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Assimilation of remotely sensed soil moisture data in a hydrological forecasting model of the Overijsselse Vecht.

Luijkx, G. (2020) Assimilation of remotely sensed soil moisture data in a hydrological forecasting model of the Overijsselse Vecht.

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Abstract:Hydrological models are widely used in the field of water management and are used, among other things, to support decisions which are made by water managers. One example of such a model that supports the decision making is the Flood Early Warning System (FEWS). By water board Drents Overijsselse Delta (WDOD), FEWS is used to forecast the discharge and water levels in the Overijsselse Vecht. This model consists of two sub-models, a hydrodynamic model, and a hydrological model. In this study there was looked at the hydrological model of the FEWS, the HBV model. Due to the increased resolution and availability of satellite data, the water board wants to know what the added value of this data could be for them. One of the questions of WDOD is whether the HBV model performance could be improved by assimilation of remotely sensed soil moisture data. In this study, 3 (out of 14) sub-catchments of the Overijsselse Vecht are investigated, namely the Ommerkanaal, Sallandse Wetering and the Dinkel. For these 3 sub-catchments, the following steps were executed. First, the HBV models of the 3 used sub-catchments were recalibrated. For this step, the parameter sensitivity was studied, from which the parameters for the calibration were selected. The calibration was done with a Monte Carlo simulation with 2.5 million runs. For all sub-catchments, the model performance did improve in comparison to the HBV models used in FEWS. The sensitivity analysis (different then the parameter sensitivity) for the initial conditions showed that the model is the most sensitive for the initial condition of the soil moisture, for 2 out of the 3 sub-catchments. For the Dinkel, the sensitivity for the soil moisture was not the highest but still relatively large. Therefore, it was expected that changes in the initial condition of the soil moisture have an effect in the simulated discharge. Subsequently, the correlation between the HBV modelled soil moisture and the remotely sensed soil moisture content was investigated. For both the daily measured soil moisture content and the 3-day moving average, a good correlation was found for all of the 3 sub-catchments, meaning there are similarities in the pattern of both datasets. The correlation between the 3-day moving average and the HBV modelled soil moisture was higher for all of the 3 sub-catchments because the peaks are smoothed. The values of the correlation coefficients are ranging from 0.85 for the Sallandse Wetering to 0.91 for the Ommerkanaal. The daily measured data is highly depending on the moment when the satellite passes over. If it has just rained, all the water is still in the top few centimetres of the soil, so the value is an overestimation of the real situation. Using the 3-day moving average instead dampens this effect and reflects the behaviour of the HBV modelled soil moisture better. The remotely sensed soil moisture delivered by VanderSat is in the unit of m3/m3 while the HBV soil moisture is in mm, therefore a transformation was needed. This is done by using two methods which linearly transformed the data. The transformed data was assimilated into the HBV model as initial condition for the soil moisture storage, which is one of the three storages the HBV model has. The other two initial conditions are made by a model run with a warm-up period of 1 year. With the assimilation the model forecasted a discharge for the next 5 day, with as input the measured precipitation and the potential evaporation. The assimilation of remotely sensed soil moisture in the HBV model did not showed an improvement overall. There are a few exceptions in which the model with assimilation showed an improvement; this was sometimes the case when the peak flow occurred during a dry period. The approach of the HBV model without assimilation is to store the precipitation in the soil moisture, which will lead to a lower discharge. With the assimilation, in this case, there was a higher forecasted discharge, because the initial soil moisture is higher. In the rest of the cases, the HBV simulated soil moisture was performing better than the assimilated soil moisture. This can be explained if looked at the transformation done with the remotely sensed soil moisture, this transformation is not representing the pattern in the data, which is not linear. Out if this research a few recommendations are derived both for the water board and for the study. One of which is to further research another transformation of the remotely sensed soil moisture content to the unit used in the HBV model. The method used is an oversimplification of the pattern which can be found in the data. Furthermore, the HBV model could have been calibrated with the use of remotely sensed soil moisture content as input. By already using the soil moisture data in the calibration the parameters could be adapted to the remotely sensed soil moisture content. This could improve the performance of the assimilation. Furthermore, the high correlation found in this study, between the remotely sensed soil moisture and HBV modelled soil moisture, could be a potential for the use of remotely sensed soil moisture in a other way in the HBV model or in other models. At last the recalibration of the model leads to an improvement of the simulated discharge and could therefore be done for the other sub-catchments of the Vecht in order to improve the model performance.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Programme:Civil Engineering and Management MSc (60026)
Link to this item:http://purl.utwente.nl/essays/83526
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