Search Results (617)

In early 2018, Cape Town, South Africa, experienced severe water shortages during the worst drought in nearly a century (2015–2017), underscoring the need to diversify water resources, including groundwater. This study evaluated infiltration rates and hydraulic properties of three representative stormwater ponds in the Zeekoe Catchment, Cape Town, to assess their feasibility as recharge basins for transferring detained stormwater runoff into the underlying aquifer. Field infiltration data were analysed to estimate hydraulic properties, while laboratory permeability tests and material classification on 36 soil samples provided inputs for numerical modelling using HYDRUS 2-D software. Simulations estimated recharge rates and indicated wetting front movement from pond surfaces to the water table (~5.5 m depth) ranged between 15 and 140 h. The results revealed field hydraulic conductivity values of 0.3 to 19.9 cm/h, with laboratory estimates up to 103% higher due to controlled conditions. Simulated infiltration rates were 67–182% higher than field measurements, attributed to idealised assumptions. Despite these variations, ponds in the central catchment exhibited the highest infiltration rates, indicating suitability for artificial recharge. Explicit recognition of pond-specific infiltration variability significantly contributes to informed urban water security planning, enabling targeted interventions to optimise groundwater recharge initiatives. Full article

Beavers play a key role in creating temporary water reservoirs that significantly impact the natural environment and local river hydrology. The primary aim of this study was to assess the potential of increasing the number of beaver dams (Castor spp.), as an alternative method of water retention in the environment. Research conducted on three small lowland streams in central Poland revealed that beaver dams, even in modified riverbeds, enable the formation of shallow floodplains and ponds. Innovative analyses considered the structural materials of the dams and their impact on river hydromorphology and sediment transport. The findings emphasise the importance of beavers in water retention processes, the stabilisation of water levels during low flows and the protection of biodiversity. The study also demonstrated that beaver dams play a critical role in storing surface- and groundwater, mitigating drought impacts, reducing surface runoff, and stabilising river flows. These constructions influence local hydrology by increasing soil moisture, extending water retention times, and creating habitats for numerous species. The collected data highlight the potential of beaver dams as a tool in water resource management in the context of climate change. Further research could provide guidance for the sustainable utilisation of beavers in environmental conservation strategies and landscape planning. Full article

We present a study that addresses the critical need for a prototype Decision Support System for forest fire information and management in Uttarakhand, India. The study’s main objective was to carry out statistical analysis of large fire incident datasets to understand trends of fires in the region and develop essential spatial decision support tools. These tools address the necessary fire management decision-making along with comprehensive datasets that can enable a decision maker to exercise better management. Moreover, this DSS addresses three major components of forest fire decision support: (i) pre-fire (forest information visualization) tools, (ii) during-fire terrain-based spatial decision support tools, and (iii) post-fire restoration tools. The efforts to develop this DSS included satellite lidar dataset-based fuel load estimations, the Keetch–Byram Drought Index, and the integration of spatial tools that ensure better spatial decisions in fire suppression planning. In addition, based on the bibliographic literature, the study also uses ecological and community-based knowledge, including financial aspects, for fire prevention and post-fire restoration planning. The development of this DSS involves an open-source R Shiny framework, enabling any decision maker at the execution or planning level to access these key datasets and simulate the spatial solutions cost-effectively. Hence, this study aimed to internalize key decision support tools and datasets based on extensive statistical analysis for data-driven forest fire planning and management. Full article

Analyzing drought evolution requires dynamic three-dimensional methods to capture spatiotemporal continuity. Existing approaches oversimplify drought patch connectivity by relying on overlapping logic, thereby neglecting dynamic evolution. We propose a novel three-dimensional identification method incorporating spatial autocorrelation and anisotropy. Using the ERA5 dataset and the multi-model ensemble mean (MEM) of CMIP6, we investigate meteorological drought characteristics and migration patterns in China during 1961–2010 (historical) and 2031–2080 (future, SSP2-4.5/SSP5-8.5). Results indicate future drought frequency may decline by over 70% compared to historical levels, but severity, duration, affected area, and migration distance could increase significantly. Most future droughts (96.3% for SSP2-4.5; 95.0% for SSP5-8.5) are projected in spring and summer. Drought trajectories may predominantly shift northeastward (33% for SSP2-4.5; 38% for SSP5-8.5), with migration hotspots transitioning from the upper Yangtze River Basin to the upper Yellow River Basin. These findings enhance the understanding of drought dynamics and support the development of improved drought monitoring frameworks. The methodology and projections provide critical insights for drought risk management and adaptive water resource planning under climate change. Full article

Rainfall datasets from the Uganda National Meteorological Authority (UNMA) for 1981–2017 and two reanalysis datasets (Climate Hazards Group Infrared Precipitation with Stations data (CHIRPS) and Tropical Applications of Meteorology using Satellite data (TAMSAT) were used to compute drought and flood tendencies from 1981 to 2017. The cumulative departure index (CDI) and rainfall anomaly index (RAI) were computed to show drought and flood tendencies in the region. Meanwhile, dry days (DD) and wet days (WD) were computed based on the definition as a day of the season with rainfall amounts less than 1.0 mm and greater than 1.0 mm, respectively. The CDI graphics indicated below-average rainfall during 1981–1987 and relatively wetter conditions during 1989–1995 for all stations in the region. Generally, seasonal rainfall declined over the first 27 years but an increasing trend in both MAM (March–April–May) and SOND (September–October–November–December) was observed in most stations during 2006–2017. The highly variable seasonal rainfall in the region is expected to impact the livelihoods of the communities. This study recommends that the use of tailor-made weather and climate information for planning economic development programs such as agriculture will play a critical role in improving the livelihood of the communities in the region. Full article

Bioclimatic classifications provide critical insights into the relationships between climatic variables and the geographic distribution of organisms. Advances in high-resolution climate data, geobotanical integration, and spatial analysis techniques have improved the delineation of bioclimatic units, enabling more precise characterization of terrestrial ecosystems. This study characterizes the bioclimatic conditions of Jalisco, Mexico, through the identification of bioclimatic units and variants using bioclimatic indices and parameters. High-resolution climate data (1980–2018) from the CHELSA database and GIS-based spatial analysis were employed to delineate bioclimatic patterns and their correlation with climatophyllous potential vegetation. The results identified one macrobioclimate and two bioclimates—Tropical pluviseasonal (56.62%) and Tropical xeric (43.38%)—as well as two bioclimatic variants, six thermotypes, and seven ombrotypes. Notably, 49.84% of the territory exhibits bioclimatic variants, and a total of 42 isobioclimates were associated with 14 types of climatophyllous potential vegetation. These findings provide a foundation for understanding vegetation dynamics and support territorial planning and land management. The integration of remote sensing and bioclimatic analysis enhances the identification of spatial heterogeneity in climate–vegetation relationships, facilitating applications in ecological modeling, drought assessment, and conservation planning. This study contributes to ongoing research on terrestrial ecosystem functioning, aligning with current advancements in remote sensing-based environmental analysis. Full article

This article examines the application of nature-based solutions in stormwater management in the context of the Global South, focusing on a co-created green infrastructure plan and a pilot intervention project in the city of Paranoá-DF, Brazil. Urban challenges such as extreme floods, droughts, landslides, heatwaves, and biodiversity loss call for innovative planning strategies to enhance adaptation and resilience. The research methodology combined technical analyses, field work, community participation, and stormwater runoff modelling to develop integrated and culturally sensitive solutions to the city’s environmental and socio-economic challenges. This article then presents the outcomes of the community-based participatory workshops, which informed the definition of a green and blue infrastructure network incorporating a range of NBS. Community-identified priorities were used to design urban landscape interventions aimed at enhancing water-related ecosystem services and improving quality of life. Additionally, and supported by hydrological modelling, this article details a localised landscape intervention project that provides new perspectives on urban resilience in this context. Acknowledging the unique challenges faced by cities in the Global South—where social inequities and infrastructure deficits intersect with environmental vulnerabilities—this study highlights the importance of adapting NBS to the contexts of precarious urbanisation patterns. With hydrological stress expected to intensify under climate change, the proposed solutions address the heightened risks faced by low- and middle-income households, promoting more equitable and sustainable urban transformations. Full article

Inadequate water supply during droughts, leading to socio-economic drought, has become a global issue. In this context, the drought resistance and disaster mitigation capabilities of reservoirs play a crucial role during drought events. Taking the downstream Yellow River Basin (DYRB) as the study area, this research analyzes the evolution and characteristics of socio-economic drought in the region from 1956 to 2016 at different time scales (3 months, 6 months, 9 months, and 12 months). The copula function is used to calculate the joint recurrence period of socio-economic drought in the downstream area. In addition, this study constructs a reservoir optimization operation model to explore the drought resistance capabilities of water supply strategies in response to downstream socio-economic droughts. The results show that the indices across the four time scales indicate that the DYRB faced the most severe socio-economic drought from the 1990s to the early 21st century, with long durations and widespread impacts. Compared with conventional scheduling methods, water supply restriction strategies can cope with more severe socio-economic droughts. However, the maximum drought resistance capacity corresponding to its recurrence period still cannot cope with the socio-economic droughts of the early 21st century. Therefore, the implementation of basin-wide unified water planning is of great importance to improve drought resistance capacity. Full article

Wildfires represent an increasing threat to ecosystems and communities, driven by climate change, fuel dynamics, and human activities. In Ambato, Ecuador, a city in the Andean highlands, these risks are exacerbated by prolonged droughts, vegetation dryness, and urban expansion into fire-prone areas within the Wildland–Urban Interface (WUI). This study integrates climatic, ecological, and socio-economic data from 2017 to 2023 to assess wildfire risks, employing advanced geospatial tools, thematic mapping, and machine learning models, including Multinomial Logistic Regression (MLR), Random Forest, and XGBoost. By segmenting the study area into 1 km² grid cells, microscale risk variations were captured, enabling classification into five categories: ‘Very Low’, ‘Low’, ‘Moderate’, ‘High’, and ‘Very High’. Results indicate that temperature anomalies, reduced fuel moisture, and anthropogenic factors such as waste burning and unregulated land-use changes significantly increase fire susceptibility. Predictive models achieved accuracies of 76.04% (MLR), 77.6% (Random Forest), and 76.5% (XGBoost), effectively identifying high-risk zones. The highest-risk areas were found in Izamba, Pasa, and San Fernando, where over 884.9 ha were burned between 2017 and 2023. The year 2020 recorded the most severe wildfire season (1500 ha burned), coinciding with extended droughts and COVID-19 lockdowns. Findings emphasize the urgent need for enhanced land-use regulations, improved firefighting infrastructure, and community-driven prevention strategies. This research provides a replicable framework for wildfire risk assessment, applicable to other Andean regions and beyond. By integrating data-driven methodologies with policy recommendations, this study contributes to evidence-based wildfire mitigation and resilience planning in climate-sensitive environments. Full article

Droughts have been exacerbated by climate change, posing significant risks to ecosystems, hydrology, agriculture, and human society. In this paper, we present the development and evaluation of a high-resolution 1 km SPEI (Standardized Precipitation-Evapotranspiration Index) dataset to enhance drought monitoring at finer spatial scales. The high-resolution SPEI datasets, derived using high-resolution TPDC precipitation and satellite-based MODIS potential evapotranspiration data, were compared with a coarse-resolution 50 km SPEI dataset derived from CRU measurements, as well as vegetation health indices (VHIs) and root zone soil moisture (SM), over two climatically contrasting regions in China: Northeast China (NEC) and Southwest China (SWC). The evaluation highlights the MODIS-based high-resolution SPEI’s ability to capture regional drought dynamics and improved correlation with vegetation and soil moisture dynamics. NEC, with its relatively flat topography and recent experience of significant droughts, and SWC, characterized by complex terrain and high precipitation variability, provided ideal testbeds for examining the performance of the 1 km SPEI. The results demonstrate that the high-resolution dataset offered superior spatial detail in detecting drought conditions, making it valuable for agricultural planning and water resource management in diverse climates. Full article

Climate change, particularly the increasing frequency of droughts, poses a critical challenge for agriculture. Rising temperatures and water scarcity threaten both agricultural productivity and ecosystem stability, making the identification of effective drought mitigation strategies essential. This study introduces an innovative approach to agricultural drought monitoring in Poland, utilizing remote sensing (RS) satellite data, collected from 2001 to 2020, and the Drought Identification Satellite System (DISS) index at a 1 km × 1 km spatial resolution, in combination with Copernicus High-Resolution Layers (HRL). To assess areas’ capacities to mitigate drought risks, a multi-criteria decision (MCD) analysis of regional environmental conditions was conducted. Focusing on the Mazowieckie Voivodeship, an algorithm was developed to evaluate regional susceptibility to drought. Spatial datasets were used to analyze environmental indicators, producing a map of communal temperature mitigation capacities. Statistical analysis identified drought vulnerability, highlighting areas in need of urgent intervention, such as increased mid-field tree planting. The study revealed that the frequency of droughts in this region during the growing season from 2001 to 2020 exceeded 40%. As a result, 40 LAU 2 administrative units have been affected by multiple negative environmental factors that contribute to drought formation and its long-term persistence. The proposed methodology, integrating diverse satellite data sources and spatial analyses, offers an effective tool for drought monitoring, mitigation planning, and ecosystem protection in a changing climate. This approach provides valuable insights for policymakers and land managers in addressing agricultural drought challenges and enhancing regional resilience to the impacts of climate change. Full article

Drought raises significant challenges and consequences in the socioeconomic environment in Dobrogea, Romania. This research aimed to assess the spatiotemporal dynamics of agrometeorological droughts from 2001 to 2021 using a multi-index approach that includes the Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI). Severe-to-extreme drought events were detected in 2001, 2007, 2012, 2015, 2016, 2019, and 2020, when temperatures in the area reached as high as 40.91 °C. Regarding area coverage, 2012 and 2020 were the worst drought years, with 66% and 71% of the region affected. Mild and moderate droughts were consistently identified across almost the entire period, while normal wet conditions were indicated in 2004–2006. The spatial analysis and the drought frequency maps revealed that the central, southern, and northwestern areas were particularly vulnerable, underlining the need for targeted drought mitigation measures. The trend analysis results indicated a nonuniform spatial feature of the negative (drying)/positive (wetting) trends at the regional level, with statistically significant trends identified only over small areas. Further results showed a robust relationship among the VHI and SPEI, particularly on 1-month and seasonal timescales. The extended correlation analysis results showed very strong positive relationships among all the vegetation indices, positive relations with rainfall, and strong negative ties with land surface temperature. Moreover, the seasonal VHI proved to be effective for drought monitoring across areas with diverse crop types. The results we obtained are consistent with previous studies on the incidence of drought in the area and hold practical significance for decision-makers responsible for drought management planning within Dobrogea, including setting up an early warning system using the VHI. Full article

Analyzing the spatiotemporal characteristics of meteorological droughts (MD) and agricultural droughts (AD) and their propagation in different climate zones is important for effective drought management, climate adaptation, and food security. This study takes a unique approach by comparing irrigated and rainfed croplands. A comprehensive framework is developed using drought indices, statistical analysis, trend tests, and wavelet transforms. The spatiotemporal evolution patterns, trends, and correlations of MD and AD in Xinjiang and the Middle-lower Yangtze Plain (MYP) are investigated. The main results showed that severe MD events (e.g., Xinjiang 2005–2009 and MYP 2004–2009) significantly impacted rainfed agricultural systems, leading to a decline in vegetation condition. Long-term irrigation can substantially alleviate AD under MD conditions. From 2000 to 2019, AD on irrigated croplands in Xinjiang continuously improved, while rainfed croplands deteriorated significantly during MD events. In contrast, although overall AD in MYP was mitigated, the benefits of irrigation were only evident during severe AD periods and weakened after 2013. Correlation and wavelet analyses revealed different drought propagation mechanisms between irrigated and rainfed croplands, highlighting the key role of local climate conditions and spatial heterogeneity in determining irrigation efficiency. The findings provide important guidance for optimizing drought management strategies, agricultural planning, and sustainable water resource management. Full article

Drought is a critical hydrological challenge with ecological and socio-economic impacts, but its long-term variability and drivers remain insufficiently understood. This study proposes a deep learning-based framework to explore drought dynamics and their underlying drivers across China’s major basins over the past four decades. The Long Short-Term Memory network was employed to reconstruct gaps in satellite-derived soil moisture (SM) datasets, achieving high accuracy (R² = 0.928 and RMSE = 0.020 m³m⁻³). An advanced explainable artificial intelligence (XAI) approach was applied to unravel the mechanistic relationships between SM and critical hydrometeorological variables. Our results revealed a slight increasing trend in SM value across China’s major basins over the past four decades, with a more pronounced downward trend in cropland that was more sensitive to water resource management. XAI results demonstrated distinct regional disparities: the northern arid regions displayed pronounced seasonality in drought dynamics, whereas the southern humid regions were less influenced by seasonal fluctuations. Surface solar radiation and air temperature were identified as the primary drivers of droughts in the Haihe, Yellow, Southwest, and Pearl River Basins, whereas precipitation is the dominant factor in the Middle and Lower Yangtze River Basins. Collectively, our study offers valuable insights for sustainable water resource management and land-use planning. Full article

Known as the “Ear of the Earth”, Lop Nor has become one of China’s four largest uninhabited areas due to environmental changes. Lop Nor is rich in mineral resources, including potassium salt, which has good quality and has been largely mined since 2002. This study focuses on the surrounding area of the Lop Nor Potash Salt Field, which covers an area of 80,036.39 square kilometers, spanning from 39.29° N to 41.84° N and 88.92° E to 92.26° E. The research is based on 1 km resolution precipitation, potential evapotranspiration, temperature data, and 250 m resolution NDVI data spanning 2002–2022. This study is devoted to exploring the trend of precipitation changes in the region surrounding the Lop Nor salt field since the start of the construction of the salt field, exploring the climatic impacts of the construction of the salt field on the surrounding region, and analyzing the correlations related to the changes in precipitation by selected meteorological factors. The Sen and Trend-Free Pre-Whitening Mann–Kendall trend analysis method was used to analyze the trend of precipitation data over the years. Combining with the data of the salt field location, the influence of the development of the salt field on regional precipitation was analyzed both temporally and spatially. The bias correlation analysis method was used to explore the correlation between maximum temperature, potential evapotranspiration, Normalized Difference Vegetation Index, and precipitation. The results of this analysis indicate that between 2002 and 2022, the study area exhibited both increasing and decreasing trends in precipitation. The region experiencing decreasing precipitation is predominantly located in the southwestern part of the study area, encompassing approximately 62% of the total area. Conversely, the area showing increasing precipitation is situated in the northeastern part, accounting for 38% of the total area. Field visits and survey data further corroborated the observed trend of increased precipitation in the northeastern region. Based on these findings, it is hypothesized that the development of salt flats has contributed to the increased precipitation, thereby alleviating regional drought conditions. Additionally, a partial correlation analysis of meteorological factors and precipitation revealed significant correlation. Temperature, potential evapotranspiration (PET), and the Normalized Difference Vegetation Index (NDVI) all exhibited varying degrees of correlation with precipitation. Temperature and potential evapotranspiration were the primary meteorological factors showing significant individual correlations. This study discusses the impact of salt field development and other climatic factors on the drought situation in Lop Nor and quantitatively analyzes the trend of precipitation changes in the study area and the factors affecting it. Water resources are scarce in China’s desert areas, and this research can provide a scientific basis for the state to formulate long-term plans for ecological protection and desert management, and it can also provide guidance for industrial development in desert areas. At the same time, it can provide important data and cases for global climate change research, offering experience and technical support for international cooperation in desertification control. Full article