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Soil moisture plays a critical role in agricultural productivity and water resource management, especially in a diverse and populous country like India. Understanding variations in soil moisture across different regions and seasons is essential for adapting agricultural practices and water management strategies to local conditions. This study examines changes in soil moisture levels across India, comparing contemporary data from 2023 with historical averages from 2000 to 2005 using advanced remote sensing and GIS technologies. The primary objective of this study is to identify Soil Moisture Anomalies (SMA) across India, quantify their impacts on agriculture and water resources, and provide recommendations for targeted management strategies. By comparing recent soil moisture data against historical averages, the study aims to highlight trends and changes that could influence future water resource planning and agricultural practices. The research utilizes data from the Famine Early Warning Systems Network's (FEWS NET) i.e. Land Data Assimilation System (FLDAS), obtained from NASA's data archives. The study employs a systematic approach to analyze seasonal variations in soil moisture across different Indian states. Soil moisture levels were analyzed using zonal statistics in GIS to classify regions into categories based on the degree of anomaly observed. This classification helped in understanding the spatial distribution of soil moisture during the pre-monsoon, monsoon, and post-monsoon seasons. The study found significant regional and seasonal variations in soil moisture across India. During the monsoon period, areas such as Bihar and Jharkhand consistently showed significant moisture deficits, indicative of drought conditions, affecting agricultural output and necessitating urgent water conservation measures. Conversely, regions like Punjab benefited from positive soil moisture anomalies, enhancing agricultural productivity. The pre-monsoon and post-monsoon seasons also showed variations, with some areas experiencing deficits requiring careful water management while others had surpluses that increased the risk of flooding. The analysis of SMA in India underscores the need for region-specific agricultural and water management strategies that consider significant variability in soil moisture conditions. The study highlights the importance of integrating soil moisture monitoring into national policy frameworks to enhance climate resilience and sustainable agricultural practices. Future research should focus on updating soil moisture assessments with more recent data and refining predictive models to improve the accuracy and effectiveness of water management and agricultural interventions. [ABSTRACT FROM AUTHOR]

Crop yield has been analyzed under different hydrological conditions by many researchers. However, little information is available about the behavior of rice yield for different conditions during its growth. This study investigated the behavior of rice yield under varying hydrological conditions in two regions of India, one rainfed and the other irrigated, for the period 2000–2018. Additionally, it examined how individual, coincidental, and sequential compound extremes, such as rainfall, temperature, and soil moisture, affected rice yield. Four individual, two coincidental, and two sequential compound extremes regression models were developed. These models were designed with yield as a function of individual and compound extremes. Individual extreme models focused on heat and water metrics independently, while compound extremes occurred when heat and water stress coincided or followed each other closely. Linear panel models were used to assess the dependency of rice yield on hydrological parameters. Results indicate that excessive heat negatively affects rice yield, particularly when coupled with low soil moisture. However, excess soil moisture mitigates heat-related damage, highlighting the significance of controlling soil moisture levels. Additionally, coincidental compound extremes pose greater threats to rice yield than sequential ones. The study underscores the importance of considering geographical variations and hydrological variables in understanding crop yield behaviour. Overall, the findings suggest the potential for optimizing soil moisture management to enhance rice yield amidst changing climatic conditions. [ABSTRACT FROM AUTHOR]

Fluoride and its constituents in soil affect plant growth and public health. In this study, soil fluoride was measured for the semi-arid regions in southern India, using Sentinel-1 data in conjunction with the dual polarimetric saline-associated fluoride model (also known as fluoride model). A loss angle was estimated from laboratory-based dielectric components of soil samples with strong electrical conductivity under high and low fluoride conditions. The conductivity loss angle and real and imaginary dielectric constants were used to study fluoride salt's dielectric behavior. The imaginary dielectric component sensitive to dielectric loss could predict fluoride across large areas over time. This was statistically analyzed with R2 = 0.86, RMSE = 1.90, and bias = 0.35 showing a promising depiction that C-band SAR data can distinguish fluoride levels over varied clay soil and soil with varying vegetation development. Moreover, the association between biomass and simulated fluoride helped to identify fluoride-tolerant and non-tolerant crops. The study found that Sorghum and Oryza sativa tolerate saline-associated fluoride, whereas Peanut and Allium do not. Furthermore, the model successfully retrieves fluoride from saline salts based on tangent loss. [ABSTRACT FROM AUTHOR]

Prioritization of watersheds is a crucial step in integrated river basin management. It guides resource allocation, planning, decision-making, stakeholder engagement, targeted interventions, and monitoring and evaluation efforts. By identifying and addressing priority watersheds, comprehensive and sustainable management of water resources can be achieved within a river basin. The aim of the current study is to prioritize watersheds using the Soil and Water Assessment Tool (SWAT) and site suitability analysis for the implementation of soil and water conservation structures (SWCS) in the prioritized watersheds by using an analytical hierarchy process (AHP) of the Lower Sutlej Sub-basin, India. The model was calibrated with observed data from 2017 to 2019 with the first 2 years (2015–16) as a warm-up period. Furthermore, validation of the model was done using 2-year data from 2020 to 2021. The model showed successful performance in terms of the R2 range from 0.72 to 0.89, the NSE range from 0.67 to 0.73, the PBIAS range from − 26.70 to 11.30, and the RSR range from 0.51 to 0.57 for stream flow and sediment yield during calibration and validation period. The basin's average annual soil loss ranged from 3.08 to 21.63 t/ha/year, a watershed with the WS2 index seeing the highest rate of soil erosion (21.63 t/ha/year). Hence, WS2 was found to be a top priority. In addition, the site suitability analysis of the prioritized watershed (WS2) reveals that about 1.42% of the area is extremely suitable, 16.14% is highly suitable, and 35.58% is moderately suitable for SWCS. According to the site suitability map validation, both dam locations were found in highly suitable areas, so the developed site suitability map is accurate. This research will help sustainability planners and managers make more informed decisions when building SWCS at suitable sites for better land and water conservation. [ABSTRACT FROM AUTHOR]

Air temperature variability and prediction are important considering the potential implications on human health, water resource management, agriculture and land-atmosphere interaction. In this paper, we investigate the delayed influence of El Niño on spring Surface Air Temperature (SAT) variability over India using observations and assess its predictability in coupled climate models. Analysis suggests that El Niño, at its decaying phase, exerts a strong influence on the spring SAT over Southern Peninsular India (SPI). This warming is mainly induced by an anomalous anticyclone, which extends from the Western North Pacific (WNP) region to the SPI. The anomalous anticyclone-induced downdraft reduces the cloud cover and increases incoming shortwave radiation at surface, which in turn causes a dry atmospheric column and dry soil moisture over this region. As a result of this decreased soil moisture, latent heat flux into atmosphere reduces and enhances sensible heat transfer, further contributing to dry soil moisture. Both dry soil moisture and anomalous anticyclone contributes to enhanced SAT over SPI region. It is found that 6 years out of the top 10 warmest years over the SPI region correspond to the decaying phase of El Niño, suggesting the importance of the present study. It is also found that the intense SAT warming over SPI aids in increasing frequencies of Discomfort Index hours (exceeding 28 and 30 °C in a day) and the Universal Thermal Climate Index during El Niño decaying spring years. Further, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models. It is noted that most of the APCC models reasonably predicted the spatial distribution of warm SAT over India during the El Niño decaying spring season at 1 and 3 month lead. This study highlights the predictability of spring SAT a season in advance over India, which would help in planning better adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

The climate over North India during winter has been endorsed with a good amount of precipitation that occurs due to the interaction between the tropical–extratropical flows. We examined two land surface parameterizations namely Biosphere Atmosphere Transfer (BAT) scheme and the Community Land Model (CLM) with the Regional Climate Model (RegCM) on simulation of North Indian winter climate. RegCM model is configured for the Indian region and was integrated from 1 January 1982 to 31 December 2016 using the initial–boundary condition of ERA-interim reanalysis. Our results reveal that the CLM features of rainfall, temperature, and circulation at 700 hPa provided an added value with respect to BAT. The analysis of extreme rainfall seasons revealed the relative resemblance of CLM with CRU by means of sensible heat flux and soil moisture analyses. Various verification methods such as Taylor metrics, Improvement parameter, and root mean square deviation also reveal the consistency of CLM in its better performance over BAT simulations. These findings suggest that the implementation of CLM scheme with RegCM results in the added value of winter climate simulations than BAT over north India and thus the usage of the same are encouraged over the region. [ABSTRACT FROM AUTHOR]

Aim of the water balance study is to evaluate the distribution of hydrological responses towards water balance components for a watershed. The present study highlights the water balance study for a watershed of Mahanadi river basin of India. Impact estimation of Landuse Land Cover (LULC) on runoff using Soil and Water Assessment Tool (SWAT) model under varying levels of hydrological response unit (HRU) has also been determined. For the purpose, LULC maps are prepared and utilized in decadal intervals for the years 1994, 2004 and 2014. The sensitivity analysis was performed using local and global methods, where eight most sensitive parameters have been utilized for calibration and validation. The model showed good agreement between observed and estimated time scale and showed satisfactory results during calibration and validation. As per impact estimation of LULC during 1994 to 2014, it was noticed that significant percentage of runoff has been increased from 27.6 to 30.95%, respectively. It was also noticed that the annual average ground water contribution decreased with LULC, from 20.1% (1994) to 15.24% (2014). Sub-watershed (SWS-6) and SWS-2 have been identified as high priority for implementation of conservation measures and better water management. Because these sub-watersheds yielded maximum runoff. The HRU role in hydrological modelling revealed that a greater number of HRUs simulate runoff more precisely and more accurately useful for water management studies. [ABSTRACT FROM AUTHOR]

Time series modeling is a way to predict future values by examining temporal data. The present study analyzes the monthly mean soil moisture data at various depths: surface, profile, and root soil moisture, spanning from 1981 to 2022. The analysis employs two distinct approaches: the statistical seasonal autoregressive integrated moving average (SARIMA) and a deep learning long short-term memory (LSTM). The models are trained on a data set, covering the period from 1981 to 2021, acquired from the agricultural site at Andhra Loyola College in Vijayawada, Andhra Pradesh, India. Subsequently, the data from 2021 to 2022 is reserved for testing purposes. The study provides comprehensive insights into the design of both SARIMA and LSTM models, along with an evaluation of their performance using established error metrics such as the model mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE). In the context of surface soil moisture prediction, the LSTM model demonstrates superior performance compared to SARIMA. Specifically, LSTM achieves a notably lower MAPE of 0.0615 in contrast to SARIMA's 0.1541, a reduced MAE of 0.0316 compared to 0.0871, and a diminished RMSE of 0.0412 as opposed to 0.1021. This pattern of enhanced accuracy persists across profile and root soil moisture predictions, further establishing LSTM's supremacy in predictive capability across diverse soil moisture levels. [ABSTRACT FROM AUTHOR]

Land degradation has become a key concern worldwide due to changes in meteorological variables and human-caused activities. This study primarily focuses on the rate, impact, and pattern of land degradation in western India. In this instance, we evaluated the effectiveness of land degradation neutrality (LDN) between 2000 and 2020 using an integrative approach based on a PSR (pressure-state-response) model developed by the OECD-UNEP under the UNCCD framework. Here, we mainly used MODIS products (e.g., NDVI, PET, LULC, and NPP). Also, soil organic carbon (SOC) and climatic variables (e.g. precipitation, aridity index and soil moisture) were taken into account. These indicators were analysed using the Google Earth Engine (GEE) code editor platform, and post-processing was done through Q-GIS software. The analysed parameters indicate that the NDVI and NPP values are + 0.20 to + 0.3 and 4.27 × 109 to 7.74 × 109 kg Cm−2, respectively. However, overall precipitation and soil moisture depicted a positive trend, and the aridity index adeptly followed a negative trend. Hence, the land degradation rate has increased in the north-western region besides the Aravalli range and neutrality work in the southwest part of the study area. The overall land degradation trend is negative over the last two decades. Therefore, this study anticipates the policymakers and government bodies to understand about land degradation of western India. [ABSTRACT FROM AUTHOR]

Streamflow simulation encounters significant uncertainties due to the intricate and unpredictable nature of the data. It is highly challenging to accurately simulate and predict streamflow time series in catchments using physical-based models. To address this, the Soil and Water Assessment Tool (SWAT) proves to be an effective tool for quantifying streamflow variations based on meteorological inputs. In the present study, the Soil and Water Assessment Tool (ArcSWAT) was employed to simulate streamflow in Sot river catchment located in the Uttar Pradesh, India. The model's calibrated and validated were conducted using daily streamflow data from 2009 to 2016. The SWATCUP 2012 calibration uncertainty program was utilized for this purpose. The SWAT-CUP's algorithm called sequential uncertainty fitting method (SUFI-2) is utilized for the sensitivity analysis, calibration, and validation of the streamflow for daily time steps. The calibration and validation phases of the study yielded promising results, indicating a strong agreement between the observed and simulated streamflow data. This was evident through the correlation coefficient (R) values of 0.73 and 0.84, as well as the Nash-Sutcliffe efficiency (NSE) values of 0.49 and 0.63, respectively. These metrics serve as indicators of model performance and highlight the SWAT model's capability to accurately predict streamflow. The model's satisfactory performance, as indicated by the high values of correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE), reinforces its credibility and suitability for hydrological modeling in the context of this study. The obtained results provide evidence of the SWAT model's proficiency in capturing the non-linear behavior of hydrological time series data and producing precise predictions of streamflow. These findings highlight the model's robustness and validate its effectiveness in simulating hydrological processes, reinforcing its value as a tool for decision-making and resource management in similar hydrological studies. [ABSTRACT FROM AUTHOR]

The design and selection of ideal emitter discharge rates can be aided by accurate information regarding the wetted soil pattern under surface drip irrigation. The current field investigation was conducted in an apple orchard in SKUAST- Kashmir, Jammu and Kashmir, a Union Territory of India, during 2017–2019. The objective of the experiment was to examine the movement of moisture over time and assess the extent of wetting in both horizontal and vertical directions under point source drip irrigation with discharge rates of 2, 4, and 8 L h−1. At 30, 60, and 120 min since the beginning of irrigation, a soil pit was dug across the length of the wetted area on the surface in order to measure the wetting pattern. For measuring the soil moisture movement and wetted soil width and depth, three replicas of soil samples were collected according to the treatment and the average value were considered. As a result, 54 different experiments were conducted, resulting in the digging of pits [3 emitter discharge rates × 3 application times × 3 replications × 2 (after application and 24 after application)]. This study utilized the Drip-Irriwater model to evaluate and validate the accuracy of predictions of wetting fronts and soil moisture dynamics in both orientations. Results showed that the modeled values were very close to the actual field values, with a mean absolute error of 0.018, a mean bias error of 0.0005, a mean absolute percentage error of 7.3, a root mean square error of 0.023, a Pearson coefficient of 0.951, a coefficient of correlation of 0.918, and a Nash–Sutcliffe model efficiency coefficient of 0.887. The wetted width just after irrigation was measured at 14.65, 16.65, and 20.62 cm; 16.20, 20.25, and 23.90 cm; and 20.00, 24.50, and 28.81 cm in 2, 4, and 8 L h−1, at 30, 60, and 120 min, respectively, while the wetted depth was observed 13.10, 16.20, and 20.44 cm; 15.10, 21.50, and 26.00 cm; 19.40, 25.00, and 31.00 cm, respectively. As the flow rate from the emitter increased, the amount of moisture dissemination grew (both immediately and 24 h after irrigation). The soil moisture contents were observed 0.4300, 0.3808, 0.2298, 0.1604, and 0.1600 cm3 cm−3 just after irrigation in 2 L h−1 while 0.4300, 0.3841, 0.2385, 0.1607, and 0.1600 cm3 cm−3 were in 4 L h−1 and 0.4300, 0.3852, 0.2417, 0.1608, and 0.1600 cm3 cm−3 were in 8 L h−1 at 5, 10, 15, 20, and 25 cm soil depth in 30 min of application time. Similar distinct increments were found in 60, and 120 min of irrigation. The findings suggest that this simple model, which only requires soil, irrigation, and simulation parameters, is a valuable and practical tool for irrigation design. It provides information on soil wetting patterns and soil moisture distribution under a single emitter, which is important for effectively planning and designing a drip irrigation system. Investigating soil wetting patterns and moisture redistribution in the soil profile under point source drip irrigation helps promote efficient planning and design of a drip irrigation system. [ABSTRACT FROM AUTHOR]

Early-season agricultural drought is frequent over South Asian region due to delayed or deficient monsoon rainfall. These drought events often cause delay in sowing and can even result in crop failure. The present study focuses on monitoring early-season agricultural drought in a semi-arid region of India over 5-year period (2016–2020). It utilizes hydro-climatic and biophysical variables to develop a combined drought index (CDI), which integrates anomalies in soil moisture conditions, rainfall, and crop-sown area progression. Synthetic aperture radar (SAR)–based soil moisture index (SMI) represents in situ measured soil moisture with reasonable accuracy (r=0.68). Based on the highest F1-score, SAR backscatter in VH (vertical transmit-horizontal receive) polarization with specific values for parameter threshold (−18.63 dB) and slope threshold (−0.072) is selected to determine the start of season (SoS) with a validation accuracy of 73.53%. The CDI approach is used to monitor early-season agricultural drought and identified drought conditions during June–July in 2019 and during July in 2018. Conversely, 2020 experienced consistently wet conditions, while 2016 and 2017 had near-normal conditions. Overall, the study highlights the use of SAR data for early-season agricultural drought monitoring, which is mainly governed by soil moisture–driven crop-sowing progression. The proposed methodology holds potential for effective monitoring, management, and decision-making in early-season agricultural drought scenarios. [ABSTRACT FROM AUTHOR]

The agricultural drought significantly affects the socio-economic sectors in the agrarian country like India. Though there is a larger variability in the drought characteristics, the time to propagation from meteorological to agricultural drought is not investigated at regional scale in India. The Standardised Precipitation Evapotranspiration Index (SPEI), and Standardised Soil moisture Index (SSI) are computed incorporating large-scale climatic oscillations and regional hydro-meteorological variables. The time to propagation is calculated based on three different approaches. In addition, the internal characteristics of agricultural drought propagation is computed. The important findings from the study suggest that the time of propagation varies between 5 to 7 months for drought initiation, 9 to 15 months for drought peak, and 10 to 20 months for drought termination. The internal drought development and recover periods varies from 3.1 to 6 months. Over most of the area, the instantaneous drought development and recovery speed magnitude varies between 0.20 and 0.60. Lastly, it is observed that the exclusion of physical covariates leads to underestimation of agricultural drought propagation characteristics over India. The results of the current study can be used to guide future early warning and monitoring systems for agricultural drought as well as the study of agricultural drought at the regional level. [ABSTRACT FROM AUTHOR]

Soil Water Retention (SWR) is an important process in drainage, surface, and groundwater partitioning, hydrological modeling, water supply for irrigation, etc. Assessment of SWR characteristics is complex and difficult to conduct spatially in varied locations. Therefore, Pedotransfer Functions (PTF) which are empirical relations with easily available physical properties are commonly used. In the present study, the evaluation of soil moisture at different suction pressure using the adaptive neuro-fuzzy inference systems (ANFIS) approach based on soil texture (percentage of gravel, sand, silt, and clay) and compare with the PTF approach. The analysis was conducted for a total of eleven sites of two adjoining commands in India. The pressure plate apparatus along with coarse and fine sieve analysis, titration, and other tests were carried out to determine SWR, texture, organic carbon, and bulk density. The comparative analysis of Nash–Sutcliffe efficiencies of the best-fitted PTF models and ANFIS model confirmed that the ANFIS model can capture all variations of soil texture across all sites with Nash–Sutcliffe efficiency of nearly 1.0 indicative of an exact match, while no single PTF-based model can be used for all the sites. Therefore, the ANFIS model can be used to model soil water retention for the central India region using easily available texture properties of soils. [ABSTRACT FROM AUTHOR]

Analysis of soil samples collected from 16 rice paddy fields located in the Western Ghats region was performed to quantify the concentration of Cu, Zn, Mn, Fe, Ni, Cr, Cd, and Pb using atomic absorption spectroscopy. High concentrations of these heavy metals were found in rice paddy fields regularly cultivated using agrochemicals. We compared this concentration with soils of rice paddy field that was not under cultivation. Cu, Zn, Mn, Fe, Cr, Ni, Pb, and Cd showed increases of 1.2, 1.3, 2.3, 2.2, 1.8, 2.8, 1.8, and 8.5 times, respectively, in the rice paddy fields cultivated with synthetic fertilizers such as NPK, urea, potash, diammonium phosphate, etc., and several categories of pesticides belonging to the class organophosphates, carbamates, and acetanilide herbicide. In contaminated sites, the heavy metals exhibited maximum correlation with soil moisture content (SMC) (Zn, Fe, Cr, Ni, and Cd), soil organic content (SOC) (Fe, Cr, Ni, and Cd), and water holding capacity (WHC) (Cu, Pb, and Cd) than those observed for the reference site. The principal component analysis (PCA) revealed a total of 77.944% variance of heavy metals contributed from WHC (40.259%), SMC (20.854%), and SOC (16.832%). This indicates the build-up of heavy metals in rice paddy soils under the strong influence of moisture content, water holding capacity, and organic carbon content of the soil. [ABSTRACT FROM AUTHOR]

Soil moisture (SM) plays a crucial role in altering climate extremes through complex land-atmosphere feedback processes. In the present study, we investigated the impact of SM perturbations on temperature extremes (ExT) over India for the historical period (1951–2010) and future climate projection (2051–2100) under 4 K warming scenario. We note that more than 70% area of the Indian landmass has experienced significant changes in characteristics of ExT due to SM perturbations. In particular, we see larger impact of SM perturbations on ExT over the north-central India (NCI), which is a hotspot of strong SM-temperature coupling. Over NCI, a 20% departure in SM significantly revamps frequency, duration and intensity of ExT by 2–5 events/year, 1-2 days/event and 0.5–2.1 °C, respectively, through modulating surface energy partitioning, evapotranspiration and SM memory. Importantly, the impact of SM perturbations on frequency and duration of ExT events becomes less prominent with intensification of global warming. [ABSTRACT FROM AUTHOR]

India receives more than 70% of its annual rainfall in the summer monsoon from June to September. The rainfall is scanty and scattered for the rest of the year. Combining satellite data and model simulations, we show that the soil-vegetation continuum works as a natural capacitor of water, storing the monsoon pulse and releasing the moisture to the atmosphere through evapotranspiration over approximately 135 days when the moisture supply from precipitation is less than the evapotranspiration losses. The total Gross Primary Productivity of vegetation in India during the capacitor period accounts for almost 35% of the total annual GPP value. It primarily depends on the soil moisture at the beginning of the period, a measure of moisture capacitance of soil, with a correlation of 0.6. Given that India is the second largest contributor to recent global greening, its soil-vegetation water capacitance plays a significant role in the global carbon balance. [ABSTRACT FROM AUTHOR]

Aims: To study the impact of salinity and water logging on Eucalyptus growth, carbon (C) sequestration in tree biomass and organic C pool under mulched and unmulched ridges. Methods: We studied the performance of five Eucalyptus clones viz. C-413, C-2135, C-7, PE-8 and PE-11 on mulched and unmulched ridges, their impact on C sequestration in tree biomass and soil organic C pool, and soil properties. Results: Electrical conductivity (E.C.) of soil under mulched ridges (3.54 ± 0.3 dS m−1) was decreased by ~ 3.9-times, compared with the unmulched ridges (13.9 ± 0.3 dS m−1) due to Eucalyptus plantation. Tree survival rate on mulched ridges was ~ 78.1%, while on unmulched ridges was ~ 63.4%; C-7 clone had the lowest (~ 55.0%), while the PE-8 had significantly (p < 0.05) higher (~ 89.5%) survival rate. Mulching increased the tree girth (~ 8.7%), height (~ 13.8%), timber wood (~ 18.4%), branches (~ 21.8%), twigs + leaves (~ 22.6%) and root biomass (~ 34.6%) after one year of plantation, compared with their plantation on unmulched ridges. Regardless of the plantation method, C-2135 clone had the lowest, while the PE-11 had the highest timber dry biomass, branches, twigs + leaves and root biomass. After 4-years of plantation, trees planted on mulched ridges had significantly higher C sequestration in timber dry biomass by 147.8 ± 18.4 Mg C ha−1 than those on unmulched ridges. Conclusions: Eucalyptus plantation offers a low cost strategy for C sequestration in waterlogged saline soils. Plastic mulching has overwhelming significance in enhancing tree survival rate and decreasing soil salinity with co-benefits of increased biomass productivity of Eucalyptus. [ABSTRACT FROM AUTHOR]

The occurrence of heavy metals (HMs) in water and soil sediments represents a serious environmental concern. This study revealed the presence and distribution of HMs in water and soil sediments of various places in Vellore District, Tamil Nadu, India. Twenty-one sites were selected along the study area, and inductively coupled plasma-optical emission spectrometry (ICP-OES) was used to analyze the concentration of the heavy metals. The dominance of various HMs in the soil sediment sample follows the order strontium (Sr) > Manganese (Mn) > Barium (Ba) > Zinc (Zn) > Nickel (Ni) and Sr > Mn > Zn > Boron (B) > , respectively. It was found that the concentration of HMs in water and soil sediments in Ambur market and Mottukollai area was significantly higher than the recommended limits. Thus, the results showed that the presence of HMs in water and soil sediments could be threatened pollution factors unsafe for irrigation, drinking, and other human activities. [ABSTRACT FROM AUTHOR]

Most land surface system models and observational assessments ignore detailed soil characteristics while describing the drought attributes such as growth, duration, recovery, and the termination rate of the event. With the national-scale digital soil maps available for India, we assessed the climate-catchment-soil nexus using daily observed streamflow records from 98 sites in tropical rain-dominated catchments of peninsular India (8–25° N, 72–86° E). Results indicated that climate-catchment-soil properties may control hydrological drought attributes to the tune of 14–70%. While terrain features are dominant drivers for drought growth, contributing around 50% variability, soil attributes contribute ~ 71.5% variability in drought duration. Finally, soil and climatic factors together control the resilience and termination rate. The most relevant climate characteristics are potential evapotranspiration, soil moisture, rainfall, and temperature; temperature and soil moisture are dominant controls for streamflow drought resilience. Among different soil properties, soil organic carbon (SOC) stock could resist drought propagation, despite low-carbon soils across the Indian subcontinent. The findings highlight the need for accounting feedback among climate, soil, and topographical properties in catchment-scale drought propagations. [ABSTRACT FROM AUTHOR]

Floods in India affect millions of people during the summer monsoon (June–September) every year. Atmospheric factors, land surface conditions, and moisture source and pathways ride a combined signature in producing floods in rain-fed river basins. However, floods in Indian river basins are mostly studied considering the role of extreme precipitation, and critical contribution of factors related to land and atmosphere is often neglected. Using the Narmada River basin in central India as a testbed, we investigated the combined signature of extreme precipitation, soil moisture, and moisture sources on floods in the basin during 1951–2018 period. We find that most of the high-flow events in the basin occur during August and September and, about 80% of them are caused by extreme precipitation combined with higher antecedent soil moisture. The major flood producing storms in the Narmada basin are linked with high amount of vertically integrated water vapor flux, low mean sea level pressure, and strong winds. The Arabian Sea is the primary moisture source for the flood producing storms. However, previously unrecognized north Indian plain is also a dominant source of flood producing storms in the basin during the mid and late monsoon season. The moisture recycled from land-surface predominates the moisture sources of storm events in the upstream sub-basins while the contribution of moisture from the Arabian Sea is higher in the downstream sub-basins. These combined linkages of land, ocean and atmosphere, can assist in developing skillful early flood warning system for the Indian river basins. [ABSTRACT FROM AUTHOR]

Endocrine disrupting chemicals are exogenous chemicals which alter the functions of endocrine system. There are very few studies of occurrence and distribution of endocrine disrupting chemicals in water, soil and their potential human health risks under Indian conditions. In the present work, occurrence of eleven endocrine disrupting chemicals namely bisphenol-A, triclosan, triclocarbon, 4-nonylphenol, 4-tert octylphenol, methyl paraben, propyl paraben, butyl paraben, benzyl butyl phthalate, di (2-ethylhexyl) phthalate and di-n-butyl phthalate have been investigated in 20 river water samples, 5 ground water samples and 23 agricultural soil samples collected from 26 sampling points of different agro-climatic zones of India during 2019–2020. Instrument detection and quantification limits of different endocrine disrupting chemicals using LC–MS/MS varied from 0.001 to 0.05 and 0.005–0.1 µg/mL, respectively. Detection frequencies of bisphenol A, 4-nonylphenol, 4-tert octylphenol and di-n-butyl phthalate were 100% in river water samples. In ground water samples di-n-butyl phthalate, benzyl butyl phthalate, di (2-ethylhexyl) phthalate, triclosan, triclocarbon, 4-tert octylphenol, 4-nonylphenol, propyl paraben and butyl paraben and in soils 4-tert octylphenol, 4-nonylphenol, bisphenol-A and di-n-butyl phthalate were detected in 100% sample. High mean concentrations of di (2-ethylhexyl) phthalate were recorded in river waters (2706.135 µg/L), ground waters (3203.33 µg/L) and agricultural soils (1005.490 ng/g). Other chemicals detected in high concentrations were 4-tert octylphenol (180.680–829.93 µg/L), di-n-butyl phthalate (117.492–182.29 µg/L) and bisphenol-A (13.99–228.03 µg/L). Widespread distribution and high concentrations of these chemicals in water and soil is a cause of concern and, therefore, continued monitoring of these chemicals in different environmental matrices is the need of the hour. [ABSTRACT FROM AUTHOR]

Soil moisture and vegetation water content play an important role in studies involving vegetation, drought, and climate change. The present paper gives details of how to calculate the Soil Moisture Index (SMI), Normalized Difference Moisture Index (NDMI), and Normalized Difference Water Index (NDWI) using Landsat 7 ETM + and Landsat 8 OLI (Operational Land Imager) and Thermal Infrared Sensor (TIRS) data and how spatially and temporally it has been changed over Palakkad District, Kerala. The present study develops an image processing method using band 3 (Green), band 4(Red), band 5 (NIR), band 6 (SWIR 1), and band 10 (TIR) of Landsat 8 and band 2 (Green), band 3 (Red), band 4 (NIR), band 5 (SWIR) and band 6 (TIR) of Landsat 7 data for determining the different spectral indices. The relationship between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) are based on experimental parameterization for soil moisture index. Both LST and NDVI are considered indispensable data to obtain SMI calculation. The statistical analysis of NDVI and LST was exposed in the standardized regression coefficient. The study found a weak negative correlation between NDVI and LST in both 2001 (R2 = 0.37) and 2021 (R2 = 0.16). As the LST increases, the vegetative cover reduces due to the record of high temperatures in the study area. The SMI result for 2021 shows that the majority of the study area experienced extreme anomalies when compared to the 2001 result. Second, a significant negative correlation existed between SMI and LST (R2 = 0.664 in 2001 and R2 = 1 in 2021), which indicates that the surface soil moisture reduces with LST. The normalized difference moisture index (NDMI) value ranges from – 0.71 to 0.75 in 2001 and – 0.33 to 0.40 in 2021. In 2001 and 2021, the normalized difference water index (NDWI) ranges from – 0.47 to 0.64 and – 0.531 to 0.154, respectively. Therefore, the current study gives a clear understanding of considerable variations in soil moisture and vegetation water content in the Palakkad District during the study period. [ABSTRACT FROM AUTHOR]

The soil moisture observation data from the Qinghai-Tibetan Plateau permafrost and alpine wetland (PAW) monitoring network and from the National Center for Environmental Prediction (NCEP) Final Analysis (FNL) from 1989 to 2019 were adopted to drive the Lagrangian backward trajectory model to simulate the water vapour transportation paths and source regions to the three-river source region during dry and wet episodes. The results showed that the water vapour transport paths mainly came from the land and ocean in the west during the extreme drought episodes based on the standardized precipitation index (SPI), including the northern African continent, the eastern European plains, and the mid-latitudes of the Atlantic Ocean. The water vapour source regions were mainly concentrated in the Kashmir region, northeast India and southern Motuo on the Qinghai-Tibet Plateau. When the SPI indicated an extremely wet state, the transport mainly occurred to the east and south, including the Pacific Ocean and the South China Sea, where water vapour entered the mainland from East/South China, passed through the Yangtze River Basin, and finally reached the three-river source region from east or south of the Qinghai-Tibet Plateau, while the water vapour from the Indian Ocean and Arabian Sea passed through the Indian Peninsula and the Bay of Bengal and entered the three-river source region from the western and southern sides of the Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

Changes in the nature of vegetation imply changes in land surface parameters such as surface albedo, roughness length, root depth, and surface emissivity which results in alteration of land surface conditions. This study aims to understand the effect of enhanced forest conditions on land surface states and land surface fluxes over central India during different seasons. In this study, the following simulations were carried out using Noah3.6 Land Surface Model (LSM) in Land Information System (LIS) for 5 years from May 2013 to May 2018 over the Indian region: (i) Control (Ctl) run, that represents the cropland(normal) scenario with Modis IGBP land cover map, (ii) Experimental (Exp) run, that represents enhanced forest conditions, where the cropland cover was replaced by evergreen broadleaf forest over the targeted region (central India). The results of this study indicate that when the model outputs were averaged over the targeted region, enhanced forest conditions led to increased soil moisture content as compared to the Cropland scenario due to dense cover of the surface and higher water holding capacity of the soil. Due to the canopy interception of solar radiation and increased soil moisture availability in the Exp-run, a decrease in both soil temperature and daily soil temperature range was seen as compared to Ctl-run. Net surface radiation increased during all the seasons in Exp-run as compared to Ctl-run due to a decrease of surface albedo. On conversion from cropland to the evergreen broadleaf forest, an increase in the latent heat flux was observed during all the seasons due to an increase in soil moisture and an increase in surface roughness length. A reduction in the sensible heat flux was observed during the post-monsoon season and beginning of winter season, however, for other seasons, an increase in the sensible heat flux was seen. The above behavior of sensible heat flux is due to variation of precipitation and the varying absorbed surface energy during different seasons. Furthermore, the results suggest that the effect of this enhanced forest conditions on land surface characteristics is more pronounced during the drier periods. A similar behavior of land surface characteristics due to enhanced forest conditions is observed for all the seasons during different years from 2013 to 2018. [ABSTRACT FROM AUTHOR]

This study examines the trends in MODIS/TERRA derived Normalized Difference Vegetation Index (NDVI) and its correlation with Land Surface Temperature (LST), Soil Moisture (SM), and precipitation over Gautam Buddh Nagar (India), during the period 2005–2018. The region have a sub-humid and quite moderate climate, scattered into cultivable land, forest and fast growing urbanization zone, making it suitable for monitoring vegetation trends and its accompanying factors. The NDVI-derived vegetation growth patterns over the study region of District Gautam Buddh Nagar, illustrate vigorous seasonal cycles, and interannual variations. The correlation between NDVI, and LST (− 0.45) was observed to be higher than the correlation of NDVI with SM (r = 0.43), and precipitation (r = 0.341), suggesting NDVI as more sensitive to LST as compare to SM, and precipitation, while SM shows the worthy positive correlation (r = 0.63) with the precipitation. On a seasonal basis, NDVI shows high values during winter (0.45 ± 0.02) followed by monsoon (0.44 ± 0.04), post-monsoon (0.41 ± 0.02), and pre-monsoon (0.37 ± 0.04). This study also aims to determine the phase wise status of NDVI and associated parameters. [ABSTRACT FROM AUTHOR]

Floods are one of the frequent natural hazards occurring in Kerala because of the remarkably high annual rate of rainfall. The objective of this study is to prepare the flood susceptibility maps of Ernakulam district by integrating remote sensing data, GIS, analytical hierarchy process (AHP), and fuzzy-analytical hierarchy process methods. Ernakulam is one of Kerala's most flood-prone districts. The development of this map can help to raise awareness about the risks of flooding. Factors such as slope angle, soil types (texture), land use/land cover, stream density, water ratio index, normalized difference built-up index, topographic wetness index, stream power index, aspect, and sediment transport index have been selected. The area of the final maps is grouped into five flood susceptible zones, ranging from very low to very high. The major reasons for flood occurrence in Ernakulam district are the combined effect of multiple factors such as excess silting, reduction of stream width due to anthropogenic activities, and changes in land cover and land-use pattern, lower slope, higher soil moisture content, lower stream capacity, and poor infiltration capacity of soils. The prepared map was validated using the receiver operating characteristic (ROC) curve method. The area under the ROC curve (AUC) values of 0.75 and 0.81 estimated by the ROC curve method for the AHP and F-AHP methods are considered acceptable and excellent, which confirms the prediction capability of the prepared maps. The very high susceptible zone constitutes around 19% of the district. This map is useful for land-use planners and policymakers to adopt strategies that will reduce the impact of flood hazards and damage in the future. [ABSTRACT FROM AUTHOR]

Climate change is adversely affecting the development, management, and planning of surface and groundwater resources. The meteorological drought becomes a severe natural problem, and it can occur in any climatic region of the world. Thus, monitoring and minimizing drought is crucial for analyzing and predicting drought impacts. The meteorological drought is considered basin or region-specific since atmospheric conditions are highly variable from region to region. However, agricultural drought links the various characteristics of hydrological or meteorological drought to agricultural impacts focusing on groundwater deficit, soil moisture deficit, etc. A single drought index cannot assess each aspect of the meteorological drought. In this study, we considered seven drought indices such as the Standardized Precipitation Index (SPI), China Z Index (CZI), Modified China Z Index (MCZI), Percent Normal Drought Index (PNI), Deciles Index (DI), Rainfall Anomaly Index (RAI), and Z-score Index (ZSI). The drought was analyzed for 3, 6, 9, and 12 months' time step, and drought classification and threshold values were estimated. SPI showed maximum correlation values (0.389, 0.412, 0.560, and 0.996) for 3, 6, 9, and 12 month time steps compared to the other drought indices. The value of correlation is increased with the increase in time step for all drought indices; therefore, the accuracy of drought assessment also increases with an increase in time step. The Mann–Kendall's trend test was analyzed at a 5% significance level for drought assessments. The drought magnitude and the severity of the Betwa River basin were estimated based on the meteorological data (Rainfall) from 1970 to 2014. [ABSTRACT FROM AUTHOR]

Field experiments were conducted at Biswanath, Assam, India (26° 42′ N and 93° 15′ E), during 2016, 2017, and 2018, to evaluate the effect of microclimates on growth, yield, and disease incidence in the ginger crop. The ginger variety Nadia was grown under six microclimates, viz., under shade net for the entire crop season (T1), under shade net from planting to mid-October (T2), with pigeon pea (T3), with maize (T4), with okra (T5), and as a sole crop (T6) in three replicated RBD. Photosynthetically active radiation (PAR), net radiation (Rn), temperature above the ginger canopy, soil temperature, and soil moisture were measured during the critical crop growth period under different microclimates. Recording of rhizome rot disease incidence was done periodically and genomic analysis of pathogen was carried out. PAR recorded above the ginger canopy under T6 was 1688.1 μ mol s−1 m−2, which was attenuated up to 80.1% in other microclimates. The Rn load of the ginger canopy was maximum (446.4 W m−2) under T6, which reduced to below 50 W m−2 under both T3 and T4. Both air temperatures above the ginger canopy and soil temperatures under T3 and T4 were reduced by 3.3 °C and 4.6 °C, respectively, as compared to T6. The pathogen causing the disease in the experimental site was identified as Fusarium oxysporum. Considerable increase in soil and air temperature and soil moisture favored disease incidence (90.3%) under shade net (T1 and T2) treatments, while opposite reason causing significant reduction in disease incidence (16.1%) was observed under T3 and T4. More yield of ginger recorded in treatments T3 (6.21 t ha−1) or T4 (6.48 t ha−1) was attributed to better crop growth and diminutive disease incidence, while the crop was almost damaged due to severe disease incidence under shade net (T1 and T2) treatments. [ABSTRACT FROM AUTHOR]

In this paper, we present the variation of natural radioactivity in soil and water samples of Tehri Garhwal district of Garhwal Himalaya. The soil and water samples collected from different locations were studied and analyzed using Gamma ray spectrometry and Laser Fluoremetry techniques, respectively. The activity concentrations 226Ra, 232Th and 40K in the soil samples were found to vary 31 ± 6 to 67 ± 7 Bq kg−1 with an average 46 Bq kg−1, 38 ± 7 to 86 ± 10 Bq kg−1 with an average of 52 Bq kg−1 and 612 ± 120 to 1277 ± 168 Bq kg−1 with an average of 902 Bq kg−1, respectively. Uranium concentration in water samples was found to vary from BDL to 2 ppb with an average 0.6 ppb. The above measured values uranium in drinking water ware also be compare the uranium concentrations in drinking water in Ukimath area. The measured values of activity concentrations in the soil and water samples were found within permissible limits. [ABSTRACT FROM AUTHOR]

Efficiency of soil water conservation (SWC) structure depends on its precise locations as well as its success in reducing surface runoff, soil erosion and increasing biomass for sustainable watershed development. The present study focuses on the identification of site suitability potential for locating SWC structures in micro-watersheds of Shivganga basin from Deccan Volcanic Province, India. First time, micro-watershed prioritization coupled with modified weighted sum analysis has been used to identify the potential areas for SWC. The main advantage of this modified method is removing biases in ranking micro-watersheds having same weighted sum scores. This method principally assists to provide suitable location for SWC structures so as to reduce future losses such as rupture of earthen wall, flash flood losses and deprived storage capacity. The suitability potential analysis incorporates the Integrated Mission for Sustainable Development (IMSD) specifications designed for various SWC structures along with the linear, areal and relief morphometric parameters. Derived site suitability potential areas were validated by field observations and ground truth verification with reference to the existing SWC sites, which show 65–100% accuracy. In conclusion, adopted modified methodology demands less data that deciphers the site suitability zones more precisely with respect to micro-watersheds. [ABSTRACT FROM AUTHOR]

The study was undertaken in coffee agroforests of Kodagu, Karnataka, India from 2016 to 2018. The coffee plantation farmers were categorised into large, medium and small based on the land holding and assessed for their role in conservation of indigenous tree species. Circa situm conservation is the preservation of planted and/or remnant trees and wildings in farmland where natural forest or woodland containing the same trees was once found, but where natural vegetation has been lost or modified significantly through agricultural expansion. Making questionnaire survey and interaction with the coffee planters, the knowledge in tree planting was assessed. There were some indigenous trees that really played well in protection, conserving the soil and moisture, helped in pollination of coffee, supply of timbers and small scale timbers and fire wood. By known use value of tree species and respondents attitude towards planting indigenous trees, Dalbergia latifolia, Ficus racemosa and Acrocarpus fraxinifolius were planted in coffee farms which aided in creation of C. situm forest gene bank. [ABSTRACT FROM AUTHOR]

Spatio-temporal evolution of soil moisture is a complex process and controlled by several factors including hydro-meteorological forcings. This study borrows a recently developed concept of temporal networks to capture the time-varying association between hydro-meteorological forcings and spatio-temporal evolution of soil moisture. Climate change and dynamic terrestrial environment cause slow but continuous change in the characteristics of hydro-meteorological forcings leading to variation in spatio-temporal distribution of soil moisture. Keeping this in the focus of the study, temporal networks based time-varying modelling framework is adapted for one-month to one-season (three-months) in advance prediction of monthly soil moisture for entire Indian mainland. Results indicate that the association among the hydro-meteorological forcings varies with both space and time. With the increase in prediction lead-time, the strength of association with the variables, such as pressure, wind and temperature, decreases and that with the variables like leaf area index remains informative. Among different seasons, the model shows superior performance for the monsoon and post monsoon periods. Next, the soil moisture based extremes are assessed by utilizing two deficit indices and two wetness indices. The model performance is highly satisfactory but varies over space and seasons, with a marginally better performance for the wetness indices. Overall, given the vast spatial extent of the Indian mainland, the proposed model performs robustly for almost all the climatic regions and may be promising for other parts of the world as well. [ABSTRACT FROM AUTHOR]

The soil biochemical properties are sensitive to change in land use systems and seasons. The variations in soil management practices and soil moisture content affect the sustainability of the systems. To study the sustainability in lower Shiwalik, a total of 144 soil samples (0–0.15 m) were undertaken to monitor the changes in the soil biochemical properties under rainfed land use systems, i.e., agri-horticulture, agroforestry, cultivated and barren system and seasons, i.e., summer, rainy and winter. Among soil biochemical properties, soil microbial biomass carbon, soil microbial quotient, dehydrogenase activities, basal soil respiration, fluorescein diacetate and urease activities ranged from 77 to 122 μg g−1, 0.023 to 0.027, 18.3 to 30.6 μg TPF g −1 h−1, 13.2 to 22.7 μg CO2-C g−1 day−1, 1.3 to 2.2 μg g−1 and 5.83 to 6.38 μg NH4-Ng−1 h−1, respectively. Among 15 soil properties, principal component analysis specified that four major soil properties, i.e., basal soil respiration, metabolic quotient, electrical conductivity and clay content contributed 73% of the soil quality index with contribution of 44, 13, 9 and 7%, respectively. Among seasons, the values for soil biochemical properties were higher in rainy season as compared to winter and summer season. In systems, agri-horticulture followed by agroforestry was the best systems in terms of sustainability in Shiwalik foothills of northwest India. [ABSTRACT FROM AUTHOR]

Jatropha curcas L. and Pongamia pinnata (L.) Pierre, are promising biofuel species in peninsular India. We quantified greenhouse gas fluxes from managed 9 year old plantations of these species for all seasons in three consecutive years (2012–2013, 2013–2014 and 2014–2015) using manually-operated closed chamber method. CH4, N2O and CO2 were analysed by using Varian 450-GC based on Gas Chromotography. The mean soil CO2, CH4, and N2O emissions in the Jatropha curcas plantation recorded were 7.720 t ha−1 y−1, 0.023 t ha−1 y−1 and 0.017 t ha−1 y−1, respectively. While in Pongamia pinnata soil CO2, CH4, and N2O emissions were recorded to be 7.633 t ha−1 y−1, 0.008 t ha−1 y−1 and 0.019 t ha−1 y−1, respectively. Our results indicate that Jatropha curcas has higher GHG emissions than Pongamia pinnata. A pronounced seasonal variability was observed in CH4, N2O and CO2 flux rates. The highest emissions for Jatropha curcas occur during rainy season, whereas for Pongamia pinnata it occurs during winter. Soil moisture and temperature being the most influencing factors for CO2 fluxes from soil. We conclude that these plantation has an effect on soil-atmosphere GHG exchanges and can act as a good source of mitigation of greenhouse gases. [ABSTRACT FROM AUTHOR]

The present study was undertaken to determine the seasonal changes in soil microbial biomass carbon, nitrogen, and phosphorus in the temperate forest of Uttarakhand Himalaya. Seasonally, the microbial biomass C and N were recorded maximum in the rainy season and minimum during winter whereas the microbial biomass P was minimum in summer and maximum in winter. Microbial biomass C, N and P ranged from 192–6210 µg/g, 8.61–229 µg/g to 0.20–68.25 µg/g respectively. Microbial biomass C contributed 6% of the total soil organic C, the maximum during the rainy season and the minimum in summer. The percentage contribution of microbial biomass N and P to total N and total P ranged from 1.07 to 0.67% respectively. Microbial biomass N was contributed maximum in winter and minimum during rainy whereas microbial biomass P was contributed maximum during summer and minimum in winter. The microbial C/N, C/P and N/P ratio were 54.35, 2329 and 94.25 respectively. Overall, this study determined the seasonal variations in microbial biomass C, N and P and its relationship with soil physicochemical properties for better understanding the microbial biomass contribution in organic matter and nutrient dynamics in the temperate forest of Uttarakhand Himalaya, India. Determining these interactions between soil physicochemical properties and microbial dynamics support for stabilization of soil organic carbon, provide information on microbe turnover and eventually improve the ecosystem function. Soil moisture, organic carbon and potassium have had a strong influence on the microbial biomass C. Simultaneously, soil total C influences soil microbial biomass N, and the soil moisture on the microbial biomass P. All these trends seems to be unique to this temperate forest and may have a bearing on its ecological functioning, as the soil C dynamics through organic matter and the labile microbial biomass are inter-linked for the inclusive health of the soil. [ABSTRACT FROM AUTHOR]

Bamboos due to high soil water conservation potential are gaining increased attention in plantation programs across the globe. Large-scale plantation of fast-growing bamboo, however, can have important hydrological consequences. The study aims to quantify the eco-hydrological parameters, viz., throughfall (TF), stemflow (SF), and interception (I) in seven important sympodial bamboo species in north western Himalayan foothills of India. The species selected include Bambusa balcooa, Bambusa bambos, Bambusa vulgaris., Bambusa nutans, Dendrocalamus hamiltonii, Dendrocalamus stocksii, and Dendrocalamus strictus. Throughfall versus gross rainfall (GR) relationship in different species indicated high throughfall production during high rainfall events with r2 > 0.90. Average throughfall was lowest (62.1%) in D. hamiltonii and highest in B. vulgaris (74.6%). SF ranged from 1.32% in B. nutans to 3.39% in D. hamiltonii. The correlation coefficient (r) between leaf area index (LAI), number of culms, and crown area with the interception were 0.746, 0.691, and 0.585, respectively. The funneling ratio (F) was highest (27.0) in D. hamiltonii and least in B. nutans. Canopy storage capacity was highest in D. strictus (3.57 mm) and least in D. hamiltonii (1.09 mm). Interception loss was highest (34.4%) in D. hamiltonii and lowest in B. vulgaris (23.5%) and D. strictus (23.6%). Higher interception in bamboos make them suitable for soil conservation, but careful selection of species is required in low rainfall areas. [ABSTRACT FROM AUTHOR]

Cyanobacteria are oxygenic photosynthetic microorganisms known for their agricultural and industrial importance. Unavailability of efficient and fast isolation and purification methods of cyanobacteria has impeded our understanding of cyanobacterial diversity. A number of techniques for isolation and purification of cyanobacteria are available, but most of them are cumbersome as well as time-consuming. In the present study, we modified and validated a uni-algal isolation technique named as Microscope Assisted Uni-algal isolation through Dilution (MAU-D) which used dilution of mixed algal population on slide and isolation of single type of cyanobacterial cells using light microscope. Using this technique, we obtained 81 cyanobacterial isolates belonging to various species from 19 different genera from soil and water samples collected from rice fields of Uttar Pradesh, India. This technique also resulted in isolation of six distinct genera, viz., Cyanobacterium, Toxopsis, Desertifilum, Chroococcidiopsis, Halomicronema, and Alkalinema, which were previously not reported from rice fields of India. Hence, the MAU-D technique presents a simple, comparatively fast method of isolation and purification of cyanobacteria which can help to isolate those cyanobacteria which are difficult to isolate through routine sub-culturing. [ABSTRACT FROM AUTHOR]

Soil moisture and temperature (SM and ST) have been identified for modeling of extreme weather and hydrological processes. The coarser resolution global analyses are limited in capturing realistic heterogeneity. This study focuses on evaluating regional land surface conditions developed from a high-resolution (4 km grid spacing) land data assimilation system (HRLDAS) over India from 2000 to 2013 against in situ and global analyses. Global analyses such as the European Space Agency Climate Change Initiative (ESACCI), Moderate Resolution Imaging Spectroradiometer (MODIS), Climate Forecasting System (CFS), and Global Land Data Assimilation System (GLDAS) have been considered to assess the credibility of the regional analysis. The regional SM from the HRLDAS is superior to global and satellite products, particularly in the orography (altitude > 300 m) regions followed by the plane regions (altitude ≤ 300 m). The probability distribution function (PDF) indicates that the regional SM and satellite analysis exhibited less error (~ 0.02 m3 m−3 at ~ 28%) in the plane regions. The regional SM analysis in the orography regions is reliable (0.015 m3 m−3 at 28% frequency) with a high equitable threat score (~ 0.6) compared to other analyses. The HRLDAS is consistently superior for soil temperature (ST) to other global analyses. The mean diurnal variation of HRLDAS-ST is close to in situ observation. The HRLDAS performs better for spatial representation of SM and ST for different months and monsoon seasons. The improved representation of land conditions from the HRLDAS could provide a realistic distribution of latent and sensible heat fluxes when compared with other global products. This study demonstrates the value of high-resolution regional analyses and recommends usefulness in hydrological applications. [ABSTRACT FROM AUTHOR]

The monsoon-driven river basins are more vulnerable to the flash-floods triggered by intense rainfall activities. Due to the lack of adequate high resolution forecast of real-time hydro-meteorological variables, a reliable forecast of flash floods remains a challenge. One plausible way to generate such a high-resolution forecast of hydro-meteorological variables is to use a coupled atmospheric–hydrologic modelling system. Thus, in this study, a physically-based, fully distributed, multi-scale hydrologic modelling framework, WRF-Hydro with optimized configurations (stand-alone and coupled-mode) is used to simulate the important hydro-meteorological variables like precipitation, runoff, soil moisture, and land surface heat fluxes over Cauvery river basin, India. In stand-alone mode, the model is driven by the high-resolution gridded data from the Global Land Data Assimilation System; while the coupled model is run with the WRF atmospheric model. In this study, the ability of a fully coupled WRF–WRF-Hydro modelling framework , with 3 km grid spacing is used to simulate the hydro-meteorological conditions during an extreme rainfall event (08–09 August 2019). The innermost domain of WRF-Hydro in conjunction with a high resolution hydrological routing grid (300 m) is also utilized, to include the subgrid scale disaggregation–aggregation weighting procedures to generate the land–atmospheric feedbacks on the hydrometeorological variables. The resulting variables have been validated through relevant observations; the overall performance of the coupled WRF-Hydro is shown to be relatively good when compared to the WRF-only simulations. [ABSTRACT FROM AUTHOR]

Bio-energy crops need to be grown on marginal salt and drought lands in India as per policy. Understanding environmental stress response in bio-energy crops might help in promoting cultivation of bio-energy feedstock on marginal salty and drought land. This is one of the first report for vegetative propagation of Bamboo (Bambusa balcooa) under salt and drought stress to understand antioxidant enzymes' gene regulations to combat stress through activation of antioxidant enzymes and osmo-protectant molecules to scavenge reactive oxygen species as measured by physiological changes. Morphological, physiological, and biochemical traits were noted as indicators of plant health upon different sodium chloride (NaCl) salt-stress while various drought conditions with correlation analysis. A significant up-regulation of genes related to most of the antioxidant enzymes was observed up to salinity of 14 mS cm− 1 electric conductivity (EC) at 150 mM NaCl experimental salt stress which declined with higher salt-stress. While in the case of drought-stress, all genes remained up-regulated while proline dehydrogenase (PDH) remained down-regulated up-to 100% drought-stress having 4% soil moisture. The gene expressions of antioxidant enzymes were significantly correlated with their corresponding gene-products namely super-oxide dismutase (SOD), catalase (CAT), glutathione reductase (GR) and ascorbate peroxidase (APX) activities. Biochemical parameters such as, soluble sugar, proline, malondialdehyde (MDA), total amino acids, hydrogen peroxide and electrolyte leakage ratio also showed positive correlation (p = 0.001) with salt condition. Genetic and biochemical test parameters were significantly correlated with physiological attributes of plant health at soil EC of 14 mS cm− 1 shown as 150 mM NaCl salt stress and 60% drought-stress having 17% soil moisture content, were the optimum stress tolerance limits observed. Application of these data would be useful to cultivate 0.63 million ha of salinity affected land and 10.05 million ha of drought affected land among wastelands in India to meet biofuel need. [ABSTRACT FROM AUTHOR]

Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data were analyzed to study the vegetation dynamics over different meteorological subdivisions in India for the period 2000–2016. Soil moisture (SM), rainfall (RF), and land use land cover (LULC) data were analyzed to identify the climatic and anthropogenic drivers that cause vegetation changes at the subdivision scale. Principal component analysis and MK (Mann-Kendall) test showed significant greening trend over semi-arid regions of Northwest India (NWI) and South India (SI) while slight browning trend seen over some of the subdivisions in Indo-Gangetic (IG) plains and Western Ghats (WG). It is found that the NDVI has superior correlation with soil moisture compared with rainfall and the croplands (CL) found to have significant increasing trend over the NWI and SI. Increasing trend in soil moisture over the NWI and SI may have contributed to increase in CL area and the greening trend. Over IG plains, the NDVI showed moderate correlation with SM and RF, and the greening trend (browning trend) in some regions can be attributed to increase in natural vegetation mosaic (decrease of CL). The NDVI has shown browning trend over the core monsoon regions of Madhya Pradesh (an increase of barren lands over west MP and decrease of CL over east MP) and Western Ghats (significant decrease of CL over Konkan and Goa). This study revealed that the soil moisture and LULC changes are the major driving factors for the vegetation changes over majority of the subdivisions in India. [ABSTRACT FROM AUTHOR]

Most of the urban heat island (UHI) studies are carried out in densely populated cities but core industrial areas are also potential sites of heat island effect despite having a comparatively lower population. In the present study, heat island assessment has been carried out for Angul-Talcher industrial area (ATIA) which is one of the oldest industrial areas of India and is still undergoing a transformation to accommodate more industries and mining operations. As the major contributors towards influencing local meteorology were expected to be industrial (and mining) activities, the heat island was studied as "industrial heat island" (IHI) rather than urban heat island. Industrial and mining sites were the most frequent nighttime canopy-layer heat island intensity (HIN) hotspots due to anthropogenic heat of associated industrial processes as well as built structures. During the daytime, croplands experienced the most frequent canopy-layer HIN hotspots which could be attributed to low moisture of the soils during the non-farming period of the field campaign. Hourly maximum atmospheric heat island intensities were observed in the range of 7–9 °C. Monthly maximum HINs ranged from 2.97 to 4.04 °C while 3-month mean HINs varied from 1.45 to 2.74 °C. Amongst different land use/land cover classes, the highest mean canopy-layer heat island intensity for the entire 3-month-long duration of field campaign during nighttime was assessed at the mining sites (3-month mean 2.74 °C) followed in decreasing order by the industrial sites (2.52 °C), rural and urban settlements (2.13 °C), and croplands (2.06 °C). Corresponding daytime canopy-layer heat island intensity was highest for the croplands (2.07 °C) followed in decreasing order by the mining sites (1.70 °C), rural and urban settlements (1.68 °C), and industry (1.45 °C). [ABSTRACT FROM AUTHOR]

The Indian subcontinent is one of the most highly irrigated regions of the world where water use has intensified since the second half of the twentieth century. This agricultural intensification has resulted in changes in the water and energy balance between land surface and atmosphere is affected by these changes, which further influences climatic parameters that directly affect us. Over India, the practice of irrigation has changed in recent years resulting in an increased agricultural productivity as well as a depletion of groundwater. In this study, we use the Community Earth System Model CESM1.2 (Community Land Model (CLM4.5) with an active crop model coupled to the Community Atmospheric Model and a slab ocean) to examine the sensitivity of India's climate to the amount of irrigation. We vary the amount of irrigation water by varying the irrigation factor in the CLM4.5 to produce a range of conditions from a bare minimum of no water stress in the crop to saturated soil. By holding all other forcings constant at year 2000 levels, we are able to examine the changes in temperature and precipitation over India due to varying amounts of irrigation. We find that irrigation decreases the maximum and minimum temperatures by nearly 3 °C and 4 °C respectively over the most heavily irrigated parts of the Indian subcontinent as a result of increased latent heat partitioning of energy. The resultant cooling (about 4% in annual mean temperature across India) impacts the monsoon circulation and reduces the moisture transport resulting in a statistically significant decrease of 1.46–4.17% in the all India summer monsoon rainfall with a decrease over the eastern part of the Gangetic basin and an increase over the Punjab region of Pakistan and India. While the large effect on temperature and precipitation may be a result of the model simulations having an irrigation seasonality that peaks in the pre-monsoon months, there is significant disagreement across different model based irrigation estimates on the timing and amount of the peak irrigation water added. This uncertainty in irrigation needs to be resolved to better understand its effects on India's climate. [ABSTRACT FROM AUTHOR]

Complementary use of resources is considered a strong driver of enhanced performance in mixed-species assemblages. In income-producing agroforestry systems, economically valuable species will ideally benefit from resource partitioning. In agroforests in southern India, we assessed soil water uptake depths of coffee and different shade tree species at the end of the dry season, when water availability is regarded critical. Water isotopes ratios (δD and δ18O) from soil and plant samples were analyzed and an isotope mixing model was applied. Results suggest that coffee plants drew water mainly from the top soil (56% from 0 to 20 cm), and to a lower extent from the subsoil. Jackfruit trees had very similar uptake patterns, which suggest competition. Mango trees showed reverse patterns of water uptake with soil depth, i.e. drew more than 75% from the subsoil, which suggests spatial complementarity to coffee. Within and across shade tree species, soil water uptake from the top soil increased with increasing diameter. We suggest that shade tree species choice and diameter regulation are viable options in management for soil water use complementarity. [ABSTRACT FROM AUTHOR]

A model experiment with a regional climate model (RegCM4.6) coupled with an updated land–surface scheme (Community Land Model; CLM4.5) is integrated for 34 years to study the Indian summer monsoon (ISM). An eastward shift (towards Bay of Bengal; BoB) in simulated monsoon features due to the secular shift of the moist convective systems is observed. Further diagnostics show lower temperature over India as compared to BoB; while the Findlater jet is significantly weakened over the Arabian Sea. The low-pressure drags the southwesterly towards BoB through India. The Bowen ratio and evaporation reflect less soil moisture availability over the land. The analysis of simulated omega, moist static energy and Hadley circulation illustrates that an atmospheric condition dominates over land which further suppresses the convection and hence the rainfall. However, favorable atmospheric condition supporting convection and moisture availability leads to heavy precipitation over BoB and hence causes the eastward shift in simulated ISM rainfall. The convective fraction contributes the major share to the total precipitation. An evaluation of appropriate land surface spin-up period in order to investigate the optimum stabilization period for CLM4.5, indicates a possible weak coupling between land and atmosphere components in model simulation. This led to the improper energy and moisture exchanges between land and atmosphere in the simulation. The present work also suggests that, the simulated mean rainfall is not predominantly influenced by the length of different spin-up periods (up to 25 years) in case of weak coupling of land and atmosphere. [ABSTRACT FROM AUTHOR]

Quantification of soil properties and identification of soil water holding capacity (WHC) are essential for proper cropping and irrigation planning. The traditional approach of calculating WHC involves the application of pedo-transfer functions (PTFs), which are unavailable in many developing countries especially in the Himalayan region. Therefore, this study evaluates the effect of elevation on the physical and chemical properties of the surface soil in a Himalayan river basin in India. Furthermore, regression models were generated for calculating maximum WHC at different agroecological zones (AEZs). A total of 129 soil samples were randomly collected from three AEZs (subtropical, temperate and trans-himalayan). Laboratory analysis was done to identify the pH, organic carbon (OC), particle size distribution, particle density, bulk density (BD), soil water (air dry) (SW), total porosity (TP) and maximum WHC at the three AEZs. Pearson correlation coefficients were derived among the soil attributes. Also, stepwise (forward) multiple linear regression (MLR) models were generated for the predictability of WHC. The results illustrate a statistically significant variation in soil attributes for pH, OC, sand, silt, BD and WHC at all three AEZs. Furthermore, WHC exhibits significant correlation with BD, TP and SW through all three AEZs. Stepwise multiple linear regression analysis shows high predictability of maximum WHC while using appropriate soil attributes; with an adjusted coefficient of determination of 0.87, 0.82 and 0.78 for subtropical, temperate and trans-himalayan AEZs, respectively. Based on the results, it is concluded that soil properties vary significantly through AEZs in the Himalayan region. Also, several properties are inter-correlated at the AEZs and simple MLR models are recommended while estimating maximum WHC. The outputs of this study can be used as a guide to decide crop suitability and develop MLR models for the calculation of WHC in the areas where PTFs are not readily available. [ABSTRACT FROM AUTHOR]

The decreasing trend in rainfall in the last few decades over the Indo-Gangetic Plains of northern India as observed in ground-based observations puts increasing stress on groundwater because irrigation uses up to 70% of freshwater resources. In this work, we have analyzed the effects of extensive irrigation over the Gangetic Plains on the seasonal mean and intra-seasonal variability of the Indian summer monsoon, using a general circulation model and a very high-resolution soil moisture dataset created using extensive field observations in a state-of-the-art hydrological model. We find that the winter-time (November–March) irrigation has a positive feedback on the Indian summer monsoon through large scale circulation changes. These changes are analogous to a positive North Atlantic Oscillation (NAO) phase during winter months. The effects of the positive NAO phase persist from winter to spring through widespread changes in surface conditions over western and central Asia, which makes the pre-monsoon conditions suitable for a subsequent good monsoon over India. Winter-time irrigation also resulted in a reduction of low frequency intra-seasonal variability over the Indian region during the monsoon season. However, when irrigation is practiced throughout the year, a decrease in June–September precipitation over the Gangetic Plains, significant at 95% level, is noted as compared to the no-irrigation scenario. This decrease is attributed to the increase in local soil moisture due to irrigation, which results in a southward shift of the moisture convergence zone during the active phase of monsoon, decreasing its mean and intraseasonal variability. Interestingly, these changes show a remarkable similarity to the long-term trend in observed rainfall spatial pattern and low-frequency variability. Our results suggest that with a decline in the mean summer precipitation and stressed groundwater resources in the Gangetic Plains, the water crisis could exacerbate, with irrigation having a weakening effect on the regional monsoon. [ABSTRACT FROM AUTHOR]

A study was conducted from 2008–2009 to 2012–2013 to evaluate the performance of bael-based hortipasture (agroforestry) system under different soil moisture conservation practices in rainfed condition for optimization of system productivity and improvement in soil health. Two cultivars of bael, NB-9 and CISH B-2 were transplanted under square system of planting with 6 × 6 m spacing. The understorey of each plot was intercropped with perennial pastures (grass + range legume) viz; Cenchrus ciliaris + Stylosanthes seabrana to utilize interspaces available between rows. Tree growth, forage yield (green & dry), physico-chemical quality of bael fruit, nutrient buildup and moisture content were found to be better in different moisture conservation treatments over control (without treatment). During the fifth year, tree height (NB-9) ranges from 3.11 to 3.31 m and 2.99 to 3.42 m (CISH B-2) under different treatments. Collar diameter varied from 6.91 to 7.86 cm and 6.81 to 7.56 cm in NB-9 and CISH B-2, respectively. Tree canopy (EW) varied from 1.94 to 2.49 m and 1.68 to 2.17 m and canopy spread (NS) ranges from 1.84 to 2.21 m and 1.85 to 2.28 m in cultivars NB-9 and CISH B-2, respectively. Green and dry forage yield was found to be maximum in staggered trenches (17.88 t/ha and 5.78 tDM/ha). Fruit yield in NB-9 varied from 1.14 to 2.41 t/ha and cultivar CISH B-2 gave 1.74 t/ha fruit yield in staggered trench and 1.11 t/ha in control. Increased Nitrogen, Phosphorus and Potash buildup in soil of experimental site ranged from 3.84 to 15.16%, 7.05 to 21.1% and 4.73 to 15.53%, respectively. Moisture content in staggered trench was 37.08% (at 15 cm depth) and 33.51% (at 30 cm depth) higher than control. Overall productivity of fruit and pasture increased with application of in situ moisture conservation treatments in bael-based hortipasture system. Results revealed that bael-based hortipasture system is a potential agroforestry model for degraded land. [ABSTRACT FROM AUTHOR]

Past studies using observational data suggested an increase in day time temperatures and frequency and duration of heat waves over India during the pre-monsoon season (April–June). In this study, the characteristics (frequency and duration) of heat waves over India in future warming scenario has been examined using nine CMIP5 models. The RCP4.5 scenario and the period 2020–2064 were used for the analysis to examine the possible changes in the characteristics of heat waves. In spite of moderate biases in day time temperatures, the CMIP5 models showed modest skill in realistic simulation of observed heat waves in terms of spatial pattern and frequency. The models suggest an increase of about two heat waves and increase of 12–18 days in heat wave duration during the period 2020–2064. In the future climate change scenario, southern parts of India and coastal part of India which are presently unaffected by heat waves, are likely to be affected by heat waves. The spatial trend analysis of Heat Wave frequency (HWF) and Heat wave Duration (HWD) suggested that both the HWF and HWD will increase significantly over central and northwest India by 0.5 events per decade and 4–7 days per decade respectively. The study also suggests that the future increase in heat waves is caused due to strengthening of mid-tropospheric high and associated subsidence over central and northwest India. Land surface processes like depletion of soil moisture and increased sensible heat fluxes are also responsible for the increase in heat waves. The CMIP5 models also suggest that El Nino Modoki events may be responsible for the prolonged and more frequent future heat waves over India in the future climate scenario. [ABSTRACT FROM AUTHOR]