Search Results (1,551)

To explore the spatiotemporal evolution of extreme temperature events in Guangxi (1940–2023), reveal regional response mechanisms, and assess future trends of persistence under climate warming, a multi-scale analysis was conducted using ERA5 reanalysis data. Methodologies included RH tests for homogeneity correction, collaborative kriging for data optimisation, Mann–Kendall tests for trend and abrupt change detection, Morlet wavelet analysis for cyclic pattern identification, Exploratory Spatio-Temporal Data Analysis (ESTDA) for spatial heterogeneity quantification, and Rescaled Range (R/S) analysis to calculate Hurst indices for future persistence assessment. Results showed the following: (1) The ERA5 dataset exhibited high applicability in Guangxi (R = 0.9989, RMSE = 1.9492 °C), supporting robust evidence of continuous warming—warm indices (e.g., SU25, TX90p) increased significantly (SU25 at 0.2044 d/10a), while cold indices (e.g., TN10p, FD0) declined (TN10p at −0.0519 d/10a); abrupt changes of cold indices were concentrated in 1942–1950, with warm indices accelerating post-2000 and TXx exhibited the highest warming rate (0.23 °C/decade). (2) Extreme temperature indices displayed a primary 19–21-year oscillation cycle (dominant in warm indices) and a secondary 13-year cycle (prominent in cold indices). (3) Spatial heterogeneity featured northwest–southeast cold–heat inversion, coastal–inland intensity gradients, and latitudinal zonation of extreme indices; ESTDA revealed intensified polarisation, with warm indices clustering in low-latitude regions (e.g., Baise) and cold indices declining homogeneously in mountainous areas (e.g., Guilin), indicating an irreversible transition to a warming steady state. (4) R/S analysis indicated all indices had Hurst indices of 0.65–0.92, reflecting persistent future trends consistent with historical evolution, with warm indices (e.g., TNn, SU25) showing stronger persistence (H > 0.85). This work clarifies the spatial polarisation mechanism and future persistence of extreme temperature dynamics in Guangxi, providing a multi-scale scientific basis for disaster early warning and adaptation planning in climate-sensitive karst-monsoon regions. Full article

This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov–Smirnov tests ($p=1.000$ for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000–2003 and 2011–2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann–Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (${\rho }_1 > 0.3$) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations ($\alpha =0.05$). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall’s $\tau$ range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies ($\tau$ range: −0.0377 to −0.0008), Sen’s slope estimates were negligible (range: −0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude ($\rho =-0.022$, $p>0.05$) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato’s severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture. Full article

Identifying the impact of climate change and changes in underlying surface conditions on river runoff changes is critical for sustainable water resource use and watershed management in arid regions. The Kriya River is not only a key support for water resources in the arid environment of the Tarim Basin, but also a solid foundation for the survival and development of agricultural oases. In this study, the Kriya River Basin in Xinjiang, China, was taken as the research object, and the Mann–Kendall, Sen’s Slope, Cumulative Sum, and other methods were used to systematically analyze the temporal evolution law and multi-modal characteristics of runoff in the basin. Based on the Budyko hydrothermal coupling equilibrium equation, the contribution of temperature, evaporation, and the underlying surface to runoff variation was quantitatively interpreted. The study found that the annual runoff depth of the Kriya River Basin has shown a significant positive evolution trend in the past 60 years, with an increase rate of 0.5189 mm/a (p ≤ 0.01). Through the identification of mutation points, the runoff time series of the Kriya River was divided into the base period 1957–1999 and the change period 2000–2015. Without considering the supply of snowmelt runoff, the contribution rate of precipitation to runoff change was 75.23%, followed by the change in underlying surface (23.08%), and the potential evapotranspiration was only 1.69%. The results of this study provide a good scientific reference for water resources management and environmental governance in the Kriya River Basin. Full article

Evapotranspiration (ET) variation is typically influenced by climatic factors, which are considered the primary drivers of agricultural water requirements. Any changes in ET rates directly affect crop water demands. In this study, temporal trends and magnitudes of key climatic variables, and their impacts on reference evapotranspiration (ETo) during 1981–2020, were evaluated across 36 districts of Punjab, Pakistan. Positive serial correlations, ranging from 0.29 to 0.48, were identified and removed using the pre-whitening technique. Increasing trends in maximum temperature (Tmax) and wind speed (WS) across Punjab and its subregions were observed, while relative humidity (RH) exhibited both increasing and decreasing trends. No significant trends were detected for the minimum temperature (Tmin). On a monthly scale, in the Southern Punjab (SP) region, Sen’s slope estimated an increase in ETo, ranging from 0.239 mm/year in November to 0.636 mm/year in May, at a significance level of α = 0.05 (5%). At the provincial scale, significant upward trends in ETo were observed for the annual, Kharif, and autumn seasons, with Z-values of 2.04, 2.16, and 3.13, respectively, at α = 0.05 and 0.01. It was determined that, on an annual scale in Punjab, ETo sensitivity to climatic parameters followed the following order: Tmax > wind speed (WS) > Tmin > RH. The best-fitted models for Tmax, Tmin, WS, and RH were Gaussian, exponential, and spherical. ETo was found to increase spatially from North to South Punjab, with an approximate rise of 70–80 mm/decade. The results provide a scientific basis for understanding hydroclimatic drivers of ETo in semi-arid regions and contribute to improving climate impact assessments on agricultural water use. The observed ETo increases, particularly in South Punjab and lower Central Punjab, highlight the need for region-specific irrigation scheduling and water allocation. These findings can guide cropping calendars, improve irrigation efficiency, and increase canal water supplies to high-ETo areas, supporting adaptive strategies against climate variability in Punjab. Full article

River spring and flash floods are highly dependent on variations in meteorological conditions. In the Baltic States, substantial changes in air temperature and precipitation have been observed between the two most recent climate normal periods (1961–1990 and 1991–2020). Therefore, changes in the magnitude of spring and flash floods across different hydrological regions between these periods were analyzed to better understand shifting hydrological patterns. Daily flow data from 1961 to 2020 were obtained from 68 water gauging stations on 55 rivers. The Pettitt and Mann–Kendall tests, as well as Sen’s slope estimator, were applied to analyze the time series of flood maximum discharges. The most pronounced negative trends in spring and flash floods were observed in Lithuanian rivers, with the magnitude of these trends gradually weakening toward Latvia and Estonia. The maximum flood heights (h_MAX) generally declined during 1961–2020, particularly in Lithuania and western Latvia. Spring flood data showed the most significant decrease, particularly during 1991–2020, when h_MAX declined on average by 0.14 mm/year in Lithuania and 0.05 mm/year in Latvia. Flash floods exhibited smaller declines, also concentrated in 1991–2020. In the major rivers (Nemunas, Neris, and Daugava), peak discharges of both floods declined consistently throughout the study period. Full article

Background/Objectives: Cancer is a major public health burden in Serbia and a factor influencing long-term disaster readiness by straining health system capacity. This study examined spatial and temporal trends in incidence and mortality for eight major cancers among women in Central Serbia (1999–2021) to inform targeted prevention and preparedness strategies. Methods: Standardised rates from national datasets were analysed using the Mann–Kendall trend test and Sen’s slope estimator. Geographic disparities were mapped in ArcGIS Pro 3.2. Mortality trends were assessed only for statistically reliable series. Results: Breast cancer incidence increased in six counties, while cervical cancer declined in several areas, likely reflecting screening success. Colorectal, bladder, pancreatic, and lung and bronchus cancers showed rising incidence; lung and bronchus cancer mortality increased in 16 counties, indicating growing demand for chronic respiratory care. These shifts may reduce surge capacity during disasters by increasing the baseline burden on healthcare infrastructure. Regional disparities highlight uneven system resilience. Conclusions: Aligning cancer control measures—especially for high-burden cancers like lung—with emergency preparedness frameworks is essential to strengthen health system resilience, particularly in resource-limited regions. Full article

Monitoring hydrologic variables over rivers and reservoirs is crucial for gaining insight into, preparing for, and mitigating future extreme weather events. This study aims to determine whether rainfall activity has contributed to changes in the rivers and reservoirs of Puerto Rico. Data from 114 stations across 19 watersheds between 1990 and 2022 were used to evaluate historical precipitation, reservoir surface elevation, river discharge, and gauge height. The Mann-Kendall test was used to detect trends, Sen’s slope test was applied to assess their magnitude, and correlation was used to determine the relationship between hydrological variables. Trend results showed that precipitation has decreased on average over the past 30 years, while surface elevation in reservoirs has increased. A similar tendency was observed for discharge and stream gauges in rivers, where contradictory trends may be due to factors other than precipitation. Correlations reflected these observations, where precipitation had a weak relationship with surface elevation and a strong relationship with river variables, but not across a large number of stations. Factors such as inadequate maintenance or sediment accumulation may be more significant contributors to this trend. Full article

The objective of this article is to analyze the impacts of climatic, physiographic, and land use/cover factors on the spatiotemporal variability of 7-day low-flow occurrence dates for 17 rivers during the period 1950–2023 in winter and summer in southern Quebec. Regarding spatial variability, correlation analysis revealed that these occurrence dates are primarily negatively correlated with agricultural surface area (early occurrence) during both seasons. In winter, they are also negatively correlated with total rainfall and daily mean maximum temperatures, but positively correlated with forest area and mean watershed slopes. Regarding temporal variability, the application of three Mann–Kendall tests showed that in summer, 7-day low flows tend to occur late in the season due to increased rainfall, particularly in the most agricultural watersheds. In contrast, in winter, very few significant changes were observed in the long-term trend of the analyzed hydrological series. Correlation analysis using redundancy analysis between eight climate indices and the occurrence dates of 7-day low flows showed that in summer, these dates are positively correlated with the global warming climate index, while they are not correlated with any climate index in winter. This study demonstrated that the spatiotemporal variability of the occurrence dates and magnitude of 7-day low flows are not influenced by the same factors in southern Quebec, except for the global warming climate index in summer. Finally, this study shows that the timing is much less sensitive to changes in climate change than the magnitude of low flows in southern Quebec. Full article

Driven by both natural and anthropogenic factors, farmland abandonment and recultivation constitute complex and widespread global phenomena that impact the ecological environment and society. In the Inner Mongolia Yellow River Basin (IMYRB), a critical tension lies between agricultural production and ecological conservation, characterized by dynamic bidirectional transitions that hold significant implications for the harmony of human–nature relations and the advancement of ecological civilization. With the development of remote sensing, it has become possible to rapidly and accurately extract farmland changes and monitor its vegetation restoration status. However, mapping abandoned farmland presents significant challenges due to its scattered and heterogeneous distribution across diverse landscapes. Furthermore, subjectivity in questionnaire-based data collection compromises the precision of farmland abandonment monitoring. This study aims to extract crop phenological metrics, map farmland abandonment, and recultivation dynamics in the IMYRB and assess post-transition vegetation changes. We used Landsat time-series data to detect the land-use changes and vegetation responses in the IMYRB. The Farmland Abandonment and Recultivation Extraction Index (FAREI) was developed using crop phenology spectral features. Key crop-specific phenological indicators, including sprout, peak, and wilting stages, were extracted from annual MODIS NDVI data for 2020. Based on these key nodes, the Landsat data from 1999 to 2022 was employed to map farmland abandonment and recultivation. Vegetation recovery trajectories were further analyzed by the Mann–Kendall test and the Theil–Sen estimator. The results showed rewarding accuracy for farmland conversion mapping, with overall precision exceeding 79%. Driven by ecological restoration programs, rural labor migration, and soil salinization, two distinct phases of farmland abandonment were identified, 87.9 kha during 2002–2004 and 105.14 kha during 2016–2019, representing an approximate 19.6% increase. Additionally, the post-2016 surge in farmland recultivation was primarily linked to national food security policies and localized soil amelioration initiatives. Vegetation restoration trends indicate significant greening over the past two decades, with particularly significant increases observed between 2011 and 2022. In the future, more attention should be paid to the trade-off between ecological protection and food security. Overall, this study developed a novel method for monitoring farmland dynamics, offering critical insights to inform adaptive ecosystem management and advance ecological conservation and sustainable development in ecologically fragile semi-arid regions. Full article

Predicting future hydrological drought characteristics can assist relevant departments in taking proactive measures to mitigate drought losses. Based on the SWAT model and the Sixth International Coupled Model Comparison Program, this study employs an improved Mann–Kendall test, cumulative anomaly method, and continuous wavelet transform to investigate future runoff and hydrological drought characteristics in the upper reaches of the Xin’an River under different Shared Socioeconomic Pathways (SSPs). The SSPs scenario consists of three typical paths. SSP126 represents the sustainable development path (low carbon emissions, ecological protection first), SSP245 is the intermediate balance path (equal emphasis on economic growth and environmental protection), and SSP585 is the fossil fuel-intensive path (high emissions, high development intensity). The results indicate that from 2000 to 2020, under the influence of ecological compensation policies, the upper reaches of the Xin’an River transitioned from hydrological drought to hydrological wetness in 2012. Under the three future scenarios, runoff volumes increased by 41.72%, 40.74%, and 40.72% compared to the historical period, respectively, with peak runoff occurring in May, June, and July, alleviating hydrological drought conditions. Under the SSP245 and SSP585 scenarios, drought characteristics were more pronounced, with the number of drought-free months increasing by 21 and 30 months, respectively, compared to the SSP126 scenario, and the number of extremely dry months increased by 9 months and 17 months, respectively. The standard runoff index in the SSP126 scenario exhibits two oscillation cycles of 400 months and 359 months, respectively, while SSP245 and SSP585 both exhibit an oscillation cycle of 835 months. After discussion, it was concluded that ecological compensation policies can improve hydrological drought conditions. Drought characteristics become increasingly pronounced as carbon emissions intensify. This research can provide theoretical references for water allocation and drought prevention in river basins. Full article

In the context of global climate change and growing food security challenges, this study provides a comprehensive analysis of the yields of three staple crops (wheat, corn and rice) in the Yellow River Basin of China, employing multiple quantitative analysis methods including the Mann–Kendall trend test, center of gravity transfer model and hotspot analysis. Our research integrates yield data covering these three crops from 72 prefecture-level cities across the Yellow River Basin, during 2000 to 2023, to systematically examine the temporal variation, spatial variation and spatial agglomeration characteristics of the yields. The study uses GeoDetector to explore the impacts of natural and socioeconomic factors on changes in crop yields from both single-factor and interactive-factor perspectives. While traditional statistical methods often struggle to simultaneously handle complex causal relationships among multiple factors, particularly in effectively distinguishing between direct and indirect influence paths or accounting for the transmission effects of factors through mediating variables, this study adopts Structural Equation Modeling (SEM) to identify which factors directly affect crop yields and which exert indirect effects through other factors. This approach enables us to elucidate the path relationships and underlying mechanisms governing crop yields, thereby revealing the direct and indirect influences among multiple factors. This study conducted an analysis using Structural Equation Modeling (SEM), classifying the intensity of influence based on the absolute value of the impact factor (with >0.3 defined as “strong”, 0.1–0.3 as “moderate” and <0.1 as “weak”), and distinguishing the nature of influence by the positive or negative value (positive values indicate promotion, negative values indicate inhibition). The results show that among natural factors, temperature has a moderate promoting effect on wheat (0.21) and a moderate inhibiting effect on corn (−0.25); precipitation has a moderate inhibiting effect on wheat (−0.28) and a moderate promoting effect on rice (0.17); DEM has a strong inhibiting effect on wheat (−0.33) and corn (−0.58), and a strong promoting effect on rice (0.38); slope has a moderate inhibiting effect on wheat (−0.15) and a moderate promoting effect on corn (0.15). Among socioeconomic factors, GDP has a weak promoting effect on wheat (0.01) and a moderate inhibiting effect on rice (−0.20), while the impact of population is relatively small. In terms of indirect effects, slope indirectly inhibits wheat (−0.051, weak) and promotes corn (0.149, moderate) through its influence on temperature; DEM indirectly promotes rice (0.236, moderate) through its influence on GDP and precipitation. In terms of interaction effects, the synergy between precipitation and temperature has the highest explanatory power for wheat and rice, while the synergy between DEM and precipitation has the strongest explanatory power for corn. The study further analyzes the mechanisms of direct and indirect interactions among various factors and finds that there are significant temporal and spatial differences in crop yields in the Yellow River Basin, with natural factors playing a leading role and socioeconomic factors showing dynamic regulatory effects. These findings provide valuable insights for sustainable agricultural development and food security policy-making in the region. Full article

Global warming has intensified the hydrological cycle, resulting in more frequent extreme precipitation events and altered spatiotemporal precipitation patterns in urban areas, thereby increasing the risk of urban flooding and threatening socio-economic and ecological security. This study investigates the characteristics of summer extreme precipitation in the Poyang Lake City Group (PLCG) from 1971 to 2022, utilizing the China Daily Precipitation Dataset and NCEP/NCAR reanalysis data. Nine extreme precipitation indices were examined through linear trend analysis, Mann–Kendall tests, wavelet transforms, and correlation methods to quantify trends, periodicity, and atmospheric drivers. The key findings include: (1) All indices exhibited increasing trends, with RX1Day and R95p exhibiting significant rises (p < 0.05). PRCPTOT, R20, and SDII also increased, indicating heightened precipitation intensity and frequency. (2) R50, RX1Day, and SDII demonstrated east-high-to-west-low spatial gradients, whereas PRCPTOT and R20 peaked in the eastern and western PLCG. More than over 88% of stations recorded rising trends in PRCPTOT and R95p. (3) Abrupt changes occurred during 1993–2009 for PRCPTOT, R50, and SDII. Wavelet analysis revealed dominant periodicities of 26–39 years, linked to atmospheric oscillations. (4) Strong subtropical highs, moisture convergence, and negative OLR anomalies were closely associated with extreme precipitation. Warmer SSTs in the eastern equatorial Pacific amplified precipitation in preceding seasons. This study provides a scientific basis for flood prevention and climate adaptation in the PLCG and highlighting the region’s vulnerability to monsoonal shifts under global warming. Full article

Global warming impacts agricultural production and food security, particularly in high-latitude regions with high temperature sensitivity. As a major grain-producing area in China and one of the fastest-warming regions globally, Northeast China (NEC) has received considerable research attention. However, the existing literature lacks sufficient exploration of the spatiotemporal heterogeneity in climate change impacts. Based on data on rice, corn, and soybean yields, as well as temperature, rainfall, and sunshine duration in NEC from 1993 to 2022, this study employs Sen’s slope estimation, the Mann–Kendall (MK) test, spatial autocorrelation analysis, and the Geographically and Temporally Weighted Regression (GTWR) model to analyze the spatiotemporal evolution of grain yields and their responses to climate change. The results show that ① 1993–2022 witnessed an overall rise in grain yields per unit area in NEC, with Liaoning growing fastest. Rice yields increased regionally; corn yields rose in Liaoning and Jilin, while soybean yields increased only in Liaoning. During the growing season, rainfall trended upward with fluctuations, temperatures rose steadily, and sunshine duration declined in Heilongjiang. ② Except for corn and soybeans in the early period, other crops exhibited significant yield spatial agglomeration. High–high agglomeration areas first expanded, then shrank, eventually shifting northward to the region of Jilin Province. ③ Climatic factors show marked spatiotemporal heterogeneity in impacts: positive effect areas of rainfall and temperature expanded northward; sunshine duration’s influence weakened, but its negative effect areas spread. ④ Differences in crop responses are closely linked to their physiological characteristics, regional climate evolution, and agricultural adaptation measures. This study provides a scientific basis for formulating region-specific agricultural adaptation strategies to address climate change in NEC. Full article

Atmospheric pollution constitutes one of the key environmental challenges hindering Atmospheric pollution is a key environmental challenge constraining the sustainable development of Gansu Province’s land-based Belt and Road corridor and its regional ecological barrier function. The spatiotemporal heterogeneity of aerosol optical depth (AOD) profoundly impacts regional environmental quality. Based on MODIS AOD, NCEP reanalysis, and emission data, this study employed trend analysis (Mann–Kendall test) and attribution analysis (multiple linear regression combined with LMG and Spearman correlation) to investigate the spatiotemporal evolution of AOD over Gansu Province during 2009–2019 and its meteorological and emission drivers. Key findings include the following: (1) AOD exhibited significant spatial heterogeneity, with high values concentrated in the Hexi Corridor and central regions; monthly variation showed a unimodal pattern (peak value of 0.293 in April); and AOD generally declined slowly province-wide during 2009–2019 (52.8% of the area showed significant decreases). (2) Following the implementation of the Air Pollution Prevention and Control Action Plan in 2013 (2014–2019), AOD trends stabilized or declined in 99.8% of the area, indicating significant improvement. (3) Meteorological influences displayed distinct regional-seasonal specificity—the Hexi Corridor (arid zone) was characterized by strong negative correlations with relative humidity (RH2) and wind speed (WS) year-round, and positive correlations with temperature (T2) in spring but negative in summer in the north; the Hedong region (industrial zone) featured strong positive correlations with planetary boundary layer height (PBLH) in summer (r > 0.6) and with T2 in spring/summer; and the Gannan Plateau (alpine zone) showed positive WS correlations in spring and weak positive RH2 correlations in spring/autumn, highlighting the decisive regulatory role of underlying surface properties. (4) Emission factors (PM_2.5, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{NO}}_3^{-}$, ${\mathrm{NH}}_4^{+}$, OM, and BC) dominated (>50% relative contribution) in 80% of seasonal scenarios, prevailing in most regions (Hexi: 71–95% year-round; Hedong: 68–80% year-round; and Gannan: 69–72% in spring/summer). Key components included BC (contributing > 30% in 11 seasons, e.g., 52.5% in Hedong summer), ${\mathrm{NO}}_3^{-}$ + ${\mathrm{NH}}_4^{+}$ (>57% in Hexi summer/autumn), and OM (20.3% in Gannan summer, 19.0% province-wide spring). Meteorological factors were the primary driver exclusively in Gannan winter (82%, T2-dominated) and province-wide summer (67%, RH2 + WS-dominated). In conclusion, Gansu’s AOD evolution is co-driven by emission factors (dominant province-wide) and meteorological factors (regionally and seasonally specific). Post-2013 environmental policies effectively promoted regional air quality improvement, providing a scientific basis for differentiated aerosol pollution control in arid, industrial, and alpine zones. Full article

Throughout history, humans have modified the environment, transforming natural biomes into agricultural areas. In the 1990s, economic policies accelerated the expansion of agricultural frontiers in Brazil, including the Triângulo Mineiro and Alto Paranaíba regions. This study analyzes rainfall variability from 1990 to 2021 and its relationship with land use. For this purpose, satellite data from MapBiomas, ERA5, and NASA POWER were processed using Google Earth Engine and QGIS. Statistical methods included the Spearman correlation and the Mann–Kendall trend test. The results revealed that average annual precipitation decreased from 1663.35 mm in 1991 to 1128.94 mm in 2022—a 32.14% reduction. Simultaneously, agricultural and urban areas increased by 365% and 237.59%, respectively. Spearman analysis showed negative correlations between precipitation and agriculture (ρ = −0.51) and urbanization (ρ = −0.51), and positive correlations with pasture (ρ = +0.52) and water bodies (ρ = +0.46). These trends suggest that land use intensification significantly affects regional rainfall patterns. Unlike studies focusing mainly on Amazon deforestation, this research emphasizes the Cerrado biome’s climatic vulnerability. The use of long-term, high-resolution remote sensing data allows a robust analysis of land use impacts. By highlighting a clear link between land transformation and precipitation decline, this study offers insights for policymaking aimed at balancing agricultural development and water resource preservation. This research underscores the importance of sustainable land management practices, such as agroecology, reforestation, and ecological corridors, for regional climate resilience. Full article