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Sensitivity of discharge characteristics to the spatial resolution of regional climate models

Brink, Ingrid van den (2017) Sensitivity of discharge characteristics to the spatial resolution of regional climate models.

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Abstract:Regional Climate Models (RCMs) coupled with General Circulation Models (GCMs) are among the most important tools to generate future climate projections. The output of these models is used for various effect studies, such as the effect of climate change on discharge characteristics. To simulate the discharge, hydrological models are used. These models need reliable precipitation, temperature and data to calculate the potential evapotranspiration as input. These datasets are simulated by the RCMs and are often adjusted using bias correction and/or statistical downscaling before forcing the hydrological models. An important improvement which has been carried out last decades is the increase in RCM spatial resolution. A higher resolution improves the lands surface representation and the possibility to simulate important small-scale precipitation. However, there are some constraints on increasing the RCM spatial resolution. First, this process is time consuming and second, a higher resolution demands significant computational resources. Therefore, it is important to study the balance between the effect of increasing the resolution on the model output and the investments needed to increase the resolution. The effect of increasing RCM spatial resolution on the simulated precipitation and temperature has often been studied. However, the effect of increasing RCM spatial resolution on simulated discharges has been rarely explored. Previous studies expected beforehand that an increase in RCM spatial resolution leads to better simulated discharges. However, these studies concluded that the effect of RCM spatial resolution on discharge characteristics depend on the size of the catchment, the topography of the catchment and the hydrological model choice. This has led to the following research objective: To assess the sensitivity of discharge characteristics to RCM spatial resolution (12.5, 25 and 50 km) simulated by different versions of HBV having different parameterizations for catchments with different characteristics (sizes and topography) in the Rhine basin. To assess the sensitivity of discharge characteristics to RCM spatial resolution, the total model performance is obtained. This total model performance is reflected by the ratio of the mean and standard deviation of the simulated discharge for the three RCM resolutions and the mean and standard deviation of the observed discharge. The influence of the RCM resolution on the total model performance is analysed for four sub-catchments in the Rhine catchments having different characteristics (sizes and topography), the Main (large and lowland), the West Alpine (large and mountainous), Kinzig (small and lowland) and Reuss Seedorf (small and mountainous). Further, to obtain the sensitivity of discharge characteristics to RCM spatial resolution when simulated by different hydrological models, different versions of HBV are used. These versions are the calibrated, semi-calibrated an un-calibrated HBV model having the same model structure, but different parameter sets. Therefore, not the choice of hydrological model, but the choice of hydrological model – parameter estimation is analysed. However, not only the total model performance is analyzed. The RCM spatial resolution is one of the many components which need to be chosen within the modeling chain. Other choices are for example the choice of bias correction technique and the choice of hydrological model. Each choice leads to a different model output and therefore a different total model performance. To make sure that the results showing the sensitivity of discharge characteristics to RCM spatial resolution are really caused by the change in spatial resolution and not influenced by other aspects, no bias correction or statistical downscaling are applied on the output of the RCMs. Further, the two most important contributions to the total model performance are the hydrological model performance and the RCM performance. To be able to understand the results of the total model performance, the contribution of the hydrological model performance and RCM performance are analysed as well. The hydrological model performance is obtained by comparing the simulated discharges forced with observed meteorological data to observed discharge data. The RCM performance is analysed by comparing the simulated discharge forced with RCM data to the simulated discharges forced with observed meteorological data. The RCM performance is further analysed by comparing the output of 5 the RCM, namely the simulated precipitation, temperature and potential evapotranspiration (calculated using the Makkink method) with the observed meteorological data. To be able to analyse the total model performance, the hydrological model performance and the RCM performance, some other choices needed to be made as well. First, the RCM RACMO has been selected having three different spatial resolutions (12.5, 25 and 50 km). This RCM is forced with re-analysis data which show a clear representation of historical climate conditions. Therefore, comparison with observations is possible. Second, the hydrological model HBV-96 has been selected since this model is often used for hydrological modelling. Third, the selected study area is the Rhine catchment since among others a lot of observed datasets are available for this catchment. At last, although this study does not focus on climate change impacts, both low and high flow conditions are considered in the validation since RCMs are often applied for climate change impact studies. The results show that the topography does not influence the sensitivity of discharge characteristics to RCM spatial resolution. The discharge characteristics are not sensitive to RCM spatial resolution in terms of hydrological model – parameter estimation. Only the size of the sub-catchments influences the sensitivity of discharge characteristics to RCM spatial resolution. In general, an increase in RCM spatial resolution leads to a small increase in total model performance for the two larger sub-catchments West Alpine and Main. This conclusion is supported by previous research as well. Further, this increase in total model performance is larger for high discharges than for annual discharges. Only for low discharges this increase is not observed. Beforehand it was expected that the increase in total model performance of smaller sub-catchments when increasing the RCM spatial resolution would be larger. The reason for this is that an increase in RCM spatial resolution leads to a better representation of small scale precipitation patterns. For catchments having a size of around 20000 km2 and for the runoff evolution of a daily timescale, the fine-scale distribution of precipitation within the catchment is less important. However, for smaller sub-catchments it would be expected that the fine scale precipitation is more important. This study did analyse smaller sub-catchments, Reuss Seedorf (836 km2) and Kinzig (928 km2) where this appeared to be not the case. The reason for this could be that no bias correction has been applied in this research. Previous research concluded that another advantage of an increase in RCM spatial resolution is that this leads to biases which are less spatially variable and more systematic and therefore easier to correct. In conclusion, this study shows that for larger sub-catchments an increase in RCM spatial resolution results in a small increase in total model performance. Further, the hydrological model choice and topography are not relevant for the sensitivity of discharge characteristics to the increase of RCM spatial resolution. It is recommended to focus further research on the dependency of the bias correction method and increase in RCM spatial resolution. Furthermore, in order to generalize the findings, it would be good to analyse performances at least for pairs of catchments with similar characteristics to evaluate whether the results are random or do apply to similar catchments. At last, if the total model performance shows an increase or decrease when increasing the RCM spatial resolution, this is not necessarily caused by only the changes in spatial RCM resolutions. These results can as well be influenced by for example a very low performance of the hydrological model or a bias correction method.
Item Type:Essay (Master)
Faculty:ET: Engineering Technology
Subject:56 civil engineering
Programme:Civil Engineering and Management MSc (60026)
Link to this item:http://purl.utwente.nl/essays/73798
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