the tested centroids exhibited gaps in covering the whole range of elevations and precipitation of the test site. The nine centroids with the five existing rain gauges were used as a basis to calculate the error kriging. This procedure enabled decreasing the error by increasing the number of the proposed gauges. The resulting points were tested again by ECDF and this confirmed the optimum of thirty-one suggested additional gauges in covering the whole range of elevations and precipitation records at the study site.
Highlights
Arid regions cover 30% of the world’s land areas [1,2]
The aim of this study is to present an innovative approach for extension and optimization of rain gauge networks by using remote sensing data from satellites
To achieve the study’s aim, two mathematical approaches were integrated in a systematic way: k-means clustering and standard error kriging; k-means clustering was utilized to divide the whole range of numerical values collected by GPM and Shuttle Radar Topography Mission (SRTM) into tiny divisions of closely related values [25], and kriging of standard error was used to discover the locations with the greatest error for further optimizing the gauge placements [11]
Summary
Arid regions cover 30% of the world’s land areas [1,2]. These regions are generally experiencing a rise in socio-economic development and population density [3]. For arid areas, determining the water cycle equilibrium, including precipitation, infiltration rate, and the evaporation rate, is crucial. Heavy pumping speeds, devastating flash flooding, and soil erosion are all examples of how the knowledge of the water cycle can assist in a better understanding of important aspects in a broad range of hydrological disciplines. The understanding of the water cycle components could contribute to setting the limits of potential expansions in a number of economic activities
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