University of Twente Student Theses
The effect of precipitation data and parameter estimation on peak flow simulation in the Jinhua river basin
Daling, N. (2018) The effect of precipitation data and parameter estimation on peak flow simulation in the Jinhua river basin.
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| Abstract: | Precipitation is the main driving force of the generation of run off. It is therefore an important model input for hydrological models. However, precipitation is highly variable in space and time and is hard to measure at an appropriate resolution. A number of studies have been done to find how precipitation affects the discharge generation. The effect on peak discharges is however less known, even though the correct modelling of flood peaks in general is desired for disaster prediction and prevention and sustainable river management. Therefore this study focuses on using precipitation data in the simulation of peak discharges. To perform this study a case study is set up using the Distributed Hydrology-Soil-Vegetation Model (DHSVM) in the Jinhua river basin, East China. For this study the precipitation data is obtained from two overlapping networks of 5 and 21 stations respectively throughout the entire river basin. It is assumed that the dense network provides improved rainfall representation and improves the peak discharge simulation. An important element in this study has been the estimation of parameters to calibrate the model against measured discharge data. This study was divided into two parts. First the effects of the precipitation and parameter estimation were investigated. For this the entire discharge series is used as well as a shortened time series that only included the individual peak flows. In the second part of the research an attempt has been made to improve the model performance, based on the results from the first part of the research. The first focus was on how the precipitation affects the peak discharge and what still can be done to improve the model by correcting the precipitation data for various measurement errors. It has become clear that the precipitation data that is currently used has been cleared of measurement errors due to failure of equipment. Also, the model already corrects for height when interpolating the precipitation. However, structural errors in the measurements such as wind induced errors and, wetting and evaporation loss are not corrected for. After this the relation between precipitation and (peak) discharges was examined through a sensitivity analysis. It was found that precipitation correlates with discharge in a non linear way and the precipitation and the peak discharges are correlated as well. It was concluded that the precipitation has a significant influence on peak discharge simulation and it is worthwhile to use this as a way to improve the model performance in the second part of the research. Secondly a small sensitivity analysis was performed to investigate the effect of the parameter estimation on the discharge simulation and model performance for the entire discharge time series and peak discharges. For this sensitivity analysis a univariate approach was chosen using the range based on a previous study. The parameters that were looked into were chosen based on the parameters that the discharge simulation in DHSVM was found to be sensitive to according to a previous study in the Jinhua river basin. As expected it was found that the parameter estimation indeed has an influence on the peak discharge simulation. The parameters, however, influence the peak discharge simulation in a lesser extent than the precipitation did. The parameters that influenced the (peak) discharge simulation most are the porosity (ø), the field capacity (θfc), the wilting point (θwp) and the lateral conductivity (K) of clay loam soil, and the rain LAI multiplier (Rj). Next, an attempt has been made to improve the model performance, with a focus on the peak discharges. This was done in three steps, namely the correction of structural measurement errors in the precipitation data, the implementation of additional gauge stations and the optimisation of the model parameters. The removal of the structural measurement errors had an effect on the simulated discharges, and with respect to the peak discharges the model performance improved. However, for the entire discharge series the model performance decreased. The increased density of the precipitation data was more difficult to examine, due to the decreased time period. This because of the limited amount of available data from the additional stations and missing observed discharges after 2008. After the implementation of the 16 additional stations the model performance was assessed again. The hydrographs also have been analysed visually. The model performance for the 21 stations did not improve greatly over the entire discharge and even decreased drastically when focussing on the peak discharges compared to the situation with five meteorological stations. Therefore the choice was made to perform a calibration procedure using the automatic genetic calibration algorithm ε-NSGA-II with three objectives: the Nash Sutcliffe coeffcient, PBIAS and Mean fourth power error. These are all statistical functions that indicate the similarity between the observed and simulated discharges. The five parameters that were found to affect the peak discharges most were used as calibration parameter. This was done for the situation with corrected precipitation with 21 stations as well as 5 stations to make the results more comparable, since a new objective has been added. The available data was split into two periods in such a way that for calibration 1.25 year was available and 1 year of data was used for validation. After the calibration it turned out that for both situations the model's performance increased drastically compared to the initial state of the model in the calibration period. However, the model with 21 meteorological stations only showed slightly better results to the one with 5 meteorological stations. The improvement here was only visible in the PBIAS value during this period, the NS coeffcient was constant for the entire discharge series as well as for the peak discharges. This indicated an improvement in the base flows, instead of the peak flows. For the validation period the model performance increased slightly for the two calibrated models compared to the initial state of the model. The model performance did not improve when comparing the model with 21 stations to the model with 5 stations, it even declined. It was believed that 16 additional stations would increase correctness of rainfall representation enough to be able to capture the spatial variability better. However, in the model with 21 stations the total annual precipitation in the area decreased, causing lower peak flows. The precipitation station that are used in this study are all located at the lower elevations of the study area near river branches. To get a better rainfall representation the response of the precipitation to an increase in elevation should be further examined, since this is not known in this study area. For the improvement of the model there can still much be won by for example investing in gauge stations at higher elevations. In general it can be concluded that the increase in gauge density does not necessarily improve the peak discharge simulation. For further research on this subject the sensitivity analysis performed in this study can be extended, since it was based on an earlier study that focussed on the entire discharge series using a two-step sensitivity analysis. However, it is possible that the peak discharges respond more strongly to other parameters that were not included in the sensitivity analysis performed in this study. Also, the model was calibrated and validated with a limited time period. There is more observed data available at the meteorological bureau, however it was not accessible during this study. If the additional observed discharges would be accessible for future research the calibration and validation of the model could be done more extensively which would help with improving the peak discharge simulation and also the analysis of the model performance with regard to the peak flows. Finally, other discharge stations in the study area can be used to see whether the underestimation of peak discharges is a problem in the entire catchment or only at the Jinhua outlet. |
| Item Type: | Essay (Master) |
| Faculty: | ET: Engineering Technology |
| Subject: | 56 civil engineering |
| Programme: | Civil Engineering and Management MSc (60026) |
| Link to this item: | https://purl.utwente.nl/essays/76613 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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