During the past few decades, precision horticulture has led ways for technological developments in agricultural policies and practices. It has also served as a medium to increase productivity while reducing risks associated with conventional farming. Sustainability in agricultural systems can only be assured with optimal utilization of resources, smart data analytics, IoAT (Internet of Agricultural Things) and data/process/event driven models that can precisely analyze plant-soil-water-atmosphere interactions. Main focus of this study is to develop an integrated modelling and prediction framework (CZO – Critical Zone Observatory) that relates various Horticultural (CWR, Nutrient dynamics, Phenology, Pest and Disease etc.), Hydrological (surface, subsurface and groundwater resource), Atmospheric (agrometeorological conditions at micro/macro scales) and remote sensing (high resolution data, spectroscopy, NDVI, time-series analysis etc.) aspects at field and regional scale using multi-modal process driven models that help in better understanding the long term dynamics between different agricultural systems. CZO’s tend to have massive state-of-the-art infrastructure (sensors and systems, extensively used scientific instruments, long term observatories, remote sensing data etc.) and requires dedicated data analysis approaches to efficiently mine the possible interaction between components of biogeochemical cycles. This gigantic infrastructure generates large datasets (Big data) which are effectively managed and processed using big data analytics and machine learning algorithms that explore the nexus between plant-soil-water-atmosphere-disease-yield and interactions.

Spatio-temporal root zone soil moisture (STRZSM) plays a vital role in the understanding of land-surface dynamics, climate change, soil-water-balance, hydrologic processes, vegetation growth and yield modeling. STRZSM trails can be captured using remote sensing, in-situ or ground based measurements or through land surface models. Apart from this, new generation of low-cost soil moisture sensors present cost effective way of in-situ measurement of soil moisture, but suffer from inherent heterogeneity between sensing techniques, protocol variations and non-standard calibration methods. Thus, long term monitoring of spatial and temporal variations in soil moisture trails requires interoperability between sensing platforms, process-based and statistical modeling and inclusion of remote sensing techniques. In this study, an attempt has been made to design and deploy an interoperable sensing platform that complies with Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) framework. Proposed system uses sensors to estimate point-based soil moisture and temperature observation at different depths of 15, 30, 45 and 60cm each. Pilot feasibility study was carried out for the duration of three months from Jan-Mar 2016 in the study area. The results include visualizations for soil moisture and soil temperature profiles at different depths over the period of three months. This study also explores the possibility of deploying dense interoperable sensor networks to capture spatio-temp

Satellite based earth observation (EO) platforms have proved capability to spatio-temporally monitor changes on the earth's surface. Long term satellite missions have provided huge repository of optical remote sensing datasets, and United States Geological Survey (USGS) Landsat program is one of the oldest sources of optical EO datasets. This historical and near real time EO archive is a rich source of information to understand the seasonal changes in the horticultural crops. Citrus (Mandarin / Nagpur Orange) is one of the major horticultural crops cultivated in central India. Erratic behaviour of rainfall and dependency on groundwater for irrigation has wide impact on the citrus crop yield. Also, wide variations are reported in temperature and relative humidity causing early fruit onset and increase in crop water requirement. Therefore, there is need to study the crop growth stages and crop evapotranspiration at spatio-temporal scale for managing the scarce resources. In this study, an attempt has been made to understand the citrus crop growth stages using Normalized Difference Time Series (NDVI) time series data obtained from Landsat archives (https://earthexplorer.usgs.gov/). Total 388 Landsat 4, 5, 7 and 8 scenes (from year 1990 to Aug. 2015) for Worldwide Reference System (WRS) 2, path 145 and row 45 were selected to understand seasonal variations in citrus crop growth. Considering Landsat 30 meter spatial resolution to obtain homogeneous pixels with crop cover orchards larger than 2 hectare area was selected. To consider change in wavelength bandwidth (radiometric resolution) with Landsat sensors (i.e. 4, 5, 7 and 8) NDVI has been selected to obtain continuous sensor independent time series. The obtained crop growth stage information has been used to estimate citrus basal crop coefficient information (Kcb). Satellite based Kcb estimates were used with proximal agrometeorological sensing system observed relevant weather parameters for crop ET estimation. The results show that time series EO based crop growth stage estimates provide better information about geographically separated citrus orchards. Attempts are being made to estimate regional variations in citrus crop water requirement for effective irrigation planning. In future high resolution Sentinel 2 observations from European Space Agency (ESA) will be used to fill the time gaps and to get better understanding about citrus crop canopy parameters.

Wide range of applications of WSN in agriculture, Crop /pest disease monitoring, Crop water management, Crop yield prediction

As citrus is progressively contributing to horticultural production, wealth and economy of a country, it is necessary to understand the factors impacting citrus production. Gummosis is one of the most serious diseases causing considerable loss of overall citrus production and yield quality. A qualitative and quantitative analysis of citrus leaf biochemical properties are necessary to monitor the crop health, disease /pest stress and production. Total leaf chlorophyll content (Cab) represents one of the key biochemical factors which contributes in water, carbon, and energy exchange processes. Photosynthesis process in citrus will be disturbed as gummosis disease life cycle progresses. It is important to study Cab to evaluate the photosynthesis rate and disease stress. In this study the potential of Radiative Transfer (RT) PROSPECT model to retrieve Cab in citrus orchards was undertaken at different sites. The main goal is to evaluate the relationship between Cab and gummosis disease stress for citrus at various phenological stages. Inversion of PROSPECT model on measured hyperspectral data is carried out to extract the leaf level parameters influencing the disease. This model was inverted with the ground truth hyperspectral reading. The testing was separately initiated for healthy and infected plant leaves. This can lead to understand the disease stress on citrus leaves. For accuracy, raw spectra are filtered and processed which is an input parameter for Inversion PROSPECT model. Here, retrieved Cab content was correlated with gummosis disease stress in terms of oozing with R2 = 0.6021 and RMSE= 0.481272.

An experiment was conducted in which different combinations of botanicals viz., Neem, Tulsi, Onion, Garlic, and Chrysanthemum and bioagents were used under in vitro and green house conditions for reducing the intensity of Phytophthora root rot. The bioagent Trichoderma viride gave highest growth inhibition (75.33%) of Phytophthora parasitica by dual culture method. In poisoned food technique complete inhibition of Phytophthora parasitica was recorded in Garlic (5%), whereas Tulsi found to be less effective to inhibit the growth of P. parasitica (54.44%). Under green house experiment combined application of Trichoderma viride @ 4g/kg + Garlic clove extract @ 5% reduced per cent root rot incidence (11.32%). Similar treatment showed maximum shoot and root length (4.5cm, 5.96cm) of Citrus jambhiri.

An experiment was conducted in which different combinations of botanicals viz., Neem, Tulsi, Onion, Garlic, and Chrysanthemum and bioagents were used under in vitro and green house conditions for reducing the intensity of Phytophthora root rot. The bioagent Trichoderma viride gave highest growth inhibition (75.33%) of Phytophthoraparasitica by dual culture method. In poisoned food technique complete inhibition of Phytophthora parasitica was recorded in Garlic (5%), whereas Tulsi found to be less effective to inhibit the growth of P. parasitica (54.44%). Under green house experiment combined application of Trichoderma viride @ 4g/kg + Garlic clove extract @ 5% reduced per cent root rot incidence (11.32%). Similar treatment showed maximum shoot and root length (4.5cm, 5.96cm) ofCitrus jambhiri.

The Olidih watershed hydrology was affected by opencast mines for the past five decades. This study explores the potential hydrological effect of these mines using Soil and Water Assessment Tool (SWAT2012). The calibration and validation of the model was performed using daily streamflow and sediment yield data (2005–2008) at the outlet of the water shed. The model performed satisfactorily during simulation when tested with statistical indicators. The alternative scenario of no-mines was also modelled to assess the potential impact of abandoned opencast mines for the period 2005–2010. Results show that the abandoned opencast mines play a crucial role in altering hydrological processes of the watershed with 16% increase in the annual sediment yield and reduction of 51% and 6% in annual surface flow and water yield, respectively. This may be due to surface soil disturbance and accumulation of surface runoff in large depressions that resulted in less surface runoff and 13% more groundwater flow. The contribution of this analysis is the application of SWAT in modelling potential hydrological effect of abandoned opencast mines by defining large opencast mines as pothole during simulation.

India leads the world in banana production, producing around 18 % of the worldwide crop of 139 million metric tonnes. In spite of this, its exports are minimal for various reasons. External appearance, internal and market quality of bananas are influenced by several factors, including production practices. Banana bunch cover is a physical protection method which will improves the visual quality of fruit by promoting skin colouration and reducing blemishes, but can also change the micro-environment for fruit development, which can have several beneficial effects on internal fruit quality. Bunch cover can also reduce the incidence of disease, insect pest and/or mechanical damage, sunburn of the skin, fruit cracking, agrochemical residues on the fruit, and bird damage. Bunch covering is laborious and its benefit cost ratio must be investigated in order to promote adoption of the method in much of the World. Few researches have been conducted studies on the effects of banana bunch covers in different parts of the World, but the results have not been compiled. We have therefore attempted to compile all the scattered information on banana bunch cover to assist researchers and extension personnel working in this area.

A field experiment was carried out in two crop seasons in the lateritic sandy loam soils of Kharagpur, West Bengal, India, to investigate the response of banana (Musa acuminata L.) cv. Grand Naine at different levels of nitrogen, phosphorous and potassium nutrients applications through drip fertigation and plastic mulch. A randomized complete block design was used with four fertigation levels in conjunction with mulch and without mulch. Fertigation levels caused a significant increase in fruit yield and determined the response to N, P and K fertilizers. The results of recommended dose of fertilizers application through drip either alone or in conjunction with black plastic mulch conditions were compared with other fertigation treatments in terms of growth and crop of yield. Both the main and ratoon crops performed best for 80 per cent of the recommended fertigation dose (160 N: 48 P: 240 kg plant-1 year-1) covered with plastic mulch in respect of (a) growth parameters; maximum plant height, stem girth, functional leaves, yield parameters and shortened total crop duration for 34 days and for (b) quality parameters; higher levels of TSS, reducing sugar and non-reducing sugar, pulp:peel ratio and lower content of acidity. Hence, fertigation with 80 per cent of the recommended dose coupled with plastic mulch was found to be optimum and economical for banana cultivation.

This manuscript presents importance of fertigation, types of fertilizers, their solubility and compatibility problems, fertig and types of fertigation equipment. The research work carried out on fertigation with drip in fruits, vegetable, field crops and rose in India and abroad is also presented in this manuscript. Extensive work carried out by the author and his research group in the Preci Development Centre project at IIT Kharagpur is also presented in this paper. The study shows that the use of micro irrigation improves the water and fertilizer use efficiency

To estimate alteration in physico-chemical and hydrological properties of soil of eucalyptus plantation (Eu) in comparison to soil of natural grassland (NG). To estimate spatio-temporal variations in soil moisture under eucalyptus plantation. To develop relationship of temperature, relative humidity, leaf area index with available soil moisture (ASM).

Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA’s Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, sub-tropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture.

The major objective of this study was to analyze the trend and variability of rainfall in the middle Mahandi river basin located in eastern India. The trend of variation of extreme rainfall events has predominant effect on agricultural water management and extreme hydrological events such as floods and droughts. Mahanadi river basin is one of the major river basins of India having an area of 1,41,589 km2 and divided into three regions: Upper, middle and delta region. The middle region of Mahanadi river basin has an area of 48,700 km2 and it is mostly dominated by agricultural land, where agriculture is mostly rainfed. The study region has five Agro-climatic zones namely: East and South Eastern Coastal Plain, North Eastern Ghat, Western Undulating Zone, Western Central Table Land and Mid Central Table Land, which were numbered as zones 1 to 5 respectively for convenience in reporting. In the present study, analysis of variability and trends of annual, seasonal, and monthly rainfall was carried out, using the daily rainfall data collected from the Indian Meteorological Department (IMD) for 35 years (1979-2013) for the 5 agro-climatic zones. The long term variability of rainfall was investigated by evaluating the mean, standard deviation and coefficient of variation. The long term trend of rainfall was analyzed using the Mann-Kendall test on monthly, seasonal and annual time scales. It was found that there is a decreasing trend in the rainfall during the winter and pre monsoon seasons for zones 2, 3 and 4; whereas in the monsoon (rainy) season there is an increasing trend for zones 1, 4 and 5 with a level of significance ranging between 90-95%. On the other hand, the mean annual rainfall has an increasing trend at 99% significance level. The estimated seasonality index showed that the rainfall distribution is asymmetric and distributed over 3-4 months period. The study will help to understand the spatio-temporal variation of rainfall and to determine the correlation between the current rainfall trend and climate change scenario of the study region for multifarious use.

Since the global climate change altering the characteristics of extreme rainfall events, modeling the behavior of extreme rainfall is becoming indispensable. Among various physical processes, the El Niño Southern Oscillation (ENSO) cycle is a significant process that can trigger occurrences of extreme hydro-climatological events, such as floods, droughts and cyclones. Consequently, modeling the relationship between ENSO cycle and regional cli- mate anomaly is gaining more attention in recent years. Studies in literature used various ENSO indices to quantify ENSO cycle in the statistical models which aim to model/forecast extreme rainfall and fail to report the best ENSO index for modeling extreme rainfall over India. Hence, this study is carried out to answer two research questions: (1) What is the best ENSO index for modeling extreme rainfall intensity over India? (2) What is the significance of identifying the best ENSO indicator for modeling extreme rainfall intensity over India? Towards answering these questions, the change in the linear relationship between ENSO cycle and extreme rainfall over India due to the choice of ENSO cycle indicator is examined. Results of this study indicate that the ENSO index Southern Oscillation Index (SOI) is the best ENSO index for modeling extreme rainfall over India. The results also show a significant change in ENSO-Extreme rainfall relationship due to the choice of ENSO index. In particular, 42% is the average increase in correlation coefficient between monsoon season extreme rainfall and ENSO cycle when the best ENSO index is chosen instead of the second-best ENSO index and it is around 37% for the non- monsoon season.

Since the substantial evidence of non-stationarity in the extreme rainfall series is already reported, the current realm of hydrologic research focusing on developing methodologies for a non-stationary rainfall condition. As the rainfall intensity duration frequency (IDF) curve is primarily used in storm water management and infrastructure design, developing rainfall IDF curves in a non-stationary context is a current interest of water resource researchers. In order to construct non-stationary rainfall IDF curve, the probability distribution’s parameters are allowed to change according to covariate value and the current practice is to use time as a covariate. However, the covariate can be any physical process and the recent studies show that the direct use of time as a covariate may increase the bias. Moreover, the significance of selecting covariate in developing non-stationary rainfall IDF curve is yet to be explored. Therefore, this study aims to find the uncertainties in rainfall return levels due to the choice of the covariate (covariate uncertainty). In addition, since the uncertainty in parameter estimates (parameter uncertainty) is the major source of uncertainty in the stationary IDF curve, the relative significance of covariate uncertainty, when compared to the parameter uncertainty, is also explored. The study results reveal that the covariate uncertainty is significant, especially when a number of covariates produce significantly superior non-stationary model and, remarkably, it is nearly equivalent to the parameter uncertainty in such cases.

Variation of precipitation extremes over the relatively small spatial scales of urban areas could be significantly different from those over larger regions. An understanding of such variation is critical for urban infrastructure design and operation. Urban climatology and sparse spatial data lead to uncertainties in the estimates of spatial precipitation. In this paper, Bayesian hierarchical model is used to obtain spatial distribution of return levels of precipitation extremes in urban areas and quantify the associated uncertainty. The 10 Generalised Extreme Value (GEV) distribution is used for modelling precipitation extremes. 11 A spatial component is introduced in the parameters of the GEV through a latent spatial 12 process by considering geographic and climatologic covariates. A Markov Chain Monte 13 Carlo algorithm is used for sampling the parameters of GEV distribution and the latent 14 process model. Applicability of the methodology is demonstrated with data from telemetric rain gauge stations in Bangalore city, India. For this case study, it is inferred that the elevation and mean monsoon precipitation are the predominant covariates for annual maximum precipitation. Variation of seasonal extremes is also examined in the paper. For the monsoon maximum precipitation, it is observed that the geographic covariates dominate while for the summer maximum precipitation, elevation and mean summer precipitation are the predominant covariates. A significant variation in spatial return levels of extreme precipitation is observed over the city.

Present infrastructure design is primarily based on rainfall Intensity-Duration-Frequency (IDF) curves with so-called stationary assumption. However, in recent years, the extreme precipitation events are increas- ing due to global climate change and creating non-stationarity in the series. Based on recent theoretical developments in the Extreme Value Theory (EVT), recent studies proposed a methodology for developing non-stationary rainfall IDF curve by incorporating trend in the parameters of the Generalized Extreme Value (GEV) distribution using Time covariate. But, the covariate Time may not be the best covariate and it is important to analyze all possible covariates and find the best covariate to model non-stationarity. In this study, five physical processes, namely, urbanization, local temperature changes, global warming, El Niño-Southern Oscillation (ENSO) cycle and Indian Ocean Dipole (IOD) are used as covariates. Based on these five covariates and their possible combinations, sixty-two non-stationary GEV models are con- structed. In addition, two non-stationary GEV models based on Time covariate and one stationary GEV model are also constructed. The best model for each duration rainfall series is chosen based on the cor- rected Akaike Information Criterion (AICc). From the findings of this study, it is observed that the local processes (i.e., Urbanization, local temperature changes) are the best covariate for short duration rainfall and global processes (i.e., Global warming, ENSO cycle and IOD) are the best covariate for the long dura- tion rainfall of the Hyderabad city, India. Furthermore, the covariate Time is never qualified as the best covariate. In addition, the identified best covariates are further used to develop non-stationary rainfall IDF curves of the Hyderabad city. The proposed methodology can be applied to other situations to develop the non-stationary IDF curves based on the best covariate.

The rainfall Intensity-Duration-Frequency (IDF) relationship is the primary input for storm water management and other engineering design applications across the world and it is developed by fitting an appropriate theoretical probability distribution to annual maximum (AM) series or partial duration series (PDS) of rainfall. The existing IDF relationship devel- oping methods consider the extreme rainfall series as a stationary series. There exist few studies that compared AM and PDS datasets for developing rainfall IDF relationship in a stationary condition. However, during the last few decades, the intensity and frequency of extreme rainfall events are increasing due to global climate change and creating a non- stationary component in the extreme rainfall series. Therefore, the rainfall IDF relationship developed with the stationary assumption is no longer tenable in a changing climate. Hence, it is inevitable to develop non-stationary rainfall IDF relationship and to understand the differ- ences in non-stationary rainfall IDF relationships derived using AM and PDS datasets. Consequently, the objectives of this study are: (1) to develop non-stationary rainfall IDF relationships using both AM and PDS datasets; (2) to compare them in terms of return level estimation. In particular, the non-linear trend in different durations’ PDS and AM datasets of Hyderabad city (India) rainfall is modeled using Multi-objective Genetic Algorithm (MGA) generated Time based covariate. In this study, the PDS datasets are modeled by the Generalized Pareto Distribution (GPD) while the AM datasets are modeled by the Generalized Extreme Value Distribution (GEVD). The time-varying component is introduced in the scale parameter of the GPD and the location parameter of the GEVD by linking the MGA generated covariate. In addition, the complexity of each non-stationary model is identified using the corrected Akaike Information Criteria (AICc) and the statistical signifi- cance of trend parameter in the non-stationary models is estimated using the Likelihood Ratio (LR) test. Upon detecting significant superiority of non-stationary models, the return levels of extreme rainfall event for 2-, 5-, 10- and 25-year return periods are calculated using non- stationary models. From the results, it is observed that the non-stationary return levels estimated with PDS datasets are higher than those estimated with AM datasets for short durations and smaller return periods while the non-stationary return levels estimated with AM datasets are higher than those estimated with PDS datasets for long durations and higher return periods.

In urban areas, quantification of extreme precipitation is important in the design of storm water drains and other infrastructure. Intensity Duration Frequency (IDF) relationships are generally used to obtain design return level for a given duration and return period. Due to lack of availability of extreme precipitation data for sufficiently large number of years, estimating the probability of extreme events is difficult. Typically, a single station data is used to obtain the design return levels for various durations and return periods, which are used in the design of urban infrastructure for the entire city. In an urban setting, the spatial variation of precipitation can be high; the precipitation amounts and patterns often vary within short distances of less than 5 km. Therefore it is crucial to study the uncertainties in the spatial variation of return levels for various durations.

The global physical processes such as El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) have a significant impact on Indian extreme rainfall. Recent studies show that the impact of ENSO cycle on Indian rainfall has changed and understanding the changes in the effects of these physical processes on extreme rainfall of an urban area may help us to reduce the damage caused by urban floods. In this study, the changes in ENSO and IOD effects on the Hyderabad city monsoon/non-monsoon extreme rainfall between 1901-1950 and 1951-2004 are analyzed. The findings of this study indicate that the effect of IOD on non-monsoon months’ extreme rainfall of the Hyderabad city is significantly reduced while there is no significant change in monsoon season relationship. In addition, a significant increase in ENSO effects on non-monsoon months’ very extreme rainfall of the Hyderabad city is observed.

With the limited availability of meteorological variables in many remote areas, estimation of evapotranspiration (ET) at different spatio-temporal scales for efficient irrigation water management and hydro-meteorological studies is becoming a challenging task. Hence, in this study, the indirect ET estimation methods, such as, the MODIS satellite-based remote sensing techniques and the water budget approach in built into the semi-distributed variable infiltration capacity (VIC-3L) land surface model are evaluated using the FAO-56 Penman-Monteith (PM) equation and crop coefficient approach. To answer the research question whether the regional or local controls of a river basin with tropical monsoon-type climatology affect the accuracy of the VIC and MODIS-based ET estimates, these methodologies are applied in the Kangsabati River basin in eastern India at 25 km×25 km resolutions attributed with dominant paddy land uses. The results reveal that the VIC-estimated ET values are reasonably matched with the FAO-56 PM based ET estimates with the Nash-Sutcliffe efficiency (NSE) of 54.14-71.94%; however, the corresponding MODIS-ET values are highly underestimated with a periodic shift which may be attributed to the cloud cover and leaf shadowing effects. To enhance the field applicability of the satellite-based MODIS-ET products, these estimates are standardized by using the genetic algorithm-based transformation that improves the NSE from -390.83% to 99.57%. Hence, this study reveals that there is the need of a regional-scale standardization of the MODIS-ET products using the FAO-56 PM or lysimeters data or possible modification in the MOD16A2 algorithm built-into the MODIS for generalization. Conversely, the satisfactory grid-scale ET estimates by the VIC model shows that this model could be reliably used for the world river basins; although at smaller temporal scales, the estimates could be slightly inconsistent

The depletion of groundwater resources threatens food and water security in India. However, the relative influence of groundwater pumping and climate variability on groundwater availability and storage remains unclear. Here we show from analyses of satellite and local well data spanning the past decade that long-term changes in monsoon precipitation are driving groundwater storage variability in most parts of India either directly by changing recharge or indirectly by changing abstraction. We find that groundwater storage has declined in northern India at the rate of 2 cm yr-1 and increased by 1 to 2 cm yr-1 in southern India between 2002 and 2013. We find that a large fraction of the total variability in groundwater storage in north-central and southern India can be explained by changes in precipitation. Groundwater storage variability in northwestern India can be explained predominantly by variability in abstraction for irrigation, which is in turn influenced by changes in precipitation. Declining precipitation in northern India is linked to Indian Ocean warming, suggesting a previously unrecognized teleconnection between ocean temperatures and groundwater storage.

The advent of the sensor-cloud framework empowers the traditional wireless sensor networks (WSNs) in terms of dynamic operation, management, storage, and security. In recent times, the sensor-cloud framework is applied to various real-world applications. In this paper, we highlight the benefits of using sensor-cloud framework for the efficient addressing of various agricultural problems. We address the specific challenges associated with designing a sensor-cloud system for agricultural applications. We also mathematically characterize the virtualization technique underlying the proposed sensor-cloud framework by considering the specific challenges. Furthermore, the energy optimization framework and duty scheduling to conserve energy in the sensor-cloud framework is presented. The existing works on sensor cloud computing for agriculture does not specifically define the specific components associated with it. We categorize the distinct features of the proposed model and evaluated its applicability using various metrics. Simulation-based results show the justification for choosing the framework for agricultural applications.

Water resources and agriculture are often affected by the weather anomalies in India resulting in disproportionate damage. While short to sub-seasonal prediction systems and forecast products are available, a skilful hydrologic forecast of runoff and root-zone soil moisture that can provide timely information has been lacking in India. Using precipitation and air temperature forecasts from the Climate Forecast System v2 (CFSv2), the Global Ensemble Forecast System (GEFSv2) and four products from the Indian Institute of Tropical Meteorology (IITM), here we show that the IITM ensemble mean (mean of all four products from the IITM) can be used operationally to provide a hydrologic forecast in India at a 7–45-day accumulation period. The IITM ensemble mean forecast was further improved using bias correction for precipitation and air temperature. Bias corrected precipitation forecast showed an improvement of 2.1 mm (on the all India median mean absolute error – MAE), while all-India median bias corrected temperature forecast was improved by 2.1 degree C for a 45-day accumulation period. Moreover, the Variable Infiltration Capacity (VIC) model simulated forecast of runoff and soil moisture successfully captured the observed anomalies during the severe drought years. The findings reported herein have strong implications for providing timely information that can help farmers and water managers in decision making in India

The purpose of the present study is to recognize the best infiltration model in soil among the infiltration methods available in HEC-HMS model for an agricultural dominated watershed upstream of the Hadaf dam. The area is predominant with agricultural land and falls under semiarid zone, where water resources planning and management is necessary for irrigation scheduling, water harvesting, flood control, drought mitigation and design of various engineering structures. In this respect, the hydrologic HECHMS model is used to compare various infiltration models and the best model that gives some results more likely to be real

Land cover/land use (LCLU) maps are essential inputs for environmental analysis. Remote sensing provides an opportunity to construct LCLU maps of large geographic area in a timely fashion. Remote sensing is one of the tool which is very important for the production of land use and land cover maps through a process called image classification. For the image classification process to be successfully, several factors should be considered including availability of quality of imagery and secondary data, a precise classification process and user’s experiences and expertise of the procedures. The objective of this research is to classify the landuse/land cover map and its accuracy assessment. In this study the LISS III imagery is used and classified into five categories like forest, agriculture land, waste land, built up and water bodies. In this study it is found that the overall accuracy derived from the stratified random sampling method is 76.00 % with an overall kappa coefficient of 0.67.The kappa coefficient is rated as substantial and hence the classified image found to be fit for further research. This study is essential source of information whereby planners and decision makers can use to sustainably plan the environment

Climate change may pose profound implications for hydrologic processes in Indian sub-continental river basins. Using downscaled and bias corrected future climate projections and the Soil Water Assessment Tool (SWAT), we show that a majority of the Indian sub-continental river basins are projected to shift towards warmer and wetter climate in the future. During the monsoon (June to September) season, under the representative concentration pathways (RCP) 4.5 (8.5), the ensemble mean air temperature is projected to increase by more than 0.5 (0.8), 1.0 (2.0), and 1.5 (3.5) °C in the Near (2010–2039), Mid (2040–2069), and End (2070–2099) term climate, respectively. Moreover, the sub-continental river basins may face an increase of 3–5 °C in the post-monsoon season under the projected future climate. While there is a large intermodel uncertainty, robust increases in precipitation are projected in many sub-continental river basins under the projected future climate especially in the Mid and End term climate. A sensitivity analysis for the Ganges and Godavari river basins shows that surface runoff is more sensitive to change in precipitation and temperature than that of evapotranspiration (ET). An intensification of the hydrologic cycle in the Indian sub-continental basins is evident in the projected future climate. For instance, for Mid and End term climate, ET is projected to increase up to 10% for the majority of the river basins under both RCP 4.5 and 8.5 scenarios. During the monsoon season, ensemble mean surface runoff is projected to increase more than 40% in 11 (15) basins under the RCP 4.5 (8.5) scenarios by the end of the 21st century. Moreover, streamflow is projected to increase more than 40% in 8 (9) basins during the monsoon season under the RCP 4.5 (8.5) scenarios. Results show that water availability in the sub-continental river basins is more sensitive towards changes in the monsoon season precipitation rather than air temperature. While in the majority of the sub-continental river basins, water availability is projected to increase, spatial and temporal (interannual) variability in the monsoon season precipitation under the projected future climate may play a significant role. Changes in the hydrologic processes under the projected future climate indicate that substantial efforts may be required to develop water management strategies in the Indian sub-continental river basins in the future

This paper presents a proposed design, modeling and simulation of the wireless smart flow meter networks with own power generation. The advantage of this system is that a smart meter node in the network generates power required for its operation within the node, thus eliminating the need for an external power source to be connected or maintained periodically. A power storage unit is also designed which charges during power generation. This unit supplies power to the circuit when generation stops. RF inter-node communication among the smart meters is proposed in this system. the proposed smart meter has a power rectification and regulation system and a power storage system along with a power generator. Super capacitor is used as power storage element. The simulation of the power rectification, regulation, storage and sensor interfacing are done is Proteus 8 software. A laboratory set up has been built and experimentally tested to examine the proposed system. Experimental results are computed and shown to be in good agreement.