Narendra Singh Raghuwanshi

Bio: Narendra Singh Raghuwanshi is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Evapotranspiration & Surface irrigation. The author has an hindex of 31, co-authored 136 publications receiving 4298 citations. Previous affiliations of Narendra Singh Raghuwanshi include Indian Institutes of Technology & North Eastern Regional Institute of Science and Technology.

Estimating Evapotranspiration using Artificial Neural Network

Abstract: This study investigates the utility of artificial neural networks (ANNs) for estimation of daily grass reference crop evapotranspiration (ETo) and compares the performance of ANNs with the conventional method (Penman–Monteith) used to estimate ETo. Several issues associated with the use of ANNs are examined, including different learning methods, number of processing elements in the hidden layer(s), and the number of hidden layers. Three learning methods, namely, the standard back-propagation with learning rates of 0.2 and 0.8, and backpropagation with momentum were considered. The best ANN architecture for estimation of daily ETo was obtained for two different data sets (Sets 1 and 2) for Davis, Calif. Using data of Set 1, the networks were trained with daily climatic data (solar radiation, maximum and minimum temperature, maximum and minimum relative humidity, and wind speed) as input and the Penman–Monteith (PM) estimated ETo as output. The best ANN architecture was selected on the basis of weighted sta...

Temporal Trends in Estimates of Reference Evapotranspiration over India

Abstract: Evapotranspiration (ET) is likely to be greatly affected by global warming because of the dependence of ET on surface temperature. The increasing atmospheric concentration of carbon dioxide (C O2 ) and other greenhouse gases is expected to increase precipitation and evaporation proportionally. However, a few studies have shown a decreasing trend for evaporation over the last 50 years globally. In India, earlier works showed that there was a significant increasing temporal trend in surface temperature and a decreasing trend in grass reference ET (ETo). To study the temporal trend of ETo along with its regionwise spatial variation, 32 years (1971–2002) monthly meteorological data were collected for 133 selected stations evenly distributed over different agro-ecological regions (AERs) of India. ETo was estimated by the globally accepted Food and Agriculture Organization (FAO) Penman Monteith (PM) method (FAO-56 PM). These ETo values were then analyzed by a nonparametric Mann–Kendall (MK) test (with modified ...

Artificial neural networks approach in evapotranspiration modeling: a review

Abstract: The use of artificial neural networks (ANNs) in estimation of evapotranspiration has received enormous interest in the present decade. Several methodologies have been reported in the literature to realize the ANN modeling of evapotranspiration process. The present review discusses these methodologies including ANN architecture development, selection of training algorithm, and performance criteria. The paper also discusses the future research needs in ANN modeling of evapotranspiration to establish this methodology as an alternative to the existing methods of evapotranspiration estimation.

The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions

Abstract: The Soil and Water Assessment Tool (SWAT) model is a continuation of nearly 30 years of modeling efforts conducted by the USDA Agricultural Research Service (ARS). SWAT has gained international acceptance as a robust interdisciplinary watershed modeling tool as evidenced by international SWAT conferences, hundreds of SWAT-related papers presented at numerous other scientific meetings, and dozens of articles published in peer-reviewed journals. The model has also been adopted as part of the U.S. Environmental Protection Agency (USEPA) Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) software package and is being used by many U.S. federal and state agencies, including the USDA within the Conservation Effects Assessment Project (CEAP). At present, over 250 peer-reviewed published articles have been identified that report SWAT applications, reviews of SWAT components, or other research that includes SWAT. Many of these peer-reviewed articles are summarized here according to relevant application categories such as streamflow calibration and related hydrologic analyses, climate change impacts on hydrology, pollutant load assessments, comparisons with other models, and sensitivity analyses and calibration techniques. Strengths and weaknesses of the model are presented, and recommended research needs for SWAT are also provided.

Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions, The

Abstract: The Soil and Water Assessment Tool (SWAT) model is a continuation of nearly 30 years of modeling efforts conducted by the U.S. Department of Agriculture (USDA), Agricultural Research Service. SWAT has gained international acceptance as a robust interdisciplinary watershed modeling tool, as evidenced by international SWAT conferences, hundreds of SWAT-related papers presented at numerous scientific meetings, and dozens of articles published in peer-reviewed journals. The model has also been adopted as part of the U.S. Environmental Protection Agency's BASINS (Better Assessment Science Integrating Point & Nonpoint Sources) software package and is being used by many U.S. federal and state agencies, including the USDA within the Conservation Effects Assessment Project. At present, over 250 peer-reviewed, published articles have been identified that report SWAT applications, reviews of SWAT components, or other research that includes SWAT. Many of these peer-reviewed articles are summarized here according to relevant application categories such as streamflow calibration and related hydrologic analyses, climate change impacts on hydrology, pollutant load assessments, comparisons with other models, and sensitivity analyses and calibration techniques. Strengths and weaknesses of the model are presented, and recommended research needs for SWAT are provided.

Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions

Abstract: In 2011, Lake Erie experienced the largest harmful algal bloom in its recorded history, with a peak intensity over three times greater than any previously observed bloom. Here we show that long-term trends in agricultural practices are consistent with increasing phosphorus loading to the western basin of the lake, and that these trends, coupled with meteorological conditions in spring 2011, produced record-breaking nutrient loads. An extended period of weak lake circulation then led to abnormally long residence times that incubated the bloom, and warm and quiescent conditions after bloom onset allowed algae to remain near the top of the water column and prevented flushing of nutrients from the system. We further find that all of these factors are consistent with expected future conditions. If a scientifically guided management plan to mitigate these impacts is not implemented, we can therefore expect this bloom to be a harbinger of future blooms in Lake Erie.