Impact of small-scale spatial rainfall variability on runoff modeling

Faurès, Jean‐Marc; Goodrich, David C.; Woolhiser, David A.; Sorooshian, Soroosh

doi:10.1016/0022-1694(95)02704-s

Cited by 317 publications

(247 citation statements)

References 19 publications

Supporting

Mentioning

216

Contrasting

Unclassified

Order By: Relevance

“…The configurations of the two types of networks rarely coincide, in agreement with Eagleson (1967a, b) and Faurès et al (1995), because the best positions for the estimation of runoff are influenced by the rainfallrunoff transformation and thus by the soil distribution. As already observed for the estimation of the precipitation, the minimum RMSE Q is never obtained with the complete network.…”

Section: Hydrograph Reconstructionsupporting

confidence: 61%

“…7 shows the value of NRMSE min;Q for each event as a function of the number of gauges and for fixed soil configuration. When rainfall spatial variability is high (events Hh-Mh-Lh), there is an increased influence of the soil configuration on the network performance, and the gauge location becomes a crucial parameter in modeling the storm hydrograph, as also stated by Faurès et al (1995). For medium or low COV S , as the soil configuration changes, the NRMSE min;Q curves have a similar pattern.…”

Section: Hydrograph Reconstructionmentioning

confidence: 88%

“…In fact, the increase of the number of rain gauges caused an elevated improvement in the estimation of both runoff and flood peak. Faurès et al (1995), through analysis at the event scale, demonstrated that the uncertainty in runoff estimation is strongly related to the number of gauges providing the input data. In case of increased spatial rainfall gradients, the location of the gauge also becomes a crucial parameter in the modeling of the storm hydrograph.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Influence of Spatial Precipitation Sampling on Hydrological Response at the Catchment Scale

2014

View full text Add to dashboard Cite

Retrieving precipitation data from a rain gauge network is a classical and common practice in hydrology and climatology. These data represent the key input in hydrological modeling to reproduce, for example, the characteristics of a flood phenomenon. The accuracy of the model results is strongly dependent on the consistency of the monitoring network in terms of spatial scale, i.e., network density and location of the rain gauges and time resolution. In this context, several studies have been carried out to analyze how the rainfall sampling influences the estimation of total runoff volume. The aim of this paper is to use a physically based and distributed-parameter hydrologic model to investigate how the number and the spatial distribution of a rain gauge network influence the estimation of the hydrograph and its characteristics in conjunction with different spatial and temporal characteristics of rainfall forcing and different soil-type characteristics. The TIN-based real-time integrated basin simulator (tRIBS) hydrologic model was used to simulate hydrologic response at Baron Fork Basin, Oklahoma. Downscaled next-generation radar (NEXRAD) measurements were assumed to represent the true precipitation distribution over the basin. Additional precipitation fields have been derived from the interpolation of eight fictitious rain gauges randomly placed in the area. The hydrological response from tRIBS that is driven by these precipitation fields has been compared with the response of the model forced with NEXRAD precipitation. The analysis has been carried out assuming first simplified spatial distributions of soil characteristics and then the real soil-type distribution. Results have shown the dependence of the best rain gauges configuration for the estimation of runoff on the spatiotemporal characteristics of storm events and the soil-type distribution.

show abstract

Section: Hydrograph Reconstructionsupporting

confidence: 61%

Section: Hydrograph Reconstructionmentioning

confidence: 88%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of Spatial Precipitation Sampling on Hydrological Response at the Catchment Scale

2014

View full text Add to dashboard Cite

show abstract

“…In laboratory studies of a conical watershed with spatially varied roughness under sprinklers, Wu et al [1982] observed 14% average and 30% maximum errors in predicted versus observed peak discharge when applying an equivalent uniform roughness in numerical simulations. Faures et al [1995] report coefficients of variation in peak discharge and runoff volumes up to 76% and 65%, respectively, in physically based simulations of a 4 ha catchment as a result of selective rain gauge sampling during thunderstorm rainfall. Smith et al [1994] provide an excellent discussion on the fact that nature is not very good at repeating itself given similar antecedent conditions and forcing.…”

Section: Point 2: Evaluating Model Performance For Subcatchmentsmentioning

confidence: 99%

Reply [to “Comment on ‘On the calibration and verification of two‐dimensional, distributed, Hortonian, continuous watershed models‘ by Sharika U. S. Senarath et al.”]

Ogden¹,

Senarath²,

Downer³

et al. 2001

Water Resources Research

View full text Add to dashboard Cite

“…For example, Wilson et al [21] indicated that the differences in simulated runoff could be quite significant when the rainfall inputs were changed based on different combinations of rainfall stations in the Fajardo basin, with an area of 68.6 km 2 in Puerto Rico. Faurès et al [22] proved that a single rainfall station with a uniform rainfall assumption could lead to large uncertainty in runoff estimation. Lopes [23] examined the effect of uncertainty in spatial estimates of rainfall on the prediction of runoff volume and the results showed that the density of the rainfall station network could greatly influence the catchment response predictions when rainfall stations were randomly excluded one by one from the analysis.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

Zhang

Shi

et al. 2018

Atmosphere

View full text Add to dashboard Cite

Abstract:Rainfall stations of a certain number and spatial distribution supply sampling records of rainfall processes in a river basin. Uncertainty may be introduced when the station records are spatially interpolated for the purpose of hydrological simulations. This study adopts a bootstrap method to quantitatively estimate the uncertainty of areal rainfall estimates and its effects on hydrological simulations. The observed rainfall records are first analyzed using clustering and correlation methods and possible average basin rainfall amounts are calculated with a bootstrap method using various combinations of rainfall station subsets. Then, the uncertainty of simulated runoff, which is propagated through a hydrological model from the spatial uncertainty of rainfall estimates, is analyzed with the bootstrapped rainfall inputs. By comparing the uncertainties of rainfall and runoff, the responses of the hydrological simulation to the rainfall spatial uncertainty are discussed. Analyses are primarily performed for three rainfall events in the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River; moreover, one rainfall event in the Longxi River basin is selected for the analysis of the areal representation of rainfall stations. Using the Digital Yellow River Integrated Model, the results show that the uncertainty of rainfall estimates derived from rainfall station network has a direct influence on model simulation, which can be conducive to better understand of rainfall spatial characteristic. The proposed method can be a guide to quantify an approximate range of simulated error caused by the spatial uncertainty of rainfall input and the quantified relationship between rainfall input and simulation performance can provide useful information about rainfall station network management in river basins.

show abstract

Impact of small-scale spatial rainfall variability on runoff modeling

Cited by 317 publications

References 19 publications

Influence of Spatial Precipitation Sampling on Hydrological Response at the Catchment Scale

Influence of Spatial Precipitation Sampling on Hydrological Response at the Catchment Scale

Reply [to “Comment on ‘On the calibration and verification of two‐dimensional, distributed, Hortonian, continuous watershed models‘ by Sharika U. S. Senarath et al.”]

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

Contact Info

Product

Resources

About