An entropy-based approach for the optimization of rain gauge network using satellite and ground-based data

Claudia Bertini,Leonardo Alfonso,Elena Ridolfi,Luiz Henrique Resende De Padua,Francesco Napolitano,Fabio Russo

doi:10.2166/nh.2021.113

Abstract

Abstract Accurate and precise rainfall records are crucial for hydrological applications and water resources management. The accuracy and continuity of ground-based time series rely on the density and distribution of rain gauges over territories. In the context of a decline of rain gauge distribution, how to optimize and design optimal networks is still an unsolved issue. In this work, we present a method to optimize a ground-based rainfall network using satellite-based observations, maximizing the information content of the network. We combine Climate Prediction Center MORPhing technique (CMORPH) observations at ungauged locations with an existing rain gauge network in the Rio das Velhas catchment, in Brazil. We use a greedy ranking algorithm to rank the potential locations to place new sensors, based on their contribution to the joint entropy of the network. Results show that the most informative locations in the catchment correspond to those areas with the highest rainfall variability and that satellite observations can be successfully employed to optimize rainfall monitoring networks.

Highlights

Quantification of precipitation is essential for improving knowledge about hydrological and water resources applications, including water allocation, water resources monitoring and risk assessment
Similar results can be observed for experiment S (Figure 3(a)). These findings confirm that entropy is mainly driven by precipitation variability and, time series variance (Alfonso et al ) and that Center MORPhing technique (CMORPH) precipitation estimates have high capability of capturing rainfall variability (Xie et al )
In this paper we present a method to optimize rain gauge networks using satellite observations with an entropybased approach

Summary

Introduction

Quantification of precipitation is essential for improving knowledge about hydrological and water resources applications, including water allocation, water resources monitoring and risk assessment. For this reason, it is desirable to have dense rain gauge networks (Li et al ). The amount of information content and of redundancy given by a monitoring network can be measured using IT (Shannon ). The concept of entropy can be extended to a random discrete variable X (Shannon & Weaver ), with discrete values x1, x2, . . . , xn and corresponding probabilities p(x1), p(x2), . . . , p(xn): Xn

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Methods

Results

Discussion

Conclusion