ABSTRACT Reliable rainfall estimates are essential for understanding extreme rainfall patterns and supporting climate adaptation in vulnerable areas. In Ethiopia’s Jemma sub-basin, limited surface weather observations challenge accurate characterization of rainfall extremes, necessitating the use of satellite and reanalysis products. We evaluated five rainfall datasets, CHIRPS v2.0, TAMSAT v3.1, ERA5, MERRA v2, and MSWEP v2.8, for their ability to replicate daily rainfall, detect rainfall events, and represent rainfall indices across sub-tropical and temperate agro-ecological zones (AEZs). A point to pixel evaluation approach was used, and statistical metrics including continuous metrics such as root mean square error (RMSE), percent bias (PBIAS), Kling Gupta efficiency (KGE), and correlation coefficient (R), and categorical metrics such as probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and frequency bias index (FBI) were used to assess performance and assign ranks using a comprehensive rating index (CRI). Results show that MSWEP v2.8 is most reliable for daily rainfall across both AEZs, while CHIRPS v2.0 excels in capturing extreme rainfall in the sub-tropical AEZ. MSWEP v2.8 also performs well in the temperate AEZ but shows limitations for indices like R20mm in sub-tropical AEZ. ERA5 and MERRA v2 exhibit higher biases, lower resolution, and weaker correlations. These findings highlight the importance of selecting rainfall products based on indices and regional conditions. MSWEP v2.8 and CHIRPS v2.0 are recommended for flood and drought forecasting, water management, and agricultural planning. Integrating multiple datasets with local observations and establishing additional meteorological stations can further enhance rainfall assessments and modeling.

ABSTRACT Landslide hazard assessment is vital for disaster risk reduction in the Raigad district of Maharashtra, India, a geologically complex and landslide-prone region. This study addresses the gap in landslide susceptibility mapping (LSM) by evaluating four models Fuzzy Analytic Hierarchy Process (FAHP), Analytic Hierarchy Process (AHP), Frequency Ratio (FR), and Shannon Entropy (SE), which have not been applied to Raigad District. Previous research has largely focused on broader regions, neglecting localized assessments. To conduct LSM in this area, 14 geo-environmental factors were analyzed, including topographic, climatic, geological, and land use characteristics. 174 landslide inventories with 70% allocated for training and 30% for validation. The FAHP model showed superior performance across all evaluation metrics with an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.84, Precision of 0.86, Recall of 0.82, Accuracy of 0.85, and F1-Score of 0.84. Comparatively, AHP, FR, and SE achieved AUC-ROC values of 0.81, 0.79, and 0.71, respectively. According to the statistics, Poladpur, Mahad, Mangaon, and Shrivardhan are administrative subdivisions in the southern and southwestern parts that are highly susceptible to landslides. This work provides new insights to support informed decision-making for sustainable land use planning and disaster management.

ABSTRACT This study evaluates the flood extent, depth, velocity, and flood hazard analyses across 100, 500, and 1000 return periods using 0.1 m, 0.5 m, and 1 m DEM data derived from Unmanned Aerial Vehicles (UAV). The Ulus settlement (Türkiye) was selected as the case study area. The primary input data include DEMs produced from the DJI Mavic 2 Pro drone and flood flows derived from the SWAT rainfall-runoff model for the Ulus River and its tributaries. Flood hazard analyses were conducted using the 2D LISFLOOD-FP hydrodynamic model using a constant Manning n value (n = 0.035) to evaluate spatial resolution variability. The findings indicate that as the resolution improves from 1 m to 0.1 m, the model runtime increases significantly (711 times on average), as do average calculation errors. Meanwhile, the spatial distribution of flood extent and hazard classes for people decreases by approximately 0.7% to 9%. However, the differences between resolutions from 0.1 m to 1 m in terms of depth, velocity, and inundation extent are not substantial. Therefore, using a 0.1 m UAV DEM resolution is not cost-effective, especially for large areas, due to the increased model instability caused by surface roughness and the longer processing time required.

ABSTRACT Addressing flood risk by integrating flood hazard, vulnerability and exposure is crucial for managing growing flood threats under warming climate. Among various flood risk indicators, flood hazard is significant, as it quantifies the spatial extent, depth and intensity of flooding. Hence, accurate assessment of flood hazard is essential for effective flood risk assessment. Recent developments in machine-learning (ML) techniques enhanced flood hazard assessment through integrating multi model dataset and increased predictive accuracy. The present investigation developed a weighted ensemble ML technique by integrating - Generalised Linear Model, Support Vector Machine, Random Forest, and Maximum Entropy models and tested in a tropical river basin (Kadalundi River basin) of southern India, prone to frequent flooding. Results show that weighted ensemble ML technique outweigh standalone models with an accuracy over 90% (AUROC 0.910) and a kappa index of 0.769. Subsequently, the Analytical Heirarchy Process (AHP) technique is applied to estimate flood vulnerability and exposure by combining various physical and socio-economic parameters. Finally, improved flood risk is estimated by combining hazard, vulnerability and exposure. The proposed methodology, integrating ensemble ML technique with physical and socio-economic factors is useful for effective disaster risk reduction and achieving Sustainable Development Goals (SDGs), particularly SDG 11 and SDG 13.

ABSTRACT The fragmentation of fish populations caused by dams has been extensively studied within the scientific community. However, there is limited data regarding the impact of riverine fragmentation on the loss of the functional quality of ecosystems, a key aspect for the optimal development of environmental, ecological, and landscape functions within river systems. In this context, an assessment of fragmentation was conducted across the Sierra Morena river network in the Guadalquivir basin using the Dendritic Connectivity Index (DCI). This analysis revealed that 88.4% of the network was disconnected from its regional base level, with 64.6% exhibiting a high degree of fragmentation. Furthermore, hydrogeomorphological alterations were investigated in three rivers from the basin – Bembézar, Guadalbarcar, and Rivera de Huéznar – characterized by different levels of fragmentation. Hydrological data from the period 2010–2021 were analyzed, and geomorphological diagrams were created to compare the functional floodplains of the three rivers in both pre-dam (1956) and post-dam (2010) conditions. Preliminary results indicate a strong correlation between the fragmentation level and various parameters: the degree of hydrological regulation (r = 0.98), reduction in flow circulation (r = 0.86), sediment retention (r = 0.92), and the loss of floodplain functionality (r = 0.86). Moreover, a very strong correlation (r = 0.999) was found between fragmentation and the decline in functional quality across the studied rivers.

ABSTRACT This paper presents a brief overview and history of “phenoclimatology”, a subdiscipline of climatology, emphasizing atmosphere-biosphere interactions. Here, we describe the establishment and recent growth in models and forecasts created using in situ phenology observations and the factors enabling these advancements, with focus on North America. Most notably, large-scale phenological models paved the way for development of synthetic indices. Such indices can supply an assessment of a location’s general phenological response over a standard period, context for comparing regional or local-scale studies, the ability to analyze changes in damage risks for plants, and reconstruction of the timing of events in years past across many regions. As such, synthetic phenological indices have seen wide adoption in estimating spring-season evolution in real time, anticipating short-term impacts of an early or late start to spring, and in assessing changes in the timing of seasonal transitions associated with climate change.

ABSTRACT River bank erosion is a fluvio-hydrological hazard, and sometimes, it turns into disaster in the human-encroached river bank and flood plain. The principal objective of this study is to detect the river bank erosion potential zone using new Bank Erosion Susceptibility Index (BESI) model and spatiotemporal shifting of river Raidak-II (1980–2020). Sedimentary bank facies (SBF) analysis was conducted to identify the nature of cohesiveness of bank materials. The result showed that the maximum average lateral shifting (213.20 m) was recorded in the year 1990 (right bank), whereas the minimum average shifting (77.32 m) was measured in the year 2020 (right bank). The result also showed that the right bank of Raidak-II was mostly oscillated due to poorly sorted quaternary non-cohesive bank materials. The Bank Erosion Hazard Index (BEHI) model showed that 46.15% and 50% bank erosion sites are fallen in high-to-extreme bank erosion susceptibility zones in the years 2020 and 2022. In the case of BESI model, 69.23% and77.77% bank erosion sites are confined in high-to-extreme bank erosion susceptibility zones in the years 2020 and 2022. Therefore, BSEI model gives more precise results compared with BEHI due to three additional factors. The poorly sorted non-cohesive quaternary sediments stimulate high rate of bank erosion within Himalayan foreland basin.

ABSTRACT Discharge values from the National Water Model (NWM) were compared to USGS stream gage discharge observations for the suburban Red Clay Creek watershed (drainage area ~140 km2 and mixed land-use), in Pennsylvania and Delaware, from 2016 to 2018. 18-hour retrospective simulations from the NWM were used with concurrent hourly USGS discharge observations from three locations along the Red Clay Creek. Results indicate that the mean of discharge estimates from the NWM and from USGS observations significantly differed and that the NWM generally underestimates low-flow conditions and overestimates high-flow conditions. Watershed size also impacted NWM performance (with performance degrading in smaller watersheds). A meteorological analysis determined that convective rainfall events were associated with 66% of the largest differences between NWM discharge estimates and USGS observations while mid-latitude cyclone stratiform precipitation events accounted for the other 34%. Lastly, of the largest 15 differences between the NWM and observations, 13 occurred with pre-cursor soil moisture that was below the mean (dry soil conditions), in conjunction with heavy rainfall. Given the NWM’s recent operational implementation, and its status as Prototype guidance, the results of this study present specific geographical and climatological findings that can aid in the NWM’s continued validation and improvement for similar regions.

ABSTRACT Formation and evolution processes of natural tombolos involve many interrelated geomorphological, sedimentary, oceanographic and anthropogenic factors, making their measurement and simulation an extremely difficult task. The present study attempts to fill this knowledge gap by detecting the natural tombolos in Greece, formed in the current sea level, through the statistical analysis of their morphometric and socio-environmental parameters. In addition, the long-term evolution of these coastal depositional landscapes has been determined by comparing old aerial photos taken in 1945 or 1960 and recent satellite images taken from 2020 to 2022. Two thirds of the twenty tombolos studied are subject to erosion, while eight are sporadically destroyed and turned into salients. The Greek tombolos are mildly or heavily exploited for touristic purposes, and fourteen of them are protected by national and international environmental laws. Future studies need to focus on the unstable type of these coastal landforms that are modified from tombolo to salient and vice versa to obtain helpful information about the morphodynamic conditions necessary for their evolution.

ABSTRACT Within Alpine catchments, glacial landforms are subject to post-depositional reworking during and following deglaciation. Ice-marginal moraines are thought to rapidly stabilise within ~200 years in this topographic context, although ice-proximal slopes are particularly prone to alteration by debris flows and solifluction. This study investigates landform transformation, documenting geomorphological change at the Bas Glacier d’Arolla, Switzerland. Gully development on a moraine slope was assessed using archive image sets obtained in 1977, 1988 and 2009 to derive historical elevation models. Raster differencing suggests that the mean rate of surface lowering on the upper moraine slope was 7.15 ± 1.83 m (± minimum level of detection) over the observation period (1977–2009), a rate of 0.22 m yr−1. The erosion of the landform resulted in an incontiguous moraine crestline. Whilst some landforms may undergo limited transformation upon deglaciation, selected sites are subject to rapid geomorphologic change, involving crestline retreat via the initial dissection by gullies, followed by the removal of inter-gully slopes.