Search Results (66)

Worldwide, flash floods are among the most unpredictable and hazardous hydrological phenomena, particularly in arid and semi-arid regions such as the Kingdom of Saudi Arabia, where sudden heavy rainfall follows prolonged periods of drought. This work presents an effective integrated model for flood hazard evaluation in the Hanefah Catchment, a socioeconomically vital area in the central part of Saudi Arabia that includes the capital city, Riyadh. Using high-resolution ALOS PALSAR 12.5 m Digital Elevation Model spatial data, we extracted and investigated indicative linear, areal, and relief morphometric keys of 64 sub-catchments. This paper employs a dual-method concept that integrates a multi-criteria ranking method and the El-Shamy approach in conjunction with morphotectonic analysis to model flood-susceptibility zones. Furthermore, this paper suggests a comparative assessment of low-cost morphometric models under data-scarce conditions, assessing the multi-criteria ranking method against El-Shamy’s approach, using the topographic position index (TPI) as an internal terrain scale benchmark. The ranking method successfully assigned 85.7% of the historically recorded flood locations to the high-hazard zone that covers ~24.22% of the Hanefah catchment. In contrast, the El-Shamy approach systematically underestimated flood susceptibility because regional tectonic activity increases bifurcation ratios, resulting in just ~42.9% of the historical floods being assigned to the high-hazard zone. The final results highlight the northern and northwestern parts of the catchment as high-hazard zones, characterized by high drainage density and steep relief. This study provides a refined, cost-effective model that aligns with the strategic objectives of Saudi Vision 2030 for sustainable water resources management and significant urban development. Full article

An extraordinary episode is a torrential rainfall event that produces significant societal impacts, which poses a major natural hazard in the western Mediterranean, particularly along the Valencia coast. This study evaluates the feasibility and added value of an explicitly spatial approach for estimating return periods of extraordinary precipitation in the Júcar and Turia basins, moving beyond traditional point-based or micro-catchment analyses. Our methodology consists of progressive spatial aggregation of time series within a basin to better estimate return periods of exceeding specific catastrophic rainfall thresholds. This technique allows us to compare 10 min rainfall data of a reference station (e.g., Turís, València, 29 October 2024 catastrophe) with long-term annual maxima from 98 stations. Temporal structure is characterized using the fractal–intermittency n-index, while tail behavior is modeled using several extreme-value distributions (Gumbel, GEV, Weibull, Gamma, and Pareto) and guided by empirical errors. Results show that $n\approx 0.3$–0.4 is consistent for extreme rainfall, while return periods systematically decrease as stations are added, stabilizing with about 15–20 stations, once the relevant spatial heterogeneity is sampled. Specifically, the probability of exceeding extraordinary thresholds is between 3 and 10 times higher for the areal than the point approach, so recurrence of a catastrophe would be once a few decades rather than centuries. Overall, the results demonstrate that spatially integrated return-period estimation is operational, physically consistent, and better suited for basin-scale risk assessment than purely point-based approaches, providing a relevant baseline for interpreting recent catastrophic events in the context of ongoing climatic warming in the Mediterranean region. Full article

Against the backdrop of increasingly evident climate change and frequent extreme weather events, flash floods have emerged as a major challenge for flood disaster prevention and mitigation in China. Flash flood early warning systems are crucial means to address this challenge, primarily comprising rainfall-based warnings (RW) and discharge-based warnings (DW). To support precise flash flood warnings, this study compares the effectiveness of RW and DW and summarizes their applicable scenarios through both case study analysis and model simulations. The results demonstrate that DW outperforms RW under the following scenarios: ① During persistent moderate-intensity rainfall events when antecedent soil moisture is moderate to high, RW is prone to missed or delayed warnings. ② When rainfall exhibits significant spatial heterogeneity, RW tends to produce false alarms. Conversely, RW outperforms DW in the following scenarios: ① For localized short-duration heavy rainfall events, DW is prone to missed or delayed warnings. ② In basins where numerous small- and medium-sized reservoirs exist upstream without operational data, DW is prone to false alarms. ③ When sparse or unevenly distributed rain gauges result in poor representativeness of areal rainfall, DW is prone to missed warnings. To enhance flash flood disaster management, future warning systems should integrate both RW and DW approaches to deliver more timely, reliable, and scientifically grounded warning information for local authorities. Full article

The continuous release of global precipitation products offers a stable data source for flood forecasting in areas without rainfall gauges. However, due to constraints of forecast timeliness, only no/short-lag precipitation products can be utilised for flood forecasting, but these products are prone to significant errors. Therefore, the keys of flood forecasting in areas lacking rainfall gauges are selecting appropriate precipitation products, improving the accuracy of precipitation products, and reducing the errors of precipitation products by combination with hydrology models. This paper first presents the current no/short-lag precipitation products that are continuously updated online and for which the download of long series historical data is supported. Based on this, this paper reviews the utilisation methods of multi-source precipitation products for flood forecasting in areas with insufficient rainfall gauges from three perspectives: methods for precipitation product performance evaluation, multi-source precipitation fusion methods, and methods for coupling precipitation products with hydrological models. Finally, future research priorities are summarized: (i) to construct a quantitative evaluation system that can take into account both the accuracy and complementarity of precipitation products; (ii) to focus on the improvement of the areal precipitation fields interpolated by gauge-based precipitation in multi-source precipitation fusion; (iii) to couple real-time correction of flood forecasts and multi-source precipitation; and (iv) to enhance global sharing and utilization of rain gauge–radar data for improving the accuracy of satellite-based precipitation products. Full article

Rainstorm-induced landslides are a widespread geomorphological hazard that can lead to major emergencies, causing severe damage to life and property. Due to the extent of the areas usually affected by these phenomena (up to thousands of km²) and/or their typical high areal density, in the early stages of the emergency it can be useful to reconstruct a comprehensive, albeit preliminary, overview of the landslides. With this aim, in this work we provide an outline of the landslides that occurred in the eastern part of the Emilia-Romagna region (northern Italy) after two severe rainstorms in May 2023. By combining information collected during the emergency through direct field inspections and UAV (unmanned aerial vehicle) surveys with preliminary analyses of historical rainfall/landslide data, we inferred the main characteristics of the landslides (e.g., movement type, involved materials, triggering mechanisms) and the relation with antecedent landslide phenomena, rainfall exceptionality, and anthropogenic activities. The latter were found to have likely contributed to landslides triggering by increasing water discharge and, in turn, infiltration and runoff erosion (i.e., inadequate drainage devices) and steepening slope gradients (e.g., road cuts). The vastness of the territory hit by the May 2023 landslides and their exceptional areal density can be explained not only with the extreme rainfall intensity (>500 years at several rainfall stations), but also with the widespread occurrence of slope materials which are very sensitive to sudden changes in hydraulic conditions. The high landslide susceptibility of the area is confirmed by the fact that many of the May 2023 landslides occurred at or close to previously identified and mapped landslide sites. Full article

This study reports on the impact of rainfall patterns and land surface changes on flood dynamics in the Meijiang River Basin, located in the upper reaches of the Ganjiang River. We formulated a range of rainfall patterns and spatial distribution scenarios and employed the MIKE SHE model to evaluate variations in flood volume, flood peak, and the timing of flood peaks. We found that under comparable areal rainfall conditions, flood volumes fluctuated by up to 6.22% among the different rainfall patterns, whereas flood peaks exhibited differences of up to 36.23%. When the rainfall center moved from upstream to downstream, both flood volume and flood peak initially increased before decreasing, with maximum values of 4.2 billion m³ and 4900 m³/s, respectively. We selected three basin scales (i.e., 10,000, 1000, and 100 km²) for comparative analysis. In the period between 1985 and 2020, the changes in land surface conditions resulted in decreases in the flood peaks of the three basins by 7.61, 11.53, and 15.79%, respectively; a reduction in the flood volumes of the three basins by 6.58, 9.60, and 10.48%, respectively; and delayed peak times by 3, 2, and 2 h, respectively. The results of this study show the significant influence exerted by rainfall patterns, the location of the rainfall centers, and the impact of changes in land surface conditions on flood processes. In particular, when the area of the basin was reduced, the influence of the underlying surface was more obvious. These results also show that flood prediction needs to consider the complex interaction of multiple factors. Full article

Drought is a widespread natural hazard resulting from an extended period of reduced rainfall, with significant socioeconomic and ecological consequences. Drought severity can impact food security globally due to its high spatial and temporal coverage. The primary objective of this paper consists of a comparative spatiotemporal analysis of environmental extremes (drought/wetness) through the estimation of a twelve-month Standardized Precipitation Index (SPI₁₂) between three distinct vulnerable agricultural regions in the Mediterranean basin (i.e., Spain, Lebanon and Tunisia), under a climate change environment in the last 38 years (1982–2020). The added value of this paper lies in the simultaneous estimation of temporal and spatial variability of drought and wetness periodic events, paying special attention to the geographical patterns of these extremes both in annual and interannual (seasonal) time scales. The results indicated that Spain and Tunisia (western Mediterranean) exhibit similar patterns over the studied period, while Lebanon demonstrates contrasting trends. Comparing the two extreme dry hydrological years, the Spanish study area faced the highest drought intensity, areal extent and duration (SPI₁₂ = −1.18; −1.84; 28–78%; 9–12 months), followed by the Lebanese (SPI₁₂ = −1.28; −1.39; 37–50%; 7–12 months) and the Tunisian ones (SPI₁₂ = −1.05; −1.08; 10–34%; 8 months). Concerning the wettest hydrological years, the Lebanese study domain has recorded the highest SPI₁₂ values, areal extent and duration (SPI₁₂ = 1.58; 2.28; 66–83%; 8–11 months), followed by the Tunisian (SPI₁₂ = 1.55; 1.79; 49–73%; 7–10 months) and Spanish one (SPI₁₂ = 1.07; 1.99; 21–73%; 4–11 months). The periodicity of drought/wetness episodes is about 20 years in Spanish area and 10 years in the Lebanese area (for drought events), whereas there seems no periodicity in the Tunisian one. Understanding the spatial distribution of drought is crucial for targeted mitigation strategies in high-risk areas, potentially avoiding broad, resource-intensive measures across entire regions. Full article

Runoff forecasting is crucial for water resource management and flood safety and remains a central research topic in hydrology. Recent advancements in machine learning provide novel approaches for predicting runoff. This study employs the Competitive Adaptive Reweighted Sampling (CARS) algorithm to integrate various machine learning models into a data-driven rainfall–runoff simulation model. We compare the forecasting performance of different machine learning models to improve rainfall–runoff prediction accuracy. This study uses data from the Maduwang hydrological station in the Bahe river basin, which contain 12 measured flood events from 2000 to 2010. Historical runoff and areal mean rainfall serve as model inputs, while flood data at different lead times are used as model outputs. Among the 12 flood events, 9 are used as the training set, 2 as the validation set, and 1 as the testing set. The results indicate that the CARS-based machine learning model effectively forecasts floods in the Bahe River basin. Under the prediction period of 1 to 6 h, the model achieves high forecasting accuracy, with the average NSE ranging from 0.7509 to 0.9671 and the average R² ranging from 0.8397 to 0.9413, though the accuracy declines to some extent as the lead time increases. The model accurately predicts peak flow and performs well in forecasting high flow and recession flows, though peak flows are somewhat underestimated for longer lead times. Compared to other machine learning models, the SVR model has the highest average RMSE of 0.942 for a 1–6 h prediction period. It exhibits the smallest deviation among low-, medium-, and high-flow curves, with the lowest NRMSE values across training, validation, and test sets, demonstrating better simulation performance and generalization capability. Therefore, the machine learning model based on CARS feature selection can serve as an effective method for flood forecasting. The related findings provide a new forecasting method and scientific decision-making basis for basin flood safety. Full article

Operational forecasters desire information about how their reservoir and riverine systems will evolve over monthly to seasonal timescales. Seasonal traces of hydrometeorological variables at a daily or sub-daily resolution are needed to drive hydrological models at this timescale. Operationally available models such as the Climate Forecast System (CFS) provide seasonal precipitation forecasts, but their coarse spatial scale requires further processing for use in local or regional hydrologic models. We focus on three methods to generate such forecasts: (1) a bias-adjustment method, in which the CFS forecasts are bias-corrected by ground-based observations; (2) a weather generator (WG) method, in which historical precipitation data, conditioned on an index of the El Niño–Southern Oscillation, are used to generate synthetic daily precipitation time series; and (3) a historical analog method, in which the CFS forecasts are used to condition the selection of historical satellite-based mean areal precipitation (MAP) traces. The Yaque del Norte River basin in the Dominican Republic is presented herein as a case study, using an independent dataset of rainfall and reservoir inflows to assess the relative performance of the methods. The methods showed seasonal variations in skill, with the MAP historical analog method having the strongest overall performance, but the CFS and WG methods also exhibited strong performance during certain seasons. These results indicate that the strengths of each method may be combined to produce an ensemble forecast product. Full article

Flood forecasting helps anticipate floods and evacuate people, but due to the access of a large number of data acquisition devices, the explosive growth of multidimensional data and the increasingly demanding prediction accuracy, classical parameter models, and traditional machine learning algorithms are unable to meet the high efficiency and high precision requirements of prediction tasks. In recent years, deep learning algorithms represented by convolutional neural networks, recurrent neural networks and Informer models have achieved fruitful results in time series prediction tasks. The Informer model is used to predict the flood flow of the reservoir. At the same time, the prediction results are compared with the prediction results of the traditional method and the LSTM model, and how to apply the Informer model in the field of flood prediction to improve the accuracy of flood prediction is studied. The data of 28 floods in the Wan’an Reservoir control basin from May 2014 to June 2020 were used, with areal rainfall in five subzones and outflow from two reservoirs as inputs and flood processes with different sequence lengths as outputs. The results show that the Informer model has good accuracy and applicability in flood forecasting. In the flood forecasting with a sequence length of 4, 5 and 6, Informer has higher prediction accuracy, and the prediction accuracy is better than other models under the same sequence length, but the prediction accuracy will decline to a certain extent with the increase in sequence length. The Informer model stably predicts the flood peak better, and its average flood peak difference and average maximum flood peak difference are the smallest. As the length of the sequence increases, the number of fields with a maximum flood peak difference less than 15% increases, and the maximum flood peak difference decreases. Therefore, the Informer model can be used as one of the better flood forecasting methods, and it provides a new forecasting method and scientific decision-making basis for reservoir flood control. Full article

In order to prevent and manage damage caused by localized torrential downpours, the quantitative observation of rainfall is crucial. Considering the spatial complexity and vertical variability of rainfall, it is important to obtain low-altitude, high-resolution radar observations to reduce uncertainty in radar rainfall estimates. In this paper, we present an electromagnetic wave rainfall gauge system (EWRG) that detects rainfall within the observation area and estimates the areal rainfall using electromagnetic waves. The EWRG system was developed based on a subminiature size antenna, a K-band dual-polarization transceiver, and advanced high-resolution, high-speed signal processing technology. The system design and signal processing techniques are described in detail. The EWRG has the advantage of overcoming the limitations of conventional cylindrical ground rain gauges, such as the contamination and spatial inaccuracy of rain gauges, which cause uncertainty in quantitative precipitation measurement. Full article

Current design storms used in hydrological modeling, urban planning, and dimensioning of structures are typically point-scale rainfall events with a steady rainfall intensity or a simple temporal intensity pattern. This can lead to oversimplified results because real rainfall events have more complex patterns than simple design series. In addition, the interest of hydrologists is usually in areal estimates rather than point values, most commonly in river-basin-wide areal mean rainfall estimates. By utilizing weather radar data and the short-term ensemble prediction system pySTEPS, which has so far been used for precipitation nowcasting, ensembles of high-resolution stochastic design storms with desired statistical properties and spatial structure evolving in time are generated. pySTEPS is complemented by adding time-series models for areal average rainfall over the simulation domain and field advection vectors. The selected study area is the Kokemäenjoki river basin located in Western Finland, and the model parametrization is carried out utilizing the Finnish Meteorological Institute’s weather radar data from the years 2013 to 2016. The results demonstrate how simulated events with similar large-scale mean areal rainfall can produce drastically different total event rainfalls in smaller scales. The sampling method, areal vs. gauge estimate, is also shown to have a prominent effect on total event rainfall across different spatial scales. The outlined method paves the way towards a more thorough and wide-spread assessment of the hydrological impacts of spatiotemporal rainfall characteristics. Full article

Enhancing spatial data attributes is crucial for effective basin-scale environmental modelling and improving our understanding and management of precipitation patterns. In this study, we focused on reconstructing homogeneous areal precipitation data in the complex terrain of the Calore River Basin (CRB) in Southern Italy. Until 1869, weather observations in the region were inconsistent, unstandardised, and lacked coordination, but the establishment of meteorological observatories brought a more unified approach to weather monitoring. We relied on the rainfall data obtained from two of these historical observatories: Benevento (1869–present) and Montevergine (1884–present). We utilised a statistical regression framework that considered rainfall measurements and temporal properties from specific locations to reconstruct and visually analyse the evolution patterns of annual mean areal precipitation (MAP) in the CRB from 1869 to 2020. The analysis revealed that mean MAP decreased from 1153 mm yr⁻¹ (1869–1951) to 998 mm yr⁻¹ (1952–2020). This decrease was accompanied by a reduction in interannual variability (from 168 mm yr⁻¹ to 147 mm yr⁻¹ standard deviation), and the difference between the means was significant (p < 0.0001), suggesting a sudden shift in the time-series. These findings provide a basis for CRB water resource management and insights for modelling other complex Mediterranean basins. Full article

Futuristic rainfall projections are used in scale and various climate impact assessments. However, the influence of climate variability on spatial distribution patterns and characteristics of rainfall at the local level, especially in semi-arid catchments that are highly variable and are not well explored. In this study, we explore the influence of climate variability on the spatial distribution and rainfall characteristics at a local scale in the semi-arid Shashe catchment, Northeastern Botswana. The LARS-WG, Long Ashton Research Station Weather Generator downscaling method, three representative scenarios (RCP 2.6, RCP 4.5, and RCP 4.5), three trend detection methods (Mann-Kendall, Sen’s slope, and innovative trend analysis) and L-moment method were used to assess climate change impacts on rainfall. Two data sets were used; one with 40 years of observed data from 1981–2020 and the other with 70 years from 1981–2050 (40 years of observed and 30 years of projected data from 2021–2050). Generally, the study found trend inconsistencies for all the trend detection methods. In most cases, Sen’s Slope has a high estimate of observed and RCP 2.6, while ITA overestimates rainfall totals under RCP 4.5 and RCP 8.5. The trend is increasing for annual total rainfall in most gauging stations while decreasing for annual maximum rainfall. The catchment is homogeneous, and Generalized Logistic distribution is the dataset’s best-fit distribution. Spatial coverage of a 100-year rainfall between 151–180 mm will be 81% based on observed data and 87% based on projected data under RCP 2.6 scenario when it happens. A 200-year rainfall between 196–240 mm under RCP 4.5 and 8.5 has high spatial areal coverage, at least 90% of the total catchment. The outcomes of this study will provide insightful information for water resource management and flood risk assessment under climate change. There is a need, however, to assess the transferability of this approach to other catchments in the country and assess the performance of other advanced modelling systems, such as machine learning, in this region. Full article

The monitoring of water resources is becoming increasingly important for humid temperate climates in light of climate change, which shows a generalised increase in temperatures and a decrease in precipitation, which is not generalised but relative to the area of interest. In this context, it is interesting to understand what the climatic changes have been, in terms of precipitation and how they have affected streamflows, by analysing them on a monthly basis. At the basin scale, interpolations were carried out with geostatistical methods using GIS software, spatialising the areal distribution of precipitation and obtaining an average value that can be correlated with water flows. As a pilot project, this research analysed the Upper Potenza basin in relation to the flow rates of the Potenza River over two reference periods, from 1964 to 1979 and from 2005 to 2020. The results show a decreasing trend in streamflows within the studied basin, while the precipitation trend decreases for the period 1964–1979 and increases for the period 2005–2020. Effective precipitation, in turn, shows a rather pronounced decrease in the more recent 2005–2020 period, due to climate change influencing the increase in temperature and consequently, the increase in evapotranspiration. In this context, it is significant to note that the Pearson correlation coefficient of streamflow to effective rainfall for both periods is about 0.8, suggesting that the net of anthropogenic disturbances, streamflow and actual precipitation maintain a high correlation. This model could be exported to other territories, in order to gain a global view for a better understanding and subsequent adaptation to ongoing climate change. Full article