Future climate change impacts on runoff of scarcely gauged Jhelum river basin using SDSM and RCPs

Climate change is a global issue and causes great uncertainties in runoff and streamflow projections, especially in high-altitude basins. The quantification of climatic indicators remains a tedious job for the scarcely gauged mountainous basin. This study investigated climate change by incorporating GCM (CCSM4) using the SDSM method for RCPs in the Jhelum river basin. Historical climatic data were coupled with Aphrodite data to cope with the scarcity of weather stations. SDSM was calibrated for the period 1976–2005 and validated for the period 2006–2015 using R2 and RMSE. Future climatic indicators were downscaled and debiased using the MB-BC method. The de-biased downscaled data and MODIS data were used to simulate discharge of Jhelum river basin using SRM. Simulated discharge was compared with measured discharge by using Dv% and NSE. The R2 and RMSE for SDSM range between 0.89–0.95 and 0.8–1.02 for temperature and 0.86–0.96 and 0.57–1.02 for precipitation. Projections depicted a rising trend of 1.5 °C to 3.8 °C in temperature, 2–7% in mean annual precipitation and 3.3–7.4% in discharge for 2100 as compared to the baseline period. Results depicted an increasing trend for climatic indicators and discharge due to climate change for the basin.

Trend Analysis of Hydro-meteorological Parameters in the Jhelum River Basin, North Western Himalayas

The Jhelum River basin drains the entire Kashmir valley and is susceptible to floods, surrounded Himalayan Mountain range. The trend analysis of Hydro-meteorological data is crucial for planning and management of various activities (agriculture, design of hydraulic structures) in the basin. The purpose of the present study is to analyze the trends in the annual maximum and annual average discharge, annual maximum, and annual average rainfall for the Jhelum River basin. The trend analysis was performed by using Mann-Kendall (M-K), Sen’s slope, and innovative trend analysis (ITA) at various Hydro-meteorological stations. The outcomes of trend analysis using the ITA test showed non-monotonic trends at various stations for different time series data and bring forth more significant data to analyze changes in Hydro-meteorological data. Moreover, the overall trend shows a significant decreasing trend in annual average rainfall and discharge, while annual maximum rainfall and discharge revealed a significant increasing trend via ITA. The trend analysis depicts changes in Hydro-meteorological data which would be useful for future management of water resources. Moreover, changes in the discharges in the Jhelum River are due to climatic change and anthropogenic activities in the basin.

Evaluating the Performance of Different Artificial Intelligence Techniques for Forecasting: Rainfall and Runoff Prospective

The forecasting plays key role for the water resources planning. Most suitable technique is Artificial intelligence techniques (AITs) for different parameters of weather forecasting and generated runoff. The study compared AITs (RBF-SVM and M5 model tree) to understand the rainfall runoff process in Jhelum River Basin, Pakistan. The rainfall and runoff of Jhelum river used from 1981 to 2012. The Different rainfall and runoff dataset combinations were used to train and test AITs. The data record for the period 1981–2001 used for training and then testing. After training and testing, modeled runoff and observed data was evaluated using R2, NRMSE, COE and MSE. During the training, the dataset C2 and C3 were found to be 0.71 for both datasets using M5 model. Similar results were found for dataset of C3 using RBF-SVM. Over all, C3 and C7 were performed best among all the dataset. The M5 model tree was performed better than other applied techniques. GEP has also exhibited good results to understand rainfall runoff process. The RBF-SVM performed less accurate as compare to other applied techniques. Flow duration curve (FDCs) were used to compare the modeled and observed dataset of Jhelum River basin. For High flow and medium high flows, GEP exhibited well. M5 model tree displayed the better results for medium low and low percentile flows. RBF-SVM exhibited better for low percentile flows. GEP were found the accurate and highly efficient DDM among the AITs applied techniques. This study will help understand the complex rainfall runoff process, which is stochastic process. Weather forecasting play key role in water resources management and planning.

Implications for water cycle dynamics in the Upper Jhelum River Basin of North-Western Himalayas based on hydrogen and oxygen isotope signatures of precipitation, surface water, and groundwater

<p>We have used stable isotopes of oxygen and hydrogen (δ<sup>18</sup>O and δD) which are important tracers for understanding various hydrological processes, to assess the spatial and temporal variability due to dual moisture sources in the Upper Jhelum River Basin (UJRB) of the north-western Himalayan region. The HYSPLIT back trajectory analysis shows large variability in spatial moisture transport pathways over the region during Southwest monsoon (SWM) and is mainly restricted to the Mediterranean Sea during Western disturbances (WDs). The isotopic composition of precipitation is significantly controlled by temperature and Relative Humidity during precipitation events from WDs; however, this control is found to be weak during the SWM.</p><p>Stable isotope signatures of precipitation are found to show a well-defined altitudinal effect (δ<sup>18</sup>O=0.19‰/100m) and a negative correlation with ambient temperature (R² = 0.65, p<0.01 for WDs & R²=0.48, p>0.1 for SWM). Mixing various tributary waters with different isotopic compositions leads to variability in the Jhelum River’s (JR) isotopic composition along its course. The observed spatial variability of δ<sup>18</sup>O and d-excess results from the exchange processes between groundwater and surface water. The higher depletion of precipitation during WDs leads to depletion of surface and groundwater and produces enrichment due to the evaporative loss of heavier isotopes due to drier weather conditions during SWM. Evaporation signals are more prominent in shallow groundwater (SGW) and lake water, indicating SGW being discharged in the proximity of lake water bodies. The isotopic values in the upper reaches are observed to be depleted, potentially due to inputs from melting glaciers and snow. In the middle, it reaches slightly enriched, likely due to shifts in groundwater and rainfall inputs. In the downstream, due to increased residence time and flat topography, the isotopic composition is relatively enriched, potentially related to the evaporative losses of heavier isotopes. The d-excess values in UJRB are found to vary between 11‰ to 20‰ with an average value of ~17‰, which is relatively higher than the long-term average observed for the Indian summer monsoon (~8‰), and Upper Indus in the Ladakh region (11.7‰) but almost similar to observed for Lower Indus (18‰).</p><p>The contribution of moisture from each source (WDs and SWM) are estimated using a two-component mixing model. The moisture source contribution over UJRB via WDs is 75%(±20) from the Mediterranean Sea and 20%(±10) from SWM. WDs contribution over UJRB is higher than in the Trans-Himalayan region in the Ladakh (Indian sector in the east) but smaller in Lower Indus Basin (Pakistan sector in the west). Hence, the influence of moisture of WDs decreases from west to east along the Himalayan region. This work based on stable isotope geochemistry of oxygen and hydrogen highlights the effects of meteorological and physiographic controls on the moisture dynamics and contributes to explain the spatial and temporal variability of hydrologic processes in the region.</p>