Multi-Facet analysis of analytical and numerical models to resolve sustainable artificial recharge rates in unconfined aquifers.

Managed aquifer recharge (MAR) has emerged as a potential solution to resolve water insecurity, globally. However, integrated studies quantifying the surplus source water, suitable recharge sites and safe recharge capacity is limited. In this study, a novel methodology is presented to quantify transient injection rates in unconfined aquifers and generate MAR suitability maps based on estimated surplus water and permissible aquifer recharge capacity (PARC). Subbasin scale monthly surplus surface runoff was estimated at 75% dependability using a SWAT model. A linear regression model based on numerical solution was used to capture the aquifer response to injection and to calculate PARC values at subbasin level. The available surplus runoff and PARC values was then used to determine the suitable site and recharge rate during MAR operation. The developed methodology was applied in the semi-arid region of Lower Betwa River Basin (LBRB), India. The estimated surplus runoff was generally confined to the monsoon months of June to September and exhibited spatial heterogeneity with an average runoff rate of 5000 m3/d in 85% of the LBRB. Analysis of the PARC results revealed that thick alluvial aquifers had large permissible storage capacity and about 50% of the LBRB was capable of storing over 3500 m3/d of water. This study revealed that sufficient surplus runoff was generated in the LBRB, but it lacked the adequate safe aquifer storage capacity to conserve it. A total 65 subbasins was identified as the best suited sites for MAR which had enough surplus water and storage capacity to suffice 20% of the total water demand in the LBRB. The developed methodology was computationally efficient, could augment the field problem of determining scheduled recharge rates and could be used as a decision-making tool in artificial recharge projects.

Analysing the relationship between human modification and land surface temperature fluctuation in the Ramganga basin, India.

In many regions across the world, including river basins, population growth and land development have enhanced the demand for land and other natural resources. The anthropogenic activities can be detrimental to the vital ecosystems that sustain the river basin region. This work assessed the impact of human modification on land surface temperature (LST) for the Ramganga basin in India. It has been hypothesised that the footprints of anthropogenic activities in the region have been connected to the LST fluctuation for the region, which could indicate environmental degradation. The LST variation between 2000 and 2016 has been estimated to test this hypothesis. The spatio-temporal correlation between human modification and LST has been computed. LST has been calculated with MODIS satellite data in the Google earth engine (GEE) platform, and anthropogenic activities can be visualised using an LU/LC map of the basin created by the Classification and Regression (CART) technique. The statistical parameters (average, maximum and standard deviation) of annual temperature for each pixel in 17 years (2000-2016) have been assessed to establish the links with human modification. The result of this work portrays a positive correlation of 0.705 between maximum LST and human modification. The forest class in the basin region has the lowest average human modification value (0.37), and it also possesses the lowest mean LST of 26.72 °C. Similarly, the settlement class has the highest average human modification value (0.85), and the mean LST temperature of this class has been on the higher side, having a value of 31.07 °C.

Analysis of algal bloom intensification in mid-Ganga river, India, using satellite data and neural network techniques.

River Ganga is one of the most significant rivers in the country. This river is the adobe for numerous aquatic species and microorganisms. The color of the river suddenly changed to green due to the rise of algal bloom in the Varanasi and nearby regions of the river Ganga during May-June 2021. These algal blooms can be detrimental to the aquatic animals of the river. This study analyzes the occurrence and the possible reasons for the algal bloom generation in the river for the considered stretch. Several factors like nutrient accumulation in the river through agricultural run-off, warm river temperature, low flow condition of the river, thermal stratification, and less turbid river water can be considered as possible reasons for algal bloom development. In this work, the optical remote sensing-based Sentinel 2 datasets have been used for the duration of mid-May 2021 to mid-June 2021. These datasets have been processed in the Google Earth Engine (GEE) platform, and chlorophyll concentration has been calculated using different satellite-based indices or band ratios. The chlorophyll concentration measurements have quantified the algal bloom growth. These indices or band ratios have been analyzed using several artificial neural network (ANN) architectures like multilayer perceptron (MLP) and radial basis function (RBF) along with the in situ values. It has been found that chlorophyll concentration has been highest for the mid-June 2021 time period in the considered river stretch.

Time series forecasting of temperature and turbidity due to global warming in river Ganga at and around Varanasi, India.

The fluctuation in the river ecosystem network due to climate change-induced global warming affects aquatic organisms, water quality, and other ecological processes. Assessment of climate change-induced global warming impacts on regional hydrological processes is vital for effective water resource management and planning. The global warming effect on river water quality has been analyzed in this work. The river Ganga stretch near the Varanasi region has been chosen as the study area for this analysis. The air temperature has been predicted using the seasonal autoregressive integrated moving average (SARIMA) and the Prophet model. The Prophet model has shown better accuracy with a root mean square percent error (RMSPE) value of 3.2% compared to the SARIMA model, which has an RMPSE value of 7.54%. The river temperature, turbidity, and nighttime radiance values have been predicted for the years 2022 and 2025 using the long short-term memory (LSTM) algorithm. The anthropogenic effect on the river has been evaluated by using the nighttime radiance imageries. The predicted average river temperature shows an increment of 0.58 °C and 0.63 °C for the city and non-city river stretches, respectively, in 2025 compared to 2022. Similarly, the river turbidity shows an increment of 1.21 nephelometric turbidity units (NTU) and 1.17 NTU for the city and non-city stretch, respectively, in 2025 compared to 2022. For future predicted years, the nighttime radiance values for the region situated near the city river stretch show a significant rise compared to the region that lies nearby the non-city river stretch.

Drivers of functional diversity in the hyporheic zone of a large river.

The effects of regional (hydrogeology and geomorphology) and local (sediment and hydrology) characteristics on hyporheic assemblages were studied along a 40-km reach of a large gravel-bed river. Hyporheic water and fauna were sampled at the upstream and downstream positions of 15 large gravel bars. The resulting 30 stations varied in their sediment grain size, stability and direction of river-aquifer exchanges. The study concludes that at the 40-km (sector) scale, the longitudinal distribution of hyporheic fauna was controlled by 1) the hydrogeology of the valley (i.e. gaining vs loosing sectors) that modifies abundance and taxonomic richness of stygobites 2) current channel morphometry of the river (i.e. shape and location of meanders), and 3) historical changes (i.e. river incision) which modify abundance and richness of assemblages. At the local scale, we found that surface grain size and stability of the sediment evaluated by visual observation were poor predictors of hyporheos composition. In contrast, the local hydrology (i.e. downwellings, upwellings, low vertical exchanges) explained a large part of the abundance, taxonomic richness and composition of the hyporheic assemblages. Stations with low vertical exchanges were found poorly colonized, while the upwelling zones were rich in stygobites and downwelling areas harbor abundant and species-rich temporary hyporheos. It was also observed that functional diversity was controlled by the same parameters, with high relative abundances of stygobites in upwelling zones and POM feeders in downwelling zones. The heterogeneity of hydrological patterns, with alternation of upwellings and downwellings may represent the optimal spatial structure for hyporheic biodiversity conservation and resilience in rivers.

Flood inundation mapping and monitoring using SAR data and its impact on Ramganga River in Ganga basin.

Remote sensing-based flood inundation mapping and monitoring is very crucial input before, during, and after floods. Ganga-Ramganga doab is one of the prolonged flood-affected area in middle Ganga plain due to seasonal monsoon which leads to rise in water levels of Ganga and Ramganga rivers. The focus of the present study is to map severe flood condition captured through synthetic aperture radar (SAR) data during August-September 2018, and to explain the impact on Ramganga river morphology. SAR data is preferred for flood mapping and real-time monitoring in all weather conditions. In this study, dual-polarized (VV and VH) Sentinel-1 SAR images coupled with hydrological data (river water level) were used to produce flood inundation maps. Thresholding technique has been applied to determine the flood mapping through Sentinel-1 data. VH and VV polarisation methods have been applied for a comparison of their respective accuracies in delineating surface water. Results have been validated against a Sentinel-2 optical image, and both polarisations produced a total accuracy of more than 93%. VV polarisation has high accuracies than VH polarisation as similar results are observed in previous studies as well. The finding reveals that severe bank erosion took place in the Ramganga channel which significantly affected the channel morphology, mainly the massive mobilisation of channel sediments. The results show that the average channel width increased from 46 to 336 m. The proposed approach demonstrates that the microwave remote sensing data along with GIS can be used efficiently for flood inundation mapping, monitoring, and analysing its effect on channel morphology. Therefore, the results of this study will help to take the initiative to reduce the flood hazard impact in the doab area and increase the flexibility in the process of flood management.