This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO3, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH3, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH3, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.
Trend analyses of monthly, seasonal and annual rainfall, air temperature, and streamflow were performed using Mann‐Kendall test within the Langat River basin to identify gradual trends and abrupt shifts for 1980 − 2010. Annual rainfall showed an increasing trend in upstream flow, a combination of decreasing and increasing trends in middle stream flow, and a decreasing trend in downstream flow. Monthly rainfall in most months displayed an insignificant increasing trend upstream. Stations with significant increasing trends showed larger trends in summer than those of other seasons. However, they were similar to the trends observed in annual rainfall. Annual minimum air temperature showed a significant decreasing trend upstream and significant increasing trends in the middle stream and downstream areas. Annual maximum air temperature portrayed increasing trends in both upstream and middle stream areas, and a decreasing trend for the downstream area. Both monthly and seasonal maximum air temperatures exhibited an increasing trend midstream, whereas they demonstrated trends of both decreasing and/or increasing temperatures at upstream and downstream areas. Annual streamflow in upper, middle and lower catchment areas exhibited significant increasing trend at the rates of 0.036, 0.023 and 0.001 × 103 m3/y at α = 0.01, respectively. Seasonal streamflow in the upstream, midstream and downstream areas displayed an increasing trend for spring (0.55, 0.33 and 0.013 m3/y respectively) and summer (0.51, 0.37, 0.018 m3/y respectively). The greatest magnitude of increased streamflow occurred in the spring (0.54 m3/y). Significant increasing trends of monthly streamflow were noticed in January and August, but insignificant trends were found in May, September and November at all stations. Annual streamflow records at the outlet of the basin were positively correlated with the annual rainfall variable. This study concludes that the climate of the Langat River basin has been getting wetter and warmer during 1980‐2010.
Hydrological models are commonly used to quantify the hydrological impacts of climate change using general circulation model (GCM) simulations as input. However, application of the model results with respect to future changes in streamflow scenarios remains limited by the large uncertainties stemming from various sources. Therefore, this study aimed to explore uncertainties involved in climate change impact assessment in Hulu Langat Basin, Malaysia, and define the contribution of uncertainty sources to the final uncertainty level. Hydrological model parameters, GCMs, and emission scenario uncertainties were considered the main uncertainty contributors in local-scale impact studies. The equidistant quantile matching method is used to bias-correct simulations of 19 GCMs under two emission scenarios of RCP4.5 and RCP8.5. The Soil and Water Assessment Tool (SWAT) hydrological model is next run by the bias-corrected GCM data to generate a wide spectrum of future streamflow scenarios. Projected monthly streamflow pattern under RCP8.5 showed a different temporal pattern from the observed one. Hydrological model parameter uncertainty was proven to be a larger uncertainty contributor than emission scenario during baseline climate. GCM and emission scenario uncertainties escalated as progressed in time and GCM uncertainty showed larger increments. The monthly pattern of effect of each uncertainty source varied when comparing the two periods of 2030s and 2080s. Therefore, for a superior management of water resources, a study of climate change impacts and uncertainty sources on a smaller scale than the decadal or annual scales can be more informative to the decision makers.
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