Redirigiendo al acceso original de articulo en 16 segundos...
Inicio  /  Water  /  Vol: 14 Par: 4 (2022)  /  Artículo
ARTÍCULO
TITULO

Incorporating the Filling?Spilling Feature of Depressions into Hydrologic Modeling

Lan Zeng    
Haoyong Shen    
Yali Cui    
Xuefeng Chu and Jingli Shao    

Resumen

Surface depressions are one of the important impact factors of hydrologic processes and catchment responses. However, in many hydrologic models, the influence of depressions is often simulated in a lumped manner, which results in the insufficient characterization of the filling?spilling?merging?splitting dynamics of depressions and the threshold behavior of the overland flow. The objective of the research reported in this paper is to improve the simulation of depression-influenced hydrologic processes by capturing the threshold control of depressions. To achieve this objective, a Depression-oriented Soil and Water Assessment Tool (SWAT-D) is developed. Specifically, the intrinsic changing patterns of contributing area and depression storage are first determined and further incorporated into the SWAT to simulate the filling?spilling of depressions and depression-influenced overland flow dynamics. The SWAT-D was applied to a depression-dominated watershed in the Prairie Pothole Region to evaluate its performance and capability. The simulated and observed hydrographs at the watershed outlet showed good agreement, with only a 7% deviation between the simulated and observed volumes of discharges in 2004. The NSE values for the simulated monthly average discharges during calibration and validation periods were 0.78 and 0.71, respectively, indicating the ability of the SWAT-D in reproducing the depression-influenced catchment responses. In addition, the SWAT-D was compared with other depression-oriented modeling techniques (i.e., the lumped depression approach and probability distribution models), and the comparisons emphasized the improvement of the SWAT-D and the importance of the research reported in this paper.

 Artículos similares

       
 
Jianjun Wu, Yuxue Hu, Zhongqiang Huang, Junsong Li, Xiang Li and Ying Sha    
Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often i... ver más
Revista: Applied Sciences

 
Suping Wang, Ligu Zhu, Lei Shi, Hao Mo and Songfu Tan    
Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and advance the field. Although previous reviews have primarily focused on binary and real-value coding methods, there is a scarcity of techniques grounded in deep repres... ver más
Revista: Applied Sciences

 
Simegnsh Bekele Dekebo, Gi-Taek Oh and Min-Woo Lee    
A moving window decision-making algorithm is proposed for the cleaning schedule optimization of heat exchanger network system subject to fouling in refinery crude preheat train. This algorithm is designed by incorporating the moving window scheme into a ... ver más
Revista: Applied Sciences

 
Charalampos M. Liapis and Sotiris Kotsiantis    
The use of deep learning in conjunction with models that extract emotion-related information from texts to predict financial time series is based on the assumption that what is said about a stock is correlated with the way that stock fluctuates. Given th... ver más
Revista: Information

 
Xulong Yu, Qiancheng Yu, Qunyue Mu, Zhiyong Hu and Jincai Xie    
Traditional manual visual detection methods are inefficient, subjective, and costly, making them prone to false and missed detections. Deep-learning-based defect detection identifies the types of defects and pinpoints their locations. By employing this a... ver más
Revista: Applied Sciences