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Climate change, variability and their impact assessment are major concerns of the scientific community across the world. Changes and variations in meteorological variables have caused deleterious effects on water, agriculture, and forests globally. Manipur is a high rainfall deficit state in India. Therefore, the lower Thoubal River watershed is highly sensitive to minor climatic variations, which may significantly affect the socio-economic conditions of around 54% of the total population depending on agricultural activities. Hence, it has become imperative to analyze past trends of climate and ascertain future scenarios. Several researchers have investigated climatic variations; however, the existing literature has paid less attention to micro-level variations. To address this gap, the present study attempts to quantify temperature and precipitation trends in the lower Thoubal river watershed during 1981–2020 using daily gridded meteorological data. Sen's slope estimator was used to quantify the rate of change in rainfall and temperature, and the Mann–Kendall (MK) test was utilized to examine the direction of change and significance level. The study also provides a new insight to forecast climate scenarios in the watershed during 2021–2030 using two machine learning algorithms: random forest and artificial neural network-multilayer perceptron (ANN-MLP). Three statistical performance assessors and coefficient of determination (R2) were used to select the best forecasting model. The trend analysis results revealed a declining trend of rainfall at the rate of 10.30 mm/year with high variability. The annual maximum, minimum, and mean temperatures, as well as the diurnal temperature range (DTR), have also exhibited a statistically significant increasing trend, with rates of change at 0.035 °C, 0.01 °C, 0.025 °C, and 0.017 °C/year, respectively. The seasonal forecasting result indicate increase in temperature and decrease in rainfall were anticipated for the next 10 years. The random forest model has proved effective for forecasting of meteorological variables in micro-scale level. Such a trend will likely affect the agricultural productivity, streamflow and flooding, groundwater recharge, vegetation cover and water supply in the watershed. The findings of the study will be helpful for the local community and policy makers for management of natural resources in the watershed. The methodology adopted in the study could be expanded for other geographical regions.

The magnitude and intensity of landslides due to changing climate have created environmental and socio-economic implications for society. Through an in-depth analysis of the existing research on landslides in a changing climate from 1996 to 2021, this paper aims to carry out bibliometric and thematic analyses, identify the research gaps in the existing literature, and suggest a future framework for climate change-induced landslide risk assessment and mitigation. The data for review was collected from the Web of Science and Scopus platforms using a set of relevant keywords. After meeting the exclusion and inclusion criteria, 200 studies were finally selected to analyze the current state of research. The findings revealed that most of the reviewed studies focused on economic vulnerability to landslides, while social and ecological aspects of vulnerability at the micro-scale were scant in the past literature. Uncertainty in landslide-climate modeling, lack of advanced models for predicting landslide risk, and lack of early warning systems were identified as the major research gaps. A holistic methodological approach is proposed for assessing landslide risk and devising landslide mitigation strategies. The identified research gaps and the proposed framework may help in the future progression of climate change-induced landslide research in spatial information science.

The accurate and efficient monitoring, assessment, and prediction of water resources is crucial for the sustenance of life and health of the environment. Traditional methods for assessing water quality can be laborious and time-consuming, but the use of machine learning algorithms can improve the speed and precision of these predictions. The study aimed to evaluate the quality of surface water in the lower Thoubalriver using a comprehensive approach that analyzed 12 physico-chemical properties of the water samples collected from 16 different sites. Four machine learning algorithms namely, deep neural network (DNN), gradient boost model (GBM), generalized linear model (GLM), and random forest (RF) were applied and compared for the prediction efficiency of a water quality index (WQI). Sensitivity and uncertainty analysis of the parameters were carried out using a DNN-based model. The study incorporated model evaluation using learning curve along with five key performance assessors. The findings revealed marked variation in the WQI in the watershed. Of the samples tested, about 70 % were found to have good water quality, 18 % had excellent water quality, 11 % had poor water quality, 1 % had very poor water quality, and less than 1 % were deemed unsuitable. Turbidity, BOD, and COD were found to be the most influential parameter towards WQI prediction. The effectiveness of the predictive models ranked as GLM>DNN>RF>GBM, GLM>GBM>DNN>RF, and GLM>DNN>RF>GBM; based on testing, training and 5-fold cross-validation. The GLM model consistently showed superior performance with the lowest RMSEs of 0.14783 in training, 0.15936 in validation, and 0.26115 in 5-fold cross-validation. Conversely, during the training, RF displayed the worst RMSEs of 5.38354, 4.79754 by GBM in validation, and 7.04887 by GBM in 5-fold cross-validation. The analysis using the Taylor diagram and learning curve further supported that GLM was the most effective model for predicting surface water quality. Biological oxygen demand, chemical oxygen demand, electrical conductivity, turbidity and total hardness were found to be the key parameters influencing the water quality. Unregulated and illegal sand mining from the riverbed, garbage dumping into the water bodies, runoffs from agricultural fields have significantly deteriorated the water quality in the study area. The scalable approach of the study and its results can benefit the local water managers and water research community.