Assessment of the impact of meteorological network density on the estimation of basin precipitation and runoff: a case study

St‐Hilaire, André; Ouarda, Taha B. M. J.; Lachance, Marius; Bobée, Bernard; Gaudet, Jocelyn; Gignac, Claude

doi:10.1002/hyp.1350

Cited by 60 publications

(34 citation statements)

References 19 publications

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“…It has been successfully used and tested by the hydropower company Hydro-Québec for over 20 years to predict runoff for their reservoirs (St-Hilaire et al, 2003;Chaumont and Chartier, 2005;Minville et al, 2008). HSAMI is particularly appropriate for this study because it takes into account the locally important processes of snow accumulation, snowmelt, and soil freezing/thawing, as well as evapotranspiration, and because it has already been calibrated to the watersheds concerned (it is used for daily forecasting of natural infl ows on 84 watersheds ranging in area from 160 km 2 to 69195 km 2 : Minville et al, 2008).…”

Section: Discharge Scenariosmentioning

confidence: 99%

Numerical modelling of climate change impacts on Saint‐Lawrence River tributaries

Verhaar

Biron

Ferguson

et al. 2010

Earth Surf Processes Landf

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The impacts of climate-induced changes in discharge and base level in three tributaries of the Saint-Lawrence River, Québec, Canada, are modelled for the period 2010-2099 using a one-dimensional morphodynamic model. Changes in channel stability and bed-material delivery to the Saint-Lawrence River over this period are simulated for all combinations of seven tributary hydrological regimes (present-day and those predicted using three global climate models and two greenhouse gas emission scenarios) and three scenarios of how the base level provided by the Saint-Lawrence River will alter (no change, gradual fall, step fall). Even with no change in base level the projected discharge scenarios lead to an increase in average bed material delivery for most combinations of river and global climate model, although the magnitude of simulated change depends on the choice of global climate model and the trend over time seems related to whether the river is currently aggrading, degrading or in equilibrium. The choice of greenhouse gas emission scenario makes much less difference than the choice of global climate model. As expected, a fall in base level leads to degradation in the rivers currently aggrading or in equilibrium, and amplifi es the effects of climate change on sediment delivery to the Saint-Lawrence River. These differences highlight the importance of investigating several rivers using several climate models in order to determine trends in climate change impacts.

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Section: Discharge Scenariosmentioning

confidence: 99%

Numerical modelling of climate change impacts on Saint‐Lawrence River tributaries

Verhaar

Biron

Ferguson

et al. 2010

Earth Surf Processes Landf

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“…Generally, point measurements of raingauge accumulations are distributed in space over the catchment by interpolation techniques (e.g., kriging, Thiessen polygons, and inverse distance method). A large number A. Bárdossy and T. Das: Rainfall network on model calibration and application of earlier studies investigated the influence of the density of the raingauge network on the simulated discharge, with both real and synthetic precipitation and discharge data sets (Krajewski et al, 1991;Peters-Lidard and Wood, 1994;Seed and Austin, 1990;Duncan et al, 1993;St-Hilarie et al, 2003). Michaud and Sorooshian (1994) observed that inadequate raingauge densities in the case of the sparse network produced significant errors in the simulated peaks in a midsized semi-arid catchment.…”

Section: Introductionmentioning

confidence: 99%

Influence of rainfall observation network on model calibration and application

Bàrdossy

Das

2008

Hydrol. Earth Syst. Sci.

178

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Abstract. The objective in this study is to investigate the influence of the spatial resolution of the rainfall input on the model calibration and application. The analysis is carried out by varying the distribution of the raingauge network. A meso-scale catchment located in southwest Germany has been selected for this study. First, the semi-distributed HBV model is calibrated with the precipitation interpolated from the available observed rainfall of the different raingauge networks. An automatic calibration method based on the combinatorial optimization algorithm simulated annealing is applied. The performance of the hydrological model is analyzed as a function of the raingauge density. Secondly, the calibrated model is validated using interpolated precipitation from the same raingauge density used for the calibration as well as interpolated precipitation based on networks of reduced and increased raingauge density. Lastly, the effect of missing rainfall data is investigated by using a multiple linear regression approach for filling in the missing measurements. The model, calibrated with the complete set of observed data, is then run in the validation period using the above described precipitation field. The simulated hydrographs obtained in the above described three sets of experiments are analyzed through the comparisons of the computed Nash-Sutcliffe coefficient and several goodness-of-fit indexes. The results show that the model using different raingauge networks might need re-calibration of the model parameters, specifically model calibrated on relatively sparse precipitation information might perform well on dense precipitation information while model calibrated on dense precipitation information fails on sparse precipitation information. Also, the model calibrated with the complete set of observed precipitation and run with incomplete observed data associated with the data estimated using multiple linear regressions, at the locations treated as missing measurements, performs well.

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“…For such a reason, design of rain gauge networks both in terms of density (number of rain gauges), structure (location of a single rain gauge), and temporal resolution has been an issue widely investigated in scientific literature to better understand its implications in hydrological modeling, e.g., its influence on basin discharge (Krajewski et al 1991;St-Hilarie et al 2003;Meselhe et al 2009). …”

Section: Introductionmentioning

confidence: 99%

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

2014

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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.

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Assessment of the impact of meteorological network density on the estimation of basin precipitation and runoff: a case study

Cited by 60 publications

References 19 publications

Numerical modelling of climate change impacts on Saint‐Lawrence River tributaries

Numerical modelling of climate change impacts on Saint‐Lawrence River tributaries

Influence of rainfall observation network on model calibration and application

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

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