Influence of Karst Reservoir Capacity on Flood in Lijiang Basin Based on Modified HEC-HMS through Soil Moisture Accounting Loss
| Autor: | Junfeng Dai, Saeed Rad, Jingxuan Xu, Zupeng Wan, Zitao Li, Linyan Pan, Asfandyar Shahab |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Atmosphere, Vol 13, Iss 10, p 1544 (2022) |
| Druh dokumentu: | article |
| ISSN: | 13101544 2073-4433 44298099 |
| DOI: | 10.3390/atmos13101544 |
| Popis: | The objective of this work was to modify the HEC-HMS flood prediction for the karstic watershed of the Lijiang River, South China, through the quantitative inclusion into the model of the available reservoir capacity of karst (ARCK) as a case study. Due to the complexities caused by hidden drainage networks in karst hydrology, as a new approach, soil moisture accounting loss was used to reflect the ARCK in flood forecasting. The soil moisture loss was analyzed against daily rainfall runoff data across 1.5 years by using an artificial neural network via phyton programming. Through the correlations found among the amounts of soil moisture and river flow fluctuations in response to precipitation and its intervals, coefficients were introduced to the model for output modifications. ARCK analysis revealed that while heavy rainfalls with longer intervals (i.e., 174 mm/2d after 112 days of the dry season) may not cause considerable changes in the river flow magnitude (0.1–0.64 higher owing to high ARCK), relatively small rainfalls with higher frequency (i.e., 83 mm/4d during the wet season) can cause drastic raise of river flow (10–20 times greater at different stations) due to lower ARCK. Soil moisture accounting loss coefficients did enhance the model’s simulated hydrographs accuracy (NSE) up to 16% on average as compared to the initial forecasting via real data. However, the modifications were valid for flood events within a few years from the soil moisture observation period. Our result suggested that the inclusion of ARCK in modeling through soil moisture accounting loss can lead to increased prediction accuracy through consistent monitoring. |
| Databáze: | Directory of Open Access Journals |
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