Search Results (19)

Under the 1960 Indus Water Treaty, Pakistan owned the Western rivers (Indus, Jhelum, and Chenab) and India the Eastern rivers (Ravi, Suleimanki, and Beas). Pakistan’s per capita water availability will reduce from 5260 m³ to less than 1000 m³ by 2025, causing water stress. The Indus Basin’s water availability was examined at inflow and outflow gauges between 1991 and 2015. The Indus Basin inflow and outflow gauges indicated exceptionally low and high flows before, during, and after floods. Lower flow values vary greatly for the Indus, Chenab, and Jhelum rivers. During Rabi and Kharif, the Indus and Chenab rivers behaved differently. Lower flows (Q90 to Q99) in Western Rivers are more periodic than higher flows (Q90 to Q99) and medium flows (Q90 to Q99). The outflow gauge Kotri reported 35% exceedance with zero flows during pre-flood and post-flood seasons and 50% during flood season, indicating seasonal concerns. Outflow and inflow both fell, particularly after the year 2000, according to data collected over a longer period (1976–2015). Low storage and regulating upstream capacity caused the Indus Basin outflow to reach 28 MAF (million acre feet) between 1976 and 2015, which is 70% more than the permitted 8.6 MAF downstream Kotri gauge. For 65 percent of the year, the Indus Basin does not release any water downstream of Kotri. As a result, the ecosystem relies on an annual influx of at least 123 MAF to sustain itself, and an outflow of 8.6 MAF from the Indus Basin necessitates an inflow of 113.51 MAF. At high-flow seasons, the Indus Basin experiences devastating floods, yet it dries out at a frightening rate before and after floods. The preservation of ecosystems and riparian zones downstream depends on the large environmental flows in eastern rivers. This is achievable only by fully implementing IWT and improving water management practices at western rivers. Full article

Seasonal snowpacks, characterized by their snow water equivalent (SWE), can play a major role in the hydrological cycle of montane environments with months of snow accretion followed by episodes of melt controlling flood risk and water resource availability downstream. Quantifying the temporal and spatial patterns of snowpack accumulation and its subsequent melt and runoff is an internationally significant challenge, particularly within mountainous regions featuring complex terrain with limited or absent observational data. Here we report a new approach to snowpack characterization using open-source global satellite and modelled data products (precipitation and SWE) greatly enhancing the utility of the widely used Soil and Water Assessment Tool (SWAT). The paper focusses on the c. 23,000 km² Chenab river basin (CRB) in the headwaters of the Indus Basin, globally important because of its large and growing population and increasing water insecurity due to climate change. We used five area-weighted averaged satellite, gridded and reanalysis precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE and CPC UPP. As well as comparison to local weather station data, these were used in SWAT to model streamflow for evaluation against observed streamflow at the basin outlet. ERA5-Land data provided the best streamflow match-ups and was used to infer snowpack (SWE) dynamics at basin and sub-basin scales. Snow reference data were derived from remote sensing and modelled SWE re-analysis products: ULCA-SWE and KRA-SWE, respectively. Beyond conventional auto-calibration and single-variable approaches we undertook multi-variable calibration using R-SWAT to manually adjust snow parameters alongside observed streamflow data. Characterization of basin-wide patterns of snowpack build-up and melt (SWE dynamics) were greatly strengthened using KRA-SWE data accompanied by improved streamflow simulation in sub-basins dominated by seasonal snow cover. UCLA-SWE data also improved SWE estimations using R-SWAT but weakened the performance of simulated streamflow due to under capture of seasonal runoff from permanent snow/ice fields in the CRB. This research highlights the utility and value of remote sensing and modelling data to drive better understanding of snowpack dynamics and their contribution to runoff in the absence of in situ snowpack data in high-altitude environments. An improved understanding of snow-bound water is vital in natural hazard risk assessment and in better managing worldwide water resources in the populous downstream regions of mountain-fed large rivers under threat from climate change. Full article

The fate of agriculture in Pakistan is predominantly concerned with excessive water mining threats to the subsurface water resources. The current study integrates the Visual MODFLOW-2000 application to estimate the water balance of an aquifer bounded by the Chenab River in the West and the Ravi River in the East, which covers an area of about 2.98 million hectares. An assimilated method of groundwater flow is employed to characterize the flow dynamics of the Rechna Doab aquifer. The Digital Elevation Model (DEM) produced by the Shuttle Radar Topography Mission (SRTM) and a mesh of discretized cell size (2500 m) were incorporated into the model design. The conceptual model of the alluvial aquifer involves trifold vertical boundaries (an initial fold thickness set up to 150 m). The model input parameters are precipitation, seepage through irrigation, return flow, recharge, hydraulic conductivity and evapotranspiration. Empirical relations are established (at the basin scale) for the discharge input of irrigation canals. Model results confirm that groundwater flow follows the topographic configuration of the study area (i.e., northeast to southwest), and the seepage from irrigating canals and rainfall appeared to be the main source of groundwater recharge among various resources. The zone budget study under steady state simulation showed that the total direct recharge to the aquifer is calculated as 522,910 acre foot. The simulated water balance of the studied aquifer reflects more fluctuations in river leakage. The predictive optimized model reflects an adaptation of canal lining and installation of additional tube wells that will minimize canal seepage by 70% and lead to the reclamation of 37,000 acres of water-logged land for normal cropping. Full article

Dams and reservoirs trap most sediments, and clear water can cause downstream riverbed degradation or aggradation. As a result, the river adjusts its dynamics and channel geometry to regain equilibrium between sediment supply and transport capacity. This study aimed to assess the river regime of the Chenab River in the post-Chiniot Dam Project scenario using a one-dimensional numerical model. After calibration and validation using historic flows and river surveys, simulations were carried out for 5, 10, and 30 years. The sediment model was validated with Brune’s curve, which showed a Nash–Sutcliffe efficiency value of 0.734. The results showed that the river experienced continuous degradation of sediments for the first 16 years and showed a maximum erosion of 8 m at 680 m downstream of the dam. The reach experienced aggradation at 15 km downstream of the dam for the first 10 years and then became stable and showed a maximum deposition of 0.9 m. The ratio of sediments passed through the dam to sediments transported out of reach varied from 0.833 to 0.921, showing that the river reach would continue to attain equilibrium even after 30 years of reservoir operation. The study would be helpful for the prediction of possible future changes in the Chenab River. Full article

Remote sensing precipitation or precipitation from numerical weather prediction (NWP) is considered to be the best substitute for in situ ground observations for flood simulations in transboundary, data-scarce catchments. This research was aimed to evaluate the possibility of using a combination of a satellite precipitation product and NWP precipitation for better flood forecasting in the transboundary Chenab River Basin (CRB) in Pakistan. The gauge-calibrated satellite precipitation product, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event in the Chenab River at the Marala Barrage gauging site in Pakistan. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model to predict the flood hydrograph in the Chenab River. The hydrologic simulations, with global GFS forecasts, were unable to predict the flood peak for all lead times. Then, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. In the WRF model, the CRB was centered in two domains of 25 km and 5 km resolutions. The downscaled precipitation forecasts were subsequently supplied to the IFAS model, and the predicted simulations were compared to obtain the optimal flood peak simulation in the Chenab River. It was found in this study that the simulated hydrographs, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R² and Nash–Sutcliffe efficiency (NSE) and the lowest percent bias (PBIAS) compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation. Full article

The contamination of surface and groundwater is of major concern around the globe due to the fast industrialization and urbanization. The groundwater and water quality of rivers, Ravi and Chenab in Faisalabad, Pakistan are contaminated due to the industrial wastewater. The aim of this study was the assessment of the physiochemical contaminants of Faisalabad’s industrial wastewater area and the adsorptive removal of ions present in high concentrations following the National Environmental Quality Standards (NEQS) for the municipal and industrial liquid effluents of Pakistan. One of the two samples was collected from a drain carrying wastewater from different industries and other from the outlet of a drain discharging wastewater into river Chenab. The analysis results obtained indicate that most of the contaminants were below the acceptable limit of industrial wastewater NEQS, Pakistan. However, contaminants like sulfate ions (714 mg/L), total dissolved solids (33,951–34,620 mg/L) and barium ions (11–15 mg/L) were found to be higher than the allowable level of NEQS for the municipal and industrial liquid effluents for Pakistan. A novel biosorbent synthesized indigenously from Monotheca buxifolia seeds was used for the removal of sulfate, barium and TDS from the wastewater effluent samples. This biosorbent successfully reduced the sulfate ion concentration in the wastewater sample from 714 to 420 mg/L at pH 6 in 1 h. Similarly, the concentration of TDS reduced to 33,951 from 6295 mg/L at pH 4, whereas barium ions were removed from 15 to 1 mg/L at pH 10 in 1 h. Treatment of wastewater through the synthesized biosorbent efficiently removed the high concentration ions and could potentially be applied to reduce the toxic effects of these contaminants on local public health. Full article

Flash floods are the most dangerous kinds of floods because they combine the destructive power of a flood with incredible speed. They occur when heavy rainfall exceeds the ability of the ground to absorb it. The main aim of this study is to generate flash flood maps using Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) models in the river’s floodplain between the Jhelum River and Chenab rivers. A total of eight flash flood-causative physical parameters are considered for this study. Six parameters are based on remote sensing images of the Advanced Land Observation Satellite (ALOS), Digital Elevation Model (DEM), and Sentinel-2 Satellite, which include slope, elevation, distance from the stream, drainage density, flow accumulation, and land use/land cover (LULC), respectively. The other two parameters are soil and geology, which consist of different rock and soil formations, respectively. In the case of AHP, each of the criteria is allotted an estimated weight according to its significant importance in the occurrence of flash floods. In the end, all the parameters were integrated using weighted overlay analysis in which the influence value of drainage density was given the highest weight. The analysis shows that a distance of 2500 m from the river has values of FR ranging from 0.54, 0.56, 1.21, 1.26, and 0.48, respectively. The output zones were categorized into very low, low, moderate, high, and very high risk, covering 7354, 5147, 3665, 2592, and 1343 km², respectively. Finally, the results show that the very high flood areas cover 1343 km², or 6.68% of the total area. The Mangla, Marala, and Trimmu valleys were identified as high-risk zones of the study area, which have been damaged drastically many times by flash floods. It provides policy guidelines for risk managers, emergency and disaster response services, urban and infrastructure planners, hydrologists, and climate scientists. Full article

Amphibians and reptiles have interacted with humans for millennia. However, humans interact with amphibian and reptile species in different manners, which depend on their culture and traditions. This study was designed to better understand the interactions between amphibian and reptile species and their usage among the native peoples in the vicinity of the Jhelum and Chenab rivers, Pakistan. Information was collected through semi-structured interviews and questionnaires, and was analyzed by using different indices, including the frequency of citation, corrected fidelity level, fidelity level, relative importance level, and informant major ailment. Two amphibians and twenty-six reptile species were used in therapeutic medicine in the study area. Based on the cultural analysis, we found that Naja naja (black cobra) was highly cited across all cultural groups. A 100% Fidelity Level was calculated for the following species: Naja naja (eye infection), Varanus bengalensis (joint pain), Eurylepis taeniolatus (cataract), and Acanthodactylus cantoris (cancer). We found five endangered species in the study area, i.e., Aspideretes gangeticus, A. hurum, Chitra indica, Varanus flavescens, and Geoclemys hamiltonii, that were used to cure joint pain, muscle stretching and pain, backbone pain, paralysis, and psoriasis, respectively. Likewise, Lissemys punctata andersoni, a vulnerable species as labelled by the International Union for Conservation of Nature, was extensively used for the treatment of joint pain, body pain, paralysis, and arthritis in the study area. In terms of conservation, it is critical to protect the highly vulnerable and endangered species that are being used in therapeutic medicines. Our findings may be helpful for the conservation of amphibian and reptile species by helping to make an effective plan to prevent their extinction. The main threats to the diversity of amphibian and reptile species in the area are hunting, trading, and cultural use. These threats could potentially lead to the extinction of these species. Therefore, with the involvement of concerned authorities, e.g., local stakeholders, the Ministry of Climate Change, provincial wildlife departments, academia, and conservation managers, immediate conservation measures should be taken for the protection and sustainable utilization of medicinal species. Full article

Flooding is frequent in the province of Punjab, Pakistan, because the Indus River is a confluence point of five rivers. Researchers have primarily focused on the northern parts of the Indus basin and they have reported on simulation models that can be applied to the evaluation of flood risk. However, the inundation risks in the southern parts of the basin, including the impact of urbanization in this region, require a further assessment. The severity of flood disasters in the upper and lower reaches of the Indus basin are equally important because flash floods and riverine flooding pose a threat to densely populated areas. In this work, we aim to simulate flooding and the effects of landcover changes on inundation in the upper and lower Indus basin. Inundation was determined using the Rainfall–Runoff–Inundation (RRI) model with rainfall data from the monsoon season (00:00 UTC 1 July 2015–00:00 UTC 1 September 2015) as the input. After validating the model, sensitivity experiments were conducted to analyze the effect of landcover changes on the inundation of the Indus basin. The RRI model results showed that planting in the bare and vegetated areas led to minimum inundation in the Indus basin. Based on these results, planting between the Indus River and Chenab River could prevent flood disasters downstream of the confluence point as the discharge values reduced from 15,695.2 m³/s to 12,078.3 m³/s and 4373.7 m³/s to 2934.6 m³/s in the Indus River and Chenab River, respectively, before the confluence point. In contrast, urbanization in Punjab increased the risk of inundation after the confluence point caused by an increased discharge from 12,078.3 m³/s to 14,190.4 m³/s and 2934.6 m³/s to 4229.5 m³/s in the Indus River and Chenab River, respectively, before the confluence point. Full article

This study focused on the impact assessment of the wild flora and environmental gradients encompassed by the River Chenab headworks using quantitative ecological indices. Quadrats of 1 × 1, 5 × 5 and 10 × 10 m² sizes were used for vegetation. Considering environmental data, grazing and anthropogenic effects, soil analysis based on different physical and chemical properties was studied. The relationship between different sites and their surroundings was analyzed by Canonical and Detrended Correspondence Analysis. Similarity indices were revealed through the use of a heat map and dendrogram. As many as 130 plant species, 60 families, comprising 104 species of dicots, 17 monocots, 7 pteridophytes and 2 species of bryophytes were recorded. Different soil properties including organic matter, soil moisture and soil pH affect the vegetation on different sites. Anthropogenic activities such as construction, fishing and animal grazing were the main threats for vegetation that need to be restricted strategically to conserve surrounding vegetation. Full article

Near real-time estimation of soil loss from river catchments is crucial for minimizing environmental degradation of complex river basins. The Chenab river is one of the most complex river basins of the world and is facing severe soil loss due to extreme hydrometeorological conditions, unpredictable hydrologic response, and complex orography. Resultantly, huge soil erosion and sediment yield (SY) not only cause irreversible environmental degradation in the Chenab river catchment but also deteriorate the downstream water resources. In this study, potential soil erosion (PSE) is estimated from the transboundary Chenab river catchment using the Revised Universal Soil Loss Equation (RUSLE), coupled with remote sensing (RS) and geographic information system (GIS). Land Use of the European Space Agency (ESA), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data, and world soil map of Food and Agriculture Organization (FAO)/The United Nations Educational, Scientific and Cultural Organization were incorporated into the study. The SY was estimated on monthly, quarterly, seasonal, and annual time-scales using sediment delivery ratio (SDR) estimated through the area, slope, and curve number (CN)-based approaches. The 30-year average PSE from the Chenab river catchment was estimated as 177.8, 61.5, 310.3, 39.5, 26.9, 47.1, and 99.1 tons/ha for annual, rabi, kharif, fall, winter, spring, and summer time scales, respectively. The 30-year average annual SY from the Chenab river catchment was estimated as 4.086, 6.163, and 7.502 million tons based on area, slope, and CN approaches. The time series trends analysis of SY indicated an increase of 0.0895, 0.1387, and 0.1698 million tons per year for area, slope, and CN-based approaches, respectively. It is recommended that the areas, except for slight erosion intensity, should be focused on framing strategies for control and mitigation of soil erosion in the Chenab river catchment. Full article

Despite spending ample resources and procedural development in flood management, flood losses are still increasing worldwide. The losses caused by floods and costs incurred on management are two components of expected annual damages (EAD) due to floods. This study introduces a generalized approach for risk-based design where a range of probable floods are considered before and after a flood mitigation measure is implemented. The proposed approach is customized from the ISO Guide 31000 along with additional advantages of flood risk visualization. A Geographic Information System (GIS)-based design of a flood-protection dike is performed to exhibit the risk redistribution. The Chenab River is selected for the existing dike system. Detailed hazard behaviour and societal vulnerability are modelled and visualized for a range of all probable floods before and after the implementation of flood-protection dikes. EAD maps demonstrate the redistribution of induced and residual risks. It can be concluded that GIS-based EAD maps not only facilitate cost-effective solutions but also provide an accurate estimate of residual risks after the mitigation measures are applied. EAD maps also indicate the high-risk areas to facilitate designing secondary measures. Full article

Rapid and reliable flood information is crucial for minimizing post-event catastrophes in the complex river basins of the world. The Chenab River basin is one of the complex river basins of the world, facing adverse hydrometeorological conditions with unpredictable hydrologic response. Resultantly, many vicinities along the river undergo destructive inundation, resulting in huge life and economic losses. In this study, Hydrologic Engineering Centre–Hydrologic Modeling System (HEC-HMS) and HEC–River Analysis System (HEC-RAS) models were used for flood forecasting and inundation modeling of the Chenab River basin. The HEC-HMS model was used for peak flow simulation of 2014 flood event using Global Precipitation Mission (GMP) Integrated Multisatellite Retrievals-Final (IMERG-F), Tropical Rainfall Measuring Mission_Real Time (TRMM_3B42RT), and Global Satellite Mapping of Precipitation_Near Real Time (GSMaP_NRT) precipitation products. The calibration and validation of the HEC-RAS model were carried out for flood events of 1992 and 2014, respectively. The comparison of observed and simulated flow at the outlet indicated that IMERG-F has good peak flow simulation results. The simulated inundation extent revealed an overall accuracy of more than 90% when compared with satellite imagery. The HEC-RAS model performed well at Manning’s n of 0.06 for the river and the floodplain. From the results, it can be concluded that remote sensing integrated with HEC-HMS and HEC-RAS models could be one of the workable solutions for flood forecasting, inundation modeling, and early warning. The concept of integrated flood management (IFM) has also been translated into practical implementation for joint Indo-Pak management for flood mitigation in the transboundary Chenab River basin. Full article

Agricultural damage due to floods in the Indus basin’s fertile land has been the most damaging natural disaster in Pakistan so far. Earthen dikes are protecting the vast areas of the floodplain from regular flooding. However, the floodplain is attractive to farmers due to its fertility and experiences regular crop production within and out of the dike area. This paper evaluates the flood risk in a floodplain of the Chenab river in Pakistan and recommends land-use changes to reduce the flood risk for crops and associated settlements within the study area. The objective of the land-use change is not just to reduce flood losses but also to increase the overall benefits of the floodplain in terms of its Economic Rent (ER). This preliminary study analyses the economic impacts of the risk-based land-use improvements on existing floodplain land uses. Expected Annual Damage (EAD) maps were developed using hydrodynamic models and GIS data. The developed model identified the areas where maize can be economically more productive compared to rice under flood conditions. Promising results were obtained for the settlement relocations. It was also observed that the infra-structure, running parallel to the river, plays a significant role in curtailing the extent of floods. The results show that a combination of structural and non-structural measures proves more effective. The study also recommends the inclusion of social and environmental damages as well as other types of non-structural measures to develop the most effective flood management strategy. Full article

Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975–2004; (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern; (3) detecting future precipitation change trends using projected data (2006–2099) from the selected model for two future scenarios. The comprehensive framework, subject to some limitations and assumptions, provides divisional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted. Full article