Precipitation Uncertainty and Raingauge Network Design  within Folsom Lake Watershed

Tsintikidis, Dimitris; Georgakakos, Konstantine P.; Sperfslage, Jason A.; Smith, Diane E.; Carpenter, T. M.

doi:10.1061/(asce)1084-0699(2002)7:2(175)

Cited by 68 publications

(38 citation statements)

References 18 publications

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“…Since the two storm events started at the beginning of the day, the AMSR-E descending data were selected as the initial conditions. The downloaded data have units of g/cm 3 and these units were converted into volumetric water content to use in the GSSHA model.…”

Section: Amsr-e Soil Moisturementioning

confidence: 99%

“…For example, Chintalapudi et al [4] found that a dense rain gauge network (one rain gauge for every 97 km 2 of basin area) significantly underestimated the streamflows for the June 2002 storm event over the Upper Guadalupe River Basin. Tsintikidis et al [3] also concluded that rain gauge precipitation underestimated the streamflows in Folsom Lake Watershed. However, other studies concluded that dense rain gauge networks provided superior model performance (e.g., [33]).…”

Section: Introductionmentioning

confidence: 96%

“…Rain gauges have problems in capturing the spatial variability of precipitation because they are point measurements. Tsintikidis [3] and Chintalapudi et al [4] have shown that when rain gauges are sparsely distributed over the watershed, they will not be able to capture the spatial variability of rainfall needed for hydrologic modeling. In many practical applications rain gauges are unevenly distributed over the basin and interpolation is required to achieve reasonable spatial distribution of precipitation over the watershed [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products

Chintalapudi

Sharif

Xie

2014

Water

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Abstract:In this study, seven precipitation products (rain gauges, NEXRAD MPE, PERSIANN 0.25 degree, PERSIANN CCS-3hr, PERSIANN CCS-1hr, TRMM 3B42V7, and CMORPH) were used to force a physically-based distributed hydrologic model. The model was driven by these products to simulate the hydrologic response of a 1232 km 2 watershed in the Guadalupe River basin, Texas. Storm events in 2007 were used to analyze the precipitation products. Comparison with rain gauge observations reveals that there were significant biases in the satellite rainfall products and large variations in the estimated amounts. The radar basin average precipitation compared very well with the rain gauge product while the gauge-adjusted TRMM 3B42V7 precipitation compared best with observed rainfall among all satellite precipitation products. The NEXRAD MPE simulated streamflows matched the observed ones the best yielding the highest Nash-Sutcliffe Efficiency correlation coefficient values for both the July and August 2007 events. Simulations driven by TRMM 3B42V7 matched the observed streamflow better than other satellite products for both events. The PERSIANN coarse resolution product yielded better runoff results than the higher resolution product. The study reveals that satellite rainfall products are viable alternatives when rain gauge or ground radar observations are sparse or non-existent.

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Section: Amsr-e Soil Moisturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

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Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products

Chintalapudi

Sharif

Xie

2014

Water

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“…Precipitation uncertainty due to spatiotemporal distribution strongly influences flood forecasting and warnings associated with incorrect flow simulations (Berndtsson and Niemczynowicz 1988;Morin et al 1995;Johnson et al 1999;Tsintikidis et al 2002;Younger et al 2009;Arnaud et al 2011). In particular, mountainous regions require more reliable precipitation estimation because the interaction between mountainous terrain and the atmosphere increases the variability of precipitation patterns and the precipitation amounts related to the mesoscale precipitation process (Krajewski and Georgakakos 1994;Wheater et al 2000).…”

Section: Introductionmentioning

confidence: 99%

“…Rationalization based on multivariate analyses was used to eliminate rain-gauge redundancy for the optimum rain-gauge network (Burn and Goulter 1991). Geostatistical frameworks have been utilized for optimal distribution of the rain-gauge monitoring network, because these can produce unbiased estimators with minimum error variance (Tsintikidis et al 2002;Barca et al 2008;Chen et al 2008;Cheng et al 2008;Chebbi et al 2011). In the geostatistical applications, Bastin et al (1984) and Kassim and Kottegoda (1991) used the iterative manner to select rain-gauge locations associated with minimum kriging variance.…”

Section: Introductionmentioning

confidence: 99%

Rain-Gauge Network Evaluations Using Spatiotemporal Correlation Structure for Semi-Mountainous Regions

Jung¹,

Kim²,

Baik³

et al. 2014

Terr. Atmos. Ocean. Sci.

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A reliable network of rain gauges is a crucial component of rainfall estimation in a watershed. To provide a better evaluation method for rain-gauge networks, a new evaluation method using average inter-gauge correlation coefficients (averaged CC) for estimating an effective radius for each rain gauge was developed. In this study, averaged CCs were obtained from the values of inter-gauge correlation coefficients after choosing a minimum number of rainfall data sets as a threshold. The Nam River Basin (2400 km 2 ) and its 24 rain gauges were selected with 8 years (2003 -2010) rainfall data to validate a new evaluation method. In the spatial correlation coefficient fitting process for generating correlation distances, averaged CCs increased fitness accuracy (maximum 37%) in terms of coefficient of determination (R 2 ) compared with a commonly used method (the last value of the inter-gauge correlation coefficient as the number of data sets is increased: last CC). In the evaluation of effective radii for 8 years, the robustness of the averaged CCs was supported by lower standard deviations for all rain gauges. For the optimum coverage of rainfall estimation in terms of effective radius, the Nam River Basin requires 20 rain gauges. Investigation of altitude effects presented that the effective radii were minimally influenced by the altitude of rain-gauge locations for this area.

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Optimal design of rain gauge network in the Middle Yarra River catchment, Australia

Adhikary

Yilmaz

Muttil

2014

Hydrological Processes

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Abstract:Rainfall data are a fundamental input for effective planning, designing and operating of water resources projects. A welldesigned rain gauge network is capable of providing accurate estimates of necessary areal average and/or point rainfall estimates at any desired ungauged location in a catchment. Increasing network density with additional rain gauge stations has been the main underlying criterion in the past to reduce error and uncertainty in rainfall estimates. However, installing and operation of additional stations in a network involves large cost and manpower. Hence, the objective of this study is to design an optimal rain gauge network in the Middle Yarra River catchment in Victoria, Australia. The optimal positioning of additional stations as well as optimally relocating of existing redundant stations using the kriging-based geostatistical approach was undertaken in this study. Reduction of kriging error was considered as an indicator for optimal spatial positioning of the stations. Daily rainfall records of 1997 (an El Niño year) and 2010 (a La Niña year) were used for the analysis. Ordinary kriging was applied for rainfall data interpolation to estimate the kriging error for the network. The results indicate that significant reduction in the kriging error can be achieved by the optimal spatial positioning of the additional as well as redundant stations. Thus, the obtained optimal rain gauge network is expected to be appropriate for providing high quality rainfall estimates over the catchment. The concept proposed in this study for optimal rain gauge network design through combined use of additional and redundant stations together is equally applicable to any other catchment.

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Precipitation Uncertainty and Raingauge Network Design within Folsom Lake Watershed

Cited by 68 publications

References 18 publications

Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products

Sensitivity of Distributed Hydrologic Simulations to Ground and Satellite Based Rainfall Products

Rain-Gauge Network Evaluations Using Spatiotemporal Correlation Structure for Semi-Mountainous Regions

Optimal design of rain gauge network in the Middle Yarra River catchment, Australia

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