Suyyas’s Flood: Numerical Models of Kashmir’s Medieval Megaflood and Ancient Lake Kerewa Drainage Events

In the mid-ninth century, an earthquake triggered a landslide that blocked the narrow gorge of the Jhelum River where it exits the Kashmir Valley. The landslide impounded a lake that extended ≈100 km along the floor of the valley, implying an impounded volume of ≤21 km3, flooding the capital, Srinagar, and much agricultural land. An engineered breach of the landslide was contrived by a Medieval engineer resulting in the catastrophic release of flood waters. Using reasonable assumptions we calculate the probable minimum drainage time of this Medieval flood (<4 days) and maximum downstream surge velocities (≈12 m/s). These would have been sufficient to transport boulders in the bed of the Jhelum with dimensions of ≈6 m, consistent with those currently present in some reaches of the river. Given the morphology of the Jhelum gorge we consider that landslide outburst floods may have been common in Kashmir’s history. Ancient shorelines indicate that paleo-lake volumes in the Kashmir Valley may have exceeded 400 km3 which, were they released in catastrophic floods, would have been associated with potential downstream outburst velocities >32 m/s, able to transport boulders with dimensions ≈40 m, far in excess of any found in the course of the Jhelum or in the Punjab plains. Their absence suggests that Kashmir’s ancient lakes were not lowered by outburst mechanisms much exceeding those associated with Suyya’s flood. Present-day floods have been many tens of meters shallower than those impounded by landslides in the Jhelum in the past several thousands of years. A challenge for future study will be to date Kashmir’s ancient shorelines to learn how often landslides and major impoundment events may have occurred in the valley.

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.

Comparative study of length-weight relationships and biological indices of Himalayan snow trout, Schizothorax labiatus, inhabiting two lotic water bodies in the Kashmir Valley

The length-weight relationship (LWR) plays an important role in fishery management as it can be used to estimate the average weight of a specific length group. The results of the current study revealed that the mean values of regression coefficient b for both sexes of Schizothorax labiatus (McClelland) in the Jhelum River indicated positive allometric growth, while in the Sindh River, the b value for males indicated positive allometric growth and for females negative allometric growth. The value of coefficient of determination r2 for both sexes of S. labiatus was equal to or greater than 0.90 in both water bodies, except for females in the Sindh River. Higher significant (P < 0.05) average values of Fulton’s condition factor (K) and the hepatosomatic index (HSI) for both sexes of S. labiatus were noted in the Jhelum River than in the Sindh River. In contrast, higher significant (P < 0.05) values of the gonadosomatic index (GSI) were recorded in the Sindh River. The data generated during the current study provide basic information for researchers and fish biologists for the conservation and sustainable management of this commercially important food fish species in the Kashmir Valley.

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.

Investigating the 2017 Erratic Fishkill Episode in the Jhelum River, Kashmir Himalaya

Globally, the frequency of fishkill episodes is increasing, owing to natural and human-induced modification of aquatic ecosystems. A massive fishkill took place on 22 October 2017 along an approximately 1.5 km stretch of the Jhelum River in Srinagar City, India. Thousands of fish died during this specific event, not lasting more than three hours, creating chaos and panic among the local population and government circles. In this context, affected fish were assessed for three morphological parameters, which include skin color, eye appearance, and skin texture. To back our findings, three critical water-quality parameters, including pH, water temperature, and dissolved oxygen essential for the survival of fishes were assessed in the affected river stretch. This study assumes importance given that water-quality observation stations for monitoring the health of the Jhelum River are lacking in the highly urbanized Srinagar City. The morphological examination of fish samples revealed discoloration, bulging eyes, and rough skin texture, indicating chemical contamination of waters in the affected river stretch. The water quality analysis revealed neutral pH (7.2), normal temperature (15.6 °C), and mildly depleted dissolved oxygen (6 mg L−1) levels. While the morphological examination of the affected fish indicated chemical contamination, the physicochemical parameters exhibited a typical scenario of river water. For avoiding any such further incident and to precisely ascertain the cause of such fishkill episodes in future, it is suggested that a few continuous water-quality monitoring sites along Jhelum River should be set up, supplemented with robust ecological modeling simulations.

Landslide Susceptibility Assessment Using Bivariate Method: A Case Study from River Neelum and Jehlum Catchment Area

Landslide is a frequently occurring natural calamity in the northern areas of Pakistan. The current study is aimed to assess the susceptibility of landslide hazard to highlight the vulnerable areas for the purpose of risk reduction along Neelum and Jehlum rivers in district Muzaffarabad. A data-driven predictive approach was adopted to conduct this study by using Weight of Evidence (WOE) model along with eleven conditioning factors. A spatial distribution map of landslides was prepared using orthophoto, previous records, and derivatives (hill shad, topographic openness, slope, aspect, curvature) of Digital Elevation Model (DEM). The results show that the roads, lithology, and rivers are the most important triggering factors for landslides in both valleys. Approximately 30% of the area is under low susceptibility zone in Jhelum valley while only 13% of the area falls under low susceptibility zone in Neelum river valley. In Jhelum river valley the medium susceptibility zone covers 35% of the total area whereas, Neelam river valley has 26% of the total area under medium susceptibility zone. Around 61% of the land in the Neelam river valley and 35% of the land in the Jhelum river valley are under high susceptibility zone. The area under high hazard lies in the north-east of the district due to multiple conducive factors to trigger landslides including weak lithology (mudstone, sandstone, shales, and clays), high altitude along steep slopes and excessive precipitation (1800 mm/ year). Furthermore, the high hazard zone in study area is not suitable for construction purpose but was suitable for plantation. The validation result (89.41%) is justifying the performance of this model.