Conclusions

This investigation concluded that the HEC-HMS, a standard rainfall-runoff model, can simulate daily streamflow efficiently in high-altitude scarcely gauged catchments of the Jhelum River (Mangla Basin). Despite being a standard rainfall-runoff model, it is a good tool for the seasonal snow- covered watersheds. Rainfall data are the most important input for precise simulation, specifically in scarcely gauged catchments. Therefore, the 0.25×0.25 gridded TRMM rainfall products (3B42) can be successfully integrated with the HEC-HMS in the Himalayan range catchments, where hydrometeorological data are scarce or unavailable. It is also concluded that while the HEC-HMS is a rainfall-runoff model, its efficiency is rather low for the simulation of rapid flow variations, like other watershed models. In contrast, it performs more conveniently over catchments where the streamflow varies gradually.

Furthermore, this investigation concludes that the snowmelt runoff model (SRM) and HEC-HMS based on the temperature index method can efficiently simulate the daily discharges in snow-fed transboundary catchments. The efficiency of both models is based on their input data and the characteristics of the catchment. However, the SRM‘s efficiency depends on the use of MOD10A2 remotely sensed cryosphere data as input to model the snowmelt runoff, and its efficiency will be high in catchments where snowmelt is a major source of inflows. Therefore, this model is not likely to be affected by the well-known rainfall measurement deficit errors in mountainous regions, where a considerable part of the runoff is in the form of snowmelt. Similarly, as the HEC-HMS is a rainfall-based runoff model, rainfall data is the most important factor for precise simulation. However, both models showed very similar performance for the Mangla Basin. The SRM performed poorly at capturing rainfall runoff over the winter period

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compared with HEC-HMS. The SRM allows some flexibility to change the parameters over the temporal scale, i.e. calibrated parametric values can be changed for the winter and summer months and even for individual days, whereas the HEC-HMS user can change some parameters over the spatial scale (along the sub-basins). Moreover, the TRMM satellite rainfall and MODIS snow cover data products were found to be a feasible choice for the ungauged sub-catchments in transboundary high elevation catchments such as the Jhelum River Basin.

A considerable temporal and spatial variation in parameters over the calibration of each time window is observed. Obviously, this change occurs because of the change in datasets over the temporal and spatial scale. This catchment is mainly influenced by monsoon rainfall, so the major change occurs in parameters related to the rainfall-runoff contribution. A slight change is also observed in the snowmelt model parameters, but only for the sub-basins of the Jhelum River catchment at Azad Pattan station. Due to this change in parameters, the efficiency of the model also significantly increased by comparison with the validation.

These results have allowed us to simulate the water availability at Mangla Dam for the planning and management of water resources. Moreover, this model can be applied for further daily flows simulation in neighbouring catchments of the Indus River. This model can also be applied under different scenarios for future predictions, as will be presented in the next chapter.

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Chapter 4

Climate Change Analysis and its Impact

on Sustainability of Water Resources at

Mangla Dam

Foreword: This chapter is included with application of Global Circulation

Models (GCMs) to downscale climate variables (precipitation and temperature) and subsequently used hydrological models to compute water resources availability under climate change at the end of this century in Mangla Basin. In order to downscale climate variables two downscaling techniques are used namely Statistical Downscaling Model (SDSM) and smooth support vector machine (SSVM). From the aforementioned techniques, a comparison is made to choose best downscaling results for examining future climate change impact by using hydrological model HEC- HMS, as illustrated in section 4.3 to 4.7. Additionally, SRM is used to exploit the impact of climate change by developing some future scenarios based on change in temperature and SCA in study basin at the end of this century, as presented in section 4.8.

4.1

Brief Introduction and Background

The global temperature is expected to be increased by 1.5 to 4.5 °C, with a

‗best estimate‘ of 2.0°C due to the doubling of CO2 in next century (IPCC

2007). The global circulation models (GCMs) are the primary source for the estimation of the expected future climate variations due to increase in the concentration of greenhouse gases in the atmosphere (Busuioc et al. 2001; Dibike and Coulibaly 2005). The GCMs spatial resolution is too coarse to compute the impact of climate change on a regional scale, and it is essential

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to convert it into a suitable resolution to inform local meteorological analysis. The methods used for the extraction of regional scale climate variables from GCM outputs are known as downscaling techniques. Generally, downscaling techniques can be categorized into four, including regression (empirical) methods (Enke and Spekat 1997; Faucher et al. 1999; Li and Sailor 2001; Raje and Mujumdar 2011; Wilby et al. 2002), weather pattern approaches (Anandhi et al. 2011; Bardossy and Plate 1992; Bárdossy et al. 2002; Wetterhall et al. 2009; Yarnal et al. 2001), stochastic weather generators (Bates et al. 1998; Semenov and Barrow 1997), a regression- based empirical approach (Raje and Mujumdar 2011), and regional climate models (Mearns et al. 2003).

Compared to other downscaling methods (e.g. dynamical downscaling), the statistical method is relatively easy to use and provides station-scale climate information from GCM-scale outputs (Wilby et al. 2002). Thus, statistical downscaling methods are widely used in hydrologic impact studies under climate-change scenarios. Finding the empirical relationships between the global and local scale of climate circulation is the basic requirement of any statistical downscaling method. According to this assumption, correlation of global GCM meteorological variables (predictors) and local meteorological variables such as observed precipitation and temperature (predictands) is the key point of this type of downscaling procedure. The most well-known regression-based downscaling methods are structured for separate estimation of the occurrence and amount of meteorological variables. The merits and demerits of statistical regression-based downscaling approaches have been discussed with detail by Hessami et al. (2008).

Moreover, GCMs do not provide direct hydrological and meteorological responses related to climate change. Therefore, the hydrological models are much needed for the simulation of streamflows under climate change

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scenarios. A number of hydrological models are offered to simulate daily streamflows, but mostly models perform less efficiently in high-altitude scarce data catchments, where the proportion of snowmelt runoff is dominant in the water resources (Martinec et al. 2007).

Recently, a number of studies have been carried out to examine the impact of climate change on the hydrological regime of rivers by the application of global emission scenarios. Yimer et al. (2009) and Meenu et al. (2013) successfully applied the Hydrological Modelling System (HEC-HMS) for the streamflow simulation by using downscaled meteorological variables (precipitation and temperature) under different global scenarios for the Beles River in Ethiopia and Tunga-Bhadra River catchment in India, respectively. Furthermore, Chen et al. (2012) provided a comparison of different downscaling models for the evaluation and comparison of different hydrological models by using GCMs. In recent years, several researchers have worked on assessing the impact of climate change on hydropower generation as well.