This study uses optical and radar satellite images (i.e., Landsat and Sentinel-1 & 2) to monitor seasonal waterlogging in the Kosi Fan from the 1987-2021 period. We used Google Earth Engine (GEE) platform to process the satellite images. We applied Random Forest (RF) classifier to classify the image pixels that correspond to waterlogging from optical and microwave images. The optical images detect the waterlogging patches more accurately (70-80%) as compared to the radar images (50-65%). We observed that the waterlogging patches located along the road and stream networks show a high probability of occurrence. We have computed the probability of occurrence of waterlogging patches near the road and stream network intersection. The result indicates a high correlation of the occurrences of waterlogging patches in the proximity of structural interventions (rail, road, network embankment, etc) on the Kosi Fan.

We estimate river discharge by utilizing limited in-situ and multi-satellite data through Bootstrap Reduced Major Axis (BRMA) optimization method (Qstat$$ {Q}_{\mathrm{stat}} $$). We establish the BRMA by analyzing the functional relationships (H, W, H.W vs. Q) at four distinct locations along the Ganga (Shahzadpur and Azmabad) and the Narmada (Hoshangabad and Mandleshwer) rivers. To measure the channel width (W) we have used the Landsat 5, 7, and 8 images (2006-2019). We use the water level (H) data from satellite altimeter (Jason 2, Jason 3, Envisat, and Sentinel 3A). We have used BRMA to establish functional relationships between channel width (W), water level (H), and H.W to their corresponding discharge at each gauge stations in the study reach. Efficacy of the proposed approach is evaluated through a comparative analysis with the traditional ordinary least squares (OLS) regression method. We observed that the BRMA exhibits better performance as compared to the best fit curve obtained by using the OLS regression. We noticed that the functional relationship between the WH-Q outperforms as compared to the other empirical curves at the both gauge stations of the Ganga River. The accuracy estimates of the Ganga River is in a range of NSEoverall$$ {\mathrm{NSE}}_{\left(\mathrm{overall}\right)} $$ (0.76-0.95), PBIASoverall$$ {\mathrm{PBIAS}}_{\left(\mathrm{overall}\right)} $$ (1-23) and RSRoverall$$ {\mathrm{RSR}}_{\left(\mathrm{overall}\right)} $$ (0.25-0.44). In the Narmada River, the functional relationship between H-Q outperforms. The accuracy of discharge at both the gauge stations of the Narmada River are found to be in a range NSEoverall$$ {\mathrm{NSE}}_{\left(\mathrm{overall}\right)} $$ (0.76-0.95), PBIASoverall$$ {\mathrm{PBIAS}}_{\left(\mathrm{overall}\right)} $$ (1-23), to RSRoverall$$ {\mathrm{RSR}}_{\left(\mathrm{overall}\right)} $$ (0.25-0.44). This study is a step towards estimating discharge from satellites data.

We propose a hybrid machine learning algorithm (i.e., P2CA−PSO−ANN) to model malaria outbreak in three districts (Barmer, Bikaner, and Jodhpur) of Rajasthan in the Western India. We have used different meteorological variables (i.e., relative humidity, temperature, and rainfall) as input features to predict malaria. We have also considered the combined impact of these variables through a linear data fusion. We then extract the uncorrelated information from the feature set by applying Probabilistic Principal Component Analysis (P2CA). We trained the fully connected feed-forward Artificial Neural Network (ANN) by optimising its hyperparameters iteratively through a bio-inspired optimisation algorithm (Particle Swarm Optimisation). We train and evaluate the performance of this algorithm using monthly meteorological variables from 2009 - 2012. This accurately predicts the malaria cases with the coefficient of correlation (R = 0.99), and Root Mean Square Error (RMSE = 1.76). Finally, we compare our model with different benchmark algorithms (Generalised Regression Neural Network (GRNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest, and Radial Basis Neural Networks (RBNN)) in terms of accuracy. We observed the performance of hybrid machine learning model relatively high. This study can be used as an early warning intelligent system to predict the malaria outbreaks solely from meteorological data.

This overview of the well-documented, ~ 11,200 km2 Kosi megafan updates many aspects of its geomorphology, and maps the detail of its modern trunk channel. The axis of the Kosi megafan is orthogonal to the Himalaya and Ganga trunk river, but with a mean annual flow of 52 × 109 m3 it has constructed a relatively small megafan (~150 km long) constricted between neighbouring megafans and the Ganga floodplain. However, while the coarser sediment load of other megafans farther to the west, such as the Gandak, has been trapped upstream in piggyback basins of the Terai belt, this upstream filter does not exist in the case of the Kosi River. Consequences for the Kosi channel are thought to be a more continuous supply of sediment, a higher proportion of coarse debris, higher rates of bed aggradation, and a more avulsive style of river behaviour on the megafan. Extensive construction of artificial levees, initially designed to mitigate the hazards arising from excessive flooding, has accelerated natural rates of channel aggradation – thereby raising the channel bed in several reaches and resulting in more frequent levee breaching and flood-related damage than on other Himalayan megafans.

The Ganga River ecosystem is under severe anthropogenic stress. Flow regulations through structural barriers alter the geomorphic and hydraulic geometry of riverine habitats. Determining the ecological health of river habitats under the contemporary modification is detrimental to its restoration and management. This study evaluates reach averaged hydraulic habitat of the endangered Ganga River dolphin (Platanista gangetica) in a stretch between Bijnor and Narora barrages. We consider an optimal minimum flow depth as the determining factor of habitat suitability. Field measurement of the hydraulic geometry and flow characteristics show that the optimal flow depth is available in the study reach during the monsoon period, while in the pre-monsoon, the minimum depth is present only in the reach upstream to Narora barrage. We use a geomorphic instream flow tool (GIFT) and satellite altimeter water level data to simulate reach averaged hydraulic habitat in varying flow conditions in area upstream of Narora barrage. We observe that to maintain the minimum flow depth which supports the dolphin habitat in the study reach, an optimal discharge of about >280 m3s-1$$ >280\ {m}<^>3{s}<^>{-1} $$ is essential. Furthermore, we develop a water-level (altimeter) and discharge (simulated from GIFT) rating curve for the study reach. It can be used to get a first-order estimate of discharge for a given water level or vice versa. This study indicates that the altimetry datasets are good precursors for estimating averaged hydraulic habitat of rivers in the data-scarce regions. The application of altimeter data can be a boon in the effective management of river habitat health over a reach scale.

This review provides a detailed synthesis of various in-situ, remote sensing, and machine learning approaches to estimate soil moisture. Bibliometric analysis of the published literature on soil moisture shows that Time-Domain Reflectometry (TDR) is the most widely used in-situ instrument, while remote sensing is the most preferred application, and random forest is the widely applied algorithm to simulate surface soil moisture. We have applied ten most widely used machine learning models on a publicly available dataset (in-situ soil moisture measurement and satellite images) to predict soil moisture and compared their results. We have briefly discussed the potential of using the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission images to estimate soil moisture. Finally, this review discusses the capabilities of physics-informed and automated machine learning (AutoML) models to predict the surface soil moisture at higher spatial and temporal resolutions. This review will assist researchers in investigating the applications of soil moisture in the broad domain of earth sciences.

It has been suggested over a century ago that the Saraswati was a large river that flowed in the Sutlej-Yamuna interfluve, a region that is now devoid of any such large river system. This large river was commonly related to the Saraswati River described in the Rig-veda, and was correlated with the discovery of several Harappan sites in the region. Presently, there is only the ephemeral Ghaggar River that flows here with its limited discharge along the abandoned course of the 'lost' Saraswati. Also, it was hypothesised earlier that this region was drained by the waters from the drainage basins of both the glacier/monsoon-fed Sutlej and Yamuna rivers. It therefore stands to reason that this region should preserve evidence of the record of the past discharge variability that impacted this region prior to the major drainage reorganisation. This study is an attempt to reconstruct the palaeohydrology of the Saraswati River. We investigate the hypothesis, that the ancient Saraswati River used to carry a combined flow of the Sutlej, Ghaggar and Yamuna river catchments. To examine this important question, we use the channel belt width, catchment area and average annual discharge of different rivers presently flowing on Indus-Ganga-Brahmaputra plains in the Himalayan Foreland. We use these variables to establish the empirical scaling relationships between the channel belt width and average annual discharge to the catchment area. We observed rivers having a larger catchment usually carry a higher discharge and have a wider channel belt. Finally, we use these empirical scaling relationships to estimate the channel belt width and average annual discharge of the lost Saraswati River at the time when it possibly carried the combined flow of the Sutlej, Ghaggar, and Yamuna rivers catchments. We obtained the average annual discharge of the Saraswati River of an order of 3000 m3s- 1 and channel belt width of about 11 km at the location downstream of the postulated confluence of the Sutlej and Yamuna rivers at Suratgarh.

We estimate the combined effect of climate and landuse-landcover (LU-LC) change on the streamflow of the Betwa River; a semi-arid catchment in Central India. We have used the observed and future bias-corrected climatic datasets from 1980–2100. To assess the LU-LC change in the catchment, we have processed and classified the Landsat satellite images from 1990–2020. We have used Artificial Neural Network (ANN) based Cellular Automata (CA) model to simulate the future LU-LC. Further, we coupled the observed and projected LU-LC and climatic variables in the SWAT (Soil and water assessment tool) model to simulate the streamflow of the Betwa River. In doing so, we have setup this model for the observed (1980–2000 and 2001–2020) and projected (2023–2060 and 2061–2100) time periods by using the LU-LC of the years 1990, 2018, and 2040, 2070, respectively. We observed that the combined effect of climate and LU-LC change resulted in the reduction in the mean monsoon stream flow of the Betwa River by 16% during 2001–2020 as compared to 1982–2000. In all four CMIP6 climatic scenarios (SSP126, SSP245, SSP370, and SSP585), the mean monsoon stream flow is expected to decrease by 39–47% and 31–47% during 2023–2060 and 2061–2100, respectively as compared to the observed time period 1982–2020. Furthermore, average monsoon rainfall in the catchment will decrease by 30–35% during 2023–2060 and 23–30% during 2061–2100 with respect to 1982–2020.

We use Soil and Water Assessment Tool (SWAT) to simulate the combined effects of land use/land cover (LU/LC) and climate change on the hydrological response of the Upper Betwa River Catchment (UBRC), a semi-arid region in Central India. We execute this model for two different time periods, 1982–2000 and 2001–2018, using the LU/LC data of 1990 and 2018, respectively. We classified the Landsat satellite images of 1990 and 2018 to obtain the dominant LU/LC classes (water body, built-up, forest, agriculture, and open land) in the catchment. The water body, built-up areas, and cropland have increased by 63%, 65%, and 3%, respectively, whereas forest cover and open land decreased by 16% and 23% in the UBRC from 1990 to 2018. The observed climate data in UBRC shows an increase in the average temperature and decrease in the total rainfall during the period between 1980 to 2018. Once the model is set up, we perform the calibration and validation by using the SWAT Calibration Uncertainty Program (SWAT-CUP). We considered two time periods (1991–1994 and 2001–2007) for the calibration and (1995–1998 and 2008–2014) for the validation. For both these time periods, the calibration and validation result of our model is satisfactory. The output of our calibrated model shows a relative decrease in rainfall (12%), surface runoff (21%), and percolation (9%) in the catchment during the period between 2001–2018 as compared to 1982–2000. Finally, we simulate the surface runoff and percolation in the UBRC using the future climate change scenario. We used the bias-corrected multi-model ensemble of CMIP6 GCMs for four different climate scenarios (2023–2100) by assuming no change in the existing LU/LC. We do this for two different time slices: one from 2023–2060 and the other from 2061–2100. For all the climate scenarios, rainfall and surface runoff in the catchment are expected to decrease by 15–40% and 50–79% as compared to the baseline period of 1982–2018. Percolation in the catchment will have a mixed response. It is expected to decrease by 18% in the middle part of the catchment and increase about 25% in the remaining parts of the catchment.

We propose an innovative methodology to estimate the formative discharge of alluvial rivers from remote sensing images. This procedure involves automatic extraction of the width of a channel from Landsat Thematic Mapper, Landsat 8, and Sentinel-1 satellite images. We translate the channel width extracted from satellite images to discharge using a width–discharge regime curve established previously by us for the Himalayan rivers. This regime curve is based on the threshold theory, a simple physical force balance that explains the first-order geometry of alluvial channels. Using this procedure, we estimate the formative discharge of six major rivers of the Himalayan foreland: the Brahmaputra, Chenab, Ganga, Indus, Kosi, and Teesta rivers. Except highly regulated rivers (Indus and Chenab), our estimates of the discharge from satellite images can be compared with the mean annual discharge obtained from historical records of gauging stations. We have shown that this procedure applies both to braided and single-thread rivers over a large territory. Furthermore, our methodology to estimate discharge from remote sensing images does not rely on continuous ground calibration.

Microwave remote sensing has emerged as an efficient tool for the estimation of soil moisture due to its higher sensitivity to the dielectric properties of soil. Synthetic Aperture Radar (SAR) sensors have been used to estimate soil moisture at large scale. The penetration depth of microwave signals into the ground soil vary significantly with the available moisture content. It has been found that the longer wavelengths have a higher capability to penetrate soil, however, their penetration capability decreases with increasing dielectric behaviour of the soil. Moisture content in the soil increases the dielectric behaviour of soil.

We study the functional relationship between the dielectric constant of soil-water mixture and penetration depth of microwave signals into the ground at different frequency (L&S) band and incidence angles. Penetration depth of microwave signals into the ground depends on the incidence angle and wavelength of radar pulses and also on the soil properties such as moisture content and textural composition. It has been observed that the longer wavelengths have higher penetration in the soil but the penetration capability decreases with increasing dielectric behaviour of the soil. Moisture content in the soil can significantly increase its dielectric constant. Various empirical models have been proposed that evaluate the dielectric behaviour of soil-water mixture as a function of moisture content and texture of the soil. In this analysis we have used two such empirical models, the Dobson model and the Hallikainen model, to calculate the penetration depth at L-and C-band in soil and compared their results. We found that both of these models give different penetration depth and show different sensitivity towards the soil composition. Hallikainen model is more sensitive to soil composition as compared to Dobson model. Finally, we explore the penetration depth at different incidence angle for the proposed L-and S-band sensor of upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission by using Hallikainen empirical model. We found that the soil penetration depth of SAR signals into the ground decreases with the increase in soil moisture content, incident angle and frequency.

Though, liberalisation of services trade is a new phenomenon, it has witnessed sustained growth and acceptance in recent decades. IT/ITES and its trade became the fastest growing sub-sector of trade in services. Services trade of SAARC countries became more dynamic in the post Uruguay Round. These economies are more integrated with the other economies of the world in services. Volatile comparative advantage with knife-edge specialisation pioneered the phrase, kaleidoscopic comparative advantage applicable equally to trade in services. In this backdrop, this study applied the qualitative (analysis of IT and telecommunications policy of all the SAARC countries) as well as quantitative methods (revealed comparative advantage - RCA index and services trade openness has been calculated) to capture the potentialities and competitiveness of IT/ITES trade of SAARC. This paper argues that the kaleidoscopic comparative advantage is not fixed only to bigger economies like India, but it has also extended to comparatively smaller economies like Sri Lanka, Bangladesh, and Pakistan.

Four genetical population of mustard (P1, P2, F1 & F2) developed by crossing of six cultivars/strains (viz. Kanti, Maya, Ashirvad, Pusa Jagganath, RLM 198 and Durgamani) were used for the estimation of gene effects for 11 quantitative characters. Theresults indicated that both additive and non additive gene action contributed in the inheritance of yield and yield contributing characters. Duplicate type of epistasis was more prevalent. The simple or pedigree selection followed by biparental mating is suggested for needful.