scholarly journals Regional Regression Equations to Estimate Flow-Duration Statistics at Ungaged Stream Sites in Connecticut

2010 ◽  
Author(s):  
Elizabeth A. Ahearn
Keyword(s):  
Regression Equations ◽  
Flow Duration ◽  
Regional Regression ◽  
Estimate Flow

scholarly journals Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves

2018 ◽  
Vol 22 (11) ◽  
pp. 5741-5758 ◽  
Author(s):  
William H. Farmer ◽  
Thomas M. Over ◽  
Julie E. Kiang
Keyword(s):  
Bias Correction ◽  
Temporal Structure ◽  
Daily Streamflow ◽  
Flow Duration ◽  
Regional Regression ◽  
Daily Data ◽  
Potential Efficacy ◽  
Flow Duration Curves ◽  
Future Work ◽  
Streamflow Timing

Abstract. In many simulations of historical daily streamflow distributional bias arising from the distributional properties of residuals has been noted. This bias often presents itself as an underestimation of high streamflow and an overestimation of low streamflow. Here, 1168 streamgages across the conterminous USA, having at least 14 complete water years of daily data between 1 October 1980 and 30 September 2013, are used to explore a method for rescaling simulated streamflow to correct the distributional bias. Based on an existing approach that separates the simulated streamflow into components of temporal structure and magnitude, the temporal structure is converted to simulated nonexceedance probabilities and the magnitudes are rescaled using an independently estimated flow duration curve (FDC) derived from regional regression. In this study, this method is applied to a pooled ordinary kriging simulation of daily streamflow coupled with FDCs estimated by regional regression on basin characteristics. The improvement in the representation of high and low streamflows is correlated with the accuracy and unbiasedness of the estimated FDC. The method is verified by using an idealized case; however, with the introduction of regionally regressed FDCs developed for this study, the method is only useful overall for the upper tails, which are more accurately and unbiasedly estimated than the lower tails. It remains for future work to determine how accurate the estimated FDCs need to be to be useful for bias correction without unduly reducing accuracy. In addition to its potential efficacy for distributional bias correction, this particular instance of the methodology also represents a generalization of nonlinear spatial interpolation of daily streamflow using FDCs. Rather than relying on single index stations, as is commonly done to reflect streamflow timing, this approach to simulation leverages geostatistical tools to allow a region of neighbors to reflect streamflow timing.

scholarly journals Regional flow duration curves for ungauged sites in Sicily

2010 ◽  
Vol 7 (5) ◽  
pp. 7059-7078
Author(s):  
F. Viola ◽  
L. V. Noto ◽  
M. Cannarozzo ◽  
G. La Loggia
Keyword(s):  
Water Allocation ◽  
Water Quality Assessment ◽  
Regression Equations ◽  
Large Dataset ◽  
Flow Duration ◽  
Ungauged Sites ◽  
Regional Flow ◽  
Streamflow Data ◽  
Flow Duration Curves ◽  
Gauged Basins

Abstract. Flow duration curves are simple and powerful tools to deal with many hydrological and environmental problems related to water quality assessment, water-use assessment and water allocation. Unfortunately the scarcity of streamflow data enables the use of these instruments only for gauged basins. A regional model is developed here for estimating flow duration curves at ungauged basins in Sicily, Italy. Due to the complex ephemeral behaviour of the examined region, this study distinguishes dry periods, when flows are zero, from wet periods using a three parameters power law to describe the frequency distribution of flows. A large dataset of streamflows has been analysed and the parameters of flow duration curves have been derived for about fifty basins. Regional regression equations have been developed to derive flow duration curves starting from morphological basin characteristics.

scholarly journals Topological and canonical kriging for design-flood prediction in ungauged catchments: an improvement over a traditional regional regression approach?

2012 ◽  
Vol 9 (10) ◽  
pp. 12193-12226 ◽  
Author(s):  
S. A. Archfield ◽  
A. Pugliese ◽  
A. Castellarin ◽  
J. O. Skøien ◽  
J. E. Kiang
Keyword(s):  
United States ◽  
Spatial Interpolation ◽  
The United States ◽  
Low Flow ◽  
Regression Equations ◽  
Ungauged Catchments ◽  
Interpolation Methods ◽  
Regional Regression ◽  
Flood Quantiles ◽  
Spatial Interpolation Methods

Abstract. In the United States, estimation of flood frequency quantiles at ungaged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e. flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, Canonical kriging, CK, (or physiographical-space based interpolation, PSBI) and Topological kriging, TK, (or Top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized-least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10-, 50-, 100- and 500-yr floods for 61 streamgauges in the Southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatment of spatial correlation when using regression-based versus spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS.

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