Rainfall Frequency Research Articles

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.

Persistence of Fungicides Applied to Control Snow Mold of Turfgrass as Influenced by Snow Cover and Antitranspirants.

Turfgrass managers rely on fungicides to suppress snow mold diseases in areas with persistent snow cover, which are commonly applied once or twice in late fall prior to snow cover. Fungicide applications targeting snow mold are expected to control snow mold diseases for the duration of winter; however, climate change is increasing the frequency of winter rainfall and snowmelts and may alter the duration of snow mold control. A 3-year study was conducted in Madison, WI, to assess the impact of snow cover and antitranspirants on the persistence of the commonly used fungicides chlorothalonil and propiconazole. Snow cover, fungicide treatment, and the use of antitranspirants had minimal impacts on fungicide persistence and disease control. The most important factors influencing persistence of the fungicides were snowmelt and winter rainfall events, yet antitranspirants demonstrated modest evidence of extending chlorothalonil persistence in certain years. These results demonstrate that increasing snowmelt and winter rainfall events associated with climate change will increase dissipation of fungicides used for snow mold suppression, which will likely impact disease control.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Investigation of the functional relationship between antecedent rainfall and the probability of debris flow occurrence in Jiangjia Gully, China

Abstract. A larger antecedent effective precipitation (AEP) indicates a higher probability of a debris flow (Pdf) being triggered by subsequent rainfall. Scientific topics surrounding this qualitative conclusion that can be raised include what kinds of variation rules they follow and whether there is a boundary limit. To answer these questions, Jiangjia Gully in Dongchuan, Yunnan Province, China, is chosen as the study area, and numerical calculation, a rainfall scenario simulation, and the Monte Carlo integration method have been used to calculate the occurrence probability of debris flow under different AEP conditions and derive the functional relationship between Pdf and AEP. The relationship between Pdf and AEP can be quantified by a piecewise function. Pdf is equal to 15.88 %, even when AEP reaches 85 mm, indicating that debris flow by nature has an extremely small probability compared to the rainfall frequency. Data from 1094 rainfall events and 37 historical debris flow events are collected to verify the reasonability of the functional relationship. The results indicate that the piecewise functions are highly correlated with the observation results. Our study confirms the correctness of the qualitative description of the relationship between AEP and Pdf, clarifies that debris flow is a small-probability event compared to rainfall frequency, and quantitatively reveals the evolution law of debris flow occurrence probability with AEP. All the above discoveries can provide a clear reference for the early warning of debris flows.

Research on the Method of Determining Rainfall Thresholds for Waterlogging Risk in Subway Stations

With the frequency of extreme rainfall increasing, the risk of waterlogging is significantly exacerbated in subway systems. It is imperative to first identify the rainfall threshold for waterlogging risk for subway stations in order to develop effective waterlogging prevention and control plans. This study focuses on Line 11 of the Beijing Subway, using InfoWorks ICM to construct a model of the research area and simulate waterlogging at various subway stations under different rainfall scenarios. The results indicate that there is a risk of waterlogging at Jinanqiao station, Moshikou station, and Beixinan station on Line 11. The accumulated water may enter the subway station through exits A, B, C, and D of Jinanqiao Station. The inlet sequence of Jinanqiao Station always follows A(B), C, and D, and the difference in waterlogging time for each outlet does not exceed 10 min. We derived the rainfall threshold formula for waterlogging risk at Jinanqiao subway station. Among the three influencing factors of topographic features, step height, and drainage capacity of the pipeline network, step height has a significant effect on increasing the rainfall threshold for waterlogging risk. The conclusions obtained can provide reference for the refined management of waterlogging risks in subway stations.

Hydrological Research Evolution: A Large Language Model‐Based Analysis of 310,000 Studies Published Globally Between 1980 and 2023

AbstractHydrology plays a crucial role in understanding Earth's intricate water system and addressing water‐related problems, including against the backdrop of ongoing climate change. A retrospective review of the evolution of hydrology up to the current state of research is of great importance for understanding this role. While there have been some quantitative reviews of large numbers of hydrological publications, there still remains a lack of overarching hydrological research assessment, particularly with the focus on hydrological basins as fundamental spatial‐geographic units of hydrological analysis. Large language models, represented by OpenAI's ChatGPT, have demonstrated powerful textual understanding capabilities, making it possible to extract such overarching and basin information from hydrological publications. Here, we considered publications related to hydrology from Web of Science spanning January 1980 to October 2023, and parsed the information from this extensive body of literature by integrating a large language model and geocoding. These techniques enable quantitative analysis of research characteristics across different spatio‐temporal scales, focusing on hotspot topics, collaboration networks, and various basins worldwide. Our study revealed an increase in hydrological research since the 1990 s, with shifts in research priorities from groundwater and nutrients to climate change and ecohydrology. Some basins in North America and Europe have consistently been hotspots for hydrological research. Since the 2010s, there has been a noteworthy increase in interest toward basins in China and South Asia, but attention to many regions with frequent extreme rainfall remains insufficient. Geographical patterns show different preferred research topics for different basins, but climate change has emerged as the most prominent topic across all regions in the last decade. In conclusion, our study provides an effective approach to quantitative analysis of research trends, offering a fresh view on the evolution of hydrology as a research field, its focus on various hydrological basins around the world, and the emergence of overarching and basin‐specific hot topics over time.

High‐intensity rainfall over northeast India: Spatial pattern, short‐term fluctuations and associated multiscale oscillations

AbstractNortheast India (NEI) receives most of its rainfall in the southwest monsoon (SWM) season. The region is known to have frequent and persistent rainfall events of high intensities and the region is vulnerable to potential high‐impact meteorological events. This study analyses observed daily rainfall data for the SWM months (JJAS) during 1991–2020 to better understand the climatology of high‐intensity rainfall (HIR) occurrences and their persistence. The agglomerative hierarchical cluster analysis has delineated four distinct clusters and Cramer's t test indicates no significant fluctuations at 5% significant level in the HIR events in these clusters. ERA‐5 reanalysis data have been used to find the moisture transport for synoptic‐scale (3–7 days), quasi‐bi‐weekly oscillations (10–20 days) and intraseasonal oscillations (30–60 days) for each cluster. Under positive synoptic‐scale phases, HIR in the clusters on southern latitudes of NEI occurs due to moisture incursion from the Bay of Bengal from southwesterlies at 850 hPa, and in the clusters on the northern latitudes, it is primarily due to westerlies. For, quasi‐bi‐weekly oscillations, westerlies at 850 hPa are favourable for moisture transport in most of the clusters during its positive phase. On the other hand, for positive phase of intraseasonal oscillations, westerlies at 850 hPa dominate the moisture transport in all the clusters. Also, most of the HIR events occur whenever Madden–Julian Oscillation (MJO) is in phase 1 and 2 with higher amplitude (RMM ≥1).

The adaptability and irrigation constraints analysis of the WOFOST model for grain production in the Songhua River Basin.

The Songhua River Basin, a vital grain-producing area in China, faces challenges due to the uneven distribution of water resources and the intensive water demands of agriculture. To enhance agricultural development and effectively manage water scarcity, it is essential to identify the water-saving potential of major staple crops - corn, wheat, and rice. This study enhances the World Food Studies (WOFOST) model by refining the day of year for the developmental vegetative stage (DVS), thereby improving the representation of phenological stages for spring maize, spring wheat, and rice within the model. This refinement offers a detailed analysis of the potential and rainfed yields. The results from the modified WOFOST model show promising simulation outcomes for the biomass and yield of maize, wheat, and rice, with Nash-Sutcliffe efficiency (NS) and index of agreement (IoA) values all exceeding 0.7. An analysis of photothermal potential yields (Yp) and rainfed yields (Yr) revealed minimal differences in yields for spring maize and rice across various rainfall frequencies. Specifically, the average photothermal utilization rates (LTs) are 93.57% for maize and 85.25% for rice. In contrast, the rainfed yield for wheat is lower than its photothermal yield, with an LT of 43.66%. These findings suggest that in the Songhua River Basin, maize and rice offer greater potential for water conservation compared to wheat. It is recommended to judiciously reduce irrigation during the growing seasons of spring maize and rice to help alleviate agricultural water use pressures. © 2024 Society of Chemical Industry.

Farmers’ use of Indigenous knowledge on climate change adaptation across farming systems and agroecological zones of Sierra Leone

The qualitative analysis of farmers’ use of Indigenous knowledge on climate change adaptation across farming systems and agroecological zones of Sierra Leone was conducted using a combination of interpretive phenomenological analysis, Focus Group Discussions, and participant observation, which was analyzed with a qualitative interpretative approach. Farmers respond to climate change through the use of wind and cloud patterns, animal and bird behavior, moon shape, and position of the sun to predict changes in temperature, intensity, drying up of rivers, and frequency of rainfall, as well as the incidence of pests and diseases. Other Indigenous knowledge used by farmers were rotational grazing, migration, crop-livestock integration, and the use of manure for composting with herbal and biological treatment for disease management. The study recommended that in the current context of climate change, the promotion of adaptation strategies should explore the interdependency of different knowledge systems and knowledge hybridity in agriculture.

Aerial photography and machine learning for estimating extremely high flamingo numbers on the Makgadikgadi Pans, Botswana

Monitoring biodiversity over time and space is essential for effective conservation of habitats, processes, and dependent organisms. Estimating large abundances of individuals can be challenging (e.g. birds and mammals), demanding efficient and effective methods. The Makgadikgadi Pans in north-eastern Botswana have high concentrations of breeding and feeding flamingos (Phoenicopterus roseus and Phoeniconaias minor), among the world’s most important breeding sites. No published estimates of flamingos exist since 2009, with historical estimates providing limited details on methods. We developed a semi-supervised machine learning method for counting a large feeding concentration of flamingos (2 June 2019) in aerial photographs from northern Sua Pan of the Makgadikgadi Pans. We also analysed rainfall and flooding frequency and extent, using satellite imagery, estimating likely frequency of these flamingo concentrations. Lastly, we reviewed the site’s global importance as flamingo breeding habitat. Our analysis successfully provided an estimate of 372,172 to 689,473 flamingos, with methods producing over 97 % test accuracy. Uncertainty related primarily to data coverage and collection rather than methodology. This estimate underlined the Makgadikgadi Pans’ significance for flamingos, supported by a high frequency of flooding (>5:10 years). Sua Pan ranked in the top ten breeding sites for the lesser and greater flamingos in the world. These methods can be applied to other large concentrations of flamingos and other animals. Our techniques provide considerable promise for tracking flamingo populations to ensure their protection. We provide code, for use in Google Earth Engine.

Frequency analysis of extreme rainfall over the Japanese archipelago by leveraging gauge-adjusted radar and satellite estimates

With the increasing frequency of extreme rainfall and difficulty in capturing their spatial variability by rain gauges, especially over regions with complex topographic and climatic features like Japan, there is a growing interest in using radar and satellite-derived precipitation estimates for building intensity–duration–frequency (IDF) curves. Such a statistical method provides references for studying extreme rainfall statistics at varying spatiotemporal scales. Yet, a comparative evaluation of the efficiency of these datasets to derive reliable IDFs over the Japanese archipelago is lacking. This study compares IDFs developed from four rain gauge-adjusted remotely sensed datasets (RSDs) derived from weather radars (R/A) and satellites (GsMAP_G, CMORPH_G, and IMERG_G) with those created from long-term rain gauge records (i.e., reference IDFs) for evaluating their reliabilities, particularly for 10- and 100-year return periods. A Bayesian Generalized Extreme Value model was adopted to estimate the IDFs. The results show that the four RSDs exhibited varying performances in deriving reliable IDFs within the 5–95% confidence bounds of the reference IDFs depending on rainfall duration and climate settings. While R/A performed better for one-hour rainfall return levels, particularly in the cold climate with warm summers, satellite-driven RSDs, especially IMERG_G, showed better performances for long-duration rainfall return levels. The performance discrepancies were primarily linked to the differences in rainfall estimation and bias adjustment schemes rather than the differences in record lengths and spatial resolutions among the tested RSDs. Thus, it is crucial to consider the rainfall duration and climate settings when using the derived IDFs for diverse practical applications.

Trend analysis of extreme rainfall indices from CHIRPS precipitation estimates over the Lake Tana sub-basin, Abbay Basin of Ethiopia.

Ethiopia is among the African nations most susceptible to climate change because of its frequent droughts and heavy rainfall. Therefore, hydrological and water management problems require an investigation of regional variability and extreme rainfall patterns. This study analyzed the spatiotemporal trends of extreme rainfall in the Lake Tana sub-basin (LTSB) of Ethiopia's upper Blue Nile basin (UBNB) between 1981 and 2019. The trend and geographic patterns of ten extreme rainfall indices are evaluated using high-resolution data from Climate Hazards Group InfraRed Precipitation Stations (CHIRPS). The researcher used RClimDex, an R software tool, to analyze the ten severe rainfall indices. The variability of the extreme rain indices was also assessed by applying the standard anomaly index (SAI). The trend analysis shows that the majority of rainfall indices decreased in the majority of station locations. Among the rainfall locations, the decreasing trend was only significant in 40% consecutive wet days (CWD), 13.33% (R95p and R99p), and 6.66% highest rainfall amount in a 1-day period (RX1day). In contrast, significant positive patterns were revealed in the incidence of rainfall events of number of heavy precipitation days (R10mm), annual total wet day rainfall (PRCPTOT), and consecutive dry days (CDD), with significant positive trends of 26.66% (R10mm) and 40% (PRCPTOT). Furthermore, a spatial distribution result of extreme rainfall trends reveals considerable variations between stations location. Thus, these findings point to the necessity of creating adaptation and mitigation plans for climate change variability within the sub-basin.

The Association between Rainfall and Taxi Travel Activities: A Case Study from Wuhan, China

Rainfall has a significant impact on urban population mobility, posing great challenges to traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we devised a multiscale comparative research framework to explore the spatiotemporal effects of rainfall on taxi travel patterns, aiming to provide a new perspective on the investigation of rainfall’s impact on urban human mobility. More specifically, at the macroscopic scale, we computed taxi travel indicators across the entire study area and used kernel density estimates to observe the spatiotemporal distribution patterns influenced by rainfall. Subsequently, complex traffic networks were constructed by considering urban road intersections as nodes and combined with visualization methods to understand changes in taxi travel patterns visually at the microscopic level. We selected Wuhan City, a typical urban area in southern China with frequent rainfall, as the study area and used meteorological data along with a large volume of taxi spatiotemporal trajectory data for investigation. Results indicated a 4.16% decrease in weekly travel volume due to rainfall, with a 3.96% decrease on workdays and a 4.64% decrease on weekends. However, nighttime rainfall between 19:00 and 22:00 on weekdays increased the demand for taxi travel. Furthermore, the impact of rainfall on weekends exceeded that on workdays, restricting people’s mobility and leisure activities, resulting in reduced travel to recreational tourist spots and commercial pedestrian streets. Rainfall altered residents’ travel preferences to some extent, with more residents choosing taxis during rainy weather, which led to decreased transportation efficiency and increased traffic congestion. These findings contribute to a deeper understanding of the complex relationship between population mobility patterns and the urban ecological environment, providing valuable insights for planning resident travel and taxi dispatching under adverse weather conditions.

Disinfection by-product formation potential in response to variability in dissolved organic matter and nutrient inputs: Insights from a mesocosm study

Changes in rainfall patterns driven by climate change affect the transport of dissolved organic matter (DOM) and nutrients through runoff to freshwater systems. This presents challenges for drinking water providers. DOM, which is a heterogeneous mix of organic molecules, serves as a critical precursor for disinfection by-products (DBPs) which are associated with adverse health effects. Predicting DBP formation is complex due to changes in DOM concentration and composition in source waters, intensified by altered rainfall frequency and intensity. We employed a novel mesocosm approach to investigate the response of DBP precursors to variability in DOM composition and inorganic nutrients, such as nitrogen and phosphorus, export to lakes. Three distinct pulse event scenarios, mimicking extreme, intermittent, and continuous runoff were studied. Simultaneous experiments were conducted at two boreal lakes with distinct DOM composition, as reflected in their color (brown and clear lakes), and bromide content, using standardized methods. Results showed primarily site-specific changes in DBP precursors, some heavily influenced by runoff variability. Intermittent and daily pulse events in the clear-water mesocosms exhibited higher haloacetonitriles (HANs) formation potential linked to freshly produced protein-like DOM enhanced by light availability. In contrast, trihalomethanes (THMs), associated with humic-like DOM, showed no significant differences between pulse events in the brown-water mesocosms. Elevated bromide concentration in the clear mesocosms critically influenced THMs speciation and concentrations. These findings contribute to understanding how changing precipitation patterns impact the dynamics of DBP formation, thereby offering insights for monitoring the mobilization and alterations of DBP precursors within catchment areas and lake ecosystems.

Urban development pattern’s influence on extreme rainfall occurrences

Growing urban population and the distinct strategies to accommodate them lead to diverse urban development patterns worldwide. While local evidence suggests the presence of urban signatures in rainfall anomalies, there is limited understanding of how rainfall responds to divergent urban development patterns worldwide. Here we unveil a divergence in the exposure to extreme rainfall for 1790 inland cities globally, attributable to their respective urban development patterns. Cities that experience compact development tend to witness larger increases in extreme rainfall frequency over downtown than their rural surroundings, while the anomalies in extreme rainfall frequency diminish for cities with dispersed development. Convection-permitting simulations further suggest compact urban footprints lead to more pronounced urban-rural thermal contrasts and aerodynamic disturbances. This is directly responsible for the divergent rainfall responses to urban development patterns. Our analyses offer significant insights pertaining to the priorities and potential of city-level efforts to mitigate the emerging climate-related hazards, particularly for countries experiencing rapid urbanization.

Impact of Climate Change on the Farming Community of Bhaktapur District of Nepal

This study was carried out to evaluate the impact of climate change on the farming community in Bhaktapur, Nepal. The study considered rainfall, temperature, flood/drought, harvest and sowing time, and pest and disease incidence over 20 years as primary factors to be considered. A household survey of 120 houses was conducted following the set of questionnaires prepared. As a result, significant changes were observed in climatic aspects as well as biological aspects. The obtained result demonstrated that the precipitation observed drastic change as there was an incidence of erratic and heavy rainfall on summer days while the intensity and frequency of rainfall decreased during winter. These abnormalities certainly do not favor agriculture and farming. Similarly, the temperature of the study area has increased annually over the last two decades. The data suggests that the average annual temperature is increasing at the rate of 0.038°C per year. Moreover, the survey showed that the time of harvest and time of sowing seeds have been delayed respectively to the time a few decades back while the climatic aberrations have increased the incidence of pests and diseases. In a nutshell, the changes in climatic conditions have a drastic change in the farming system and thus the agricultural produce and productivity. The study highlights the significant impact of climate change on traditional farming practices in Bhaktapur, Nepal. It underscores how changes in rainfall patterns, temperature, and the incidence of extreme weather events like floods and droughts have disrupted agricultural activities such as planting and harvesting cycles. The research not only identifies challenges but also emphasizes the need for adaptation strategies to mitigate the impacts of climate change on farming communities. By linking research findings to policy recommendations and practical interventions, the study offers actionable insights for policymakers, practitioners, and local stakeholders striving to build resilience in agricultural systems.

Characterizing the performances of different observational precipitation products and their uncertainties over Africa

Validation of observed gridded precipitation datasets sourced from satellites or reanalysis over Africa remains a challenge due to the dearth of in-situ products that can act as a true estimate. To address this gap, this study compares the performance of different precipitation products (gauge, reanalysis, and satellite-based) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over Africa. Satellite products are classified as corrected (incorporating gauge observations into their algorithms) or uncorrected, which implies that temporal variations depend entirely on the satellite. The main aim is to identify regions where precipitation products depict minimal uncertainties, supporting the use of the datasets in understanding precipitation variability in the specific regions. This is achieved by applying the triple collocation approach, which takes advantage of three collocated datasets of the same variable to derive the mean square error without requiring knowledge of the true value. The results show that light precipitation (1–5 mm d−1) was prevalent in most regions of Africa during the study duration (2001–2016). Estimating the spatial distribution of daily precipitation greater than the 90th percentiles suggests that extreme precipitation is mainly detected over the Central Africa region and coastal regions of West Africa, where the majority of uncorrected satellite products show consistent performance. The satellite product CMORPH_V1_RAW shows higher estimates of 90th percentile precipitation among the uncorrected satellite products. The ability of precipitation products to detect rainy or non-rainy days shows that corrected satellite products depict notable agreement for probability of detection and false alarm ratio over most regions of Africa. Overall, better performance is demonstrated by the IMERG products, ARCv2, CHIRPSv2 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0 and REGEN_ALL (gauge) during the study period. Among the reanalysis products, ERA5 datasets shows good performance in estimating daily precipitation over Africa. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage by different stakeholders.

Urbanizational impact on climatic variables and geographical analysis of physical land use land cover variation of a city using Remote Sensing.

<p>Climate change has serious implications foron the rise and variation of the average earth’s temperature, and intensified rainfall. In urban areas, temperatures and intensified rainfall variation are noticeably seenin the city which disrupts the normal life of a living things. Also, it affects the storage of rainfall runoff water in tanks and lakes. Green cover reduction due to the increase ofagriculture, industrialization, population explosion and urbanization have a direct impact on the climate pattern.The average temperature of a city is increasing in every decade due to a drastic reduction in the green cover.This is visible today from the abnormal melting of glaciers. The rainfall pattern is highly intensified, and the frequency of rainfall is on the a decreasing trend. This intensified rainfall creates high surface runoff during rainfall and disrupts the normal life in a city. The Standard Precipitation Index (SPI) for a city indicates a noticeable shift in its climatic pattern, particularly with consistent rainfall during the southwest monsoon. Over the span of four decades, LULC research has revealed an increase in residential areas and a decrease in green cover. This urbanization process is associated with changes in LULC, resulting in decreased vegetation and diminished storage water bodies.Variations in the temperature of a city are on an increasing trend. Urban forestry or the dense green coverof an area reduces the temperature and creates cool pockets in the city. The regression model for temperature and vegetation indicates the negative correlation of temperature with high vegetation growth. Keywords: Urbanization, Climate change, Rainfall-runoff, Temperature, Green Cover</p>

Rainstorm erosion difference and topographical changes induced by heavy rainfall between afforestation and grassland restoration catchments on the Chinese Loess Plateau

The frequency and intensity of heavy rainfall on the Chinese Loess Plateau (CLP) are increasing due to global climate change, which has caused severe soil erosion. However, precise evaluation of heavy rainfall-induced soil erosion, deposition, and related topographical changes at the catchment scale is lacking, and there is uncertainty as to how effectively trees can inhibit erosion, especially during heavy rainfall. In this study, we selected a pair of catchments with afforestation and natural grassland restoration on the CLP. Precise evaluation of erosion and topographic changes induced by heavy rainfall was conducted based on airborne light detection and ranging (LiDAR) data. In the grassland catchment, the gully bed area increased by 17.6 %, the gully slope and inter-gully land area decreased by 1.4 %, and the length of the gully shoulder line decreased by 4.4 %. Conversely, the forestland catchment experienced a decrease in gully bed area of 8.8 %, an increase in gully slope area of 5.3 %, a decrease in inter-gully land area of 9.9 %, and a decrease in the length of the gully shoulder line of 11.4 %. Following heavy rainfall, both catchments experienced a substantial increase in terrain relief. Soil erosion in the gully slope accounted for >90 % of the total erosion in both catchments, while deposition in the same area contributed to >75 % of the total deposition. The grassland catchment experienced a unit area erosion of 233,908 m3 km−2, which was 4.7 times greater than that of the forestland catchment (49,768 m3 km−2). In both catchments, erosion predominantly transpired in the steep slope region adjacent to the gully shoulder line, accounting for >15 % of the gully slope erosion area. The grassland catchment exhibited a continuous distribution pattern of erosion and deposition, whereas the forestland catchment exhibited a scattered and intertwined distribution pattern. This study is essential for further research on erosion and topographic changes induced by extreme weather events and to determine appropriate measures.

Development and Evaluation of Flood Early Warning System for the Small Streams

As the frequency of local extreme rainfall increases due to climate change, frequent flood damage is occurring in small stream watersheds. Casualties in these small-streams can only be significantly reduced through non-structural measures, such as the development of flood early warning systems. Howeve owever, the development and evaluation of flood early warning systems have been limited due to a lack of measurement data in small streams. Recently, the National Disaster Management Institute operated five test beds for small streams across the country. A dditionally, the Ministry of Interior and Safety, along with local governments, established a smart measurement and management system for approximately 10% of all small streams, totaling 2,200, by 2027, making it possible to secure measurement data in small streams. Considering onsidering the flow characteristics of small streams, an optimization system is developed using the rainfall-discharge nomograph and rating curve rather than a numerical-based method that requires considerable calculation time. To evaluate the developed system, the discharge and depth data measured at the time of a flood accident on August 16, 2021, are compared on a one-to-one basis with the prediction results. The prediction results show that the measured discharge and depth distribution of the five small streams over time were accurately reproduced. Based on these results, the developed rainfall-discharge nomograph and rating curve are expected to serve as effective tools to support flood early warning systems.

Short-term fate of nitrogen fixed by moss-cyanobacteria associations under different rainfall regimes

Nitrogen (N) fixation by moss-cyanobacteria associations has been recognized as an important N input pathway in many ecosystems from arctic tundra to tropical forests. However, the transfer of fixed N2 from mosses to the soil as well as the effects of rainfall frequency and volume on this N transfer has hardly been studied – even though mosses can leach nutrients upon rewetting. In this study, we investigated the transfer of fixed N2 by moss-cyanobacteria associations in one month under four watering regimes with a combination of high and low volume and frequency. For this, we used two morphologically similar moss species collected from ecosystems with different climate and N availability (subarctic - Hylocomium splendens; and tropical - Thuidium delicatulum). Acetylene reduction assays were conducted as a measure of N2 fixation rates in mosses, and 15N-N2 tracing was used to follow the fixed N2 from moss to the underlying substrate. Nitrogen fixation rates were higher in T. delicatulum than in H. splendens, but rainfall volume and frequency did not show strong effects on N2 fixation rates. Nonetheless, the extent of N leached from mosses was more sensitive to an increase in rainfall volume than to an increase in frequency, and more N was lost from T. delicatulum under high volume precipitation than from H. splendens. Both total nitrogen and 15N enrichment results demonstrate that the fixed N2 was mostly stored in moss tissues with less than 1 % leached to the substrate. Our results show that both moss species retain almost all fixed N2 within their tissues under small rainfall disturbances within one month, while increased N availability under higher precipitation volume renders some moss species an important N source for the soil.