Muhammad Uzair Qamar

Monthly Runoff Regime Regionalization Through Dissimilarity-Based Methods

A number of procedures can be cited in the literature to perform stream flow prediction in an ungauged basin. Stream flow functions as flow duration curve and flood frequency curves can be obtained by hydrological or statistical models. Also flow regime curves are needed for water resources assessment: they are complex (non monotonic) functions and require special care in the parameterization. Here we propose a dissimilarity-based regionalization model to estimate this particular feature of the stream flow process, as the monthly flow regime. The proposed regional statistical frame work is based on the measure of the dissimilarity (sometimes also referred to as distance) between all the possible pairs of flow regimes available in the region. Each regime is considered as a whole hydrological object and the distance between each pair of regime curves is computed through a suitable metric in a non-parametric way. Dissimilarity values then compose a distance matrix which characterizes the variability of the regime shapes in the region of interest. The prediction of regimes in ungauged basins is obtained by creating corresponding distance matrices of basin features taken among geographic, geomorphologic and climatic attributes, usually referred to as descriptors. Suitable basin descriptors are those whose distance matrices are reasonably correlated to the flow regime distance matrix. This choice allows us to use complex descriptors, like the rainfall regime curve. Identification of the suitable descriptors is performed through an unsupervised procedure based on multiple regressions on distance matrices. Once identified the relations, the candidate descriptors of the ungauged basin can be used to select the most similar gauged basins to use as neighbours for estimation of the required runoff regime. The procedure is applied to a set of 118 basins located in northwestern Italy. The performance of the regional estimation is assessed by means of a cross-validation procedure and through comparison with other parametric regional approaches. In most of the cases, the distance-based model produces better estimates of flow regimes than the “standard” procedure, using only few catchment descriptors, with the advantage of demonstrating the role of complex basin features, as for instance the rainfall regime curve.

Flow duration curve regionalization with enhanced selection of donor basins

A non-parametric regionalization procedure for the assessment of flow duration curve (FDC) at ungauged basins is presented. This modeling approach is fundamentally based on the quantification of dissimilarity between FDCs, thus allowing the grouping of most similar basins. An analogous grouping procedure, performed in the space of selected basin characteristics, allows the estimation of FDCs also at ungauged sites; however, for a fixed set of basin characteristics, some ungauged basins cannot be properly represented due to the scarcity of close (similar) donor basins. For these cases, the proposed method allows for the selection of an alternative set of basin characteristics as a support for similarity grouping. The results of the study show that the statistical error can be significantly reduced by following the proposed methodology. About 10% of all the basins involved in the analysis can benefit from the model swapping procedure, thus improving the final predicted curve.

Rainfall Extremes: a Novel Modeling Approach for Regionalization

The rainfall events of extreme magnitude over the past few decades have caused destructive damages to lives and properties, especially in the subcontinent (e.g. Pakistan, India, Bangladesh etc). Rainfall hazard maps for these areas can be of great practical and theoretical interests. In our work, we used extreme value analysis and spatial interpolation techniques to provide such maps through a combination of the Tropical Rainfall Measuring Mission Precipitation (TRMM) 3B42 product and raingauge data. This mixed approach takes advantage of both the long time series available at a limited number of stations, and the large spatial coverage of the satellite data which, instead, has a poor temporal extent. The methodology is implemented by (1) creating a unique growth curve for the homogeneous region by utilizing in-situ rainfall data and (2) mapping the parameters of intensity-duration functions for the entire length of the study area by using TRMM 3B42 product. The regional results obtained by using mixed approach and TRMM 3B42 are compared with the estimates obtained by using in-situ data. The comparison showed that the overall output of mixed approach is more consistent with what transpired by in-situ data for a pre-defined return period.

Future climate and cryosphere impacts on the hydrology of a scarcely gauged catchment on the Jhelum river basin, Northern Pakistan

Streamflow projections are fundamental sources for future water resources strategic planning and management, particularly in high-altitude scarcely-gauged basins located in high mountain Asia. Therefore, quantification of the climate change impacts on major hydrological components (evapotranspiration, soil water storage, snowmelt-runoff, rainfall-runoff and streamflow) is of high importance and remains a challenge. For this purpose, we analysed general circulation models (GCMs) using a multiple bias correction approach and two different hydrological models i.e. the Hydrological Modelling System (HEC-HMS) and the Snowmelt Runoff Model (SRM), to examine the impact of climate change on the hydrological behaviour of the Jhelum River basin. Based on scrutiny, climate projections using four best fit CMIP5 GCMs (i.e. BCC-CSM1.1, INMCM4, IPSL-CM5A-LR and CMCC-CMS) were chosen by evaluating linear scaling, local intensity scaling (LOCI) and distribution mapping (DM) approaches at twenty climate stations. Subsequently, after calibration and validation of HEC-HMS and SRM at five streamflow gauging stations, the bias corrected projected climate data was integrated with HEC-HMS and SRM to simulate projected streamflow. Results demonstrate that the DM approach fitted the projections best. The climate projections exhibited maximum intra-annual rises in precipitation by 183.2 mm (12.74%) during the monsoon for RCP4.5 and a rise in Tmin (Tmax) by 4.77 °C (4.42 °C) during pre-monsoon, for RCP8.5 during 2090s. The precipitation and temperature rise is expected to expedite and increase snowmelt-runoff up to 48% and evapotranspiration and soil water storage up to 45%. The projections exhibited significant increases in streamflows by 330 m3/s (22.6%) for HEC-HMS and 449 m3/s (30.7%) for SRM during the pre-monfaf0000soon season by the 2090s under RCP8.5. Overall, our results reveal that the pre-monsoon season is potentially utmost affected under scenario-periods, and consequently, which has the potential to alter the precipitation and flow regime of the Jhelum River basin due to significant early snow- and glacier-melt.