Frequency Of Extreme Rainfall Events Research Articles

Clarifying urban flood response characteristics and improving interpretable flood prediction with sparse data considering the coupling effect of rainfall and drainage pipeline siltation

With climate warming and accelerated urbanisation, severe urban flooding has become a common problem worldwide. Frequent extreme rainfall events and the siltation of drainage pipes further increase the burden on urban drainage networks. However, existing studies have not fully considered the effects of rainfall and pipeline siltation on the response characteristics of flooding when constructing numerical models of urban flooding simulations. To solve this problem, a surface-subsurface coupling model was constructed by combining the Saint-Venant equation, Manning equation, a one-dimensional pipeline model (SWMM), and a two-dimensional surface overflow model (LISFLOOD-FP). Then, the SWMM model considering pipeline siltation and the two-dimensional surface overflow model (LISFLOOD-FP) were coupled with the flow exchange governing equation, and the urban flooding response characteristics considering the coupling effect of “rainfall and drainage pipeline siltation” were analysed. To enhance the solvability of waterlogging prediction, an intelligent prediction model of urban flooding based on Bayes-CNN-BLSTM was established by combining a convolutional neural network (CNN), bidirectional long short-term memory neural network (BLSTM), Bayesian optimisation (Bayes), and an interpretable loss function error correction method. The actual rainfall events and flooding processes recorded by the monitoring equipment at Huizhou University were used to calibrate and verify the model. The results show that in the Rainfall 1 and Rainfall 2 scenarios, the overload rates of the pipelines in the current siltation scenario were 60.06 % and 68.37 %, respectively, and the proportions of overflow nodes were 24.87 % and 25.89 %, respectively. When the drainage network was initially put into operation, the overload rates of the pipeline were 36.67 % and 41.16 %, and the overflow nodes accounted for 3.05 % and 4.06 %, respectively. The inundated area and volume of urban flooding increased when the combined siltation coefficient (CSC) was 0.2; therefore, two desilting schemes were determined. Under Rainfall 1, Rainfall 2, and the four rainfall recurrence periods, the Bayes-CNN-BLSTM model had clear advantages in terms of accuracy, reliability, and robustness.

Response of hydrology and nutrient losses to different extreme rainfall conditions in a coastal watershed influenced by orchards

Global warming is altering the frequency of extreme rainfall events and introducing uncertainties for non-point source pollution (NPSP). This research centers on orchard-influenced planting areas (OIPA) in the Wulong River Watershed of Shandong Province, China, which are known for their heightened nitrogen (N) and phosphorus (P) pollution. Leveraging meteorological data from both historical (1989–2018) and projected future periods (2041–2100), this research identified five extreme rainfall indices (ERI): R10 (moderate rain), R20 (heavy rain), R50 (rainstorm), R95p (Daily rainfall between the 95th and 99th percentile of the rainfall), and R99p (>99th percentile). Utilizing an advanced watershed hydrological model, SWAT-CO2, this study carried out a comparison between ERI and average conditions and evaluated the effects of ERI on the hydrology and nutrient losses in this coastal watershed. The findings revealed that the growth multiples of precipitation in the OIPA for five ERI varied between 16 and 59 times for the historical period and 14 to 65 times for future climate scenarios compared to the average conditions. The most pronounced increases in surface runoff and total phosphorus (TP) loss were observed with R50, R95p, and R99p, showing growth multiples as high as 352 and 330 times, and total nitrogen (TN) growth multiples varied between 4.6 and 30.3 times. The contribution rates of R50 and R99p for surface runoff and TP loss in the OIPA during all periods exceeded 55%, however, TN exhibited the opposite trend, primarily due to the dominated NO3-N leaching in the sandy soil. This research revealed how the OIPA reacts to different ERI and pinpointed essential elements influencing water and nutrient losses.

Evaluation of Future Changes in Climate Extremes over Southeast Asia Using Downscaled CMIP6 GCM Projections

This study presented an assessment of climate extremes in the Southeast Asia (SEA) region, utilizing downscaled climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs). The study outputs uncovered statistically significant trends indicating a rise in extreme precipitation and temperature events throughout SEA for both the near-term (2021–2060) and long-term (2061–2100) future under both SSP245 and SSP585 scenarios, in comparison to the historical period (1950–2014). Moreover, we investigated the seasonal fluctuations in rainfall and temperature distributions, accentuating the occurrence of drier dry seasons and wetter rainy seasons in particular geographic areas. The focused examination of seven prominent cities in SEA underscored the escalating frequency of extreme rainfall events and rising temperatures, heightening the urban vulnerability to urban flooding and heatwaves. This study’s findings enhance our comprehension of potential climate extremes in SEA, providing valuable insights to inform climate adaptation, mitigation strategies, and natural disaster preparedness efforts within the region.

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

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

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.

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

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

Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir

With the advancement of society and the impact of various factors such as climate change, surface conditions, and human activities, there has been a significant increase in the frequency of extreme rainfall events, leading to substantial losses from flood disasters. The presence of numerous small and medium-sized water conservancy projects in the basin plays a crucial role in influencing runoff production and rainwater confluence. However, due to the lack of extensive historical hydrological data for simulation purposes, it is challenging to accurately predict floods in the basin. Therefore, there is a growing emphasis on flood simulation and forecasting that takes into account the influence of upstream water projects. Moushan Reservoir basin is located in a hilly area of an arid and semi-arid region in the north of China. Flooding has the characteristics of sudden strong, short confluence time, steep rise, and steep fall, especially floods caused by extreme weather events, which have a high frequency and a wide range of hazards, and has become one of the most threatening natural disasters to human life and property safety. There are many small and medium-sized reservoirs in this basin, which have a significant influence on the accuracy of flood prediction. Therefore, taking Moushan Reservoir as an example, this paper puts forward a flash flood simulation method for reservoirs in hilly areas, considering upstream reservoirs, which can better solve the problem of flood simulation accuracy. Using the virtual aggregation method, the 3 medium-sized reservoirs and 93 small upstream reservoirs are summarized into 7 aggregated reservoirs. Then, we construct the hydrological model combining two method sets with different runoff generation and confluence mechanisms. Finally, after model calibration and verification, the results of different methods are analyzed in terms of peak discharge error, runoff depth error, difference in peak time, and certainty coefficient. The results indicate that the flooding processes simulated by the proposed model are in line with the observed ones. The errors of flood peak and runoff depth are in the ranges of 2.3% to 15% and 0.1% to 19.6%, respectively, meeting the requirements of Class B accuracy of the “Water Forecast Code”. Method set 1 demonstrates a better simulation of floods with an average flood peak error of 5.63%. All these findings illustrate that the developed model, utilizing aggregate reservoirs and dynamic parameters to reflect regulation and storage functions, can effectively capture the impact of small water conservancy projects on confluence. This approach addresses challenges in simulating floods caused by small and medium-sized reservoirs, facilitating basin-wide flood prediction.

Biogeochemical Response of the Water Column of Concepción Bay, Chile, to a New Regime of Atmospheric and Oceanographic Variability

Concepción Bay is a socio-economic and ecologically important embayment whose hydrographic variability has been historically regulated by wind-modulated seasonal upwelling events during spring–summer and by freshwater from precipitation and river discharges during fall–winter. This system is subject to several anthropogenic and environmental strains due to the intense port activity and the increasing occurrence of extreme natural events. This study determines a new hydrographic regime and characterizes and analyzes the biogeochemical response of the water column to changes in rainfall and upwelling patterns. Despite the intrusion of nitrate-rich upwelled waters that enhance biological productivity remains more intense during spring–summer, the system remains fertilized year-long due to the occurrence of persistent upwelling pulses during fall–winter. The hydrographic structure presented a two-layer water column that was stratified thermally in spring–summer and primarily by freshwater inputs in fall–winter. Nevertheless, the regular pattern of the rainfall has changed (a decrease in precipitation and an increased frequency of extreme rainfall events), together with recurrent upwelling-favorable wind pulses during the non-upwelling season. This new regime has altered the seasonality of the physicochemical conditions and the structure of the microplanktonic communities, with productive and sanitary implications affecting the biogeochemical status of CB.

Long-term developments in seasonal hypoxia and response to climate change: A three-decade modeling study in the Ariake Sea, Japan

Hypoxia in the Ariake Sea, Japan, is steadily increasing in both duration and spatial coverage. Hypoxia, defined as dissolved oxygen (DO) below 3 mg/L, is strongly associated with the amplified frequency of extreme rainfall events driven by climate change, which poses a mounting threat to marine ecosystems on a global scale. In this study, we employed a general three-dimensional (3-D) hydrodynamic coastal model and a phytoplankton-based ecosystem model to identify the potential cause of seasonal hypoxic events over three decades. The results indicated a substantial decrease in bottom DO levels from 1992 to 2021, with the rate of increase in hypoxic area being 8 km2/yr (95 % CI: −0.38, 16.2) and the anoxic area increasing from almost non-existent to 100 km2. Notably, among various environmental drivers, increased river discharge was identified as a pivotal factor in the occurrence of hypoxia. Large-scale river discharge events can potentially increase water stratification, leading to the formation of hypoxia. River discharge volume and the duration of bottom hypoxia in the Ariake Sea were correlated. The duration of hypoxia was strongly associated with river discharge magnitude, with correlation coefficients ranging from 0.56 to 0.82 across six observational stations. Furthermore, analysis of varied simulated environmental factors over multiple years revealed diverse responses to climate change, indicating that the Ariake Sea is prone to experiencing a decline in its physical and water quality conditions.

Response of road erosion to hydrological connectivity under a heavy rainstorm in an agricultural watershed on the Loess Plateau

Road network extension is closely associated with soil erosion in watersheds during frequent extreme rainfall events. Identifying potential risk areas that actively deliver surface runoff to road structures and channel networks is critical for comprehending hydrological processes and controlling road erosion. In this study, the road erosion (landslides, collapses, scour pits, and gullies on monitored roads) on the Loess Plateau during a heavy rainstorm was analyzed using field survey and unmanned aerial vehicle (UAV) data. We defined hydrological connections as the intersections of roads and channel networks, and the activated hydrological connections represent hydrological connectivity. The results of this study highlight the exacerbating effect of hydrological connectivity on road erosion during a rainstorm in an agricultural watershed in the Loess Plateau region. The results showed that the road erosion intensity ranged from 2.16 × 102 to 1.02 × 104 t/ha. Strong hydrological connectivity resulted in average soil erosion amounts of landslides, collapses, scour pits, and gullies in the downslope area that were 2.16, 1.78, 5.46, and 6.98 times greater than those without hydrological connectivity, respectively. The presence of hydrological connectivity generated a 1.88 times higher road surface erosion intensity than that without hydrological connectivity. The Upslope-Road connections created the most significant enhancement effect on road erosion, and up to 65.37 % landslide erosion and 41.97 % collapse erosion amounts were influenced by these connections.The enhancement effect of Upslope–Road connections on road erosion varied with different road types, this impact of Upslope–Road connections on road erosion were only observed on the landslide erosion of petroleum transport roads and the collapse erosion of agricultural roads. Conversely, Road–Downslope connections did not significantly impact road erosion (p > 0.05). To effectively control the negative effect of road erosion, hydrological connections should be identified and regulated to impede hydrological connectivity activation during rainstorms by modifying tillage activities, improving road maintenance, and strengthening drainage systems.

A Critical Review of Existing Methods to Evaluate the Performance of Nature-Based Solutions (NBS) on Commercial Roofs (CR) to Mitigate Urban Flooding

The intensity and frequency of extreme rainfall events have increased in North America and the world due to climate change. Extreme rainfall events, characterized by a heavy volume of rainfall in a short duration, have triggered the onset of urban flash floods. Over the years, flash flooding has been reported in different cities in Canada, which resulted in many losses. Subsequently, different green roofing systems have been adopted to control urban stormwater runoff as part of Nature-Based Solutions (NBS) to mitigate urban flood and build a flood-resilient city. Currently, no specific widely recognized standard or code is dedicated to determining the hydrological performance of green roofs as a whole system. Moreover, there are no test protocols to regulate the design of green roof systems in the market. A comprehensive literature review examines existing research methods adopted to evaluate influencing parameters affecting the hydrological performance of NBS-CR. The results indicate several limitations in experimental and field investigations. Consequently, to address these limitations, it is essential to formulate a multi-functional work plan to develop a standardized test method that can become a common platform for the roofing industry to test and quantify the hydrological performance of their systems.

Hysteresis analysis reveals how phytoplankton assemblage shifts with the nutrient dynamics during and between precipitation patterns

The escalation of global eutrophication has significantly increased due to the impact of climate change, particularly the increased frequency of extreme rainfall events. Predicting and managing eutrophication requires understanding the consequences of precipitation events on algal dynamics. Here, we assessed the influence of precipitation events throughout the year on nutrient and phytoplankton dynamics in a drinking water reservoir from January 2020 to January 2022. Four distinct precipitation patterns, namely early spring flood rain (THX), Plum rain (MY), Typhoon rain (TF), and Dry season (DS), were identified based on rainfall intensity, duration time, and cumulative rainfall. The study findings indicate that rainfall is the primary driver of algal dynamics by altering nutrient levels and TN:TP ratios during wet seasons, while water temperature becomes more critical during the Dry season. Combining precipitation characteristics with the lag periods between algal proliferation and rainfall occurrence is essential for accurately assessing the impact of rainfall on algal blooms. The highest algae proliferation occurred approximately 20 and 30 days after the peak rainfall during the MY and DS periods, respectively. This was influenced by the intensity and cumulative precipitation. The reservoir exhibited two distinct TN/TP ratio stages, with average values of 52 and 19, respectively. These stages were determined by various forms of nitrogen and phosphorus in rainfall-driven inflows and were associated with shifts from Bacillariophyta-dominated to Cyanophyta-dominated blooms during the MY and DS seasons. Our findings underscore the interconnected effects of nutrients, temperature, and hydrological conditions driven by diverse rainfall patterns in shaping algal dynamics. This study provides valuable insights into forecasting algal bloom risks in the context of climate change and developing sustainable strategies for lake or reservoir restoration.

Framework for risk assessment of economic loss from structures damaged by rainfall-induced landslides using machine learning

ABSTRACT Given the increased frequency of extreme rainfall events, pre-disaster countermeasures against landslides triggered by heavy rainfall are important to enhance disaster resilience. This study presents a methodology for economic risk assessment of structures affected by rainfall-induced landslides using machine learning (ML). Random Forest and LightGBM algorithms were applied to develop ML-based landslide prediction models considering the spatial distributions of landslide conditioning and triggering factors. The rainfall index was calculated considering the temporal variation in rainfall and was used as a feature associated with rainfall intensity. The rainfall hazard curve, representing the relationship between the rainfall index and its annual exceedance probability, was statistically estimated using a generalised extreme value distribution. Rainfall-induced landslide susceptibility was assessed using an ML-based landslide prediction model and rainfall hazard curve. Finally, the risk curve associated with the economic loss from structures damaged by rainfall-induced landslides was estimated based on landslide susceptibility and structure distribution maps. In this study, LightGBM showed better prediction performance for evaluating rainfall-induced landslide susceptibility than Random Forest. An illustrative example is presented to demonstrate that the proposed methodology can be used to develop an appropriate risk-based disaster mitigation strategy.

Climate‐Smart IPM: Key to U.S. Ag Resilience

AbstractFarmers, ranchers, and foresters are facing increasing challenges related to changing climate and weather, manifested in rising average temperatures, more frequent extreme rainfall events, extended drought, and heat waves. These changes are also impacting pest abundance and distribution, creating new pest problems and straining current pest management options and decision‐making. Key factors for U.S. agriculture and producer resilience in tackling new environmental and pest challenges lie in the ability of producers to apply effective, economical integrated pest management (IPM) approaches and in support for research and development of new and renewed IPM tactics and systems to meet 21st century needs. Earn 0.5 CEUs in Integrated Pest Management by reading this article and taking the quiz at https://web.sciencesocieties.org/Learning‐Center/Courses.

Assessing the risk of check dam failure due to heavy rainfall using machine learning on the Loess Plateau, China

Check dams are widely used throughout the world to tackle soil and water loss. However, the frequency of extreme rainfall events has increased owing to global climate change and the main structure of check dam is gradually aging, which lead to an increase in the failure risk of check dams. Thus, it is necessary to carry out the study on failure risk diagnosis and assessment of check dams. In the study, machine learning algorithms (ML), including random forests (RF), support vector machine (SVM), and logistic regression (LR), were used to integrate the environmental and engineering factors and then assess the risk of check dam failure due to the “7.26” rainstorm on July 26, 2017, in the Chabagou watershed, located in the hilly-gully region of the Loess Plateau, China. To verify the generalizability of the model in this study, these models were used for the Wangmaogou catchment north of the Loess Plateau. The accuracy assessment by the receiver operating characteristic (ROC) curve indicated that the RF model with an area under the ROC curve (AUC) greater than 0.89 was the most precise model and had a higher generalization ability. In addition, the model dataset was relatively small and easy to obtain, which make the risk modeling of check dam failure in the study has the potential for application in other regions. In the RF model, each factor selected was confirmed to be important, and the importance values for engineering factors were generally higher than those for the environmental factors. The risk map of check dam failure in the RF model indicated that 56.34% of check dams in the study area had very high and high risks of dam failure under high-intensity rainfall in 2017. Based on the importance of factors and the risk map of check dam failure, the prevention and control measures for reducing the risk of check dam failure and promoting the construction of check dam are proposed. These proposals provide a scientific basis for the reinforcement of check dams and the future layout of check dams in the Chinese Loess Plateau.

Cloud Modelling of Property-Level Flood Exposure in Megacities

Surface water flood risk is projected to increase worldwide due to the growth of cities as well as the frequency of extreme rainfall events. Flood risk modelling at high resolution in megacities is now feasible due to the advent of high spatial resolution terrain data, fast and accurate hydrodynamic models, and the power of cloud computing platforms. Analysing the flood exposure of urban features in these cities during multiple storm events is essential to understanding flood risk for insurance and planning and ultimately for designing resilient solutions. This study focuses on London, UK, a sprawling megacity that has experienced damaging floods in the last few years. The analysis highlights the key role of accurate digital terrain models (DTMs) in hydrodynamic models. Flood exposure at individual building level is evaluated using the outputs from the CityCAT model driven by a range of design storms of different magnitudes, including validation with observations of a real storm event that hit London on the 12 July 2021. Overall, a novel demonstration is presented of how cloud-based flood modelling can be used to inform exposure insurance and flood resilience in cities of any size worldwide, and a specification is presented of what datasets are needed to achieve this aim.

Canopy gaps and associated losses of biomass – combining UAV imagery and field data in a central Amazon forest

Abstract. Understanding mechanisms of tree mortality and the dynamics of associated canopy gaps is relevant for robust estimates of carbon balance in forests. We combined monthly RGB images acquired from an unoccupied aerial vehicle with field surveys to identify gaps in an 18 ha plot installed in an old-growth central Amazon forest. We measured the size and shape of gaps and analyzed their temporal variation and correlation with rainfall over a period of 28 months. We further described associated modes of tree mortality (i.e., snapping, uprooting and standing dead) and branch fall and quantified associated losses of biomass. In total, we detected 32 gaps either in the images or field ranging in area from 9 to 835 m2. Relatively small gaps (< 39 m2) opened by branch fall were the most frequent (11 gaps). Out of 18 gaps for which both field and image data were available, three could not be detected remotely. Gaps observed in the field but not captured on the imagery were relatively small and mainly formed by the fall of branches from live and standing dead trees. Our data show that ∼ 17 % of the tree-mortality and branch-fall events only affected the lower canopy and the understory of the forest and are likely neglected by top-of-the-canopy assessments. Regardless of the detection method, the size distribution was best described by a lognormal function for gaps starting from the smallest detected size (9 and 10 m2 for field and imagery data, respectively), and the Weibull and Power functions for gaps larger than 25 m2. Properly assessing associated confidence intervals requires larger sample sizes. Repeated field measurements reveal that gap area does not differ significantly among modes of tree mortality or branch fall in central Amazon forests, with the last contributing the least to biomass loss. Predicting mechanisms of gap formation based on associated area and biomass loss remains challenging, which highlights the need for larger datasets. The rate of gap area formation was positively correlated with the frequency of extreme rainfall events, which may be related to a higher frequency of storms propagating extreme rain and wind gusts. While remote sensing has proven to be an accurate and precise method for mapping gaps compared to field data (i.e., ground truth), it is important to note that our sample size was relatively small. Therefore, the extrapolation of these results beyond our study region and landscape shall be made cautiously. Apart from improving landscape assessments of carbon balance, regional information on gap dynamics and associated mechanisms of formation are fundamental to address forest responses to altered disturbance regimes resulting from climate change.

Effects of rainfall amount and frequencies on soil net nitrogen mineralization in Gahai wet meadow in the Qinghai-Tibetan Plateau

Global climate change has led to a significant increase in the frequency of extreme rainfall events in the Qinghai-Tibetan Plateau (QTP), thus potentially increasing the annual rainfall amounts and, consequently, affecting the net soil nitrogen (N) mineralization process. However, few studies on the responses of the soil net N mineralization rates to the increases in rainfall amounts and frequencies in alpine wet meadows have been carried out. Therefore, the present study aims to assess the effects of rainfall frequency and amount changes on the N fixation capacity of wet meadow soils by varying the rainfall frequency and amount in the Gahai wet meadow in the northeastern margin of the QTP during the plant-growing season in 2019. The treatment scenarios consisted of ambient rain (CK) and supplementary irrigation at a rate of 25 mm, with different irrigation frequencies, namely weekly (DF1), biweekly (DF2), every three weeks (DF3), and every four weeks (DF4). According to the obtained results, the increased rainfall frequency and amount decreased the soil mineral N stock and increased the aboveground vegetation biomass (AB) amounts and soil water contents in the wet meadows of the QTP. Ammonium (NH4+-N) and nitrate N (NO3–-N) contributed similarly to the mineral N contents. However, the ammonification process played a major role in the soil mineralization process. The effects of increasing rainfall amount and frequency on N mineralization showed seasonal variations. The N mineralization rate showed a single-peaked curve with increasing soil temperature during the rapid vegetation growth phase, reaching the highest value in August. In addition, the N mineralization rates showed significant positive correlations with soil temperatures and NH4+-N contents and a significant negative correlation with AB (P < 0.05). The results of this study demonstrated the key role of low extreme rainfall event frequencies in increasing the net soil N mineralization rates in the vegetation growing season, which is detrimental to soil N accumulation, thereby affecting the effectiveness of soil N contents.

El Niño–Southern Oscillation (ENSO)-driven hypersedimentation in the Poechos Reservoir, northern Peru

Abstract. Although extreme El Niño events (EENEs) have always impacted hydrological anomalies and sediment transport in South America, their intensification by global warming and their association with changes in human activities and land cover after humid periods may lead to the acceleration of sediment transfers in river systems and dam reservoirs. This situation may threaten soil and water resources in arid and semiarid regions highly dependent on water originating from large dams. In this study, we investigated the sediment sequence accumulated in the Poechos Reservoir (northern Peru) and provided a retrospective reconstruction of the interactions of El Niño–Southern Oscillation (ENSO), agricultural practices and vegetation cover changes with sediment dynamics (1978–2019). To this end, a sediment core was dated and characterized by physical and chemical analyses (e.g., scanner tomography, X-ray fluorescence, particle size analysis) to estimate the evolution of sedimentation rates and changes in sediment sources during the last 5 decades. Sediment tracing results indicated the occurrence of changes in sediment sources associated with positive and negative phases of the Eastern Pacific index with a greater contribution of the lowland dry-forest area in comparison to that of the Andean area to sediment during the El Niño events (mean contribution of 76 %; up to 90 % during the coastal El Niño events (CENEs) of 2016–2017). This source contribution was mostly controlled by the stationary rainfall occurring during the EENEs in the lowland dry-forest area characterized by a low vegetation cover. Overall, after an extreme phase of ENSO, like after the EENE 1982–1983, the normal discharges and persistent sediment supplies from the middle- and upper-catchment parts led to river aggradation and the storage of substantial amounts of sediment in alluvial plains. In the absence of a significant EENE between 1983 and 1997, the large volume of sediment stored in the alluvial plains was exported by the EENE 1997–1998 resulting in an increase in sedimentation rate of 140 % after 1997 with a significant aggradation of the deltaic zone of the reservoir. In addition to the impact of extreme climate events on sediment dynamics, the development of agriculture along the riverine system after an extreme phase of ENSO increased the availability of sediments in the main channel of the rivers, easily transported by the next EENE. This study suggests that intensification of human activities associated with a higher frequency of extreme rainfall events amplified the quantity of sediment transported by the river system, which will significantly decrease the lifespan of the reservoir, which is essential to meeting the freshwater demands of the farmers and the populations living in this arid and semiarid region.

Possible factors for the recent changes in frequency of central Indian Summer Monsoon precipitation extremes during 2005–2020

The transitions of short-term trends of Indian Summer Monsoon daily precipitation extremes remain poorly understood. Here, we show a short-term declination of temporal frequency of monsoonal precipitation extremes over Central India for the last 15 years starting from 2005 to 2020 while the low and moderate events show increasing trends. The possible dynamical and thermodynamical factors for the observed decline in precipitation extremes include depletion in moisture transport to Central India via cross-equatorial flow and the low-level Somali jet, caused by reduced evaporation from the regional moisture sources. In addition, the vertical gradient of the moist static energy exhibits favorable conditions for increased lower atmospheric stability, supporting the declining trends. Lastly, the Atlantic Zonal Mode seems to be a potential large-scale climate driver for the less frequent central Indian extreme rainfall events during 2005–2020.