N. S. Raghuwanshi

Identification and Prioritisation of Critical Sub-watersheds for Soil Conservation Management using the SWAT Model

Abstract A few areas of the watershed are critical and responsible for high amount of soil and nutrient losses. Implementation of best management practices is required in those critical erosion prone areas of the watershed for controlling the soil and nutrient losses. Identification of these critical areas is essential for the effective and efficient implementation of watershed management programmes. In this study, a calibrated Soil and Water Assessment Tool (SWAT) model was verified for a small watershed (Nagwan) and used for identification and prioritisation of critical sub-watersheds to develop an effective management plan. Daily rainfall, runoff and sediment yield data of 7 years (1992–1998) were used in this study. Data related to nutrient losses for few storm events of 1997 were also used. Besides these data, the topographical map, soil map, land resources data and satellite imageries of the study watershed were used in this study. A geographical information system was used for generating the watershed and sub-watershed boundaries, drainage networks, slope, soil series and texture maps. Supervised classification method was used for land use/cover classification from satellite imageries. The weighted average values of parameters such as runoff curve number, surface slope, channel length, average slope length, channel width, channel depth, soil erodibility factor and other soil layer data were taken for each sub-watershed to verify the model. The calibrated SWAT model was verified for the monsoon season on daily basis for the year 1997 and monthly basis for the years 1992–1998 for both surface runoff and sediment yield. It was also tested for the available data on nutrient losses. Critical sub-watersheds were identified on the basis of average annual sediment yield and nutrient losses during the period of 3 years 1996–1998. The erosion rates and their classes were used as a criterion for identifying the critical sub-watersheds. Out of the 12 sub-watersheds, one sub-watershed fell under moderate soil loss group and five sub-watersheds fell under high soil loss group of soil erosion classes whereas other sub-watersheds fell under slight erosion classes. The study revealed that the SWAT model could successfully be used for identifying and prioritising critical sub-watersheds for management purposes.

Temporal Trends in Estimates of Reference Evapotranspiration over India

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 ...

Wireless sensor networks for agriculture

The existing state-of-the-art in wireless sensor networks for agricultural applications is reviewed thoroughly.The existing WSNs are analyzed with respect to communication and networking technologies, standards, and hardware.The prospects and problems of the existing framework are discussed with case studies for global and Indian scenarios.Few futuristic applications are presented highlighting the factors for improvements for the existing scenarios. The advent of Wireless Sensor Networks (WSNs) spurred a new direction of research in agricultural and farming domain. In recent times, WSNs are widely applied in various agricultural applications. In this paper, we review the potential WSN applications, and the specific issues and challenges associated with deploying WSNs for improved farming. To focus on the specific requirements, the devices, sensors and communication techniques associated with WSNs in agricultural applications are analyzed comprehensively. We present various case studies to thoroughly explore the existing solutions proposed in the literature in various categories according to their design and implementation related parameters. In this regard, the WSN deployments for various farming applications in the Indian as well as global scenario are surveyed. We highlight the prospects and problems of these solutions, while identifying the factors for improvement and future directions of work using the new age technologies.

Artificial neural networks approach in evapotranspiration modeling: a review

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.

Flood Forecasting Using ANN, Neuro-Fuzzy, and Neuro-GA Models

Flood forecasting at Jamtara gauging site of the Ajay River Basin in Jharkhand, India is carried out using an artificial neural network (ANN) model, an adaptive neuro-fuzzy interference system (ANFIS) model, and an adaptive neuro-GA integrated system (ANGIS) model. Relative performances of these models are also compared. Initially the ANN model is developed and is then integrated with fuzzy logic to develop an ANFIS model. Further, the ANN weights are optimized by genetic algorithm (GA) to develop an ANGIS model. For development of these models, 20 rainfall–runoff events are selected, of which 15 are used for model training and five are used for validation. Various performance measures are used to evaluate and compare the performances of different models. For the same input data set ANGIS model predicts flood events with maximum accuracy. ANFIS and ANN model perform similarly in some cases, but ANFIS model predicts better than the ANN model in most of the cases.

Development and testing of an irrigation scheduling model

An irrigation scheduling model (ISM) consisting of a database management system (DBMS), model base and graphical user interface (GUI) was developed for performing irrigation scheduling under various management options for both single and multiple fields. The ISM is based on a daily water balance approach and uses climatological, crop and soil data as input. Depending on the availability of climatological data, the model offers a choice of one or more methods of estimating reference evapotranspiration (ETo). The GUI is based on mouse-driven approach with pop-up windows, pull-down menus and button controls. The model was tested against field data and the CROPWAT model. The model-predicted soil moisture contents were compared with the field-measured data for both single and multiple field cases. The two models, ISM and CROPWAT, gave similar values of soil moisture but showed some variation after the second irrigation. For both the single- and multiple-field cases, simulated bean yield was slightly higher than measured yield. Also, except for the Turc method, all ETo estimation methods resulted in higher yield as compared to measured yield. The ISM is a flexible and user-friendly irrigation-scheduling tool, which can be used for efficient use of irrigation water.

Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin

Indian Ganga Basin (IGB), one of the most densely populated areas in the world, is facing a significant threat to food grain production, besides increased yield gap between actual and potential production, due to climate change. We have analyzed the spatial variability of climate change impacts on rice and wheat yields at three different locations representing the upper, middle and lower IGB. The DSSAT model is used to simulate the effects of climate variability and climate change on rice and wheat yields by analyzing: (i) spatial crop yield response to current climate, and (ii) impact of a changing climate as projected by two regional climate models, REMO and HadRM3, based on SRES A1B emission scenarios for the period 2011-2040. Results for current climate demonstrate a significant gap between actual and potential yield for upper, middle and lower IGB stations. The analysis based on RCM projections shows that during 2011-2040, the largest reduction in rice and wheat yields will occur in the upper IGB (reduction of potential rice and wheat yield respectively by 43.2% and 20.9% by REMO, and 24.8% and 17.2% by HadRM3). In the lower IGB, however, contrasting results are obtained, with HadRM3 based projections showing an increase in the potential rice and wheat yields, whereas, REMO based projections show decreased potential yields. We discuss the influence of agro-climatic factors; variation in temperature, length of maturity period and leaf area index which are responsible for modeled spatial variability in crop yield response within the IGB.

Evapotranspiration Modeling Using Second-Order Neural Networks

AbstractThis study introduces the utility of the second-order neural network (SONN) method to model the reference evapotranspiration (ET0) in different climatic zones of India. The daily climate data of minimum and maximum air temperatures, minimum and maximum relative humidity, wind speed, and solar radiation from 17 different locations in India were used as the inputs to the SONN models to estimate ET0 corresponding to the FAO-56 Penman-Monteith (FAO-56 PM) method. With the same inputs, for all 17 locations the first-order neural networks such as feed forward back propagation (FFBP-NN) models were also developed and compared with the SONN models. The developed SONN and FFBP-NN models were also compared with the estimates provided by the FAO-56 PM method. The performance criteria adopted for comparing the models were root-mean-squared error (RMSE), mean-absolute error (MAE), coefficient of determination (R2), and the ratio of average output to average target ET0 values (Rratio). Based on the comparisons,...

OPTIMAL FURROW IRRIGATION SCHEDULING UNDER HETEROGENEOUS CONDITIONS

Abstract In California, better farm water management practices are needed to meet the increasing water demands by competing water users (industrial, urban, wildlife, etc.), increasing environmental awareness and cost of water. Management can be improved through better irrigation scheduling and irrigation system designs. In this study, optimal furrow irrigation schedules, designs and irrigation adequacy were determined for heterogeneous soil conditions. The seasonal performance between optimal and full irrigation was compared. For the bean crop studied, the maximum return to water was achieved with the irrigation adequacies of 63, 59, 54, 49 and 50%, respectively, for irrigation intervals of 10, 12, 14, 18, and 21 days. An irrigation interval of 10 days with 63% adequacy gave the global maximum return to water. However, the Natural Resource Conservation Service recommended irrigation adequacy for homogeneous soil condition is 87·5%. For any given irrigation interval, optimal irrigation required less (48–63%) water than full irrigation. This also reduced both the deep percolation and runoff losses and caused a 31–43% increase in the application efficiency. Furthermore, loss in revenue due to yield reduction was less than the savings in irrigation cost, which resulted in higher (32–54%) net return to water under the optimal irrigation compared with full irrigation. These results indicate that the optimal irrigation strategy has potential not only for water conservation, but also for reducing non-point source pollution.

Generalized Quadratic Synaptic Neural Networks for ETo Modeling

This study aims at developing generalized quadratic synaptic neural (GQSN) based reference evapotranspiration (ETo) models corresponding to the Hargreaves (HG) method. The GQSN models were developed using pooled climate data from different locations under four agro-ecological regions (semi-arid, arid, sub-humid, and humid) in India. The inputs for the development of GQSN models include daily climate data of minimum and maximum air temperatures (Tmin and Tmax), extra terrestrial radiation (Ra) and altitude (alt) with different combinations, and the target consists of the FAO-56 Penman Monteith (FAO-56 PM) ETo. Comparisons of developed GQSN models with the generalized linear synaptic neural (GLSN) models were also made. Based on the comparisons, it is concluded that the GQSN and GLSN models performed better than the HG and calibrated HG (HG-C) methods. Comparison of GQSN and GLSN models, reveal that the GQSN models performed better than the GLSN models for all regions. Both GLSN and GQSN models with the inputs of Tmin, Tmax and Ra performed better compared to other combinations. Further, GLSN and GQSN models were applied to locations of model development and model testing to test the generalizing capability. The testing results suggest that the GQSN and GLSN models with the inputs of Tmin, Tmax and Ra have a good generalizing capability for all regions.