Abstract
Precipitation is a key constituent of the water cycle and its accurate measurement is essential for a wide range of hydroclimatic studies. Although advances in technology have provided new sources of precipitation data (e.g., satellite data), gauge measurements are still considered as the most reliable source of information. Accuracy of ground measurements highly depends on the network density and positions of the rain gauges and thus, many studies have attempted to re-design the rain gauge networks around the globe. This is while, a limited number of researches have investigated the potential of utilizing global gridded precipitation products in network optimization context. In this study, a two-step framework was proposed to remove redundant rain gauge stations of the Central Plateau watershed of Iran. In the first step, number of excess rain gauges was detected based on transinformation–distance (T–D) analysis. In the second step, exact location of the extra rain gauges was determined through high degree of freedom and low degree of freedom (LDF) scenarios by a combination of harmony search optimization algorithm and a geostatistical model. According to the results, 58 out of 580 rain gauges were detected to be removed. Results of the proposed scenarios differed in detecting 13 out of 58 gauges. While LDF scenario required less computational time, network re-designed by this scenario was about 32% less accurate than the other scenario. Performance of the both optimized networks was equally similar to the initial network in terms of developing precipitation rasters.
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AG: Conceptualization, methodology, software, formal analysis, investigation, resources, data curation, writing-original draft, and visualization. MN: Conceptualization, methodology, software, investigation, resources, writing-review and editing, and supervision. BB: Conceptualization, investigation, resources, and writing-review and editing.
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Ghomlaghi, A., Nasseri, M. & Bayat, B. Large-scale precipitation monitoring network re-design using ground and satellite datasets: coupled application of geostatistics and meta-heuristic optimization algorithms. Stoch Environ Res Risk Assess 37, 4445–4458 (2023). https://doi.org/10.1007/s00477-023-02517-x
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DOI: https://doi.org/10.1007/s00477-023-02517-x