National Institute of Hydrology

Roorkee, India

National Institute of Hydrology

Roorkee, India
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Thayyen R.J.,Wadia Institute of Himalayan Geology | Thayyen R.J.,National Institute of Hydrology | Gergan J.T.,Wadia Institute of Himalayan Geology
Cryosphere | Year: 2010

A large number of Himalayan glacier catchments are under the influence of humid climate with snowfall in winter (November-April) and south-west monsoon in summer (June-September) dominating the regional hydrology. Such catchments are defined as "Himalayan catchment", where the glacier meltwater contributes to the river flow during the period of annual high flows produced by the monsoon. The winter snow dominated Alpine catchments of the Kashmir and Karakoram region and cold-arid regions of the Ladakh mountain range are the other major glaciohydrological regimes identified in the region. Factors in- fluencing the river flow variations in a "Himalayan catchment" were studied in a micro-scale glacier catchment in the Garhwal Himalaya, covering an area of 77.8 km2. Three hydrometric stations were established at different altitudes along the Din Gad stream and discharge was monitored during the summer ablation period from 1998 to 2004, with an exception in 2002. These data have been analysed along with winter/summer precipitation, temperature and mass balance data of the Dokriani glacier to study the role of glacier and precipitation in determining runoff variations along the stream continuum from the glacier snout to 2360ma.s.l. The study shows that the inter-annual runoff variation in a "Himalayan catchment" is linked with precipitation rather than mass balance changes of the glacier. This study also indicates that the warming induced an initial increase of glacier runoff and subsequent decline as suggested by the IPCC (2007) is restricted to the glacier degradation-derived component in a precipitation dominant Himalayan catchment and cannot be translated as river flow response. The preliminary assessment suggests that the "Himalayan catchment" could experience higher river flows and positive glacier mass balance regime together in association with strong monsoon. The important role of glaciers in this precipitation dominant system is to augment stream runoff during the years of low summer discharge. This paper intends to highlight the importance of creating credible knowledge on the Himalayan cryospheric processes to develop a more representative global view on river flow response to cryospheric changes and locally sustainable water resources management strategies.

Agency: European Commission | Branch: FP7 | Program: CP-FP-SICA | Phase: ENV.2011.3.1.1-2 | Award Amount: 4.78M | Year: 2011

Saph Pani addresses the improvement of natural water treatment systems such as river bank filtration (RBF), managed aquifer recharge (MAR) and wetlands in India building on a combination of local and international expertise. The project aims at enhancing water resources and water supply particularly in water stressed urban and peri-urban areas in different parts of the sub-continent. The objective is to strengthen the scientific understanding of the performance-determining processes occurring in the root, soil and aquifer zones of the relevant processes considering the removal and fate of important water quality parameters such as pathogenic microorganisms and respective indicators, organic substances and metals. Moreover the hydrologic characteristics (infiltration and storage capacity) and the eco-system function will be investigated along with the integral importance in the local or regional water resources management concept (e.g. by providing underground buffering of seasonal variations in supply and demand). The socio-economic value of the enhanced utilisation of the attenuation and storage capacity will be evaluated taking into account long-term sustainability issues and a comprehensive risk management. The project focuses on a set of case study areas in India covering various regional, climatic, and hydrogeological conditions as well as different treatment technologies. The site investigations will include hydrological and geochemical characterisation and, depending on the degree of site development, water quality monitoring or pre-feasibility studies for new treatment schemes. Besides the actual natural treatment component the investigation may encompass also appropriate pre- and post treatment steps to potabilise the water or avoid clogging of the sub-surface structures. The experimental and conceptual studies will be complemented by modelling activities which help to support the transferability of results.

Singh S.K.,National Institute of Hydrology
Journal of Irrigation and Drainage Engineering | Year: 2012

The suitability of the widely used existing solution for calculating groundwater mound due to artificial recharge from rectangular areas is examined for its applicability to unconfined aquifers, and this solution has been found applicable only to confined aquifers. The solution applicable for confined aquifers is derived and shown equivalent to the existing solutions. A computationally simple function is proposed for accurately approximating the integral appearing in this or existing solutions. A procedure involving analytical approximation is outlined for using this solution for unconfined aquifers. A method to calculate groundwater mound height in unconfined aquifers due to arbitrarily varying temporal recharge (percolation) is also proposed. It is hoped that the proposed methods would be of help to field engineers and practitioners. © 2012 American Society of Civil Engineers.

Lohani A.K.,National Institute of Hydrology | Kumar R.,National Institute of Hydrology | Singh R.D.,National Institute of Hydrology
Journal of Hydrology | Year: 2012

Time series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir. © 2012 Elsevier B.V.

Nayak P.C.,National Institute of Hydrology
Journal of Hydrologic Engineering | Year: 2010

This paper presents a popular fuzzy rule-based model for river flow forecasting for an Indian basin. To set up the fuzzy rules, a cluster estimation method is adopted to determine the number of rules and the membership functions of variables involved in the premises of the rules. The most appropriate set of input variables was determined by trial and error procedure to test the coherence of the different input variables in forecasting flood. It is observed that the last time steps of measured runoff are dominating the forecast. The developed model is used to forecast up to 12 h in advance. The values of three performance evaluation criteria namely, the coefficient of efficiency, the root-mean-square error and the coefficient of correlation, were found to be very good and consistent for flows forecasted 1 h in advance by the model. The performance is decreasing as the forecast horizon is increasing and a reasonable forecast is obtained up to 9 h ahead. A set of fuzzy rules is extracted and used for understanding of the behavior of the developed model. It is observed that the developed model follows the trend of the input membership grade in antecedent part of the fuzzy model. © 2010 ASCE.

Mishra K.,Hemwati Nandan Bahuguna Garhwal University | Sharma R.C.,Hemwati Nandan Bahuguna Garhwal University | Kumar S.,National Institute of Hydrology
Ecotoxicology and Environmental Safety | Year: 2012

Organochlorine pesticides, dichlorodiphenyltrichloroethane (DDT) and hexachlorocyclohexane (HCH), are potential chemical pollutants extensively used for agriculture and vector control purposes due to low cost and high effectiveness. Concentrations of HCH and DDT were determined in 175 surface soil samples from different agricultural fields, fallow and urban lands of districts Nagaon and Dibrugarh, Assam, India. The mean concentrations of total HCH and total DDT were 825. ng/g (range: 98-1945. ng/g) and 903. ng/g (range: 166-2288. ng/g) in district Nagaon while 705. ng/g (range: 178-1701. ng/g) and 757. ng/g (range: 75-2296. ng/g) in district Dibrugarh, respectively. The soils from paddy fields contained highest amounts of HCH and DDT residues. Total organic carbon was found to be positively associated with soil HCH and DDT residues. Ratios of DDT/(DDD+DDE) were 1.25 and 1.82 while of α/γ HCH were 2.78 and 2.51 for districts Dibrugarh and Nagaon, respectively. Source identification revealed that soil residue levels have originated from long past and recent mixed source of technical HCH and Lindane for HCHs and mainly technical DDT for DDTs. Spatial distribution was also investigated to identify the areas with higher pesticide loadings in soil. © 2011 Elsevier Inc.

Singh S.K.,National Institute of Hydrology
Journal of Irrigation and Drainage Engineering | Year: 2010

Simple method and explicit equations are proposed for estimating the parameters of leaky aquifers from drawdown at an observation well, which avoid the curve matching or initial estimate of the parameter. The proposed method is computationally simple and the calculations can be performed even on a handheld calculator. The application of the methods is illustrated, using published data sets. The new method yields quick and accurate estimates of the leaky-aquifer parameters, if observed drawdowns do not contain large errors. The proposed method can also analyze the early drawdowns for accurate characteristics/parameters of a confined aquifer, if the conductance of the aquitard is assigned a zero value. It is hoped that the proposed method would be of help to field engineers and practitioners. © 2010 ASCE.

Budu K.,National Institute of Hydrology
Journal of Hydrologic Engineering | Year: 2014

The present study demonstrates the capability of two preprocessing techniques such as wavelets and moving average (MA) methods in combination with feed-forward neural networks-namely, back propagation (BP) and radial basis (RB) and multiple linear regression (MLR) models-in the prediction of the daily inflow values of the Malaprabha reservoir in Belgaum, India. Daily data on 11 years of rainfall, inflow, and streamflow at an upstream gauging station have been used. The observed inputs are decomposed into subseries using discrete wavelet transform with different mother wavelet functions, and then the appropriate subseries is used as input to the neural networks for forecasting reservoir inflow. Model parameters are calibrated using 7 years of data, and the remaining data are used for model validation. More statistical indices have been used to determine the optimal models. Optimum architectures of the wavelet neural network (WNN) models are selected according to the obtained evaluation criteria in terms of the Nash-Sutcliffe efficiency coefficient, root mean squared error, and correlation coefficient. The result of this study has been compared by developing two standard neural network models and a multiple linear regression (MLR) model and MA. The results of this study indicate that the WNN model performs better compared to artificial neural network (ANN) and MLR models in forecasting the inflow hydrograph effectively. The study only used reservoir inflow data from one area, and further studies using data from various areas may be required to strengthen these conclusions. © 2014 American Society of Civil Engineers.

Lohani A.K.,National Institute of Hydrology | Goel N.K.,Indian Institute of Technology Roorkee | Bhatia K.K.S.,Poornima Group of Institutions
Journal of Hydrology | Year: 2014

In order to improve the real time forecasting of foods, this paper proposes a modified Takagi Sugeno (T-S) fuzzy inference system termed as threshold subtractive clustering based Takagi Sugeno (TSC-T-S) fuzzy inference system by introducing the concept of rare and frequent hydrological situations in fuzzy modeling system. The proposed modified fuzzy inference systems provide an option of analyzing and computing cluster centers and membership functions for two different hydrological situations, i.e. low to medium flows (frequent events) as well as high to very high flows (rare events) generally encountered in real time flood forecasting. The methodology has been applied for flood forecasting using the hourly rainfall and river flow data of upper Narmada basin, Central India. The available rainfall-runoff data has been classified in frequent and rare events and suitable TSC-T-S fuzzy model structures have been suggested for better forecasting of river flows. The performance of the model during calibration and validation is evaluated by performance indices such as root mean square error (RMSE), model efficiency and coefficient of correlation (R). In flood forecasting, it is very important to know the performance of flow forecasting model in predicting higher magnitude flows. The above described performance criteria do not express the prediction ability of the model precisely from higher to low flow region. Therefore, a new model performance criterion termed as peak percent threshold statistics (PPTS) is proposed to evaluate the performance of a flood forecasting model. The developed model has been tested for different lead periods using hourly rainfall and discharge data. Further, the proposed fuzzy model results have been compared with artificial neural networks (ANN), ANN models for different classes identified by Self Organizing Map (SOM) and subtractive clustering based Takagi Sugeno fuzzy model (SC-T-S fuzzy model). It has been concluded from the study that the TSC-T-S fuzzy model provide reasonably accurate forecast with sufficient lead-time. © 2013 Elsevier B.V.

Kumar V.,National Institute of Hydrology | Jain S.K.,Indian Institute of Technology Roorkee
Quaternary International | Year: 2010

The study has analysed seasonal and annual rainfall and rainy days at five stations namely Srinagar, Kulgam, Handwara, Qazigund and Kukarnag to decipher rainfall trends over the Kashmir Valley. Rainfall data collected by the India Meteorological Department (IMD) were used. Due to the varying length of the available data, analysis was performed for two common periods: 1903-1982 (80 years) at three stations and 1962-2002 (41 years) at three stations. The 102 years of data at Srinagar were also analysed to examine the trends for last century. Time series of annual and seasonal rainfall/rainy days were examined for trends by analysis of anomalies and application of statistical tests. During the period 1903-1982, Srinagar, Kulgam and Handwara stations experienced a decreasing trend in annual rainfall; the maximum decrease was found for Kulgam (-20.16% of mean/100 years) and minimum for Srinagar (-2.45% of mean/100 years). All three stations showed a decreasing trend in monsoon and winter rainfall and an increasing trend in pre-monsoon and post-monsoon seasonal rainfall. The decreasing trend in winter rainfall was found to be statistically significant (95% confidence) at Kulgam and Handwara, whereas none of the increasing trend in the pre-monsoon and post-monsoon season was significant. Srinagar and Handwara witnessed a decreasing (non-significant) trend in annual rainy days, whereas Kulgam experienced the opposite trend. All the stations experienced a decreasing trend in monsoon and winter rainy days. Qazigund and Kukarnag experienced decreasing annual rainfall, and Srinagar showed increasing annual rainfall during the period 1962-2002. Pre-monsoon and post-monsoon rainfall decreased at all three stations. None of the increasing/decreasing trends were found to be significant. Annual, pre-monsoon, post-monsoon and winter rainfall increased (non-significant) whereas monsoon rainfall decreased (non-significant), at Srinagar during the last century. © 2009 Elsevier Ltd and INQUA.

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