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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]

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]

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]

This observational study mainly examines the impact of rainfall on Indian soil moisture (SM) variability in four homogeneous rainfall zones (i.e., central India (CI), northwest India (NWI), south peninsula India (SPIN), and northeast India (NEI)) as defined by India Meteorological Department (IMD) during strong, weak, and normal Indian summer monsoons (ISMs), which are determined regionally for each homogeneous rainfall zone separately. This study uses the daily gridded (0.25° × 0.25°) rainfall data set provided by IMD and the daily gridded (0.25° × 0.25°) SM combined product version 06.1 from European Space Agency Climate Change Initiative (ESA CCI) over the period 1992–2020. Results reveal that monthly and seasonal mean SM in NWI, CI, and SPIN are overall higher during strong than during weak ISMs. The daily SM and its dependence on rainfall appear to be region-locked in space and phase-locked in time: Strong correlation and large response to rainfall generally occur in most parts of SPIN and NWI during June (June–July) of strong (weak) ISMs where SM values are relatively small; Weak correlation and small response generally occur in CI and NEI in July-September (August–September) of strong (weak) ISMs where SM values are relatively large. The phase-locked feature is associated with the features of ISMs. The region-locked feature is presumably associated with the local features, such as soil and vegetation types and/or environmental conditions. Both region-locked and phase-locked features cause regional distinct features in SM persistence. [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]

Soil moisture plays an essential role in food production and land‐atmosphere feedback. However, the relative contribution of precipitation and air temperature on dry season (October–December) soil moisture has not been systematically examined in India. Using observations and simulations from the Variable Infiltration Capacity model, a significant (4%, p value = 0.04) decline in soil moisture (dry season drying hereafter) was found in a large part of India during the 1951–2018. The decline in the summer monsoon precipitation in India during 1951–2018 is mainly due to a decrease in rainfall during the late monsoon season (August–September). A substantial decline (−18.6%, p value = 0.009) in the late monsoon and (−26%, p value = 0.19) dry season precipitation was observed. A significant warming (0.75°C, p value = 0.002) has occurred during the dry season between 1951 and 2018. The relative contribution of decline in precipitation in the late monsoon (30%) and dry season (34%) is considerably higher than the dry season warming (15%). The previously unexplored role of climate warming on the dry season drying was analyzed using Maximum Covariance Analysis between precipitation and sea surface temperature, which suggests both large‐scale and localized influences. Climate warming over the Indian and Atlantic Oceans is associated with declining precipitation during the late monsoon and dry seasons over central India. Also, climate warming over land exacerbates the dry season drying by depleting soil moisture. The dry season drying over a large part of India contributed by climate warming has substantial implications for irrigation water management and groundwater abstraction. Key Points: A well‐calibrated hydrology model was used to show a significant decline in the dry season soil moisture during 1951–2018The decline in soil moisture is associated with the decline in the late monsoon and dry season precipitation and dry season warmingThe climate warming contributes to the dry season drying by reducing precipitation and by depleting soil moisture [ABSTRACT FROM AUTHOR]

Understanding the spatial and temporal variations of soil moisture is important since the feedback between soil moisture and precipitation is a key control of the climate. This is also important as soil moisture data is a key input in regional climate models. The inter‐annual and inter‐decadal variability of soil moisture has been investigated here using the European Space Agency's Climate Change Initiative active and passive combined soil moisture product over different climate regimes of India. The observed soil moisture and rainfall are positively correlated and their relationship in this study suggests that both positive and negative feedback occur. The analysis showed an increasing soil moisture trend with a significant increase in the monsoon and autumn seasons for the past two decades over central India, where the frequency of extreme precipitation increased in the same period. An overall increase in soil moisture over the western arid region is also significant. A decreasing trend in soil moisture prevailed throughout the monsoon season over eastern India, where monsoon depressions make landfall, signalling the recent climate trends over the Indian subcontinent. [ABSTRACT FROM AUTHOR]

Hydrological components in a river basin can get adversely affected by climate change in coming future. Manipur River basin lies in the extreme northeast region of India nestled in the lesser Himalayan ranges and it is under severe pressure from anthropogenic and natural factors. Basin is un-gauged as it lies in remote location and suffering from large data scarcity. This paper explores the impact of climate change towards understanding the inter-relationships between various complex hydrological factors in the river basin. An integrated approach is applied by coupling Soil and Water Assessment Hydrological Model and Hadley Center Coupled Model based on temperature, rainfall and geospatial data. Future representative concentration pathways 2.6, 4.5 and 8.5 scenarios for 2050s and 2090s decades were used to evaluate the effects of climatic changes on hydrological parameters. Both annual mean temperature and annual precipitation is predicted to be increased by 2.07oC and 62% under RCP 8.5 by the end of 21st century. This study highlights that change in meteorological parameters will lead to significant change in the hydrological regime of the basin. Runoff, actual evapotranspiration and water yield are expected to be increased by 40.96 m3/s, 52.2% and 86.8% respectively under RCP 8.5. This study shows that water yield and evapotranspiration will be most affected by increase in precipitation and temperature in the upper and middle subbasins. Different region within the basin is likely to be affected by frequent landslides and flood in coming decades. [ABSTRACT FROM AUTHOR]

Changes in ecosystem structure and function can be revealed by examining the prevailing patterns in vegetation growth and the forces that shape those patterns. The mechanism of ecosystem behaviour may be better understood if the trend of vegetation change and its sensitivity to climatic variation are well understood. The interaction of vegetation and climatic factors (it's driving variables) is non-linear in behaviour and affected by time lag and time accumulation. Jharkhand has a typical plateau in eastern India, having a mixed climate (arid and semi-arid), taken as the study area, and the spatiotemporal distributions of the normalized difference vegetation index (NDVI) were explored with interaction driving factors. This study investigated the time-lag and time-accumulation effects of the NDVI response to climate factors Evapotranspiration (ET), Land Surface Temperature (LST), Potential Evapotranspiration (PET), Precipitation (PREC), and Soil Moisture (SM) and identified the primary controlling factors that affect the vegetation dynamics. The observations indicate that the correlation between NDVI and summer LST (- 0.838) was discovered to be greater than the correlation of NDVI with SM (r = 0.90) and PREC (r = 0.751), showing NDVI as more sensitive to LST when comparing to SM, and PREC, while PET exhibits the significant positive correlation (r = −0.751) with the NDVI in autumn during the studied duration. Higher NDVI values were seen during the monsoon (0.54 ± 0.12), which is correlated with a decrease in the monsoon LST (25.8 ± 0.20), followed by the winter (0.47 ± 0.13), summer (0.33 ± 0.18), and fall (0.37 ± 0.04). Vegetation growth is influenced by both the time-lag and time-accumulation effects of temperature and the time-accumulation impact of precipitation. Regarding the climate-vegetation response mechanism, the application of the Granger Causality (GC) Test and GC-based Vector Auto-Regressive Neural Network (VARNN) Model test reveals that the 0–2 month optimum time lag effect is prevalent in the study area. In addition, the LST and SM have a more prominent stimulating influence on plant growth in the study region than precipitation. The above findings highlight the need to effectively monitor vegetation dynamics under environmental changes by considering the temporal impacts of vegetation response to climate when the current climate models research vegetation-climate interactions. [Display omitted] • The PREP exhibits leptokurtic behaviour. • The GC test detects 0 and 1 month lag but fails to detect 2 and 3 month lag. • VARNN model identify 0–2 month lag in the response were more significant. • LST, PREP and SM are the prevalent factor responsible for the vegetation growth. [ABSTRACT FROM AUTHOR]

Change in rainfall pattern with longer dry period depletes soil water content (SWC) and incorrect sowing time adversely affects rainfed sorghum production in Semi-Arid Tropics (SATs). The present study was conducted to develop agricultural water management strategies for improving SWC and to evaluate sowing time as climate change agro-adaptations for sorghum production in SATs. The field experiments on two land-water management (flatbed, broad bed furrows (BBF)) and four nutrient management (application of macro-and micronutrients through combination of chemical and organic fertilizers) were conducted in 2014 and 2015 at International Crops Research Institute for the Semi-Arid Tropics, India. The average SWC in 'BBF' was higher over 'flatbed' by 0.90 cm and 1.06 cm in 0–30 cm soil depth, 0.67 cm and 1.02 cm in 30–60 cm depth, 0.51 cm and 0.84 cm in 60–90 cm depth and, 0.34 cm and 0.67 cm in 90–120 cm during 2014 and 2015, respectively. The SWC in BBF was higher over flatbed by 7.28% throughout 0–120 cm soil depth during longest dry period of 26 days in the year 2014. The simulation analyses using DSSAT Version 4.6 for Coupled Model Intercomparison Project Phase 5 with RCP 4.5 stated that postponing the normal sowing time (30 June) to 10 July resulted in lower grain yield reduction i.e. 14.75% in 2030 and 19.37% in 2050 as compared to base period (1988–2007) yield with normal sowing in Parbhani location of India. The BBF combined with macro-and micronutrients application through chemical fertilizer and postponing sowing time was found the effective climate change agro-adaptation strategies for improving sorghum production in SATs. This study indicates the need for desired policy orientation by the government to promote integrated land-water-nutrient management as the effective agro-adaptations to climate change in SATs. • Broad bed furrows (BBF) recorded higher soil water content (SWC) than flatbed. • Average SWC in BBF was more over flatbed by 0.75 cm (7.28%) in 0–120 cm soil depth. • BBF increased rainwater use efficiency by 19.5% over flatbed in ICRISAT, India. • BBF found efficient land-water management technology during longest dry period. • Sowing postponement is an agro-adaptation strategy for sorghum production in SAT. [ABSTRACT FROM AUTHOR]

The annual water loss through evapotranspiration (ET) is an uncertain but significant component of India’s water budget. The present study generated independent estimates of baseline annual ET, calibrated with in situ micrometeorological data over Indian sub-continent, using surface energy balance framework and satellite-based long-term thermal remote sensing, visible and near-infrared observations as the primary data sources. Thirty years’ (1981–2010) of satellite-based ET estimates at 0.08° grid resolution were used to assess trend in regional ET, to find out change hot-spots and probable causes. Long-term collateral data, influencing ET, such as gridded (0.5° × 0.5°) annual rainfall (RF), annual mean surface soil moisture (SSM) at 25 km resolution from ESA scatterometers and annual mean incoming shortwave radiation from MERRA-2D reanalysis were also analyzed. Mean annual ET loss was found to be the highest for Indian cropland (890 Cubic Km) than forest (575 Cubic Km). Annual water consumption pattern over vegetation systems showed declining ET trend at the rate of -16 Cubic Km yr −1 upto 1995 during 30 years which might be due to declining rainfall and solar dimming. This was followed by increasing ET trend (34 Cubic Km yr −1 ). During 2001–2010, irrigated cropland showed a steep increase in water consumption pattern with an average rate of 4 Cubic Km yr −1 while grassland and forest showed declining consumption patterns since 2003 and 2007, respectively thus showing crossover points of their consumption patterns with irrigated cropland. Four agriculturally important Indian eastern, central, western and southern states showed significantly increasing ET trend with S-score of 15–25 and Z-score of 1.09–2.9 during this period. Increasing ET in western and southern states was found to be coupled with increase in annual rainfall and SSM. But in eastern and central states, no significant trend in rainfall was observed though significant increase in ET was noticed. Region-specific correlation of annual ET with natural forcing variables was higher for incoming shortwave radiation as compared to rainfall. The increase in ET over irrigated croplands as well as over some of the Indian states could be due to increase in anthropogenic factors which need more detailed investigations in future. [ABSTRACT FROM AUTHOR]

Compound dry and hot extremes (CDHE) are periods of prolonged dry and hot weather. Their joint occurrence typically impacts society and nature stronger compared to the occurrence of the single hazards. Understanding the likelihood, variability and drivers of CDHE is challenging due to the complexity of the climate system involving interactions and feedbacks among atmosphere-land processes. In this study, we first investigate the role of the dependence between precipitation and temperature for the likelihood of CDHEs. We demonstrate that both the dependence strength and its type, i.e. the degree of tail dependence, substantially affect the CDHE likelihood. We then analyze the space-time variation of CDHE characteristics during the Indian Summer Monsoon across India for the period 1961–2014. We find strong negative association and substantial tail dependence between precipitation and temperature in some regions. Event coincidence analysis reveals that low soil moisture preconditioned by dry extremes is responsible for 55–65% of CDHE occurrence. Our analysis of the temporal evolution of CDHE characteristics finds an increasing negative association between precipitation and temperature leading to a 2 to 3-fold rise of CDHE frequency for some regions of India. • CDHE likelihood depends strongly on the type and strength of precipitation-temperature dependence. • Dependence between low precipitation and high temperature exhibits strong space-time variation. • Low soil moisture preconditioned by dry extremes contributes to CDHE occurrence and its spatial diversity. • CDHE frequency shows a 2 to 3-fold rise for some regions for the period 1961–2014. [ABSTRACT FROM AUTHOR]

In North-Western India, wheat is normally irrigated at an IW: CPE of 0.9, with 75 mm depth of irrigation water (conventional irrigation practice, CP) resulting in wastage of water. An effective irrigation strategy is required that will save irrigation water without compromising yield penalty. So, an experiment was conducted at Punjab Agricultural University, Ludhiana during 2014–15 and 2015–16 in split plot design, keeping four sowing dates {25th October (D 1 ), 10th November (D 2 ), 25th November (D 3 ) and 10th December (D 4 )} in the main plots and five irrigation schedules {irrigation at 15 (FC 15 ), 25 (FC 25 ), 35 (FC 35 ) and 45 (FC 45 )% depletion of soil moisture from field capacity (FC) and a conventional practice} in sub plots. The objectives of the study were to evaluate the effect of drip irrigation amounts on field water balance, yield and water productivity of wheat. The results revealed that mean grain yield decreased by 8.3 & 8.7, 10.7 & 10.6 and 13.1 & 13.4% from D 1 to D 2 , D 2 to D 3 and D 3 to D 4 during 2014-15 and 2015-16, respectively. Pooled grain yield decreased by 29% with delay in sowing from D 1 to D 4. Reduction in ETc was 10% in D 4 as compared to D 1 during 2014-15 and 24% during 2015-16. The highest grain yield was obtained with irrigation applied at 15% depletion from FC. The pooled grain yield decreased by 30%, ETc by 21% and water productivity by 29% in FC 45 as compared to FC 15 . The water saving in drip irrigation during 2014-15 was 62, 70, 77 and 83% in FC 15 , FC 25 , FC 35 and FC 45 respectively as compared to CP. The respective values during 2015-16 were 38, 44, 54 and 60%. The results demonstrate that irrigating wheat at 15% depletion of FC using drip method of irrigation as a novel concept that saves irrigation water in addition to higher grain yield. [ABSTRACT FROM AUTHOR]

The assessment of land use land cover (LULC) and climate change over the hydrology of a catchment has become inevitable and is an essential aspect to understand the water resources-related problems within the catchment. For large catchments, mesoscale models such as variable infiltration capacity (VIC) model are required for appropriate hydrological assessment. In this study, Ashti Catchment (sub-catchment of Godavari Basin in India) is considered as a case study to evaluate the impacts of LULC changes and rainfall trends on the hydrological variables using VIC model. The land cover data and rainfall trends for 40 years (1971-2010) were used as driving input parameters to simulate the hydrological changes over the Ashti Catchment and the results are compared with observed runoff. The good agreement between observed and simulated streamflows emphasises that the VIC model is able to evaluate the hydrological changes within the major catchment, satisfactorily. Further, the study shows that evapotranspiration is predominantly governed by the vegetation classes. Evapotranspiration is higher for the forest cover as compared to the evapotranspiration for shrubland/grassland, as the trees with deeper roots draws the soil moisture from the deeper soil layers. The results show that the spatial extent of change in rainfall trends is small as compared to the total catchment. The hydrological response of the catchment shows that small changes in monsoon rainfall predominantly contribute to runoff, which results in higher changes in runoff as the potential evapotranspiration within the catchments is achieved. The study also emphasises that the hydrological implications of climate change are not very significant on the Ashti Catchment, during the last 40 years (1971-2010). [ABSTRACT FROM AUTHOR]

The present study has developed the time series database surface soil moisture over India, for June, July and August months for the period of 20 years from 1991 to 2010, using data products generated under Climate Change Initiative Programme of European Space Agency. These three months represent the crop sowing period in the prime cropping season in the country and the soil moisture data during this period is highly useful to detect the drought conditions and assess the drought impact. The time series soil moisture data which is in 0.25 degree spatial resolution was analyzed to generate different indicators. Rainfall data of same spatial resolution for the same period, generated by India Meteorological Department was also procured and analyzed. Geospatial analysis of soil moisture and rainfall derived indicators was carried out to study (1) inter annual variability of soil moisture and rainfall, (2) soil moisture deviations from normal during prominent drought years, (3) soil moisture and rainfall correlations and (4) drought exposure based on soil moisture and rainfall variability. The study has successfully demonstrated the potential of these soil moisture time series data sets for generating regional drought surveillance information products, drought hazard mapping, drought exposure analysis and detection of drought sensitive areas in the crop planting period. [ABSTRACT FROM AUTHOR]

Previous studies on the response of the South Asian summer monsoon to the direct radiative forcing caused by anthropogenic absorbing aerosols have emphasized the role of premonsoonal aerosol forcing. This study examines the roles of aerosol forcing in both pre- and postonset periods using the Community Earth System Model, version 1.0.4, with the Community Atmosphere Model, version 4. Simulations were perturbed by model-derived radiative forcing applied (i) only during the premonsoonal period (May-June), (ii) only during the monsoonal period (July-August), and (iii) throughout both periods. Soil water storage is found to retain the effects of premonsoonal forcing into succeeding months, resulting in monsoonal central India drying. Monsoonal forcing is found to dry all of India through local responses. Large-scale responses, such as the meridional rotation of monsoon jet during June and its weakening during July-August, are significant only when aerosol forcing is present throughout both premonsoonal and monsoonal periods. Monsoon responses to premonsoonal forcing by the model-derived 'realistic' distribution versus a uniform wide-area distribution were compared. Both simulations exhibit central India drying in June. June precipitation over northwestern India (increase) and southwestern India (decrease) is significantly changed under realistic but not under wide-area forcing. Finally, the same aerosol forcing is found to dry or moisten the July-August period following the warm or cool phase of the simulations' ENSO-like internal variability. The selection of years used for analysis may affect the precipitation response obtained, but the overall effect seems to be an increase in rainfall variance over northwest and southwest India. [ABSTRACT FROM AUTHOR]

Understanding changes in monsoon variability over a decade requires thorough knowledge of the seasonal and inter-annual variability in surface energy flux and its forcing parameters (land surface and meteorology) in response to climate change. In the present study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua climate model gridded global products (0.05° × 0.05° spatial resolution) of land surface temperature (LST;Ts), normalized difference vegetation index (NDVI), and surface albedo (α) were used to generate seasonal (June–September) and inter-annual (2003–2012) variation in surface energy flux and its forcing parameters over different agro-climatic regions (ACRs) of India. Energy fluxes were retrieved using a single-source surface energy balance model (here vegetation and soil is considered as a single unit). Energy flux observations over different ACRs allowed comparison of the seasonal transition of latent heat flux (LE), net radiation (Rn), soil heat flux (G), available energy (Q = Rn – G), and evaporative fraction (EF) as terrestrial links to the atmosphere. The seasonal and inter-annual variation in EF was investigated by plotting against the soil moisture information retrieved from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) global monthly data product (1° × 1° spatial resolution). Decadal and seasonal analysis showed that energy fluxes vary widely in time and space due to variability in surface radiation parameters (Ts,α), vegetation cover, soil moisture, and air temperature (Ta), which influence the seasonal transition of monsoon through LE and EF. Among the ACRs, LE and EF were found lowest in the Western Dry Region (WDR) and highest in the Western Himalayan Region (WHR). The spatiotemporal depiction of MODIS LE and MODIS EF over a span of 10 years can identify the hotspots and monsoon intensity over different ACRs. Climatic parameters that are susceptible to changes resulting from climate change are thoroughly studied in the present analysis. [ABSTRACT FROM AUTHOR]

We examined monthly and seasonal changes in soil CO2 flux in the grassland, bamboo and Dipterocarpus forest ecosystems of Manipur, North East India. Soil CO2 flux was recorded to be highest during rainy season and lowest during cool and dry winter season. Soil CO2 flux rate was recorded to be highest in forest followed by grassland and bamboo forest. Multiple regression revealed that up to 92% of variation in soil CO2 flux could be explained by soil moisture, soil temperature and soil organic carbon in three different ecosystems. Annual amount of CO2 flux from soil was estimated at 694.86, 671.16 and 1029.25 g C m-2 y-1 in grassland, bamboo and Dipterocarpus forest ecosystems respectively. Thus the results indicate that soil CO2 flux rate is highly influenced by seasons, environmental factors and types of vegetation in the different ecosystems of NE India. [ABSTRACT FROM AUTHOR]

In climate-change studies, a macroscale hydrologic model (MHM) operating over large scales can be an important tool in developing consistent hydrological variability estimates over large basins. MHMs, which can operate at coarse grid resolutions of about 1° latitude by longitude, have been used previously to study climate change impacts on the hydrology of continental scale or global river basins. They can provide a connection between global atmospheric models and water resource systems on large spatial scales and long timescales. In this study, the variable infiltration capacity (VIC) MHM is used to study large scale hydrologic impacts of climate change for Indian river basins. Large-scale changes in runoff, evapotranspiration and soil moisture for India, as well as station-scale changes in discharges for three major river basins with distinct climatic and geographic characteristics are examined in this study. Climate model projections for meteorological variables (precipitation, temperature and wind speed) from three general circulation models (GCMs) and three emissions scenarios are used to drive the VIC MHM. GCM projections are first interpolated to a 1° by 1° hydrologic model grid and then bias-corrected using a quantile-quantile mapping. The VIC model is able to reproduce observed statistics for discharges in the Ganga, Narmada and Krishna basins reasonably well, even at the coarse grid resolution employed using a calibration period for years 1965-1970 and testing period from 1971-1973/1974. An increasing trend is projected for summer monsoon surface runoff, evapotranspiration and soil moisture in most central Indian river basins, whereas a decrease in runoff and soil moisture is projected for some regions in southern India, with important differences arising from GCM and scenario variability. Discharge statistics show increases in mid-flow and low flow at Farakka station on Ganga River, increased high flows at Jamtara station upstream of Narmada, and increased high, mid-flow and low flow for Vijayawada station on Krishna River in the future. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The annual cycle of solar radiation, together with the resulting land-ocean differential heating, is traditionally considered the dominant forcing controlling the northward progression of the Indian monsoon. This study makes use of a state-of-the-art atmospheric general circulation model in a realistic configuration to conduct 'perpetual' experiments aimed at providing new insights into the role of land-atmosphere processes in modulating the annual cycle of precipitation over India. The simulations are carried out at three important stages of the monsoon cycle: March, May, and July. Insolation and SSTs are held fixed at their respective monthly mean values, thus eliminating any external seasonal forcing. In the perpetual May experiment both precipitation and circulation are able to considerably evolve only by regional internal land-atmosphere processes and the mediation of soil hydrology. A large-scale equilibrium state is reached after approximately 270 days, closely resembling mid-summer climatological conditions. As a result, despite the absence of external forcing, intense and widespread rains over India are able to develop in the May-like state. The interaction between soil moisture and circulation, modulated by surface heating over the northwestern semi-arid areas, determines a slow northwestward migration of the monsoon, a crucial feature for the existence of desert regions to the west. This also implies that the land-atmosphere system in May is far from being in equilibrium with the external forcing. The inland migration of the precipitation front comprises a succession of large-scale 35-50 day coupled oscillations between soil moisture, precipitation, and circulation. The oscillatory regime is self-sustained and entirely due to the internal dynamics of the system. In contrast to the May case, minor changes in the land-atmosphere system are found when the model is initialized in March and, more surprisingly, in July, the latter case further emphasizing the role of northwestern surface heating. [ABSTRACT FROM AUTHOR]

Abstract: Soils are the third biggest sink of carbon on the earth. Hence, suitable land uses for a climatic condition are expected to sequester optimum atmospheric carbon in soils. But, information on how climatic conditions and land uses influence carbon accumulation in the soils on the Himalayan Mountains is not known. This study reports the impact of four climatic conditions (sub-tropical, altitude: 500–1200m; temperate 1200–2000m; lower alpine 2000–3000m; upper alpine, 3000–3500m) and four land uses (forest, grassland, horticulture, agriculture) on the concentrations and stocks of soil organic carbon (SOC) in upper (0–30cm) and deeper (30–100cm) soil depths on the western Himalayan Mountains of India. The study also explored the drivers which influenced the SOC stock build up on the mountains. Rainfall and soil moisture showed quadratic relations, whereas temperature declined linearly with the altitude. SOC stock as well as concentration was the highest (101.8Mgha−1 in 0–30cm, 227.97Mgha−1 in 0–100cm) in temperate and the lowest in sub-tropical climate (37Mgha−1 in 0–30cm, 107.04Mgha−1 in 0–100cm). Pattern of SOC stock build up across the altitude was: temperate>lower alpine>upper alpine>sub-tropical. SOC stocks in all land uses across the climatic conditions, except agriculture in lower alpine, was higher (0.7 to 41.6%) in the deeper than upper soil depth. SOC stocks in both the depths showed quadratic relations with soil temperature and soil moisture. Other factors like fine soil particles, land-use factor and altitude influenced positively whereas slope and pH, negatively to the SOC stocks. In all climatic conditions, other than temperate, SOC stocks were greater in natural ecosystems like forests and pastures (112.5 to 247.5Mgha−1) than agriculture (63 to 120.4Mgha−1). In temperate climate, SOC stock in agriculture (253.6Mgha−1) on well formed terraces was a little higher than forest (231.3Mgha−1) on natural slope. These observations, suggest that land uses on temperate climate may be treated as potential sinks for sequestration of the atmospheric carbon. However, agriculture in subtropical climate need to be pursued with due SOC protection measures like the temperate climate for greater sequestration of the atmospheric carbon. [Copyright &y& Elsevier]

The objective of this study is to apply and test a simple parametric water balance model for prediction of soil moisture regime in the presence of vegetation. The intention was to evaluate the differences in model parameterization and performance when applied to small watersheds under three different types of land covers ( Acacia, degraded forest and natural forest). The watersheds selected for this purpose are located in the sub-humid climate within the Western Ghats, Karnataka, India. Model calibration and validation were performed using a dataset comprising depth-averaged soil moisture content measurements made at weekly time steps from October 2004 to December 2008. In addition to this, a sensitivity analysis was carried out with respect to the water-holding capacity of the soils with the aim of explaining the suitability and adaptation of exotic vegetation types under the prevailing climatic conditions. Results indicated reasonably good performance of the model in simulating the pattern and magnitude of weekly average soil moisture content in 150 cm deep soil layer under all three land covers. This study demonstrates that a simple, robust and parametrically parsimonious model is capable of simulating the temporal dynamics of soil moisture content under distinctly different land covers. Also, results of sensitivity analysis revealed that exotic plant species such as Acacia have adapted themselves effectively to the local climate. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a participatory approach to investigate vulnerability and adaptive capacity to climate variability and water stress in the Lakhwar watershed in Uttarakhand State, India. Highly water stressed microwatersheds were identified by modelling surface runoff, soil moisture development, lateral runoff, and groundwater recharge. The modelling results were shared with communities in two villages, and timeline exercises were carried out to allow them to trace past developments that have impacted their lives and livelihoods, and stimulate discussion about future changes and possible adaptation interventions. [Copyright &y& Elsevier]

This study investigates the direct radiative effects of aerosols on the evolution of atmospheric boundary layer (ABL) over a tropical site, Anand, for 4 days, representative of each season, using land surface processes experiment (LASPEX) data sets used in a one-dimensional ABL and radiative-transfer models. Simulations with the ABL model incorporating fixed aerosol loading with three different levels of absorption and its perturbation with aerosol-free conditions were analyzed. The reduction in net available flux (NAF) increases with increase in aerosol absorption, resulting in maximum reduction for strongly absorbing type. In dry seasons, soil being dry, NAF is partitioned almost equally by latent (LE) and sensible (H) heat fluxes. In wet season, since soil moisture is abundant, LE dominates about 75% in compensating the reduction in NAF. The larger the absorption, the lesser the gradient between the surface and 2-m air temperature and hence more stable the surface layer. The reduction in vertical temperature gradient ranges from 1.74 (dry season) to 0.6K (wet season). This stabilization of the surface layer reduces sensible heat flux and surface evaporation. Aerosol absorption decreases the turbulent heating but simultaneously increases the solar heating, and in turn increases the air temperature. This affects the inversion layer and hence the ABL height. Reduction in NAF at the surface decreases the ABL height, and the average decrease during the daytime is 167, 204 and 247m for SSA=1.0, 0.9 and 0.8, respectively, during dry seasons. It is also found that absorbing aerosols delay the growth and promote early collapse of the ABL in all seasons. [Copyright &y& Elsevier]

• Vertical soil moisture profile (root zone) database is developed for entire India. • Remote sensing based ESA-CCI surface soil moisture is assimilated using SSMP model. • Developed database is validated with observed station based data collected by IMD. • Reconstruction of droughts is demonstrated as an exemplary utility of the database. • Outcome will be useful for many hydro-climatological and agricultural studies. The Climate Change Initiative (CCI) program of the European Space Agency (ESA) (ESA-CCI) provides a global surface Soil Moisture (SM) product by merging the information from various Active and Passive sensors on board for the period of 1971–2014. This study aims to assimilate this surface SM product into a recently developed model, namely Statistical Soil Moisture Profile (SSMP) model, to investigate the efficacy of this combination in developing a vertical soil moisture profile database over a climatologically vast area, such as Indian mainland. The SSMP model is a spatially varying statistical framework that couples the memory and forcing of SM from the overlying layers and utilizes the information of Hydrologic Soil Groups (HSGs) that makes it spatially transferable. The model estimates SM at four depths i.e. 10, 20, 51 and 102 cm using the surface SM information (~0–5 cm). The developed database is named as 'ESA-CCI Driven Vertical Soil Moisture Profile (root zone) Database (EDVSMPD)' and the simulated values are validated with observed SM data at many monitoring stations across India, maintained by India Meteorological Department (IMD). As an exemplary demonstration of the utility of the data, basin scale reconstruction of the historical agricultural droughts and its spatial distribution, using the developed EDVSMPD product, is illustrated for a medium sized drought-stricken river basin, namely Wardha, in the central part of India. The resulting SM product for a considerable length of period (more than 30 years) will be immensely useful for many hydro-climatological studies. [ABSTRACT FROM AUTHOR]

The Himalayas have become synonymous with the hydropower developments for larger electricity demands of India's energy sector. In the Himachal Himalayas though, there are only three large storage dams with more than 1000 megawatts (hereafter MW) capacity that have very serious environmental issues. However, hundreds of small runoff-river hydropower plants across the Himachal Himalayas are a serious threat to the river regimes and Himalayan biota. There are 965 identified hydropower projects (hereafter HPPs) having a potential capacity of 27,436 MW in the Himachal Pradesh as of December 2019 as per the Directorate of Energy of the state. Out of the 965 identified, 216 are commissioned, including less than 5 MW plants, with an installed capacity of 10,596 MW, and were operational by December 2019. Only 58 projects are under construction among the identified with an installed capacity of 2351 MW, 640 projects are in various stages of clearance and investigation with an installed capacity 9260 MW, 30 projects are to be allotted with 1304 MW installed capacity, and merely four projects are disputed/cancelled with installed capacity of 50.50 MW. The large number of HPPs are sanctioned without proper consideration of negative environmental and geohazard impacts on the Himalayan terrestrial biota. In this work, our focus was on the hydropower and climate change impact on the Himalayan river regimes of the Chenab, the Ravi, the Beas, the Satluj, and the Yamuna river basins. We analyzed basin-wise rainfall, temperature, and soil moisture data from 1955 to 2019 to see the trend by applying the Mann–Kendall test, the linear regression model, and Sen's slope test. A basin-wise hazard zonation map has been drawn to assess the disaster vulnerability, and 12 hydropower sites have been covered through the primary survey for first-hand information of local perceptions and responses owing to hydropower plants. [ABSTRACT FROM AUTHOR]

It is imperative to know the spatial distribution of vegetation trends in India and its responses to both climatic and non-climatic drivers because many ecoregions are vulnerable to global climate change. Here we employed the NDVI3g satellite data over the span of 35 years (1981/82–2015) to estimate vegetation trends and corresponding climatic variables trends (i.e., precipitation, temperature, solar radiation and soil moisture) by using the Mann–Kendall test (τ) and the Theil–Sen median trend. Analysis was performed separately for the two focal periods—(i) the earlier period (1981/82–2000) and (ii) later period (2000–2015)—because many ecoregions experienced more warming after 2000 than the 1980s and 1990s. Our results revealed that a prominent large-scale greening trend (47% of area) of vegetation continued from the earlier period to the later period (80% of area) across the northwestern Plain and Central India. Despite climatologically drier regions, the stronger greening trend was also evident over croplands which was attributed to moisture-induced greening combined with cooling trends of temperature. However, greening trends of vegetation and croplands diminished (i.e., from 84% to 40% of area in kharif season), especially over the southern peninsula, including the west-central area. Such changes were mostly attributed to warming trends and declined soil moisture trends, a phenomenon known as temperature-induced moisture stress. This effect has an adverse impact on vegetation growth in the Himalayas, Northeast India, the Western Ghats and the southern peninsula, which was further exaggerated by human-induced land-use change. Therefore, it can be concluded that vegetation trend analysis from NDVI3g data provides vital information on two mechanisms (i.e., temperature-induced moisture stress and moisture-induced greening) operating in India. In particular, the temperature-induced moisture stress is alarming, and may be exacerbated in the future under accelerated warming as it may have potential implications on forest and agriculture ecosystems, including societal impacts (e.g., food security, employment, wealth). These findings are very valuable to policymakers and climate change awareness-raising campaigns at the national level. [ABSTRACT FROM AUTHOR]

• Climate change is severely affecting the agro-ecosystems in ravine lands. • Extreme rainfall events adversely affected crops compared to tree and grasses. • Soil and water conservation enhanced the growth, biomass and carbon stock of plants. • Terrace was found superior to enhance climate resilience and carbon sequestration. Agro ecosystems in degraded lands are subjected to climate change impacts throughout the globe. Previous work in degraded lands has focused on afforestation and soil conservation, which has obvious limitations in terms of generality and scope of application for production systems in climate change scenario. A key challenge in degraded lands is to develop climate smart systems those are resilient to extreme weather and have high potential for carbon sequestration. Therefore, we examined the effectiveness of Sapota trees (Achras zapota) and regional crops/grasses (annuals) combination (agroforestry) with soil water conservation (SWC) for enhancing climate resilience and carbon sequestration of agro ecosystem in semi-arid degraded lands of Western India. The experiment was conducted for eight years (2010–2017) in four systems: (i) Sapota + Crops on terrace (SCTe) (ii) Sapota on terrace (STe); (iii) Sapota and Trenches on slope (STrS); and (iv) Sole Sapota on slope (SS) and their performance was assessed during different annual rainfall events (Normal, Heavy, High, Abnormal, Drought). Our findings explained that, among all the annual rainfall events, drought resulted in the mortality of Sapota tree and reduction in biomass production of the grasses, while, terrace and trench measures reduced the tree mortality and enhanced the grass biomass production. Plant population of Cowpea and Castor crops were recorded significantly higher during normal rainfall, but a decline was observed during rest of the rainfall events. Height and diameter growth increment in Sapota was recorded higher during normal rainfall compared to the drought period. SWC measure such as terraces and trench contributed higher growth increment and biomass production in Sapota tree, compared to sole slope, under all the rainfall conditions. Rainfall variability also affected the Cowpea and Caster and their yield was recorded maximum during normal rainfall and minimum during drought period, respectively. Moreover, SWC measure such as terrace and trench enhanced fruit yield of Sapota during the drought period. These measures also improved carbon stock and carbon sequestration in Sapota and grasses during the eight years. Overall, carbon stock and carbon sequestration in plants and soil were observed greater in uncultivated terrace (STe), followed by cultivated terrace (SCTe) and trench (STr) systems, respectively, while, overall, their lower value was observed in slope system (SS). The results suggested that tree and annuals combination (agroforestry) along with soil water conservation measures enhanced agro ecosystem resilience to extreme weather and improved the carbon stock and sequestration potential. Therefore, soil and water conservation should be considered, along with only those trees and annuals, which have the capability to enhance climate resilience and potential of high carbon sequestration, in semi-arid climate change vulnerable degraded ravine landscape. [ABSTRACT FROM AUTHOR]

The potential consequence of global climatic change caused the rise in temperature and precipitation decline, the aftermath of which has led to phenomenon like drought. As the agriculture is mainly dependent on the timely availability of water, delayed arrival of rainfall or decrease in the intensity of precipitation highly affects the growth of crops. If such situation persists for a longer period, the soil moisture content will be exploited completely and maturity of the crop will be stunted, finally adversely affecting the annual crop yield. In the present study, the capability of shortwave-infrared (SWIR) channels in detecting the agricultural crop stress in Raichur district of Karnataka state, India, using spectral vegetation indices namely Global Vegetation Moisture Index (GVMI) and Normalized Multi-band Drought Index (NMDI) has been analyzed using multi-temporal MODIS data. The vegetation health analysis by utilizing both indices were carried out from year 2002 to 2012 for the Kharif season, and it was seen that the year 2002 suffered major agricultural drought where the lower GVMI and NMDI values were covering the majority of the crop areas and in the year 2010 agricultural crop production was observed to be good. The average values of GVMI and NMDI for the years 2002 and 2010 were plotted with the average NDVI values of all months of both years and the results revealed that NDVI values were in concurrence with both NMDI and GVMI. [ABSTRACT FROM AUTHOR]