Search Results (65)

Riverine and pluvial flooding triggered by extreme monsoon rainfall is intensifying under climate change, yet flood-risk products in many coastal municipalities remain too coarse for parcel-scale prevention and climate-adaptive planning. This study presents a 1 m LiDAR–GIS flood susceptibility framework validated against consecutive record-breaking floods in Dangjin City, South Korea (July 2024: 214.6 mm; July 2025: 377.4 mm). Five terrain parameters—elevation, slope, topographic wetness index, flow accumulation, and distance to stream—were integrated into a weighted Flood Susceptibility Index ($\mathrm{FSI}=0.20\cdot \widehat{E}+0.30\cdot \widehat{S}+0.25\cdot \widehat{T}+0.15\cdot \widehat{F}+0.10\cdot \widehat{D}$) and classified into four risk strata using K-means clustering (k = 4), identifying a high-risk zone of 0.3119 km² (5.00% of the 6.18 km² analysis domain). A Monte Carlo sensitivity analysis (n = 5000; ±0.10 weight perturbation) confirmed classification robustness (CV = 5.21%, mean Pearson r = 0.992). Static bathtub inundation scenarios (Δh = 0.5–2.0 m above the 5th-percentile baseline elevation of 13.29 m AMSL) produced footprint expansion from 0.370 to 0.572 km², capturing all nine observed flood inventory points at the 2.0 m threshold, with flow-connectivity analysis confirming that 91.7–93.1% of predicted inundation is hydraulically connected to the D8-derived stream network. Spatial validation yielded a combined IoU of 6.51%, with a progressive increase from 3.33% (2024) to 6.50% (2025) confirming that the FSI correctly tracks flood-extent expansion with increasing rainfall intensity. Relying exclusively on topographic data and standard GIS algorithms, the framework supports scientifically grounded flood risk governance in data-limited municipalities, directly aligned with SDG 11, SDG 13, and Sendai Framework Target B. Full article

Vegetation in desert ecosystems is strongly affected by seasonal climatic fluctuations and soil physical and chemical properties. Rawdat Nourah is a natural watershed depression within the King Abdulaziz Royal Reserve in Saudi Arabia. It is colonized by grasses, herbs, and shrubs. Climatic variability and soil heterogeneity are influencing the vegetation dynamics and regeneration patterns in this ecosystem. Based on the literature review, no previous study analyzed and determined either the vegetation composition or the soil seed-bank of Rawdat Nourah. So, the general objective of this study is to examine the vegetation composition and its relationships with soil physicochemical properties and soil seed-bank composition across Rawdat Nourah across different seasons. Floristic analyses, vegetation composition, soil properties, and soil seed-bank were performed within two seasons (winter–spring and summer–fall seasons) of 2023–2024. The obtained data were analyzed using multivariate and statistical approaches. Six plant associations were identified: winter–spring (WVG I: Zilla spinosa–Malva parviflora; WVG II: Rhazya stricta–Zilla spinosa; WVG III: Cynodon dactylon–Convolvulus pilosellifolius) and summer–fall (SVG I: Calotropis procera–Pulicaria undulata; SVG II: Cynodon dactylon–Zilla spinosa; SVG III: Rhazya stricta–Schismus arabicus). Species richness was higher in winter–spring (2.4 species stand⁻¹) than in summer–fall (1.66 species stand⁻¹), while the seed-bank densities were 633.9 and 575.1 seeds m⁻², respectively. Vegetation responded strongly to marked seasonal contrasts in temperature and moisture (~15 °C, 11 mm vs. ~36 °C, 3 mm). Moderate human activity enhanced vegetation cover, whereas prolonged grazing exclusion reduced diversity through the dominance of a few species. The response of vegetation structure and species richness to climatic factors varies greatly depending on the increase in water availability, and moisture content during the mild weather Winter–Spring season (mean temperature is 15 °C and rainfall is 11 mm), compared to the Summer–Autumn season (mean temperature is 36 °C and rainfall is 3 mm). The richness and cover of the plants were generally affected by human activity, where long-term grazing will reduce species richness and increase competition between species, making one or two species dominant. Although above-ground vegetation exhibited clear seasonal and spatial shifts in species composition and abundance, these changes were not reflected in the soil seed-bank. This relation suggests that above-ground communities and seed-banks are regulated by different ecological processes under arid conditions. The data of the present study showed low correlation between the current vegetation and the soil seed bank, which reflects a degradation in this region. Therefore, these findings suggest that sustained protection of the King Abdulaziz Royal Reserve is essential for enhancing seed-bank persistence, vegetation recovery, and ecosystem resilience under arid conditions. Full article

Burkholderia pseudomallei is a saprophytic environmental bacterium and the causative agent of melioidosis, a serious opportunistic infection in tropical regions, including northern Australia. Infection occurs following environmental exposure via percutaneous inoculation, ingestion, or inhalation; however, the environmental reservoirs and transmission pathways responsible for human disease remain poorly defined. Groundwater has been implicated as a potential source of infection, but the factors influencing the persistence and mobility of B. pseudomallei in surface waters in North Queensland are not well understood. Water samples were collected from a groundwater-connected seasonal creek in Townsville, North Queensland, over a 12-month period encompassing wet and dry seasons. Samples were cultured on Ashdown agar and confirmed as B. pseudomallei by qPCR. Multi-locus sequence typing (MLST) was performed using targeted allele sequencing on the Oxford Nanopore MinION platform. Eighteen of 59 water samples were culture-positive for B. pseudomallei. Detection occurred exclusively in turbid, flowing water following ≥30 mm of rainfall and was observed in both wet and dry seasons. MLST of 48 isolates identified 18 sequence types, including 12 novel types. Six sequence types matched previously reported Townsville clinical isolates. These findings indicate that groundwater from a connected urban creek may function as a mobile reservoir for clinically relevant B. pseudomallei strains under specific hydrological and climatic conditions, highlighting rainfall-driven processes as key drivers of environmental exposure risk. Full article

This study aims at developing an integrated system comprising TDR technologies for continuous and 3D monitoring of the vadose zone with special focus on the aerial distribution of water during an artificial sprinkling experiment. The system was tested during field artificial infiltration experiments. The objective of this study is to evaluate a flexible long TDR sensor in the field during a sprinkling and infiltration experiment that mimics rainfall and irrigation events through zonal wetting, monitor the resulting water flows and compare the findings with those from custom rigid spatial TDR sensors. This study exclusively used the TDR technique to measure soil moisture changes during the infiltration experiment, utilizing both custom rigid spatial sensors and a flexible sensor. The results indicate that the flexible sensor, which can be installed in the soil in arrays that rigid sensors cannot, achieved logical and coherent soil moisture estimations, proving that it could also be used as a standalone sensor for soil volumetric water content measurements. The use of long flexible sensors, along with long rigid sensors, facilitates continuous, precise, and 3D monitoring of moisture changes across larger soil volumes, transcending traditional point measurements and 1D soil moisture profiles typically associated with the TDR technique. Full article

This study analyzed the hydroclimatic functioning of the Lake Fitri basin (Chad) by combining rainfall records, in situ hydrological observations, water balance analysis, and spatial remote sensing data. Results show a strong Sahelian climatic control, with rainfall concentrated in a short-wet season (July–September) and potential evapotranspiration largely exceeding precipitation. Batha River flows are highly seasonal, generating short flood pulses that drive lake level fluctuations and aquifer recharge. Water balance estimates indicate that recharge is limited and episodic (approximately 70–120 mm in 2020), representing only 14–24% of annual rainfall, occurring almost exclusively during extreme rainfall events. Compared with Lake Chad, Lake Fitri is more directly sensitive to local rainfall variability, reflecting its dependence on a single tributary. Overall, the findings underline the fragility of this hydrosystem and the need for reinforced monitoring and integrated management to ensure sustainable water resources under increasing climatic variability. This work constitutes the initial reference for the hydroclimatic characterization of Lake Fitri, thanks to a methodology combining in situ and satellite data. Full article

Climate change can alter both the amount and timing of inflows to water supply reservoirs while also increasing heat-driven demand and the likelihood of stressful warm-season conditions. Climate-driven changes in inflow to Eagle Mountain Lake Reservoir (Texas, USA) were quantified by integrating (i) a calibrated SWAT model evaluated at four USGS stream gauges, (ii) statistically downscaled CMIP6 daily precipitation and minimum/maximum temperature at seven stations/grid points for a historical baseline (2003–2022) and two future windows (2031–2050 and 2081–2100) under SSP1-2.6, SSP2-4.5, and SSP5-8.5, and (iii) observed reservoir operations (lake level, water supply releases, and flood discharge; 1990–2022). A standard watershed climate workflow is reframed through an operations-focused lens, wherein projected inflow changes are translated into decision-relevant indicators via the utilization of observed thresholds and operating mode signals. Included within this framework are spring refill-season inflow shifts, a hot–dry month metric, and storage threshold performance measures, which are coupled with screening-level probabilities linked to multi-year inflow deficits. Across models and stations, mean annual temperature increases by 0.7–1.9 °C in the 2030s and by 0.7–6.1 °C in the 2080s, while annual precipitation changes remain uncertain (−24% to +55%). Daily projections show a strong increase in extreme heat days (daily Tmax above the historical 95th percentile), from about 18 days yr⁻¹ historically to about 30–33 days yr⁻¹ in the 2030s and about 34–82 days yr⁻¹ by the 2080s. Hot–dry months (monthly mean Tmax above the historical 90th percentile and monthly precipitation below the historical median) increase modestly by mid-century and rise to about 1.5 months yr⁻¹ on average by the 2080s under SSP5-8.5. SWAT simulations indicate that the mean annual inflow declines by 17–20% across scenarios, with the largest reductions during the spring refill period (March–June). Historical operations show that hot–dry months are associated with approximately double the mean water supply release (7.2 vs. 3.5 m³/s) and a lower monthly minimum lake level (about 0.30 m; about 1.0 ft lower on average). Flood discharges occur almost exclusively when lake elevation is at or above about 197.8 m and follow multi-day rainfall clusters (cross-validated AUC = 0.99). Together, these results indicate that earlier-season inflow reductions and more frequent hot–dry stress will tighten the operational margin between refill, summer demand, and flood management, underscoring the need for adaptive drought response triggers and integrated drought–flood planning for the Dallas–Fort Worth region. Full article

Recurrent flooding poses a persistent and growing threat to West African watersheds facing rapid urbanization and climate change. Despite advances in machine learning and geospatial datasets, urban planning and flood prevention often rely on limited datasets and traditional analysis. This study addresses this research gap in the Bonoumin-Palmeraie watershed (Abidjan, Côte d’Ivoire) by developing an integrated approach leveraging remote sensing, Geographic Information Systems (GIS), and the Random Forest algorithm to assess and map flood susceptibility. Twelve conditioning factors related to topography, hydrology, land use, and climate were derived from Sentinel-1, ALOS PALSAR, and multi-source earth observation datasets. Historical flood extents were mapped in Google Earth Engine to train the Random Forest model in a Google Colab environment. The model demonstrated high discriminatory power, yielding an Area Under the Curve of 0.94 and Overall Accuracy of 0.83. Drainage density, rainfall, and altitude were identified as the primary explanatory drivers. The resulting flood susceptibility map indicates that 39% of the watershed exhibits medium to very high susceptibility, with critical hotspots in the neighborhoods of Palmeraie, Attoban, Akouedo, Djorogobité, and Riviera-Sogefiha. While limited by the exclusion of certain anthropogenic variables and ground truth constraints, the study provides a reproducible, data-driven framework for flood risk assessment in tropical urban environments. These findings offer essential scientific support for urban planners and decision-makers to enhance territorial planning and sustainable flood management in Abidjan. Full article

Increasingly frequent intense rainfall is increasing landslide occurrence and risk. In southern China in particular, steep slopes and thin residual soils produce frequent landslide events with pronounced spatial heterogeneity. Therefore, displacement prediction methods that function across sites and deformation regimes in similar settings are essential for early warning. Most existing approaches adopt a multistage pipeline that decomposes, predicts, and recombines, often leading to complex architectures with weak cross-domain transfer and limited adaptability. To address these limitations, we present CRAFormer, a causal role-aware Transformer guided by a dynamic-lag Bayesian network-style causal graph learned from historical observations. In our system, the discovered directed acyclic graph (DAG) partitions drivers into five causal roles and induces role-specific, non-anticipative masks for lightweight branch encoders, while a context-aware Top-2 gate sparsely fuses the branch outputs, yielding sample-wise attributions. To safely exploit exogenous rainfall forecasts, next-day rainfall is entered exclusively through an ICS tail with a leakage-free block mask, a non-negative readout, and a rainfall monotonicity regularizer. In this study, we curate two long-term GNSS datasets from Guangxi (LaMenTun and BaYiTun) that capture slow creep and step-like motions during extreme rainfall. Under identical inputs and a unified protocol, CRAFormer reduces the MAE and RMSE by 59–79% across stations relative to the strongest baseline, and it lowers magnitude errors near turning points and step events, demonstrating robust performance for two contrasting landslides within a shared regional setting. Ablations confirm the contributions of the DBN-style causal masks, the leakage-free ICS tail, and the monotonicity prior. These results highlight a practical path from causal discovery to forecast-compatible neural predictors for rainfall-induced landslides. Full article

Accurate precipitation nowcasting is essential for short-term forecasting, but it remains challenging due to the dynamic nature of rainfall mechanisms. This study implements and evaluates two schemes for improving precipitation nowcasting: (1) an advection–diffusion scheme and (2) an advection–diffusion scheme integrated with the linear theory of orographic precipitation. These schemes are implemented into the Local Analysis and Prediction System (LAPS) to produce short-term precipitation forecasts and applied to a case study involving a rainfall event over the Athens metropolitan area in Greece. These schemes are compared against the default LAPS nowcasting module based on a first-order advection scheme (control). The first-order advection scheme, while computationally efficient, lacks the ability to simulate rainfall field evolution due to its exclusion of diffusion processes and orographic effects, leading to inaccurate nowcasts. To address these limitations, the advection–diffusion scheme is introduced to capture the precipitation evolution, and the third scheme integrates the linear theory of orographic precipitation to account for the influence of topography. Preliminary results show improvements in the spatiotemporal distribution of the nowcasted precipitation. These findings suggest that incorporating diffusion and orographic effects can enhance the accuracy of short-term precipitation forecasts, though further evaluation across diverse meteorological events is needed to confirm general applicability. Full article

Gopher tortoises (Gopherus polyphemus) are threatened burrowing keystone ecosystem engineers indigenous to open uplands in the Southeastern United States. Perils to the species include habitat degradation and fragmentation, anthropogenic disturbances, predation, parasites, and disease. Problems are severe in the SE Florida study area due to coastal urban sprawl, confining the tortoises in small, scattered, unnatural pockets subject to novel stresses. The annual South Florida February to ca. late May dry season became a severe drought in 2025. The present project centered on the broad question of foodplant resilience through the drought. The tortoise-grazed areas host three dominant groundcover species, in order of abundance: non-native Richardia grandiflora, native grass Paspalum setaceum, and non-native sedge Fimbristylis cymosa. Key findings were as follows: 1. The most abundant and most-often grazed species, Richardia grandiflora, when tortoises were excluded, expanded despite the drought (from 39% to 49.5% mean coverage). Under combined drought and grazing, that species cover decreased slightly (42.5% to 39.4%). Tortoise-free, Paspalum setaceum declined slightly during the drought (32.7% to 27.1% mean coverage), and showed mixed results with little net effect exposed to drought and to grazing. Never observed to be grazed during the study, Fimbristylis cymosa formed a nearly monospecific lawn in a sizeable portion of the study area. During the drought, it mostly browned, retaining green rosette centers, and tortoise exclusion showed no discernable effect. With transition to late spring, however, with increased rainfall, tortoise exclusion allowed rapid competition from grasses among the Fimbristylis rosettes. Adjacent unenclosed grazing, by contrast, maintained the Fimbristylis lawn without increase in grass coverage. Conclusions are that the two chief “fodder” species, Richardia grandiflora and Paspalum setaceum, were robust to drought and grazing. The introduced Fimbristylis cymosa appears to be facilitated by selective grazing-suppressing grass competitors. Full article

This study employs k-means clustering to analyze local organizations responsible for land improvement in Greece, identifying four distinct groups with consistent geographic patterns but divergent financial and operational characteristics. By integrating unsupervised machine learning with spatial analysis, the research offers a novel perspective on irrigation water pricing and cost recovery. The findings reveal that organizations located on islands, despite high water costs due to limited rainfall and geographic isolation, tend to achieve relatively strong financial performance, indicating the presence of adaptive mechanisms that could inform broader policy strategies. In contrast, organizations managing extensive irrigable land or large volumes of water frequently show poor cost recovery, challenging assumptions about economies of scale and revealing inefficiencies in pricing or governance structures. The spatial coherence of the clusters underscores the importance of geography in shaping institutional outcomes, reaffirming that environmental and locational factors can offer greater explanatory power than algorithmic models alone. This highlights the need for water management policies that move beyond uniform national strategies and instead reflect regional climatic, infrastructural, and economic variability. The study suggests several policy directions, including targeted infrastructure investment, locally calibrated water pricing models, and performance benchmarking based on successful organizational practices. Although grounded in the Greek context, the methodology and insights are transferable to other European and Mediterranean regions facing similar water governance challenges. Recognizing the limitations of the current analysis—including gaps in data consistency and the exclusion of socio-environmental indicators—the study advocates for future research incorporating broader variables and international comparative approaches. Ultimately, it supports a hybrid policy framework that combines data-driven analysis with spatial intelligence to promote sustainability, equity, and financial viability in agricultural water management. Full article

From the urban sustainability perspective and from the steps essential for regulating/balancing the microclimate features, the creation and maintenance of urban green spaces (UGS) are vital. The UGS include vegetation of any kind in urban areas such as parks, gardens, vertical gardens, trees, hedge plants, and roadside plants. This “urban green infrastructure” is a cost-effective and energy-saving means for ensuring sustainable development. The relationship between urban landscape patterns and microclimate needs to be sufficiently understood to make urban living ecologically, economically, and ergonomically justifiable. In this regard, information on diverse patterns of land use intensity or spatial growth is essential to delineate both beneficial and adverse impacts on the urban environment. With this background, the present study aimed to address water requirements of UGS plants and trees during the non-rainy months from Panaji city (Koppen classification: Am) situated on the west coast of India, which receives over 2750 mm of rainfall, almost exclusively during June–September. During the remaining eight months, irrigating the plants in the UGS becomes a serious necessity. In this regard, the daily water requirements (DWR) of 34 tree species, several species of hedge plants, and lawn areas were estimated using standard methods that included primary (field survey-based) and secondary (inputs from key-informant survey questionnaires) data collection to address water requirement of the UGS vegetation. Monthly evapotranspiration rates (ET_o) were derived in this study and were used for calculating the water requirement of the UGS. The day–night average ET_o was over 8 mm, which means that there appears to be an imminent water stress in most UGS of the city in particular during the January–May period. The DWR in seven gardens of Panaji city were ~25 L/tree, 6.77 L/m² hedge plants, and 4.57 L/m² groundcover (=lawns). The water requirements for the entire UGS in Panaji city were calculated. Using this information, the estimated total daily volume of water required for the entire UGS of 1.86 km² in Panaji city is 7.10 million liters. The current supply from borewells of 64,200 L vis a vis means that the ET_o-based DWR of 184,086 L is at a shortage of over 2.88 times and is far inadequate for meeting the daily demand of hedge plants and lawn/groundcover. Full article

Urban green spaces are important components of city spaces that are vulnerable to degradation in soil–water–climate processes. This vulnerability is exacerbated by current climate change and park usage density. This study examines the dynamics of soil water repellency in the topsoils of selected urban parks in Berlin, aiming to assess the relationships between weather conditions, soil water content, and soil water repellency. This study is based on monthly sampled soils from spots originating from three selected parks—Fischtal Park, Stadtpark Steglitz, and Rudolph-Wilde Park—between September 2022 and October 2023; two of the parks are exclusively rainwater fed, and one is irrigated during summer months. For each sample soil, water repellency persistence and severity were analyzed. Time series analysis was conducted including soil water content. In addition, the total organic carbon content (TOC) and sample texture were analyzed. The results show that the rainfall amount, number of dry days, and maximum temperature during different time intervals prior to the sampling date predominantly control the variation in the soil water repellency via the soil water content. Soil water repellency variations observed appear more event-related than monthly or seasonal, as rainfall is evenly distributed through the years without a distinct dry or wet season in Berlin. The non-repellency of the soil samples was usually observed when the associated water content was increased, which is linked to high cumulative rainfall and short dry periods. Low rainfall amounts and long dry periods in summer result in the re-establishment of the soil water repellency, possibly affecting increased runoff generation and soil erosion risk. Spatially, the repellency properties were observed at locations under healthy vegetation cover, while soils located on the upper slope locations and on the pathways lacked repellency characteristics. Full article

Accurately selecting an appropriate probability distribution model is a critical challenge when predicting extreme rainfall in arid and semi-arid regions, especially in countries with diverse climatic conditions. This study presents a comprehensive methodology for evaluating rainfall probability distributions across Pakistan, and aims to create a probabilistic zoning map that could serve as a valuable resource to inform the development of strategies for efficient water resource management and improved flood resilience in diverse climatic and geographic conditions. Precipitation data from the Pakistan Meteorological Department (PMD) over 42 years were compared with CHIRPS, confirming their accuracy. Nine probability distributions were assessed, with five models—log Pearson type-III (LP3), Weibull (W2), log normal (LN2), Generalized Extreme Value (GEV), and gamma (GAM)—deemed most suitable for the region’s climatic variability. The spatial applicability of these distributions was identified as follows: LP3 (30%), LN2 (30%), W2 (15%), GEV (10%), and GAM (15%). The central and southern regions of Punjab were predominantly characterized by LN2, while GAM was prevalent in the coastal areas of Sindh. Balochistan exhibited a heterogeneous distribution of W2, LP3, and LN2, while the mountainous Gilgit-Baltistan region was exclusively associated with GEV. Khyber Pakhtunkhwa demonstrated a mix of GEV and LP3 distributions. Beyond provincial variations, distinct patterns emerged: GEV dominated high-altitude, cold-temperate areas; LP3 was common in mountainous regions with variable temperature profiles; and W2 was prevalent along the flood-prone Indus River. This study provides a robust framework for region-specific disaster preparedness and contributes to sustainable development initiatives by offering tailored strategies for managing extreme rainfall events across Pakistan’s diverse climatic zones. Full article

Rhamdiopsis krugi is a highly specialized troglobitic (exclusively subterranean) catfish endemic to the phreatic water bodies of twelve caves located within two separated metasedimentary basins in the region of Chapada Diamantina, Bahia state, Brazil. This species is included in the List of Endangered Fauna of Brazil, under the Vulnerable category—VU. In general, troglobites have different strategies for searching for food and reproductive partners, as well as unique behaviors. Knowledge of the reproductive periods, as well as its feeding habits, provides fundamental data for effective protection and species conservation. Biological aspects related to feeding habits and reproduction of R. krugi were addressed across six annual cycles, considering both dry and rainy seasons. For this, stomach content analysis, using the frequency of occurrence and volumetric index methods, as well as observation of the sex ratio and stage of maturation of the gonads were carried out for 148 individuals of R. krugi sampled in eight caves in Chapada Diamantina. Stomach volumes correlated with reproduction aspects across the dry and rainy seasons. These populations showed opportunistic carnivorous feeding habits, consuming both autochthonous and allochthonous items, with a preference for foraging in submerged guano deposits, which demonstrates the catfish’s strong dependence on bats. Regarding sex ratios, there was no marked seasonality; however, in rainy seasons, there was a higher proportion of maturing females, showing a reproductive tendency. During these periods, there was also a significantly higher number of stomachs with contents, showing seasonality in the diet. Specialized diet and dependence on rainy periods, especially in diet, corroborate the fragility of R. krugi, especially considering the changes in rainfall regimes in Brazil, with dry seasons exceeding eight months per year in the last ten years. Full article