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Due to climate change, extreme rainfalls happen more frequently with different patterns. Biochar and plant roots can affect soil water retention curve (SWRC) and hence slope stability. Until now, no available SWRC model considers soil–plant-biochar interaction, which can also be used for slope stability analysis under extreme rainfalls with different patterns (i.e., advanced, bimodal, and delayed). This study proposes a new SWRC model for vegetated biochar-amended soil by considering the influence of soil–plant-biochar interaction on the void ratio and pore structure. A series of numerical analyses were conducted to investigate the significance of new SWRC model on slope stability under extreme rainfall patterns. Good agreements between laboratory measurements and the new SWRC model predictions were obtained. With an increase of biochar content from 0 to 10%, the number of soil micropores increased by 22%, which improved root volume ratio (Rv) by 130% and hence air entry value (AEV) of vegetated soil by 50%. Based on numerical analysis, the factor of safety (FOS) for vegetated slope under advanced and bimodal rainfall patterns was up to 53% lower than that under the delayed one during rainfall. The rainfall influence depth and FOS of vegetated slope without biochar was nearly 100% deeper and 60% lower than that with biochar, respectively. The lowest FOS was caused by advanced rainfall pattern, which is therefore suggested for slope design. The application of 10% biochar can better stabilize shallow slopes due to its substantial effects on plant root, soil pore structure, and SWRC.

Rainfall-induced slope failures have an inherent relationship with rainfall patterns; however, it has not received adequate attention to date. To investigate the distinctive effects of various rainfall patterns on slope failures, a series of experimental tests, considering five representative rainfall patterns (the uniform, the advanced, the intermediate, the delayed, and the bimodal modes), were carried out on homogenous sandy slope models, respectively. Based on the DIC (Digital Image Correlation Technology) contour maps and physical failure behaviors, three types of failure modes were recognized, i.e., the thrust load caused failure, the surface slide failure, and the retrogressive failure. Different failure mechanisms were investigated in-depth with the help of CFD-DEM numerical simulations. Given the same rainfall amount and duration, the delayed rainfall pattern was identified as the most dangerous one to slope stability, which usually resulted in high-value pore water pressure (PWP) and hence triggered the largest failure depth (D) and distance of crest (L) at the peak rainfall intensity; the advanced rainfall pattern with fastest increase in PWP had the shortest rainfall duration threshold for incurring slope instability. It is disclosed that the rainfall amount thresholds to trigger slope failures were insensitive to different rainfall patterns.

The present study majorly focused on the degree of rainfall alteration (DIRA) using an indicator-based novel approach. Most of the previous works majorly focused on only magnitude parameters for measuring monthly rainfall change; however, alteration may also come from other relevant aspects like frequency, timing, rate of change, and duration of rainfall. The present paper considered 33 indicators to assess alterations in rainfall, encompassing various such aspects. Subsequently, it computed the degrees of rainfall alteration categorized as low, moderate, and high range of variability (RVA). This computation was based on the gridded rainfall data endorsed by the Indian Meteorological Department (IMD). Getis-ord was applied to compute the significance of rainfall patterns. The study revealed that middle RVA-based DIRA is low (< 0.33) in the case of magnitude indicators across IMD-defined meteorological sub-divisions, but moderate DIRA (0.33–0.67) was detected in some parts in the case of rate of change, frequency, duration, and timing indicators. This change was detected rather high (moderate to high) over wider parts of India (six and sixteen sub-divisions ) in the case of low and high DIRA. In monsoon and post-monsoon seasons, north-eastern hilly hot spots (95–99% level) were weakened, while it was found enhanced in north-western India. A new rainfall hot spot (95%) was identified in Odisha, Telangana, and Vidarbha transition in 1991–2021. Taking into account the notable success achieved through the IRA approach, the study strongly recommends its application across various related domains.

Historically, most precipitation data have been measured by collecting rainfall, usually at intervals of 24 h, with a fixed starting time. Nonetheless, it is known that the use of fixed time intervals to measure rainfall quantities could lead to an underestimation of the true maximum precipitation amounts for the considered duration, so a single multiplicative correction factor is commonly applied, generally without taking into account the rainfall pattern of the place, nor regional or seasonal considerations. In the present work, hourly measurements from 120 stations of Catalonia (northeast of the Iberian Peninsula) have been used to analyse how the ratio between rainfall amounts measured by fixed and unrestricted intervals, i.e. the correction factor, depends on the considered duration and on the specific starting time of the fixed interval (local 00:00, 08:00, 12:00 or 16:00), as well as the influence of geographical location and seasonality and actual rainfall duration. For fixed sampling intervals starting at 16:00, the mean correction factor has been found to be higher (1.137) than at the usual 08:00 starting time (1.129). Some geographical patterns of the correction factor over Catalonia arose which, moreover, depend on the season, with a mean value of 1.161 in spring and a value of 1.093 in summer. Also, the value of the correction has been found to increase with the actual duration of the maximum rainfall events used in the analysis. Some of these extreme events had actual mesoscale durations between 6 and 9 h, linked to highly convective mesoscale organisations acting mainly in summer and the beginning of autumn. Other maxima episodes, with more advective rainfall lasting more than 12 h registered in the northern area of the territory, presented the highest values of the correction factor, especially in spring.

Flash flood early warning is a very effective way to reduce casualties induced by rainstorm flash flood in mountainous area. The forecasting of flash flooding remains challenging because of the short response time and inaccurate warning threshold. So far, the flash flood disaster defenses often adopt the critical rainfall amounts inducing the peak discharge or water level to establish an early warning threshold in China. However, the runoff peak discharge depends on rainfall patterns including rainfall intensity and accumulation, result in the critical rainfall threshold has a significant uncertainty. To reduce this uncertainty, herein we present a dual-threshold method for flash flood early warning with consideration of rainfall patterns based on above two-rainfall metrics. Moreover, applying this new method in the flash flood disasters occurred in the Zhongdu river basin, Sichuan province of China to evaluate the early warning reliability. Firstly, five most likely rainfall patterns of this basin were determined according to the timing of rain peak in historical rainfall events, and then, we determined the critical rainfall thresholds under different rainfall patterns and soil moisture conditions. The result showed that the rainfall thresholds uncertainty caused by rainfall pattern is more pronounced than soil moisture. Next, using the cumulative rainfall depth and maximum rainfall intensity corresponding to disaster discharge in different flood processes to establish the dual-thresholds. We found the dual-threshold method comprehensively considers the impacts of soil moisture, rainfall temporal distribution and flood rising property, which can achieve early warning for the four protected objects along the Zhongdu River, with an average lead duration of 46.2 min. Compared with the other three single-threshold methods, the critical rainfall and the critical rainstorm curve methods frequently created false or missing warnings, making it difficult to achieve the effect of early warning. Although reliability of flood water level rising rate method is high, the lead time is relatively short and only lasts for a few minutes in some cases. As a result, the new proposed dual-threshold method, accounting for both the reliability and long lead time, can be a potential candidate for the flash flood disaster early warning.

Urban flooding is a perennial problem, especially in developing countries with relatively weak infrastructure under ever‐increasing stress due to climate change and human activities. We simulate the temporally variable flood‐water depth and inundation area under four designed rainfall patterns in the typical tropical rainforest city of Hue, Vietnam. The four rainfall types are R1 (peak at fifth hour), R2 (peak at 20th hour), R3 (peak at first hour), and R4 (peak at 13th hour). Results show that temporal rainfall pattern R4 with peak rainfall in the middle of the total period yielded the maximum water depth of 1.88 m. R3, with peak rainfall in the first hour, yields the shallowest maximum water depth and the largest inundation extent. When the water depth for R3 is 0.1–0.2 m, the inundated area caused by R3 is 3–4 times that of the other three patterns. Analysis of urban flood inundation in Hue provides a management tool to facilitate flood risk management in the context of extreme rainfall.

Global warming and anthropogenic activities have imposed noticeable impacts on rainfall pattern changes at both spatial and temporal scales in recent decades. Systematic diagnosis of rainfall pattern changes is urgently needed at spatiotemporal scales for a deeper understanding of how climate change produces variations in rainfall patterns. The objective of this study was to identify rainfall pattern changes systematically under climate change at a subcontinental scale along a rainfall gradient ranging from 1800 to 200 mm yr⁻¹ by analyzing centennial rainfall data covering 23-0 sites from 1910 to 2017 in the Northern Territory of Australia. Rainfall pattern changes were characterized by considering aspects of trends and periodicity of annual rainfall, abrupt changes, rainfall distribution, and extreme rainfall events. Our results illustrated that rainfall patterns in northern Australia have changed significantly compared with the early period of the twentieth century. Specifically, 1) a significant increasing trend in annual precipitation associated with greater variation in recent decades was observed over the entire study area, 2) temporal variations represented a mean rainfall periodicity of 27 years over wet to dry regions, 3) an abrupt change of annual rainfall amount occurred consistently in both humid and arid regions during the 1966–75 period, and 4) partitioned long-term time series of rainfall demonstrated a wetter rainfall distribution trend across coastal to inland areas that was associated with more frequent extreme rainfall events in recent decades. The findings of this study could facilitate further studies on the mechanisms of climate change that influence rainfall pattern changes.

The hydrological response of groundwater to rainfall plays a key role in the initiation of deep-seated bedrock landslides; however, the mechanisms require further investigation due to the complexity of groundwater movement in fissured bedrock. In this study, an active translational landslide along nearly horizontal rock strata was investigated. The hydrological response of groundwater to rainfall was analyzed, using the data from a four-year real-time field monitoring program from June 2013 to December 2016. The monitoring system was installed along a longitudinal section of the landslide with severe deformation and consisted of two rainfall gauges, nine piezometers, three water-level gauges, and two GPS data loggers. Much research effort has been directed to exploring the relationship between rainfall and groundwater response. It is found that both the pore-water pressure (PWP) and groundwater level (GWL) responses were significantly influenced by the rainfall pattern and the hydrological properties of the underlying aquifer. The rapid rise and fall of PWP and GWL were observed in the rainy season of 2013 with high-frequency, long-duration, and high-intensity rainfall patterns, especially in the lower section of the landslide dominated by the porous aquifer system. In contrast, a slower and prolonged response of PWP and GWL to rainfall was observed in most monitoring boreholes in 2014 and 2015 with two rainstorms of short duration and high intensity. In the lower section of the landslide, the peak GWL exhibited a stronger correlation with the cumulative rainfall than the daily rainfall in a single rainfall event whereas the peak groundwater level fluctuation (GWLF) exhibited a strong correlation with API with a half-life of 7 days. In the middle section of the landslide, however, relatively lower correlation between rainfall and groundwater response was observed. Three types of groundwater flow were identified based on the recession coefficients of different segments of water-level hydrographs in the landslide area, corresponding to the quick flow through highly permeable gravely soil and well-developed vertical joints in the bedrock, the slow and diffuse flow through the relatively less-permeable bedrock, and the transition between them in the aquifer system.

Slope debris flow (SDF) is a common geological disaster with complex formation processes and strong destructive forces causing significant casualties and economic losses in mountainous areas worldwide. Experimental research and models of the trigger mechanism of SDF are the key scientific issues as they provide the basis for studying technologies for the prevention, mitigation, prediction, and forecasting of these disasters. This paper summarizes the methods of data collection, analysis, and status of recent experimental research on the trigger mechanism and models of SDF under the action of artificial rainfall. The main progress and theoretical achievements related to the SDF are discussed in terms of the experimental parameter settings, the mechanism of water–soil coupling action, and the start-up model of SDF. On this basis, the suggestions for experimental research on the mechanism and models of triggers for debris flows are proposed. First, future experiments on debris flow triggering should increase the similarities between rainfall patterns and loose soil characteristics. Second, the mechanism research of SDF is needed on the changes in the physical and mechanical characteristics of soil and the response to debris flow triggers under enhanced rainfall. Third, the parameters of the debris flow trigger model should be simplified, and the model’s applicability should be improved with artificial intelligence. Through these efforts, the debris flow trigger test under artificial rainfall should be developed and refined, and the microscopic and multi-factor correlations of water–soil coupling should be applied to reveal the debris flow trigger mechanism in greater detail and establish a more applicable model of debris flow triggering.

Increased rainfall associated with climate change can increase sediment discharge. The supply of fine sediment from slope failures inhibits bed armoring of mountain rivers and increases sediment discharge to the downstream reaches. Floods without slope failures lead to bed erosion and armoring and may ultimately decrease sediment discharge. Thus, it is important to consider sediment discharge from slope failure and bed erosion as factors affecting sediment production. Climate change affects not only the rainfall amount, but also the temporal rainfall pattern; consequently, the pattern affects the sediment production factors and the amount of sediment discharge. However, changes in sediment discharge due to climate change based on sediment production sources have not yet been clarified. In this study, we statistically analyzed 1200 results simulated using a physics-based sediment runoff model to assess the impact of changes in temporal rainfall patterns on sediment discharge and sediment production sources in the Pekerebetsu River Basin. In the simulations, we used the rainfall predicted in d4PDF (Database for policy decision-making for future climate change), a large ensemble climate simulation database at 5 km and 20 km resolutions. Our results showed that the climate-driven increase in sediment discharge was considerably larger than that of rainfall. An increase in short-term heavy rainfall increased the supply of fine sediments from slope failure. This resulted in the suppression of bed armoring and a large increase in sediment discharge. Thus, the increase in sediment discharge is not only caused by an increase in rainfall but also by changes in temporal rainfall patterns and sediment production factors. The sediment discharge calculated for the 20 km resolution climate projection was nearly one order of magnitude smaller than that for the 5 km resolution. This suggests that the 20 km resolution climate projections do not adequately represent orographic rainfall in the mountains and thus, do not adequately reproduce extreme sediment discharge events. An increased sediment supply causes bed aggradation and decreases the river conveyance capacity of the downstream channel. The model developed in this study will contribute to flood risk analysis and flood control planning for increased rainfall due to climate change.