Beas River Basin Research Articles

Projection of future rainfall events over the Beas River basin, Western Himalaya, using shared socioeconomic pathways (SSPs) from CMIP6

ABSTRACT The analysis of rainfall variability has significant implications for environmental studies since it influences the agrarian economy of regions such as the western Himalayas. The main objective of this research is to identify future precipitation trends in parts of the Beas River basin using modeled data from three models employed in the Climate Model Intercomparison Project Phase 6. The ACCESS, CanESM, and NorESM models were utilized to obtain modeled meteorological data from 2015 to 2100 (86 years). Data from global climate models were downscaled to the regional level and validated with the India Meteorological Department (IMD). Mention that the modeled data were downscaled from the regional level to the local level. The nonparametric trends test, modified Mann–Kendall, and Sen's slope estimator (Q) were employed to detect the trend and magnitude. Furthermore, the sub-trends of the data series were evaluated utilizing the innovative trend analysis (ITA) approach. Results have shown a significant increasing trend in future timescales, indicating the more frequent extreme events in the basin under all scenarios. The basin has shown a maximum slope of 24.9 (ITA) and 12.2 (Sen's slope).This study findings hold significant implications for policymakers and water resource managers.

Snow Cover Variability in the Beas River Basin and its relation with climate parameters during 2007-2018

Snow Cover Variability in the Beas River Basin and its relation with climate parameters during 2007-2018

Landslide susceptibility evaluation in the Beas River Basin of North-Western Himalaya: A geospatial analysis employing the Analytical Hierarchy Process (AHP) method

In the North-Western Himalayas, particularly within the Beas Basin of Himachal Pradesh, landslide incidents are frequent, primarily due to the unique interplay of adverse geological conditions, heavy rainfall, and human factors. These incidents result in substantial loss of life and property each year. To mitigate such issues, systematic landslide research is essential, encompassing aspects like inventory mapping and risk assessment. This study leverages the Analytical Hierarchy Process (AHP) for an in-depth Landslide Susceptibility Index (LSI) mapping in the Beas River basin, employing remote sensing data to analyze key factors contributing to the region's instability. The process involved a detailed selection and mapping of landslide conditioning variables, guided by validated landslide inventory data and high-resolution remote sensing images, ensuring an accurate representation of the basin's geographical variations. The creation of the landslide susceptibility map utilized the weighted overlay approach, categorizing the area into five levels of susceptibility: very low, low, moderate, high, and very high. This classification incorporated ten critical factors influencing landslide occurrence, including elevation, slope aspect, slope angle, distance from drainage, lithology, distance from lineament, geomorphology, rainfall, and land use/land cover (LULC). The LSI was calculated through the Weighted Linear Combination (WLC) technique, leveraging the weights and ratings derived via the AHP method. This analysis revealed that approximately 634.1 square kilometers, or 12.8% of the region, face very high landslide susceptibility, followed by 22.6% at high, 25.8% with moderate, 24.4% at low, and 14.5% at very low susceptibility. The LSI map's accuracy in predicting landslides was affirmed through Receiver Operating Characteristic (ROC) and Area Under Curve (AUC) evaluations, showcasing an 86.3% precision rate. This classification facilitates focused interventions in high-risk areas, guiding planners in landslide-conscious development and infrastructure planning. It directs engineers toward engineering solutions like slope stabilization and drainage improvements to mitigate landslide effects. Moreover, this approach supports the creation of evacuation and emergency response plans, bolstering community resilience to landslide threats in the river basin.

Groundwater hydrogeochemistry and non-carcinogenic health risk assessment in major river basins of Punjab, India.

The Indian Punjab state is drained by the four rivers, along with a well-connected network of canals, and is now dealing with a slew of water quality issues and problems. In this study, basin-wise hydrogeochemical modelling of 323 groundwater samples and identification of NO3- and F- enrichment pathways in aquifer systems of Punjab were studied using different plots and multivariate statistics. To evaluate the groundwater quality and human health risks, an entropy-based water quality index and Monte Carlo simulation were used, respectively. Spatial distribution of NO3- indicated that its very high values were prominent in parts of southwestern Punjab falling under LSRB, along with few pockets in eastern and northeastern Punjab falling under MSRB and GRB. High NO3- values (> 45.0mg/L) were found in 15.0% of Ravi River Basin (RRB) groundwater samples, 22.86% of Beas River Basin (BRB), 23.52% of Middle Sutlej River Basin (MSRB), 36.9% of Lower Sutlej River Basin (LSRB), and 21.31% of Ghaggar River Basin (GRB). The spatial distribution of NO3- revealed elevated concentrations (> 100mg/L) in the southwestern part of Punjab, particularly in LSRB and localized pockets in the eastern and northeastern areas of Punjab within MSRB and GRB. High F- concentration (> 1.5mg/L) was observed in 15.12% and 21.31% groundwater samples of LSRB and GRB, respectively. Spatially southern parts falling under LSRB and GRB reflected high F- content (> 1.5mg/L) in groundwater. In LSRB, evaporative and anthropogenic processes influence the groundwater quality. The results of interionic relationships and statistical analysis revealed that NO3- has anthropogenic origin and that is being aggravated by leaching,the evaporation processes, animal excreta, septic tanks and irrigation return flows in LSRB and GRB, while F- is geogenic in nature. Hazard index (HI) values in 14.63%, 22.2%, 24.6%, 49.58%, and 34.42% samples for adults and 21.95%, 27.7%, 42.0%, 72.3%, and 52.46% samples for children were higher than unity in RRB, BRB, MSRB, LSRB, and GRB, respectively. The basin-wise demarcation of various groundwater quality parameter and assessment of human health risk would be of significance for the management of water resources.

Snow Cover Response to Climatological Factors at the Beas River Basin of W. Himalayas from MODIS and ERA5 Datasets.

Glaciers and snow are critical components of the hydrological cycle in the Himalayan region, and they play a vital role in river runoff. Therefore, it is crucial to monitor the glaciers and snow cover on a spatiotemporal basis to better understand the changes in their dynamics and their impact on river runoff. A significant amount of data is necessary to comprehend the dynamics of snow. Yet, the absence of weather stations in inaccessible locations and high elevation present multiple challenges for researchers through field surveys. However, the advancements made in remote sensing have become an effective tool for studying snow. In this article, the snow cover area (SCA) was analysed over the Beas River basin, Western Himalayas for the period 2003 to 2018. Moreover, its sensitivity towards temperature and precipitation was also analysed. To perform the analysis, two datasets, i.e., MODIS-based MOYDGL06 products for SCA estimation and the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Global Climate (ERA5) for climate data were utilized. Results showed an average SCA of ~56% of its total area, with the highest annual SCA recorded in 2014 at ~61.84%. Conversely, the lowest annual SCA occurred in 2016, reaching ~49.2%. Notably, fluctuations in SCA are highly influenced by temperature, as evidenced by the strong connection between annual and seasonal SCA and temperature. The present study findings can have significant applications in fields such as water resource management, climate studies, and disaster management.

Statistical evaluation of snow accumulation and depletion from remotely sensed MODIS snow time series data using the SARIMA model

AbstractIn the remote and challenging terrain of the Himalayan region, accurate measurement of cyclic snow accumulation and depletion is a significant challenge. To overcome this, an attempt has been made in the present study by applying a statistical analysis of MODIS snow time series data with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model from 2003 to 2018 over the Beas river basin. The Box–Jenkins methodology of forecasting is based on the identification using seasonality, stationarity, ACF, and PACF plots; and estimation based on maximum likelihood techniques; and the last diagnostic checking based on the residual and error values have been used. Later, forecasting models have been proposed separately for the snow accumulation period (October–February) as (1,1,1) (0,1,3)19 and for the snow depletion period (March–September) as (1,1,1) (1,1,2)27 after calibration of the data (2003–2015) and the same were then validated using data (2016–2018). The accuracy assessment of the models has been checked using performance criteria like AIC, MSE, and RSS. The comparison of the forecasting models with the observed data showed a good agreement with R2 of 0.83 and 0.89 for snow accumulation and snow depletion, respectively. This research highlights the potential of utilizing satellite data and statistical modeling to address the challenges of monitoring snow cover in remote and inaccessible regions.

Interpretation of land use/land cover dynamics with the application of geospatial techniques in sarbari khad watershed of Himachal Pradesh, India

Land Use/Land Cover (LULC) changes represent anthropogenic transformation of natural landscape. These changes are an important tool in developing strategies for the management of natural resources as well as monitoring environmental changes. LULC mapping and monitoring is required to understand the causes and consequences of over exploitation of natural resources such as land, soil, forest and water. By assessing the uses and misuses of resources and identifying the reasons for misuses, the measures for sustainable development of resources in the fragile Himalayan watersheds can be identified and implemented. The main objective of the present study is to assess the land use/land cover changes in the Sarbari Khad watershed of Kullu Valley. The Sarbari Khad watershed is part of Beas River basin. The changes in LULC have been detected by using remote sensing and GIS (Geographical Information System) techniques for a period of twenty-one years (2000–2021). The satellite images of the National European Space Agency Sentinal-2 of November, 2021 and LandSat-7 image of November 2000 were processed with the help of ERDAS Imagine (2015) and Arc View (3.2a) software. This study reveals that substantial amount of deforestation and snow cover retreat has occurred during this period of two decades due to anthropogenic activities such as expansion of agriculture, especially horticulture, tourism and infrastructure development.

Validation of Landsat-8 satellite-derived radiative energy fluxes using wireless sensor network data over Beas River basin, India

ABSTRACT In the present paper, spatio-temporal variability in surface radiative energy fluxes has been estimated for snow/ice-covered Beas River basin, lower Western Himalaya, India using remote sensing technology and evaluated with wireless sensor network (WSN) collected data at various elevation levels. Surface energy fluxes are derived using Landsat-8 satellite images and digital elevation model (DEM) at fine spatial resolution (~30-m) during clear-sky days of the year 2016–2017. RMSE have been estimated in incoming shortwave, incoming longwave, outgoing longwave, net shortwave and net radiation flux and observed to be ~5%, ~ 6.3%, ~2.4%, ~18.9%, and ~28.3% of the respective mean values. Landsat-8 images in conjunction with DEM have shown the potential to retrieve variability in surface radiative energy fluxes at fine resolution in mountain terrain. Additionally, temporal variability in radiative radiation fluxes have also been explored on different elevation ranges and aspects slopes. The high energy fluxes have been estimated on southern slopes during analysis as compared to northern slopes in the present study. The paper appears to be the first for reporting net radiation flux for Himalayan snow cover at 30-m spatial resolution. The satellite-derived energy fluxes may be valuable in various applications associated with climatology, hydrology, wet-snow avalanche detection, land use land cover, mass balance, ecology, etc., especially in the absence of in situ data.

Identification of flash-floods-prone river reaches in Beas river basin using GIS-based multi-criteria technique: validation using field and satellite observations

In view of an exponential increase in the negative impacts of flash-floods globally, the present work aims at the identification of flash-floods-prone river reaches in the Beas river basin, Himachal Pradesh, India using a multi-criteria indexing technique. The flood hazard index (FHI) was computed by implementing analytical hierarchy process (AHP) model on 6 hydrologic parameters influencing flood hazard, namely rainfall intensity, curve number (CN) grid, time of travel, slope, Manning's roughness coefficient and drainage density. The CN grid (empirical parameter to estimate direct surface runoff) was used as one of the parameters which depend upon the land use, hydrologic soil group and hydrologic conditions. It is imperative to mention that remote sensing and geographical information system (GIS) techniques played a crucial role in the preparation of these 6 parameter layers. The AHP model calculates the normalized weights for each parameter using pair-wise comparison matrices. The rainfall intensity and curve number were the factors having the highest normalized weight of 34.52 each. Subsequently, the estimated weights of the parameters and hazard level-wise rating scores were used in a GIS environment to generate FHI. The generated FHI raster was masked using floodplain layer within geomorphology map and river buffer to identify flash-floods-affected river reaches. The generated flash-floods map was validated by historical flash-floods ground points, field observations and remote sensing data. The results depicted that the river reaches in the north and east of the Beas basin are susceptible to flash-floods which are mainly governed by heavy rainfall intensity and high runoff characteristics. The river stretches namely Bahang–Manali (Beas), Kullu–Bhuntar (Beas) and Manikaran–Kheer-Ganga (Parvati) have been categorized into very high and high flash-floods zones. Decreasing trend of normalized differential vegetation index (NDVI) was observed for river reaches falling within the very high and high zones indicating the vegetation loss post successive flash-floods events. The river order 2 lies in the very high and high flash-floods zones, indicating the fact that the contribution of tributaries is significant to flood events. Flash-floods map will serve as catastrophic product, which will help policymakers to take suitable measures to reduce the risk of flash-floods.

Suitability of spring water from the Upper Beas River Basin in Kullu Valley (Western Himalaya, India) for drinking and irrigation purposes

Spring water is a reliable source of potable water to many communities and habitants in western Himalayan region of India. The present study evaluates the hydrochemical nature of spring water using various drinking parameters and agricultural indices in upper Beas basin of Kullu Valley, Himachal Pradesh, India. Fifty springs were sampled for the estimation of physico-chemical parameters and major ions. The results indicate that majority of the spring waters in the study area are suitable for drinking as well as irrigation purposes except for few locations. About 14% of springs showed high nitrate content (45 to 92.6 mg/L) more than BIS permissible limit of 45 mg/L. The source of contamination could be sewage disposal, livestock waste and fertilizers. Fluoride (0.16–0.49 mg/L) was found to be within permissible limits for drinking. Drinking Water Quality Index ranges from 1.74 to 108, and Irrigation Water Quality Index ranges from 0.27 to 8.21. Both these indices indicate that the spring water falls in excellent to good category and is suitable in terms of potability and irrigation uses. Hydrogeochemical characteristics of the spring waters indicate that alkaline earths (Ca2+ + Mg2+) dominate alkalies (Na+ + K+) and strong acids (SO42− + Cl−) dominate weak acids (CO32− + HCO3−). Based on Piper’s classification, the spring water data falls in no cation–no anion dominant zone followed by carbonate hardness (secondary alkalinity) zone and hydrochemical trends (Piper’s and Gibb’s plots) inferred that spring water chemistry is mainly controlled by water rock interaction followed by rainwater chemistry.

Evaluation of reanalysis and global meteorological products in Beas river basin of North-Western Himalaya

It is a great challenge to obtain reliable gridded meteorological data in some data-scarce and complex territories like the Himalaya region. Less dense observed raingauge data are unable to represent rainfall variability in the Beas river basin of North-Western Himalaya. In this study four reanalyses (MERRA, ERA-Interim, JRA-55 and CFSR) and one global meteorological forcing data WFDEI have been used to evaluate the potential of the products to represent orographic rainfall pattern of Beas river basin using hydrology model. The modeled climate data have compared with observed climate data for a long term basis. A comparison of various rainfall and temperature products helps to determine uniformity and disparity between various estimates. Results show that all temperature data have a good agreement with gridded observed data. ERA-Interim temperature data is better in terms of bias, RMSE (Root Mean Square Error), and correlation compared to other data. On the other hand, MERRA, ERA-Interim and JRA-55 models have overestimated rainfall values, but CFSR and WFDEI models have underestimated rainfall values to the measured values. Variable Infiltration Capacity (VIC), a macroscale distributed hydrology model has been successfully applied to indirectly estimate the performance of five gridded meteorological data to represent Beas river basin rainfall pattern. The simulation result of the VIC hydrology model forced by these data reveals that the discharge of ERA-Interim has a good agreement with observed streamflow. In contrast there is an overestimated streamflow observed for MERRA reanalysis estimate. JRA-55, WFDEI, and CFSR data underestimate the streamflow. The reanalysis products are also poor in capturing the seasonal hydrograph pattern. The ERA-Interim product better represents orographic rainfall for the Beas river basin. The reason may be the ERA-Interim uses a four-dimensional variational analysis model during assimilation. The major drawback of MERRA is the non-inclusion of observed precipitation data during assimilation and modeling error. The poor performance of JRA-55, CFSR and WFDEI is due to the gauge rainfall data assimilation error. This research finding will help for broader research on hydrology and meteorology of the Himalayan region.

Flow regime changes under future climate and land cover scenarios in the Upper Beas basin of Himalaya using SWAT model

ABSTRACT The present study aims to analyse the effect of climate and land cover changes on the discharge of the Upper Beas River basin. The study used climate, soil, land use/land cover, and elevation data in the Soil and Water Assessment Tool (SWAT) model to estimate the discharge that is calibrated and validated using the observed discharge data of the Thalout gauge site. The findings suggest that basin hydrology is more sensitive to climate change than land cover changes. Mean annual discharge shows a rise between 0.31 and 9.65% under climate change scenarios, but it may decline by 9% under land cover change scenarios by the mid-21st century. Under all the climate change and land cover scenarios, seasonal variations in discharge are more prominent than annual changes. Water availability would be more in pre-monsoon season because of warming in the future. These changes would be beneficial in the short run, but may adversely affect the water availability in the long run.

A case study for the assessment of the suitability of gridded reanalysis weather data for hydrological simulation in Beas river basin of North Western Himalaya

The major problem of estimating snowmelt runoff for Beas river basin is inadequacy of observed meteorological data distributed across the basin. In this study, ERA-Interim global reanalysis data have been used for assessing the stream flow and sediment yield in Beas river basin of North Western Himalaya. The snow module of ARCSWAT hydrology model has been simulated by integration of subbasin-wise elevation band files for modeling snowmelt runoff process including sediment yield due to rainfall and temperature change for different elevation bands varying from 361 to 6188 m. The gridded reanalysis (0.125° × 0.125°) dataset produces a decreased maximum and minimum temperature and increased precipitation at higher elevation in comparison with IMD gridded weather data. The outcome of this study conveys that the reanalysis data represent better snowmelt runoff (NSE = 0.76, 0.70 and R2 = 0.80, 0.70) and sediment yield (NSE = 0.50, 0.53 and R2 = 0.72, 0.57) mechanism at Pong and Pandoh dams than IMD gridded weather data (NSE = 0.50, 0.47 and R2 = 0.65, 0.60) for stream flow and (NSE = 0.50, 0.53 and R2 = 0.65, 0.60) sediment yield during the period 1996–1999 and 1999–2002 for these two locations.

Twenty-first-century glacio-hydrological changes in the Himalayan headwater Beas River basin

Abstract. The Himalayan Mountains are the source region of one of the world's largest supplies of freshwater. The changes in glacier melt may lead to droughts as well as floods in the Himalayan basins, which are vulnerable to hydrological changes. This study used an integrated glacio-hydrological model, the Glacier and Snow Melt – WASMOD model (GSM-WASMOD), for hydrological projections under 21st century climate change by two ensembles of four global climate models (GCMs) under two Representative Concentration Pathways (RCP4.5 and RCP8.5) and two bias-correction methods (i.e., the daily bias correction (DBC) and the local intensity scaling (LOCI)) in order to assess the future hydrological changes in the Himalayan Beas basin up to Pandoh Dam (upper Beas basin). Besides, the glacier extent loss during the 21st century was also investigated as part of the glacio-hydrological modeling as an ensemble simulation. In addition, a high-resolution WRF precipitation dataset suggested much heavier winter precipitation over the high-altitude ungauged area, which was used for precipitation correction in the study. The glacio-hydrological modeling shows that the glacier ablation accounted for about 5 % of the annual total runoff during 1986–2004 in this area. Under climate change, the temperature will increase by 1.8–2.8 ∘C at the middle of the century (2046–2065), and by 2.3–5.4 ∘C until the end of the century (2080–2099). It is very likely that the upper Beas basin will get warmer and wetter compared to the historical period. In this study, the glacier extent in the upper Beas basin is projected to decrease over the range of 63 %–87 % by the middle of the century and 89 %–100 % at the end of the century compared to the glacier extent in 2005. This loss in glacier area will in general result in a reduction in glacier discharge in the future, while the future streamflow is most likely to have a slight increase because of the increase in both precipitation and temperature under all the scenarios. However, there is widespread uncertainty regarding the changes in total discharge in the future, including the seasonality and magnitude. In general, the largest increase in river total discharge also has the largest spread. The uncertainty in future hydrological change is not only from GCMs, but also from the bias-correction methods and hydrological modeling. A decrease in discharge is found in July from DBC, while it is opposite for LOCI. Besides, there is a decrease in evaporation in September from DBC, which cannot be seen from LOCI. The study helps to understand the hydrological impacts of climate change in northern India and contributes to stakeholder and policymaker engagement in the management of future water resources in northern India.

EXPERIMENTAL FLOOD EARLY WARNING SYSTEM IN PARTS OF BEAS BASIN USING INTEGRATION OF WEATHER FORECASTING, HYDROLOGICAL AND HYDRODYNAMIC MODELS

Abstract. The flood early warning for any country is very important due to possible saving of human life, minimizing economic losses and devising mitigation strategies. The present work highlights the experimental flood early warning study in parts of Beas Basin, India for the monsoon season of 2015. The entire flood early warning was done in three parts. In first part, rainfall forecast for every three days in double nested Weather Research and Forecasting (WRF) domain (9 km for outer domain and 3 km for inner domain) was done for North Western Himalaya NWH using National Centres for Environmental Prediction (NCEP) Global Forecasting System (GFS) 0.25 degree data as initialization state. Rainfall forecast was validated using Indian Meteorological Department (IMD) data, the simulation accuracy of WRF in rainfall prediction above 100 mm is about 60%. Rainfall induced flood event of August 05–08, 2015 in Sone River (tributary of Beas River) Basin, near Dharampur, Mandi district of Himachal Pradesh caused very high damages. This event was picked three days in advance by WRF model based rainfall forecast. In second part, mean rainfall at sub-basin scale for hydrological model (HEC-HMS) was estimated from forecasted rainfall at every three hours in netcdf format using python script and flood hydrographs were generated. In third part, flood inundation map was generated using Hydrodynamic (HD) model (MIKE 11) with flood hydrographs as boundary condition to see the probable areas of inundation.

Automated Retrieval of Snow/Ice Surface Broadband Albedo in Beas River Basin, India Using Landsat-8 Satellite Images and Validation with Wireless Sensor Network Data

This paper proposed an algorithm for automated retrieval of snow/ice surface albedo using Landsat-8 satellite images and validation with wireless senor network data in Beas River basin, India. Cloud-free Landsat-8 images of winter season 2016–2017 have been used to estimate snow/ice broadband albedo. Albedo maps generated using satellite images have been compared and validated with in situ recorded albedo values at wireless sensor network stations. Root-mean-square error of 0.053, R-square (R2) of 0.96, bias of − 0.034 and mean absolute percentage error of ~ 3.5% have been observed. Existing global albedo model for Landsat ETM+ was also used to estimate albedo in the study area using Landsat-8 images. Algorithm proposed in this paper for Landsat-8 has shown comparatively better results. Simultaneously, albedo values for snow/ice class have also been estimated using MODIS images. High correlation has been observed between the albedo values estimated from Landsat-8 and MODIS images. Proposed algorithm appears to be the first to estimate broadband albedo of snow/ice class from narrowband reflectances using Landsat-8 images for Indian Himalaya.

A Framework for Assessing Instream Supporting Ecosystem Services Based on Hydroecological Modelling

River systems provide diverse ecosystem services (ES), such as flood regulation (regulating), fresh water (provisioning), nutrient cycling (supporting), and recreation (cultural), among others. The construction of infrastructure (e.g., for hydropower, irrigation) enhances the delivery of tangible ES for example food or energy (generally provisioning) to meet human needs. However, the resulting change to river flows threatens both the ecological health of a river and its ability to provide intangible but vital ES, for example those which support the delivery of other services. Understanding these supporting ES processes in river systems is essential to fully recognise the impact of water resources development on ES delivery. Whilst approaches for assessing instream supporting ES are under development, to date few provide quantitative methods for assessing delivery. Thus, this paper sets out a framework for the assessment of instream supporting ES using hydroecological modelling. It links supporting ES delivery to fluvial hydrological indicators through the use of ecologically relevant hydrological indices and macroinvertebrate flow preferences. The proposed framework is demonstrated on the Beas River basin (Western Himalayas, India), and is flexible enough to be transferred to a basin-wide model, thereby allowing ES relationships to be accounted for in basin-wide water resources planning.

Split-Window Algorithm for Retrieval of Land Surface Temperature Using Landsat 8 Thermal Infrared Data

Land surface temperature (LST) plays a vital role in global climate change, radiation budgets, heat balance, vegetation, snowmelt, glacier hydrology, and geo-biophysical processes. It is, therefore, essential to determine LST precisely over large areas. With advancements in remote sensing, LST can now be estimated. In this study, a critical appraisal of various LST inversion algorithms is presented. These algorithms include mono-window (MW), split-window (SW), dual-angle (DA), single-channel (SC), and Sabmao method. The main objective is to derive an SW algorithm to retrieve LST from Landsat 8 satellite data and demonstrate the application to the Beas River basin in India. Located within the Himalayan range, the study area is characterized by heterogeneity and rugged terrain with areas covered by snow and glacier. The satellite imagery is a product of the Optical Land Imager (OLI) with a spatial resolution of 30 m and a thermal infrared sensor (TIRS) having a spatial resolution of 100 m. The SW algorithm requires spectral radiance and emissivity from two bands of the TIRS as input for the estimation of LST. The spectral radiance has been estimated using the TIRS bands 10 and 11. The normalized difference vegetation index (NDVI) and the threshold technique of OLI bands (2 to 5) have been used to derive the emissivity. The estimates of LST from the TIRS and OLI bands using the SW algorithm are found to be accurate and close to the in situ air temperature measurements and the LST values obtained from the MW algorithm. Results obtained show that the values of LST are high in the barren/rocky areas and low in the snow/glacier areas. The study reveals that the LST estimates from SW and MW algorithms are linearly transferable with negligible loss of accuracy. The LST estimates from the SW algorithm differs at most by up to 5 °C with the measured air temperature.

Assessment of freshwater ecosystem services in the Beas River Basin, Himalayas region, India

Abstract. River systems provide a diverse range of ecosystem services, examples include: flood regulation (regulating), fish (provisioning), nutrient cycling (supporting) and recreation (cultural). Developing water resources through the construction of dams (hydropower or irrigation) can enhance the delivery of provisioning ecosystem services. However, these hydrologic alterations result in reductions in less tangible regulating, cultural and supporting ecosystem services. This study seeks to understand how multiple impoundments, abstractions and transfers within the upper Beas River Basin, Western Himalayas, India, are affecting the delivery of supporting ecosystem services. Whilst approaches for assessing supporting ecosystem services are under development, the immediate aim of this paper is to set out a framework for their quantification, using the macroinvertebrate index Lotic-Invertebrate Index for Flow Evaluation (LIFE). LIFE is a weighted measure of the flow velocity preferences of the macroinvertebrate community. Flow records from multiple gauging stations within the basin were used to investigate flow variability at seasonal, inter-annual and decadal time scales. The findings show that both mean monthly and seasonal cumulative flows have decreased over time in the Beas River Basin. A positive hydroecological relationship between LIFE and flow was also identified, indicative of macroinvertebrate response to seasonal changes in the flow regime. For example, high LIFE scores (7.7–9.3) in the winter and summer seasons indicate an abundance of macroinvertebrates with a preference for high flows; this represents a high potential for instream supporting ecosystem services delivery. However, further analysis is required to understand these hydroecological interactions in the study basin and the impact on instream supporting ecosystem services delivery.

Automated mapping of snow/ice surface temperature using Landsat-8 data in Beas River basin, India, and validation with wireless sensor network data

In this paper, an automated method for retrieval of snow surface temperature (SST) in Beas River Basin, India, using Landsat-8 thermal data is proposed. Digital number (DN) values of thermal data were converted into Top of Atmospheric (TOA) radiance. Surface radiance has been estimated from TOA radiance using a single channel method. The estimated surface radiance was then converted into SST. Cloud free Landsat-8 data for January and February 2017 has been used to estimate SST. Snow and Avalanche Study Establishment (SASE) has established a wireless sensor network (WSN) in an avalanche prone slope in Beas River Basin, India. Landsat-8 retrieved SST has been compared and validated with recorded SST at WSN stations. The retrieved SST using proposed algorithm was in good agreement with SST recorded on ground by sensor network. The mean absolute error (MAE) and root-mean-square error (RMSE) between estimated and recorded SST has been observed as ~ 1.1 K and ~ 1.5 K for 23 January 2017 and ~ 0.7 and ~ 1.6 K for 24 February 2017. Algorithm has shown a potential for automated mapping of snow and ice surface temperature using Landsat-8 data for snow cover and glaciers in Himalaya.