Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty

2013 ◽  
Vol 28 (4) ◽  
pp. 2033-2045 ◽  
Author(s):  
R. Cibin ◽  
P. Athira ◽  
K. P. Sudheer ◽  
I. Chaubey
Keyword(s):  
Hydrological Models ◽  
Ungauged Basins ◽  
Predictive Uncertainty ◽  
Distributed Hydrological Models ◽  
Predictions In Ungauged Basins

scholarly journals Hydrological modelling for ungauged basins of arid and semi-arid regions: review

Vestnik MGSU ◽  
2019 ◽  
pp. 1023-1036
Author(s):  
Anghesom A. Ghebrehiwot ◽  
Dmitriy V. Kozlov
Keyword(s):  
Arid Regions ◽  
Hydrological Modelling ◽  
Hydrological Models ◽  
Ungauged Basins ◽  
Local Data ◽  
Recent Developments ◽  
Planning Development ◽  
Predictions In Ungauged Basins ◽  
Resources Planning ◽  
Semi Arid

Introduction: hydrological modelling is a powerful tool for water resources planning, development, design, operation, and management in a catchment. It becomes more important when it is applied to areas that suffer from inadequate hydrological field data. The existing methods which are appropriate for predictions in ungauged basins include extrapolation from gauged to ungauged basins, remote sensing-based measurements, process-based hydrological models, and application of combined meteorological–hydrological models without the need to specify precipitation inputs. Nonetheless, numerous works indicate that these methods have had limitations when it comes to predictions from ungauged basins. Materials and methods: the methods employed in this work include a detailed review of related materials on the historical development, significance, classification, selection, and recent developments of hydrological modelling in ungauged basins with an emphasis on arid and semi-arid regions. Results: the review indicates that the development of comprehensive and effective approaches that address the unique characteristics of arid and semi-arid regions in general and similar areas within developing countries, in particular, are yet to be developed. Conclusions: in the absence of reliable hydrometeorological data, the best approach to streamflow predictions from ungauged basins and the considered catchment would be intercomparison of two or more hydrological models. The models accommodate global, regional, and local data (if any).

Split-parameter structure for the automatic calibration of distributed hydrological models

2007 ◽  
Vol 332 (1-2) ◽  
pp. 226-240 ◽  
Author(s):  
Félix Francés ◽  
Jaime Ignacio Vélez ◽  
Jorge Julián Vélez
Keyword(s):  
Automatic Calibration ◽  
Hydrological Models ◽  
Split Parameter ◽  
Distributed Hydrological Models

Runoff Predictions in Ungauged Basins Using Sequence-to-Sequence Models

2021 ◽  
pp. 126975
Author(s):  
Hanlin Yin ◽  
Zilong Guo ◽  
Xiuwei Zhang ◽  
Jiaojiao Chen ◽  
Yanning Zhang
Keyword(s):  
Ungauged Basins ◽  
Predictions In Ungauged Basins

scholarly journals Estimation of baseflow parameters of variable infiltration capacity model with soil and topography properties for predictions in ungauged basins

2011 ◽  
Vol 8 (4) ◽  
pp. 7017-7053 ◽  
Author(s):  
Z. Bao ◽  
J. Liu ◽  
J. Zhang ◽  
G. Fu ◽  
G. Wang ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Model Simulation ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Variable Infiltration Capacity ◽  
Infiltration Capacity ◽  
Ungauged Catchments ◽  
Vic Model ◽  
Predictions In Ungauged Basins

Abstract. Equifinality is unavoidable when transferring model parameters from gauged catchments to ungauged catchments for predictions in ungauged basins (PUB). A framework for estimating the three baseflow parameters of variable infiltration capacity (VIC) model, directly with soil and topography properties is presented. When the new parameters setting methodology is used, the number of parameters needing to be calibrated is reduced from six to three, that leads to a decrease of equifinality and uncertainty. This is validated by Monte Carlo simulations in 24 hydro-climatic catchments in China. Using the new parameters estimation approach, model parameters become more sensitive and the extent of parameters space will be smaller when a threshold of goodness-of-fit is given. That means the parameters uncertainty is reduced with the new parameters setting methodology. In addition, the uncertainty of model simulation is estimated by the generalised likelihood uncertainty estimation (GLUE) methodology. The results indicate that the uncertainty of streamflow simulations, i.e., confidence interval, is lower with the new parameters estimation methodology compared to that used by original calibration methodology. The new baseflow parameters estimation framework could be applied in VIC model and other appropriate models for PUB.

scholarly journals Data-driven catchment classification: application to the PUB problem

2011 ◽  
Vol 8 (1) ◽  
pp. 391-427 ◽  
Author(s):  
M. Di Prinzio ◽  
A. Castellarin ◽  
E. Toth
Keyword(s):  
Cross Validation ◽  
Principal Component ◽  
Annual Runoff ◽  
Data Driven ◽  
Spatiotemporal Variability ◽  
Ungauged Basins ◽  
Streamflow Prediction ◽  
Streamflow Regime ◽  
Predictions In Ungauged Basins ◽  
Unsupervised Neural Networks

Abstract. Objective criteria for catchment classification are identified by the scientific community among the key research topics for improving the interpretation and representation of the spatiotemporal variability of streamflow. A promising approach to catchment classification makes use of unsupervised neural networks (Self Organising Maps, SOM's), which organise input data through non-linear techniques depending on the intrinsic similarity of the data themselves. Our study considers ~300 Italian catchments scattered nationwide, for which several descriptors of the streamflow regime and geomorphoclimatic characteristics are available. We qualitatively and quantitatively compare in the context of PUB (Prediction in Ungauged Basins) a reference classification, RC, with four alternative classifications, AC's. RC was identified by using indices of the streamflow regime as input to SOM, whereas AC's were identified on the basis of catchment descriptors that can be derived for ungauged basins. One AC directly adopts the available catchment descriptors as input to SOM. The remaining AC's are identified by applying SOM to two sets of derived variables obtained by applying Principal Component Analysis (PCA, second AC) and Canonical Correlation Analysis (CCA, third and fourth ACs) to the available catchment descriptors. First, we measure the similarity between each AC and RC. Second, we use AC's and RC to regionalize several streamflow indices and we compare AC's with RC in terms of accuracy of streamflow prediction. In particular, we perform an extensive cross-validation to quantify nationwide the accuracy of predictions in ungauged basins of mean annual runoff, mean annual flood, and flood quantiles associated with given exceedance probabilities. Results of the study show that CCA can significantly improve the effectiveness of SOM classifications for the PUB problem.

scholarly journals Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

2019 ◽  
Vol 55 (12) ◽  
pp. 11344-11354 ◽  
Author(s):  
Frederik Kratzert ◽  
Daniel Klotz ◽  
Mathew Herrnegger ◽  
Alden K. Sampson ◽  
Sepp Hochreiter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ungauged Basins ◽  
Predictions In Ungauged Basins
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