Search Results (224)

Climate change is projected to significantly alter hydrological conditions across the Northern Hemisphere, with increased precipitation variability, more intense rainfall events, and earlier, rain-driven spring floods in regions like northern Sweden. These changes will affect both natural ecosystems and hydropower-regulated rivers, particularly during ecologically sensitive periods such as the grayling spawning season in late spring. This study examines the impact of extreme spring flow conditions on grayling spawning habitats by analyzing historical runoff data and simulating high-flow events using a 2D hydraulic model in Delft3D FM. Results show that previously suitable spawning areas became too deep or experienced flow velocities beyond ecological thresholds, rendering them unsuitable. These hydrodynamic shifts could have cascading effects on aquatic vegetation and food availability, ultimately threatening the survival and reproductive success of grayling populations. The findings underscore the importance of integrating ecological considerations into future water management and hydropower operation strategies in the face of climate-driven flow variability. Full article

The construction and operation of high dam projects at high altitudes have led to concerns about the effectiveness of flood discharge security predictions resulting from the greater flood discharge atomized rain caused by ambient pressure reduction. In this study, self-similar characteristics and variation in atomized raindrop size distributions are analyzed to understand the phenomenon of increased atomized rain intensity under low ambient pressure from a mesoscopic scale. The monographic experiments are characterized by a low ambient pressure range (0.66P₀–1.02P₀) and a high waterjet velocity range (13.89–15.74 m/s). When the ambient pressure decreases by 0.10P₀ (P₀ = 101.325 kPa) from the reference atmospheric pressure condition as the other conditions remain fixed, the total number concentration in a two-dimensional atomized raindrop spectrum (number/(54 cm²)) and the peak value of the individual three-dimensional number concentration (number/(m³·mm) increase, which can lead to the required industry standard protective level of atomized zones increasing by one level in some cases. In addition, the spectrum trend and typical particle size ranges of the atomized raindrop size distributions present self-similarity as the ambient pressure decreases. The above studies further confirm the effects of low-ambient pressure enhancement on flood discharge atomized rain intensity, which can provide a theoretical basis for the development of random splash simulation models characterized by low pressure for high-altitude hydropower stations. Full article

Flooding poses a significant hazard to urban areas, particularly under the pressures of climate change and rapid urbanization. This study evaluates the flood risk in the Wadi Ibrahim watershed, located in Makkah Al-Mukarramah City, Kingdom of Saudi Arabia (KSA), by analyzing the impacts of climate change on flood hazards. The analysis incorporates projections from the Coordinated Regional Climate Downscaling Experiment (CORDEX) regional climate model (RCM) for three climate scenarios: representative concentration pathway (RCP) 2.6, RCP 4.5 and RCP 8.5. A novel aspect of this study is the integration of 2D HEC-RAS rain-on-grid (RoG) hydrodynamic modeling with climate change projection analysis, which has not been previously applied in this watershed. Flood risk maps are generated for each scenario at three return periods: 50, 100, and 200 years. The results indicate an increasing flood volume and depth under future climate scenarios. The flood risk mapping shows an expansion of medium- and high-risk zones compared to current conditions. Under the current climate, the low-risk areas (0–0.5 m) slightly decrease from 13.9 km² (50 years) to 13.8 km² (200 years), while the medium- (0.5–2 m) and high-risk areas (>2 m) increase from 6.5 km² to 7.0 km² and from 7.2 km² to 9.8 km², respectively. Under RCP 2.6, the low-risk zones decline from 13.6 km² to 13.0 km², the medium-risk zones grow from 14.5 km² to 16.2 km², and the high-risk zones rise from 4.3 km² to 6.5 km². The higher emissions scenarios show greater risk increases, with the high-risk areas expanding from 5.3 km² to 12.0 km² under RCP 4.5, and from 9.5 km² to 16.6 km² under RCP 8.5. These findings underscore the escalating flood risks due to climate change and highlight the need for mitigation in the Wadi Ibrahim watershed. Full article

More than half of the cultivated land belongs to the Irrigation Association. Therefore, there have been no farmland consolidation, irrigation, and drainage projects. The cultivation in the non-irrigation area suffers from poor geographical conditions and a lack of water sources. A practical planning strategy is required for expanding irrigation services. The mountainous area of Nantou County, Taiwan, has 7477 ha of available land and 4656 ha of agricultural land outside the irrigation area. Rain and streams are the main water source. There are 82 ponds, 80% of which belong to the loam soil, and the rainfall from October to February is limited. The water requirement of crops is 1.5–3.1 mm/day. Wild streams, groundwater, and rainwater are the only potential water sources due to elevation and terrain. The potential runoff is estimated to be 0–0.927 cms (m³/s) when using the SCS-CN method. Water supply and demand from October to April are limited, and the rainfall comprises 22% of the total water supply. Large reservoirs and water storage towers are required for flooding and in dry seasons. To address water storage challenges and stabilize the balance between water supply and demand, it is essential to construct additional ponds. Full article

Accurate simulation of extreme precipitation events is crucial for managing climate-vulnerable sectors in Southern Africa, as such events directly impact agriculture, water resources, and disaster preparedness. However, global climate models frequently struggle to capture these phenomena, which limits their practical applicability. This study investigates the effectiveness of three bias correction techniques—scaled distribution mapping (SDM), quantile distribution mapping (QDM), and QDM with a focus on precipitation above and below the 95th percentile (QDM95)—and the daily precipitation outputs from 11 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset was served as a reference. The bias-corrected and native models were evaluated against three observational datasets—the CHIRPS, Multi-Source Weighted Ensemble Precipitation (MSWEP), and Global Precipitation Climatology Center (GPCC) datasets—for the period of 1982–2014, focusing on the December-January-February season. The ability of the models to generate eight extreme precipitation indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) was evaluated. The results show that the native and bias-corrected models captured similar spatial patterns of extreme precipitation, but there were significant changes in the amount of extreme precipitation episodes. While bias correction generally improved the spatial representation of extreme precipitation, its effectiveness varied depending on the reference dataset used, particularly for the maximum one-day precipitation (Rx1day), consecutive wet days (CWD), consecutive dry days (CDD), extremely wet days (R95p), and simple daily intensity index (SDII). In contrast, the total rain days (RR1), heavy precipitation days (R10mm), and extremely heavy precipitation days (R20mm) showed consistent improvement across all observations. All three bias correction techniques enhanced the accuracy of the models across all extreme indices, as demonstrated by higher pattern correlation coefficients, improved Taylor skill scores (TSSs), reduced root mean square errors, and fewer biases. The ranking of models using the comprehensive rating index (CRI) indicates that no single model consistently outperformed the others across all bias-corrected techniques relative to the CHIRPS, GPCC, and MSWEP datasets. Among the three bias correction methods, SDM and QDM95 outperformed QDM for a variety of criteria. Among the bias-corrected strategies, the best-performing models were EC-Earth3-Veg, EC-Earth3, MRI-ESM2, and the multi-model ensemble (MME). These findings demonstrate the efficiency of bias correction in improving the modeling of precipitation extremes in Southern Africa, ultimately boosting climate impact assessments. Full article

Multiple monsoon systems impact autumn precipitation in West China; however, their synergistic influence is unknown. Here, we employed statistical analysis of Global Precipitation Climatology Project Version 3.2 precipitation data, European Center for Medium-Range Weather Forecasts ERA5 reanalysis data, and Coupled Model Intercomparison Project model data, and calculated four monsoon indices to analyze the features of the East Asian Monsoon, South Asian Monsoon, Asia Zonal Circulation, and Tibetan Plateau Monsoon, as well as their synergistic impacts on autumn precipitation in West China. The East Asian Monsoon negatively influences autumn precipitation in West China through closed high pressure over Northeast China. The South Asian Monsoon encloses West China between two areas of closed high pressure; strong high pressure to the north guides the abnormal transport of cold air in Northwest China, whereas strong western Pacific subtropical high pressure guides the transport of warm and wet air to West China, which is conducive to the formation of autumn precipitation in West China. During years of strong Asia Zonal Circulation, West China is controlled by an anomalous sinking airflow, which is not conducive to the occurrence of autumn rain. During strong Tibetan Plateau Monsoon, western and southwestern China are affected by plateau subsidence flow, resulting in less precipitation. Based on the CMIP6 model data, the study found that under the SSP5-8.5 emission scenario, the future trends of the four monsoon systems will show significant differences, and the amplitude of autumn and interannual precipitation oscillations in west China will increase. Full article

The medium river basins (MRBs) in Nepal originate from mid-hills. These medium-range rivers are typically non-snow-fed, relying on rain and other water sources. These rivers are typically small, and the sizes of medium river basins vary between 500 and 5000 km². These MRBs are often used for irrigation and other agricultural purposes. In this analysis, we first set up, calibrated, and validated three hydrological models (i.e., HBV, HEC HMS, and SWAT) at the Kankai River Basin (one MRB in eastern Nepal). Then, the best-performing SWAT hydrological model was forced with cutting-edge climate models (CMs) using thirteen CMIP6 models under four shared socioeconomic pathways (SSPs). We employed ten bias correction (BC) methods to capture local spatial variability in precipitation and temperature. Finally, the likely streamflow alteration during two future periods, i.e., the near-term timeframe (NF), spanning from 2031 to 2060, and the long-term timeframe (FF), covering the years 2071 to 2100, were evaluated against the historical period (baseline: 1986–2014), considering the uncertainties associated with the choice of CMs, BC methods, or/and SSPs. The study results confirm that there will not be any noticeable shifts in seasonal variations in the future. However, the magnitude is projected to alter substantially. Overall, the streamflow is estimated to upsurge during upcoming periods. We observed that less deviation is expected in April, i.e., around +5 to +7% more than the baseline period. Notably, a higher percentage increment is projected during the monsoon season (June–August). During the NF (FF) period, the flow alteration will be around +20% (+40%) under lower SSPs, whereas the flow alteration will be around +30% (+60%) under higher SSPs during high flow season. Thus, the likelihoods of flooding, inundation, and higher discharge are projected to be quite high in the coming years. Full article

This study investigated the effectiveness of nature-based solutions (NBSs) as a resilient strategy for mitigating urban flood risks in a developing hot arid country. The research method included the following steps: (a) performing a flood hazard risk assessment for the Fifth Settlement district in New Cairo, Egypt, (b) selecting best-fit NBSs, and (c) performance assessment. The process started with flood hazard analysis using hydrological data, topographical maps, urban planning, and land use maps, in addition to the history of storm events. This step defined the urban areas located in flood depth zones and categorized their flood hazard level. Exposure assessment considered the number and characteristics of population and buildings exposed to flood hazards. Vulnerability assessment determined the vulnerable characteristics of exposed populations and buildings to flood risk. The result of this assessment step indicated that there were 2000 buildings distributed in almost twenty neighborhood areas facing high flood risk. One of these urban areas with 72 building units, including residential, public, and services buildings, was selected to test the potential of integrating NBSs for flood-resilient land use planning and disaster preparedness. The selection of best-fit NBSs was based on a weighted-average sum matrix considering their climatic and contextual suitability and applicability. As a final step, numerical simulation models helped assess the efficiency of the selected NBSs for stormwater runoff reduction and the percentage of the volume capture goal. Five simulation models tested the efficiency of each NBS individually. Rain gardens achieved the highest stormwater capture percentage, while green roofs performed the least effectively, with capture rates of 43.6% and 9.9%, respectively. Two more simulation models were developed to evaluate the efficiency of NBSs when implemented in combination compared to the base case of using no NBSs. Permeable paving demonstrated the highest effectiveness in volume capture. The result indicated that applying combined measures of NBSs over 54.1% of the total site area was able to capture 8% more than the required volume capture goal. Consequently, this study underscores the necessity of adopting tailored solutions and integrated approaches using NBSs for flood risk mitigation. This necessitates testing their performance under site-specific conditions and future climate projections. Full article

Over the last century, significant climate changes, including more intense droughts and floods, have impacted agriculture and socio-economic development, particularly in rain-dependent regions like Marrakech–Safi (MS) in Morocco. Limited data availability complicates the accurate monitoring and assessment of these natural hazards. This study evaluates the role of satellite data in drought monitoring in the MS region using rain gauge observations from 18 stations, satellite-based precipitation estimates from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), and temperatures from the fifth generation of the atmospheric global climate reanalyzed Era5-Land data. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated at various timescales to characterize droughts. Statistical analysis was then performed to assess the correlation between the SPEI and the cereal yields. The results show that CHIRPS effectively monitors droughts, demonstrating strong statistically significant correlations (r ~ 0.9) with the observed data in the plains, the plateaus, Essaouira–Chichaoua Basin, and the coastal zones, along with a good BIAS score and lower root mean square error (RMSE). However, discrepancies were observed in the High Atlas foothills and the mountainous regions. Correlation analysis indicates the significant impact of droughts on agricultural productivity, with strong correlations between the Standardized Yield Residual Series (SYRS) and SPEI-6 in April and SPEI-12 in June (r ~ 0.80). These findings underscore the importance of annual and late-season precipitation for cereal yields. Analysis provides valuable insights for decision-makers in designing adaptation strategies to enhance small-scale farmers’ resilience to current and projected droughts. Full article

The carbon sink over land plays a key role in the mitigation of climate change by removing carbon dioxide (CO₂) from the atmosphere. Accurately assessing the land sink capacity across regions should contribute to better future climate projections and help guide the mitigation of global emissions towards the Paris Agreement. This study estimates terrestrial CO₂ fluxes over India using a high-resolution global inverse model that assimilates surface observations from the global observation network and the Indian subcontinent, airborne sampling from Brazil, and data from the Greenhouse gas Observing SATellite (GOSAT) satellite. The inverse model optimizes terrestrial biosphere fluxes and ocean-atmosphere CO₂ exchanges independently, and it obtains CO₂ fluxes over large land and ocean regions that are comparable to a multi-model estimate from a previous model intercomparison study. The sensitivity of optimized fluxes to the weights of the GOSAT satellite data and regional surface station data in the inverse calculations is also examined. It was found that the carbon sink over the South Asian region is reduced when the weight of the GOSAT data is reduced along with a stricter data filtering. Over India, our result shows a carbon sink of 0.040 ± 0.133 PgC yr⁻¹ using both GOSAT and global surface data, while the sink increases to 0.147 ± 0.094 PgC yr⁻¹ by adding data from the Indian subcontinent. This demonstrates that surface observations from the Indian subcontinent provide a significant additional constraint on the flux estimates, suggesting an increased sink over the region. Thus, this study highlights the importance of Indian sub-continental measurements in estimating the terrestrial CO₂ fluxes over India. Additionally, the findings suggest that obtaining robust estimates solely using the GOSAT satellite data could be challenging since the GOSAT satellite data yield significantly varies over seasons, particularly with increased rain and cloud frequency. Full article

Africa is grappling with severe food security challenges driven by population growth, climate change, land degradation, water scarcity, and socio-economic factors such as poverty and inequality. Climate variability and extreme weather events, including droughts, floods, and heatwaves, are intensifying food insecurity by reducing agricultural productivity, water availability, and livelihoods. This study examines the projected threats to food security in Africa, focusing on changes in temperature, precipitation patterns, and the frequency of extreme weather events. Using an Exponential Growth Model, we estimated the population from 2020 to 2050 across Africa’s five sub-regions. The analysis assumes a 5% reduction in crop yields for every degree of warming above historical levels, with a minimum requirement of 225 kg of cereals per person per year. Climate change is a critical factor in Africa’s food systems, with an average temperature increase of approximately +0.3 °C per decade. By 2050, the total food required to meet the 2100-kilocalorie per adult equivalent per day will rise to 558.7 million tons annually, up from 438.3 million tons in 2020. We conclude that Africa’s current food systems are unsustainable, lacking resilience to climate shocks and relying heavily on rain-fed agriculture with inadequate infrastructure and technology. We call for a transformation in food systems through policy reform, technological and structural changes, solutions to land degradation, and proven methods of increasing crop yields that take the needs of communities into account. Full article

Precipitation is a fundamental component of the hydrologic cycle and is an extremely important variable in meteorological, climatological, and hydrological studies. Reliable climate information including accurate precipitation data is essential for identifying precipitation trends and variability as well as applying hydrologic models for purposes such as estimating (surface) water availability and predicting flooding. In this study, I compared precipitation rates from five reanalysis datasets and one analysis dataset—the European Centre for Medium-Range Weather Forecasts Reanalysis Version 5 (ERA-5), the Japanese 55-Year Reanalysis (JRA-55), the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis 1 (NCEP/NCAR R1), the NCEP/Department of Energy Reanalysis 2 (NCEP/DOE R2), and the NCEP/Climate Forecast System Version 2 (NCEP/CFSv2)—with the merged satellite and rain gauge dataset from the Global Precipitation Climatology Project in Version 2.3 (GPCPv2.3). The latter was taken as a reference due to its global availability including the oceans. Monthly mean precipitation rates of the most recent five-year period from 2019 to 2023 were chosen for this comparison, which included calculating differences, percentage errors, Spearman correlation coefficients, and root mean square errors (RMSEs). ERA-5 showed the highest agreement with the reference dataset with the lowest mean and maximum percentage errors, the highest mean correlation, and the smallest mean RMSE. The highest mean and maximum percentage errors as well as the lowest correlations were observed between NCEP/NCAR R1 and GPCPv2.3. NCEP/DOE R2 showed significantly higher precipitation rates than the reference dataset (only JRA-55 precipitation rates were higher), the second lowest correlations, and the highest mean RMSE. Full article

This paper presents the design and development project of an engine test stand specifically constructed for ground testing of miniature turbine jet engines (MTJEs) along with conclusive results of the conducted investigations. The tested engines serve as the propulsion system for an unmanned aerial vehicle (UAV) platform. The engine test stand was used to determine various operating parameters of the engine, with a particular focus on recording variations and changes in temperature and pressure at the engine control cross-sections: behind the compressor, the combustion chamber, and at the final cross-section of the nozzle. The analysis of the direct test results allowed the evaluation of the engine’s behavior under hydration conditions and documents the quantitative and qualitative response of the control system of the engine. Of particular interest are the results showing an increase in exhaust system temperature with a decrease in the temperature in combustion chamber under hydrated conditions. The test program assumed and considered the acting loads and forces in both standard and specific flight conditions, including scenarios for a heavy rain. The preliminary evaluation of the investigation results provided data and insights required for further analysis. Quantitatively, the measured temperature value in the exhaust system does not exceed 700 °C and the temperature increase resulting from the introduction of water and the engine’s response to the out-of-operation event is approximately 50 °C for the JetCat 140. Qualitatively different effects were observed in the combustion moment, consisting in a drop in temperature values during the introduction of water into the engine flow channel. The introduction of water into the GTM 140 inlet revealed no significant changes in the variations of pressure and temperature measured in selected engine design sections. Based on the knowledge and experience gained, a fully operational test stand to monitor the parameters and performance of the MTJEs, which are used for aerial target propulsion, was developed. Full article

Landslides are among the most serious and frequent environmental disasters, involving the fall of large masses of rock and soil that can significantly impact human structures and inhabited areas. Anticipating these events is crucial to reduce risks through real-time monitoring of areas at risk during extreme weather events, such as heavy rains, allowing for early warnings. This study aims to develop a methodology to enhance the prediction of landslide susceptibility, creating a more reliable system for early identification of risk areas. Our project involves creating a model capable of quickly predicting the susceptibility index of specific areas in response to extreme weather events. We represent the terrain using cellular automata and implement a random forest model to analyze and learn from weather patterns. Providing data with high spatial accuracy is vital to identify vulnerable areas and implement preventive measures. The proposed method offers an early warning mechanism by comparing the predicted susceptibility index with the current one, allowing for the issuance of alarms for the entire observed area. This early warning mechanism can be integrated into existing emergency protocols to improve the response to natural disasters. We applied this method to the area of Prunella, a small village in the municipality of Melito di Porto Salvo, known for numerous historical landslides. This approach provides an early warning mechanism, allowing for alarms to be issued for the entire observed area, and it can be integrated into existing emergency protocols to enhance disaster response. Full article

Rainfall in the Brazilian Legal Amazon (BLA) is vital for climate and water resource management. This research uses spatial downscaling and validated rainfall data from the National Water and Sanitation Agency (ANA) to ensure accurate rain projections with artificial intelligence. To make an initial approach, Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) were employed to forecast rainfall from 2012 to 2020. The RNN model showed strong alignment with the observed patterns, accurately predicting rainfall seasonality. However, median comparisons revealed fair approximations with discrepancies. The Root Mean Square Error (RMSE) ranged from 6.7 mm to 11.2 mm, and the coefficient of determination (R²) was low in some series. Extensive analyses showed a low Wilmott agreement and high Mean Absolute Percentage Error (MAPE), highlighting limitations in projecting anomalies and days without rain. Despite challenges, this study lays a foundation for future advancements in climate modeling and water resource management in the BLA. Full article