A priori parameter estimates for a distributed, grid-based Xinanjiang model using geographically based information

Yao, Cheng; Li, Zhijia; Yu, Zhongbo; Zhang, Ke

doi:10.1016/j.jhydrol.2012.08.025

Cited by 77 publications

(63 citation statements)

References 61 publications

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“…The values of all these parameters were roughly estimated as the means of their value ranges determined from the literature. Parameter W m of the iCRESTRI-GRS model was determined from topography using the a priori estimation method developed by Yao et al (2012), while parameter b is set to a constant (1.5) across the study region based on our past experience. For the rest of the iCRESTRI-GRS parameters that are inherited from CREST, we determined their distributed values using the parameter look-up table provided by the CREST user manual based on the land cover map and the soil texture map.…”

Section: Model Parameters and Initializationmentioning

confidence: 99%

iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

Zhang

Xue

Hong

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Severe storm-triggered floods and landslides are two major natural hazards in the US, causing property losses of USD 6 billion and approximately 110-160 fatalities per year nationwide. Moreover, floods and landslides often occur in a cascading manner, posing significant risk and leading to losses that are significantly greater than the sum of the losses from the hazards when acting separately. It is pertinent to couple hydrological and geotechnical modeling processes to an integrated flood-landslide cascading disaster modeling system for improved disaster preparedness and hazard management. In this study, we developed the iCRESTRIGRS model, a coupled flash flood and landslide initiation modeling system, by integrating the Coupled Routing and Excess STorage (CREST) model with the physically based Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) landslide model. The iCRESTRIGRS system is evaluated in four river basins in western North Carolina that experienced a large number of floods, landslides and debris flows triggered by heavy rainfall from Hurricane Ivan during 16-18 September 2004. The modeled hourly hydrographs at four USGS gauge stations show generally good agreement with the observations during the entire storm period. In terms of landslide prediction in this case study, the coupled model has a global accuracy of 98.9 % and a true positive rate of 56.4 %. More importantly, it shows an improved predictive capability for landslides relative to the stand-alone TRIGRS model. This study highlights the important physical connection between rainfall, hydrological processes and slope stability, and provides a useful prototype model system for operational forecasting of flood and landslide.

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Section: Model Parameters and Initializationmentioning

confidence: 99%

iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

Zhang

Xue

Hong

et al. 2016

Hydrol. Earth Syst. Sci.

Self Cite

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“…To explicitly express the effect of the spatial-temporal rainfall and soil moisture state, the rainfall excess Rðx; y; tÞ [L T -1 ] at time t and at point (x, y) can be thought as the product of rainfall field Pðx; y; tÞ [L T -1 ] is runoff coefficient Wðx; y; tÞ [-]. In this study, the runoff coefficient Wðx; y; tÞ is estimated by the Grid-XAJ model (Liu et al 2009;Yao et al 2012), which is a distributed hydrologic model widely used in humid and semi-humid area of China. The catchment-and stormaveraged rainfall excess R xyt [L T -1 ] can be computed by integrating the rainfall excess Rðx; y; tÞ in time and space domain: …”

Section: Catchment and Storm-averaged Rainfall Excessmentioning

confidence: 99%

“…For the parameters of the Grid-XAJ model refer to Yao et al (2012). The flow velocity in hillslope and channel are calibrated by four flood events during 1996-1999 at hourly time step.…”

Section: Model Evaluationmentioning

confidence: 99%

Quantifying the spatial variability of rainfall and flow routing on flood response across scales

et al. 2015

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The different spatial patterns of hydrologic processes always lead to different flood responses, and this effect varies with environment and scale. In this paper, we evaluate the sensitivity of hydrologic response to the spatial variability of rainfall and flow routing by a variability framework, which explicitly expresses the main features of hydrology in terms of the spatial-temporal variability of rainfall, runoff and flow routing. For a more general conclusion, a stochastic rainfall generator is used as input of the variability framework. We perform the numerical experiments at the Yanduhe Basin and its 64 subcatchments, with area ranging over three orders of magnitude. The results suggest that the sensitivity of hydrologic response to the spatial variability of rainfall depends on the characteristics of rainfall and antecedent soil moisture. The contribution of spatial variability of rainfall reaches the peak in the case of relatively small rainfall event or antecedent dry condition. Influenced by the characteristics of local rainfall, the contribution of spatial variability increases with the subcatchment size at the Yanduhe Basin. The hillslope routing is dominant for slow runoff or small catchment. With the increasing of flow velocity and catchment size, the importance of channel routing significantly increases.

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“…The power function provides an analytical solution of runoff generation but lacks definite physical interpretation. Following the work of Williams et al [23] and Anderson et al [24], Yao et al [25] utilized the soil texture and the land cover attributes to estimate the spatial distribution of the soil storage capacity. However, limited by resolution of soil property, the small-scale variability information is not readily available.…”

Section: Subgrid Scale Parameterization Of the Soil Storage Capacitymentioning

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

show abstract

A priori parameter estimates for a distributed, grid-based Xinanjiang model using geographically based information

Cited by 77 publications

References 61 publications

iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

iCRESTRIGRS: a coupled modeling system for cascading flood–landslide disaster forecasting

Quantifying the spatial variability of rainfall and flow routing on flood response across scales

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

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