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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]

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]

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]

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

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

Rainfall anomaly during crop-growing season can have large impact on the agricultural output of a country, especially like India, where two-thirds of the crop land is rain-fed. In such situation, decreased agricultural production not only challenges food security of the country but directly and immediately hits the livelihood of its farming community. In a vast country like India, rainfall or its anomalies hardly follow a specific pattern, rather it is having high variability in spatial domain. This study focused on the trends of national and regional rainfall anomalies (wetness/dryness) along with their interrelationship using time series data of past 158 years. The significant reducing wetness trend (p < 0.05) over north mountainous India was prominent with an increasing trend over southern peninsular India (p < 0.10). However, long-term annual wetness was increasing over entire peninsular India. The results of change point tests indicate that major abrupt changes occurred between early to mid-twentieth century having regional variations. The regional interrelationship was studied using principal component, hierarchical clustering, and pair-wise difference test, which clearly indicated a significantly different pattern in rainfall anomalies for north east India (p = 0.022), north central India (p = 0.022), and north mountainous India (p = 0.011) from that of the all India. Result of this study affirmed high spatial variability in rainfall anomaly and most importantly established the unalike pattern in trends of regional rainfall vis-à-vis national level, ushering towards paradigm shift in rainfall forecast from country scale to regional scale for pragmatic planning. [ABSTRACT FROM AUTHOR]

Understanding the relationship between gradually varying soil moisture (SM) conditions and monsoon rainfall anomalies is crucial for seasonal prediction. Though it is an important issue, very few studies in the past attempted to diagnose the linkages between the antecedent SM and Indian summer monsoon rainfall. This study examined the relationship between spring (April-May) SM and June rainfall using observed data during the period 1979-2010. The Empirical Orthogonal Function (EOF) analyses showed that the spring SM plays a significant role in June rainfall over the Central India (CI), South India (SI), and North East India (NEI) regions. The composite anomaly of the spring SM and June rainfall showed that excess (deficit) June rainfall over the CI was preceded by wet (dry) spring SM. The anomalies in surface-specific humidity, air temperature, and surface radiation fluxes also supported the existence of a positive SM-precipitation feedback over the CI. On the contrary, excess (deficit) June rainfall over the SI and NEI region were preceded by dry (wet) spring SM. The abnormal wet (dry) SM over the SI and NEI decreased (increased) the 2-m air temperature and increased (decreased) the surface pressure compared to the surrounding oceans which resulted in less (more) moisture transport from oceans to land (negative SM-precipitation feedback over the Indian monsoon region). [ABSTRACT FROM AUTHOR]

There is a lack of observed data-based studies examining the role of enhanced soil moisture conditions (due to irrigation) on the prevailing precipitation. Therefore, in the present study, we have examined the impacts of the Green Revolution (GR) related expansion of irrigation and changes in dry season (the rabi (November to May) and the zaid (March to June)) precipitation in India. The results for some regions indicated decreasing and increasing trend in precipitation during the pre- and post-GR periods, respectively. For example, in eastern Madhya Pradesh, the pre- and post-GR precipitation trends for the zaid season were −0.45 and 2.40 mm year, respectively. On the other hand, some regions reported lower rate of decline in precipitation during the post-GR period. This paper suggests that both positive and lower declining trend during the post-GR period were linked to increased precipitation due to the introduction of irrigation. The study has found up to 69 mm (121%) increase in total amount of precipitation for growing seasons during the post-GR period. Moreover, a 175% increase in average precipitation was also recorded. All irrigated regions show a notable increase in precipitation during post-GR growing seasons. It was found that differences in growing season average precipitation between the pre- and post-GR periods were statistically significant for most of the regions. For further verification of results, the MM5 and Noah land surface model were applied. These applications show changes in precipitation and various precipitation controlling factors due to changes in soil moisture. [ABSTRACT FROM AUTHOR]