A multivariate statistical approach for the evaluation of spatial and temporal dynamics of surface water quality from the small reservoir located in the drought-prone area of South-West India: a case study of Tiru reservoir (India).

With the use of different multivariate statistical analysis methods, spatio-temporal fluctuations in the water parameters of Tiru reservoir located at the Marathwada drought-prone area of Maharashtra, India, have been analysed and reported in this case study. Tiru reservoir, situated on the tributary of the Godavari River, was regularly monitored at five different sites from August 2017 to January 2019 for the estimation of 20 water quality parameters. Various multivariate methods such as pattern reorganisation using cluster analysis (CA), factor analysis/principal component analysis (FA/PCA), and discriminant analysis (DA) were used for handling complex datasets. CA extracted three different clusters from five sampling sites with similar water quality characteristics. FA/PCA extracted thirteen factors (65% of 20 measured) required to explain 74% of the data variability and identified the factors accountable for variation in water quality and also evaluated the prevalence of each cluster on the overall dissimilarity at five different sampling sites. Discriminant analysis extracted a total of 16 parameters with 97.7% right assignations. Varifactors (VFs) acquired by factor analysis recommended that the water quality parameters accounted for variation were linked to two groups. The first group included water quality parameters like T, DO, SDD, turbidity, TDS, PA, and MA, whereas the second group covered most of the nutrients Cl-, silicates, PP, TP, NO3-N, NO2-N, and NH3-N; hardness; and CHL-a and mainly entered the reservoir during surface runoff from agriculture fields and the surrounding area containing domestic as well as animal waste. Thus, the present work showed the efficiency of multivariate methods for the assessment of spatial as well as a temporal variation in the water quality of a small reservoir.

Trophic state modeling for shallow freshwater reservoir: a new approach.

Lakes and reservoirs around the world are facing a substantial water quality degradation problem that poses significant environmental, social, and economic impacts. Reservoir productivity is influenced primarily by the climatic, morphometric, and hydro-edaphic features. High nutrient loadings in the reservoir from agriculture runoff often provide ideal conditions for algal blooms, leading to eutrophication. Reservoir and lake management to prevent or reduce eutrophication, therefore, has become the need of the hour. The traditional approach of trophic state monitoring by rigorous field surveys and eutrophication modeling has been revised in the present study by developing a new trophic state index (TSI)-based model for tropical shallow freshwater reservoirs. The new model has been constructed based on Carlson's Limnology and Oceanography, 22, 361-369, (1977) guidelines by establishing an empirical relationship between trophic parameters including total phosphorus (TP), Secchi disk depth (SDD), and chlorophyll (Chl-a). After comparing the new model with various earlier models for its applicability and validation with actual field conditions, it was found to be most precise over previous TSI models. Temporal and spatial fluctuations in the water quality of the Tiru reservoir were primarily attributed to the changing climatic conditions during the study period. Seasonal monsoon with less frequency, heavy nutrient loading from agriculture runoff, and increased turbidity due to a high level of sediment inflow during monsoon raised the TSI (SDD) values of the Tiru reservoir to place it in the hyper-eutrophic class. Average TSI values during winter for SDD, Chl-a, and TP were indicative of the meso-eutrophic to eutrophic state. Saturation of nutrients due to low water level during summer season caused the poly-eutrophic condition for TSI (SDD)- and TSI (TP)-based estimates and eutrophic condition as per TSI (Chl-a) estimates. However, seasonal deviations of the TSI values based on the relationship between TSI (Chl-a) and TSI (SD) indicated a predominance of smaller particles (non-algal turbidity) during all seasons. Even though TP present in the Tiru reservoir is controlling the algal production, it is also affected by low-light conditions due to non-algal turbidity. The recommendation from this study is that the TSI method for estimating the health of the water bodies is the efficient, cost-effective, and time-saving approach. The model developed during the study would help managers and policy makers to take necessary steps to reduce eutrophication levels in the reservoir and would be helpful for researchers in developing new concepts and protocols, mainly focusing on shallow freshwater reservoirs.

Multi-temporal Land Use Land Cover (LULC) change analysis of a dry semi-arid river basin in western India following a robust multi-sensor satellite image calibration strategy.

Multi-temporal and multi-sensor satellite data calibration is an inherent problem in remote sensing-based applications. If multiple satellite scenes cover the study area, it is difficult to compare and process the images for change detection and long-term trend analysis of the same day and/or seasons from different satellites or sensors. Moreover, the validation of all the past images is a challenge due to unavailability of past ground truth datasets. The proposed calibration paradigm in this study is based on radiance normalization in the spatial and spectral domain to ease the alignment of multiple images into an identical radiometric foundation. In this study, an intuitive radiometric correction technique (at a daily, monthly and yearly scale) was proposed, aimed at all Landsat sensors' datasets for long-term Land Use Land Cover (LULC) trend analysis for a dry semi-arid river basin in western India, facing drought conditions. The post-calibration mosaiced images were smooth, and meaningful LULC classification results could be obtained easily for all the years. The LULC change dynamics were analyzed and compared for the years 1972, 1980, 1991, 2001, 2011 and 2016 in Shivna River Basin. During these study periods, wasteland was found to be the most altered class, followed by agricultural land and forest. The spatial extent of agricultural land was found to decrease linearly, while forest cover showed an exponential decrease; a linear increase was observed in wasteland. Though during the 44 years study period (1972-2016), 241.48 km2 area was converted to agricultural land from wasteland, but more than double that land was converted to wasteland from agricultural land; alarmingly, 5.18% (8.12% in 1972 and 2.94% in 2016) forest cover decreased. The existing forest cover in 2016 is approximately one-third compared to 1972. The present work provides a generic framework for the calibration of multi-temporal and multi-sensor satellite images for long-term LULC trend analysis, which can be adopted for other satellite datasets.

Monitoring and trend mapping of sea surface temperature (SST) from MODIS data: a case study of Mumbai coast.

Sea surface temperature (SST) is one of the most important parameters in monitoring ecosystem health in the marine and coastal environment. Coastal ecosystem is largely dependent on ambient temperature and temperature fronts for marine/coastal habitat and its sustainability. Hence, thermal pollution is seen as a severe threat for ecological health of coastal waters across the world. Mumbai is one of the largest metropolises of the world and faces severe domestic and industrial effluent disposal problem, of which thermal pollution is a major issue with policy-makers and environmental stakeholders. This study attempts to understand the long-term SST variation in the coastal waters off Mumbai, on the western coast of India, and to identify thermal pollution zones. Analysis of SST trends in the near-coastal waters for the pre- and post-monsoon seasons from the year 2004 to the year 2010 has been carried out using Moderate Resolution Imaging Spectro-radiometer (MODIS) Thermal Infra-red (TIR) bands. SST is calculated with the help of bands 31 and 32 using split window method. Several statistical operations were then applied to find the seasonal averages in SST and the standard deviation of SST in the study area. Maximum variation in SST was found within a perpendicular distance of 5 km from the shoreline during the study period. Also, a warm water mass was found to form consistently off coast during the winter months. Several anthropogenic sources of thermal pollution could be identified which were found to impact various locations along the coast.