Antecedent Rainfall Conditions Research Articles

Evaluation of the Antecedent Saturation and Rainfall Conditions on the Slope Failure Mechanism Triggered by Rainfalls

The stability analysis of rainfall-induced slope failures considers a number of factors including the characteristics of the rainfall, vegetation, geometry of the slope, unsaturated soil characteristics, infiltration capacity, and saturation degree variations. Amongst all these factors, this study aims to investigate the effects of the antecedent rainfall and saturation conditions. A numerical modeling study was conducted using finite difference code software on a representative slope geometry with two different soil types. Two scenarios were followed: The first involved the application of three different rainfall intensities for varying initial saturation levels between 40% and 60%, representing the antecedent saturation conditions. The second scenario involved modeling successive rainfalls for a typical initial saturation degree of 50%. The impact of antecedent rainfall was assessed by determining the time required for failure during the application of a main extreme rainfall after a preceding rainfall of varying durations. Consequently, a zone of susceptible time for failure was suggested for use as a criterion in hazard management, allowing for the tracking of rainfall and its duration through the proposed chart for potential failures. Once the anticipated critical rainfall intensities have been determined through a meteorological analysis, a risk assessment for a specific slope can be conducted using the proposed practical procedure. Accordingly, a control mechanism may be established to detect the potential for a natural hazard. Furthermore, the proposed procedure was applied to a case study, whose modeling insights were in harmony with the real conditions of the slope failure. Thus, this demonstrated the significance of the antecedent conditions in modeling landslides triggered by rainfalls.

Extreme rainfall and landslides as a response to human-induced climate change: a case study at Baixada Santista, Brazil, 2020

In March 2020, an extreme rainfall in Baixada Santista, Brazil, led to a series of landslides affecting more than 2800 people and resulting losses exceeding USD 43 million. This attribution study compared extreme rainfall in two large ensembles of the UK Met Office Hadley Centre HadGEM3-GA6 model that represented the event with and without the effects of anthropogenic climate change. Antecedent rainfall conditions on two different timescales are considered, namely extreme 60-day rainfall (Rx60day) which relates to the soil moisture conditions and extreme 3-day rainfall (Rx3day) which represents landslide triggering heavy rainfall. In the scenario including both natural and human-induced factors the antecedent 60 day rainfall became 74% more likely, while the short-term trigger was 46% more likely. The anthropogenic contribution to changes in rainfall accounted for 20–42% of the total losses and damages. The greatest economic losses occurred in Guarujá (42%), followed by São Vicente (30%) and Santos (28%). Landslides were responsible for 47% of the homes damaged, 85% of the homes destroyed, all reported injuries, and 51% of the deaths associated with heavy rainfall. Changes in land cover and urbanization showed a pronounced increase in urbanized area in Guarujá (107%), São Vicente (61.7%) and Santos (36.9%) and a reduction in farming area. In recent years, the region has experienced an increase in population growth and a rise in the proportion of irregular and/or precarious housing in high-risk areas. Guarujá has the highest number of such dwellings, accounting for 34.8%. Our estimates suggest that extreme precipitation events are having shorter return periods due to climate change and increased urbanization and population growth is exposing more people to these events. These findings are especially important for decision-makers in the context of disaster risk reduction and mitigation and adaptation to climate change.

Mesoscale Influences of Land Use, Topography, Antecedent Rainfall, and Atmospheric Conditions on Summertime Convective Storm Initiation under Weak Synoptic-Scale Forcing

Abstract Throughout the summer months in the Southeast United States (SEUS), the initiation of isolated convection (CI) can occur abundantly during the daytime with weak synoptic support (e.g., weak wind shear). Centered around this premise, a dual-summer, limited-area case study of CI events concerning both geographical and meteorological features was conducted. The goal of this study was to help explain SEUS summertime CI in weak synoptic environments, which can enhance CI predictability. Results show that spatial CI nonrandomness event patterns arise, with greater CI event density appearing over high elevation by midday. Later in the day, overall CI event counts subside with other mechanisms/factors emerging (e.g., urban heat island). Antecedent rainfall, instability, and moisture features are also higher on average where CI occurred. In a random forest feature importance analysis, elevation was the most important variable in dictating CI events in the early to midafternoon while antecedent rainfall and wind direction consistently rank highest in permutation importance. The results cumulatively allude to, albeit in a muted, nonsignificant statistical signal, and a degree of spatial clustering of CI event occurrences cross the study domain as a function of daytime heating and contributions of features to enhancing CI probabilities (e.g., differential heating and mesoscale thermal circulations). Significance Statement Widespread isolated thunderstorms in the Southeast United States summer season with weak synoptic support have been commonly observed. With forecasting these remaining a challenge, a dual-summer intercomparison of geographical/meteorological features with convective initiation events was conducted. Radar data with a minimum threshold for convective initiation detection (35 dBZ) were used. Spatial nonrandomness was discovered with greater event density appearing over higher elevation by midday. Features such as prior rainfall and atmospheric instability/moisture were higher on average where initiation occurred. In a feature importance analysis, elevation ranked higher in the early to midafternoon hours while antecedent rainfall and wind direction ranked highest overall in permutation importance. These results allude to the contribution of localized phenomena to the nonrandomness (e.g., mesoscale circulations).

Transformation of organic carbon through medium pressure (polychromatic) UV disinfection of wastewater effluent during wet weather events

An observed decrease in total organic carbon (TOC) and dissolved organic carbon (DOC) concentrations following wastewater disinfection with medium pressure (MP, polychromatic) ultraviolet (UV) irradiation during wet weather flows is investigated. When antecedent rainfall in the previous 7-days was >2 in (5 cm), TOC and DOC concentrations decreased dramatically following MP-UV disinfection. Organic carbon surrogate measurements of biological oxygen demand (BOD), TOC, DOC, turbidity, UVA – 254 nm, SUVA (specific UVA), scanning UV–Visible spectra (200–600 nm), fluorescence excitation-emission matrix (EEM) spectra, and light scattering data are presented for wastewater resource recovery facility (WRRF) influent, secondary effluent (pre-UV-disinfection), and MP-UV-disinfected (final effluent) samples. TOC and DOC in wastewater influent and secondary effluent (i.e., pre-UV disinfection) correlated with antecedent rainfall conditions. The percent TOC and DOC removal through secondary treatment (i.e., from influent to effluent pre-UV) and the percent TOC and DOC removal through MP-UV disinfection (i.e., from effluent pre-UV to effluent post-UV) were compared and the latter approached 90 % through MP-UV disinfection during high antecedent rainfall conditions. Spectroscopy (UV, visible, or fluorescence) was performed on samples after filtration through 0.45 μm filters, i.e., the operationally defined DOC fraction of aquatic carbon. Scanning UV–visible spectra indicated transformation of an unidentified wastewater component into light-scattering entities regardless of antecedent rainfall conditions. The types of organic carbon (diagenetic, biogenic, or anthropogenic) and the significance of wet weather are discussed. An organic carbon contribution via infiltration and inflow was attributed as a source-of-interest in this research.

A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data

Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights.

Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China

There is no doubt that land cover and climate changes have consequences on landslide activity, but it is still an open issue to assess and quantify their impacts. Wanzhou County in southwest China was selected as the test area to study rainfall-induced shallow landslide susceptibility under the future changes of land use and land cover (LULC) and climate. We used a high-resolution meteorological precipitation dataset and frequency distribution model to analyse the present extreme and antecedent rainfall conditions related to landslide activity. The future climate change factors were obtained from a 4-member multi-model ensemble that was derived from statistically downscaled regional climate simulations. The future LULC maps were simulated by the land change modeller (LCM) integrated into IDRISI Selva software. A total of six scenarios were defined by considering the rainfall (antecedent conditions and extreme events) and LULC changes towards two time periods (mid and late XXI century). A physically-based model was used to assess landslide susceptibility under these different scenarios. The results showed that the magnitude of both antecedent effective recharge and event rainfall in the region will evidently increase in the future. Under the scenario with a return period of 100 years, the antecedent rainfall in summer will increase by up to 63% whereas the event rainfall will increase by up to 54% for the late 21st century. The most considerable changes of LULC will be the increase of forest cover and the decrease of farming land. The magnitude of this change can reach + 22.1% (forest) and –9.2% (farmland) from 2010 until 2100, respectively. We found that the negative impact of climate change on landslide susceptibility is greater than the stabilizing effect of LULC change, leading to an over decrease in stability over the study area. This is one of the first studies across Asia to assess and quantify changes of regional landslide susceptibility under scenarios driven by LULC and climate change. Our results aim to guide land use planning and climate change mitigation considerations to reduce landslide risk.

Centrifuge modelling of unsaturated slopes subjected to the integrated effect of groundwater and rainfall infiltration

The stability of unsaturated soil slopes is comprehensively discussed with the rainfall characteristics. However, pre-existing groundwater table is also common in many tropical mountainous regions. Additionally, perched water table could be generated due to antecedent rainfall conditions on the soil-bedrock interface. The complex hydrological response process of slopes subjected to the integrated effect of groundwater and rainfall infiltration has not yet been entirely realized. Therefore, the current studyaimed at investigating the influence of initial groundwater table on initiation of landslides in slopes exposed to rainfall infiltration. The centrifuge modelling technique is utilized in this study. A centrifuge container was newly designed to reproduce the rainfall and groundwater table effects. Identical soil slopes made from silty sand were tested under 50g conditions. The unsaturated slopes behaviour was evaluated under two cases during the analysis: 01) rainfall only and 02) rainfall on a pre-existing groundwater table. The results demonstrated that the failure happened in case (02) was quicker, larger in volume and faster to respond to rainfall infiltration. The factor of safety calculated using the infinite slope equations portrayed a slight mismatch of failure timing compared to the experimental results. These results suggested i) a higher risk ofslope failure under pre-existing groundwater flow and ii) acceleration of the progression of landside due to a potential surcharge flow.

Flow partitioning modelling using high-resolution electrical conductivity data during variable flow conditions in a tropical montane catchment

Tracer-aided hydrological models (TAHMs) are one of the most powerful tools to identify new (event) and old (pre-event) water fractions contributing to stormflow because they account both for streamflow and tracer mixing dynamics in model calibration. Nevertheless, their representativeness of hydrograph dynamics is often limited due to the unavailability of high-resolution conservative tracer data (e.g., water stable isotopes or chloride). Hence, there is a need to identify alternative tracers yielding similar flow partitioning results than “ideal” ones while requiring fewer financial resources for high-frequency monitoring (e.g., sub-hourly). Here, we compare flow partitioning results of a TAHM calibrated using high-frequency electrical conductivity (EC) and water stable isotope (18O) data collected during 37 rainfall-runoff events monitored during variable hydrometeorological conditions in the Zhurucay Ecohydrological Observatory, a tropical alpine catchment located in southern Ecuador. When the model was calibrated using the sampling resolution of stables isotopes (6-hours to 1-hour), no statistically significant differences of pre-event water fractions (PEWFs) using both tracers for model calibration were found. PEWF differences between both tracers for 89% of the events were < 20% regardless of the events’ antecedent moisture and rainfall conditions. Model transfer functions were also similar suggesting that catchment internal processes inferred using both tracers are comparable. Events presenting larger differences (n = 4; up to 27% PEWF difference) had no samples collected during peak flow. Calibration of the model using EC data collected at sub-hourly intervals (every 5-minutes) showed a significant increase in model performance as compared to the frequency of collection of isotopic data. Similarity in flow partitioning results can be attributed to a quasi-conservative nature of EC due to the presence of organic-rich riparian soils (peat-type) overlying compact bedrock across the catchment. Findings also highlight the importance of capturing rapidly occurring catchment mixing processes though high-temporal frequency monitoring of tracer data. Our study encourages the value of assessing the use of alternative tracers, such as EC, to identify fast occurring rainfall-runoff processes, while lowering the costs needed to implement and sustain tracer data collection for long time periods.

Forest cover lessens the impact of drought on streamflow in Puerto Rico

AbstractTropical regions are experiencing high rates of forest cover loss coupled with changes in the volume and timing of rainfall. These shifts can compromise streamflow and water provision, highlighting the need to identify how forest cover influences streamflow generation under variable rainfall conditions. Although rainfall is the key driver of streamflow regimes, the role of forests is less clear, particularly in tropical regions where forest loss is an ongoing risk. Forest cover loss alters evapotranspiration, rainfall infiltration and storage, and may increase stream ecosystem vulnerability to rainfall extremes. Puerto Rico, an island with spatially heterogenous forest cover and a marked geographic rainfall gradient, is projected to experience more frequent droughts and flash flooding. Using 15‐min streamflow data collected between 2005 and 2016 from 20 US Geological Survey stream gages and 3‐hourly Multi‐Source Weighted‐Ensemble Precipitation rainfall estimates, we utilized flow‐duration curves and linear mixed regression models to examine the role of forest cover in regulating the timing and volume of streamflow. The mixed model approach helps to account for differences in watershed characteristics. We determined the effects of rainfall and forest cover on low and peak flows in Puerto Rican streams, then evaluated changes in these relationships under dry and wet antecedent rainfall conditions. Watersheds with high forest cover had consistently greater low and peak streamflow than deforested ones under all rainfall conditions, although the effect was more marked during wet antecedent conditions, suggesting that peak flow is largely the result of saturation excess overland flow. During dry antecedent rainfall conditions, highly forested watersheds had higher streamflow than deforested ones, suggesting greater hillslope storage and release may also be at play. Our results demonstrate that forest cover generated a net increase in hillslope infiltration and storage and may lessen drought impacts on streamflow in Puerto Rico. Resilience to prolonged drought may be limited by finite water storage potential in this steep, mountainous setting, highlighting maintenance of forest cover as an important water management strategy to increase infiltration.

Drivers and Subseasonal Predictability of Heavy Rainfall in Equatorial East Africa and Relationship with Flood Risk

AbstractEquatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with preemptive action; however, currently available early warnings are limited to a few days’ lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However, before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper-quintile rainfall within a season, at lead times of 2 weeks and beyond. We demonstrate the link between the Madden–Julian oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia River in western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper-quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed.

Enhancing Flood Hazard Assessments in Coastal Louisiana Through Coupled Hydrologic and Surge Processes

Traditional coastal flood hazard studies do not typically account for rainfall-runoff processes in quantifying flood hazard and related cascading risks. This study addresses the potential impacts of antecedent rainfall-runoff, tropical cyclone (TC)-driven rainfall, and TC-driven surge on total water levels and its influence in delineating a coastal flood transition zone for two distinct coastal basins in southeastern Louisiana (Barataria and Lake Maurepas watersheds). Rainfall-runoff from antecedent and TC-driven rainfall along with storm surge was simulated using a new rain-on-mesh module incorporated into the ADCIRC code. Antecedent rainfall conditions were obtained for 21 landfalling TC events spanning 1948–2008 via rain stations. A parametric, TC-driven, rainfall model was used for precipitation associated with the TC. Twelve synthetic storms of varying meteorological intensity (low, medium, and high) and total rainfall were utilized for each watershed and provided model forcing for coastal inundation simulations. First, it was found that antecedent rainfall (pre-TC landfall) is influential up to 3 days pre-landfall. Second, results show that antecedent and TC-driven rainfall increase simulated peak water levels within each basin, with antecedent rainfall dominating inundation across the basin's upper portions. Third, the delineated flood zones of coastal, transition, and hydrologic show stark differences between the two basins.

The optimal rainfall thresholds and probabilistic rainfall conditions for a landslide early warning system for Chuncheon, Republic of Korea

The purpose of this study is to establish the criteria for a landslide early warning system (LEWS). We accomplished this by deriving optimal thresholds for the cumulative event rainfall–duration (ED) and identifying the characteristics of the rainfall variables associated with a high probability of landslide occurrence via a Bayesian model. We have established these system criteria using rainfall and landslide data for Chuncheon, Republic of Korea. Heavy rainfall is the leading cause of landslides in Chuncheon; thus, it is crucial to determine the rainfall conditions that trigger landslides. Hourly rainfall data spanning 1999 to 2017 from seven gauging stations were utilized to establish the ED thresholds and the Bayesian model. We used three different calibration periods of rainfall events split by 12, 24, 48, and 96 non-rainfall hours to calibrate the ED thresholds. Finally, the optimal threshold was determined by comparing the results of the contingency table and the skill scores that maximize the probability of detection (POD) score and minimize the probability of false detection (POFD) score. In the LEWS, by considering the first level as “normal,” we developed subsequent step-by-step warning levels based on the Bayesian model as well as the ED thresholds. We propose the second level, “watch,” when the rainfall condition is above the ED thresholds. We then adopt the third level, “warning,” and the fourth level, “severe warning,” based on the probability of landslide occurrence determined via a Bayesian model that considers several factors including the rainfall conditions of landslide vs. non-landslide and various rainfall variables such as hourly maximum rainfall and 3-day antecedent rainfall conditions. The proposed alert level predicted a total of 98.2% of the landslide occurrences at the levels of “severe warning” and “warning” as a result of the model fitness verification. The false alarm rate is 0% for the severe warning level and 47.4% for the warning level. We propose using the optimal ED thresholds to forecast when landslides are likely to occur in the local region. Additionally, we propose the ranges of rainfall variables that represent a high landslide probability based on the Bayesian model to set the landslide warning standard that fits the local area’s characteristics.

Heavy Rainfall Events and Diarrheal Diseases: The Role of Urban-Rural Geography.

Diarrheal diseases remain a significant contributor to the global burden of disease. Climate change may increase their incidence by altering the epidemiology of waterborne pathogens through changes in rainfall patterns. To assess potential impacts of future changes in rainfall patterns, we analyzed 33,927 cases of diarrhea across all Ministry of Health clinical facilities in Esmeraldas Province, Ecuador, for a 24-month period from 2013 to 2014, using mixed-effects Poisson regression. We assessed the association between the incidence of diarrheal diseases and heavy rainfall events (HREs) and antecedent rainfall conditions. In rural areas, we found no significant associations between HREs and incidence. In urban areas, dry antecedent conditions were associated with higher incidence than wet conditions. In addition, HREs with dry antecedent conditions were associated with elevated incidence by up to 1.35 (incidence rate ratio, 95% CI: 1.14-1.60) times compared with similar conditions without HREs. These patterns may be driven by accumulation of fecal contamination during dry periods, followed by a flushing effect during HREs. This phenomenon is more important in dense urban environments with more impervious surfaces. These findings suggest that projected increases in rainfall variability and HREs may increase diarrhea burden in urban regions, which are rapidly expanding globally.

Effect of antecedent rainfall conditions and their variations on shallow landslide-triggering rainfall thresholds in South Korea

The intensity-duration (I-D) threshold is considered an effective indicator for landslides triggered by short-term high-intensity rainfall and long-term low-intensity rainfall. However, previous studies have not considered the influence of antecedent rainfall. Herein, we analyzed hourly rainfall data for 613 shallow landslides that occurred from 1963 to 2018 in South Korea to obtain rainfall thresholds and investigated the effect of antecedent rainfall conditions on threshold variations. The I-D and IMAP-D, which is normalized by dividing I by mean annual precipitation (MAP), thresholds were determined to be I = 10.40D–0.31 and IMAP = 0.006D–0.26 (4 ≤ D (h) ≤ 84), respectively, at the 2nd percentile level through quantile regression analysis. These thresholds were lower than other local thresholds (i.e., excluding global and regional thresholds), suggesting that the southern region of the Korean Peninsula was more susceptible to rainfall-induced landslides. Although the effective length of antecedent rainfall was not presented herein, the I-D threshold of landslide-triggering rainfall was confirmed to be low for the absolute and/or calibrated antecedent rainfalls greater than event cumulative rainfall at 5, 7, 10, and 20 days prior to the event. Such differences in I can be greater at shorter durations; however, these differences gradually decrease as D increases, suggesting that they lose their effect as soil water content increases. The results of the current study can improve the understanding of the effect of antecedent rainfall conditions on landslide occurrence and should be further tested with respect to the hydrologic response of hillslopes by considering regional climate and local site conditions.

Temporal Probability Assessment and Its Use in Landslide Susceptibility Mapping for Eastern Bhutan

Landslides are one of the major natural disasters that Bhutan faces every year. The monsoon season in Bhutan is usually marked by heavy rainfall, which leads to multiple landslides, especially across the highways, and affects the entire transportation network of the nation. The determinations of rainfall thresholds are often used to predict the possible occurrence of landslides. A rainfall threshold was defined along Samdrup Jongkhar–Trashigang highway in eastern Bhutan using cumulated event rainfall and antecedent rainfall conditions. Threshold values were determined using the available daily rainfall and landslide data from 2014 to 2017, and validated using the 2018 dataset. The threshold determined was used to estimate temporal probability using a Poisson probability model. Finally, a landslide susceptibility map using the analytic hierarchy process was developed for the highway to identify the sections of the highway that are more susceptible to landslides. The accuracy of the model was validated using the area under the receiver operating characteristic curves. The results presented here may be regarded as a first step towards understanding of landslide hazards and development of an early warning system for a region where such studies have not previously been conducted.

Rainwater harvesting for urban flood management – An integrated modelling framework

It is well known that rainwater harvesting (RWH) can augment water supply and reduce stormwater pollutant discharges. Due to the lack of continuous 2D modelling of urban flood coverage and its associated damage, the ability of RWH to reduce urban flood risks has not been fully evaluated. Literature suggests that small distributed storage spaces using RWH tanks will reduce flood damage only during small to medium flooding events and therefore cumulative assessment of their benefits is needed. In this study we developed a new integrated modelling framework that implements a semi-continuous simulation approach to investigate flood prevention and water supply benefits of RWH tanks. The framework includes a continuous mass balance simulation model that considers antecedent rainfall conditions and water demand/usage of tanks and predicts the available storage prior to each storm event. To do so, this model couples a rainfall-runoff tank storage model with a detailed stochastic end-use water demand model. The available storage capacity of tanks is then used as a boundary condition for the novel rapid flood simulation model. This flood model was developed by coupling the U.S. EPA Storm Water Management Model (SWMM) to the Cellular-Automata Fast Flood Evaluation (CA-ffé) model to predict the inundation depth caused by surcharges over the capacity of the drainage network. The stage-depth damage curves method was used to calculate time series of flood damage, which are then directly used for flood risk and cost-benefit analysis. The model was tested through a case study in Melbourne, using a recorded rainfall time series of 85 years (after validating the flood model against 1D-2D MIKE-FLOOD). Results showed that extensive implementation of RWH tanks in the study area is economically feasible and can reduce expected annual damage in the catchment by up to approximately 30 percent. Availability of storage space and temporal distribution of rainfall within an event were important factors affecting tank performance for flood reduction.

Rainfall Thresholds for Prediction of Landslides in Idukki, India: An Empirical Approach

Idukki is a South Indian district in the state of Kerala, which is highly susceptible to landslides. This hilly area which is a hub of a wide variety of flora and fauna, has been suffering from slope stability issues due to heavy rainfall. A well-established landslide early warning system for the region is the need of the hour, considering the recent landslide disasters in 2018 and 2019. This study is an attempt to define a regional scale rainfall threshold for landslide occurrence in Idukki district, as the first step of establishing a landslide early warning system. Using the rainfall and landslide database from 2010 to 2018, an intensity-duration threshold was derived as I = 0.9D−0.16 for the Idukki district. The effect of antecedent rainfall conditions in triggering landslide events was explored in detail using cumulative rainfalls of 3 days, 10 days, 20 days, 30 days, and 40 days prior to failure. As the number of days prior to landslide increases, the distribution of landslide events shifts towards antecedent rainfall conditions. The biasness increased from 72.12% to 99.56% when the number of days was increased from 3 to 40. The derived equations can be used along with a rainfall forecasting system for landslide early warning in the study region.

Precipitation and Salmonellosis Incidence in Georgia, USA: Interactions between Extreme Rainfall Events and Antecedent Rainfall Conditions.

Background:The southeastern United States consistently has high salmonellosis incidence, but disease drivers remain unknown. Salmonella is regularly detected in this region’s natural environment, leading to numerous exposure opportunities. Rainfall patterns may impact the survival/transport of environmental Salmonella in ways that can affect disease transmission.Objectives:This study investigated associations between short-term precipitation (extreme rainfall events) and longer-term precipitation (rainfall conditions antecedent to these extreme events) on salmonellosis counts in the state of Georgia in the United States.Methods:For the period 1997–2016, negative binomial models estimated associations between weekly county-level extreme rainfall events ( percentile of daily rainfall) and antecedent conditions (8-week precipitation sums, categorized into tertiles) and weekly county-level salmonellosis counts.Results:In Georgia’s Coastal Plain counties, extreme and antecedent rainfall were associated with significant differences in salmonellosis counts. In these counties, extreme rainfall was associated with a 5% increase in salmonellosis risk (95% CI: 1%, 10%) compared with weeks with no extreme rainfall. Antecedent dry periods were associated with a 9% risk decrease (95% CI: 5%, 12%), whereas wet periods were associated with a 5% increase (95% CI: 1%, 9%), compared with periods of moderate rainfall. In models considering the interaction between extreme and antecedent rainfall conditions, wet periods were associated with a 13% risk increase (95% CI: 6%, 19%), whereas wet periods followed by extreme events were associated with an 11% increase (95% CI: 5%, 18%). Associations were substantially magnified when analyses were restricted to cases attributed to serovars commonly isolated from wildlife/environment (e.g., Javiana). For example, wet periods followed by extreme rainfall were associated with a 34% risk increase (95% CI: 20%, 49%) in environmental serovar infection.Conclusions:Given the associations of short-term extreme rainfall events and longer-term rainfall conditions on salmonellosis incidence, our findings suggest that avoiding contact with environmental reservoirs of Salmonella following heavy rainfall events, especially during the rainy season, may reduce the risk of salmonellosis. https://doi.org/10.1289/EHP4621

Effectiveness of water-oriented thinning in two semiarid forests: The redistribution of increased net rainfall into soil water, drainage and runoff

Water is the key element that modulates the provision of goods and services together with global/climate stressors affecting semiarid forests. In this sense, there is a need to improve the understanding and quantification of forest and water relationships as affected by forest management. This work addresses this issue by comparing net rainfall (Pn) redistribution into different belowground hydrological processes (BHP) in two forest types after a thinning treatment: a holm oak coppice (HU) and a post-fire Aleppo pine regeneration (CAL). The relative contribution (RI) of forest structure, antecedent soil moisture (θst), rainfall and meteorological conditions on the BHP was assessed through boosted regression trees models. In both sites, the RI of the forest structure itself was limited (<10%). However, θst, which clearly increased significantly with thinning, received an average RI of 29%. Surface and subsurface lateral flows showed values <1% of gross rainfall (Pg) in either site and were not significantly affected by thinning. On the other hand, soil moisture and drainage were affected by the thinning treatment, although with different extent depending on the site: in the drier site (CAL), the increased Pn in the thinning was mainly allocated into increased soil water content, with very limited improvement in drainage (<10 mm/year); in contrast, in the wetter continental site of HU, drainage to deeper soil layers was the most remarkable effect of thinning (50 mm/year higher than in control), given the higher θst and hence the lower soil water storage available. Thinning also improved the response of BHP during drought, making these processes more elastic and less vulnerable to climatic extremes. The results presented here complement those previously reported on rainfall partitioning in these sites and all together provide a comprehensive understanding of the short-term effect (3–4 years) of water-oriented silviculture in Quercus ilex and Pinus halepensis low-biomass semiarid forests. Questions such as the long-term effects of thinning remain open for these ecosystems.

Precipitation thresholds for fecal bacterial indicators in the Chesapeake Bay

Many coastal states of the United States restrict harvest of shellfish from select areas based on some environmental trigger. Such areas are classified as being conditionally approved. In Maryland, the trigger is an inch or more of rainfall that has fallen in the last 24 h. This study used 11 years of monitoring data to test the relationship between daily rainfall totals and densities of fecal indicators in Maryland shellfish harvest waters. Precipitation and fecal coliform (FC) water monitoring data from 2004 to 2014 were matched by date and watershed. The influence of antecedent rainfall conditions (i.e. rainfall in the preceding days or weeks) and the distance of each monitoring station to land were compared to the percent of samples exceeding the FDA criterion for managing shellfish harvest areas. Sample stations beyond 1000m from land had FC densities consistently below the FDA criterion and were excluded from further analysis. Rainfall events greater than an inch tended to result in significantly elevated FC for the following two days, followed by lower levels thereafter. The total amount of rain in the last three weeks was positively related to the proportion of samples with FC greater than the FDA criterion. Bay-wide, the percent of samples exceeding the FDA criterion rose from seven percent for rainfall less than an inch to 37% following one or more inches of rain. Watersheds were classified based on the percent of FC densities over the criterion when rainfall was an inch or more, with 41 of 81 watersheds showing FC responses indicative of potential conditionally approved areas, those shellfish growing areas where the one inch precipitation trigger may be applied. These areas largely overlapped the current conditionally approved areas defined by Maryland. The percent of open water, wetlands, and poorly drained soils explained a significant amount of the variability (R2 = 0.72) in the difference in percent of samples exceeding the FDA criterion when rainfall was greater than an inch and when it was less than an inch. Logistic regression analysis showed that the current trigger of one inch of rain in 24 h is predictive of FC densities over the FDA criterion, though the appropriate threshold will most likely depend on how far the particular shellfish growing area is from land and antecedent rain conditions. In watersheds with relatively high percentages of open water to total watershed size, higher rainfall thresholds might be appropriate. The approach taken in this study could be applied to individual stations and sub-watersheds, potentially allowing the reclassification of some shellfish harvest areas.