Freshwater macrophytes thrive in tropical and subtropical climates, providing valuable feed ingredients for a variety of aquatic and terrestrial animals while also influencing hydrology, sediment dynamics, and biogeochemical cycles. The purpose of this review is to assess the viability of freshwater macrophytes as a sustainable source of minerals for agriculture. To provide a comprehensive understanding of the topic, over 120 studies published across diverse geographic regions with diverse climates, precipitations and topography, were reviewed, with a focus on different species and methodologies. Key findings show that anthropogenic introductions of exotic freshwater macrophytes pose significant ecological risks due to their rapid growth in new habitats, which can disrupt local ecosystems. However, the biomass derived from these plants has a wide range of applications, including animal feed, biofuel, and ceramics, as well as biological markers and environmental remediators. Furthermore, fresh tissues, dried matter, manure, ensilage, and biochar are examples of in-situ applications of freshwater macrophytes that have shown to have major benefits for agricultural practices. These applications promote healthy plant growth, raise crop yields, lessen the need for chemical fertilizers, and decrease crop disease rates. The review also covers these organisms’ ability to produce antibacterial compounds, remove heavy metals and other pollutants from soil and water, and enhance soil quality and biodiversity in general. The results collectively highlight the significance of freshwater macrophytes in fostering ecological balance and resource efficiency, as well as their potential to support environmentally friendly farming methods and environmental conservation.

The present study aims to identify potential locations for small-scale hydroelectric power (HEP) stations in hilly regions for the purpose of generating renewable energy. A rainfall-runoff (R-R) model of the Beas River catchment was established using the MIKE 11 NAM to estimate the available discharge. The model was calibrated and validated over the period of June-2015-May-2018 and June-2018-May-2020, respectively, using daily observed discharge data at the Pandoh Dam site. The model exhibited good performance with a coefficient of determination (R2) of 0.82 during calibration and 0.70 during validation and a water balance of -0.01% and -18%, respectively. However, Lmax, CK1, CK2 and CQOF are found most sensitive parameters during the calibration. Further, thirteen major streams of order five or higher were selected for the assessment of hydropower potential, resulting in the identification of 131 potential run-of-river (ROR) hydropower sites. The hydropower potential at two proposed sites, Bhang SHEP (9 MW) and Raison SHEP (18 MW), was estimated to be 11 and 15 MW, respectively, using 90% dependable flow. The results demonstrate the effectiveness of using Digital Elevation Model (DEM) and Geographic Information System (GIS) techniques for determining hydropower potential in ungauged basins in the Himalayas.

The current study presents a comprehensive assessment of hydrological models, including the Snowmelt Runoff Model (SRM), MIKE HYDRO RIVER NAM model, and Soil and Water Assessment Tool (SWAT), for simulating runoff dynamics in the Beas River Basin (BRB). Utilizing data spanning seven years from 2014 to 2020, the models underwent rigorous calibration and validation processes to evaluate their performance under varying hydrological conditions. The SRM model exhibited commendable performance, with high correlation coefficients (R²) of 0.85 during calibration and 0.82 during validation, indicating strong agreement between observed and simulated runoff volumes. Similarly, the MIKE HYDRO RIVER NAM model demonstrated satisfactory performance, albeit with a slightly higher root mean square error (RMSE), indicating a reasonable fit between observed and simulated data. In contrast, the SWAT model exhibited relatively lower performance metrics, particularly regarding R² and Nash-Sutcliffe Efficiency (NSE) values, suggesting limitations in accurately capturing runoff dynamics, especially during peak flow events. Comparison of model performance highlighted the superior capability of the SRM and MIKE HYDRO RIVER NAM models in simulating runoff dynamics, attributed to their robust representation of hydrological processes and comprehensive consideration of relevant parameters. Analysis of water resource management in the BRB emphasized the importance of understanding flow dynamics, particularly seasonal variations in water availability, for effective water resource management. Overall, this study underscores the significance of accurate hydrological modelling for informed decision-making in water resource management and highlights the potential of the SRM and MIKE HYDRO RIVER NAM models for such applications.

Studying geo-morphometric parameters using Remote Sensing (RS) and Geographic Information System (GIS) tools is crucial to routing runoff and remaining hydrological processes. A geo-spatial model and principal component analysis (PCA) approach are used in this study to prioritize sub-watersheds of the upper Beas river up to Pandoh dam. Dendritic drainage patterns throughout its sub-watersheds characterized the 6 th -order Beas river. The sub-watersheds show a lithological uniformity that indicates that the entire watershed has structurally impermeable materials at both surface and sub-surface levels. Moreover, the aerial and relief aspects of the sub-watershed indicate fine drainage textures, steep slopes, immediate peak flows, a hydrograph with multiple peaks, and a low concentration time. In other words, the sub-watershed may not be able to manage flash floods during the storm period. Surface runoff and sediment production rates (SPR) were estimated in the present study ranged from 3.576–5.240 sq. km-cm/sq.km and 0.101–0.234 ha-m/100sq.km/year, respectively. Finally, the study concluded that the sub-watersheds in the upper regions produced high runoff and sediments, usually carried into the mainstream. Further, the PCA technique was applied to find the redundant morphometric parameters and then the same results were utilized to determine the effective way to prioritize the watershed. The present study will serve as a basis for developing appropriate policies and practices for peak flooding and promoting the sustainability of the watershed.

In 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.

The current study uses remote sensing derived products, high resolution gridded rainfall and temperature, and SWAT inside a Geographic Information System (GIS) to assess the Kuttiyadi River's hydrological response and water balance components. As Kuttiyadi basin lacks a rainfall monitoring station, making hydrological studies difficult. Thus, we used satellite-based rainfall data as a solution to data shortages in the basin. The basin has separated into 104 numbers of hydrological response units (HRUs) based on unique land use, soil, and slope. The available streamflow data was divided for the calibration (2004–2013) and validation (2014–2017) for the modelling of both daily and monthly streamflow. The simulation of the streamflow was observed to be good on the daily time step (R2 = 0.65, NSE = 0.62 and R2 = 0.62, NSE = 0.60 for the calibration and validation respectively) which is further improved for the monthly time step (R2 = 0.90, NSE = 0.80 and R2 = 0.88, NSE = 0.85 for the calibration and validation respectively). During the monsoon, PBIAS value for the daily validations exceeded from the permissible limit due to the higher fluctuations in the daily streamflow. Our modelling results found that NE monsoon has a greater influence than the SW monsoon, generating almost 75% of total surface runoff in the basin. Study of the basin's water balance indicates that surface runoff is more prevalent, and contributes 35% to annual precipitation. The curve number, hydraulic conductivity of a channel and soil water capacity are highly sensitive parameters which showed rapid changes in land-use and hydraulic conductivity of the mainstream channel owing to the bi-directional interaction of the groundwater with the streamflow. The current study found that the PET and ET were fairly high, and that ET accounted for 24% of the total precipitation.

Assessment of the geomorphometric parameters using Remote Sensing (RS) and Geographic Information System (GIS) tools forms an important part in routing the runoff and other hydrological processes. The current study uses a geospatial model based on geomorphometric parameters for the categorization of surface runoff and identification of the erosion-prone areas in the watershed of the Kuttiyadi River. The 4th order Kuttiyadi river is dominated by a dendritic to semi-dendritic drainage pattern in the subwatersheds. The linear aspect of the subwatersheds indicates towards the presence of permeable surface and subsurface materials with uniform lithology. The aerial and relief aspects of the subwatersheds shows fine drainage texture, gentle slopes, delayed peak flow, flatter hydrograph, and large concentration time which shows that subwatersheds are quite capable of managing flash floods during storm events. The estimated values of surface runoff (Q) and sediment production rate (SPR) are range from 2.13 to 32.88 km2-cm/km2 and 0.0004–0.017 Ha-m/100km2/year respectively and suggest that Subwatershed 1 (SW1) will generate more surface runoff and is prone to soil erosion followed by subwatershed 2 (SW2) in comparison to other subwatersheds. This paper aims to fill the knowledge gap regarding categorization of flow and erosion dynamics in a coastal river watershed. We believe that our work may work help in providing the crucial information for decision-makers and policymakers responsible for establishing suitable policies and sustainable land use practices for the watershed.

Snow sustains Himalayan rivers as an abundant source of water. Although, seasonal snow cover is the significant parameter in a hydrological system of a basin, its contribution is restricted to the spring season when snow-melt and base-flow comprise two parts of the inflow. The current study assessed the snowmelt contribution in the total streamflow for the Beas River up to the Pandoh dam using Snowmelt Runoff Model (SRM). The study area is divided into seven elevation classes and snow cover has been computed. The snow-covered area varies from 10% to 80% in the basin. The efficiency of the model was evaluated using Coefficient of Determination (R2), Nash-Sutcliffe Efficiency and Volume difference. The R2, NSE and Volume Difference during the calibration and validation period was ranges from 0.79 to 0.87, 0.72 to 0.79, -0.025% to 7.2% and 0.72, 0.67, -4.65 % respectively. The major finding of the present study suggest that a major part of the streamflow is generated in summer and monsoon season and the contribution of the snowmelt was ranges from 10-45%. The present study will provide a baseline information towards the contribution of snowmelt in the streamflow and also provide the information towards the availability of freshwater.

Abstract Autoregressive Integrated Moving Average (ARIMA) and seasonal ARIMA (SARIMA) models are statistical techniques generally used in analyzing and forecasting seasonal, periodic cyclic, and non-stationary time series data. This paper presents the use of the Seasonal Autoregressive Integrated Moving Average (SARIMA) method for developing a forecasting model that computes seasonal snow accumulation and depletion in the snow dominant area of the Beas river catchment. A time-series data of 8- days average snow covers acquired by Terra and Aqua sensors of MODIS (Moderate Resolution Imaging Spectro-radiometer) optical satellite has been utilized (2003 – 2018). The Box – Jenkins methodology has been performed separately by splitting yearly data into two main seasons snow accumulation (Oct. – Feb.) and snow depletion (March – Sept.). Two SARIMA models, one for snow accumulation as (1,1,1) (0,1,3)19 and the second for snow depletion as (1,1,1) (1,1,2)27 were identified by visual inspection of ACF and PACF plots using data (2003 – 2015) and then accuracy assessment has been done using performance criterion like Akaike’s Information Criterion (AIC), MSE and RSS, etc. The performance of the resulting models was then validated using data (2016 - 2018) and the comparison of both the models showed a good agreement between the simulated and observed data with a coefficient of determination (R2) of 0.829 in snow accumulation and 0.893 in snow depletion. Finally, the study advised, that the identified models could be adequate to forecast the weekly snow accumulation and depletion at least for the next 3- years to predict hydraulic events such as flood forecasting, runoff estimation, and hydropower assessment.

Hydrological modeling system represents a part of hydrologic cycle in a simplified and conceptual way. Hydrological cycle represents the circulation, occurrence, distribution and conservation of earth water and it is never-ending process. Hydrological models are primarily used for hydrologic prediction and helps in understanding the hydrologic response of a particular catchment area. Hydrological modeling and its operations requires a larger set of temporal and spatial data. Indeed, the accessibility and accuracy of this data usually becomes a concern to cope with and this puts a more considerable effect on the precision of model. Due to lack of data accuracy, efficiency of the model is compromised for hydrological model simulation and its operations like calibration and validation. The current technical note describes the literature study on the event and continuous hydrological modeling with Hydrologic Engineering Centre’s Hydrologic Modeling System (HEC-HMS). This literature review represents the development approach of hydrologic modeling by combining the finer-scale event and coarse-scale continuous hydrological modeling by using HEC-HMS.