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Research highlights: Compared performance of absorption decomposition algorithms in optically complex waters. Good performance of absorption decomposition algorithms in inherent optical property retrieval from the measured total absorption coefficient. Limited applicability of absorption decomposition algorithms in longer wavelengths. Higher errors in total absorption coefficient derived from semi-analytical algorithms. Need for better semi-analytical algorithms to derive accurate total absorption and absorption due to subcomponents. The present study evaluates the applicability of two absorption decomposition algorithms (ADA) (Zhang and Lin’s models) in the retrieval of subcomponent Inherent Optical Properties (IOPs) from coastal waters. These ADAs use measured or model-derived total non-water absorption coefficient a nw (λ) (total absorption coefficient subtracted water absorption coefficient) as input and provide absorption subcomponents – absorption due to phytoplankton a ph (λ) and coloured detrital matter, a dg (λ) as outputs. Coast Colour Round Robin match-up dataset, NASA’s bio-Optical Marine Algorithm Dataset (NOMAD) and Kochi (Indian coastal waters) datasets that consist of coincident measurements of remote sensing reflectance, R rs λ (s r - 1) and IOPs were used for the evaluation of various models. With measured a nw (λ) as input, both Zhang and Lin’s models demonstrated good performance with an average spectral mean absolute percentage error (MAPE) in the range of 19–44% for the derived absorption subcomponents for all three datasets. Quasi-Analytical Algorithm (QAA) and LS2 are two semi-analytical algorithms (SAAs) that use R rs (λ) as input and provide a nw (λ) as output. The QAA-Zhang, QAA-Lin, LS2-Zhang and LS2-Lin models (combination of SAA and ADA) resulted in higher average spectral MAPE (33–59%) values for the derived absorption subcomponents as compared to other existing SAAs, owing to errors present in both SAAs and ADAs. These results indicate that improved SAAs are required to derive accurate a nw (λ) to improve the applicability of ADAs in remote sensing applications. [ABSTRACT FROM AUTHOR]

For sustainable development in a river basin, it is crucial to understand population growth–land use/land cover (LU/LC) transformations–water quality nexus. This study investigates the effects of demographic changes and LU/LC transformations on surface water quality of rural (Ghod) and urban (Mula-Mutha) subbasins of Upper Bhima River basin. Population data (1981–2011) and LU/LC data {October 2002 [Landsat Enhanced Thematic Mapper (ETM+) data] and October/November 2009 (Indian Remote Sensing 1C Linear Imaging Self Scanner III data]} were analysed using statistical, remote sensing and geographic information system techniques to study demographic and LU/LC changes, respectively. Further, overall indices of pollution (OIPs) developed specifically for rural subbasin (OIPr: Hardness CaCO3 and Total Dissolved Solids), urban subbasin (OIPu: Biological Oxygen Demand, Chlorides, Coliform Total, Colour, Dissolved Oxygen%, pH and Turbidity) and single OIP considering all parameters (OIPa) were used for spatio-temporal water quality assessment of pre-monsoon and post-monsoon periods. Results revealed that from 1981 to 2011, population increase was higher in urban subbasin than in rural subbasin. Subsequently, from 2002 to 2009 mainly increase in built-up lands (3.82%) and agricultural lands (15.35%) in urban and rural subbasins respectively, affected their water quality. From 2002 to 2009, the highest increase in OIPr and OIPu was observed at Kashti (3.37–6.52 due to fertilizers) and Bundgarden Bridge (3.03–7.83 due to municipal and industrial wastes) stations of rural and urban subbasins, respectively. With significant increase in OIPa of 2.74–6.70, Bundgarden Bridge station affected by urbanization had the most polluted water quality. [ABSTRACT FROM AUTHOR]

The present study investigates the hydrological response of increase in urbanization on water stressed Upper Bhima River basin which lies in a semi-arid climatic zone of Maharashtra state, India. Land Use Land Cover (LULC) changes due to urbanization, industrialization and anthropogenic activities have reconfigured the waterscape of the river basin, which has affected its regional hydrology. Influence of urbanization on key hydrological components is studied using Soil and Water Assessment Tool model. Firstly, Object Based Image Analysis approach was used to prepare time series LULC maps of the years 1992, 2002, 2009 and 2014. Overall classification accuracy of 92.48% and Kappa Coefficient (K) of 0.87 were achieved. Urbanization indicators, e.g. population urbanization level (Up) and spatial urbanization level (Us), were used to quantify the growth patterns in population and urban areas respectively. Mann-Kendall trend test was performed on the average annual rainfall data (year 1985-2014) to study rainfall trends across the region. Further, combination of statistical analyses including correlation analysis and multivariate analysis of variance were performed to comprehend the causative connection and interrelationships between Us and hydrological parameters. The results reveal that during 1992-2014, with increase in Us of 0.05, the average annual surface runoff increased to 10.4 mm [standard deviation (σ) = 4.40; sum of squares (SS) = 58.20], whereas percolation decreased to 14.5 mm [σ = 6.06; SS = 110.10], and base flow decreased to 11.7 mm (σ = 4.90; SS = 72.00). These hydrological parameters are highly influenced by increase in urbanization. This study is relevant for various stakeholders such as water sources planners and policy makers for assessment of water resources to ensure sustainable development in the urbanizing tropical river basins. Remedial measures are suggested to minimize the adverse effect of urbanization on hydrological processes. [ABSTRACT FROM AUTHOR]