Impact Of Rainfall Research Articles

Analysis of characteristics and evaluation of forecast accuracy for Super Typhoon Doksuri (2023)

Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range, as well as significant and prolonged hazards. In this work, we studied Doksuri's main characteristics and assessed its forecast accuracy meticulously based on official forecasts, global models and regional models with lead times varying from 1 to 5 days. The results indicate that Typhoon Doksuri underwent rapid intensification and made landfall at 09:55 BJT on July 28 with a powerful intensity of 50 m s−1 confirmed by the real-time operational warnings issued by China Meteorological Administration (CMA). The typhoon also caused significant wind and rainfall impacts, with precipitation at several stations reaching historical extremes, ranking eighth in terms of total rainfall impact during the event. The evaluation of forecast accuracy for Doksuri suggests that Shanghai Multi-model Ensemble Method (SSTC) and Fengwu Model are the most effective for short-term track forecasts. Meanwhile, the forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) and United Kingdom Meteorological Office (UKMO) are optimal for long-term predictions. It is worth noting that objective forecasts systematically underestimate the typhoon maximum intensity. The objective forecast is terribly poor when there is a sudden change in intensity. CMA-National Digital Forecast System (CMA-NDFS) provides a better reference value for typhoon accumulated rainfall forecasts, and regional models perform well in forecasting extreme rainfall. The analyses above assist forecasters in pinpointing challenges within typhoon predictions and gaining a comprehensive insight into the performance of each model. This improves the effective application of model products.

HydroSAR: A Cloud-Based Service for the Monitoring of Inundation Events in the Hindu Kush Himalaya

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service which was co-developed with in-region partners to provide year-round, low-latency weather hazard information across the HKH. This paper describes the end user-focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring the surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which provides the capability to scale automatically with the event size. A performance assessment of our quantitative algorithms is described, demonstrating the capabilities to map the flood extent and water depth with an accuracy of >90% and <1 m, respectively. An application of the HydroSAR service to the 2023 South Asia monsoon seasons showed that monsoon floods peaked near 6 August 2023 and covered 11.6% of Bangladesh in water. At the peak of the flood season, nearly 13.5% of Bangladesh’s agriculture areas were affected.

Changes of vertical distribution of Microcystis colonies driven by short-term rainfall: Disappearance and reformation of surface bloom

Global climate warming and human activities have increased the magnitude and frequency of Microcystis surface blooms, posing significant threats to freshwater ecosystems and human health over recent decades. Heavy rainfall events have been reported to cause the disappearance of these blooms. Although some studies have employed turbulence models to analyze the movement characteristics of Microcystis colonies, the impact of rainfall is complex, comprehensive investigations on their vertical migration induced by short-term rainfall are still necessary. Utilizing monitoring data from eutrophic ponds and controlled simulation experiments, this study examines the short-term impacts of rainfall on the vertical distribution of Microcystis in the water column. Our findings indicate that rainfall contributes to the disappearance of Microcystis blooms by reducing the quantity of small to medium-sized colonies (0–100 μm) at the surface, subsequently decreasing the overall Microcystis biomass. As rainfall intensity increases, larger colonies migrate deeper into the water column. At a rainfall threshold of 666 mm, the difference in the median volume diameter (DV50) of Microcystis colonies between the surface and bottom reaches a minimal value of 3.09%. Post-rainfall, these colonies rapidly ascend, aggregate into larger formations, and re-establish surface blooms. The greater the rainfall, the smaller the resultant Microcystis biomass, albeit with larger aggregated colony sizes. When rainfall exceeds 222 mm, the recovery rate of surface Microcystis biomass remains below 100%, decreasing to 19.48% at 666 mm of rainfall, while the median volume diameter (DV50) of the colonies increases to 139.07% of its pre-rainfall level. Furthermore, compared to pre-rainfall conditions, the photosynthetic activity of the surface Microcystis colonies was enhanced and the secretion of EPS was increased under heavy rainfall conditions. Our results identify a critical response time of 30 min for Microcystis colonies to rainfall, after which the response ceases to intensify. These insights are crucial for predicting post-rain Microcystis bloom dynamics and aiding management authorities in timely interventions.

Rational reduction of planting density and enhancement of NUE were effective methods to mitigate maize yield loss due to excessive rainfall

The impact of excessive rainfall or waterlogging on maize growth and yield have been widely studied, but the effects of planting density and N management under waterlogging remain unknown. We observed the changes in maize yield caused by excessive rainfall via a short-term experiment (2017 to present) in Changchun (125°14.231′–125°14.914′ E, 43°56.603′–43°57.274′ N), China. The experiment was conducted at four planting densities (45,000, 60,000, 75,000 and 90,000 plants/ha) and three nitrogen (N) rates (120, 180, and 240 kg/ha). The objective was to explore the effect of excessive precipitation on maize yield through changes in maize growing conditions, and the uptake, allocation, and utilization of N under different planting densities and N rates from 2019 to 2022. The precipitation during the whole growth period of maize in 2019 (542.9 mm) and 2020 (560.0 mm) was normal, while it was excessive in 2021 (829.10 mm) and 2022 (953.56 mm), especially during the vegetative stage from V12 to VT (355.60–482.10 mm). Excessive rainfall negatively affected the growth, photosynthetic characteristics (Pn: −20.00 %, SPAD: −50.50 %), absorption (−56.86 %), distribution (−15.83 %), N utilization efficiency (NUE: −29.69 %), and grain yield (−44.67 %) of maize. Our results indicate that yield loss was minimized (−22.88 %) when the planting density was appropriately reduced (from 75,000 to 60,000 plants/ha) and the N rate was increased from 180 to 240 kg/ha. The effect of different waterlogging durations on yield exhibited a significantly negative linear relation (R2 > 0.80). This study revealed the physiological mechanism of the sustained effects of excessive rainfall on maize growth and yield. Waterlogging significantly affected the SPAD of maize (p < 0.01, R2 = 0.04), resulting in insufficient kernel N content (p < 0.001, R2 = 0.16) and decreased NUE (p < 0.001, R2 = 0.48). These factors significantly affected yield and exerted a significant negative correlation with planting density (p < 0.05). Our findings improved understanding of planting density and N management for growth and yield of maize under excessive rainfall conditions in mid-high latitude agriculture areas of the world.

Impact of Rainfall on Traffic Mobility and Reliability Under Indian Traffic Conditions

Impact of Rainfall on Traffic Mobility and Reliability Under Indian Traffic Conditions

Rainfall and water level fluctuations dominated the landslide deformation at Baihetan Reservoir, China

Understanding the deformation patterns of different types of landslides around reservoirs is crucial for precise prevention and control of related hazards. However, under the complex coupling effect of extreme rainfall and reservoir level fluctuations, the response between various types of landslides and triggering factors is still unclear. Our study employed the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method and wavelet analysis to undertake an early identification and quantitative analysis of deformation patterns of unstable slopes in the Baihetan Reservoir region, China, and explored the responses of different types of landslide deformation to rainfall and reservoir water level fluctuations. The findings reveal distinct differences in the deformation characteristics of unstable slopes around the reservoir. The quantitative analysis of normalized displacement indicated that the convergent-type landslide exhibits an initial rapid movement that gradually tends to be stable, following an exponential function. The steady-type landslide moves at a consistent speed along a linear trendline. The exponential-type landslide starts slowly and then progressively accelerates. The periodic-type landslide displays periodic fluctuations in displacement, which conforms to the Fourier periodic function. Furthermore, comparisons with antecedent rainfall and reservoir water levels reveal that the convergent-type landslide is significantly influenced by water level fluctuations, and both steady-type and exponential-type landslides are subject to the dual impact of rainfall and water level fluctuations, while the periodic-type landslide exhibits a notable response to rainfall. Therefore, this research aims to provide a scientific basis for the prevention and mitigation of geohazards in reservoir areas.

Influences on North‐Atlantic summer climate from the El Niño‐Southern Oscillation

AbstractSeasonal‐range predictability of summer climate in northwestern Europe is generally considered to be low. This is an increasing issue given the worsening impact of summer heatwaves, droughts and intense convective rainfall in a rapidly changing climate. In wintertime, predictive skill in the region is derived from a variety of sources, not least teleconnections with the El Niño‐Southern Oscillation (ENSO). Summer ENSO teleconnections, however, are often considered to be negligible. In this paper, we revisit the topic of summer teleconnections between ENSO and the North Atlantic‐European region. We build on previous work identifying upper tropospheric responses to tropical forcing, since dynamical teleconnections are most apparent at this level. Our results confirm that significantly increased geopotential heights are found stretching over the North‐Atlantic region and into western Europe when La Niña conditions are prevalent during summer. This pattern is part of the previously identified ‘circumglobal’ pattern of wider northern‐hemisphere height changes. We then look for these responses in a range of climate models used in operational seasonal prediction. While parts of the circumglobal pattern are weakly present, none of them produce the response seen over the North Atlantic, even when the effect of sampling on the observed teleconnection is accounted for. We additionally estimate the contribution of the previous (wintertime) phase of ENSO on the following summer. We find a significant delayed response, particularly in heights, to the earlier phase. The combination of the delayed and current responses gives height anomalies that are larger, on average, when ENSO changes phase from winter to summer. Finally, we show that a modest level of regional prediction skill from ENSO does exist. There is a contribution to skill in heights from the previous ENSO phase, but the equivalent contribution to the skill of zonal winds is smaller.

Impact of dust and temperature on photovoltaic panel performance: A model-based approach to determine optimal cleaning frequency

Enhancing the reliability of photovoltaic (PV) systems is of paramount importance, given their expanding role in sustainable energy production, carbon emissions reduction, and supporting industrial growth. However, PV panels commonly encounter issues that significantly impact their performance. Specifically, the accumulation of dust and the rise in internal temperature lead to a drop in energy production efficiency. The primary issue addressed in this paper is using mathematical modeling to determine the optimal cleaning frequency. This paper first focuses on stochastic modeling for dust accumulation and temperature changes in PV panels, considering varying environmental conditions and proposing a model-based approach to determine the optimal cleaning frequency. Dust accumulation is described using a Non-homogeneous compound Poisson process (NHCPP), while temperature evolution is modeled using Markov chains. Within this framework, we consider the impact of wind speed and rainfall on dust accumulation and temperature. These factors, treated as covariates, are modeled using a two-dimensional time-continuous Markov chain with a finite state space. A Condition-based cleaning policy is proposed and assessed based on the degradation model. Optimal preventive cleaning thresholds and cleaning frequency (periodic and non-periodic) are determined to minimize the long-term average maintenance cost. The gain achieved by non-periodic inspections compared to periodic inspections ranges from 3.83% to 9.37%. Numerical experiments demonstrate the performance of the proposed cleaning policy, highlighting its potential to improve PV system efficiency and reliability.

Climatic and Environmental Impacts on the Sedimentation of the SW Taiwan Margin Since the Last Deglaciation: Geochemical and Mineralogical Investigations

AbstractMany scientific studies have been conducted to constrain present and past source‐to‐sink processes and their controlling factors. The role of typhoon and monsoon rainfall on chemical weathering and soils erosion in east Asia is still not well established. Clay minerals and major elements, combined with Nd and Sr isotopic compositions were analyzed on sediments from Core MD18‐3569 located on the Taiwan margin in the northeastern South China Sea. The aim was to reconstruct the weathering history of small river basins of southwest Taiwan and to establish the impact of East Asian summer monsoon and typhoon rainfall on source to sink processes since the last glacial period. 87Sr/86Sr and ɛNd values and predominant illite‐chlorite indicate that the rivers of southwest Taiwan is the sole source of sediments to Core MD18‐3569 since last deglaciation. Variations in clay mineral assemblages and major elements allowed us to evaluate the intensity of past weathering in the rivers of southwest Taiwan. We demonstrated that long‐term changes of chemical weathering intensity record from Taiwan are driven by changes in the summer monsoon rainfall. The deglaciation is characterized by a progressive increase in the intensity of chemical weathering, which peaked at the beginning of the Holocene. The chemical weathering degree of sediments from Taiwan decreases during the Holocene concurrently with a weakening of the summer monsoon, increase in typhoon activity, and changes in the vegetation cover. All these processes induce soil erosion, regressive pedogenesis, due to shorter residence time of minerals in the soils of southwest Taiwan.

The Impact of Rainfall and Wind Speed on The Outcome of Catching Swanggi Fish Using Cantrang Catch Tools in Brondong National Fishing Port (PPN)

Indonesia has a wealth of natural resources, especially in the fishing sector. The waters of Lamongan are one of the areas that has the potential of swanggi fishing that dominates the fishing sector by catching cantrang fish. The research is aimed at providing information to the Brondong National Fishing Port (PPN) and fishermen around the area about the impact of rainfall and wind speed on the catch of swanggi fish. The results of linear regression method analysis using T test, F test and determination test. The result of lineary regression test T test showed that the regression value of the rainfall variable to catch of fish is the value of sig.&lt;0,05 while the variable of wind speed to Catch of Fish is the number of sig.&gt;0,05. The determination test result of R2 is 0.445, which means that the variable rainfall and wind speed influenced 44.5% on the catch of the swanggi fish and the rest was affected by other factors.

Nexus between Climate Change, Technological Inputs, Energy Consumption and Cereal Production in India: An ARDL approach

Agriculture is vital to India's economy and food security but is highly vulnerable to climate change. This study examines the impact of rainfall, temperature, arable land, energy consumption, fertilizer, and technology inputs on cereal production in India from 1965 to 2018 using ARDL and Toda-Yamamoto Granger Causality techniques. Results show temperature negatively affects cereal production, while rainfall, arable land, energy, fertilizer, and technology have a positive impact. There is a unidirectional causal relationship between these factors and cereal production. The study suggests adopting modern technology, prioritizing organic farming, educating farmers, investing in agricultural R&amp;D, and developing sustainable irrigation infrastructure.

Heavy rainfall and moisture susceptibility of pavement foundation: A case study coupling finite element method and MnROAD moisture monitoring data

The Midwest region, including Minnesota, has been experiencing increased heavy precipitation events due to climate change, and the Minnesota Department of Transportation (MnDOT) is currently investigating the effect of climate change on pavement foundation and other transportation assets. As part of this effort, a study was conducted to investigate the impact of heavy rainfall on pavement foundation performance by focusing on moisture dynamics and resilient modulus changes in the pavement base layer. This study is aimed at understanding the adverse effects of heavy rainfall on moisture fluxes within pavement foundation and corresponding stiffness of the base aggregate layer. A two-step approach was adopted for predicting changes in saturation when estimating corresponding resilient modulus values using the resilient modulus prediction equation employed in AASHTOWare Pavement Mechanistic-Empirical (ME) Design. PLAXIS 3D, a finite-element analysis tool, was utilized to simulate the movement of moisture within the pavement layers under varying heavy rainfall scenarios. By incorporating predicted saturation from PLAXIS 3D simulations into the Pavement ME equation, corresponding resilient modulus values were estimated for the base layer. To ensure its accuracy and reliability, the model was validated using field sensor data from the MnROAD facility. Multiple linear regression models were developed to provide a means for estimating resilient modulus changes due to heavy rainfall. This study highlights the importance of considering moisture effects in pavement design and maintenance in regions prone to heavy rainfall events, and findings can be used by transportation agencies as part of their transportation/geotechnical asset management programs.

Assessing the impact of rainfall on water quality in a coastal urban river utilizing the environmental fluid dynamics code

The Haihe River, flowing through Tianjin City, grapples with pollution challenges, notably high levels of nitrogen (N) and phosphorus (P) leading to eutrophication and unpleasant odors, particularly aggravated by rainfall. This study used Environmental Fluid Dynamics Code to comprehensively analyze the water environment under three scenarios: low flow year, normal flow year, and high flow year. The investigation delves into N and P concentrations, computes atmospheric deposition fluxes, and evaluates the loads and retention ratios of N and P in the urban river network of Tianjin City. Scenario analysis reveals distinct trends, with high flow years associated with elevated N and hydrodynamics levels, while low flow years exhibit contrasting patterns. Key findings include 1) wet deposition fluxes contributing 21% of NH4+-N and 9% of TP river loads, 2) continuous rainfall and runoff resulting in immediate or delayed increases in NH4+-N, TN, and TP concentrations in surface water, 3) an increase in average N:P mole ratios downstream along the Haihe River, and 4) N and P mean retention ratios were 48% and 63%, respectively. The study suggests that implementing sponge city construction and sustainable drainage systems may improve water quality by mitigating the direct discharge of rainfall runoff into the river.

Delineation of the Groundwater Potential Zones using Integrated Geospatial Techniques and Multi-criteria Decision Approach in the Cretaceous Lower Benue and Niger Delta Basins of Abia State, Southern Nigeria

Delineation of the groundwater potential zones in the Cretaceous Lower Benue and Niger Delta basins of Abia state, Nigeria with geographic information system (GIS) / remote sensing (RS) multi-criteria decision approach (MCDA) considered the significant hydrogeomorphological factors affecting the availability of groundwater resources. The factors (geology, rainfall, slope, drainage density, lineament density, soil type, and land use land cover) were ranked using the analytical hierarchical process, a multi-criteria decision-making tool. The integration of the factors into a geographic information system (GIS) and weighted overlay analysis were then carried out using ArcGIS software. The result indicates 31.5% of zones of high to very high groundwater occurrence are in the southern section, and 41.6% of poor to very poor zones in the northern section. While the moderate zones make up the 26.8% of the study area. The study demonstrates that the groundwater occurrence in the study area is primarily controlled by the interactive impact of geology, rainfall, and slope factors, thus revealing the causes of the incessant random borehole failures in the moderate zones. Thus, the groundwater resources can be sustainably exploited, developed, and managed with the help of the map produced from the resulting demarcation of groundwater potential zones in the study.

Delineation of the Groundwater Potential Zones using Integrated Geospatial Techniques and Multi-criteria Decision Approach in the Cretaceous Lower Benue and Niger Delta Basins of Abia State, Southern Nigeria

Delineation of the groundwater potential zones in the Cretaceous Lower Benue and Niger Delta basins of Abia state, Nigeria with geographic information system (GIS) / remote sensing (RS) multi-criteria decision approach (MCDA) considered the significant hydrogeomorphological factors affecting the availability of groundwater resources. The factors (geology, rainfall, slope, drainage density, lineament density, soil type, and land use land cover) were ranked using the analytical hierarchical process, a multi-criteria decision-making tool. The integration of the factors into a geographic information system (GIS) and weighted overlay analysis were then carried out using ArcGIS software. The result indicates 31.5% of zones of high to very high groundwater occurrence are in the southern section, and 41.6% of poor to very poor zones in the northern section. While the moderate zones make up the 26.8% of the study area. The study demonstrates that the groundwater occurrence in the study area is primarily controlled by the interactive impact of geology, rainfall, and slope factors, thus revealing the causes of the incessant random borehole failures in the moderate zones. Thus, the groundwater resources can be sustainably exploited, developed, and managed with the help of the map produced from the resulting demarcation of groundwater potential zones in the study.

Deep learning prediction of rainfall-driven debris flows considering the similar critical thresholds within comparable background conditions

Machine learning has been widely applied to predict the spatial or temporal likelihood of debris flows by leveraging its powerful capability to fit nonlinear features and uncover underlying patterns or rules in the complex formation mechanisms of debris flows. However, traditional approaches, including some current machine learning-based prediction models, still have limitations when used for debris flow prediction. These include the lack of a specific network structure or model to consider the updating of debris flow critical conditions in relation to geographical background conditions, limiting the universality of prediction models when transferring them to different places. In this study, this article proposes a deep learning network designed to predict the spatiotemporal probability of rainfall-induced debris flows, incorporating the Similarity Mechanism of Debris Flow Critical Conditions (SM-DFCC). The model comprehensively integrates the mining of rainfall-triggering features and couples them with geographical background features to fit the nonlinear relationship with debris flow formation. The model underwent training using data on various historical debris flows triggered by different storms across Liangshan Prefecture from 2020 to 2022. The results indicated that: (i) the method is effective in predicting the spatiotemporal likelihood of debris flows under catchment units, with accuracy scores (ACC) ranging from 0.724 to 0.835; (ii) after optimization using the AVOA algorithm, the predictive performance of the model significantly improved, with an increase of 27.24% in ACC scores for SVC and 8.81% for XGBoost; and (iii) factor importance analysis revealed that rainfall triggering factors have higher cumulative contribution rates when distinguishing between the occurrence and non-occurrence of debris flows. In addition, taking a rainfall storm on 06, September 2020 as a case, this research quantitatively revealed the pattern of debris flow formation, where high-frequency disaster areas exhibit lower rainfall thresholds of debris flows, represented by absolute energy (AE). Despite these findings, the accuracy and reliability of rainfall data still remain the most challenging obstacle in basin/regional-scale debris flow prediction when applying this method. The integration of multiple sources of rainfall data, including station data, satellite rainfall, radar rainfall, etc., is necessary to accurately quantify the impact of rainfall on debris flow formation when applying this method to debris flow monitoring and early warning tasks. Overall, this method shows great potential in providing a scientific reference for the construction of debris flow monitoring and early warning systems in the future.

Microplastics in Hong Kong's marine waters: Impact of rainfall and Pearl River discharge

This study provided a systematic investigation of microplastics in Hong Kong's surface marine waters during the pandemic from 2019 to 2021. Microplastics (2.07 ± 4.00 particles/m3) exhibited significant temporal variations with higher abundance in the wet season, without a consistent trend after the mandatory mask-wearing requirement was announced. The impact of pandemic restrictions on microplastic distribution was found to be relatively minor. However, significant correlations between microplastic abundances and rainfall highlighted the substantial contribution of local emissions through surface runoff. Notably, sites in closer proximity to the Pearl River Delta exhibited higher microplastic abundances, indicating their association with emission sources. The influence of rainfall and adverse weather on marine microplastic loads demonstrated different sensitivities among various locations but can generally last for one month. These results revealed the impact of seasonal rainfall on coastal microplastics and emphasized the need for efforts to reduce microplastic discharge from land-based sources.

Integrated modeling of urban mobility, flood inundation, and sewer hydrodynamics processes to support resilience assessment of urban drainage systems.

With the increasing frequency of extreme weather events and a deepening understanding of disasters, resilience has received widespread attention in urban drainage systems. The studies on the resilience assessment of urban drainage systems are mostly indirect assessments that did not simulate human behavior affected by rainfall or semi-quantitative assessments that did not build simulation models, but few research characterizes the processes between people and infrastructure to assess resilience directly. Our study developed a dynamic model that integrates urban mobility, flood inundation, and sewer hydrodynamics processes. The model can simulate the impact of rainfall on people's mobility behavior and the full process including runoff generation, runoff entering pipes, node overflow, flood migration, urban mobility, and residential water usage. Then, we assessed the resilience of the urban drainage system under rainfall events from the perspectives of property loss and urban mobility. The study found that the average percentage increase in commuting time under different return periods of rainfall ranged from 6.4 to 203.9%. Calculating the annual expectation of property loss and traffic obstruction, the study found that the annual expectation loss in urban mobility is 9.1% of the annual expectation of property loss if the rainfall is near the morning commuting peak.

Flood risk assessment in arid and semi-arid regions using Multi-criteria approaches and remote sensing in a data-scarce region

Flooding is a natural disaster that poses a threat to both people and the environment, necessitating proactive assessment and mitigation strategies to protect vulnerable communities and ecosystems. These measures are necessary to reduce the risk of flooding and moderate the impact of rainfall. In this study, an Analytical Hierarchy Process (AHP) was used to evaluate flood risk in a data-limited region by integrating Remote Sensing (RS) and Geographic Information System (GIS) methods. The study identified several key flood risk indicators, including topographic wetness index, elevation, slope, land cover, precipitation, distance to river, distance to road, and NDVI. The flood risk map had a score range of 8.71–30.99 %, with higher scores indicating a greater susceptibility to flooding. These scores were then used to classify the flood risk into five categories: very low, low, moderate, high, and very high. The percentages of regions falling into each category were 8.71 %, 23.52 %, 30.99 %, 22.68 %, and 14.09 % respectively. The area under the Curve (AUC) approach was used to validate the flood risk map, which showed a high degree of accuracy (0.86). The results of this study provide valuable insights for monitoring, and forecasting the probability of floods in the Dosso Region.

Impact of extreme rainfall events on soil erosion on karst Slopes: A study of hydrodynamic mechanisms

As global warming intensifies, frequent extreme rainfall events cause severe soil erosion. ​However, the impact of extreme rainfall on erosion on karst slopes and its hydrodynamic mechanisms are still poorly understood. This study aims to examine how extreme rainfall events and slope gradient affect soil erosion on karst slopes and its underlying dynamic mechanisms. Simulation experiments were performed in a steel tank that mimicked the dual hydrological structure of a karst slope, using various rainfall intensities (50, 105, 115, 125, and 145 mm·h−1) and slope gradients (5°, 15°, and 25°). The results show that in the event of extreme rainfall (105 – 145 mm·h−1), there was a substantial increase in the surface runoff coefficient, rising from 48.85 % to 89.79 %, representing an 83.81 % increase. Surface erosion was found to be the predominant factor on the slope, contributing to 98.74 % to 99.98 % of the overall sediment output, while underground sediment coefficient ranged from only 0.02 % to 1.26 % under same conditions. The unit stream power emerged as the most suitable hydrodynamic parameter for characterizing surface sediment yield (SS), with a critical threshold of unit water flow power for surface erosion identified at 0.006 m·s−1. Constructing a surface erosion model (R2 = 0.969) based on runoff velocity, rain intensity, and slope yields higher accuracy than the model (R2 = 0.966) based solely on runoff modulus, rain intensity, and slope. For the most precise predictions, it is recommended to combine runoff modulus and velocity with rainfall intensity and slope when constructing the surface erosion prediction model(R2 > 0.999). This study enhances understanding of extreme rainfall’s effect on soil erosion in karst slopes and backs the creation of prediction models for such events. Our insights offer fresh perspectives for soil and water conservation, aiding in the formulation of precise erosion control strategies for karst slopes.