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Senthil Kumar, A. R.
- Investigating the Performance of Snowmelt Runoff Model Using Temporally Varying Near-Surface Lapse Rate in Western Himalayas
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Authors
Affiliations
1 Inter Disciplinary Program (IDP) in Climate Studies and Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, ID
2 Inter Disciplinary Program (IDP) in Climate Studies and Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
3 Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
4 National Institute of Hydrology, Roorkee 247 667, IN
1 Inter Disciplinary Program (IDP) in Climate Studies and Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, ID
2 Inter Disciplinary Program (IDP) in Climate Studies and Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
3 Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400 076, IN
4 National Institute of Hydrology, Roorkee 247 667, IN
Source
Current Science, Vol 114, No 04 (2018), Pagination: 808-813Abstract
The present study assesses the effect of accounting the temporal variation of near-surface lapse rate in the conceptual, degree-day snowmelt runoff model simulations in a cold-desert region of Himalayas. The nearsurface lapse rate over Spiti basin shows seasonal variation during a year. The results obtained show that the inclusion of monthly variation of lapse rate in the hydrological modelling is able to capture the observed hydrograph more efficiently than when an annually constant value of lapse rate is employed. Based on our results and considering the available data, a monthly representation of near-surface lapse rates in the temperature index based models is recommended for Himalayan basins.Keywords
Himalayas, Lapse Rate, Snowmelt Runoff Model, Temporal Variation.Full Text
References
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- A Hybrid-Wavelet Artificial Neural Network Model for Monthly Water Table Depth Prediction
Abstract Views :286 |
PDF Views:83
Authors
Anandakumar
1,
A. R. Senthil Kumar
2,
Ravindra Kale
3,
B. Maheshwara Babu
1,
U. Sathishkumar
1,
G. V. Srinivasa Reddy
1,
Prasad S. Kulkarni
1
Affiliations
1 Department of Soil and Water Engineering, College of Agricultural Engineering, Raichur 584 104, IN
2 National Institute of Hydrology, Roorkee 247 667, IN
3 Western Himalayan Regional Centre, National Institute of Hydrology, Jammu 180 003, IN
1 Department of Soil and Water Engineering, College of Agricultural Engineering, Raichur 584 104, IN
2 National Institute of Hydrology, Roorkee 247 667, IN
3 Western Himalayan Regional Centre, National Institute of Hydrology, Jammu 180 003, IN
Source
Current Science, Vol 117, No 9 (2019), Pagination: 1475-1481Abstract
Groundwater is an essential natural resource in the country to fulfil the irrigation, domestic, industrial and other needs. In order to ensure sustainable use of groundwater resources, the groundwater level is used as an important indicator for balancing the groundwater withdrawal rate and replenishment rate through the recharge. Quantitatively, the recharge rate is governed by various complex large-scale hydrological processes and hence achievement of sustainability of groundwater supplies, through sustainable withdrawal rate is a complicated issue. In the present study, a data-driven prediction model by combining discrete wavelet transform (DWT) with artificial neural network (ANN) called as wavelet artificial neural network (WANN) is proposed for the groundwater table prediction. The simulation results obtained by regular ANN model were compared with those obtained by WANN model to prove the superiority of the latter model over the former. WANN model was developed using decomposed signals of rainfall, evapotranspiration and water table depth time series as inputs in the ANN model to arrive at a prediction of monthly fluctuation of the groundwater table. Rainfall time series was decomposed using Haar wavelet at third decomposition level and evapotranspiration and water table depth time series was decomposed using Daubechies wavelet at second decomposition level. The RMSE value of ANN and WANN model during validation were found to be 0.3648 m and 0.1695 m respectively, which showed decrease in RMSE value by 0.195 m when WANN was applied. Model efficiencies of ANN and WANN model during validation were 84.65% and 95.68%, indicating excellent improvement of model accuracy after applying WANN. Hence, the proposed WANN model seems to be a promising tool to predict the monthly water table fluctuation.Keywords
Artificial Neural Network, Wavelet Transformation, Wavelet Artificial Neural Network, Water Table Depth Prediction.Full Text
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