Khulgad watershed is the constituent of the Kosi river basin and is located to the west of the Almora town in the Hawalbagh Development Block of Almora district in the Uttarakhand.The watershed is bounded within 79°32'20.71"to 79°37'11.19"E longitude and 29°34'30.20"to 29°38'48.03"Nlatitude, covering an area of 32.57km 2 and having cool temperature climate with an annual average temperature of 20°C.To achieve the Morphometric analysis, toposheet No. 63 C/ 2 Survey of India (SOI) in 1:50000 scales are procured and the boundary line is extracted by joining the ridge points.This will serve as area of interest for preparing base map and thematic maps.The drainage map is prepared with the help of geographical information system tool and morphometric parameters such as linear, aerial and relief aspects of the watershed have been determined.These dimensionless and dimensional parametric values are interpreted to understand the watershed characteristics.From the drainage map of the study area dendritic drainage pattern is identified.Strahler (1964) stream ordering method is used for stream ordering of the watershed.The mean bifurcation ratio of the watershed is 3.49.

Evaporation is one of the main elements that effect water storage and temperature in the hydrological cycle and plays an important role in evaluation of water availability. Although, there are empirical formulae available for evaporation estimation but their performances are not all satisfactory due to the complicated nature of the evaporation process and the data availability. In this study, an attempt has been made to develop multiple linear regression (MLR) and artificial neural network (ANN) based evaporation estimation models using climatic parameters as inputs and evaporation as output for Udaipur of Rajasthan with the aid of Gamma test (GT). Average temperature (T), average wind speed (W), relative humidity (Rh) and sunshine hours (S) data were used as input and estimated evaporation was considered as output. The performance of the developed model was evaluated using root mean square error (RMSE) and correlation coefficient. The result showed an appropriate correlation between the estimated evaporation and actual evaporation.

This study assessed the ability of two models, Local Linear Regression (LLR) and Artificial Neural Network (ANN) to estimate monthly potential evaporation from Pantagar, US Nagar (India) which falls under sub-humid and subtropical climatic zone. Observations of relative humidity, solar radiation, temperature, wind speed and evaporation have been used to train and test the developed models. A comparison was made between the estimates provided by the LLR model and ANN model. Results shown that the models were able to well learn the events they were trained to recognize. For ANN model the correlation coefficient for training period is 0.9311 and for testing period is 0.9236 and the value of root mean square error for training period is 1.070 and for testing period it is 0.9863. In case of LLR model the correlation coefficient for training period is 0.9746 and for testing period is 0.9273 and value of root mean square error for training period is 0.6121 and for testing period it is 1.5301.