Extreme rainfall erosivity: Research advances and future perspectives

Extreme rainfall erosivity, the capacity of intense rainfall to induce soil erosion, is vital for anticipating future impacts on soil conservation. Despite extensive research, significant differences persist in terms of understanding influencing mechanisms, potential impacts, estimation models and future trends of extreme rainfall erosivity. Quantitatively describing extreme rainfall erosivity remains a key issue in existing research. In this study, we comprehensively reviewed the literature to assess the relationships between extreme rainfall characteristics and rainfall erosivity, between extreme rainfall erosivity and soil erosion, estimation models and trend prediction. The aim was to summarize previous related research and achievements, providing a better understanding of the generation, impacts and future trends of extreme rainfall erosivity. Future research directions should include identifying the thresholds of extreme rainfall events, increasing research attention on tropical cyclones in terms of rainfall erosivity, considering on the impact of extreme rainfall erosivity on soil erosion, and improving rainfall erosivity estimation and simulation prediction methods. This study could contribute to adapting to global climate change and aiding in formulating soil erosion prevention and environmental protection recommendations.

Exploring the spatial pattern of house collapse rates caused by extreme rainfall in central China: The role of natural and social factors

The collapse of houses represents a prominent hazard associated with floods, mudslides, and other disastrous events resulting from extreme rainfall. Nevertheless, previous research in this area has been insufficiently dedicated to comprehending the factors that specifically contribute to house collapse triggered by extreme rainfall. This study endeavors to address this knowledge gap by proposing a hypothesis that the occurrence of house collapse, induced by extreme rainfall, demonstrates spatial heterogeneity and is subject to the interactive impacts of various factors. In the study, we investigate the relationship between house collapse rates and natural and social factors in the provinces of Henan, Shanxi, and Shaanxi provinces in 2021. These provinces are representative of flood-prone areas in central China. Spatial scan statistics and GeoDetector model were used to analyze spatial hotspot areas of house collapse rates and determinant power of natural and social factors on the spatial heterogeneity of house collapse rates, respectively. Our analysis reveals that the spatial hotspot areas predominantly concentrated in regions characterized by high rainfall, including areas along riverbanks and low-lying regions. Multiple factors contribute to the variations in house collapse rates. Among these factors, precipitation (q = 0.32) is the most significant, followed by the ratio of brick-concrete houses (q = 0.24), per capita GDP (q = 0.13), elevation (q = 0.13) and other factors. Notably, the interaction of precipitation and slope explains 63 % of the damage pattern, making it the strongest causal factor. The results substantiate our initial hypothesis and underscore the fact that the pattern of damage does not solely rely on a singular factor but rather on the interaction of multiple factors. These findings hold significance in advancing the formulation of more precise strategies aimed at bolstering safety measures and safeguarding properties within regions susceptible to flooding.

Climate change amplified the 2009 extreme landslide event in Austria

Landslides are an important natural hazard in mountainous regions. Given the triggering and preconditioning by meteorological conditions, it is known that landslide risk may change in a warming climate, but whether climate change has already affected individual landslide events is still an open question, partly owing to landslide data limitations and methodological challenges in climate impact attribution. Here, we demonstrate the substantial influence of anthropogenic climate change on a severe event in the southeastern Alpine forelands with some estimated 952 individual landslides in June 2009. Our study is based on conditional event attribution complemented by an assessment of changes in atmospheric circulation. Using this approach, we simulate the meteorological event under observed and a range of counterfactual conditions of no climate change and explicitly predict the landslide occurrence probability for these conditions. We find that up to 10%, i.e., 95 landslides, can be attributed to climate change.

Impact of extreme rainfall on non-point source nitrogen loss in coastal basins of Laizhou Bay, China

Extreme rainfalls often lead to large amounts of nitrogen (N) loss from river basins. However, the composition and spatial variation of N loss caused by extreme events and the effects of control measures are not well understood. To shed light into this question, the Soil and Water Assessment Tool (SWAT) was used to evaluate the spatiotemporal characteristics of organic and inorganic nitrogen (ON and IN) losses in the coastal basins of Laizhou Bay during typhoons Rumbia and Lekima. The effects of best management practices on controlling N loss were also explored during such extreme rainfall events. Results showed that extreme rainfall promoted transport of ON more than IN. The mass of ON and IN transported by the two typhoons exceeded 57 % and 39 % of the average annual N flux, respectively, and the loads were positively correlated with streamflow. During the two typhoons, the loss of ON was mainly concentrated in areas with steep slopes (θ > 15°) and natural vegetation (forests, grasslands, and shrublands). The IN loss was higher in areas with a 5-10° slope. Furthermore, subsurface flow was the main IN transport mechanism in areas with steep slope (θ > 5°). Simulations showed that implementation of filter strips in areas with slopes exceeding 10° can reduce N loss, with much greater reductions in ON (>36 %) than IN (>0.3 %). This study provides important insights into N loss during extreme events and the key role filter strips can play in trapping them before they reach downstream waterbodies.

Multicriteria analysis for flood risk map development: a hierarchical method applied to Brazilian cities

Floods have caused socio-economic and environmental damage globally and, thus, require research. Several factors influence flooding events, such as extreme rainfall, physical characteristics, and local anthropogenic factors; therefore, such factors are essential for mapping flood risk areas and enabling measures that mitigate the damage they cause. This study aimed to map and analyze regions susceptible to flood risk in three different study areas belonging to the same Atlantic Forest biome, in which flood disasters are recurrent. Due to the presence of numerous factors, a multicriteria analysis using the Analytical Hierarchical Process was conducted. First, a geospatial database was composed of layers of elevation, slope, drainage distance, soil drainage, soil hydrological group, precipitation, relief, and land use and cover. Flood risk maps for the study area were then generated, and patterns in the study areas were verified, with the greatest influence being exerted by intense precipitation on consecutive days, elevation at the edges of the channel with low altimetric variation and a flat combination, densely built areas close to the banks of the main river, and an expressive water mass in the main watercourse. The results demonstrate that these characteristics together can indicate the occurrence of flooding events.

Mass tourism urban destinations and climate change in small islands: resilience to extreme rainfall in the Canary Islands

The Canary Islands are one of the main destinations for mass tourism in the European context, characterized by the absence of seasonality in tourist activity. Moreover, the level of activity increases during the winters, coinciding with a greater probability of extreme rainfall events, whose danger seems to be increasing as a result of climate change. Owing to its pronounced orography, the southern coast of the island of Gran Canaria houses several tourist settlements built along ravines and steeply sloping terrain. This scenario presents considerable risk because of spatial probability of landslide occurrence. The case of San Agustín, especially, serves to test the model of tourist urbanization along the hillside, demonstrating its high fragility in the face of extreme rainfall events. Especially owing to its importance in providing assistance in emergency situations, its vulnerability has been analyzed with regard to accessibility, which is entirely dependent on road mobility. The growth model of San Agustín serves as an example of mass tourism in small islands, allowing urban planners and designers to assess corrective measures based on managing its existing road infrastructure and open spaces right from the planning stage.

Phytoplankton functional responses induced by extreme hydrological events in a tropical reservoir

Climate change is affecting the global hydrological cycle, causing drastic changes in precipitation patterns. Extreme climatic events are becoming more frequent and intense than in the past, leading to water-level fluctuations and affecting aquatic ecosystems. Semiarid regions are very susceptible to changing climate. We analyzed a 10 years dataset from a tropical semiarid reservoir during extreme hydrological events (heavy rains and prolonged drought), and evaluated phytoplankton functional responses to environmental conditions. We found, as hypothesized, that phytoplankton functional structure change in a temporal scale due to water-volume fluctuation induced by the rainfall pattern. Depth and inorganic material acted as environmental filters selecting phytoplankton groups. High water level seems to improve water quality and low water level worsen it. Colonial and filamentous cyanobacteria dominate the wet period; however, it may have a critical threshold during severe periods of drought, which will lead to dominance of groups well adapted to low light conditions and with mixotrophic metabolism. Phytoplankton functional approaches can simplify phytoplankton identification and reflect better the environmental conditions than the taxonomic approach. Therefore, these approaches can help to understand the shifts in aquatic ecosystems under extreme hydrological events and predict functional response of phytoplankton being an important tool to water management and conservation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10750-023-05241-3.

Framework for rainfall-triggered landslide-prone critical infrastructure zonation

Rainfall-induced landslides cause frequent disruptions to critical infrastructure in mountainous countries. Climate change is altering rainfall patterns and localizing extreme rainfall events, increasing the occurrence of landslides. For planning climate-resilient critical infrastructure in landslide-prone regions, it is urgent to understand the changing landslide susceptibility in relation to changing rainfall extremes and spatially overlay them with critical infrastructure to determine risk zones. As such, areas requiring financial reinforcements can be prioritized. In this paper, we develop a framework linking changing rainfall extremes to landslide susceptibility and intensity of critical infrastructure - exemplified on a national scale using Nepal as a case study. First, we define a set of 21 different unique rainfall indices that describe extreme and localized rainfall. Second, we prepare a new annual (2016-2020) inventory of 107,900 landslides in Nepal mapped on PlanetScope satellite imagery. Next, we prepare a landslide susceptibility map by training a random forest model using the collected extreme rainfall indices and landslide locations in combination with spatial data on topography. Fourth, we construct a gridded critical infrastructure spatial density map that quantifies the intensity of infrastructure (i.e., transportation, energy, telecommunication, waste, water, health, and education) at each grid location using OpenStreetMap. The landslide susceptibility map classified Nepal's topography into low (36 %), medium (33 %), and (32 %) high rainfall-triggered landslide susceptibility zones. The landslide susceptibility map had an average area under the receiver characteristic curve value of 0.94. Finally, we overlay the landslide susceptibility map with the critical infrastructure intensity to identify areas needing financial reinforcement. Our framework reasonably mapped critical infrastructure hotspots in Nepal prone to landslides on a 1 km grid. The hotspots are mainly concentrated along major national highways and in provinces 4, 3, and 1, highlighting the need for improved land management practices. These hotspots need spatial prioritization regarding climate-resilient critical infrastructure financing and slope conservation policies. The research data, output maps, and code are publicly released via an ArcGIS WebApp and GitHub repository. The framework is scalable and can be used for developing infrastructure financing strategies for landslide mountain regions and countries.

Evaluating soil loss under land use management and extreme rainfall

Topsoil loss is a widespread environmental concern causing adverse impacts on natural and human systems. Severe weather accompanied with human activities can exacerbate this issue degrading soil health and consequently accelerating global and regional food insecurity. Erosion impairs soil physical and chemical properties such as infiltration rate, water holding capacity, loss of nutrients including soil carbon and nitrogen. Although, temporal properties of a rainfall event have meaningful implications, spatial heterogeneity of a rainfall contributes substantially and cannot be overlooked. Therefore, in this study, we investigated soil loss using weather radar NEXRAD data. We developed extreme rainfall (ER) scenarios and land use practices (nomgt, S0, S1, S2, and S3) and evaluated the watershed response. We found that grazing can manifold soil loss, and if accompanied with extreme rainfalls, soil loss accelerates impacting different subbasins each time. Our results suggest that spatial heterogeneity of ERs can be more significant in individual extreme rainfalls, however, over a year, soil moisture and type of the management practices (grazing and farming) could contribute more to topsoil loss. We classified watershed subbasins into different classes of soil loss severity to determine the soil loss hotspots. Soil loss can go as high as 350 (ton/ha/yr) under the ERs. Land use practices can increase erosion by 3600%. Slight increase in rainfall concentration (S1) can put vulnerable subbasins in extremely severe class (>150 ton/ha/yr). Under moderate increase in the rainfall concentration (S2) more subbasins fall into extremely severe category yielding approximately 200 ton/ha/yr. Under high increase in rainfall concentration (S3) almost all the subbasins fall into the extremely severe class yielding >200 ton/ha/yr. We found that in vulnerable subbasins, up to 10% increase in (Concentration Ratio Index) CI can increase annual soil loss up to 75%. Single ER can generate up to 35% of annual soil loss. Under one ER event soil loss hotspot subbasins can lose up to 160 ton/ha/day. 32% and 80% increase in rainfall amount for an ER event can increase soil loss by 94% and 285% respectively. The results, also, reveal that grazing and farming can be responsible for up 50% of soil loss. Our findings indicate the importance of site-specific managements to mitigate soil loss and all the consequences. Our study can help in better soil loss management implementation. Insights of our study may also help in water quality control and flood mitigation planning efforts.

Assessing the mechanism for flood control: a case of plain river network cities under extreme rainfalls

In recent years, frequent floods hit Chinese cities and caused heavy casualties and property losses, making China faced with severe flood problems. In this study, Nanhai Future City in the IX Flood Control Area of Yancheng City, Jiangsu Province, was selected as the research area to simulate water-level changes under different control schemes meeting extreme rainfalls. MIKE model simulated the inundation with the designed storm of different return periods. The results showed that flooding inside the research area was severe. Higher drainage capacity of the pump stations with more engineering and non-engineering measures can reduce the adverse effect of extreme rainfall. The results provide a reference for planning future infrastructure and flood control decisions for Nanhai Future City and the surrounding areas.

Extreme rainfall characterisation under climate change and rapid population growth in the city of Niamey, Niger

Since recent years, the Sahel semiarid region has experienced devastating floods-causing significant losses and damages. The present paper attempts to characterise extreme rainfalls responsible for pluvial floods in the city of Niamey, in Niger, under climate change and rapid population growth. Past damaging rainfall records spanning 1992-2015 were used to estimate the optimal temporal scale and to define a threshold for extreme rainfall. The characteristics of extreme rainfalls were then assessed under stationary and non-stationary conditions using peaks over threshold (POT) with the generalised pareto distribution (GDP). In the non-stationary POT, population data was used as threshold covariate whereas air temperature was used as scale parameter covariate. A suitable temporal scale of 3 h was found, whereas the threshold depth was 28.71 mm under stationary conditions and between 21 and 27 mm for the time dependent threshold. The analysis of the extreme rainfall series revealed no significant trend neither in the magnitude nor in the frequency. The influence of air temperature in the characterization of extreme rainfall were less compared to rapid urbanisation, represented herein by population growth. By 2040, 3-hourly rainfall depths of 20 mm could be considered as extreme rainfall.

Spatial dependence of floods shaped by extreme rainfall under the influence of urbanization

Weather system are spatially and temporally dependent, and these dependencies can result in flood events with similar behaviors. While it is well known that urbanization profoundly impacts the flood generation process, much less is known about the impacts of urbanization on the spatial dependence of floods, a major determinant of flood risk severity. To this end, a scheme was proposed to detect the flood dependence variations influenced by urbanization. Based on the scheme, we found that 1) the flood dependence can be weakened with extreme rainfall increasing from a short return period to a long return period; 2) The flood dependencies can be amplified in slightly urbanized regions and mitigated in highly urbanized regions due to intensifying urbanization. In addition, the change characteristics of the flood dependencies influenced by urbanization are first identified from the perspective of network structure. We found that urbanization can significantly affect the network structure (i.e., hub and connectivity) of flood dependence, especially in highly urbanized regions. The catchments with high hub and connectivity are prone to widespread floods and should be given more attention in flood warning and control management work, which can contribute to helping defend against floods in hazard-prone areas.

Detecting characteristics of extreme precipitation events using regional and satellite-based precipitation gridded datasets over a region in Central Europe

Perception of the spatio-temporal events of extreme precipitation and their variations is essential for diminishing the natural hazards linked with extreme events. In this research, a satellite-based precipitation dataset derived from remotely sensed soil moisture (SM2RAIN-ASCAT, obtained from ASCAT satellite soil moisture data through the Soil Moisture to Rain algorithm) was selected to evaluate the accuracy of daily precipitation and extreme events estimations against a regional gridded weather dataset by employing various performance indicators, and ETCCDI indicators (CDD, and CWD, SDII, R10mm, R20mm, R95p, R99p, Rx1day, and Rx5day). The study area includes entire Poland as well as small parts of Ukraine, Belarus, Slovakia, the Czech Republic, Russia, and Germany. According to PBIAS (~ -3.9 %) and coefficient of correlation (~0.74), SM2RAIN-ASCAT has good accuracy in the study area. Assessments reveal that, in general, over southern, mountainous part SM2RAIN-ASCAT does not have accurate estimations. According to the reference dataset, during the 2007-2019 period, on average, the length of dry days was ~22 days, while SM2RAIN-ASCAT shows ~19.6 consecutive dry days. In contrast, SM2RAIN-ASCAT overestimated (16 days/year) the consecutive wet days compared to the reference dataset (~8.7 days/year). SM2RAIN-ASCAT underestimated the number of heavy precipitation days index (R10mm) over the northern part of the region, close to the Baltic Sea), but the accuracy increased in the southern parts. SM2RAIN-ASCAT underestimated the maximum 1-day rainfall total and relative max 5-day precipitation amount indices. The total precipitation divided by the amount of wet days index shows that SM2RAIN-ASCAT has relatively acceptable accuracy in the center and south of the study area. Our results show that SM2RAIN-ASCAT should be improved for relatively higher extreme indicators.

Prolonged heavy rainfall and land use drive catchment sediment source dynamics: Appraisal using multiple biotracers

Excessive sediment loss degrades freshwater quality and is prone to further elevation and variable source contributions due to the combined effect of extreme rainfall and differing land uses. To quantify erosion and sediment source responses across scales, this study integrated work at both field and catchment scale for two hydrologically contrasting winters (2018-19 and 2019-20). Sediment load was estimated at the field scale (grassland-arable conversion system). Sediment source apportionment work was undertaken at the catchment scale (4.5 km2) and used alkanes, and both free and bound fatty acid carbon isotope signatures as diagnostic fingerprints to distinguish sediment sources: arable, pasture, woodland and stream banks. Sediment source apportionment based on bound fatty acids revealed a substantial shift in contributions, from stream banks dominating (70 ± 5%) in winter 2018-19, to arable land dominating (52 ± 7%) in the extreme wet winter 2019-20. Increases in sediment contributions from arable (∼3.9 times) and pasture (∼2.4 times) land at the catchment outlet during the winter 2019-20 were consistent with elevated sediment losses monitored at the field scale which indicated that low-magnitude high frequency rainfall alone increased sediment loss even from pasture by 350%. In contrast, carbon isotope signatures of alkanes and free fatty acids consistently estimated stream banks as a dominant source (i.e., ∼36% and ∼70% respectively) for both winters regardless of prolonged rainfall in winter 2019-20. Beyond quantifying the shifts in field scale sediment load and catchment scale sediment sources due to the changes in rainfall patterns, our results demonstrate valuable insight into how the fate of biotracers in soil and sediment manifests in the δ13C values of homologues and, in turn, their role in information gain for estimating sediment source contributions. Discrepancies in the estimated sediment source contributions using different biotracers indicate that without a careful appreciation of their biogeochemical limitations, erroneous interpretation of sediment source contributions can undermine management strategies for delivering more sustainable and resilient agriculture.

A study on correlations between precipitation ETCCDI and airborne pollen/fungal spore parameters in the NE Iberian Peninsula

Precipitation is one of the meteorological variables usually involved in the aerobiological studies, which presents a complex relationship with atmospheric levels of pollen and fungal spores and the temporal characteristics of their seasons. This complexity is due in a large part to rainfall's twofold impact of having, prior to pollination, a positive influence on subsequent pollen production and of contributing, during pollination, to pollen removal from the air through a wash-out effect. To better explore this impact, we place particular emphasis on extreme rainfall by calculating the correlation between airborne pollen and fungal spore parameters and the precipitation indices that the Expert Team on Climate Change Detection and Indices (ETCCDI) proposed for characterising climate extremes. Parameters for twenty-seven pollen and fungal spore taxa measured in six aerobiological stations in the NE Iberian Peninsula have been considered. We have distinguished between annual and winter ETCCDI in order to compare the correlations between extreme rainfall and airborne pollen concentrations and to avoid the wash-out effect as far as possible. Results show a positive influence from an increase in moderately extreme winter rainfall, specifically on subsequent pollen/fungal spore production: the percentage of all possible significant correlations is higher for winter than for annual rainfall. Furthermore, while annual rainfall in this region has nearly the same number of positive as negative correlations, the positive correlations for winter rainfall are more than twice that of the negative ones. The seasonal consideration on rainfall ETCCDI made with the aim to avoid the confounding overlapping of different rainfall impacts has led to more sharpened observations of its positive and negative effects on airborne pollen and fungal spore concentrations.

Unraveling the drivers of intensified landslide regimes in Western Ghats, India

The Western Ghats (WG) mountain range in the Indian sub-continent is a biodiversity hotspot, now faces a severe threat to the valley population and ecosystem because of changing rainfall patterns and land-use changes. Here, we use the 2018-2019 landslide inventory data together with various geo-environmental factors and show that the landslide activity in the WG region is amplified by anthropogenic disturbances. We applied a generalized feature selection algorithm and a random forest susceptibility model to demonstrate the major topographic controls of landslides and the risk associated with them in the WG region. Our results show that road cutting and slopes modified to plantations are the strongest environmental variable (50% - 73% within 300 m buffer distance) related to the landslide patterns, whereas short-duration intense precipitation in the high elevated terrain, profile concavity, and stream power contributed to the initiation of landslides. The susceptibility models made for the present, and Global Climate Models (GCM) under the representative concentration pathway (RCP) 8.5 scenario predicts the vulnerable nature of WG for future climate extremes. Our results highlight the impacts of Anthropocene hazards and sensitivity of the WG ecosystem, and a greater focus therefore should be placed to reduce the vulnerability and increase preparedness for future events.

Rainfall, groundwater, and surface water isotope data from extreme tropical cyclones (2016-2019) within the Caribbean Sea and Atlantic Ocean basins

Under a changing climate, projections estimate that over the next thirty years, extreme Tropical Cyclones (TCs) will increase in frequency, with two to three times more Category 4 and 5 hurricanes in the Atlantic basin between 20°N and 40°N. In recent years, the Caribbean Sea and Atlantic Ocean basins have experienced several extreme TCs, resulting in extensive human, ecological, and economic damage [1], [2], [3]. To improve understanding of TCs and their potential impacts in the face of climate change, physically based understanding of past climate and modern TC dynamics is necessary. Despite the well-known Atlantic hurricane season, surface observations of the isotopic evolution of TC's moisture and the propagation of isotopically distinct pulses across surface and subsurface water reservoirs are lacking. In this data article, we provide novel high frequency sampling of surface rainfall isotope compositions (δ¹⁸O, δ²H, and d-excess in ‰) for Hurricanes Otto (Costa Rica, 2016), Nate (Costa Rica, 2017), Irma (Cuba and The Bahamas, 2017), Maria (Cuba and The Bahamas, 2017), and Dorian (The Bahamas, 2019). These five TCs were characterized by unprecedented impacts during continental and maritime landfalls and passages. In total, 161 surface rainfall samples were collected in passive devices [4] with event-based and daily frequencies, resulting in the first surface isotopic tempestology anatomy across the Caribbean Sea and Atlantic Ocean basins to date. Derived rainfall from TCs often results in large input amounts of isotopically distinct water over an area from few hours to several days, and therefore this unique isotope composition is propagated through surface and shallow subsurface reservoirs. Our data also include spring (N=338) and surface water (N=334) isotope compositions following the impact of Hurricane Otto and Tropical Storm Nate in central Costa Rica. As this region is well-known for its diverse rainfall dynamics and as a climate change 'hot spot' [5], [6], [7], our data provide an opportunity to improve and complement modern and past climate interpretations often derived from satellite products and calcite-δ¹⁸O paleoclimatic archives in light of climatic forcing, TC rainfall amounts and recharge rates, and the hypothesized climatic-induced decline of past Mesoamerican civilizations.

Biochar increases nitrate removal capacity of woodchip biofilters during high-intensity rainfall

Stormwater biofilters have been increasingly used to mitigate the impact of climate change on the export of contaminants including nitrate to water bodies. Yet, their performance is rarely tested under high-intensity rainfall events, which are predicted to occur more frequently under climate change scenarios. We examined the potential of biochar to improve the resilience of woodchip biofilters under simulated high-intensity rainfall events and linked denitrification to biochar-mediated changes in hydrological (physical), chemical, and biological properties of woodchip biofilters. Results showed that nitrate removal capacity of woodchip biofilters decreased with increases in rainfall intensity or duration and decreases in antecedent drying time. However, adding biochar to woodchips significantly decreased the exhaustion rate of woodchips, only when the hydraulic residence time (HRT) was less than 5 h. At longer HRT (>5 h), the benefits of biochar became less apparent. We attributed the improved denitrification during high nitrate loading to biochar's ability to decrease dissolved oxygen in pore water and increase water holding capacity and retention of dissolved organic carbon and nitrate-all of which could increase nitrate utilization. Biochar increased the net microbial biomass but did not affect the relative abundance of denitrifying genes, which indicates that a shift in microbial biomass could not fully explain the observed increase in nitrate removal in biochar-augmented woodchip biofilters. Overall, the results showed that biochar could increase the resiliency of woodchip biofilters for denitrification in high-intensity rainfall events, a worst-case scenario, thereby mitigating the water quality degradation during climate change.

The human cost of global warming: Deadly landslides and their triggers (1995-2014)

Worldwide, landslides incur catastrophic and significant economic and human losses. Previous studies have characterized the patterns in landslides' fatalities, from all kinds of triggering causes, at a continental or global scale, but they were based on data from periods of <10 years. The research herein presented hypothesizes that climate change associated with extreme rainfall and population distribution is contributing to a higher number of deadly landslides worldwide. This study maps and identified deadly landslides in 128 countries and it encompasses their role, for a 20 years' period from January/1995 to December/2014, considered representative for establishing a relationship between landslides and their meteorological triggers. A database of georeferenced landslides, their date, and casualties' information, duly validated, was implemented. A hot spot analysis for the daily record of landslide locations was performed, as well as a percentile-based approach to evaluate the trend of extreme rainfall events for each occurrence. The relationship between casualty, population distribution, and rainfall was also evaluated. For 20 years, 3876 landslides caused a total of 163,658 deaths and 11,689 injuries globally. They occurred most frequently between June and December in the Northern Hemisphere, and between December and February in the Southern Hemisphere. A significant global rise in the number of deadly landslides and hotspots across the studied period was observed. Analysis of daily rainfall confirmed that more than half of the events were in areas exposed to the risk of extreme rainfall. The relationships established between extreme rainfall, population distribution, seasonality, and landslides provide a useful basis for efforts to model the adverse impacts of extreme rainfall due to climate change and human activities and thus contribute towards a more resilient society.

Observed data of extreme rainfall events over the West African Sahel

The data described in this article are sets of daily rainfall values derived from observed station records. The data was recorded by 72 in-situ rain gauges spread over the West African Sahel. The daily rainfall time series from synoptic, climate, agro-meteorological, and rainfall stations are assessed for quality and consistency before extreme values are extracted based on 90th, 95th, and 99th percentile thresholds. This data is free for use as part of the study "Scales for rating heavy rainfall events in West African Sahel" [1] (Salack et al., 2018). Complementary and up to date time series can be taken from WASCAL data infrastructure (WADI) geoportal https://wascal-dataportal.org/wascal_searchportal2/. This is a derived product (DP), made public in line with WASCAL׳s "3rd party data sharing policy" signed by the WASCAL member countries.

Soybean supplementation increases the resilience of microbial and nematode communities in soil to extreme rainfall in an agroforestry system

A current challenge for ecological research in agriculture is to identify ways in which to improve the resilience of the soil food web to extreme climate events, such as severe rainfall. Plant species composition influence soil biota communities differently, which might affect the recovery of soil food web after extreme rainfall. We compared the effects of rainfall stress up on the soil microbial food web in three planting systems: a monoculture of the focal species Zanthoxylum bungeanum and mixed cultures of Z. bungeanum and Medicago sativa or Z. bungeanum and Glycine max. We tested the effect of the presence of a legume on the recovery of trophic interactions between microorganisms and nematodes after extreme rainfall. Our results indicated that all chemical properties of the soil recovered to control levels (normal rainfall) in the three planting systems 45 days after exposure to extreme rain. However, on day 45, the bulk microbial community differed from controls in the monoculture treatment, but not in the two mixed planting treatments. The nematode community did not fully recover in the monoculture or Z. bungeanum and M. sativa treatments, while nematode populations in the combined Z. bungeanum and G. max treatment were indistinguishable from controls. G. max performed better than M. sativa in terms of increasing the resilience of microbial and nematode communities to extreme rainfall. Soil microbial biomass and nematode density were positively correlated with the available carbon and nitrogen content in soil, demonstrating a link between soil health and biological properties. This study demonstrated that certain leguminous plants can stabilize the soil food web via interactions with soil biota communities after extreme rainfall.

Physical and numerical modeling of an inclined three-layer (silt/gravelly sand/clay) capillary barrier cover system under extreme rainfall

As an extension of the two-layer capillary barrier, a three-layer capillary barrier landfill cover system is proposed for minimizing rainfall infiltration in humid climates. This system consists of a compacted clay layer lying beneath a conventional cover with capillary barrier effects (CCBE), which is in turn composed of a silt layer sitting on top of a gravelly sand layer. To explore the effectiveness of the new system in minimizing rainfall infiltration, a flume model (3.0 m × 1.0 m × 1.1 m) was designed and set up in this study. This physical model was heavily instrumented to monitor pore water pressure, volumetric water content, surface runoff, infiltration and lateral drainage of each layer, and percolation of the cover system. The cover system was subjected to extreme rainfall followed by evaporation. The experiment was also back-analyzed using a piece of finite element software called CODE_BRIGHT to simulate transient water flows in the test. Based on the results obtained from various instruments, it was found that breakthrough of the two upper layers occurred for a 4-h rainfall event having a 100-year return period. Due to the presence of the newly introduced clay layer, the percolation of the three-layer capillary barrier cover system was insignificant because the clay layer enabled lateral diversion in the gravelly sand layer above. In other words, the gravelly sand layer changed from being a capillary barrier in a convention CCBE cover to being a lateral diversion passage after the breakthrough of the two upper layers. Experimental and back-analysis results confirm that no infiltrated water seeped through the proposed three-layer barrier system. The proposed system thus represents a promising alternative landfill cover system for use in humid climates.