Predictions in ungauged basins: an approach for regionalization of hydrological models considering the probability distribution of model parameters

2015 ◽  
Vol 30 (4) ◽  
pp. 1131-1149 ◽  
Author(s):  
P. Athira ◽  
K. P. Sudheer ◽  
R. Cibin ◽  
I. Chaubey
Keyword(s):  
Probability Distribution ◽  
Model Parameters ◽  
Hydrological Models ◽  
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 Enhancing Physical Similarity Approach to Predict Runoff in Ungauged Watersheds in Sub-Tropical Regions

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 528 ◽  
Author(s):  
Santiago Narbondo ◽  
Angela Gorgoglione ◽  
Magdalena Crisci ◽  
Christian Chreties
Keyword(s):  
Weather Conditions ◽  
Climatic Conditions ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Physical Similarity ◽  
Tropical Regions ◽  
Runoff Prediction ◽  
Tropical Areas ◽  
Rainfall Runoff Model ◽  
Predictions In Ungauged Basins

Regionalization techniques have been comprehensively discussed as the solution for runoff predictions in ungauged basins (PUB). Several types of regionalization approach have been proposed during the years. Among these, the physical similarity one was demonstrated to be one of the most robust. However, this method cannot be applied in large regions characterized by highly variable climatic conditions, such as sub-tropical areas. Therefore, this study aims to develop a new regionalization approach based on an enhanced concept of physical similarity to improve the runoff prediction of ungauged basins at country scale, under highly variable-weather conditions. A clustering method assured that watersheds with different hydrologic and physical characteristics were considered. The novelty of the proposed approach is based on the relationships found between rainfall-runoff model parameters and watershed-physiographic factors. These relationships were successively exported and validated at the ungauged basins. From the overall results, it can be concluded that the runoff prediction in the ungauged basins was very satisfactory. Therefore, the proposed approach can be adopted as an alternative method for runoff prediction in ungauged basins characterized by highly variable-climatic conditions.

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).

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 Recurrence time distribution and temporal clustering properties of a cellular automaton modelling landslide events

2013 ◽  
Vol 20 (6) ◽  
pp. 1071-1078 ◽  
Author(s):  
E. Piegari ◽  
R. Di Maio ◽  
A. Avella
Keyword(s):  
Time Series ◽  
Probability Distribution ◽  
Cellular Automaton ◽  
Time Window ◽  
Fano Factor ◽  
Recent Analysis ◽  
Recurrence Time ◽  
Model Parameters ◽  
Reasonable Prediction ◽  
First Time

Abstract. Reasonable prediction of landslide occurrences in a given area requires the choice of an appropriate probability distribution of recurrence time intervals. Although landslides are widespread and frequent in many parts of the world, complete databases of landslide occurrences over large periods are missing and often such natural disasters are treated as processes uncorrelated in time and, therefore, Poisson distributed. In this paper, we examine the recurrence time statistics of landslide events simulated by a cellular automaton model that reproduces well the actual frequency-size statistics of landslide catalogues. The complex time series are analysed by varying both the threshold above which the time between events is recorded and the values of the key model parameters. The synthetic recurrence time probability distribution is shown to be strongly dependent on the rate at which instability is approached, providing a smooth crossover from a power-law regime to a Weibull regime. Moreover, a Fano factor analysis shows a clear indication of different degrees of correlation in landslide time series. Such a finding supports, at least in part, a recent analysis performed for the first time of an historical landslide time series over a time window of fifty years.

scholarly journals Use of Statistical Models for Simulating Transactions on the Real Estate Market

2015 ◽  
Vol 23 (2) ◽  
pp. 102-111 ◽  
Author(s):  
Radosław Cellmer ◽  
Katarzyna Szczepankowska
Keyword(s):  
Probability Distribution ◽  
Real Estate ◽  
Statistical Models ◽  
Real Estate Market ◽  
Model Parameters ◽  
The Real ◽  
The Real Estate ◽  
Transaction Prices ◽  
The Real Estate Market ◽  
Real Estate Prices

Abstract The regularities and relations between real estate prices and the factors that shape them may be presented in the form of statistical models, thanks to which the diagnosis and prediction of prices is possible. A formal description of empirical observation presented in the form of regressive models also offers a possibility for creating certain phenomena in a virtual dimension. Market phenomena cannot be fully described with the use of determinist models, which clarify only a part of price variation. The predicted price is, in this situation, a special case of implementing a random function. Assuming that other implementations are also possible, regressive models may constitute a basis for simulation, which results in the procurement of a future image of the market. Simulation may refer both to real estate prices and transaction prices. The basis for price simulation may be familiarity with the structure of the analyzed market data. Assuming that this structure has a static character, simulation of real estate prices is performed on the basis of familiarity with the probability distribution and a generator of random numbers. The basis for price simulation is familiarity with model parameters and probability distribution of the random factor. The study presents the core and theoretical description of a transaction simulation on the real estate market, as well as the results of an experiment regarding transaction prices of office real estate located within the area of the city of Olsztyn. The result of the study is a collection of virtual real properties with known features and simulated prices, constituting a reflection of market processes which may take place in the near future. Comparison between the simulated characteristic and actual transactions in turn allows the correctness of the description of reality by the model to be verified.

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