A Method for the Optimized Design of a Rain Gauge Network Combined with Satellite Remote Sensing Data

Abstract

A well-designed rain gauge network can provide precise and detailed rainfall data for earth science research; meanwhile, satellite precipitation data has been developed to generate more real spatial features, which provides new data support for the improvement of ground station network design methods. In this paper, satellite precipitation data are introduced into the design of a rain gauge network and an optimized method for designing a rain gauge network that comprehensively considers the information content, spatiotemporality, and accuracy (ISA) of the data is proposed. After screening the potential stations, the average spatial information index of the rain gauge network, which is calculated from remote sensing data, is used to address the shortcomings of applying spatial information from single-use measurement data. Then, the greedy ranking algorithm is used to rank the order in which the rain gauges are added to the network. The results of the rain gauge network design in the upper reaches of the Chaobai river show that compared with two methods that do not consider spatiality or use only measured data to consider spatiality, the proposed method performs better in terms of the spatial layout and accuracy verification. This study provides new ideas and references for the design of hydrological station networks and explores the use of remote sensing data for the layout of ground-based station networks.

A Nonlinear Radiometric Normalization Model for Satellite Imgaes Time Series Based on Artificial Neural Networks and Greedy Algroithm

“…Create a greedy algorithm [21]. A greedy algorithm is an intuitive, well-tested algorithm used in optimization problems [22,23]. The algorithm makes the optimal choice at each step as it attempts to find the overall optimal method to solve the entire problem.…”

Objective functions for information-theoretical monitoring network design: what is optimal?

“…They argued that transinformation between selected stations in the optimal set and non-selected stations should be maximized to account for the information transfer ability of a network. Meanwhile, recently proposed methods in the literature attempted to improve monitoring network design by introducing yet other additional objectives (Huang et al, 2020;Wang et al, 2018;Banik et al, 2017;Keum and Coulibaly, 2017). These additional objective are further discussed in the appendix.…”

“…These studies apply information theoretical measures on multiple of time series from a set of sensors, to identify optimal subsets. Jointly, these papers (Alfonso et al, 2010a, b;Li et al, 2012;Ridolfi et al, 2011;Samuel et al, 2013;Stosic et al, 2017;Keum and Coulibaly, 2017;Banik et al, 2017;Wang et al, 2018;Huang et al, 2020;Khorshidi et al, 2020) have proposed a wide variety of different optimization objectives. Some have suggested that either a multi-objective approach or an single objective derived from multiple objectives is necessary to find an optimal monitoring network.…”

A Bayesian Fuzzy Clustering Approach for Design of Precipitation Gauge Network Using Merged Remote Sensing and Ground‐Based Precipitation Products

“…Information in sparsely gauged and inaccessible areas could be obtained by combining/merging ground‐ and satellite‐based measurements using effective strategies (Beck et al., 2019; Li & Shao, 2010; Shi et al., 2020; Xie & Xiong, 2011; Zhang et al., 2021). Compared to conventional options (e.g., interpolation, simulation), remote sensing products have the advantage of objectively and comprehensively providing the spatiotemporal relationship of precipitation patterns due to their finer spatial resolution and exhaustive coverage (Huang et al., 2020). In the merged precipitation product, ground‐based measurements provide information necessary to devise standardized bias correction strategies for minimizing the error and inhomogeneities in the remote sensing products with respect to in‐situ measurements.…”

“…Information in sparsely gauged and inaccessible areas could be obtained by combining/merging ground-and satellite-based measurements using effective strategies (Beck et al, 2019;Li & Shao, 2010;Shi et al, 2020;Xie & Xiong, 2011;Zhang et al, 2021). Compared to conventional options (e.g., interpolation, simulation), remote sensing products have the advantage of objectively and comprehensively providing the spatiotemporal relationship of precipitation patterns due to their finer spatial resolution and exhaustive coverage (Huang et al, 2020).…”