Predicting annual and long-term flow-duration curves in ungauged basins

2007 ◽  
Vol 30 (4) ◽  
pp. 937-953 ◽  
Author(s):  
Attilio Castellarin ◽  
Giorgio Camorani ◽  
Armando Brath
2004 ◽  
Vol 27 (10) ◽  
pp. 953-965 ◽  
Author(s):  
Attilio Castellarin ◽  
Giorgio Galeati ◽  
Luigia Brandimarte ◽  
Alberto Montanari ◽  
Armando Brath

2014 ◽  
Vol 18 (8) ◽  
pp. 2993-3013 ◽  
Author(s):  
I. K. Westerberg ◽  
L. Gong ◽  
K. J. Beven ◽  
J. Seibert ◽  
A. Semedo ◽  
...  

Abstract. Robust and reliable water-resource mapping in ungauged basins requires estimation of the uncertainties in the hydrologic model, the regionalisation method, and the observational data. In this study we investigated the use of regionalised flow-duration curves (FDCs) for constraining model predictive uncertainty, while accounting for all these uncertainty sources. A water balance model was applied to 36 basins in Central America using regionally and globally available precipitation, climate and discharge data that were screened for inconsistencies. A rating-curve analysis for 35 Honduran discharge stations was used to estimate discharge uncertainty for the region, and the consistency of the model forcing and evaluation data was analysed using two different screening methods. FDCs with uncertainty bounds were calculated for each basin, accounting for both discharge uncertainty and, in many cases, uncertainty stemming from the use of short time series, potentially not representative for the modelling period. These uncertain FDCs were then used to regionalise a FDC for each basin, treating it as ungauged in a cross-evaluation, and this regionalised FDC was used to constrain the uncertainty in the model predictions for the basin. There was a clear relationship between the performance of the local model calibration and the degree of data set consistency – with many basins with inconsistent data lacking behavioural simulations (i.e. simulations within predefined limits around the observed FDC) and the basins with the highest data set consistency also having the highest simulation reliability. For the basins where the regionalisation of the FDCs worked best, the uncertainty bounds for the regionalised simulations were only slightly wider than those for a local model calibration. The predicted uncertainty was greater for basins where the result of the FDC regionalisation was more uncertain, but the regionalised simulations still had a high reliability compared to the locally calibrated simulations and often encompassed them. The regionalised FDCs were found to be useful on their own as a basic signature constraint; however, additional regionalised signatures could further constrain the uncertainty in the predictions and may increase the robustness to severe data inconsistencies, which are difficult to detect for ungauged basins.


2016 ◽  
Author(s):  
Annalise G. Blum ◽  
Richard M. Vogel ◽  
Stacey A. Archfield

Abstract. One of the most commonly used tools in hydrology, empirical flow duration curves (FDCs) characterize the frequency with which streamflows are equaled or exceeded. Finding a suitable probability distribution to approximate a FDC enables regionalization and prediction of FDCs in basins that lack streamflow measurements. FDCs constructed from daily streamflow observations can be computed as the period-of-record FDC (POR-FDC) to represent long-term streamflow conditions or as the median annual FDC (MA-FDC) to represent streamflows in a typical year. The goal of this study is to identify suitable probability distributions for both POR-FDCs and MA-FDCs of daily streamflow for unregulated and perennial streams. Comparisons of modeled and empirical FDCs at over 400 unregulated stream gages across the conterminous United States reveal that both the four-parameter kappa (KAP) and three-parameter generalized Pareto (GPA3) distributions can provide reasonable approximations to MA-FDCs; however, even four and five-parameter distributions are unable to capture the complexity of the POR-FDC behavior for which flows often range over five or more orders of magnitude. Regional regression models developed for the mid-Atlantic and Missouri regions as case studies present a simple and practical method to predict MA-FDCs at ungaged sites, which can be accurately predicted more consistently compared to POR-FDCs.


2017 ◽  
Vol 26 (8) ◽  
pp. 939-953 ◽  
Author(s):  
Gyeong hoon Kim ◽  
Heon gak Kwon ◽  
Jung min Ahn ◽  
Sanghun Kim ◽  
Tae hyo Im ◽  
...  

2009 ◽  
Vol 45 (10) ◽  
Author(s):  
D. Ganora ◽  
P. Claps ◽  
F. Laio ◽  
A. Viglione

2016 ◽  
Vol 541 ◽  
pp. 1030-1041 ◽  
Author(s):  
Muhammad Uzair Qamar ◽  
Muhammad Azmat ◽  
Muhammad Jehanzeb Masud Cheema ◽  
Muhammad Adnan Shahid ◽  
Rao Arsalan Khushnood ◽  
...  

2015 ◽  
Vol 12 (9) ◽  
pp. 9765-9811 ◽  
Author(s):  
M. F. Müller ◽  
S. E. Thompson

Abstract. The prediction of flow duration curves (FDCs) in ungauged basins remains an important task for hydrologists given the practical relevance of FDCs for water management and infrastructure design. Predicting FDCs in ungauged basins typically requires spatial interpolation of statistical or model parameters. This task is complicated if climate becomes non-stationary, as the prediction challenge now also requires extrapolation through time. In this context, process-based models for FDCs that mechanistically link the streamflow distribution to climate and landscape factors may have an advantage over purely statistical methods to predict FDCs. This study compares a stochastic (process-based) and statistical method for FDC prediction in both stationary and non-stationary contexts, using Nepal as a case study. Under contemporary conditions, both models perform well in predicting FDCs, with Nash–Sutcliffe coefficients above 0.80 in 75 % of the tested catchments. The main drives of uncertainty differ between the models: parameter interpolation was the main source of error for the statistical model, while violations of the assumptions of the process-based model represented the main source of its error. The process-based approach performed better than the statistical approach in numerical simulations with non-stationary climate drivers. The predictions of the statistical method under non-stationary rainfall conditions were poor if (i) local runoff coefficients were not accurately determined from the gauge network, or (ii) streamflow variability was strongly affected by changes in rainfall. A Monte Carlo analysis shows that the streamflow regimes in catchments characterized by a strong wet-season runoff and a rapid, strongly non-linear hydrologic response are particularly sensitive to changes in rainfall statistics. In these cases, process-based prediction approaches are strongly favored over statistical models.


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