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This study aims to quantify the uncertainty caused by various combinations of spatiotemporal scales (0.1° 0.8° and 1 24 h) of gauge network designs in the densely gauged and relatively flat Ganjiang River basin, South China, in order to evaluate the state-of-the-art satellite precipitation, the Integrated Multi-satellite Retrievals for Global Precipitation Measurement IMERGGlobal Precipitation Measurement IMERG
To evaluate the final optimal gauge network, areal average rainfall was estimated and its accuracy was compared with that obtained with the existing rain gauge network.
However, equipping and maintaining a high-density rain gauge network will also involve high costs.
First, the effectiveness of the TC method is evaluated in South China with dense gauge network.
This paper estimates rainfall frequencies over a portion of Minnesota's high-density rain gauge network.
A methodology for the design of a rain gauge network is developed in this study.
Similar(24)
However, observational gauge networks provide only point estimates of streamflow variation.
Radar rainfall fields are sampled by simulated rain gauge networks, stochastically generated with varying rain gauge densities.
Results show that all sparse gauge networks persistently underestimate the performance of IMERG according to most metrics.
It is applied to two small rain gauge networks in France (Ubaye catchment) and Austria/Germany (Salzach catchment) and is shown to well reproduce the observations.
The results and discussion underline that combining meteorological information from different rain gauge networks with different equipments remains challenging for operational purposes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com