Identification of rainfall-runoff model for improved baseflow estimation in ungauged basins

2011 ◽  
Vol 26 (3) ◽  
pp. 356-366 ◽  
Author(s):  
Jos Samuel ◽  
Paulin Coulibaly ◽  
Robert A. Metcalfe
Keyword(s):  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Can a Calibration-Free Dynamic Rainfall‒Runoff Model Predict FDCs in Data-Scarce Regions? Comparing the IDW Model with the Dynamic Budyko Model in South India

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 32 ◽  
Author(s):  
Nag ◽  
Biswal
Keyword(s):  
Model Performance ◽  
Data Availability ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Discharge Data ◽  
Study Region ◽  
Calibration Free ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Free Dynamic

Construction of flow duration curves (FDCs) is a challenge for hydrologists as most streams and rivers worldwide are ungauged. Regionalization methods are commonly followed to solve the problem of discharge data scarcity by transforming hydrological information from gauged basins to ungauged basins. As a consequence, regionalization-based FDC predictions are not very reliable where discharge data are scarce quantitatively and/or qualitatively. In such a scenario, it is perhaps more meaningful to use a calibration-free rainfall‒runoff model that can exploit easily available meteorological information to predict FDCs in ungauged basins. This hypothesis is tested in this study by comparing a well-known regionalization-based model, the inverse distance weighting (IDW) model, with the recently proposed calibration-free dynamic Budyko model (DB) in a region where discharge observations are not only insufficient quantitatively but also show apparent signs of observational errors. The DB model markedly outperformed the IDW model in the study region. Furthermore, the IDW model’s performance sharply declined when we randomly removed discharge gauging stations to test the model in a variety of data availability scenarios. The analysis here also throws some light on how errors in observational datasets and drainage area influence model performance and thus provides a better picture of the relative strengths of the two models. Overall, the results of this study support the notion that a calibration-free rainfall‒runoff model can be chosen to predict FDCs in discharge data-scarce regions. On a philosophical note, our study highlights the importance of process understanding for the development of meaningful hydrological models.

Exploring signatures-based calibration of hydrological models for prediction in ungauged basins.

2020 ◽  
Author(s):  
Marco Dal Molin ◽  
Dmitri Kavetski ◽  
Mario Schirmer ◽  
Fabrizio Fenicia
Keyword(s):  
Time Series ◽  
Accurate Estimation ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Multiple Sources ◽  
Important Challenge ◽  
Rainfall Runoff Model ◽  
Approximate Bayesian ◽  
Correct Estimation ◽  
Runoff Model

<p>One of the open challenges in catchment hydrology is prediction in ungauged basins (PUB), i.e. being able to predict catchment responses (typically streamflow) when measurements are not available. One of the possible approaches to this problem consists in calibrating a model using catchment response statistics (called signatures) that can be estimated at the ungauged site.<br>An important challenge of any approach to PUB is to produce reliable and precise predictions of catchment response, with an accurate estimation of the uncertainty. In the context of PUB through calibration on regionalized streamflow signatures, there are multiple sources of uncertainty that affect streamflow predictions, which relate to:</p><ul><li>The use streamflow signatures, which, by synthetizing the underlying time series, reduce the information available for model calibration;</li> <li>The regionalization of streamflow signatures, which are not observed, but estimated through some signature regionalization model;</li> <li>The use of a rainfall-runoff model, which carries uncertainties related to input data, parameter values, and model structure.</li> </ul><p>This study proposes an approach that separately accounts for the uncertainty related to the regionalization of the signatures from the other types; the implementation uses Approximate Bayesian Computation (ABC) to infer the parameters of the rainfall-runoff model using stochastic streamflow signatures. <br>The methodology is tested in six sub-catchments of the Thur catchment in Switzerland; results show that the regionalized model produces streamflow time series that are similar to the ones obtained by the classical time-domain calibration, with slightly higher uncertainty but similar fit to the observed data. These results support the proposed approach as a viable method for PUB, with a focus on the correct estimation of the uncertainty.</p>

scholarly journals Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria

2013 ◽  
Vol 10 (1) ◽  
pp. 449-485 ◽  
Author(s):  
A. Viglione ◽  
J. Parajka ◽  
M. Rogger ◽  
J. L. Salinas ◽  
G. Laaha ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Comparative Assessment ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Low Flows ◽  
High Flows ◽  
Seasonal Runoff ◽  
Rainfall Runoff Model ◽  
Predictions In Ungauged Basins ◽  
Runoff Model

Abstract. In a three-part paper we assess the performance of runoff predictions in ungauged basins in a comparative way. While Parajka et al. (2013) and Salinas et al. (2013) assess the regionalisation of hydrographs and hydrological extremes through a literature review, in this paper we assess prediction of a range of runoff signatures for a consistent dataset. Daily runoff time series are predicted for 213 catchments in Austria by a regionalised rainfall–runoff model and by Top-Kriging, a geostatistical interpolation method that accounts for the river network hierarchy. From the runoff timeseries, six runoff signatures are extracted: annual runoff, seasonal runoff, flow duration curves, low flows, high flows and runoff hydrograph. The predictive performance is assessed by the bias, error spread and proportion of unexplained spatial variance of statistical measures of these signatures in cross-validation mode. Results of the comparative assessment show that the geostatistical approach (Top-Kriging) generally outperforms the regionalised rainfall–runoff model. The predictive performance increases with catchment area for both methods and all signatures, while the dependence on climate characteristics is weaker. Annual and seasonal runoff can be predicted more accurately than all other signatures. The spatial variability of high flows is the most difficult to capture followed by the low flows. The relative predictive performance of the signatures depends on the selected performance measures. It is therefore essential to report performance in a consistent way by more than one performance measure.

scholarly journals Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria

2013 ◽  
Vol 17 (6) ◽  
pp. 2263-2279 ◽  
Author(s):  
A. Viglione ◽  
J. Parajka ◽  
M. Rogger ◽  
J. L. Salinas ◽  
G. Laaha ◽  
...  
Keyword(s):  
Time Series ◽  
Predictive Performance ◽  
Comparative Assessment ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
High Flows ◽  
Seasonal Runoff ◽  
Rainfall Runoff Model ◽  
Predictions In Ungauged Basins ◽  
Runoff Model

Abstract. This is the third of a three-part paper series through which we assess the performance of runoff predictions in ungauged basins in a comparative way. Whereas the two previous papers by Parajka et al. (2013) and Salinas et al. (2013) assess the regionalisation performance of hydrographs and hydrological extremes on the basis of a comprehensive literature review of thousands of case studies around the world, in this paper we jointly assess prediction performance of a range of runoff signatures for a consistent and rich dataset. Daily runoff time series are predicted for 213 catchments in Austria by a regionalised rainfall–runoff model and by Top-kriging, a geostatistical estimation method that accounts for the river network hierarchy. From the runoff time-series, six runoff signatures are extracted: annual runoff, seasonal runoff, flow duration curves, low flows, high flows and runoff hydrographs. The predictive performance is assessed in terms of the bias, error spread and proportion of unexplained spatial variance of statistical measures of these signatures in cross-validation (blind testing) mode. Results of the comparative assessment show that, in Austria, the predictive performance increases with catchment area for both methods and for most signatures, it tends to increase with elevation for the regionalised rainfall–runoff model, while the dependence on climate characteristics is weaker. Annual and seasonal runoff can be predicted more accurately than all other signatures. The spatial variability of high flows in ungauged basins is the most difficult to estimate followed by the low flows. It also turns out that in this data-rich study in Austria, the geostatistical approach (Top-kriging) generally outperforms the regionalised rainfall–runoff model.

scholarly journals Rainfall-runoff model parameter conditioning on regional hydrological signatures: application to ungauged basins in southern Italy

2016 ◽  
Vol 48 (3) ◽  
pp. 714-725 ◽  
Author(s):  
Daniela Biondi ◽  
Davide Luciano De Luca
Keyword(s):  
Parameter Estimation ◽  
Southern Italy ◽  
Parameter Distribution ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Model Parameter ◽  
Wide Range ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Hydrological Signatures

Parameter estimation for rainfall-runoff models in ungauged basins is a key aspect for a wide range of applications where streamflow predictions from a hydrological model can be used. The need for more reliable estimation of flow in data scarcity conditions is, in fact, thoroughly related to the necessity of reducing uncertainty associated with parameter estimation. This study extends the application of a Bayesian procedure that, given a generic rainfall-runoff model, allows for the assessment of posterior parameter distribution, using a regional estimate of ‘hydrological signatures’ available in ungauged basins. A set of eight catchments located in southern Italy was analyzed, and regionalized, and the first three L-moments of annual streamflow maxima were considered as signatures. Specifically, the effects of conditioning posterior model parameter distribution under different sets of signatures and the role played by uncertainty in their regional estimates were investigated with specific reference to the application of rainfall-runoff models in design flood estimation. For this purpose, the continuous simulation approach was employed and compared to purely statistical methods. The obtained results confirm the potential of the proposed methodology and that the use of the available regional information enables a reduction of the uncertainty of rainfall-runoff models in applications to ungauged basins.

scholarly journals Effects of streamflow isotope sampling strategies on the calibration of a tracer‐aided rainfall‐runoff model

2021 ◽  
Author(s):  
Jamie Lee Stevenson ◽  
Christian Birkel ◽  
Aaron J. Neill ◽  
Doerthe Tetzlaff ◽  
Chris Soulsby
Keyword(s):  
Sampling Strategies ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals Performance Evaluation of a Two-Parameters Monthly Rainfall-Runoff Model in the Southern Basin of Thailand

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1226
Author(s):  
Pakorn Ditthakit ◽  
Sirimon Pinthong ◽  
Nureehan Salaeh ◽  
Fadilah Binnui ◽  
Laksanara Khwanchum ◽  
...  
Keyword(s):  
Exchange Rate ◽  
Rainfall Runoff ◽  
Management Planning ◽  
Runoff Variation ◽  
Groundwater Exchange ◽  
Monthly Runoff ◽  
Rainfall Runoff Model ◽  
Two Parameters ◽  
Runoff Model

Accurate monthly runoff estimation is crucial in water resources management, planning, and development, preventing and reducing water-related problems, such as flooding and droughts. This article evaluates the monthly hydrological rainfall-runoff model’s performance, the GR2M model, in Thailand’s southern basins. The GR2M model requires only two parameters: production store (X1) and groundwater exchange rate (X2). Moreover, no prior research has been reported on its application in this region. The 37 runoff stations, which are located in three sub-watersheds of Thailand’s southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. The Thornthwaite method was utilized for the determination of evapotranspiration. The model’s performance was conducted using three statistical indices: Nash–Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OI). The model’s calibration results for 37 runoff stations gave the average NSE, r, and OI of 0.657, 0.825, and 0.757, respectively. Moreover, the NSE, r, and OI values for the model’s verification were 0.472, 0.750, and 0.639, respectively. Hence, the GR2M model was qualified and reliable to apply for determining monthly runoff variation in this region. The spatial distribution of production store (X1) and groundwater exchange rate (X2) values was conducted using the IDW method. It was susceptible to the X1, and X2 values of approximately more than 0.90, gave the higher model’s performance.

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