Application of a Developed Grid-Xinanjiang Model to Chinese Watersheds for Flood Forecasting Purpose

Yao, Cheng; Li, Zhijia; Bao, Hongjun; Yu, Zhongbo

doi:10.1061/(asce)he.1943-5584.0000067

Cited by 61 publications

(49 citation statements)

References 33 publications

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“…Now, the focus is to develop such conceptual model which include the merits of the conceptual rainfall-runoff models and overcome the deficiencies of physically-based models (Robinson and Sivapalan 1995;Koren et al 2003;Chen et al 2007;Yao et al 2009;Zhang and Werner 2009). Some research has shown that the conceptual distributed hydrological models are applicable in the field of flood forecasting (Chen et al 2007;Yao et al 2009). In this study, to forecast floods with high precision efficiency, three kinds of conceptual distributed hydrological models are applied to construct rainfall-runoff process.…”

Section: Introductionmentioning

confidence: 98%

“…Such models include TOPMODEL (Beven and Kirkby 1979), the HBV model (Bergström1976), Xinanjiang model (Zhao et al 1980) and ARNO model (Todini 1996). Now, the focus is to develop such conceptual model which include the merits of the conceptual rainfall-runoff models and overcome the deficiencies of physically-based models (Robinson and Sivapalan 1995;Koren et al 2003;Chen et al 2007;Yao et al 2009;Zhang and Werner 2009). Some research has shown that the conceptual distributed hydrological models are applicable in the field of flood forecasting (Chen et al 2007;Yao et al 2009).…”

Section: Introductionmentioning

confidence: 99%

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Optimal application of conceptual rainfall–runoff hydrological models in the Jinshajiang River basin, China

et al. 2015

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For specific research areas different hydrological models have shown different characteristics. By comparing different hydrological models on the same area we should get better and more authentic results. The objective of this research study is to highlight the importance of model selection for specific research areas. For the Jinshajiang River basin, three conceptual hydrological models including the Xin'anjiang model, the Antecedent precipitation index (API) model and the Tank model are applied to select the most suitable model for flood forecasting, based on the hourly rainfall and hourly discharge data. Data were analysed by comparing the simulation outputs of the three models with the Nash-Sutcliffe efficiency and Correlation coefficient index. Results showed that the performance of the three models were not very different. On the basis of data need and the characteristics of the research basin, the Xin'anjiang model was selected as the optimal and practical conceptual hydrological model for the Jinshajiang River basin.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Optimal application of conceptual rainfall–runoff hydrological models in the Jinshajiang River basin, China

et al. 2015

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“…The Grid-Xinanjiang model is an improved version of the Xinanjiang model, which is widely used for flood forecasting in humid and semi-humid area of China [17,18]. The soil storage capacity is a key variable in the Grid-Xinanjiang model, which controls runoff generation and partitioning.…”

Section: Introductionmentioning

confidence: 99%

Subgrid Parameterization of the Soil Moisture Storage Capacity for a Distributed Rainfall-Runoff Model

Guo

Wang

Zeng

et al. 2015

Water

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Spatial variability plays an important role in nonlinear hydrologic processes. Due to the limitation of computational efficiency and data resolution, subgrid variability is usually assumed to be uniform for most grid-based rainfall-runoff models, which leads to the scale-dependence of model performances. In this paper, the scale effect on the Grid-Xinanjiang model was examined. The bias of the estimation of precipitation, runoff, evapotranspiration and soil moisture at the different grid scales, along with the scale-dependence of the effective parameters, highlights the importance of well representing the subgrid variability. This paper presents a subgrid parameterization method to incorporate the subgrid variability of the soil storage capacity, which is a key variable that controls runoff generation and partitioning in the Grid-Xinanjiang model. In light of the similar spatial pattern and physical basis, the soil storage capacity is correlated with the topographic index, whose spatial distribution can more readily be measured. A beta distribution is introduced to represent the spatial distribution of the soil storage capacity within the grid. The results derived from the Yanduhe Basin show that the proposed subgrid parameterization method can effectively correct the watershed soil storage capacity curve. Compared to the original Grid-Xinanjiang model, the model performances are quite consistent at the different grid scales when the subgrid variability is incorporated. This subgrid parameterization method reduces the recalibration necessity when the Digital Elevation Model (DEM) resolution is changed. Moreover, it improves the potential for the application of the distributed model in the ungauged basin. OPEN ACCESSWater 2015, 7 2692

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“…To this end, a case study was carried out using five TIGGE forecast centres: The Grid-Xinanjiang model (Li et al, 2006;Wang et al, 2007;Yao et al, 2009) was used to simulate the discharge. The study area is the Xixian catchment, located upstream of the Huaihe River.…”

Section: Introductionmentioning

confidence: 99%

“…Outlet flow of each pixel to the outlet of the whole catchment is routed from cell to cell by using the Muskingum-Cunge method (Cunge, 1969) based on the computed order among the DEM grid cells. During the calculation of the runoff generation and runoff concentration in every computational element, the reinfiltration and influence of the river drainage network are taken into consideration (Band, 1986;Yao et al, 2009). The flow is partitioned into channel and downstream cell according to the channel inflow direction and channel outflow direction.…”

Section: Introduction Of the Grid-xinanjiang Modelmentioning

confidence: 99%

Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

Bao

Liang

et al. 2011

Adv. Geosci.

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Abstract. The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km 2 ) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.

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Application of a Developed Grid-Xinanjiang Model to Chinese Watersheds for Flood Forecasting Purpose

Cited by 61 publications

References 33 publications

Optimal application of conceptual rainfall–runoff hydrological models in the Jinshajiang River basin, China

Optimal application of conceptual rainfall–runoff hydrological models in the Jinshajiang River basin, China

Subgrid Parameterization of the Soil Moisture Storage Capacity for a Distributed Rainfall-Runoff Model

Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

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