Abstract
A reasonable rain gauge network can provide valid precipitation information that reflects the spatial and temporal fluctuation characteristics for a given basin. Thus, it is indispensable for designing an optimal network with a minimal number of rain gauges (NRGs) in an optimal location as a means of providing reliable rainfall records, both in terms of the areal average rainfall and the spatiotemporal variability. This study presents a methodological framework that couples the ordinary kriging (OK) method and spatial correlation approach (SCA) to optimize current rain gauge networks, which involves the deletion of redundant gauges and the addition of new rain gauges in the ‘blank’ monitoring area of a basin. This framework was applied to a network of 38 rain gauges in the Jinjiang Basin in southeast China. The results indicated that: (1) the number of rain gauges was reduced from 38 to 11 by using the OK method to determine the redundant rain gauges, which were removed to obtain the ‘base’ rain gauge network. The base rain gauges were mainly distributed in the midstream of this basin. (2) The SCA and OK were employed for obtaining the number and location of new rain gauges in the ‘blank’ monitoring region, respectively. Two new rain gauges in the ‘blank’ monitoring region were identified. One rain gauge was located near the Anxi hydrological station and the other was located in the lower reaches of Anxi sub-basin, respectively. The locations of the two new rain gauges were proven to be reasonable. The number of optimal rain gauges in the Jinjiang Basin was increased to 13. The method proposed in this study provides a novel and simple approach to solve the problems of redundant rain gauges and blank monitoring areas in rain gauge networks. This method is beneficial for improving the optimization level of rain gauge networks and provides a reference for such an optimization.
Highlights
The accurate estimation of rainfall in a given region is an important and challenging task [1,2]
Based on the monthly rainfall value of the 38 rain gauges, we adopted three cross-validation statistics (MSE, RMSE, and average standard error (ASE)) to comprehensively compare the connection between different numbers and interpolation errors, which enabled us to ascertain the optimum number of base rain gauges
The relationship between the cross-validation statistics and the number of rain gauges (NRGs) is presented in Figure 3, which shows that the indicator values of mean standardized error (MSE), rain gauge combinations were mean-square standardized error (RMSS), and ASE [20] generally exhibited downward trends with the increase in the NRGs
Summary
The accurate estimation of rainfall in a given region (or basin) is an important and challenging task [1,2]. Spatial distribution of the rain gauge is a vital factor in providing reliable areal rainfall [3,4]. The rain gauge network provides the necessary real-time precipitation information. It is essential for improving the accuracy of flood forecasting and hydrological model simulations, and for water resource management, including the risk assessment of regional freshwater resources, Water 2020, 12, 2252; doi:10.3390/w12082252 www.mdpi.com/journal/water. The tools for measuring rainfall can be divided into three categories: rain gauges [1], meteorological radars [6,7], and remote sensing satellites [8,9]. Meteorological radars and remote sensing satellites can describe the spatiotemporal variation of large-scale rainfall by high-resolution measurements. There are some factors that influence the measurement precision [6,10], such as measurement error, chaotic noise, and so on [11,12]
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