An entropy-based approach for the optimization of rain gauge network using satellite and ground-based data
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IWA Publishing
2021-06-01
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| Series: | Hydrology Research |
| Subjects: | |
| Online Access: | http://hr.iwaponline.com/content/52/3/620 |
| Summary: | 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
This study proposes a method to evaluate and optimize an existing rain gauge network using satellite observations.;
The most informative locations in the catchment are identified using a greedy ranking algorithm.;
The resulting optimal network provides higher joint entropy with fewer sensors.;
The most informative locations reflect those with highest variance.; |
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| ISSN: | 1998-9563 2224-7955 |