Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data

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 ra...

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Autores principales:	Mona Morsy, Ruhollah Taghizadeh-Mehrjardi, Silas Michaelides, Thomas Scholten, Peter Dietrich, Karsten Schmidt
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	rain gauge arid region GPM IMERG Empirical Cumulative Distribution Function Sinai Science Q
Acceso en línea:	https://doaj.org/article/181ba6b42fd047f69832e31418df93f7
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spelling	oai:doaj.org-article:181ba6b42fd047f69832e31418df93f72021-11-11T18:51:08ZOptimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data10.3390/rs132142432072-4292https://doaj.org/article/181ba6b42fd047f69832e31418df93f72021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4243https://doaj.org/toc/2072-4292Water 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.Mona MorsyRuhollah Taghizadeh-MehrjardiSilas MichaelidesThomas ScholtenPeter DietrichKarsten SchmidtMDPI AGarticlerain gaugearid regionGPMIMERGEmpirical Cumulative Distribution FunctionSinaiScienceQENRemote Sensing, Vol 13, Iss 4243, p 4243 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rain gauge arid region GPM IMERG Empirical Cumulative Distribution Function Sinai Science Q
spellingShingle	rain gauge arid region GPM IMERG Empirical Cumulative Distribution Function Sinai Science Q Mona Morsy Ruhollah Taghizadeh-Mehrjardi Silas Michaelides Thomas Scholten Peter Dietrich Karsten Schmidt Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data
description	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.
format	article
author	Mona Morsy Ruhollah Taghizadeh-Mehrjardi Silas Michaelides Thomas Scholten Peter Dietrich Karsten Schmidt
author_facet	Mona Morsy Ruhollah Taghizadeh-Mehrjardi Silas Michaelides Thomas Scholten Peter Dietrich Karsten Schmidt
author_sort	Mona Morsy
title	Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data
title_short	Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data
title_full	Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data
title_fullStr	Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data
title_full_unstemmed	Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data
title_sort	optimization of rain gauge networks for arid regions based on remote sensing data
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/181ba6b42fd047f69832e31418df93f7
work_keys_str_mv	AT monamorsy optimizationofraingaugenetworksforaridregionsbasedonremotesensingdata AT ruhollahtaghizadehmehrjardi optimizationofraingaugenetworksforaridregionsbasedonremotesensingdata AT silasmichaelides optimizationofraingaugenetworksforaridregionsbasedonremotesensingdata AT thomasscholten optimizationofraingaugenetworksforaridregionsbasedonremotesensingdata AT peterdietrich optimizationofraingaugenetworksforaridregionsbasedonremotesensingdata AT karstenschmidt optimizationofraingaugenetworksforaridregionsbasedonremotesensingdata
_version_	1718431688942419968

Optimization of Rain Gauge Networks for Arid Regions Based on Remote Sensing Data

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