Search Results (97)

Flash floods are catastrophic phenomena that pose a serious risk to coastal infrastructures, towns, villages, and cities. This study assesses the risk of flash floods in the ungauged Mekkah province region based on specific and effective morphometric and topographic features characterizing the study region. Shuttle Radar Topography Mission (SRTM) data were employed to construct a digital elevation model (DEM) for a detailed analysis, and the geographical information systems software 10.4 (GIS) was utilized to assess the linear, area, and relief aspects of the morphometric parameters. The ArcHydro tool was used to prepare the primary parameters, including the watershed border, flow accumulation, flow direction, flow length, and stream ordering. The study region’s flash flood hazard degrees were assessed using several morphometric characteristics that were measured, computed, and connected. Two different and effective methods were used to independently develop two models of flood vulnerability behaviors. The integrated method analysis revealed that most of the eastern and western parts of the studied province provide high levels of flood vulnerability. Due to it being one of the most helpful topographic indices, the integrated flood vulnerability final map was overlayed with the topographic position index (TPI). The integrated results aided in understanding the link between the general basins’ morphometric characteristics and their topographical features for mapping the different flood susceptibility locations over the entire studied province. Thus, this can be applied to investigate a surface-specific reduction plan against the impacts of flood hazards in the studied landscape. Full article

The freshwater discharge from catchments along the Gulf of Alaska, termed Alaska discharge, is characterized by significant quantity and variability. Owing to subarctic climate and mountainous topography, the Alaska discharge variations may deliver possible impacts beyond the local hydrology. While short-term and local discharge estimation has been frequently realized, a longer time span and a discussion on cascading impacts remain unexplored in this area. In this study, the Alaska discharge during 1982–2022 is estimated using the Soil and Water Assessment Tool (SWAT). The adequate balance between the model complexity and the functional efficiency of SWAT suits the objective well, and discharge simulation is successfully conducted after customization in melting calculations and careful calibrations. During 1982−2022, the Alaska discharge is estimated to be 14,396 ± 819 m³⋅s⁻¹⋅yr⁻¹, with meltwater contributing approximately 53%. Regarding variation in the Alaska discharge, the interannual change is found to be negatively correlated with sea surface salinity anomalies in the Alaska Stream, while the decadal change positively correlates with the North Pacific Gyre Oscillation, with reasonable time lags in both cases. These new findings provide insights into the relationship between local hydrology and regional climate in this area. More importantly, we provide rare evidence that variation in freshwater discharge may affect properties beyond the local hydrology. Full article

Geochemical measurements of stream sediments are practical for small-scale mineral exploration. However, traditional grid interpolation methods cause element concentrations to diffuse and smooth out anomalies, particularly in complex terrains, making it challenging to reflect the actual distribution of elements accurately. We applied the Dynamic Enhanced Weighted Drainage Catchment Basin (DE-WDCB) method to enhance the retention and identification of local anomalies by limiting the scope of analysis to specific drainage units. This method reduces interference from varying background values across different watersheds, effectively enhancing geochemical element anomalies and aligning better with geomorphic conditions. The DE-WDCB method was tested in the Duobaoshan–Heihe area, a significant copper polymetallic mineral district in northeastern China. Compared with traditional grid interpolation methods, the DE-WDCB method retained and strengthened low and weak abnormal information of favorable mineralization elements, particularly in the Luotuowaizi area. The method demonstrated a higher spatial coverage rate with mineral points and a more vital ore-indicating ability. Specifically, the DE-WDCB method identified anomalies with a mean accuracy of 63.57% (p < 0.05, 95% CI: 47.64%–79.50%), compared to 50.53% for traditional methods. In conclusion, in regions with a complex topography and watershed differences, the DE-WDCB method effectively reduces local geochemical background interference, accurately identifies low and weak geochemical anomalies, and better reflects the actual distribution of elements. This makes it a significantly advantageous method for geochemical anomaly extraction, delineating higher-confidence exploration targets in the Sandaowan–Luotuowaizi area in the east and the triangular area between Duobaoshan, Yubaoshan, Sankuanggou, and the midstream highlands of the Guanbird River in the west. Full article

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), to map wildfire susceptibility in Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, and vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used to predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation of the trained ML models showed that the Random Forest (RF) model outperformed XGBoost and LightGBM, achieving the highest test accuracy (95.6%). All of the classifiers demonstrated a strong predictive performance, but RF excelled in sensitivity, specificity, precision, and F-1 score, making it the preferred model for generating a wildfire susceptibility map and conducting a SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this study fills a critical gap by employing a SHAP summary and dependence plots to comprehensively assess each factor’s contribution, enhancing the explainability and reliability of the results. The analysis reveals clear associations between factors such as wind speed, temperature, NDVI, slope, and distance to villages with increased fire susceptibility, while rainfall and distance to streams exhibit nuanced effects. The spatial distribution of the wildfire susceptibility classes highlights critical areas, particularly in flat and coastal regions near settlements and agricultural lands, emphasizing the need for enhanced awareness and preventive measures. These insights inform targeted fire management strategies, highlighting the importance of tailored interventions like firebreaks and vegetation management. However, challenges remain, including ensuring the selected factors’ adequacy across diverse regions, addressing potential biases from resampling spatially varied data, and refining the model for broader applicability. Full article

Based on the data from the Chinese national ground-based (LFEDA: Low-frequency E-field Detection Array) and satellite-based lightning-detection systems (LMI: Lightning Mapping Imager), the spatial and temporal distribution statistical properties of all types of lightning over the Tibetan Plateau in the summer of 2022 and 2023 are analyzed, and were compared with those in Hainan, which are under quite different geographical conditions. The discrepancy between ground-based and space-borne lightning detection was also discussed. The main results show the following: (1) the characteristics of lightning activities over the Tibetan Plateau based on multi-source data: Most of the high-value lightning areas were located in the transition zone between lower and higher terrain; the diurnal variation of lightning activity was significant, and the most active period concentrated around 15:00 LST (Local Standard Time, the same below). In addition, lightning activities were significantly increased at 21:00 and 0:00, which was related to the unique topography and night rain phenomenon of the plateau. In terms of lightning types, the number of IC (Intra-Cloud) lightning was more than that of CG (Cloud-to-Ground). The study of IC changes is of great significance to the early warning of the plateau DCSs. The spatial distribution of IC at different altitudes was quite different. (2) Comparison of lightning activities between the Tibetan Plateau and Hainan: The hourly variation of lightning activities in Nagqu showed a single peak, while that in Hainan was characterized by a primary peak and a secondary peak, affected by the enhancement of the boundary stream in the low latitude and altitude area of China. At the peak of convection, the lightning activities in Nagqu were less than 1/3 of that in Hainan. However, the duration of high-frequency lightning activities in Nagqu (15–19:00) was about 2 h longer than that in Hainan (15–17:00), which may be related to the fact that the Tibetan Plateau is located in the west of China, where the sunset is later, and solar radiation and convective activities last longer. (3) Analysis of features of LMI: LMI has more advantages in IC detection; LMI has higher detection efficiency for the lightning in the range of 4–6 KM altitude, which is partly related to the stronger convective process and the higher proportion of IC. This work will provide deeper understanding of the characteristics of all types of lightning over the Tibetan Plateau, to reveal the indication significance of lightning for DCSs, and help to promote the development of Chinese satellite-based lightning-detection technology, the optimization of subsequent instruments and the fusion application of ground-based and satellite-based lightning data. Full article

Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales. Full article

The macroinvertebrate community, physical–chemical water variables and hydromorphological indices were studied in the Turón River (Málaga, Southern Spain). Our study aims to improve the knowledge of the most influential environmental factors at different spatial and temporal scales in Mediterranean rivers, in order to establish better management of Mediterranean river ecosystems. To this end, in this work, seasonal sampling was carried out for one year to evaluate the effect of the characteristics of the drainage basin (i.e., geology, topography, land use) on the macroinvertebrate community. To this end, the environmental variables of the catchment basins were evaluated at three different scales: (i) watershed level, (ii) valley segment level and (iii) local level. The results showed that 13 environmental variables, 3 at the watershed scale, 5 at the valley segment scale and 5 at the local scale, influenced the macroinvertebrate community. Land use is the main explanatory variable at the watershed scale, while stream channel curvature is the most common variable at the valley segment scale, and the habitat assessment index is the variable with the strongest influence at the local scale. The influence of different spatial scales presented a seasonal variation. During spring, autumn and winter, the watershed scale exhibited the highest resolution (adjusted R² = 0.20–0.29), while in summer, the local scale became the most significant in explaining the presence of macroinvertebrate taxa (adjusted R² = 0.17). The obtained results emphasize the significance of temporal and spatial scales in Mediterranean rivers for adequate river ecosystem management. Full article

Extreme climate events such as storms and severe droughts are becoming more frequent under the warming climate. In the tropics, excess rainfall carried by hurricanes causes massive flooding and threatens ecosystems and human society. We assessed recent major floodings on the tropical island of Puerto Rico after Hurricane Maria in 2017 and Hurricane Fiona in 2022, both of which cost billions of dollars damages to the island. We analyzed the Sentinel-1 synthetic aperture radar (SAR) images right after the hurricanes and detected surface inundation extent by applying a random forest classifier. We further explored hurricane rainfall patterns, flow accumulation, and other possible drivers of surface inundation at watershed scale and discussed the limitations. An independent validation dataset on flooding derived from high-resolution aerial images indicated a high classification accuracy with a Kappa statistic of 0.83. The total detected surface inundation amounted to 10,307 ha after Hurricane Maria and 7949 ha after Hurricane Fiona for areas with SAR images available. The inundation patterns are differentiated by the hurricane paths and associated rainfall patterns. We found that flow accumulation estimated from the interpolated Fiona rainfall highly correlated with the ground-observed stream discharges, with a Pearson’s correlation coefficient of 0.98. The detected inundation extent was found to depend strongly on hurricane rainfall and topography in lowlands within watersheds. Normal climate, which connects to mean soil moisture, also contributed to the differentiated flooding extent among watersheds. The higher the accumulated Fiona rain and the lower the mean elevation in the flat lowlands, the larger the detected surface flooding extent at the watershed scale. Additionally, the drier the climate, which might indicate drier soils, the smaller the surface flooding areas. The approach used in this study is limited by the penetration capability of C-band SAR; further application of L-band images would expand the detection to flooding under dense vegetation. Detecting flooding by applying machine learning techniques to SAR satellite images provides an effective, efficient, and reliable approach to flood assessment in coastal regions on a large scale, hence helping to guide emergency responses and policy making and to mitigate flooding disasters. Full article

Neom city is a unique cross-border city connecting Saudi Arabia, Jordan, and Egypt. Although Neom city is of great and critical importance for Saudi Arabia, few hydrological, natural hazard, and geomorphological studies have been undertaken on this region. This work aims to investigate the hydro-geomorphological characteristics and assess the flash flood hazards in Neom city by investigating several valuable morphometric parameters. The Shutter Radar Topography Mission (SRTM) digital elevation model and hydrological and geological data were analyzed in this study using ArcGIS software. Based on the morphometric parameter results, total stream lengths and stream orders were relatively high (17,956.03 km and 5, respectively), whereas the average bifurcation ratio was recorded to be low at 3.54. Basins 10, 12, 17, 30, 31, 32, and 34 were described as large basins, coarse-textured, elongated, with a medium drainage density, low infiltration values, long overland flows, and high values of constant maintenance. Additionally, the El-Shamy approach for flood hazard assessment was applied side by side with the morphometric analysis, which indicated that the possibility of an intense flood hazard is very low. In general, this study suggests that most of the studied basins cover similar and resistant rocks and soils. They have minimal conditions for flooding events and suitable conditions for underground and surface water resources. Therefore, they display high signals of susceptibility to erosion. The morphometric analysis and flash flood assessment techniques applied in this study were time- and cost-effective for the morphometric characterization of landforms. This text deals with the analysis of several environmental characteristics including hydro-morphological characteristics, drainage topography and lithology, soil erosion, groundwater recharge impact, and flash flood signals. Excellent sustainability plans should be reliant on extensive and varied information about the environment. Thus, integrated analyses incorporating environmental characteristics and flood hazard assessment play an important role in adjusting and adapting the suitable socioeconomic and scientific sustainability of the development of the study city. They build up the basic and essential information required to help decision-makers and sustainability managers design and adjust the most suitable sustainability plans for the study city over the long term. Full article

Anaxyrus microscaphus (The Arizona Toad) is an at-risk species that is endemic to the southwestern United States. Despite conservation concerns, little is known about the ecological drivers of its distribution and habitat use. We investigated the potential distribution of A. microscaphus at the range-wide scale and local scales (i.e., Zion National Park), using MaxEnt to model habitat suitability under current and future climate scenarios. Our models incorporated 12 environmental variables, including climatic, geomorphological, and remotely sensed data. The results showed good model accuracy, with temperature and elevation being the top contributing variables. Currently, 42.6% of the park’s area provides a suitable habitat for A. microscaphus, but projections for 2050 and 2070 indicate a significant reduction in suitable habitat across its range. Temperature was the most influential variable, with habitat suitability decreasing as the annual mean temperatures exceeded 10 °C. Precipitation, vegetation, and topography variables also significantly contributed to the models. The most suitable habitat within Zion National Park occurred along sloped rivers and streams and in valleys with sandy soils, emphasizing the importance of riparian habitat conservation for A. microscaphus survival and persistence. As climate change progresses, the species’ habitat is expected to become increasingly constrained across local and range-wide scales. Our models demonstrated a shift in the suitable habitat towards major river systems, indicating a potential reliance on larger permanent river systems as smaller, more ephemeral habitats decrease in size and abundance. Future management strategies should prioritize conserving and enhancing the resilience of these habitats. MaxEnt models can guide population survey efforts and facilitate the identification of priority conservation areas, saving time and resources for species of concern such as A. microscaphus. Further research, including field surveys and large-scale analyses, is necessary to further refine our understanding of this species’ distribution and how it may be impacted by climate and habitat change. Full article

Non-point source pollution poses a continuous threat to the quality of Great Lakes waters. To abate this problem, the Great Lakes Agricultural Stewardship Initiative (GLASI) was initiated in Ontario, Canada, with the primary aim of reducing phosphorus pollution. Therefore, a case-study analysis of the Wigle Creek watershed, one of the six priority watersheds under the GLASI program, was undertaken to evaluate the effectiveness of various existing and potential future Best Management Practices (BMPs) and to identify BMPs that might aid in mitigating the watershed’s contribution to phosphorus loads reaching Lake Erie. Given the watershed’s very flat topography, hydrological/nutrient modeling proved an extremely challenging exercise. The Soil and Water Assessment Tool (SWAT) model was used in this evaluation. Several digital elevation model (DEM) options were considered to accurately describe the watershed and represent flow conditions. A 30 m resolution DEM, implementing a modified burning in of streams based on ground truthing, was finally employed to develop the SWAT model’s drainage framework. The model was first calibrated for flow, sediment, and phosphorus loads. The calibrated model was used to evaluate the ability of potential BMPs (minimum tillage, no-till, retiring croplands into pasture, retiring croplands into forest, winter wheat cover crop, and vegetative filter strips) to reduce phosphorus loads compared to implemented practice. Converting all croplands into pasture or forest significantly decreased P loads reaching Lake Erie. Comparatively, a winter wheat cover crop had minimal effect on reducing phosphorus loading. Full article

The overall health of streams, including their surrounding urban or agricultural areas, is inextricably linked to general ecological balance and public health (physical and mental well-being). This study aims to contribute to the monitoring of rural or suburban areas adjacent to streams. Specifically, low-cost and rapid ground and Earth observation techniques were used to (a) obtain a rapid assessment of stream soil and water patterns, (b) create a database of selected parameters for the study area that can be used for future comparisons, and (c) identify soil variability in agricultural fields adjacent to streams and determine soil zones that will enable the rational use of inputs (water, fertilisers, and pesticides). Robust techniques from related fields of topography, geology, geophysics, and remote sensing were combined using GIS for two selected areas (I and II) in Heraklion, central Crete (Greece) in the eastern Mediterranean. Our results indicate that area I (east of Heraklion) is under pressure only in its coastal part, most probably due to urbanisation (land change). The agricultural fields of area II (west of Heraklion) show normal values for the distribution of electrical conductivity and magnetic susceptibility and present spatial variability indicating intra-parcel zones. Intra-parcel variability of the conductivity and magnetic susceptibility should be considered in future cropping and environmental management. Full article

The partition of surface runoff and infiltration is crucial in hydrologic modeling. To improve the flood prediction, we designed four strategies to explore the influences of the runoff partition method on the flexible hybrid runoff generation model. The runoff partition strategies consist of a hydrological model without the runoff partition module, a two-source runoff partition method, an improved two-source runoff partition method considering the heterogeneity of the subsurface topography and land cover, and a three-source runoff partition method. The Xin’anjiang hydrological model was used as the modeling framework to simulate a six-hourly stream flow for the Xun River watershed in Shaanxi Province, China. And the saturation-excess runoff generation and infiltration-excess runoff generation mechanisms were combined to construct the flexible hybrid runoff generation model. The performances of the four strategies were compared and analyzed based on the continuous flow discharge as well as the flood events. The runoff components analysis method was used to test the model’s conformity with the reality of the watershed. The results showed that the three-source runoff partition method was not applicable to the flexible hybrid runoff generation model because it overestimated the surface runoff and almost ignored the subsurface stormflow runoff. The improved two-source runoff partition method outperformed the others as it considered the heterogeneity of the watershed. Full article

The hydrological characteristics of the watershed in the southern Aseer and northern Jazan regions of Saudi Arabia (SA) were identified by integrated remote sensing (RS) and geographic information system (GIS) techniques using Shuttle Radar Topography Mission (SRTM) and Landsat data. For this purpose, the Wadi Ishran, Wadi Baysh, Wadi Itwad, Wadi Tabab, and Wadi Bayd drainage basins were extracted. Wadi Ishran is the largest, and Wadi Tabab is the smallest. Stream order and bifurcation ratio show that the Itwad and Bayd basins are permeable and of high aquifer potentiality. The multisupervised classification found seven rock units that were spread out in different ways across the basins. The areas with the highest vegetation were in the southeast, the centre, and the northwest. The bands’ ratios show more iron-rich sediments in the northeast and southwest. This paper’s outcomes serve as the basis for planning and managing groundwater resources. It finds potential groundwater zones, determines the risk of flooding, and chooses places where harvesting can be undertaken. Full article

The creation of more sustainable land use strategies is paramount to designing multifunctional agricultural landscapes that allow grasslands to continually deliver multiple ecosystem services. A mapping modelling approach would provide us with a tool for system diagnosis to better assess the value of a landscape and define place-based practices for designing more context-adjusted systems that are in synergy with the complexity of grasslands. To assess the potential capability of a high-country pastoral livestock production system in New Zealand in delivering ecosystem services, this work uses a geospatial model as a decision support tool to identify management practices that enhance grassland health. The model uses national, climatic, soil, and landcover data to assess the agricultural productivity, flood mitigation, C sequestration, erosion, and sediment delivery capacity of a case study high-country station in New Zealand. Model outcomes suggest that the station has the potential for increased agricultural productivity although varying spatially, a high flood mitigation capacity, a high capacity for C sequestration, a moderate risk of erosion, a capacity to reduce sediment delivery to streams, and overall, a low to moderate nitrogen and phosphorus accumulation. Output maps display a spatial visualisation of ecosystem services associated with the landscape topography, soil, and vegetation patterns that allow the identification of neglected areas and planning of best place-based management practices strategies to enhance the health of grasslands. Full article