PARAMETER ESTIMATION OF A DISTRIBUTED HYDROLOGICAL MODEL USING THE ADJOINT METHOD: A CASE STUDY IN THE IBO RIVER WATERSHED, JAPAN

2019 ◽  
Author(s):  
MASAYASU IRIE ◽  
HIROAKI TOI ◽  
MASAHIDE ISHIZUKA ◽  
KOHJI TANAKA ◽  
SHUZO NISHIDA(
Keyword(s):  
Parameter Estimation ◽  
Adjoint Method ◽  
Hydrological Model ◽  
Distributed Hydrological Model ◽  
River Watershed

PARAMETER ESTIMATION OF A DISTRIBUTED HYDROLOGICAL MODEL USING AN ADJOINT METHOD

2018 ◽  
Vol 74 (5) ◽  
pp. I_145-I_150 ◽  
Author(s):  
Kohei ODA ◽  
Masayasu IRIE ◽  
Hiroaki TOI ◽  
Masahide ISHIZUKA ◽  
Kohji TANAKA
Keyword(s):  
Parameter Estimation ◽  
Adjoint Method ◽  
Hydrological Model ◽  
Distributed Hydrological Model

scholarly journals Inverse distributed hydrological modelling of Alpine catchments

2006 ◽  
Vol 10 (3) ◽  
pp. 395-412 ◽  
Author(s):  
H. Kunstmann ◽  
J. Krause ◽  
S. Mayr
Keyword(s):  
Parameter Estimation ◽  
Hydrological Model ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Nonlinear Parameter ◽  
Model Complexity ◽  
Distributed Hydrological Model ◽  
Nonlinear Parameter Estimation ◽  
Estimated Parameters ◽  
Physically Based

Abstract. Even in physically based distributed hydrological models, various remaining parameters must be estimated for each sub-catchment. This can involve tremendous effort, especially when the number of sub-catchments is large and the applied hydrological model is computationally expensive. Automatic parameter estimation tools can significantly facilitate the calibration process. Hence, we combined the nonlinear parameter estimation tool PEST with the distributed hydrological model WaSiM. PEST is based on the Gauss-Marquardt-Levenberg method, a gradient-based nonlinear parameter estimation algorithm. WaSiM is a fully distributed hydrological model using physically based algorithms for most of the process descriptions. WaSiM was applied to the alpine/prealpine Ammer River catchment (southern Germany, 710 km2 in a 100×100 m2 horizontal resolution. The catchment is heterogeneous in terms of geology, pedology and land use and shows a complex orography (the difference of elevation is around 1600 m). Using the developed PEST-WaSiM interface, the hydrological model was calibrated by comparing simulated and observed runoff at eight gauges for the hydrologic year 1997 and validated for the hydrologic year 1993. For each sub-catchment four parameters had to be calibrated: the recession constants of direct runoff and interflow, the drainage density, and the hydraulic conductivity of the uppermost aquifer. Additionally, five snowmelt specific parameters were adjusted for the entire catchment. Altogether, 37 parameters had to be calibrated. Additional a priori information (e.g. from flood hydrograph analysis) narrowed the parameter space of the solutions and improved the non-uniqueness of the fitted values. A reasonable quality of fit was achieved. Discrepancies between modelled and observed runoff were also due to the small number of meteorological stations and corresponding interpolation artefacts in the orographically complex terrain. Application of a 2-dimensional numerical groundwater model partly yielded a slight decrease of overall model performance when compared to a simple conceptual groundwater approach. Increased model complexity therefore did not yield in general increased model performance. A detailed covariance analysis was performed allowing to derive confidence bounds for all estimated parameters. The correlation between the estimated parameters was in most cases negligible, showing that parameters were estimated independently from each other.

scholarly journals PUBs for Engineering Purpose: Framework Development and Case Study

10.29007/9kpv ◽  
2018 ◽  
Author(s):  
Yang Zhiyong ◽  
Gao Xichao ◽  
Liu Jiahong
Keyword(s):  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Distributed Hydrological Model ◽  
Runoff Simulation ◽  
Annual Average ◽  
Sensing Technology ◽  
Watershed Method ◽  
Predictions In Ungauged Basins

A framework of predictions in ungauged basins (PUBs, taking Paniai lakes watershed, Indonesia as an example) for hydropower exploration is developed. In this framework, remote sensing technology and similar watershed method are used to collect necessary meteorological and topographical data for runoff simulation. Besides, a modified physical based distributed hydrological model is developed to consider the characteristics (regulation capacity of the lakes) of the watershed. Finally, considering the modeling purpose, annual average runoff index is used to assess the modeling results. In the case study (Paniai lakes watershed), TRMM precipitation, HWSD soil type, and AVHRR landcover data, combined with meteorological data from two similar watersheds, are collected to drive the modified hydrological model. According to the model results, the simulated potential evapotranspiration capacities and annual average runoff coefficients are consistent between the two cases (modeling with meteorological data of the two similar watersheds), and the simulated annual average runoff coefficients of the two cases are basically consistent with the observed annual average runoff coefficient of another similar watershed located in Indonesia.

scholarly journals Towards a more representative parametrisation of hydrological models via synthesizing the strengths of particle swarm optimisation and robust parameter estimation

2011 ◽  
Vol 8 (2) ◽  
pp. 2373-2422 ◽  
Author(s):  
T. Krauße ◽  
J. Cullmann
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Hydrological Model ◽  
Data Depth ◽  
Hydrological Models ◽  
Time Step ◽  
Good Parameter ◽  
Robust Parameter ◽  
Robust Parameter Estimation

Abstract. The development of methods for estimating the parameters of hydrological models considering uncertainties has been of high interest in hydrological research over the last years. In particular methods which understand the estimation of hydrological model parameters as a geometric search of a set of robust performing parameter vectors by application of the concept of data depth found growing research interest. Bárdossy and Singh (2008) presented a first proposal and applied it for the calibration of a conceptual rainfall-runoff model with daily time step. Krauße and Cullmann (2011) further developed this method and applied it in a case study to calibrate a process oriented hydrological model with hourly time step focussing on flood events in a fast responding catchment. The results of both studies showed the potential of the application of the principle of data depth. However, also the weak point of the presented approach got obvious. The algorithm identifies a set of model parameter vectors with high model performance and subsequently generates a set of parameter vectors with high data depth with respect to the first set. These both steps are repeated iteratively until a stopping criterion is met. In the first step the estimation of the good parameter vectors is based on the Monte Carlo method. The major shortcoming of this method is that it is strongly dependent on a high number of samples exponentially growing with the dimensionality of the problem. In this paper we present another robust parameter estimation strategy which applies an approved search strategy for high-dimensional parameter spaces, the particle swarm optimisation in order to identify a set of good parameter vectors with given uncertainty bounds. The generation of deep parameters is according to Krauße and Cullmann (2011). The method was compared to the Monte Carlo based robust parameter estimation algorithm on the example of a case study in Krauße and Cullmann (2011) to calibrate the process-oriented distributed hydrological model focussing for flood forecasting in a small catchment characterised by extreme process dynamics. In a second case study the comparison is repeated on a problem with higher dimensionality considering further parameters of the soil module.

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