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Impact of rainfall and slopes on soil organic carbon (SOC) dynamics was assessed under tea and coffee plantations, land covers, in Western Ghats Mountain (WGM). Severe water erosion in the western side of WGM by heavy rainfall and land sloppiness recorded less SOC (3.07%) compared to the eastern side (4.03%) in tea plantations. Similar trend in SOC was observed in coffee plantations, with 1.98% in western side and 3.25% in eastern side. Loss of soil organic carbon stock under tea plantations in western sides was 0.346 million tonnes and in coffee plantations it was 0.190 million tonnes. Hence, the total organic carbon loss was 0.536 million tonnes. Highest SOC content and stocks were recorded in steep slopes in tea and coffee plantations due to dense biomass cover and adoption of soil and water conservation measures. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) at high spatial resolution is an essential land parameter for agricultural applications like irrigation mapping, scheduling, crop water stress assessment, etc. However, available satellite derived soil moisture products are inadequate for meeting the requirements of agricultural applications due to coarse scale soil moisture (~10–40 km). In this article, we developed an operational framework for first of its kind sub-km (~500 m) operational soil moisture product over India by utilizing ISRO’s EOS-04 C-band synthetic aperture radar (SAR) data based on active-passive approach. The potential of EOS-04 SAR for sub-km scale is demonstrated and tested over major cropland sites covering highly heterogeneous and dynamic crop conditions in different agro-climatic regions over India which shows a good agreement with in situ datasets with mean ubRMSE, ranging from 0.051 to 0.078 m³ /m³. [ABSTRACT FROM AUTHOR]

Stream flow forecasting is a crucial aspect of hydrology and water resource management. This study explores stream flow forecasting using two distinct models: the Soil and Water Assessment Tool (SWAT) and a hybrid M5P model tree. The research specifically targets the daily stream flow predictions at the MH Halli gauge stations, located along the Hemvati River in Karnataka, India. A 14-year dataset spanning from 2003 to 2017 is divided into two subsets for model calibration and validation. The SWAT model’s performance is evaluated by comparing its predictions to observed stream flow data. Residual time series values resulting from this comparison are then resolved using the M5P model tree. The findings reveal that the hybrid M5P tree model surpasses the SWAT model in terms of various evaluation metrics, including root-mean-square error, coefficient of determination (R² ), Nash-Sutcliffe efficiency, and degree of agreement (d) for the MH Halli stations. In conclusion, this study shows the effectiveness of the hybrid M5P tree model in stream flow forecasting. The research contributes valuable insights into improved water resource management and underscores the importance of selecting appropriate models based on their performance and suitability for specific hydrological forecasting tasks. [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]

A field experiment was conducted to investigate the optimum lateral spacing and irrigation frequency of a subsurface drip irrigation system for broccoli crop grown in the semi-arid region of Haryana (India). The effects on soil water dynamics, growth parameters (i.e. plant height, leaf length, no. of leaves/plant, curd weight and curd diameter), irrigation water use efficiency (IWUE), fertilize use efficiency (FUE) and yield response of broccoli were assessed under two lateral spacings (45 cm and 60 cm) and four irrigation frequencies (daily, and after one, two & three days) in the years of 2019–20 and 2020–21. The results show that soil moisture content was higher near the dripper, and it decreased with increasing radial distance from the drippers. Further, the moisture content was high at 45 cm lateral spacing as compared to 60 cm lateral spacing. The effects of selected treatments on growth and yield parameters were found significant and maximum broccoli yield (147. 41 q ha−1) was found at 45 cm lateral spacing with alternate days of irrigation. The highest IWUE and FUE of 9.93 kg/m3 and 30.68 kg/kg, respectively, were also found at alternate days irrigation with 45 cm lateral spacing. It may be concluded from the present study that alternate days irrigation with 45 cm lateral spacing is the best choice to obtain the maximum yield of broccoli grown in winter season in the semi-arid region of India. [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]

Characterizing soil salinity at the regional scale remains challenging despite decades of effort in soil mapping. Using satellite remote sensing, an effort has been made to identify the coastal saline soils in India. The study made use of the OLI sensor of the Landsat-8 satellite. The images were downloaded from the USGS EarthExplorer website. For multi-temporal studies, absolute radiometric calibration was done to minimize the impacts of changing atmospheric conditions, solar inclination and sensor view angle. Images were categorized using unsupervised classification, while a ground survey and Google Earth data were used for ground truthing. Three indices, namely normalized difference vegetation index (NDVI), salinity index (SI), and canopy response salinity index (CRSI), were used to identify soil salinity regions. For testing the vegetation index with soil salinity, 192 georeferenced soil samples from the Indian Sundarbans were collected. A relationship was developed between NDVI, SI and square of CRSI (CRSISQR) with ECe (electrical conductivity of saturation paste extract) and EC1:2 (1: 2; soil: water). For the coastal region, soils with CRSISQR < 0.16 were considered to be influenced by salinity since the relationship between ECe and CRSISQR had a maximum R² (0.50). It has been estimated that India has 12.94 lakh ha of saline soil within arable lands in all the coastal districts, and Gujarat (5.28 lakh ha), West Bengal (5.08 lakh ha), and Andhra Pradesh (1.06 lakh ha) were identified as the top three-salinity affected coastal states in the country. [ABSTRACT FROM AUTHOR]

Sustainable water resource management is the immediate priority as the rapid depletion of water resources is aggravating drought conditions in many regions worldwide. One of the possible approaches to recover from such a condition is through water resource balancing. The water balance studies require knowledge of water availability for various sectors' present and future requirements to balance the water resource. The present study uses the Palmer Drought Severity Index (PDSI) for water balancing. It evaluates the soil moisture deficit or surplus with spatial and temporal changes for the Marathwada region consisting of eight districts for 1957–2017. The analyzis shows water surplus from July to September and water deficit from November to May. The study observes that the Marathwada is drought-free during the Kharif season and water deficit in Rabi season. This study analyzes the need for crop-shift to avoid crop damage during drought. Based on the analyzis, all the urban system sectors need to augment short-term and long-term policies to overcome the water crisis for a balanced water resource. The Self-Calibrated PDSI (ScPDSI) is calculated based on PDSI, which gives more freedom in analyzing the drought period. Highlights Water balancing approach can effectively help in modelling the rapid depletion of water resources. This work expands the use of drought indicators for water balancing by evaluating the soil moisture deficit or surplus with spatial and temporal changes for the Marathwada region for 1957–2017. Urban system sectors may augment short-term and long-term policies based on the framework to overcome the water crisis for a balanced water resource. [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]

The representation of land–atmosphere coupling in forecast models can significantly impact weather prediction. A previous case study in Northern India incorporating both model and observational data identified atmospheric biases in a high‐resolution forecast linked to soil moisture that impacted the representation of the monsoon trough, an important driver of regional rainfall. The aim of the current work is to understand whether this behavior is present in operational forecasts run by the India National Centre for Medium Range Weather Forecasting (NCMRWF). We utilize satellite observations and reanalysis to evaluate model fields in June, July, August, and September forecasts from 2020. Our analysis reveals systematic rapid growth in warm boundary layer biases during the daytime over North West India, which weaken overnight, consistent with excessive daytime surface sensible heat flux. The cumulative effect of these biases produces temperatures more than 4K warmer in 60‐h forecasts. These effects are enhanced by dry surface conditions. The biases impact circulation in the forecasts, which have implications for regional rainfall. The spatial distribution of warm biases in the Indo‐Gangetic Plain is remarkably consistent with the location of areas equipped for irrigation, a process that is not explicitly represented in the model. Our results provide compelling evidence that the development of an irrigation scheme within the model is needed to address the substantial forecast biases that we document. [ABSTRACT FROM AUTHOR]

An accurate streamflow forecast is vital for flood prediction and early warning systems. Notwithstanding the rising frequency and intensity of floods during the summer monsoon season in India, efforts to examine the utility of data assimilation (DA) for streamflow prediction remain limited. We examine soil moisture and streamflow DA to improve streamflow simulations in the Narmada River basin, considered a testbed. Data assimilation was performed using the Variable Infiltration Capacity (VIC) model at four gauge stations in the basin. First, we used the ensemble Kalman filter (EnKF) to assimilate the satellite soil moisture from the European Space Agency Climate Change Initiative (ESA-CCI) to the initial state of the VIC model. We examined the usefulness of observed streamflow from the India Water Resources Information System (India-WRIS) to improve the initial hydrological conditions of the VIC model in the streamflow DA during the summer monsoon (June–September) season from 1980 to 2018. The assimilation of ESA-CCI soil moisture showed less improvement in percent error reduction (PER) and efficiency index (EFF) (less than 2%) than the streamflow DA at all of the four gauge locations in the Narmada basin. On the other hand, the streamflow DA showed a significant improvement in PER and EFF (more than 10%) at all the gauge stations for both mean and high-flow conditions. Streamflow data assimilation improved errors in the magnitude and timing for the major floods in 1994 and 2013. Significance Statement: India is considerably affected by floods during the summer monsoon. Floods have increased and are projected to become more frequent under the warming climate. Flood prediction and early warning systems are essential in reducing their impacts and exposure. In the present study, we are interested in examining the role of soil moisture and streamflow data assimilation on flood prediction in the Narmada River basin. Our results show that streamflow data assimilation is more effective in reducing errors and bias in streamflow prediction than soil moisture data assimilation. The hydrologic framework used for the Narmada basin can be extended and evaluated to other flood-prone basins in India. [ABSTRACT FROM AUTHOR]

Soil water‐groundwater interactions are important in determining the moisture profile in a soil column. However, the Noah Land Surface Model (Noah LSM), the land surface component of several general circulation models, does not consider groundwater effects. The present study investigates the impact of integrating a groundwater model into the Noah LSM. By performing experiments forced by observations at the surface, insights into the characteristics of soil moisture evolution with and without the aquifer are investigated. In the absence of surface precipitation, soil moisture is shown to exhibit an exponential decay rate without the aquifer (termed as Noah‐Cntl). This is because evapotranspiration dominates other processes governing the soil moisture evolution. Based on scale analysis, we derived an analytical equation that could well represent the aforementioned exponential variation in soil moisture. Presence of the aquifer (termed as Noah‐GW) makes the capillary processes important as well. The result is a slower decay of soil moisture as compared to Noah‐Cntl without surface precipitation. Soil moisture evolution in both the models is a function of vegetation and soil types. As a result, the aforementioned decay timescale displays significant heterogeneity over the Indian region, with Central India exhibiting relatively faster decay rates. We also performed 24‐year long experiments with observed interannually varying forcing that indicates an enhancement in the annual mean soil moisture at all levels due to groundwater effects. This enhancement is particularly prominent during the post‐monsoon season, reaffirming the results of the drain‐out experiments mentioned above. Plain Language Summary: Land surface processes are integral parts of weather and climate. Hence, an accurate representation of these processes is crucial from prediction and understanding points of view. However, state‐of‐the‐art land surface models do not incorporate some essential components that make these models erroneous. This study integrates a groundwater model into an existing land surface model and characterizes its impact on the evolution of soil moisture. We discuss factors affecting soil moisture decay rate over the Indian subcontinent without surface forcing. We show that the post‐monsoon and annual mean soil moisture increases in the presence of a groundwater model. However, spatial heterogeneity of groundwater effects is dependent on parameters like soil type and surface rain rate. Key Points: A prognostic model for groundwater is developed and integrated inside the Noah Land Surface ModelAnalytical solutions for the rate of change of soil moisture in the absence of surface water forcing are derived to explain model resultsPresence of an aquifer increases the soil moisture retention capability in the Noah Land surface model [ABSTRACT FROM AUTHOR]

Standardized precipitation index (SPI) is one of the frequently used meteorological drought indices. However, the time-varying characteristics observed in the historical precipitation data questions the reliability of SPI and motivated the development of nonstationary SPI. To overcome some of the limitations in the existing nonstationary drought indices, a new framework for drought index is proposed, incorporating the temporal dynamics in the precipitation. The proposed drought index is developed by coupling the joint deficit index with the extended time sliding window–based nonstationary modeling (TSW-NSM). The proposed nonstationary joint deficit index (NJDI) detects the signature of nonstationarity in the distribution parameter and models both long-term (i.e., trend) and short-term (i.e., step-change) temporal dynamics of distribution parameters. The efficacy of NJDI is demonstrated by employing it to identify the meteorological drought-prone areas over India. The changes observed in the distribution parameter of rainfall series reveal an increasing number of dry days in recent decades all over India, except the northeast. Comparison of NJDI and stationary joint deficit index (JDI) reveals that JDI overestimates drought when frequent severe dry events are clustered and underestimates when these events are scattered, which indicates that the traditional index is biased toward the lowest magnitude of precipitation while classifying the drought. Moreover, NJDI could closely capture historical droughts and their spatial variations, thereby reflecting the temporal dynamics of rainfall series and the changes in the pattern of dry events over India. NJDI proves to be a potentially reliable index for drought monitoring in a nonstationary climate. Significance Statement: Drought is one of the most severe natural disasters and is expected to intensify under a warming climate. There has been progress on developing newer methodologies to characterize drought severity under the changing climate. However, some limitations remain in capturing the temporal changes of the precipitation, especially the nonstationarity in variance (rapid increases in extreme precipitation versus the average precipitation). We propose an extension to the time sliding window approach to capture the nonstationarity in variance and mean while incorporating short-term (step-change) and long-term (trend) temporal dynamics. We apply the methodology to a subcontinent-sized heterogeneous country of India with gridded rainfall dataset (0.25° × 0.25°, 1901–2013) from the India Meteorological Department. We find that the majority of grids exhibit negative and positive trends in shape and scale parameters, respectively, which ultimately leads to an increase in the number of drier events, whereas a contradictory pattern is exhibited in the northwest Indian region (decrease in the number of dry days). The proposed index gives a potential framework for drought monitoring under the changing climate and can be extended to develop a multivariate nonstationary joint deficit index by incorporating other hydrological variables (e.g., soil moisture, diurnal temperature range, streamflow). [ABSTRACT FROM AUTHOR]

Plants produce variety of secondary metabolites in response to stress. The study aimed to evaluate the soil moisture stress on oil content & composition of German chamomile conducted in, 2020 and 2021 at CSIR-Central Institute of Medicinal and Aromatic Plant, Research Centre, Pantnagar, Uttarakhand, India. The treatments were T1 (just before irrigation), T2 (24 h after irrigation), T3 (72 h after irrigation), T4 (120 h after irrigation), and T5 (168 h after irrigation). Results showed that maximum oil content (0.26 & 0.57 ml/100g) was found in fresh and dry flowers, respectively of T2 & oil content decreased as the days to irrigation increased. GC results showed that the percentage of major compounds was lowest (74.02%) while maximum in acute drought conditions i.e. before irrigation (92.12%). Results suggest that soil moisture stress reduces the oil content however, the quality of oil is enhanced due to increased production of secondary metabolites. [ABSTRACT FROM AUTHOR]

Leaf litter decomposition and nutrient release dynamics of Flemingia semialata Roxb. were studied from May 2016 to April 2017 using the litter bag technique. The study on litter dynamics is needed to assess the nutrient release pattern before introduction in degraded or marginal lands or agroforestry systems for soil nutrient management practices in hilly northeast states of India. The initial N concentration, lignin content, and lignin/N ratio of leaf litter had significant positive correlations with decay coefficients. The annual decay rate constant (k) for dry matter was 0.01 day−1 (3.65 year−1) with t50 and t99 values of 96.43 and 695.73 days, respectively, indicating faster leaf litter decomposition. Initial C (51%), N (2.38%), and low lignin concentrations with low C/N (21.53) and lignin/N (4.05) ratios could be important factors in faster mass loss from the litter samples. The P mineralization kP (2.19) rate was higher than kN (0.73), indicating the slower release rate of N. The weight loss during decomposition was positively correlated with rainfall (r = 0.42) and temperature (r = 0.39). The study suggests that F. semialata leaf litter could be useful for regulating soil nutrient pool through faster litter turnover and can therefore be exploited as plantation and/or agroforestry species for soil and water conservation in degraded slopy terrain of the region. [ABSTRACT FROM AUTHOR]

In this study, an experiment was conducted to investigate the optimum lateral spacing and irrigation frequency for subsurface drip irrigated okra in the semi-arid region of Haryana (India). Two lateral spacings (45 cm and 60 cm) and four irrigation frequencies (daily, after 1, 2 and 3 days) were selected to grow okra in the Kharif season of 2019 and 2020. The effect on soil water dynamics, growth parameters, efficiency and yield were assessed using equal amounts of water under all the treatments on the basis of pan evaporation. The results from the study depict that the overall soil moisture decreased laterally, but increased vertically downward with the increase in the irrigation interval. On the basis of soil water dynamics, plant growth parameters, efficiency and yield of okra, it was concluded that subsurface drip irrigation with daily irrigation at 45 cm lateral spacing gives better performance than all other treatments in sandy loam soil. The present study highlights the significance of proper irrigation frequency and lateral spacing for maximum production of okra. Using these guidelines, the income of okra growers/farmers in the semi-arid region may be increased by choosing the best frequency and lateral spacing of subsurface drip irrigation. [ABSTRACT FROM AUTHOR]

Soil moisture deficit is an essential element in the estimation of irrigation demands, both spatially and temporarily. The determination of temporal and spatial variations of soil moisture in a river basin is challenging in many aspects; however, distributed hydrological modelling with remote sensing inputs is an effective way to determine soil moisture. In this research, a water demand module was developed for a satellite-based National Hydrological Model—India (NHM-I) to estimate distributed irrigation demands based on soil moisture deficits. The NHM-I is a conceptual distributed model that was explicitly developed to utilize the products from remote sensing satellites. MOD13Q1.5 data were used in this study to classify paddy and irrigated dry crops. Along with the above data, the DEM, Leaf Area Index, FAO soil map, and crop characteristics data were also used as inputs. The NHM-I with water demand module was evaluated in the Damodar river basin, India, from 2009 to 2018. The integrated NHM-I model simulated the irrigation demands effectively with remote sensing data. The temporal analysis reveals that soil moisture deficits in the Kharif season varied annually from 2009 to 2018; however, soil moisture deficits in the Rabi season were almost constant. The 50% Allowable Moisture Depletion (AMD-50) scenario can reduce the irrigation demand of 1966 MCM compared to the Zero Allowable Moisture Depletion (AMD-0) scenario. The highest annual irrigation demand (8923 MCM) under the AMD-50 scenario occurred in the 2015–2016 season, while the lowest (6344 MCM) happened in 2013–2014 season. With a new water demand module and remote sensing inputs, the NHM-I will provide a platform to assess spatial and temporal irrigation demands and soil moisture. [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]

India's climate is semi-arid, and most of the people in this country depend on agriculture. In this article, we present the long-term relationships of the satellite-derived Normalized Difference Vegetation Index (NDVI) with the rainfall, land surface temperature (LST), surface soil moisture (SSM), and the groundwater storage (GWS) during the Kharif (June–October) season of the monsoon core region (MCR) of India from the year 2000 to 2018. Results show that the NDVI could capture the stages of crop phenology and that it undergoes interannual variability characterized by the El Niño Southern Oscillation. A strong dependence of NDVI on SSM and GWS is evidenced by the statistically significant correlation coefficients of +0.89 and +0.88, respectively. A substantial negative correlation coefficient of −0.91 is observed between NDVI and LST. Wavelet coherence spectrum suggests the in-phase relation of NDVI with rainfall, SSM, GWS, and anti-phase connection with LST, with weaker correlations from 2004 to 2008. The results of this study attain significance since the studies on NDVI and hydrologic variables are much less than those on NDVI and climate variables over the MCR of India. [ABSTRACT FROM AUTHOR]

As a natural hazard, drought is a complex multivariate phenomenon that does not depend on only one hydrometeorological variable, as well as, considering a particular kind of drought may not be effective for drought management. Considering this, many multivariate drought indices have been developed based on linearity assumptions or conventional copulas assuming symmetric relationships among univariate drought indices. In this study, D‐vine copula was applied to construct a four‐dimensional index, named as Integrated Drought Index (IDI), by combining four univariate drought indices (Standardized Precipitation Index [SPI], Reconnaissance Drought Index [RDI], Standardized Soil moisture Index [SSI] and Standardized stream flow Drought Index [SDI]) to better reflect many hydrometeorological variables (precipitation, evapotranspiration, soil moisture and stream flow) and different kinds of drought (meteorological, agricultural and hydrological) simultaneously. Vine copula was used to solve nonlinear and asymmetric relationships among drought indices due to its flexibility over the free selection of copula(s) in each step of hierarchical structure in high dimensional modelling. The IDI was constructed for 1‐ and 4‐month timescales for the upper Tapti basin of the central region in India. The performance of IDI was tested with dependence measures (Pearson's correlation coefficient, Mutual Information) and evaluated against the Terrestrial Water Storage Anomaly data derived from the Gravity Recovery and Climate Experiment (GRACE) mission. Spatial analysis of drought was carried out by fuzzy c‐means (FCM) clustering algorithm with IDI. IDI based on vine copula solved the nonlinear and asymmetric relationships among different variables associated with the occurrence of droughts effectively with a reduction of uncertainty as compared to the single drought indices for different kinds of droughts. Analysis revealed spatially different drought risks in the upper and lower river basins. In general, the vine copula addresses nonlinear and asymmetric relationships that exist between the variables associated with natural hazards like drought. [ABSTRACT FROM AUTHOR]

This study highlights the land use/land cover change, and its implications on the long-term changes in various hydrometeorological parameters in India during 2001–2018. The study exhibited a decrease in grassland (15.1%), barren land (10.2%), and shrubland (9.5%) in contrast to an increase in deciduous broadleaf forest cover (27.4%) and croplands (25.2%) in India. The study exhibited 31.7% of the geographical area observed a low warming (<0.8°C), in contrast to 52.1% of the area observed a low cooling (> 0.8 °C) in India over the observation period. The transformation of (a) barren land to shrubland and cropland (Western India) and (b) cropland, and grassland to forest cover (Central India) led to the cooling effects. While Indo-Gangetic Plain and North-Eastern parts observed warming effects due to greenness and wetness change. The findings highlight the relevance of bio-geophysical and land-climate feedback, which may help in developing integrated and effective climate mitigation and adaptation strategies. [ABSTRACT FROM AUTHOR]

Depletion of water resources and soil moisture leading to drought is a global concern and the effective assessment and monitoring using a drought model has become essential. A detailed account of frequency, run length and temporal trend of the drought events are presented in the study from 1981 to 2019 at the 1-, 3-, 6-, 9-, 12- and 24-month timescale using the Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index. SPI recorded more drought months in the extreme category with 14 months at Gubbi station for example, while SPEI showed only 5 months. SPEI showed longer drought length in moderate and severe categories for agricultural and hydrological drought. At a 1- month timescale, both the indices reported extreme drought events where Tiptur station in May 2016 (–4.75) and Chikkanayakanahalli station in March 1992 (–2.76) were the worst-case scenarios. The study aims at providing practical results to facilitate decision-makers for drought risk management. [ABSTRACT FROM AUTHOR]

Significant changes in the photosynthesis of terrestrial vegetation are observed in recent decades due to climate variability, land use and land cover changes, frequent disasters and anthropogenic forcing, but this situation in the Indian context remains largely unexplored. Here, therefore, we estimate the long–term changes in photosynthesis and its key climate drivers in India. The gross photosynthetic activity peaks (FPAR mean = 0.57) in post–monsoon (September, October and November) due to high soil moisture (87.26 kg/m² ), good precipitation (5.2 mm/day) and optimum temperature (21.46 C) conditions, whereas it is smallest (FPAR mean = 0.4) in summer (March, April and May) due to very high temperature (27.37 C) and lower soil moisture (55.45 kg/m2 ). The trend in FPAR over 2000–2019 reveals enhanced photosynthetic activity in India. In the northwest, both soil moisture and temperature have a positive influence on greening (i.e. moisture induced greening). Conversely, the unfavourable soil moisture and temperature conditions dominate in the peninsular region to trigger browning there (i.e. warming induced moisture stress). Our analyses suggest that the photosynthetic trend is primarily controlled by trend in the soil moisture (44.31%) and air temperature (32.54%). The warming induced moisture stress can be a serious threat in the future to drive browning in more areas. Extreme events such as droughts, landslides and fires are expected to increase in the hilly regions such as the northeast due to browning. Furthermore, as food production and forest resources influence the economy of a country like India, management and conservation of greenery are inevitable for a sustainable ecosystem. [ABSTRACT FROM AUTHOR]

Land Suitability Analysis (LSA) is one of the most important methods for developing a precise land suitability map. The present study was aimed to identify suitable paddy crop-growing regions in the Koch Bihar district of West Bengal, India. For the study, 18 criteria were chosen, and their weightage was assigned based on expert opinion and the GIS-based Fuzzy Analytical Hierarchy process (F-AHP) method adopted. Results indicated the most important factors for developing LSA for paddy cultivation are rainfall (16.72%), soil texture (13.22%), slope (11.55%), elevation (09.63%), soil moisture (08.27%), temperature (07.11%), and pH (05.03%). It has been observed that highly suitable (14.54%), moderately suitable (46.07%), marginally suitable (24.20%), and not suitable (15.19%) areas for paddy cultivation. The Area Under Curve (AUC) value (0.869) confirms the reliability of the F-AHP approach in LSA. The findings of the study provide sustainable agricultural planning and delineate potential sites for paddy cultivation. [ABSTRACT FROM AUTHOR]

Drought is primarily considered as a natural hazard which is a resultant phenomenon of less rainfall. When an agricultural area constantly received low rainfall during the sowing and growing season of crop, the soil moisture of the cropland goes down and the crop began to wilt, the situation is known as agricultural drought. Present study, used the multi-temporal Sentinel 1 SAR and Landsat optical data to assess the agricultural drought in the part of Odisha state. Initially, to establish the methodology, correlation between NDVI and SAR backscattering has been carried out for the demarcated agricultural landscape of MODIS images derived landcover map of 2019. The drought assessment has been carried out for the month of Jun-July of 2020 and 2021 which is the kharif crop growing season. Thereafter, the backscattering vale range has been identified as a threshold value to delineate the agricultural drought affected areas from other SAR images of various vintages. The study area was more affected by the drought in 2020 compared to 2021. In this study, it is observed that although the area is drought affected, received less rainfall, but due to location in coastal region this area is mostly under cloud cover, therefore, it is very difficult assess the agricultural drought using optical remote sensing data. SAR data is also sensitive to soil moisture condition; therefore, it can be potentially to be used for drought assessment for the coastal agricultural area. [ABSTRACT FROM AUTHOR]

The reported study was undertaken to determine which soil health indicators showed measurable signs of improvement, during the first year of the process of introducing a Conservation Agriculture (CA) cropping system in rainfed areas in Madhya Pradesh, India. Soil health indicators of soil aggregate stability, soil-atmosphere CO2 fluxes, water infiltration, soil moisture, potentially mineralisable nitrogen, soil organic content and bulk density were measured. Results demonstrate that generally, there were improvements in all measured soil health indicators in CA soils, with decrease in CO2 emissions and increase in soil aggregates being statistically significant. [ABSTRACT FROM AUTHOR]

Summary: Soil and water are crucial resources for agriculture, especially in arid and semi-arid rain-fed areas, yet farm-level economic impacts and the factors influencing the adoption of measures for their conservation are little studied. The present study used data from 400 farm households to assess factors influencing the adoption of soil and water conservation measures (SWCMs) and their impacts on farm productivity and income in a semi-arid region of central India. We employed a probit model to determine the factors influencing the on-farm adoption of SWCMs and a propensity score matching technique for assessing their impacts. The findings indicate that farmer age and education, off-farm income, farm size and land ownership and access to training are key drivers of the adoption of SWCMs. SWCMs accentuated the input costs by INR 1689–2847 per ha during the rabi cropping season (October–February), but also increased crop productivity and net revenue from farming. The impact in the rabi season was less sensitive to the unobserved confounders than in the kharif season (June–September). Therefore, SWCMs could represent an important strategy for unlocking the cultivation potential of large rain-fed areas and for sustaining the livelihoods of farm households in the ecologically fragile arid and semi-arid tropics. [ABSTRACT FROM AUTHOR]

The 2019 extreme positive Indian Ocean dipole drove climate extremes over Indian Ocean rim countries with unclear carbon‐cycle responses. We investigated its impact on net biome productivity (NBP) and its constituent fluxes, using the Global Carbon Assimilation System (GCASv2) product, process‐based model simulations from TRENDYv9, and satellite‐based gross primary productivity (GPP). By distinguishing two separate regions, the India‐Africa and Asia‐Pacific, GCASv2 indicated enhanced terrestrial carbon uptake of 0.23 ± 0.20 PgC and release of 0.38 ± 0.15 PgC, respectively, during September–December (SOND) 2019. These NBP anomalies had comparable magnitudes to those following the 2015 extreme El Niño which, however, caused the consistent carbon release in both regions. The TRENDYv9 model ensemble confirmed these NBP responses, albeit with smaller magnitudes. These regional NBP anomalies were related to soil moisture variations with a dominant role of GPP. Understanding the impact of IOD provides new insights into mechanisms driving interannual variations in regional carbon cycling. Plain Language Summary: The extreme Indian Ocean Dipole (IOD) can drive climate extremes, such as floods, heatwaves, droughts, and wildfires, over the Indian Ocean rim countries. However, responses of regional terrestrial carbon cycling to IOD remained unclear. We used the net biome productivity (NBP) from an atmospheric inversion and multiple terrestrial biosphere models to demonstrate an enhanced terrestrial carbon uptake and release over the India‐Africa and Asia‐Pacific regions, respectively, during the extreme positive IOD (September–December) in 2019. These IOD‐induced regional NBP anomalies showed comparable magnitudes but different patterns to those following the 2015 extreme El Niño. Along with the more frequent extreme IOD under future greenhouse warming, IOD will be an important mechanism driving interannual variations in regional carbon cycling. Key Points: The 2019 extreme positive Indian Ocean Dipole caused the enhanced land carbon uptake over India‐Africa and release over Asia‐Pacific during September–DecemberThese regional net biome productivity (NBP) anomalies were closely related to soil moisture variations with a dominant role of gross primary productivityThese Indian Ocean Dipole‐induced regional NBP anomalies showed comparable magnitudes but different patterns to those following the 2015 extreme El Niño [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]

The current study is an applied analysis of sediment hydraulic process to estimate and forecast the fluvial scenario of Peddavagu. a tributary of Pranhita Sub-basin of Godavari river basin in India. The peculiarity of the drainage basin is that the catchment area under study falls in a heavy rainfall zone where the average annual rainfall is about 1040 min and the catchment area receives more than 90 percent of the rain during the South-West Monsoon (June to October) thereby consequently all the inflow is received between June and October only. Further. high floods are generally expected. between August and September. thereafter the flow in the river diminishes down considerably. A comparative performance of the applied methodologies evaluated and presented to understand the response of drainage basin in the study. Artificial Neural Networks (ANNs) and conventional Sediment Rating Curves (SRC) were applied to precisely estimate the sediment transportation at the initial stage later Soil Water Assessment Tool (SWAT) methodology applied to simulate and model the same sediment transport response of the drainage basin by considering widely varied hydrologic parameters. The present study mainly focuses on the precise estimation of the event and to see the other hydrological components influence in a broader sense. Hence, SWAT was adapted to incorporate more hydrological parameters even though the analysis results in lesser values of performance indices for better understanding of the system behavior. Therefore. the performance of the selectec two models was carried out for the appropriate prediction of average monthly runoff values during thi monsoon and non-monsoon seasons. In view of the above. for the SWAT model. an appropriate set 01 databases (DEM. landuse / landcover. soil. precipitation. and temperature. etc.) is needed which wil entail good performance (NSE) of the model. while ANN is a linear model needs typically dependeni and independent parameters. Keeping the objective problem in mind. an attempt has been made tc check the performance of both models. when the input data was limited and scarce. [ABSTRACT FROM AUTHOR]

Sensor-based irrigation scheduling in field crops like wheat for achieving optimum yield with minimum use of water needs investigation under different amendment practices. The present study was thus conducted to investigate the effect of four irrigation regimes i.e., soil moisture indicator (red level of indication, SMI), tensiometer (35 k Pa), IW/Pan-E 0.9, and IW/Pan-E 0.6 in main plots and four organic amendments i.e., farm yard manure (FYM), rice straw mulch, hydrogel and without amendments in subplots on soil physical properties and wheat productivity. The field experiment was laid out in split plot design with three replications during rabi 2020–21 at Research Farm, Department of Soil Science, PAU, Ludhiana. The use of SMI shows promising results in respect of scheduling irrigation in wheat as similar biological yield with same amount of water was achieved as that observed for recommended practice i.e., IW/PAN-E = 0.9. The results further indicate that the use of amendments, particularly FYM, leads to reduction in soil bulk density by 4%, while the soil moisture content at field capacity and water-holding capacity by 14% and 9.3%. It is, thus, concluded that scheduling irrigation based on SMI along with FYM in wheat is a suitable practice for northwest India. [ABSTRACT FROM AUTHOR]

The study investigates the influence of near-surface atmospheric parameters on land surface processes at the land–atmosphere interface through the offline simulation of the 2D Noah Land Surface Model-based High-Resolution Land Data Assimilation System (HRLDAS). The HRLDAS is used to conduct sensitive experiments by introducing perturbation in the atmospheric parameters, and the experiments were conducted for the period 2011–2013 in India. In each sensitive experiment, a single parameter is perturbed at a time, keeping the rest of the forcing parameters unchanged, and the procedure is followed for all the forcing parameters. The results revealed that the downward longwave radiation and T2 are highly sensitive to land surface processes, while wind speed is the least sensitive. The land surface process sensitivity varies with soil moisture content. The annual mean soil moisture at the surface layer is increased (decreased) by 8% when long wave radiation is decreased (increased) by 20%. Similarly, the annual mean soil temperature increased (decreased) by 2.2 °C when T2 increased (decreased) by 1%. The latent heat flux is highly sensitive to longwave radiation over the wetter soil, while its sensitivity to rainfall is higher over the drier soil. This is attributed to evapotranspiration's sensitivity to the preferred soil moisture state. Further, the land surface sensitivity varies with contrasting seasons. The sensitivity of soil moisture and latent heat flux is high in OND and JJA seasons, respectively, and are least sensitive in the MAM season. In contrast, the sensible heat flux is highly sensitive to solar radiation in the MAM season and comparatively less sensitive in the JJA season. The study suggests that the antecedent soil moisture state plays a critical role in modulating land surface process sensitivity, and, therefore, a realistic soil moisture state is important for land surface feedback processes. [ABSTRACT FROM AUTHOR]

Drought assessment in any region primarily hinges on precipitation deficiency, which is subsequently propagated to various components and sectors, leading to different drought types. In countries such as India, an intricate relationship between various governing factors, drought types, and their quantification methodologies make it elusive to timely initiate government relief measures. This also prevents comprehensive inclusion of the integrated effect of the principal drivers of drought, resulting in ambiguous categorization of severity, where groundwater storage variability is often neglected despite its significant role in irrigation. Here, we developed a multivariate Joint Drought Index (JDI) combining satellite and model-based standardized indices of precipitation and evapotranspiration (SPEI), soil moisture (SSI), groundwater (SGI), and surface runoff (SRI) with different temporal scales by employing two robust methods, principal component analysis (PCA) and Gaussian copula, and applied the index to highly drought-prone Marathwada region from central India. Our novel approach of using different scale combinations of integrated indices for two primary seasons (Kharif and Rabi) provides more realistic drought intensities than multiple univariate indices, by incorporating the response from each index, representing the seasonal drought conditions corroborating with the seasonal crop yields. JDI, with both methods, successfully identified two major drought events in 2015 and 2018, while effectively capturing the groundwater drought. Moreover, despite the high correlation between JDI using PCA and copula, we observed a significant difference in the intensities reported by these methods, where copula detected exceptional drought conditions more frequently than PCA. JDI effectively detected the onset, duration, and termination of drought, where the improved accuracy of drought detection can play a critical role in policy formation and socioeconomic security of the related stakeholders. Seasonal agriculture drought categorization for holistic quantification of drought conditions as presented in this study should provide broad methodological implications on drought monitoring and mitigation measures, especially for agriculture-dominated regions in semiarid climates. [ABSTRACT FROM AUTHOR]

Drought poses a tremendous challenge to India's socioeconomic development, livelihood, agriculture, and water management. While existing drought monitoring systems have characterized drought impact at different scales, policymaking and management require drought assessment at sub-district or taluka (sub-district) levels. Here, we develop high resolution (250 m) agriculture drought indices for the Indian region to overcome the shortcomings of the coarse resolution datasets. We used the co-kriging to downscale the Land Surface Temperature (LST) from 1000m to 250m. The LST and Enhanced Vegetation Index (EVI) are obtained at 8-day intervals at 250m spatial resolution. The high-resolution datasets show significant improvement in identifying the severity and coverage of drought. Soil Moisture Agriculture Drought Index (SMADI), which accounts for water stress and vegetation lag response, shows high reliability in drought detection. We evaluated drought extent and severity using the newly developed dataset and found that the high-resolution dataset can be used to separate the irrigation impact on drought alleviation. The high resolution drought indices from SMADI and the Normalized Vegetation Supply Water Index (NVSWI) effectively represent the drought conditions at district and taluka levels that can be used in drought impacts assessments in India. [ABSTRACT FROM AUTHOR]

The efforts to develop a hydrologic model-based operational streamflow forecast in India are limited. We evaluate the role of bias correction of meteorological forecasts and streamflow postprocessing on hydrological prediction skill in India. We use the Variable Infiltration Capacity (VIC) model to simulate runoff and root-zone soil moisture in the Narmada basin (drainage area: 97 410 km²), which was used as a testbed to examine the forecast skill along with the observed streamflow. We evaluated meteorological and hydrological forecasts during the monsoon (June--September) season for the 2000--18 period. The raw meteorological forecast displayed relatively low skill against the observed precipitation at 1--3-day lead time during the monsoon season. Similarly, the forecast skill was low with mean normalized root-meansquare error (NRMSE) more than 0.9 and mean absolute bias larger than 60% for extreme precipitation at the 1--3-day lead time. We used empirical quantile mapping (EQM) to bias-correct precipitation forecasts. The bias correction of precipitation forecasts resulted in significant improvement in the precipitation forecast skill. Runoff and root-zone soil moisture forecasts were also significantly improved due to bias correction of precipitation forecasts where the forecast evaluation is performed against the reference model run. However, bias correction of precipitation forecasts did not cause considerable improvement in the streamflow prediction. Bias correction of streamflow forecasts performs better than the streamflow forecasts simulated using the bias-corrected meteorological forecast. The combination of the bias correction of precipitation forecasts and postprocessing of streamflow resulted in a significant improvement in the streamflow prediction (reduction in bias from 40%to 5%). [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]

[Display omitted] • Soil properties and enzymatic activities varied across land use types and villages. • Agroforestry establishment accelerated soil enzymatic activities by 39.41–90.17%. • Cultivation caused massive decline in level and variability of enzymatic activities. • Climate, vegetation, and management practices determined the enzymatic activities. Soil microbial enzymatic activities, often used as indicators of soil health and fertility, are vulnerable to changes in climate and anthropogenic activities. This study analyses the responses of specific soil enzymatic activities– dehydrogenase (DHA), acid phosphatase (APH), β-glucosidase (BG) and urease (URE)– in Nitosols/Luvisols of the subtropical zone of the Central Himalaya, India. We investigated how these enzymatic activities vary with different land uses: arable land (AL), kitchen garden (KG) and peach orchard (PO), across four villages– Niglat, Kainchidham, Majhera and Syalikhet. Considering associated management practices, we quantified cumulative average soil enzymatic activities for the rabi season (2017–2018). In general, catalytic activity of the selected enzymes was highest in Syalikhet and lowest in Majhera. However, for land use types, enzymatic activities were lower in AL and KG (75.56–90.17% and 39.41–61.72%, respectively) compared to PO values. MANOVA analysis evaluated significant spatial differences (P < 0.001) in enzymatic activities. Soil enzymatic mean values were highest in the surface layer (0–10 cm) and decreased gradually with increasing depth. Parameters such as soil moisture content (SMC) and pH, along with vegetation, management practices, and nutrients such as SOC, TN, K, AP, and N forms (NH 4 +–N and NO 3 ––N) showed significant and strong correlations with all the soil enzyme activities (r = 0.40–0.97; P < 0.05). TN, SOC, K, SMC, pH, AP, NH 4 +- N, and clay content were key determining factors of soil enzymatic activities, explaining approximately 59% of the variation. These results suggest that using soil microbial enzymatic activities as soil quality indicators is a feasible approach to assess the impacts of vegetation, management practices, and soil profile depths in soils under different land use types/systems in selected impoverished villages of the fragile Central Himalaya, India. [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]

Soil salinity and seasonal dynamics are typical soil characteristics along the west coast of India. The low-to-high soil/water salinity are classified based on the nature, content and composition of salts: the salinity (SAR) and sodicity (ESP, RSC) hazards. Soil salinity has been characterized based on typical reflectance in 427, 487, 950, 1414, 1917, 2206, 2380 and 2460 nm bands using hyper-spectral remote sensing (HRS). The inverse relation between soil salinity and spectral reflectance indicated the hygroscopic properties of dominant salt. Recent studies on spectral properties of saline soils (EC 3.2 to 30.46 dS m−1) have revealed an increasing trend from visible to SWIR1 and a dip at SWIR2 bands. Spectral properties of saline–sodic soils in IGP revealed i) a higher spectral response from dry-barren surface of a strongly (pHs >10) sodic soil and ii) a change in spectral response pattern due to vegetative cover and higher moisture content in irrigated soils. The results suggest for further validation of spectral data using natural salts. Studies on technical grade salts have indicated a relatively high reflectance of NaCl and CaCO3 than Na2CO3, NaHCO3, Na2SO4, CaSO4.2H2O (gypsum) and pyrite (FeS) salts; the later show a decreasing trend at higher wavelengths. Hygroscopic salts have shown prominent energy absorption of water molecules at 1400, 1900 and 2250 nm. The reflectance from surface soils varies with colour, texture, structure and surface roughness. Statistical and index-based models have also been successfully used for the diagnosis of soil salinity. The recommended methods for soil analysis, the available forms of nutrients and spatial variability studies are suggested for better salinity appraisal. [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]