Rainfall Intensity Research Articles

Effects of rainfall intensity and mitigation measures on slope stability: a case study of Shatianpo landslide in Yunyang county, Chongqing City, Southwest China

Effects of rainfall intensity and mitigation measures on slope stability: a case study of Shatianpo landslide in Yunyang county, Chongqing City, Southwest China

Emerging Trends in Agro Climatic Parameters in Malawi: Has Rainfall Changed?

Climate change and variability in Malawi have had a wide impact on rain-fed agricultural production. The recent climatic events suggest new trends in agro-climatic parameters such as dry spell length, total annual rainfall, and planting dates. However, there is dearth of understand the emerging trends on the agro-climatic parameters in Malawi. Both parametric and non-parametric trend tests were used to quantify the strength of trends on a 49-year series of historical rainfall data from eight meteorological stations across Malawi. The results show that, while there is no change in dry spell length across the country, there is an increase in the number of dry spells and a decrease in the number of rainy days per growing season. Thus, there is a slight decrease in total annual rainfall with high variability. Suitable planting dates have also shifted towards later days in the season, resulting in a shorter growing season accompanied by high- intensity rainfall. This study indicates significant changes in the rainfall pattern in Malawi in specific areas, especially in the north. Therefore, it is imperative to consider early maturing varieties, in-field water harvesting structures, efficient irrigation systems, and extension services to farmers to adapt change of agricultural calendar.

Floodplain inundation in the Murray–Darling Basin under current and future climate conditions

Predicting floodplain inundation under a changing climate is essential for adaptive management of water resources and ecosystems worldwide. This study presents a framework combining satellite observations and hydrological modeling to explore changes in floodplain inundation. We examine variability, trends, and frequency of inundation across the Murray–Darling Basin (MDB), Australia’s largest river system, over the past 35 years (1988–2022). Our analysis shows that annual maximum 30-day runoff is a primary hydrological factor influencing floodplain inundation. Using this metric as a proxy, we found that floodplain inundation, if driven solely by hydroclimate conditions, would have been more frequent in the recent decades (1988–2022) compared to the century-long baseline (1900–2022), especially in the southern basin. Despite projected declines in water availability under climate change in MDB, floodplain inundation appears to be less affected. The projected changes in floodplain inundation vary by region, influenced by local hydroclimate, human intervention, and the balance between projected more intense extreme rainfall and drier catchment conditions. This framework provides valuable insights into water resource planning and environmental management, with potential applications beyond the MDB.

Identification of flood-prone areas in Kaziranga National Park, Assam, India

ABSTRACT Flooding poses a significant challenge in arid and semi-arid regions, where unexpected rainfall can cause catastrophic consequences. Kaziranga National Park, a UNESCO World Heritage Site in Assam, faces annual floods threatening its rich biodiversity, particularly the iconic one-horned rhinoceroses. Therefore, assessing flood risk in this region to save the animals is of utmost importance. This study assessed flood-prone zones in Kaziranga for 2015 and 2019 using GIS-based multi-criteria analysis. Factors like flow accumulation, slope, soil type, land use, rainfall intensity and elevation were integrated to map flood risk areas. The results indicated that the northeastern parts of the park had the highest flood vulnerability, with flood risk significantly increasing in 2019 compared to 2015. High and very high flood-prone areas covered 36.83% and 31.26% of the study area in 2019. Rainfall and soil type were the most influential factors, with clayey soils on the northern side near the Brahmaputra River intensifying runoff, while sandy soils in the southern region reduced flood risk due to higher infiltration. The study highlights the need for strategic flood mitigation measures to protect the park's wildlife. These findings offer valuable insights for conservation planning, aiding authorities in minimizing flood impacts and safeguarding Kaziranga's unique ecosystem.

The May 2023 Rainstorm-Induced Landslides in the Emilia-Romagna Region (Northern Italy): Considerations from UAV Investigations Under Emergency Conditions

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

Impact of Climate Variability on Maize Yield Under Different Climate Change Scenarios in Southern India: A Panel Data Approach

The changes in frequency and intensity of rainfall, variation in temperature, increasing extreme weather events, and rising greenhouse gas emissions can together have a varying impact on food grain production, which then leads to significant impacts on food security in the future. The purpose of this study is to quantify how maize productivity might be affected due to climate change in Southern India. The present study examines how the projected changes to the northeast monsoon will affect maize yield in Tamil Nadu during the rabi season, which spans from September to December, by using a three-step methodology. Firstly, global climate models that accurately represent the large-scale features of the mean monsoon were chosen. Secondly, baseline and future climate data were extracted from the selected global models and the baseline data were compared with observations. Thirdly, the panel data regression model was fitted with the India Meteorological Department’s (IMD) observed climate data to generate the baseline coefficients and projected the maize production using future climate data generated from the global climate model. The Representative Concentration Pathways (RCPs) of RCP4.5 and RCP8.5 were used from two global climate model outputs, namely GFDL_CM3 and HadGEM2_CC, to predict the climate change variability on maize yields during the middle (2021–2050) and the end (2071–2100) of this century. The maize yield is predicted to increase by 3 to 5.47 per cent during the mid-century period and it varies from 7.25 to 14.53 per cent during the end of the century for the medium- (RCP4.5) and high-emission (RCP8.5) climate change scenarios. The maize grain yield increasing during the future periods indicated that the increase in rainfall and temperature during winter in Southern India reduced the possibility of a negative impact of temperature on the maize yield.

Modelling of rainwater reduction and hydrological performance of selected green infrastructure (GI) facilities in urban catchments

Abstract Currently, effective rainwater management in urban areas requires a modern and comprehensive approach, with mathematical modelling methods at its foundation. The aim of this study was to estimate the impact of the use of dispersed green infrastructure (GI) – specifically, drainage-equipped rain gardens (bio-retention cells, or BC) – and their operational hydrological performance on the scale of an urban catchment, particularly in reducing the amount of rainwater discharged directly to the receiver. An additional objective was to determine the relationship between the total rainwater inflow per unit area and the capacity percentage reduction in individual BC facilities. In this research, a hydrodynamic single-event precipitation model was built and calibrated for an existing housing estate in Cracow (Poland) using the Storm Water Management Model (SWMM). The functioning of the GI is represented by Low Impact Development (LID), which was simulated for a number of precipitation scenarios (S0–S7) with durations of 4.2, 15, 30, 45, 60, 90, 120 and 180 minutes, and a probability of occurrence of p = 10% (indicating a 10% chance of occurrence in any given year). The results demonstrated significant potential for reducing the peak flow of rainwater by an average of 94%, and reducing the total volume of rainwater by an average of 86%, for all simulated precipitation at the outfall of the entire system. Analysis of individual BC facilities revealed reductions in rainwater ranging from 67% to 95% of capacity for GI facilities, depending on the duration of the rainfall. Notably, more significant reductions in GI facilities were observed in cases of short, intense rainfall. The analysis of the individual BC facilities with the highest efficiency showed that the maximum values of total inflow per 1 m2 of their surface area, reduced to 100% for rainwater inflow, occurred during heavy rainfall of 15 min (S1) and amounted to 29.72 L/m2 (average 5.79 L/m2).

Modelling urban stormwater drainage overflows for assessing flood hazards: application to the urban area of Dakar (Senegal)

Abstract. With the recurrence of flooding in African cities, there is growing interest in developing sufficiently informative tools to help characterize and predict overflow risks. One of the challenges is to develop methods that strike a compromise between the accuracy of simulations, the availability of basic data, and the shortening of calculation times to be compatible with real-time applications. The present study, carried out on the urban outskirts of Dakar, aims to propose a method capable of modelling flows at fine resolution (25 m2) over the entire area and provide a rapid diagnosis of how the drainage network is operating for rainfall intensities of different return periods, while taking urban conditions into account. Three methodological steps are combined to achieve this objective: (i) determination of drainage directions, including modifications induced by buildings, artificial drainage, and storage basins; (ii) application of a hydrological model to calculate flows at the outlets of elementary catchment; and (iii) implementation of a hydraulic model to propagate these flows through the drainage network and implementation of a storage model for retention basins. The modelling chain was built within the ATHYS platform. The network overflow points are detected if the difference between the calculated flows exceeds the network capacity to evacuate them. Examples are given by carrying out simulations using 10- and 100-year design rainfall. The model also provides boundary conditions to apply more complex hydraulic models to determine the local impact of drainage network overflows on limited areas. However, the capacity of the method still needs to be validated in further research by comparing it with accurate data from observed flood events.

Heavy metal(loid) contamination in forest fire affected soil and surface water: pollution indices and human health risk assessment.

Forest fires, whether natural or anthropogenic, release and mobilize heavy metal(loids) (HM). Following intense rainfall events, soil-bound HM are transported from soil to surface water through surface runoff, leading to water quality deterioration. Pollution and ecological risk indices are effective tools for assessing HM contamination. Most forest fire-affected soils and surface water exhibited a degree of contamination greater than 3 and 8 (high and moderate pollution), with associated high and extremely high ecological risks (165 and 2389, respectively). Pollution indices revealed that soils were highly contaminated with Ni, Cu, Cr, and Pb, while Ni, Cu, Hg, Cd, and As posed significant ecological risks. Surface water was heavily contaminated with Pb, Mn, Al, and Fe, with Ni and V contributing to extremely high ecological risks. This study highlights that trace HM also requires substantial removal efforts to make water potable, with removal efficiencies needed for Sb (94.49%), Be (85.83%), Ba (70.75%), V (68.19%), and Se (65.51%). Fire-affected surface water poses an elevated cancer risk to both children (0.18 and 4.5 × 10-3) and adults (0.39 and 1.53 × 10-3) through oral and dermal exposure, respectively. Children are more vulnerable to dermal cancer and noncancer risks compared to adults. Low-cost treatment methods, such as the application of immobilizing agents combined with compost, straw mulching, and seeding, can be implemented to control soil erosion in forest areas, thereby reducing the transport of soil-bound HM to surface water. These findings can aid government agencies in developing new soil and water quality standards and implementing effective treatment measures.

Adaptive Water Management Through Hybrid Infrastructure: Addressing Floods and Water Scarcity in the Sunter River Region

Objective: The Sunter River basin faces significant challenges related to flooding and water scarcity, driven by broken embankments and high rainfall intensity. This study aims to design an adaptive water management system that mitigates flood risks during the rainy season while conserving rainwater for use during the dry season. Method: This research employs area calculation using the Polygon Thiessen method to analyze rainfall distribution. Rainfall data with 90% reliability is processed using the F.J. Mock method, while the Mononobe theory is applied to determine rainfall intensity. The study proposes hybrid infrastructure that integrates rainwater harvesting (RwH), green roofs (GRo), infiltration trenches (ITre), and ground reservoirs to manage and conserve water resources effectively. Results and Discussion: The analysis indicates that the hybrid infrastructure can harvest approximately 401,345 m³ of rainwater annually. The infiltration trench system is designed to manage runoff from the Sunter River’s drainage channel, with a capacity of 143,259 m³/s, based on a 5-year return period. The trench dimensions are 2.5 meters in depth and 3 meters in width. Rainwater storage for RwH and GRo employs fiberglass reinforced panel tanks in a modular knock-down system with dimensions of 1m x 1m x 1m, reinforced with slab structures and C-channel plates. This infrastructure is strategically constructed over the Sunter River to optimize land use and mitigate flooding. Research Implications: The proposed system offers a sustainable solution to address water management issues in urban areas, reducing flood risk while enhancing water availability during dry periods. This hybrid infrastructure model can be adapted and replicated in other flood-prone and water-scarce regions, contributing to improved resilience against climate variability. Originality/Value: This study presents an innovative approach by integrating multiple water management techniques into a single hybrid infrastructure system. By addressing both flooding and water scarcity through adaptive design, the research provides a novel contribution to sustainable urban water management practices in the Sunter River region.

Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system.

Flood resilience through hybrid deep learning: Advanced forecasting for Taipei's urban drainage system.

Impact of Pacific Meridional Mode on Hawaiian Rainfall Variability

Abstract The Pacific Meridional Mode (PMM) features air-sea interactions within the subtropical and tropical eastern North Pacific, however, its influence on Hawaiian rainfall variability remains elusive. This study aims to elucidate PMM-related large-scale atmospheric patterns and their repercussions on island rainfall dynamics across diverse timescales. In reference to the peak PMM season in boreal spring, we delineate distinct atmospheric patterns during the antecedent winter and concurrent spring. Our analysis employs multiple linear regression and disentangles the roles of El Niño-Southern Oscillation (ENSO) and PMM in driving rainfall variability. ENSO emerges as the primary driver of winter rainfall variability, while PMM assumes a pivotal role in spring rainfall, particularly impacting the southern islands. During a (+) PMM phase in winter, anomalous surface westerly winds decelerate prevailing trade winds, engendering an east-west anomalous rainfall dipole pattern across the Hawaiian Islands. This, in turn, amplifies median and lower-quartile seasonal rainfall over typically arid leeward sides of the island chain. Subsequently, in spring, a (+) PMM intensifies moisture and ascent over the tropical eastern North Pacific, precipitating extensive rainfall across the state. For disturbance-driven rainfall, (+) PMM coincides with heightened frontal-related rainfall, whereas El Niño is associated with decreased rainfall from Kona lows, upper-level lows, and trade-wind events. Our evaluation of PMM-related daily rainfall intensity underscores spatial variations: (−) PMM corresponds to reduced moderate daily rainfall over windward sides, potentially exacerbating drought occurrences. Conversely, leeward sides experience an increase in extreme rainfall events in both winter and spring during (+) PMM, suggesting a heightened risk of floods.

Boundary-layer and low-level moisture fluxes during low-level jet events in South China and their relationship with early-summer rainfall

Abstract Low-level jets (LLJs) play a key role in driving early-summer rainfall in South and East China (SEC) by supplying essential moisture and energy for convective processes. LLJs in South China (SC) are classified into two distinct types: synoptic-system-related jet (SLLJ) and boundary-layer jet (BLJ), each exerting distinct influences on rainfall mechanisms. This study investigates the individual influence of SLLJ and BLJ, as well as their interplay, on earlysummer rainfall over SEC, with a focus on moisture flux dynamics. The findings reveal the independent yet equally significant contributions of SLLJs and BLJs in shaping the pattern of early-summer rainfall in SEC. Rainfall intensity over SEC correlates with the intensity of BLJs. Strong BLJs result in pronounced boundary-layer convergence and upward moisture transport, and thus favorable for enhanced rainfall over SEC. The location of rainfall is closely associated with the pattern of SLLJs. Southwesterly SLLJs along the coast of SC enhances local low-level convergence and upward motions, leading to rainfall mainly confined to SC. By contrast, a quasi-meridional-oriented SLLJ promotes the low-level northward advection of moisture, which is elevated from the boundary layer by topography in SC. This part of moisture, along with the northward-transported moisture within the boundary layer, is elevated to upper layers at the frontal zone in East China (EC), jointly contributing to enhanced rainfall in EC. Overall, the moisture for rainfall in SC is mainly contributed by the transport of BLJs, whereas the moisture for rainfall in EC is jointly attributed to the northward moisture advection by both BLJs and SLLJs.

Regulation of Preferential Flow by Soil Thickness on Small Hillslopes With Complex Topography Through Intensive High‐Frequency Soil Moisture Monitoring

AbstractPreferential flow (PF) is a relatively rapid water movement that significantly impacts geophysical processes. However, identifying PF and its environmental control mechanisms remains challenging, primarily due to soil spatial heterogeneity. In this study, 20 sensors were installed on two hillslopes with distinct soil thicknesses to monitor moisture at 5‐min intervals. PF types were identified based on moisture response sequence to rainfall across layers, and relationships among PF frequency (PFF), soil depth, and rainfall characteristics were determined. Macropore flow was the main PF type, followed by soil‒bedrock interface flow. On the hillslope with deep soil cover, PFF was significantly negatively correlated with soil depth. Comparison, on the hillslope with shallow soil cover, PFF was not influenced by soil depth but more notably controlled by rainfall intensity and antecedent soil moisture. Accordingly, these findings highlight the critical roles of the soil thickness in shaping PF characteristics.

Study on landslide hazard risk in Wenzhou based on slope units and machine learning approaches

Landslides are a prevalent and devastating form of geological disaster. These events occur when gravity causes rock and soil masses to slide along specific surfaces or zones, often triggered by intense rainfall, seismic activity, or human engineering activities. Assessing landslide hazard risk is crucial for effective disaster management, yet traditional approaches often rely on administrative or grid units, which lack the granularity needed for site-specific hazard management. This results in uniformly high-risk classifications for hilly areas, complicating practical engagement and increasing management costs. The study further combines historical landslide data and applies machine learning models such as Random Forest, XGBoost, and LightGBM to analyze landslide susceptibility in Wenzhou City, proposing a slope unit-based landslide hazard assessment method. The results are as follows: (1) Landslide Susceptibility across different slope units was categorized as low, low-moderate, moderate, moderate-high, high, and very high, with the very high-risk slope units accounting for 5.35% of the total area and the low-risk slope units covering the largest area (975.41 km2). (2) Among the machine learning models used for landslide susceptibility analysis at the slope unit level, the Random Forest model performed the best, demonstrating higher prediction reliability, with an accuracy of 77.94% for Random Forest, 76.95% for XGBoost, and 78.30% for LightGBM. (3) Extreme rainfall events significantly increased the proportion of high-risk slope units, particularly in mountainous and hilly areas. According to different rainfall return periods, the proportion of very high-risk slope units increased from 5.35 to 40.39% under the 100-year return period. (4) A case study of Xuekou Village validated the practical application of the slope unit risk assessment results and proposed preventive measures for medium-to-high-risk units, such as regular monitoring and enhanced vegetation coverage, to mitigate landslide risks.

Estimating flood inundation potential using the curve number method and satellite image processing (case study of the Qizil Uzan River in Iran)

ABSTRACT The research introduces a method of flood hazard measurement using satellite imaging coupled with the Soil Conservation Service (SCS) curve number (CN) method. The research area was selected to be the region around the Qizil Uzan River in northwest Iran, which suffers from intensive rainfall and increased susceptibility to flood. Satellite-derived groundcover data are combined with soil type and slope to calculate spatially distributed CN values that describe the stream production potential throughout the landscape. The calculated CN maps exhibited pronounced spatial variation in flood risk over the study area. Especially, Section 15 has been identified as a high-risk zone with its high CN value, indicating a high flood risk that is aggravated by the closeness of residential developments. Furthermore, locations along the riverbed (Sections 3 and 4) also present high flood risks, highlighting the necessity for an integrated river management plan. The study area overall exhibited a high to moderate risk of flooding, with some areas being very susceptible. The integration of the SCS-CN method and satellite data was found beneficial in evaluating the risk of flood, determining the priority areas for targeted intervention, and providing suggestions on sustainable land-use planning interventions.

Flood mitigation performance of low impact development practice in a coastal city from the perspective of catchment scale

Flood mitigation performance of low impact development practice in a coastal city from the perspective of catchment scale

Impact of Climate Change on Groundwater Heads Using Predicted Land Use and Projected Population in Numerical Modelling for Chennai Basin

ABSTRACT Groundwater, a crucial resource in humanity’s development and sustainability, is studied, focusing on the impact of climate change and changing land use patterns. It is carried out through numerical modelling, and it predicts future groundwater heads with precipitation from the climate model, as well as predicted decadal land use and projected population. The recharge is estimated based on precipitation and land use. Extraction is estimated based on population and land use. The model was calibrated, and a good match was found for steady (1995) and transient states from 1996 to 2020. The prediction was carried out using a calibrated model from 2021 to 2050. Groundwater head was impacted by fluctuation based on the intensity of rainfall and drought years. Similarly, the increasing population will be the dominant factor in reducing the groundwater head in the future. Due to increased extraction, groundwater declined by 1.25 to 2.00 m in all assessing wells between 2021 and 2050. Proper governance on extraction and recharge is required for future resource sustainability. Restriction on the built-up expansion should be moved apart to restrict the extraction.

Surveillance and environmental risk of very mobile pollutants in urban stormwater and rainwater in a water-stressed city.

Surveillance and environmental risk of very mobile pollutants in urban stormwater and rainwater in a water-stressed city.

Pollution and toxicity of heavy metals in wildfires-affected soil and surface water: A review and meta-analysis.

Pollution and toxicity of heavy metals in wildfires-affected soil and surface water: A review and meta-analysis.