Extreme Rainfall Events Research Articles

Microplastics promote the invasiveness of invasive alien species under fluctuating water regime

Abstract Microplastic (MP) pollution and alien plant invasions are two important threats to terrestrial ecosystems. Microplastics alter the physical and chemical characteristics of soil, potentially affecting the performance of alien plants. However, previous studies have overlooked the impact of weather on invasive plants in areas polluted by MPs. With the global increase in extreme rainfall events, it is imperative to redefine the correlation between MPs and invasive plants. Here, we conducted an experiment in a climate chamber to examine the effects of MPs on the growth and development of both native and invasive alien plants under constant and fluctuating water regime (FWR). The FWR simulated extreme water pulses during the 2016–2020 growing seasons in Wuhan, China. Our results indicated that biomass accumulation and root development were influenced by water conditions and MP pollution in both invasive and native species. The extent of the effects varied between the two groups of plant species. FWR promoted plant growth and fine root development in invasive plants but reduced the maximum quantum efficiency of photosystem II (Fv/Fm) and nonphotochemical quenching (NPQ) indices of native plants. Moreover, FWR attenuated the negative effects of polybutylene succinate (PBS, degradable MPs) on biomass and root characteristics (length, surface area and tips). FWR compensated the negative impacts of MPs on the total and below‐ground biomass of the invasive species Paspalum dilatatum and Sphagneticola trilobata, but not on the native species. Consequently, invasive species showed better performance than native species in the fine root development of biomass growth and chlorophyll fluorescence under the combined effects of MPs and FWR. Synthesis and applications. Our findings suggest that MP pollution enhances the competitiveness of invasive alien species over the native species when exposed to pronounced dry–wet water cycle conditions, potentially affecting the composition and biodiversity of the ecosystems. Thus, controlling MP pollution should be a part of the management strategy to conserve biodiversity and ecosystems.

Modeling non-stationarity in extreme rainfall data and implications for climate adaptation: A case study from southern highlands region of Tanzania

The Southern Highlands region of Tanzania has witnessed an increased frequency of severe flash floods. This study examines rainfall data of four stations (Iringa, Mbeya, Rukwa, and Ruvuma) spanning 30 years (1991–2020) to investigate drivers of extreme rainfall and non-stationarity behavior. The Generalized Extreme Value (GEV) model, commonly used in hydrological studies, assumes constant distribution parameters, which may not be true due to climate variability, potentially leading to bias in extreme quantile estimation. Recent studies have introduced a technique for constructing non-stationary Intensity-Duration-Frequency (IDF) rainfall curves. The method incorporates trends in the parameters of the GEV distribution, only using time as a covariate. However, uncertainty exists about whether time is the most suitable covariate, highlighting the need to explore all potential covariates for modeling non-stationarity. The aim of this study is to assess the influence of other time-varying covariates on extreme daily rainfall events, considering seasonality and climate change in the rainfall data. Specifically, five processes (i.e., local temperature changes (LTC), urbanization, annual Global Temperature Anomaly (GTA), the Indian Ocean Dipole (IOD), and the El Niño-Southern Oscillation (ENSO) cycle) were studied as drivers of extreme rainfall events. Sixty two non-stationary GEV models are developed based on these covariates and their combinations, alongside two non-stationary GEV models using the time covariate to capture the seasonality of the unimodal rainfall in the region, and one stationary GEV model (S0). With the use of corrected Akaike Information Criterion (AICc), the best model for each duration (i.e., 1-, 3-, and 5-days) of rainfall series is chosen. Results indicate that local processes (i.e., LTC and urbanization) are the optimal covariates for 1 day-duration rainfall, while global processes (i.e, IOD, ENSO cycle, and GTA) are identified as the most suitable covariates for 3, and 5 day-duration rainfall across all stations. The identified best non-stationary model (with their best covariates) are then used to develop non-stationary rainfall IDF curves for all stations. According to the analysis of non-stationary extreme values, the return periods of extreme rainfall events concluded a notable decrease in comparison to the stationary approach. The study also revealed strong correlations between global climate indices (ENSO, IOD, GTA) and long-duration extreme rainfall in Tanzania’s Southern Highlands. Local factors like Urbanization and temperature changes also show significant associations with 1-day duration events. These findings emphasize the need for integrated climate forecasting to inform effective adaptation strategies. Finally, the study addresses associated uncertainties in our predictions of forthcoming extreme rainfall events through rigorous analysis. The study demonstrated that return levels for extreme rainfall events exhibit a rising trend with increasing return period, indicating heightened intensity over longer time spans, whereas, a relative uncertainty analysis illustrate escalating uncertainty with increasing return periods, emphasizing challenges in long-term prediction.

Assessing Drought Vulnerability in the Brazilian Atlantic Forest Using High-Frequency Data

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation and maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from using imputation methods due to potential bias. Drought quantification involved the computation of the aridity index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann–Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting that geographical location and elevation do not strongly influence observed dryness trends.

Observed Changes in Extreme Precipitation Associated with U.S. Tropical Cyclones

Abstract Numerous recent tropical cyclones have caused extreme rainfall and flooding events in the CONUS. Climate change is contributing to heavier extreme rainfall around the world. Modeling studies have suggested that tropical cyclones may be particularly efficient engines for transferring the additional water vapor in the atmosphere into extreme rainfall. This paper develops a new indicator for climate change using the enhanced rainfall metric to evaluate how the frequency and/or intensity of extreme rainfall around tropical cyclones has changed. The enhanced rainfall metric relates the amount of rain from a storm over a given location to the 5-yr return period rainfall in that location to determine the severity of the event. The annual area exposed to tropical-cyclone-related 5-yr rainfall events is increasing, which makes it a compelling climate change indicator. Quantile regression illustrates that the distribution of tropical cyclone rainfall is also changing. For tropical storms, all quantiles are increasing. However, major hurricanes show large increases in their most extreme rainfall. This study does not attempt to make any detection claims (vs natural variability) or attribution of the observed trends to anthropogenic forcing. However, the sensitivity of the results to natural variability in tropical cyclone frequency was somewhat constrained by comparing 2 decades from the previous active era (1951–70) with two from the current era (2001–20). This comparison also shows that both the mean rainfall and the maximum rainfall associated with tropical cyclones are increasing over most areas of the eastern CONUS with the most significant increases from northern Alabama to the southern Appalachians. Significance Statement The purpose of this study is to analyze the changes in frequency and magnitude of extreme precipitation events associated with tropical cyclones with the goal of developing a new indicator for climate change. This is important because heavy rainfall and associated flooding is one of the primary causes of tropical cyclone destruction and fatalities, especially in inland locations away from where storms initially make landfall. Our results show that both the frequency and magnitude of extreme rainfall events from tropical cyclones have increased over the CONUS. The strongest storms (major hurricanes) also show more of an increase in extreme rainfall than storms of weaker intensities.

Characteristics of Atmospheric Rivers and the Impact of Urban Roof Roughness on Precipitation during the “23.7” Extreme Rainstorm against the Background of Climate Warming

In July 2023, Baoding in Hebei Province experienced unprecedented torrential rainfall, breaking historical records and causing severe flooding. However, our understanding of the multi-scale circulation systems and physical mechanisms driving this extreme precipitation event remains incomplete. This study utilizes multi-source observational data and the Weather Research and Forecasting (WRF) numerical model to conduct a weather diagnosis and numerical simulation of this extreme rainfall event, focusing on the impact of atmospheric rivers (ARS) and urban rooftop roughness on the precipitation process against the background of climate warming. The study found that this extremely heavy rainstorm occurred in the circulation background formed by the factors of subtropical high ectopics, typhoon residual vortex retention, double typhoon water-vapor transmission, and stable high-level divergence. The ARS provided abundant moisture, with its vapor pathway significantly altered following the landfall of Typhoon Doksuri. The interaction between the ARS and the Taihang Mountains was crucial in triggering and intensifying the rainstorm in the foothills. Urbanization significantly affected the distribution of precipitation, with moderate urban roughness enhancing rainfall in and around the city, whereas excessive roughness suppressed it. These results contribute to a deeper understanding of the mechanisms behind extreme precipitation under climate change and provide a scientific basis for improving the forecasting and mitigation of such events.

Enhanced resilience in urban stormwater management through model predictive control and optimal layout schemes under extreme rainfall events

Optimizing the layout of urban stormwater management systems is an effective method for mitigating the risk of urban flooding under extreme storms. However, traditional approaches that consider only economic costs or annual runoff control rates cannot dynamically respond to the uncertainties of extreme weather, making it difficult to completely avoid large accumulations of water and flooding in a short period. This study proposes an integrated method combining system layout optimization and Model Predictive Control(MPC)to enhance the system's resilience and effectiveness in flood control. An optimization framework was initially built to identify optimal system layouts, balancing annual average life cycle cost (AALCC) and resilience index. The MPC was then applied to the optimal layout selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, aiming to alleviate inundation cost-effectively. The adaptability of MPC to varying sets of control horizons and its efficacy in managing the hydrograph and flood dynamics of urban drainage system were examined. Conducted in Yubei, Chongqing, this study revealed patterns in optimal layout fronts among various extreme design rainfalls, showing that peak position rate and return period significantly influence system resilience. The contribution of MPC to the optimal system layout was particularly notable, resulting in improved instantaneous and overall flood mitigation. The application of MPC increased the resilience index by an average of 0.0485 and offered cost savings of 0.0514 million yuan in AALCC. Besides, our findings highlighted the importance of selecting an optimal set of control horizons for MPC, which could reduce maximum flood depth from 0.43m to 0.19m and decrease conduit peak flow by up to 14% at a flood-prone downstream location.

Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble

Abstract. ​​​​​​​Severe floods with extreme return periods of 100 years and beyond have been observed in several large rivers in Bavaria in the last 3 decades. Flood protection structures are typically designed based on a 100-year event, relying on statistical extrapolations of relatively short observation time series while ignoring potential temporal non-stationarity. However, future precipitation projections indicate an increase in the frequency and intensity of extreme rainfall events, as well as a shift in seasonality. This study aims to examine the impact of climate change on the 100-year flood (HF100) events of 98 hydrometric gauges within hydrological Bavaria. A hydrological climate change impact (CCI) modeling chain consisting of a regional Single Model Initial-condition Large Ensemble (SMILE) and a single hydrological model was created. The 50 equally probable members of the Canadian Regional Climate Model version 5 large ensemble (CRCM5-LE) were used to drive the hydrological model WaSiM (Water balance Simulation Model) to create a hydro-SMILE. As a result, a database of 1500 model years (50 members × 30 years) per investigated time period was established for extreme value analysis (EVA) to illustrate the benefit of the hydro-SMILE approach for a robust estimation of HF100 based on annual maxima (AM) and to examine the CCI on the frequency and magnitude of HF100 in different discharge regimes under a strong-emission scenario (RCP8.5). The results demonstrate that the hydro-SMILE approach provides a clear advantage for a robust estimation of HF100 using the empirical probability of 1500 AM compared to its estimation using the generalized extreme value (GEV) distribution of 1000 samples of typically available time series sizes of 30, 100, and 200 years. Thereby, by applying the hydro-SMILE framework, the uncertainty from statistical estimation can be reduced. The study highlights the added value of using hydrological SMILEs to project future flood return levels. The CCI of HF100 varies for different flow regimes, with snowmelt-driven catchments experiencing severe increases in frequency and magnitude, leading to unseen extremes that impact the distribution. Pluvial regimes show a lower intensification or even decline. The dynamics of HF100 driving mechanisms depict a decline in snowmelt-driven events in favor of rainfall-driven events, an increase in events driven by convective rainfall, and almost no change in the ratio between single-driver and compound events towards the end of the century.

Regional flood risk grading assessment considering indicator interactions among hazard, exposure, and vulnerability: A novel FlowSort with DBSCAN

With escalating flood risks due to global warming and frequent extreme rainfall events, it is crucial to highlight the importance of flood risk assessment for devising prudent mitigation strategies and promoting sustainable development. Against this backdrop, this study proposes a novel regional flood risk grading assessment method, namely the Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-FlowSort method, aimed at comprehensively assessing flood risks in county-level regions of Anhui Province. The innovation of this method lies in its consideration of interactions among the hazard, exposure, and vulnerability subsystems, as well as the comprehensive determination of assessment indicator weights through the Probability Language Term Set-Decision Making Trial and Evaluation Laboratory (PLTS-DEMATEL) and Entropy Weight Method (EWM). Thus, the subjectivity and objectivity of the weights of the indicators are integratedly taken into account. Additionally, this study introduces DBSCAN to generate reference profiles, improving the reliance on expert input in the traditional FlowSort method and enhancing the automation and objectivity of the evaluation process. The results of the study demonstrate that the DBSCAN-FlowSort method exhibits superior classification performance in predicting flood hazards, particularly in accurately identifying and assessing high-risk areas when considering interactions among indicators in different subsystems. This method provides a new scientific tool for flood risk assessment and management, which is crucial for devising flood resource allocation and risk mitigation measures in both theory and practice.

Historical and future extreme climate events in highly vulnerable small Caribbean Islands

Small Caribbean islands are on the frontline of climate change because of sea level rise, extreme rainfall and temperature events, and heavy hurricanes. The Archipelago of San Andrés, Providencia, and Santa Catalina (SAI), are Caribbean islands belonging to Colombia and declared a Biosphere Reserve by UNESCO. SAI is highly vulnerable to climate change impacts but no hydroclimatological study quantified the extreme climatic changes yet. This study analyzes historical (1960s-2020, 7 stations) and future (2071–2100, CMIP6 multi-model ensemble, for four scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) trends in mean and extreme precipitation and temperature duration, frequency, and intensity. We find that heatwaves have more than tripled in frequency and doubled their maximum duration since the end of the ‘80 s. Precipitation is historically reduced by 5%, with a reduction recorded in 5 stations and an increase in 2, while extreme rainfall events significantly increased in frequency and intensity in most stations. The hotter-and-drier climate is amplified in the future for all scenarios, with much drier extremes (e.g., -0.5─-17% wet days, +8%─30% consecutive dry days, and +60%─89% in hot days). Although we show that hurricanes Categories IV and V near SAI (< 600 km) more than doubled since the’60 s, only a small fraction of extreme rainfall in the archipelago is associated with hurricanes or tropical storms. La Niña events also have no substantial influence on extreme precipitation. Interestingly, opposite and heterogeneous historical extreme rainfall trends are found across such small territory (< 30 km2). Thus, downscaled hydrometeorological data and model simulations are essential to investigate future extreme climatic events and strengthen small Caribbean islands' climate change adaptation efforts.

Decadal-scale assessment of sediment denudation rates in the Zhoukou River Basin, Taiwan: insights from improved DEMs of differencing based on spectral analysis

This study assesses sediment erosion rates in the Zhoukou River Basin in southern Taiwan over the past three decades, focusing on the impact of extreme rainfall events. While various established methods determine erosion rates over different temporal scales, we employ an independent approach for decadal-scale erosion rate calculation by utilizing open-source global and regional digital elevation models (DEMs) data for two distinct periods. We introduce a new method that applies Fourier analysis to vertically register the DEMs, significantly minimizing their vertical bias. Through spectral analysis, we identify long wavelength topography crucial for correcting vertical offsets. Erosion rates, computed through DEMs of Difference (DoD), exhibit a similar trend of significant reduction in sediment export rates from 1990–2010 to 2011–2020 due to decreased extreme rainfall events, aligning with erosion rate estimates derived from mean suspended load data at gauge stations. Over the entire period from 1990 to 2020, the calculated denudation rate was 14.19 mm/yr, whereas in the recent decade (2011–2020), it decreased to 10.46 mm/yr. Our study suggests that the improved DoD method can effectively estimate sediment transport rates by leveraging underutilized DEMs captured at distinct points in time, especially when the erosional signal dominates data noise.

Analysing the variability of non-stationary extreme rainfall events amidst climate change in East Malaysia

ABSTRACT Climate change is intensifying the occurrence of extreme rainfall events, drawing attention to the importance of understanding the return period concept within the realm of extreme weather studies. This study evaluates the stationarity of extreme rainfall series on both monthly and annual series across East Malaysia, employing the Augmented Dickey–Fuller, Phillips Perron, and Kwiatkowski–Phillips–Schmidt–Shin tests. To model these extreme rainfall series, various probability distributions were applied, followed by goodness-of-fit tests to determine their adequacy. The study identified the stationary and non-stationary return values at 25-, 50-, and 100-year return periods. Additionally, maps depicting the spatial distribution for non-stationary increment were generated. The results indicated that extreme monthly rainfall exhibited stationary characteristics, while extreme yearly rainfall displayed non-stationary characteristics. Among the tested probability distributions, the generalised extreme value distribution was found to be superior in representing the characteristics of the extreme rainfall. Furthermore, a significant finding is that the non-stationary rainfall exhibits higher return values than those of stationary rainfall across all return periods. The northeast coast of Sabah highlighted as the most affected area, with notably high return values for extreme rainfall.

Identification of Possible Incoming Runoff using Different Combinations of Extreme Rainfall Events in a Semi-arid Context: Banas River, Bisalpur Dam Catchment

ABSTRACT Event-based hydrologic models are very useful to predict peak flow and flood volume, particularly in semi-arid regions. In HEC-HMS software methods selected for loss, transformation, routing and base flow were soil conservation service (SCS) curve number (CN), SCS unit hydrograph (UH), Muskingum and recession, respectively. A total of six extreme events from the year 2011 to 2019 were selected, out of which four were used for calibration, one each for validation and application. The developed model can identify the peak discharge and flood volume satisfactorily at 2-hour intervals. During validation performance statistical viz. percent difference in runoff volume (DV%), percent difference in peak flow (DP%), Nash Sutcliffe Efficiency (NSE), percent bias (%BIAS), coefficient of determination (R2) and ratio of the root mean square error to the standard deviation (RSR) were −3.68, −25.93, 0.52, 3.52, 0.53 and 0.69, respectively. The sensitivity analysis revealed that the CN is the highest sensitive parameter followed by the storage time constant (K), which affects the peak discharge. Whereas, for flood volume, CN is the highest sensitive parameter followed by the recession constant (Rc). The relative sensitivity of CN for peak flow and flood volume were 2.11 and 1.73, respectively. Out of center maximum rainfall (CeMR) and cumulative maximum rainfall (CuMR) distribution, CeMR distribution has given higher peak discharge for all rainfall duration. The rainfall characteristics of the 2016 flood event suggest a hypothetical 9-day rainfall duration can be considered. The 9-day event with CeMR gives 70.64% higher discharge as compared to the observed peak discharge during the 2016 flood.

Acidification alleviates the inhibition of hyposaline stress on physiological performance of tropical seagrass Thalassia hemprichii

Since the Industrial Revolution, increasing atmospheric CO2 concentrations have had a substantial negative impact influence on coastal ecosystems because of direct effects including ocean acidification and indirect effects such as extreme rainfall events. Using a two-factor crossover indoor simulation experiment, this study examined the combined effects of acidification and hyposaline stress on Thalassia hemprichii. Seawater acidification increased the photosynthetic pigment content of T. hemprichii leaves and promoted seagrass growth rate. Hyposaline stress slowed down seagrass growth and had an impact on the osmotic potential and osmoregulatory substance content of seagrass leaves. Acidification and salinity reduction had significant interaction effects on the photosynthesis rate, photosynthetic pigment content, chlorophyll fluorescence parameters, and osmotic potential of T. hemprichii, but not on the growth rate. Overall, these findings have shown that the hyposaline stress inhibitory effect on the T. hemprichii physiological performance and growth may be reduced by acidification.

Identification of sub-urban scale extreme precipitation variability using a geospatial algorithm

Established urban hydrometeorological literature shows the enhanced rainfall occurs generally near and downwind of urban centers. This information is often too generalized for operational use. Anomaly analysis in a spatial framework similar to operational forecast zones may provide utility in prediction of extreme events like localized flash flooding. This paper introduces a spatial analysis algorithm deploying 4-km NCEP Multisensor Precipitation Estimates Stage IV data to determine specific areas, or sectors, of anomalous precipitation across three southern United States cities (Atlanta, Houston, and Raleigh). Rainfall frequency and intensity are quantified during the afternoon and early evening across a 21-year period. Hotspot analysis and sector-based comparison of anomalies show that specific areas within each city are prone to isolated extreme rainfall events more often than other areas. Isolated precipitation in central and eastern Atlanta, specifically, has a high rate of recurrence and intensity during convectively active hours. 700 hPa wind deviations from the zonal mean are mapped to domain sectors as rainfall anomalies exceed ten standard deviations. These results are an application of the provided geospatial algorithm which combines gridded raster data with subdivisions defined by polygon features to assess the clustering of variables (environmental or otherwise) within domains of interest.

Intensity-Duration-Frequency Curves for Manicaragua city, Cuba

The Intensity-Duration-Frequency (IDF) curves are a way to visualize and represent extreme hydrometeorological rainfall events. In this article, an analysis of convective rainfall events recorded at the La Piedra Meteorological Station, Villa Clara, Cuba, was conducted. To develop IDF curves, the 2006- 2019 time series was analyzed. A partial duration series was generated, including intervals from 20 minutes to 4320 minutes, subjected to an outlier detection process. The series was divided into two categories: one for durations ≤ 720 minutes and another for durations &gt; 720 minutes. The resulting series underwent nonparametric tests to assess their independence, randomness, homogeneity, and seasonality. Subsequently, they were fitted to the Generalized Pareto probability distribution and to a parametric equation of the Montana model, and then the curves were plotted for return periods of 10, 50 and 100. The Montana model led to obtaining correlation coefficients greater than 0.90 compared to the other methods used, significantly improving the quality of the fit in both categories. This research provides information to understand and plan the management of intense climatic phenomena and adequate risk management in an area where such studies are lacking, facilitating access to crucial data essential in the design and execution of hydraulic engineering projects in the region.

Atmospheric River Rapids and Their Role in the Extreme Rainfall Event of April 2023 in the Middle East

AbstractThe mesoscale dynamics of a record‐breaking Atmospheric River (AR) that impacted the Middle East in mid‐April 2023 and caused property damage and loss of life are investigated using model, reanalysis and observational data. The high‐resolution (2.5 km) simulations revealed the presence of AR rapids, narrow and long convective structures embedded within the AR that generated heavy precipitation (&gt;4 mm hr−1) as they moved at high speeds (&gt;30 m s−1) from northeastern Africa into western Iran. Gravity waves triggered by the complex terrain in Saudi Arabia further intensified their effects. Given the rising frequency of ARs in this region, AR rapids may be even more impactful in a warming climate, and need to be accounted for in reanalysis and numerical models.

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.

Perspectives of nature-based tourism-dependent communities on climate change in the Okavango Delta, Botswana.

The intensity and frequency of climate extremes such as heat waves, droughts and extreme rainfall events are projected to rise. This will increase the severity of their impacts across socio-ecological systems. Economic sectors such as nature-based tourism become more vulnerable because of their reliance on climate and natural capital as key resources. While attempts have been made to understand how climate change may impact tourists and the industry itself, little is known about the same on tourism-dependent communities. This paper determines the extent to which tourism-dependent communities are vulnerable to climate change in the Okavango Delta, Botswana, to enhance their wider livelihood the development of strategies for improving adaptive capacity, resilience, and reduced exposure sensitivities. A household survey of 172 households was conducted in three purposively selected villages of Mababe, Sankuyo and Khwai, actively involved in community-based tourism for their socio-economic development. Information sourced related to livelihood options, peoples' resilience, local risks, and hazards. The data was analysed using descriptive statistics and thematic analysis. The results indicate that respondents have observed climatic changes over the years such as increased temperatures, decreased rainfall, and increased frequencies of extreme events. The respondents attributed changes in natural capital to these observed climatic conditions in the form of desiccation, dwindling populations of some wildlife species, decreased fish stocks and reduced vegetation cover. This renders the tourism-dependent communities vulnerable as their livelihood is threatened. The paper thus concludes that climate change adaptation is an urgent priority for local communities who are already exposed to existing climatic and non-climatic stresses.

Urbanization signature on hourly rainfall extremes of Kuala Lumpur

This study uses high-resolution satellite data to investigate the impact of urbanization on the local climate of Kuala Lumpur, Malaysia. Nonparametric statistical methods, including trend test, spatial correlation, and quantile regression, were employed to quantify interrelationships between satellite nighttime light (NTL), Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and Global Satellite Mapping of Precipitation (GSMaP) extreme rainfall events. The results revealed that urbanization increased Kuala Lumpur's LST by 3.8 to 4.1 °C and the 99th percentile rainfall by 10 to 43 % compared to surrounding areas. The correlation analysis showed an association (r = 0.62) between the trends in LST and rainfall extremes, with a significance level of p < 0.05. The NTL trend analysis showed a rapid expansion of the city, particularly in the west, and thus, an expansion of high LST areas and extreme rainfall amounts. Quantile regression results confirmed the influence of increased LST on rainfall extremes. The trends for hourly rainfall revealed increases mainly between 2pm and 9pm in the city, which is attributed to the intensification of convective rainfall caused by increased LST. The study highlights the importance of mitigating urban LST to reduce climatic extremes and promote sustainable urban development.

Integrating deep learning, satellite image processing, and spatial-temporal analysis for urban flood prediction

Urban flooding is escalating worldwide due to the increasing impervious surfaces from urban developments and frequency of extreme rainfall events by climate change. Traditional flood extent prediction and mapping methods based on physical-based hydrological principles often face limitations due to model complexity and computational burden. In response to these challenges, there has been a notable shift toward satellite image processing and Artificial Intelligence (AI) based approaches, such as Deep Learning (DL) models, including architectures like Convolutional Neural Networks (CNN). The objective of this research is to predict near real-time (NRT) flood extents within urban areas. This research integrated CNN (U-Net) with Sentinel-1 satellite imagery, Digital Elevation Model (DEM), hydrologic soil group (HSG), imperviousness, and rainfall data to create a flood extent prediction model. To detect flooded areas, a binary raster map was created using calibrated backscatter values derived from the VV (vertical transmit and vertical receive) polarization mode of Sentinel-1 imagery, which was highlighted as having a significant impact on backscatter behavior and prediction results. Application of the model was demonstrated in urban areas of Miami-Dade County, Florida. The results demonstrated the capability of the model to provide rapid and accurate flood extent predictions at a spatial resolution of 10 m, with an overall accuracy of 97.05 %, F-1 Score of 92.49 %, and AUC of 93 % in the study area. The U-Net model’s flood predictions were compared with historical floodplain data and then using GIS overlay analysis, resulting in a Ground Truth Index of 84.05 % that shows the accuracy of the model in identifying flooded areas. The research incorporated crucial flood-influencing data (including rainfall) to the flood extent prediction models and expanded the focus models beyond major rainfall events only to encompass a wider range of flood events. The presented NRT flood extent mapping model has a broad range of applicability, including, but not limited to, the continuous monitoring of flood events and their potential impacts on civil infrastructure assets (e.g., construction, operation, and maintenance of roads and bridges), early warning systems for timely evacuation and preparedness measures, and insurance risk assessment.