Adequate water resource management is essential for fulfilling ecosystem and human needs. Nainital Lake is a popular lake in Uttarakhand State in India, attracting lakhs of tourists annually. Locals also use the lake water for domestic purposes and irrigation. The increasing impact of climate change and over-exploration of water from lakes make their regular monitoring key to implementing effective conservation measures and preventing substantial degradation. In this study, dynamic change in the water spread area of Nainital Lake from 2001 to 2018 has been investigated using the multiband rationing indices, namely normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and water ratio index (WRI). The model has been developed in QGIS 3.4 software. A physical GPS survey of the lake was conducted to check the accuracy of these indices. Furthermore, to determine the trend in water surface area for a studied period, a non-parametric Mann–Kendall test was used. San’s slope estimator test determined the magnitude of the trend and total percentage change. The result of the physical survey shows that NDWI was the best method, with an accuracy of 96.94%. Hence, the lake water spread area trend is determined based on calculated NDWI values. The lake water spread area significantly decreased from March to June and July to October at a 5% significance level. The maximum decrease in water spread area has been determined from March to June (7.7%), which was followed by the period July to October (4.67%) and then November to February (2.79%). The study results show that the lake’s water spread area decreased sharply for the analyzed period. The study might be helpful for the government, policymakers, and water experts to make plans for reclaiming and restoring Nainital Lake. This study is very helpful in states such as Uttarakhand, where physical mapping is not possible every time due to its tough topography and climate conditions.

Remote sensing technology can be said to be an eye in the sky which provides detailed information on various parameters of the Earth, including climatic, vegetative, and forest variables. For many years, Landsat 8 has been used to study various natural phenomena on Earth. In the present study, Landsat 8 imagery has been used to retrieve land surface temperature (LST). For retrieving LST, TIRS (Thermal Infra-Red Sensor) Spectral Bands have been used. The LST map of Udhamsingh Nagar in Uttarakhand, India has been further prepared for the period 2018 to 2021 for the month of October. The result suggested that for the study area, LST varies from 24 oC to 39 oC for the month of October in the aforesaid study period. High temperatures have been observed in built-up areas like towns and residential areas, whereas in agricultural fields and forests, the LST is lower as compared to human-induced built-up areas. Further, the southeastern part of Udhamsingh Nagar has higher LST as compared to other regions of the study area. Doi: 10.28991/HEF-2022-03-02-02 Full Text: PDF

The effective scheduling of irrigation requires knowledge of a crop’s consumptive water use according to its metabolic activities. Conversely, to know a crop’s consumptive water use, one must know its exact evapotranspiration (ET) rate. Although the United Nations’ Food and Agricultural Organization (FAO) has recommended using the standard Penman-Monteith method to determine crop ET (ETcrop), the method’s intricacies render it impractical to use in the field in predicting agricultural and irrigation requirement-based water needs. The present study investigated the use of a new approach, extreme learning machines (ELMs), for estimating ETcrop using climatic variables such as temperature, relative humidity, rainfall, sunshine hours, and wind speed. ELM is a single, hidden layer, feed-forward network that provides a unified learning platform with widespread types of feature mappings. It can also be applied in regression. This study compares results obtained using the standard Penman-Monteith method, ...