Rain‐gauge network evaluation and augmentation using geostatistics

Abstract

Abstract:Rain-gauge networks are often used to provide estimates of area average rainfall or point rainfalls at ungauged locations. The level of accuracy a network can achieve depends on the total number and locations of gauges in the network. A geostatistical approach for evaluation and augmentation of an existing rain-gauge network is proposed in this study. Through variogram analysis, hourly rainfalls are shown to have higher spatial variability than annual rainfalls, with hourly Mei-Yu rainfalls having the highest spatial variability. A criterion using ordinary kriging variance is proposed to assess the accuracy of rainfall estimation using the acceptance probability defined as the probability that estimation error falls within a desired range. Based on the criterion, the percentage of the total area with acceptable accuracy A p under certain network configuration can be calculated. A sequential algorithm is also proposed to prioritize rain-gauges of the existing network, identify the base network, and relocate non-base gauges. Percentage of the total area with acceptable accuracy is mostly contributed by the base network. In contrast, non-base gauges provide little contribution to A p and are subject to removal or relocation. Using a case study in northern Taiwan, the proposed approach demonstrates that the identified base network which comprises of approximately two-thirds of the total rain-gauges can achieve almost the same level of performance (expressed in terms of percentage of the total area with acceptable accuracy) as the complete network for hourly Mei-Yu rainfall estimation. The percentage of area with acceptable accuracy can be raised from 56% to 88% using an augmented network. A threshold value for the percentage of area with acceptable accuracy is also recommended to help determine the number of non-base gauges which need to be relocated.

Spatiotemporal Scaling Effect on Rainfall Network Design Using Entropy

“…1 rain gauge (Alishan), there were only about half rainy days between 1992 and 2012 (total 7671 days).We do not include daily rainfall data for the same reasons even though it may be a suitable temporal scale between hour and month. Compared with the work by Cheng et al [5] at hourly and annual scale, this study only includes hourly data for typhoons with the selecting criterion that over two thirds records are non-zero data, we neglected the other three major rainfall types, i.e., convective, Mei-Yu and frontal rain. The major reason is the rain intensity of seven typhoon events in study area is large enough to represent the extreme rainfall condition in study area, in particular, the Typhoon Morakot in Aug 2009.…”

“…Applications to groundwater quality monitoring networks, stream gauge networks, and water distribution networks have increased in recent years. The methods used in network research related to entropy include least square methods and entropy [4], kriging [5], information entropy [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], and combined kriging and information entropy [23][24][25]. In particular, the information entropy approach has been widely adopted since the 1970s for hydrologic data collection network design and uncertainty evaluation [26][27][28][29][30][31][32][33].…”

Interaction of valleys and circulation patterns (CPs) on spatial precipitation patterns in southern Germany

“…The local and regional water balance is also determined by precipitation patterns at the corresponding scales (Fekete et al, 2004). Furthermore, detailed knowledge on precipitation patterns can be used to optimize the design of rain-gauge networks (Cheng et al, 2008), such that a limited number of stations can adequately represent the underlying rainfall field.…”

Estimation of rainfall distribution for the southwestern region of Saudi Arabia

“…Kriging proved to be a superior estimation method relative to the traditional techniques. Geostatistics has been widely used to estimate natural parameters having spatial variability such as solar radiation (Rehman and Ghori 2000), porosity (Pramanik et al 2004), assessment of soil and groundwater contamination (Goovaerts et al 1997, Michalak andKitanidis 2004), wind speed (Luo et al 2008), snow distribution (Erxleben et al 2002), temperature (Hudson and Wackernagel 1994), porosity, permeability, hydraulic conductivity, water tables and rainfall distribution (Pardo-Igúzquiza 1998, Goovaerts 2000, Haberlandt 2007, Cheng et al 2008. The most widely used geostatistical techniques are ordinary kriging and co-kriging.…”