Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model
<p>In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potent...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Jezik: | English |
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Copernicus Publications
2019-03-01
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| Serija: | Hydrology and Earth System Sciences |
| Online dostop: | https://www.hydrol-earth-syst-sci.net/23/1505/2019/hess-23-1505-2019.pdf |
| _version_ | 1831737704655618048 |
|---|---|
| author | J. Li D. Yuan D. Yuan J. Liu Y. Jiang Y. Chen K. L. Hsu S. Sorooshian |
| author_facet | J. Li D. Yuan D. Yuan J. Liu Y. Jiang Y. Chen K. L. Hsu S. Sorooshian |
| author_sort | J. Li |
| collection | DOAJ |
| description | <p>In general, there are no
long-term meteorological or hydrological data available for karst river
basins. The lack of rainfall data is a great challenge that hinders the
development of hydrological models. Quantitative precipitation estimates
(QPEs) based on weather satellites offer a potential method by which rainfall
data in karst areas could be obtained. Furthermore, coupling QPEs with a
distributed hydrological model has the potential to improve the precision of
flood predictions in large karst watersheds. Estimating precipitation from
remotely sensed information using an artificial neural network-cloud
classification system (PERSIANN-CCS) is a type of QPE technology based on
satellites that has achieved broad research results worldwide. However, only
a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and
the accuracy is generally poor in terms of practical applications. This paper
studied the feasibility of coupling a fully physically based distributed
hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for
predicting floods in a large river basin, i.e., the Liujiang karst river
basin, which has a watershed area of 58 270 km<span class="inline-formula"><sup>2</sup></span>, in southern China.
The model structure and function require further refinement to suit the karst
basins. For instance, the sub-basins in this paper are divided into many
karst hydrology response units (KHRUs) to ensure that the model structure is
adequately refined for karst areas. In addition, the convergence of the
underground runoff calculation method within the original Liuxihe model is
changed to suit the karst water-bearing media, and the Muskingum routing
method is used in the model to calculate the underground runoff in this
study. Additionally, the epikarst zone, as a distinctive structure of the
KHRU, is carefully considered in the model. The result of the QPEs shows that
compared with the observed precipitation measured by a rain gauge, the
distribution of precipitation predicted by the PERSIANN-CCS QPEs was very
similar. However, the quantity of precipitation predicted by the PERSIANN-CCS
QPEs was smaller. A post-processing method is proposed to revise the products
of the PERSIANN-CCS QPEs. The karst flood simulation results show that
coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a
better performance relative to the result based on the initial PERSIANN-CCS
QPEs. Moreover, the performance of the coupled model largely improves with
parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The
average values of the six evaluation indices change as follows: the
Nash–Sutcliffe coefficient increases by 14 %, the correlation
coefficient increases by 15 %, the process relative error decreases by
8 %, the peak flow relative error decreases by 18 %, the water
balance coefficient increases by 8 %, and the peak flow time error
displays a 5 h decrease. Among these parameters, the peak flow relative
error shows the greatest improvement; thus, these parameters are of<span id="page1506"/> the
greatest concern for flood prediction. The rational flood simulation results
from the coupled model provide a great practical application prospect for
flood prediction in large karst river basins.</p> |
| first_indexed | 2024-12-21T13:08:35Z |
| format | Article |
| id | doaj.art-180a0bd3d1f04f07a8b51a210d358d22 |
| institution | Directory Open Access Journal |
| issn | 1027-5606 1607-7938 |
| language | English |
| last_indexed | 2024-12-21T13:08:35Z |
| publishDate | 2019-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj.art-180a0bd3d1f04f07a8b51a210d358d222022-12-21T19:02:57ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382019-03-01231505153210.5194/hess-23-1505-2019Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological modelJ. Li0D. Yuan1D. Yuan2J. Liu3Y. Jiang4Y. Chen5K. L. Hsu6S. Sorooshian7School of Geographical Sciences of Southwest University, Chongqing Key Laboratory of Karst Environment, Chongqing 400715, ChinaSchool of Geographical Sciences of Southwest University, Chongqing Key Laboratory of Karst Environment, Chongqing 400715, ChinaKarst Dynamic Laboratory, Ministry of Land and Resources, Guilin 541004, ChinaChongqing Hydrology and Water Resources Bureau, Chongqing 401120, ChinaSchool of Geographical Sciences of Southwest University, Chongqing Key Laboratory of Karst Environment, Chongqing 400715, ChinaDepartment of Water Resources and Environment, Sun Yat-Sen University, Guangzhou 510275, ChinaCenter for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, CaliforniaCenter for Hydrometeorology and Remote Sensing, Department of Civil and Environmental Engineering, University of California, Irvine, Irvine, California<p>In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km<span class="inline-formula"><sup>2</sup></span>, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash–Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of<span id="page1506"/> the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins.</p>https://www.hydrol-earth-syst-sci.net/23/1505/2019/hess-23-1505-2019.pdf |
| spellingShingle | J. Li D. Yuan D. Yuan J. Liu Y. Jiang Y. Chen K. L. Hsu S. Sorooshian Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model Hydrology and Earth System Sciences |
| title | Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model |
| title_full | Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model |
| title_fullStr | Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model |
| title_full_unstemmed | Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model |
| title_short | Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model |
| title_sort | predicting floods in a large karst river basin by coupling persiann ccs qpes with a physically based distributed hydrological model |
| url | https://www.hydrol-earth-syst-sci.net/23/1505/2019/hess-23-1505-2019.pdf |
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