Vicky Anand

Structural uncertainty in physics-based models (PBMs) and the limited generalisability of purely data-driven techniques limit the ability of predicting daily streamflow in catchments in data-scare region. In order to enhance prediction accuracy and geographic transferability, this study proposes a coupled physics-based-machine learning (PBM-ML) framework that combines knowledge of hydrological processes with data-driven learning. The framework was tested in multiple catchments with different hydroclimatic conditions, encompassing basins in Japan and New Zealand. PBM-derived states and fluxes were fed into machine-learning models after a PBM (SWAT) was first calibrated to simulate daily streamflow. The Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2) were used to evaluate the performance of the model. Coupled PBM-ML models consistently performed better than standalone SWAT in all basins. Testing NSE improved from 0.69-0.76 for SWAT to 0.80-0.89 for coupled models in New Zealand and from 0.67-0.68 to 0.74-0.86 in Japan. SWAT-LSTM had the best prediction ability among the hybrid methods. Regionalization approaches were used to investigate the transferability of the model. The coupled models retained robust performance under partially gauged and fully ungauged conditions. These findings demonstrate that PBM-ML coupling could enhance streamflow prediction and transferability in data-scarce regions.

Planning and managing water resources necessitate the utilization of hydrologic models. However, the calibration of these models poses a significant challenge due to the high degree of parameter uncertainty, which has global implications. Moreover, regions with high seasonal variation in precipitation exhibit strong heteroskedasticity, further complicating the calibration process. This study aims to determine an optimized calibration approach and parameter optimizations by integrating two sensitivity methods with two optimization techniques. The methodology was demonstrated in the sub-basins of the Bay of Plenty, New Zealand, using the SWAT hydrological model. The performance indices of the calibrated models were analyzed, revealing striking similarities between GLUE and SUFI-2 approaches. Although GLUE marginally outperformed SUFI-2 in terms of the fit indices, indicating better model accuracy, SUFI-2 demonstrated less uncertainty, making it a more stable option. Among the parameters, the initial SCS curve number for moisture condition II, baseflow factor, groundwater delay, groundwater revap coefficient, soil evaporation compensation factor, Manning's coefficient value for the main channel, and effective hydraulic conductivity in the main channel were identified as the most sensitive. These findings provide critical insights into hydrological modeling, supporting the development of policies not only in the Bay of Plenty but also in similar regions.

Global biodiversity is largely dependent on aquatic habitats. The state of fish habitats and the health of the aquatic ecosystem as a whole are important indicators of environmental quality. In recent years, the implications of human interference and climate change on aquatic environments have gained significant attention. Hydrological modifications driven on by anthropogenic activities are steadily degrading flow conditions and threatening the abundance of fish in the geographic region, while simultaneously having an impact on the health of the watershed. Therefore, the integrated hydrological-hydrodynamic-habitat modelling under diverse flow regimes can be used to assess the eco-sustainability and habitat suitability of the fish species. This article categorizes the habitat variables of fish into three groups: eco-hydrological, hydrodynamic-water quality, and eco-biological. This categorization facilitates an understanding of the mechanisms by which each factor affects fish and their habitats. The study, as the first of its kind, provides an extensive review of eco-hydrological models, hydrodynamic-water quality models, and habitat models, along with their relevant influencing components. It proposes a scientific framework for evaluating the potential hazards of aquatic habitat degradation, with an emphasis on data-scarce regions. This strategy could potentially establish a scientific basis for the preservation and restoration of aquatic ecosystems.

Calibration of a hydrological model is a challenging task, especially in basins that are data scarce. With the incorporation of regional information and integration with satellite data, the parameters of hydrological models can be estimated for a basin with scant or no discharge records. The main objective of this study is to calibrate and validate a hydrological model based on a limited amount of in‐situ measured and remote sensing satellite datasets in a data‐sparse region. Multiple techniques were applied for the model calibration: (1) stage‐discharge curves using a spatial proximity approach, (2) Simplified Surface Energy Balance actual evapotranspiration, (3) river discharge using a physical similarity regionalization approach, and (4) a new hybrid approach by integrating remote sensing datasets along with field measured river bathymetry data to estimate the river discharge. To demonstrate the methodology, we employed the widely used Soil and Water Assessment Tool (SWAT) hydrological model in Manipur River Basin, India. The sensitivity, calibration, and validation of the SWAT model were carried out by using the Sequential Uncertainty Fitting Technique. During calibration, the coefficient of determination (R²) and the Kling Gupta Efficiency (KGE) were found to be in the range of 0.46–0.81 and 0.41–0.83, whereas during validation R² and KGE were found to be in the range of 0.40–0.79 and 0.53–0.77 for the four different techniques. Among all the four techniques applied in this study, calibration based on (i) stage‐discharge curve using spatial proximity approach and (ii) new hybrid approach by integrating remote sensing datasets and river bathymetry were found as the better approaches as indicated by the statistical indices. The performance evaluation of the model through a new hybrid approach by integrating remote sensing and in‐situ measured datasets for rivers with narrow width represents a promising technique for use in a data sparse region.

The application of remote sensing can aid the decision makers and the researchers in the field of water resources for the effective monitoring of water quality in a water sparse region.  The monitoring of water quality in a wetland dominated by the heterogeneous biomass becomes more intricate. This research study was carried out in Loktak Lake, a Ramsar site nestled in the Indo-Myanmar range between the time intervals February 2022 to December 2022. In order to carry out this study, high and very high resolution multispectral satellite imageries were used. The physical water quality parameters namely electrical conductivity, total suspended solids, pH, turbidity, and nitrates were considered for the assessment. The results of this study clearly indicate a strong correlation between the field-measured parameters and reflectance. The prediction algorithms were generally the best fit to derive the water quality parameters. The model performance indices indicates good performance of the model with correlation coefficient greater than 0.80. The outcomes of this study emphasize the use of high and very high multi-spectral satellite imageries for the monitoring of water bodies with complex dynamics.

Reliability of hydrological model simulation plays an important role in better understanding of hydrological processes, eco-hydrologic and eco-hydraulic condition of a data scarce catchment with limited baseline data. Due to the lack of baseline hydrological dataset, precise simulation of hydrological output from a hydrological model becomes essential. There has been several studies carried out to calibrate a data scarce catchment using river discharge solely on remote sensing data but they have been limited to the rivers with large width. The current study was carried out in Manipur River basin where the widths of the streams are narrow due to which direct application of remotely sensed data possesses serious challenge. This study attempts to calibrate and validate a comprehensive physically semi-distributed Soil and Water Assessment Tool (SWAT) model in Manipur River basin. The SWAT hydrological model was set-up using elevation, soil, weather, and land use land cover (LULC) dataset. For the calibration and validation of model two different approaches has been applied. In the first approach, river streamflow was generated by using stage data based on stage-discharge curve through the technique of spatial proximity, whereas in the second approach, Moderate Resolution Imaging Spectroradiometer (MODIS) evapotranspiration dataset was used at sub-basin scale to calibrate and validate the SWAT model. In the calibration period, the model returned R2 and Kling-Gupta Efficiency (KGE) of 0.78 and 0.73, whereas in the validation period R2, KGE was found to be 0.75, 0.71 while using the stage-discharge curve approach. The model performance of R2, KGE equals 0.67, 0.41, respectively during calibration and R2, KGE equals 0.79, 0.53 respectively was obtained during validation when MODIS evapotranspiration dataset was used. From the modelling result it was observed that the model performance was found to be better when streamflow dataset derived from stage-discharge curve used as compared to the MODIS evapotranspiration dataset. The main reason behind the under performance of the model while using MODIS evapotranspiration dataset was due to the underestimation of evapotranspiration by the SWAT model in the cold-dry season from December to February. 

Manipur River basin is seriously affected by soil erosion. Assessment of soil erosion in such watersheds is useful in identifying causes and associated risks. This study aims to assess spatio-temporal change in soil erosion/deposition in the past, and project it for the future using the Unit Stream Power Erosion/Deposition model based on rainfall erosivity, soil erodobility, soil cover, slope length and steepness factor as the input parameter. From the model results, the area under extreme to slight erosion intensity was found to be 3.82%, 5.54%, 5.44%, 5.28% and 6.74% for the year 2007, 2011, 2014, 2017 and 2030, whereas for the deposition it was found to be 3.72%, 4.9%, 5.21%, 5.3% and 6.3% for the year 2007, 2011, 2014, 2017 and 2030 respectively. This study also indicates that the study area is suffering from moderate soil erosion but it is most likely to transit into severe in the future.

Safe delivery of healthy newborn from the state of pregnancy is one of the main objectives of prenatal health care. Women face maternal health issues due to lack of awareness, excessive stress during pregnancies and lack of appropriate maternal health-care services. The main objectives of this research study is to identify the abortion and stillbirth prevalent hotspot zones in Manipur from the year 2011 to 2018 and identification of statistically significant factors related to the cause of abortion and stillbirth. It was observed from the hotspot results that Lamphelpat, Kakching, Thoubal, and Churachandpur are the blocks where abortion is more prevalent, whereas stillbirth cases were found to be concentrated in Lamphelpat block. Generated regression model showed good model performance with adjusted R² = 0.68, Akaike Information Criterion (AIC) = 608.72, Moran’s I = 0.37, and adjusted R² = 0.73, AIC = 433.26, Moran’s I = −0.05 for abortion and stillbirth, respectively. Through regression modeling three factors i.e., main worker female, female working as agriculture labor and female household worker were found to be significant for the cause maternal issues like abortion and stillbirth. There is a need to follow up pregnant women with these risk factors more frequently and in a more qualified way.