Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

Precipitation provides the most crucial input for hydrological modeling. However, rain gauge networks, the most common precipitation measurement mechanisms, are sometimes sparse and inadequately distributed in practice, resulting in an imperfect representation of rainfall spatial variability. The ob...

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Bibliographic Details
Published in:Sustainability 2018-09, Vol.10 (9), p.3209
Main Authors: Yin, Zhaokai, Liao, Weihong, Lei, Xiaohui, Wang, Hao, Wang, Ruojia
Format: Article
Language:English
Subjects:
Calibration
Energy transfer
Gauges
Hydrologic models
Hydrology
Model accuracy
Network reliability
Precipitation
Rain
Rain gauges
Rainfall
Reliability analysis
River basins
Rivers
Sensitivity analysis
Spatial distribution
Sustainability
Uncertainty
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Summary:Precipitation provides the most crucial input for hydrological modeling. However, rain gauge networks, the most common precipitation measurement mechanisms, are sometimes sparse and inadequately distributed in practice, resulting in an imperfect representation of rainfall spatial variability. The objective of this study is to analyze the sensitivity of different model structures to the different density and distribution of rain gauges and evaluate their reliability and robustness. Based on a rain gauge network of 20 gauges in the Jinjiang River Basin, south-eastern China, this study compared the performance of two conceptual models (the hydrologic model (HYMOD) and Xinanjiang) and one process-based distributed model (the water and energy transfer between soil, plants and atmosphere model (WetSpa)) with different rain gauge distributions. The results show that the average accuracy for the three models is generally stable as the number of rain gauges decreases but is sensitive to changes in the network distribution. HYMOD has the highest calibration uncertainty, followed by Xinanjiang and WetSpa. Differing model responses are consistent with changes in network distribution, while calibration uncertainties are more related to model structures.
ISSN:2071-1050
2071-1050
DOI:10.3390/su10093209