Land Use Land Cover Change and Rainfall Dynamics for Climate-Resilient Farm Planning: Insights from Dewgain Village, Jharkhand, India

Land Use Land Cover Change and Rainfall Dynamics for Climate-Resilient Farm Planning: Insights from Dewgain Village, Jharkhand, India | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 18 November 2025 V1 Latest version Share on Land Use Land Cover Change and Rainfall Dynamics for Climate-Resilient Farm Planning: Insights from Dewgain Village, Jharkhand, India Authors : Demisie Ejigu 0000-0001-9494-4237 , P Raji [email protected] , Sushil Himanshu , Sajithkumar KJ , and Shivapratap Gopakumar Authors Info & Affiliations https://doi.org/10.22541/au.176342427.70688540/v1 238 views 100 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Purpose: Agriculture forms the foundation of rural livelihoods but is increasingly threatened by irregular rainfall, water scarcity, and unsustainable land-use transitions. For that, understanding the interactions between land use/land cover (LULC) change and rainfall variability is essential for enhancing the resilience of rainfed farming systems. Aims: This study aims to analyze the temporal dynamics of LULC and rainfall variability to support climate-resilient farm planning in Dewgain Village, Jharkhand, India. It also seeks to assess future rainfall scenarios to evaluate potential flood and drought risks. Methods: Multi-temporal Sentinel-2 imagery (2017–2024) was processed using GIS-based change detection to identify LULC transitions. Rainfall trends were evaluated using linear regression, the Mann–Kendall test, Sen’s slope estimator, and the Rainfall Anomaly Index (RAI). Future rainfall (2025–2050) was projected using Machine Learning (XGBoost) modelling to examine climate-related hazards. Results: LULC analysis revealed substantial conversion from forest, rangeland, and built-up areas to cropland, indicating intensified cultivation pressure. Rainfall exhibited a significant upward trend (P = 0.0083, τ = 0.277) and greater interannual variability, with seven dry years observed. However, winter rainfall showed a declining trend, suggesting increasing drought stress, groundwater dependence, and irrigation costs. Future projections indicate rising extreme rainfall events, increasing flood risks in low-lying agricultural zones. Conclusions: By integrating LULC and rainfall dynamics in a spatial framework, this study provides actionable insights for local-scale climate adaptation. The findings emphasize the importance of agroforestry, water-efficient irrigation, and participatory farm planning to enhance resilience and mitigate land degradation in rainfed agricultural systems. Land Use Land Cover Change and Rainfall Dynamics for Climate-Resilient Farm Planning: Insights from Dewgain Village, Jharkhand, India Demisie Ejigu a , Raji Pushpalatha a , Sushil Kumar Himanshu b , Sajithkumar KJ a , Shivapratap Gopakumar a a Amrita School for Sustainable Futures, Amritapuri, Amrita Vishwa Vidyapeetham, Kerala, India P.O. Box: 690525, Clappana, Kollam, Kerala, India b Agricultural Systems and Engineering, Department of Food, Agriculture, and Bioresources, Asian Institute of Technology, Bangkok P.O. Box 4, 58 Moo 9, Km.42, Paholyothin, Highway, Kong Luang, Pathum Thani, 12120 Thailand Correspondence Dr Raji Pushpalatha, Amrita School for Sustainable Futures, Amritapuri, Amrita Vishwa Vidyapeetham, Kerala, India Email: [email protected] Phone number: +919496030912 P.O. Box: 690525, Clappana, Kollam, Kerala, India Short running Title: “L and Use Land Cover Change and Rainfall Trends in Dewgain” Abstract: Purpose: Agriculture forms the foundation of rural livelihoods but is increasingly threatened by irregular rainfall, water scarcity, and unsustainable land-use transitions. For that, understanding the interactions between land use/land cover (LULC) change and rainfall variability is essential for enhancing the resilience of rainfed farming systems. Aims: This study aims to analyze the temporal dynamics of LULC and rainfall variability to support climate-resilient farm planning in Dewgain Village, Jharkhand, India. It also seeks to assess future rainfall scenarios to evaluate potential flood and drought risks. Methods: Multi-temporal Sentinel-2 imagery (2017–2024) was processed using GIS-based change detection to identify LULC transitions. Rainfall trends were evaluated using linear regression, the Mann–Kendall test, Sen’s slope estimator, and the Rainfall Anomaly Index (RAI). Future rainfall (2025–2050) was projected using Machine Learning (XGBoost) modelling to examine climate-related hazards. Results: LULC analysis revealed substantial conversion from forest, rangeland, and built-up areas to cropland, indicating intensified cultivation pressure. Rainfall exhibited a significant upward trend (P = 0.0083, τ = 0.277) and greater interannual variability, with seven dry years observed. However, winter rainfall showed a declining trend, suggesting increasing drought stress, groundwater dependence, and irrigation costs. Future projections indicate rising extreme rainfall events, increasing flood risks in low-lying agricultural zones. Conclusions: By integrating LULC and rainfall dynamics in a spatial framework, this study provides actionable insights for local-scale climate adaptation. The findings emphasize the importance of agroforestry, water-efficient irrigation, and participatory farm planning to enhance resilience and mitigate land degradation in rainfed agricultural systems. Keywords: Land Use/Land Cover Change − Rainfall Trends − Rainfall Anomalies − Climate-Resilient Agriculture − Sustainable Farm Planning − Dewgain Village Introduction Agriculture remains central to global food security, employment, and economic growth. In India, it contributes about 16% to the GDP, 12.5% to exports, and provides livelihoods for over half of the population (FAO, 2020; Singh et al., 2020; World Bank, 2021). However, the sector faces increasing pressure from water scarcity, unsustainable practices, and climate change (IPCC, 2021). Erratic rainfall, droughts, floods, and temperature fluctuations threaten soil quality, water availability, and crop productivity, undermining rural livelihoods (Barati et al., 2022; Rao et al., 2019). Additional constraints such as pest outbreaks and weak extension services further limit agricultural sustainability (Yadachi et al., 2023). In many rural regions of India, rapid land transformation driven by population growth, deforestation, and infrastructure expansion is altering ecosystem balance and agricultural productivity (Pal & Ziaul, 2017; Prasad et al., 2019). Concurrently, rainfall irregularities delayed monsoons, prolonged dry spells, and uneven distribution have intensified agricultural vulnerability (Barati et al., 2024; Gadgil & Kumar, 2006). These challenges are particularly acute in Jharkhand, where agriculture employs 43% of the workforce and contributes 13% to the state’s Gross Value Added (Kumar, 2023). Dewgain village, located in this region, depends predominantly on rainfed agriculture. Rice, wheat, millet, maize, and pulses are the main crops (Guleria et al., 2020), but yields remain well below national averages 1,133.6 kg/acre for rice and 1,425.5 kg/acre for wheat (MAFWI, 2024). The average farm income (~INR 41,000 per year) reflects high climate sensitivity and economic vulnerability. A micro-level analysis of land use and rainfall variability is therefore crucial to capture localized environmental and socio-economic dynamics often overlooked in regional studies. Assessing monthly and seasonal rainfall patterns supports decisions on crop selection, irrigation, and soil moisture management (Mall et al., 2006). Integrating rainfall dynamics with land-use transitions can guide adaptive, resource-efficient, and climate-resilient farm planning. This study, therefore, investigates the spatiotemporal dynamics of land use/land cover (LULC) and rainfall variability in Dewgain, Jharkhand, to provide actionable insights for sustainable and climate-resilient agriculture aligned with Sustainable Development Goals (SDGs), particularly SDG2 (Zero Hunger), SDG13(Climate Action), and SDG 15 (Life on Land). Methodology Study Area Description The study was conducted in Dewgain Village, located in the Namkum subdivision of Ranchi district, Jharkhand, India (23.2160° N, 85.3627° E; 651 m a.s.l.), about 30 km from the district headquarters (Fig. 1). The area has a humid subtropical climate with an average annual rainfall of 1,383.30 mm (range: 914.55–2,032.86 mm), irregularly distributed high in summer and minimal in other seasons. Annual temperatures range between 13.04 °C and 31.72 °C, with 67.5% relative humidity. Livelihoods depend on crop production and animal husbandry; rice, wheat, mustard, legumes, and fruits are major crops, reflecting vulnerability to climate variability and the need for sustainable, diversified farming (Source: Preliminary Survey). Fig. 1 Map of India, Jharkhand State, Ranchi District and Dewgain Village (study area) Method of Data Collection and Analysis Land use land cover data extraction and analysis Sentinel-2 Surface Reflectance imagery (10 m, 5-day revisit) from Google Earth Engine (GEE) was used for LULC classification (Drusch et al., 2012). Images from May to December (2017–2024) were selected to minimize confusion between cropland and rangeland. The Random Forest (RF) algorithm was applied for classification due to its robustness and accuracy in remote sensing (Belgiu & Drăguţ, 2016). 70% of samples were used for training and 30% for validation. Classified maps were visualized in GEE, and raster data were converted to polygons in QGIS for pixel and area computation. Rainfall data collection Daily rainfall data were obtained from the Indian Meteorology Department (IMD), which provides high-resolution gridded datasets (0.25° × 0.25°) (Pai et al., 2014). We extracted data that covers the period from 1981 to 2024, over the study location, enabling multi-decadal analysis of rainfall variability. Rainfall analysis Temporal rainfall trends were analyzed using linear regression to estimate the direction and magnitude of change, with significance assessed by the P-value (α = 0.05) and model fit by R² (Navya et al., 2024). The non-parametric Mann–Kendall test was applied to detect monotonic trends, using Kendall’s Tau for strength and Theil–Sen estimator for slope determination (Kundan et al., 2020). Rainfall anomaly analysis and characterization The Rainfall Anomaly Index (RAI) (van Rooy, 1965) quantifies deviations of annual or seasonal rainfall from the long-term mean to classify drought and wet years: \(RAI=\frac{(X-\overset{ˉ}{X})}{(\text{Mean\ of\ 10\ Highest\ or\ Lowest\ Years}-\overset{ˉ}{X})}\times K\)———————————————————eq1 where \(X\)is annual rainfall, \(\overset{ˉ}{X}\)is the long-term mean, and \(K=\pm 3\)standardizes the index (Table 1). The Percentage Departure of Rainfall (PDR) , following IMD classification, evaluates rainfall deviation (%) from the long-term mean: \(PDR(\%)=\frac{(X_{i}-\overset{ˉ}{X})}{X_{m}}\times 100\)——————————————————————————eq2 where \(X_{m}\)is mean annual rainfall and \(X_{i}\)is yearly rainfall. The Standardized Precipitation Index (SPI) (Bartczak et al., 2014) measures precipitation anomalies using a fitted Gamma or Pearson Type III distribution transformed to normal form: \(SPI=\frac{(X-\mu)}{s}\)———————————————————————————————eq3 where \(X\)is observed precipitation, \(\mu\)is mean, and \(s\)is standard deviation. SPI values indicate drought or wet intensity (Hayes et al., 2011). Table 1. Classification of meteorological category as per SPI, PDR, and RAI values SPI Category PDR Category RAI Category > 2.00 Extremely wet 60% Large Excess RAI ≥ 1 Wet 1.50 −1.99 Severely wet 20% to 59% Excess RAI ≤ -1 Dry 1.00 −1.49 Moderately wet 19% to -19% Normal -1 < RAI < +1 Normal -0.99 to 0.99 Near Normal -20% to -59% Deficient -1.00 to -1.49 Moderately dry -60% to -99% Large Deficient -1.50 to -1.99 Severely dry -100% No Rain < -2.00 Extremely dry Time series rainfall forecasting Machine Learning (XGBoost) was used for feature rainfall projection. The model’s performance was validated using RMSE. Machine Learning (XGBoost) was used for rainfall lags and possibly climate indices. A train-test split and k-fold cross-validation were applied, including RMSE and NRMSE (Chen & Guestrin, 2016). Results Land Use and Land Cover Based on the assessment of land use and land cover, Dewgain Village land has been categorized into five classes: water, forest, cropland, built-up, and rangeland (Fig. 2 & 3). The region features moderate forest cover, while cropland dominates, reflecting the area’s agrarian character. Built-up areas comprise residential and infrastructural features, while rangeland includes grassland and scrub vegetation. Water bodies: Water covers less than 1% (1.22 ha) of the total area, declining by 0.35% from 2.780 ha to 1.226 ha (Table 2). Forest: Forest cover decreased by 1.01%, from 119.68 ha to 115.18 ha. Rangeland: Rangeland declined by 1.66%, from 121.48 ha to 114.12 ha. Cropland: Cropland increased by 3.58%, from 136.29 ha to 152.19 ha. Built-up: Built-up areas slightly decreased (−0.55%), from 62.89 ha to 60.41 ha. These changes illustrate an overall conversion from forest, rangeland, and built-up zones into cropland, suggesting intensified agricultural activity. Fig. 2 Dewgain Village LULC (2017), Source author’s work on Google Earth Engine Fig. 3 Dewgain Village LULC (2024), Source author’s work on Google Earth Engine Table 2. Area statistics of LULC classes for the years 2017 and 2024 Feature Class 2017 2024 (2017-2024) Area (ha) % Area (ha) % % Change Water 2.780 0.63 1.226 0.27 -0.35 Forest 119.684 27.01 115.175 25.99 -1.01 Crop 136.288 30.75 152.194 34.34 +3.58 Built-up 62.890 14.19 60.411 13.63 -0.55 Range 121.479 27.41 114.116 25.75 -1.66 Total 443.121 100 443.122 100 Land use land cover (LULC) change detection Change detection between 2017 and 2024 (Fig. 4; Table 3) reveals notable transitions. A large proportion of the landscape remained stable especially within forest (16.86%), cropland (15.11%), and built-up (9.43%) classes indicating persistence in these land categories. However, substantial conversions occurred: Forest → Built-up (5.79%), Cropland → Built-up (7.42%), Built-up → Cropland (9.69%) and Forest → Cropland and Rangeland (> 5%). These patterns highlight both agricultural expansion and localized land degradation. Fig. 4 Land use land cover (LULC) change detection map (2017−2024) Table 3. Land Use Land Cover (LULC) change Transition matrix (2017−2024) From\To Area (ha) Water Forest Crop Land Built-up Range land Total (2017) Water 0.67 0.62 0.40 0.22 0.87 2.88 Forest 0.15 74.74 15.65 3.45 25.69 119.68 Crop land 0.10 12.47 66.96 23.83 32.92 136.28 Built-up 0.12 5.09 26.22 18.59 12.86 62.88 Range land 0.17 22.24 42.96 14.32 41.78 121.48 Total (2024) 1.21 115.16 152.19 60.41 114.12 443.12 Rainfall Characteristics Monthly rainfall Monthly rainfall (1981–2024) shows clear seasonality (Table 4; Fig. 5). Rainfall peaks during June–September, contributing 75–80% of annual totals. December–February records the lowest precipitation (below 18.8 mm month⁻¹). Pre-monsoon (March–May) rainfall is moderate (22–76 mm), aiding early sowing. Monsoon months (June–September) exhibit high intensity (7.47–10.80 mm day⁻¹) with 29–31 rainy days per month. Post-monsoon rainfall (October–November) decreases gradually (2.9–3.84 mm day⁻¹). Fig. 5 Average monthly rainfall from 1981 to 2024 Table 4. Monthly average rainy days and intensity January 8 1.92 A few rainy days with very low rainfall intensity February 12 1.56 Slightly rainy days and low-intensity March 14 1.58 A moderately rainy day and slight intensity April 16 1.83 A moderately rainy day and slight intensity May 27 2.81 High rainy days, with moderate intensity June 29 7.47 Peak rainy days, with high intensity July 31 10.80 Peak rainy days with very high intensity August 31 10.53 Peak rainy days with very high intensity September 29 8.20 Heavy rains with slightly intense October 21 3.84 Moderate rainy day and moderate intensity. November 4 2.9 Very few rainy days, with moderate intensity December 7 1.77 Occasional rainy day and low intensity Seasonal rainfall Seasonal trend analysis (Fig. 6) indicates: Monsoon: Significant increasing trend (p = 0.038; +5.48 mm yr⁻¹). Pre-Monsoon: Strong positive trend (p = 0.005; R² = 0.17). Post-Monsoon: Slight, non-significant increase (p = 0.059). Winter: Weak declining trend (p = 0.50). SPI analysis (Table 5) revealed that out of 40 years, 26 were normal, 7 wet, and 7 dry during the monsoon; post-monsoon seasons recorded 5 wet and 9 dry years. Fig. 6 Seasonal rainfall trend analysis (1981–2024) Table 5. Standardized Precipitation Index (SPI) for seasonal rainfall anomalies SPI-range Normal years Wet years Dry years SPI-range Normal years Wet years Dry years 1981-1990(I) -1.99 to 0.7 7 - 3 -2.12 to 0.78 8 - 2 1991-2000(II) -1.38 to 1.75 7 1 2 -1.8 to 1.15 5 2 3 2001-2010 (III) -1.89 to 1.5 5 3 2 -1.45 to 2.1 6 1 3 2011-2020 (IV) -0.88 to 1.75 7 3 - -1.16 to 2.4 7 2 1 Total 26 7 7 26 5 9 Annual rainfall Annual rainfall ranged from 914.55 mm to 2032.86 mm (mean = 1383.30 mm; CV = 19.25%), showing moderate variability. The homogeneity test detected a breakpoint around 2010, after which mean rainfall increased to 1550 mm. Linear regression (p = 0.0035; R² = 0.1851) and Mann–Kendall (p = 0.0083; τ = 0.277) confirmed a statistically significant upward trend of 8.92–9.17 mm yr⁻¹ (Fig. 7). RAI and PDR analyses (Table 6; Fig. 8) identified 6 wet years (1994, 2006, 2011, 2017, 2020, 2021) and seven dry years (1983, 1986, 1992, 1989, 2000, 2005, 2010); the remaining 31 years were normal. Fig. 7 Regression (a) and Mann-Kendall (b) rainfall analysis (1981−2024) Table 6. Rainfall anomaly classification based on RAI and RAD (1981-2024). Year Rainfall (mm) RAI PDR% Percentile Anomaly Classification 1981 1551.37 0.63 12.15 77.27 Normal 1982 1261.51 -0.45 -8.80 34.09 Normal 1983 924.72 -1.72 -33.15 4.54 Dry 1984 1468.59 0.32 -6.16 65.90 Normal 1985 1261.54 -0.45 -8.80 36.36 Normal 1986 1065.52 -1.19 -22.97 15.90 Dry 1987 1128.67 -0.95 -18.40 18.18 Normal 1988 1131.07 -0.94 -18.23 20.45 Normal 1989 914.55 -1.76 -33.88 2.27 Dry 1990 1548.19 0.61 11.91 75 Normal 1991 1551.37 -0.23 -4.58 40.90 Normal 1992 1261.51 -1.62 -31.36 9.09 Dry 1993 1284.95 -0.36 -7.11 38.63 Normal 1994 1805.51 1.58 30.52 95.45 Wet 1995 1362.39 -0.07 -1.51 50 Normal 1996 1240.09 -0.53 -10.35 29.54 Normal 1997 1543.97 0.60 11.61 72.72 Normal 1998 1466.78 0.31 6.03 61.36 Normal 1999 1441.79 0.21 4.22 56.81 Normal 2000 1046.6 -1.26 -24.34 13.63 Dry 2001 1341.96 -0.15 -2.98 45.45 Normal 2002 1194.6 -0.70 -13.64 25 Normal 2003 1468.29 0.32 6.14 63.63 Normal 2004 1332.13 -0.19 -3.69 43.18 Normal 2005 1038.11 -1.29 -24.95 11.36 Dry 2006 1678.59 1.11 21.34 88.63 Wet 2007 1597.84 0.81 15.50 81.81 Normal 2008 1529.4 0.55 10.56 70.45 Normal 2009 1249.96 -0.50 -9.63 31.81 Normal 2010 925.41 -1.72 -33.10 6.81 Dry 2011 1745.67 1.36 26.19 90.90 Wet 2012 1421.17 0.14 2.73 54.54 Normal 2013 1571.69 0.71 13.61 79.54 Normal 2014 1193.92 -0.71 -13.69 22.72 Normal 2015 1343.81 -0.15 -2.85 47.72 Normal 2016 1627.9 0.92 17.68 86.36 Normal 2017 1862.87 1.80 34.66 97.72 Wet 2018 1419.78 0.14 2.63 52.27 Normal 2019 1609.36 0.85 16.34 84.09 Normal 2020 1801.94 1.57 30.26 93.18 Wet 2021 2032.86 2.44 46.95 100 Wet 2022 1443.35 0.23 4.34 59.09 Normal 2023 1202.3 -0.68 -13.08 27.27 Normal 2024 1515.32 0.49 9.54 68.18 Normal Fig. 8 The bar chart displays the annual rainfall amounts and their departure from normal (1981 to 2024) Future projections of rainfall and anomalies in rainfall The XGBoost model (Fig. 9) forecasted rainfall for 2025–2050 using 1980–2024 data, achieving RMSE = 101.52 mm and NRMSE = 0.0734. Projected rainfall (Table 7) indicates a continued increase, with near-normal to moderately wet conditions between 2025–2034, and frequent “extremely wet” years (percentile > 97.7%) from 2039–2050. This suggests intensification of rainfall and heightened flood risk in low-lying zones. Fig. 9 Projected feature rainfall by using XGBoost model Table 7. Future rainfall anomaly classification (2025−2050) No. Year Rainfall (mm) RAI Percentile Anomaly Classes Remark 0 2025 1480.2 0.13 65.90 Near Normal 1 2026 1523.5 0.04 68.18 Near Normal 2 2027 1398.7 0.36 50.00 Near Normal 3 2028 1602.1 -0.24 81.82 Moderate Wet 4 2029 1705.3 -0.47 88.64 Moderate Wet 5 2030 1550.6 -0.07 75.00 Mild Wet 6 2031 1622.4 -0.30 84.09 Moderate Wet 7 2032 1589.7 -0.16 79.54 Mild Wet 8 2033 1499.2 -0.10 65.91 Near Normal 9 2034 1650.8 -0.36 86.36 Moderate Wet 10 2035 1723.9 -0.51 88.64 Moderate Wet 11 2036 1680.5 -0.42 88.64 Moderate Wet 12 2037 1595.6 -0.18 79.54 Mild Wet 13 2038 1742.3 -0.53 88.64 Moderate Wet 14 2039 1801.7 -0.65 90.91 Sever Wet Flood risk 15 2040 1775.2 -0.59 90.91 Sever Wet Flood risk 16 2041 1698.4 -0.44 88.64 Moderate Wet 17 2042 1820.6 -0.73 95.45 Extremely Wet Flood risk 18 2043 1855.9 -0.76 95.91 Extremely Wet Flood risk 19 2044 1789.3 -0.62 90.91 Sever Wet Flood risk 20 2045 1902.1 -0.85 97.72 Extremely Wet Flood risk 21 2046 1935.8 -0.88 97.72 Extremely Wet Flood risk 22 2047 1870.4 -0.82 97.72 Extremely Wet Flood risk 23 2048 1955.2 -0.91 97.72 Extremely Wet Flood risk 24 2049 1989.7 -0.94 97.72 Extremely Wet Flood risk 25 2050 2020.3 -0.97 97.72 Extremely Wet Flood risk *Anomaly classes based on Percentile range according to Schar et al. (2016) Climate-Resilient and Sustainable Farm Planning Climate-resilient farm planning refers to a systematic approach to agricultural planning and management that helps farmers adapt to and with the adverse impacts of climate change and variability, such as irregular rainfall, drought, floods, and heatwaves, while sustaining productivity, income, and ecosystem health. Based on our analysis of land use and land cover (LULC) along with rainfall patterns, we have created a framework for planning climate-resilient farming (see Fig.10). Fig. 10 Climent’s resilient farm planning framework 5. Discussion The results demonstrate substantial land use transformation and evolving rainfall behavior in Dewgain Village, both reflecting the influence of anthropogenic and climatic factors. The expansion of cropland (+3.58%) and decline in forest and rangeland align with regional trends of agricultural intensification reported across semi-arid India (Lambin & Meyfroidt, 2011; NRSC, 2021). Similar deforestation and built-up transitions have been documented near peri-urban centers (Mehra & Swain, 2024), suggesting increasing land pressure from cultivation and habitation. The sharp reduction in water bodies (−0.35%) underscores the growing challenge of surface-water scarcity, consistent with national findings that > 70% of rural water bodies occupy < 0.5 ha (Ministry of Jal Shakti, 2023). This highlights the need for improved rainwater harvesting and sustainable groundwater extraction to meet irrigation demands (CGWB, 2023). Rainfall analysis indicates a significant positive long-term trend, mirroring patterns observed in several Indian states (Roxy et al., 2017; Ghosh et al., 2012). Although the monsoon and pre-monsoon seasons exhibit increasing rainfall, the declining winter precipitation could exacerbate dry-season water stress and irrigation costs. The post-2010 increase in rainfall variability agrees with reports of intensified extremes due to global warming and changing circulation patterns (IPCC, 2021). The SPI and anomaly classifications reveal alternating wet and dry years typical of tropical monsoon climates (Dutta & Das, 2019). Such fluctuations directly influence crop yields and water resource planning, particularly for rainfed systems dependent on timely rainfall (FAO, 2021). Projected increases in rainfall intensity and frequency of “extremely wet” years (after 2039) imply a shift toward a wetter regime, corroborating projections by Ghosh et al. (2012) and Schär et al. (2016). While higher rainfall may temporarily benefit crop yields, excessive wetness raises flood, erosion, and pest risks, demanding adaptive land-water management. Integrating LULC and rainfall dynamics into farm-planning frameworks can support resilience by promoting climate-smart interventions (Zomer et al., 2009) such as agroforestry, efficient irrigation, flood-tolerant crop varieties, and soil-conservation measures. Improved drainage, crop diversification, and participatory water management will be essential to balance productivity and sustainability. Ultimately, linking spatial LULC changes with temporal rainfall variability enables targeted adaptation strategies to safeguard livelihoods and ecosystems in monsoon-dependent agrarian landscapes. Conclusion This study highlights the growing climate challenges facing agriculture in Dewgain, Jharkhand, by analyzing land use and rainfall dynamics from 2017 to 2024. Results reveal a notable expansion of cropland at the expense of forests, rangelands, and built-up areas, alongside a significant upward rainfall trend (P = 0.0083, Tau = 0.277) and rising variability, with annual totals ranging from 914.55 mm to 2032.86 mm. While seven dry years were identified, projections for 2025–2050 suggest increased flood risks, posing further threats to agricultural stability. Declining winter rainfall also signals growing groundwater dependence and higher irrigation costs. To enhance resilience, the study recommends climate-smart interventions such as agroforestry, water-efficient farming, and proactive flood management, emphasizing the integration of land use and rainfall trends into sustainable and adaptive farm planning. Acknowledgment The authors would like to express their immense gratitude to Sri. Mata Amritanandamayi Devi (Amma), Chancellor of Amrita Vishwa Vidyapeetham, has inspired them to perform selfless service to society. This project has been funded by the E4LIFE International Ph.D. Fellowship Program offered by Amrita Vishwa Vidyapeetham. We extend our gratitude to the Amrita Live in Lab® academic program for providing all the support. Statement of Declarations: Funding: The study was funded by Amrita Vishwa Vidyapeetham (Amrita University), School of Sustainable Features. No specific grant number or funding code was provided. Conflicts of interest: The authors declare that they have no conflicts of interest. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. Author Contributions: Demisie Ejigu: Preparation of the manuscript Raji Pushpalatha: Scientific monitoring and editing Sushil Kumar Himanshu: Reviewing and technical editing Sajithkumar K Jayaprakash: Reviewing and editing Shivapratap Gopakumar: Reviewing and editing References Aggarwal, P.K, Joshi, P.K, Ingram, J.S, Gupta, R. K. (2004). Adapting food systems of the Indo Gangetic plains to the global environmental change: Key information needs to improve policy formulation. Environmental Science & Policy , 7(6),487−498. Barati, M.K., Manivasagam, V.S., Nikoo, M. R., Saravanane, P., Narayanan, A., & Manalil, S. (2022). Rainfall Variability and Rice Sustainability: An Evaluation Study of Two Distinct Rice-Growing Ecosystems. Land , 11 (8). https://doi.org/10.3390/land11081242 Barati, M. K., Soundharajan, B.S., Nikoo, M.R. (2024). Simulation of climate-adaptation responses to rainfall variability on rainfed yield anomalies. Environ. Sustainabil. Indicat , 22, 100411. https://doi.org/10.1016/j.indic.2024.100411. Bartczak.A., Glazik, R., & Tyszkowski,S. (2014). The application of Box–Cox transformation to determine the Standardised Precipitation Index (SPI), the Standardised Discharge Index (SDI) and to identify drought events: Case study in Eastern Kujawy (Central Poland). Journal of Water and Land Development , 22 , 3–15. Belgiu, M., Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing , 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011 Baghel, R., & Sharma, P. (2024). Evaluating agricultural activity dynamics over the Uttar Pradesh state of India using satellite-based datasets. Tropical Ecology , 65 (3):412-425. Central Ground Water Board (CGWB). (2023). National groundwater report: Usage, sustainability, and recharge. Ministry of Jal Shakti, Government of India. Chen, T., Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD :785–794. Dutta, D., & Das, A. (2019). Assessment of meteorological drought characteristics in India using the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI). Natural Hazards , 99 (1), 179-197. https://doi.org/10.1007/s11069-09-03707-6 Drusch, M. (2012). Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment , 120, 25–36. https://doi.org/10.1016/j.rse.2011.11.026 FAO. (2021). The State of Food and Agriculture: Climate Change and Agriculture. Food and Agriculture Organization of the United Nations. https://www.fao.org Gadgil, S & Gadgil, S. (2006). The Indian monsoon, GDP, and agriculture. Economic and Political Weekly. 4887-4895. Gadgil, S & Kumar, K.R. (2006). The Asian monsoon – agriculture and economy . In Wang B. (Ed.), The Asian Monsoon. 651–683). Springer. Gajalakshmi, K., Anantharam, V. (2024). Spatiotemporal Land use/ Land cover Changes in Bangalore Rural District. International Journal of Research and Review ,11. Ghosh, S., Das, D., Kao, S.C., Ganguly, A. R. (2012). Lack of uniform trends but increasing spatial variability in observed Indian rainfall extremes. Nature Climate Change , 2(2), 86–91. https://doi.org/10.1038/nclimate1327. Giri, C., Pengra, B., Long, J., & Loveland, T.R. (2016). Next generation of global land cover characterization, mapping, and monitoring. International Journal of Digital Earth , 9(1), 1–12. https://doi.org/10.1080/17538947.2016.1155678 Goswami, B.N., Venugopal.V., Sengupta, D., Madhusoodanan, M.S., Xavier, P.K. (2006). Increasing trend of extreme rain events over India in a warming environment. Science , 314(5804), 1442–1445. https://doi.org/10.1126/science.1132027 Guhathakurta, P., Rajeevan, M., Sikka, D.R., Tyagi, A. (2015). Observed changes in southwest monsoon rainfall over India during 1901-2011. International Journal of Climatology , 35(8), 1881-1898. https://doi.org/10.1002/joc.4095 Guleria, M., Rajesh, T., Panicker, A.J., Raja, S.R., & Soundharajan. B. (2020). Using Human-Centered Design to Improve Socio-Economic Livelihoods with Modernized Irrigation Systems. In 2020 IEE 8 th R10 Humanitarian Technology Conference (R10-HTC), 1-6. IEEE. https://doi.org/10.1109/R10-HTC 49770-2020.9357058 Hare, F.K. (1983). Climate and Desertification. World Meteorological Organization, WMO No. 506, Geneva. Indian Institute for Human Settlements (IIHS). (2022). Peri-urban land use transitions in India: Mapping growth and challenges. https://iihs.co.in IPCC. (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the IPCC. Cambridge University Press. https://doi.org/10.1017/9781009157896 Jambekar, S., Nema, Sh., & Saquib, Z. (2018). Prediction of Crop Production in India Using Data Mining Techniques. In 2016, the fourth international conference on computing communication control and automation (ICCUBEA ). 1-5. Doi: 978-1-5386-5257-2/18/$31.00@2018 IEE Kumar, D. (2023). Growth of Major Agricultural Crops in India with Special Reference to Jharkhand. Resonance , 11 (1). Kumar, D. (2022). Growth of Major Agricultural Crops in Jharkhand. Journal of Development Studies (IJDS), 15 (2) , 1-4. Kundan, M., Kamble, V.A., Sthool, S.K., Jadhav, J. D., Bagade, S.V., Giri, A.M. (2020). Trend Analysis of Rainfall and Rainy Days Using Mann Kendall Method and Sen’s Slope Estimator in Parola Tehsil of Jalgaon District of Maharashtra (India). Int.J.Curr . Microbiol. App. Sci, 9(12), 3038-3044. Krishnan, R., Sabin, T.P., Vellore, R., Mujumdar, M., Sanjay, J., & Ghosh, S. (2020). Unravelling climate change impacts on Indian monsoon rainfall patterns. Current Climate Change Reports, 6(2), 82–97. https://doi.org/10.1007/s40641-020-00153-2 Lambin, E.F., Meyfroidt, P. (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the national academy of sciences, 108 (9), 3465-3472. Lehmann, C.E., Archibald, S.A., Hoffmann, W.A., & Bond, W.J. (2011). Deciphering the distribution of the savanna biome. New Phytologist, 191 (1), 197-209. https://doi.org/10.1111/j.1469-8137.2011.03689.x MAFWI. (2023 & 2024). Directorate of Economics and Statistics, Department of Agriculture and Farmers Welfare, Ministry of Agriculture and Farmers Welfare, Govt. of India, Annual crop yield report. Mall, R. K., Gupta, A., Singh, R., Singh, R.S., & Rathore, L.S. (2006). Water resources and climate change: An Indian perspective. Current Science , 90 (12), 1610–1626. Mall, R.K. (2006). Impact of climate change on Indian agriculture: A review. Climate Change , 78:445–478. https://doi.org/10.1007/s10584-005-9042-x Mehra, N., Swain., J.B. (2024). Assessment of land use, land cover change, and its effects using an artificial neural network-based cellular automation. Journal of Engineering and Applied Science , 71(70). Ministry of Jal Shakti. (2023). First census report of water bodies in India. Government of India. https://jalshakti.gov.in Mishra and Srijit. (2008). Risks, farmers, suicides and agrarian crisis in India: Is there a way out? Indian Journal of Agricultural Economics , 63, 902-1016. Mondal, A., Kundu. S., Mukhopadhyay, A. (2012). Rainfall trend analysis by Mann–Kendall test: A case study of the North-Eastern part of Cuttack district, Orissa. International Journal of Geology, Earth and Environmental Sciences , 2(1), 70–78. National Remote Sensing Centre (NRSC). (2021). India land use/land cover atlas : 2020–2021 . Indian Space Research Organization. https://nrsc.gov.in Navya, T.N., Meghana, S., Pravalika, U., Sumalatha. M., Sahithi, S. (2024). Preiction of Rainfall Using Linear Regression. Journal of Emerging Technologies and Innovative Research (JETIR) , 11(11) Nicholson, S.E. (2001). Climatic and environmental change in Africa during the last two centuries. Climate Research . 17, 123-144. Pai, D.S., Latha. S., Rajeevan, M., Sreejith, O.P., Satbhai, N. S., Mukhopadhyay, B. (2014). Development of a new high spatial resolution (0.25° × 0.25°) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. MAUSAM , 65(1), 1–18. Pal, S., Ziaul. S. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egyptian Journal of Remote Sensing and Space Science, 20(1), 125–145. Pushpalatha, R., Santhosh, M.V., Sunitha, S. (2022). Impact of climate change on the yield of tropical root and tuber crops VS. rice and potato in India. Food Security , 14, 495-508. https://doi.org/10.1007/s12571-021-01226-z Prasad, G., Ramesh, M.V. (2019). Spatio-temporal analysis of land use/land cover changes in an ecologically fragile area-Alappuzha District, Southern Kerala, India. Natur. Resour.Res , 28, 31−42. https://doi.org/10.1007/s11053-018-9419-y. Rao, K. K., Srinivas. K., Rao, G. G. (2019). Climate change and its impact on Indian agriculture: Trends and projections. Current Science, 117 (1), 1–8. Rao, K.P., Ndegwa, W.G., Kizito, K., Oyoo. (2011). Climate variability and change: Farmer perceptions and understanding of intra-seasonal variability in rainfall and associated risk in semi-arid Kenya. Experimental agriculture , 47(2), 267-291. Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science , 18(1), 77–84. Roxy, M. K., Ghosh, S., Pathak, A., Athulya, R., Mujumdar, M., Murtugudde, R., Rajeevan, M. (2017). A threefold rise in widespread extreme rain events over central India. Nature Communications , 8(1), 708. https://doi.org/10.1038/s41467-017-00744- Schär, C., Ban, N., Fischer, E. M. (2016). Percentile indices for assessing changes in heavy precipitation events. Climatic Change, 137, 201–216. https://doi.org/10.1007/s10584-016-1669-2 Sharma, B.R., Rao, K.V., Vittal, K.P., Ramakrishna, Y.S., Amarasinghe, U. (2020). Climate variability and agriculture: Adaptation and mitigation strategies. Indian Council of Agricultural Sharma., M., Kumar, S. (2024). A systematic review of urban sprawl and land use/land cover change studies in India. Sustainable Environment , 10, 2331269. Sharma, B. R. (2010). Groundwater management in South Asia. Water International , 35(1), 95–112 Singh, D., Tsiang, M., Rajaratnam, B., Diffenbaugh, N. S. (2014). Observed changes in extreme wet and dry spells during the South Asian summer monsoon season. Nature Climate Change , 4 (6), 456-46 Singh, R., Dass, A., Singh, V.K. (2020). Sustaining Crop Production in Rainfed Areas in India: Issues and Strategies. Current Journal of Applied Science and Technology , 154–173. https://doi.org/10.9734/cjast/2020/v39i2530895 Srinivas, R., Rao, V.S. (2021). Long Term and Seasonal Rainfall Trend in Bapatla District of Andhra Pradesh Using Advanced Statistical Methods. International Journal of Environment and Climate Change , 13 (11), 3566–3577. https://doi.org/10.9734/ijecc/2023/v13i113532 Tripathi, B.P., Mahato, R.K., Yadaw, R.B., Sah, S.N., Adhikari, B.B. (2012). Adapting rice technologies to climate change. Hydro Nepal : Journal of Water, Energy and Environment , 69-72. Turner, A.G., Annamalai, H. (2012). Climate change and the South Asian summer monsoon. Nature Climate Change , 2(8), 587–595. https://doi.org/10.1038/nclimate1495 Van Rooy, M.P. (1965). A rainfall anomaly index independent of time and space. Notos, 14, 43−48. Vermeulen, S.J., Aggarwal, P.K., Ainslie, A., Angelone, B.M., Challinor, A.J., Wollenberg, E. (2012). Options for support to agriculture and food security under climate change. Environmental Science & Policy , 15(1), 136–144. https://doi.org/10.1016/j.envsci.2011.09.003 World Meteorological Organization (WMO). (2020). State of Climate Services 2020: Agriculture and Food Security. World Meteorological Organization. Zewdu, D., Krishnan, C.M., Raj, P. N., Makadi, Y.C., Arlikatti, S. (2024). Assessing climate change risks using multi-criteria decision-making (MCDM) techniques in Raichur Taluk, Karnataka, India. Stoch.Environ. Res. Risk Assess . 1−26. https://doi.org/10.1007/s00477-024-02816-x. Zewdu, D., Krishnan, C.M., Raj, P.N, Makadi,Y.C., Barati, M.K., Arlikatti, S. (2025). Interactions of land use, land cover, climate change: A case study of Raichur District, Karnataka, India. Environmental Challenges, 19:101166. https://doi.org/10.1016/j.envc.2025.101166 Zomer, R.J., Trabucco, A., Coe. R., Place. F. (2009). Trees on farm: Analysis of global extent and geographical patterns of agroforestry, World Agroforestry Centre. ICRAF Working Paper no. 89 . 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Keywords climate-resilient agriculture land cover change rainfall anomalies rainfall trends sustainable farm planning Authors Affiliations Demisie Ejigu 0000-0001-9494-4237 Amrita Vishwa Vidyapeetham School for Sustainable Futures View all articles by this author P Raji [email protected] Amrita Vishwa Vidyapeetham School for Sustainable Futures View all articles by this author Sushil Himanshu Asian Institute of Technology School of Engineering and Technology View all articles by this author Sajithkumar KJ Amrita Vishwa Vidyapeetham School for Sustainable Futures View all articles by this author Shivapratap Gopakumar Amrita Vishwa Vidyapeetham School for Sustainable Futures View all articles by this author Metrics & Citations Metrics Article Usage 238 views 100 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Demisie Ejigu, P Raji, Sushil Himanshu, et al. 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