Rainfall Levels Research Articles

New data-based analysis tool for functioning of natural flood management measures reveals multi-site time-variable effectiveness

Temporary storage areas (TSAs) are a type of Natural Flood Management measure or nature-based solution that can provide additional storage during flood events by intercepting and attenuating surface runoff. Pressures on land use and an increase in climate change induced storms means there is a need to create additional storage within multifunctional rural landscapes. Implementation of small-scale TSAs is slowly gaining momentum, but practitioners still require further evidence on their functioning during different storm events. Here we present the TSA Drainage Rate Analysis tool (TSA-DRA tool), a novel data-based mechanistic method that only requires rainfall and TSA water level data to describe individual TSA drainage rates. We developed and then used the TSA-DRA tool to perform a multi-site assessment of different TSAs, allowing comparisons of TSA functioning across different types or time-variable factors. TSA design and outlet were found to be the dominant controls on drainage rates when the feature is full. Meanwhile, time-variable differences in functioning were more evident at lower water levels, when soil infiltration was the main TSA outflow. Results from a modelling experiment using observed time-variable TSA drainage rates suggested that these can impact the TSAs flood mitigation effectiveness. Specifically, for a particular event, soil conditions of a TSA in NE Scotland were more effective during spring than winter in attenuating surface runoff. Understanding spatial and temporal differences in TSA drainage rates will help optimise existing and future TSA designs, ensuring small-scale headwater TSAs are successfully integrated within rural catchments to mitigate an increasing exposure to hydrological extremes.

Environmental factors associated with Escherichia coli concentration at freshwater beaches on Lake Winnipeg, Manitoba, Canada

At many public beaches, routine monitoring of beach water quality using fecal indicator bacteria is conducted to evaluate the risk of recreational water illness. Results from water sample analysis can take over 24-hr, which may no longer accurately reflect current water quality conditions. This study aimed to assess which combination of environmental factors best predicts fecal contamination (E. coli) levels at two of the most popular beaches on Lake Winnipeg, Manitoba (Gimli and Grand Beach), by linking water quality data and publicly available environmental data from 2007 to 2021. We developed separate mixed effects models for each beach for two outcomes, linear (continuous log-transformed E. coli concentration) and categorical (200 CFU/100 ml threshold), to explore differences in the predictors of E. coli concentrations and exceedances of the provincial health risk threshold, respectively. We used a Directed Acyclic Graph to choose which predictor variables to include in the models. For both beaches, we identified clustering of the E. coli outcomes by year, suggesting year-specific variation. We also determined that extreme weather days, with higher levels of rainfall in the preceding 48-hr, previous day average air temperature, and previous day E. coli concentration could result in a higher probability of E. coli threshold exceedances or higher concentrations in the water bodies. In Grand Beach, we identified that days with lower average UV levels in the previous 24-hr and antecedent dry days could result in a higher probability of E. coli threshold exceedances or higher concentrations. The findings can inform possible trends in other freshwater settings and be used to help develop real-time recreational water quality predictive models to allow more accurate beach management decisions and warrant enhancement of beach monitoring programs for extreme weather events as part of the climate change preparedness efforts.

Assessing the impact of climate change on soybean production in Argentina

Soy is the most important agricultural commodity in Argentina, with relevance in public revenues and international reserves accumulation. However, a proper impact evaluation of future losses in the context of climate change has not yet been developed. Therefore, the aim of this paper is to provide future estimates of impact evaluation for macroeconomic adaptation policies. By means of a multiple partial regression, the relation between total country soybean yields and different combinations of 28 territorial weather stations was estimated for the period 2001–2021. With the optimal model, the output was projected using future rainfall and temperature data from 150 climate models from 4 different climate change scenarios from the Copernicus database during 2022–2042. Results show a strong statistical relationship between rainfall levels and maximum temperature and total soybean yields, explaining on average 91.2% of the yield variation. The future projections showed that the general average of the four climate change scenarios projected future output at 3.8% higher than the current levels, with 30% of projections with lower and 70% with higher output values than the current production. Variability analysis showed an increase in the frequency and intensity of extreme negative events and higher aggregate than in the past: relative loss is on average 3.6% of production (USD million $13,000) compared to 2.3% of production in the historical data (USD million $7,492). The main application is the estimation of macro-fiscal impact for long-term budgetary and fiscal planning, with a parsimonious approach and open-access data that allows permanent actualization.

Convection‐Permitting Climate Models Can Support Observations to Generate Rainfall Return Levels

AbstractInformation about the frequency and intensity of extreme precipitation is generally derived from fitting extreme value models using point‐observations, but the regionalization of these models is challenging. Here we propose using high‐resolution convection‐permitting climate model output as covariates for the estimation of observation‐based spatial rainfall return levels. We apply the Weather and Forecasting Research (WRF) model at a 1.5 km resolution driven by ERA5 reanalysis data over southern Germany, where 1,132 rain gauges provide observations of daily rainfall. For this complex topography, we build three different smooth spatial Generalized Extreme Value (GEV) models: (a) a reference model using latitude, longitude and elevation as covariates; (b) a model adding mean annual precipitation from the WRF; (c) a model adding extreme value statistical model estimates using WRF output. We show that the additional information provided by the WRF model can improve the representation of 10‐year and 100‐year return levels of daily rainfall by lowering the percentage bias, mean absolute error, and root‐mean‐square error. Furthermore, we conduct an extensive cross‐validation, where only 5%, 10%, 20%, 50%, 80%, 90%, and 95% of all rain gauges are considered when building spatial GEV models. Again, the additional information provided by the WRF model can improve results here. This cross‐validation study also highlights the robustness of our approach, showing great potential for use in data‐scarce regions.

Numerical Analysis of Riverbank Slope Stability Considering Rainfall, Vegetation and Water Level Fluctuation

Numerical Analysis of Riverbank Slope Stability Considering Rainfall, Vegetation and Water Level Fluctuation

Microbial response to Nature-Based Solutions in urban soils: A comprehensive analysis using Biolog® EcoPlates™

The study presents a comprehensive examination of changes in soil microbial functional diversity (hereafter called microbial activity) following the implementation of Nature-Based Solutions (NBS) in urban areas. Utilizing the Biolog® EcoPlates™ technique, the study explored variations in microbial diversity in urban soil under NBSs implementation across timespan of two years.Significant differences in microbial activity were observed between control location and those with NBS implementations, with seasonal variations playing a crucial role. NBS positively impacted soil microbial activity especially at two locations: infiltration basin and wild flower meadow showing the most substantial increase after NBS implementation. The study links rainfall levels to microbial functional diversity, highlighting the influence of climatic conditions on soil microbiome. The research investigates also the utilization of different carbon sources by soil microorganisms, shedding light on the specificity of substrate utilization across seasons and locations. The results demonstrate that NBSs implementations lead to changes in substrate utilization patterns, emphasizing the positive influence of NBS on soil microbial communities. Likewise, biodiversity indices, such as Shannon-Weaver diversity (H′), Shannon Evenness Index (E), and substrate richness index (S), exhibit significant variations in response to NBS. Notably, NBS implementation positively impacted H′ and E indexes, especially in infiltration basin and wild flower meadow, underlining the benefits of NBS for enhancing microbial diversity.The obtained results demonstrated valuable insight into the dynamic interactions between NBS implementation and soil microbial activity. The findings underscore the potential of NBS to positively influence soil microbial diversity in urban environments, contributing to urban sustainability and soil health. The study emphasizes the importance of monitoring soil microbial activity to assess the effectiveness of NBS interventions and guides sustainable urban development practices.

Crop Prediction and Fertilizer Recommendation Using Regression Algorithms

Abstract: India, a nation with a strong agricultural backbone, relies heavily on the forecast for crop production and agroindustrial products for its economy. The domain of data mining is gaining traction as a valuable tool in the analysis of crop yields. Predicting yields is a crucial aspect of agriculture, as it allows farmers to anticipate their potential harvest. This involves the examination of various relatedfactors such as the pH level, which indicates soil alkalinity. Other important elements include the percentages of essential nutrients like Nitrogen (N), Phosphorus (P), and Potassium (K), as well as the temperature, rainfall, and humidity levels in the region. These data attributes are examined and used to train a range of appropriate machine learning algorithms to create a predictive model. This system aims to provide accurate crop yield predictions and offer users specific recommendations on the type of fertilizer required. The predictions are considering the atmospheric and soil parameters of the territory, with the goal of enhancing crop yield and thereby increasing the farmer’s revenue

Post-Analysis of Daniel Extreme Flood Event in Thessaly, Central Greece: Practical Lessons and the Value of State-of-the-Art Water-Monitoring Networks

Storm Daniel initiated on 3 September 2023, over the Northeastern Aegean Sea, causing extreme rainfall levels for the following four days, reaching an average of about 360 mm over the Peneus basin, in Thessaly, Central Greece. This event led to extensive floods, with 17 human lives lost and devastating environmental and economic impacts. The automatic water-monitoring network of the HIMIOFoTS National Research Infrastructure captured the evolution of the phenomenon and the relevant hydrometeorological (rainfall, water stage, and discharge) measurements were used to analyse the event’s characteristics. The results indicate that the average rainfall’s return period was up to 150 years, the peak flow close to the river mouth reached approximately 1950 m3/s, and the outflow volume of water to the sea was 1670 hm3. The analysis of the observed hydrographs across Peneus also provided useful lessons from the flood-engineering perspective regarding key modelling assumptions and the role of upstream retentions. Therefore, extending and supporting the operation of the HIMIOFoTS infrastructure is crucial to assist responsible authorities and local communities in reducing potential damages and increasing the socioeconomic resilience to natural disasters, as well as to improve the existing knowledge with respect to extreme flood-simulation approaches.

Displacement Prediction Method for Bank Landslide Based on SSA-VMD and LSTM Model

Landslide displacement prediction is of great significance for the prevention and early warning of slope hazards. In order to enhance the extraction of landslide historical monitoring signals, a landslide displacement prediction method is proposed based on the decomposition of monitoring data before prediction. Firstly, based on the idea of temporal addition, the sparrow search algorithm (SSA) coupled with the variational modal decomposition (VMD) algorithm is used to decompose the total landslide displacement into trend item, periodic item and random item; then, the displacement values of the subitems are fitted by using the long and short-term memory (LSTM) neural network, and the predicted cumulative landslide displacement is obtained by adding up the predicted values of the three subsequences. Finally, the historical measured data of the Shuping landslide is taken as an example. Considering the effects of seasonal rainfall and reservoir water level rise and fall, the displacement of this landslide is predicted, and the prediction results of other traditional models are compared. The results show that the landslide displacement prediction model of SSA-VMD coupled with LSTM can predict landslide displacement more accurately and capture the characteristics of historical signals, which can be used as a reference for landslide displacement prediction.

Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution

An effective flood early warning system is vital to take action to save lives and protect properties in urban areas which are increasingly prone to flooding. Despite substantial progress in flood early warning systems, limited available and accessible data often impede their advancement and reliability. Engaging communities affected by flooding can help address data and information gaps in flood early warning systems, facilitated by appropriate methods. This study developed and evaluated a flood threshold combination method to support a community-based flood early warning system in the Akaki catchment, home to Addis Ababa, the capital city of Ethiopia. Various flood threshold combinations were formulated, calibrated and validated by integrating multiple sources of data: rainfall, antecedent precipitation index estimates, Sentinel-1 Synthetic Aperture Radar satellite time series of flood extent, long-term simulated streamflow, citizen science data, river water level and three days lead-time numerical weather prediction rainfall forecast. During validation, the rainfall and river water level threshold combination outperformed other threshold combinations with probability of detection, false alarm ratio, and critical success index estimates of 0.74, 0.18 and 0.63, respectively. The flood threshold combination showed high detection performance for most flooding conditions. Flood forecasts with a 1-day lead-time exhibited a high likelihood in detecting historical severe flood events. The study provides a tested methodology for selecting suitable flood threshold-combinations, enhance the engagement of citizen scientists in a community–based flood early warning system in urban communities.

Prevalence and factors associated with Leishmania spp. and Toxoplasma gondii infections in apparently healthy horses in Eastern Spain

Leishmaniasis and toxoplasmosis are two of the most common parasitic zoonoses. Leishmaniasis is endemic to 98 countries around the world, whereas toxoplasmosis is widely distributed throughout the world, causing significant health expenditure. Horses can play a relevant role in the transmission of the disease, being a silent reservoir, as clinical signs are not common. Serum samples from 166 horses living in eastern Spain (Mediterranean basin) were analysed to determine the presence of antibodies against Leishmania spp. and T. gondii by ELISA (Enzyme-linked Immunosorbent Assay.) The risk factors evaluated were the geographical area and the relative humidity and average temperature, and epidemiological factors such as sex, reproductive status, age, breed, morphotype, living with other domestic animals, use and access to the outdoors. Seroprevalence of Leishmania spp. and T. gondii infection was found 28.92%, and 16.27% respectively, whereas co-infection of the two parasites was found only in two males. Leishmania seroprevalence was high in castrated males and several mesodolichomorphic equine breeds used for teaching, as well as in outdoor animals. The most elevated seroprevalence was found in winter with higher levels of rainfall, whereas high seroprevalence of T. gondii was found in crossbreeding animals and those used for breeding. High seroprevalence of Leishmania spp. and T. gondii was found in horses of the Mediterranean basin. These data suggest that horses can act as a silent reservoir and that this species has high potential for transmission to humans, outdoor animals and in geographical areas with high average rainfall.

Time series prediction of reservoir bank landslide failure probability considering the spatial variability of soil properties

Historically, landslides have been the primary type of geological disaster worldwide. Generally, the stability of reservoir banks is primarily affected by rainfall and reservoir water level fluctuations. Moreover, the stability of reservoir banks changes with the long-term dynamics of external disaster-causing factors. Thus, assessing the time-varying reliability of reservoir landslides remains a challenge. In this paper, a machine learning (ML) based approach is proposed to analyze the long-term reliability of reservoir bank landslides in spatially variable soils through time series prediction. This study systematically investigated the prediction performances of three ML algorithms, i.e. multilayer perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM). Additionally, the effects of the data quantity and data ratio on the predictive power of deep learning models are considered. The results show that all three ML models can accurately depict the changes in the time-varying failure probability of reservoir landslides. The CNN model outperforms both the MLP and LSTM models in predicting the failure probability. Furthermore, selecting the right data ratio can improve the prediction accuracy of the failure probability obtained by ML models.

Exploring Climate Change Adaptation Practices and Agricultural Livelihoods among Rice Farmers of the Brahmaputra Valley in Northeast India.

Global climate change has seriously threatened agriculture and connected sectors, especially in developing countries like India. The Brahmaputra Valley in Assam, Northeast India, is vulnerable to climate change due to its agrarian economy, fragile geo-ecological setting, recurrent floods and droughts, and poor socioeconomic conditions of the farmers. The climate-induced hindrances faced by the rice farming community of this region and the local adaptation practices they employ have not been adequately studied. Therefore, we carried out a survey among 635 rice farmers across four agro-climatic zones of Assam, namely the Upper Brahmaputra Valley Zone, North Bank Plain Zone, Central Brahmaputra Valley Zone, and Lower Brahmaputra Valley Zone, to understand how they perceive and respond to climatic changes. The survey revealed that all the respondents have perceived an increase in ambient temperature, and 65% of the respondents have perceived a slight change in rainfall characteristics over the years. Most farmers reported adjusting the existing farming practices and livelihood choices to adapt to the changing climate. Farming adjustments were made mainly in terms of field preparation and management of water, rice variety, nutrients, and pests. Environmental variables like rainfall, flood, drought, and pest level, and socioeconomic variables like family size, education, farming experience, training, digital media exposure, and land area were found to influence farmers' adaptation choices. The findings imply that policies to strengthen flood, drought, pest management, education, land-use planning, agricultural training, and digital media applications in agriculture are needed for effective climate change adaptation in this region.

Restoring the earth’s damaged temperature regulation is the fastest way out of the climate crisis. cooling the planet with plants

Reversal of global warming is accepted as an urgent necessity. Atmospheric CO2 concentration is widely used as a proxy and predictive indicator for global warming. Scientific calculation of the cooling efficiency of carbon sequestration options provides a rational basis for optimising climate investment. Based on evapotranspiration data and average rainfall over the Amazon Basin, we determine the cooling power of latent heat transport over the tropical rainforest. A healthy rainforest acts as a heat pump, cooling the Earth’s surface and releasing heat into space. Because of the heat pump, the cooling engendered by evapotranspiration and subsequent cloud-forming is two orders of magnitude greater than the cooling brought about by carbon sequestration in forming biomass. In rainforests situated far inland, the levels of rainfall, evapotranspiration, and latent heat release during cloud formation remain as high, or even higher, than near those near the coast. Whether the coastal forests attract ocean clouds through a biological pump mechanism, or are nourished by on-shore winds, the net result is the amplification of the Amazon's heat pump effect across millions of square kilometres and, therefore, deep into the interior of South-America.

Onset of Rainfall and Cumulative Analysis for Sorghum Cultivation in Baringo County

Sorghum is an essential food security crop in the majority of the ASALs regions, such as Baringo County. This study investigated the critical relationship between rainfall levels and the initiation of sorghum planting, with the aim of identifying the threshold at which rainfall is considered sufficient for germination and optimal growth. This study used historical meteorological data from 1990 to 2022, field experiments, statistical analysis, and sorghum growth patterns to discern the trigger points for planting, which are essential for ensuring successful cultivation. This study uncovered alterations in rainfall onset and seasonal rainfall accumulation. The planting window in Baringo County varied significantly, with some dates falling above and below the average onset values of April 4<sup>th</sup> for the long rainfall season and September 24<sup>th</sup> for the short rainfall season. A short rainy season is not ideal for sorghum planting, as it typically receives less than 300 mm of rainfall, which is insufficient for sorghum production. The optimal planting time for sorghum is usually late March and early April. The study found that 36.4% of the long rainy season years had high production, 42.4% had normal production, and only 21% had low or no productivity. The years 2000 and 2018 experienced minimum and maximum rainfall amounts of 198.45 mm and 941.9 mm, respectively.

Risk analysis of electrical power grids in India using best-worst method and grey causal modelling

PurposeThe study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.Design/methodology/approachA multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.FindingsThe results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.Research limitations/implicationsThe study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.Practical implicationsFrom the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.Originality/valueThe research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

A numerical investigation of the influence of hydraulic parameters on the stability of a residual soil slope

The residual tropical soils and their unsaturated condition add more complexity to the study of slope stability, introducing the suction variable to the stress state analysis. Due to the complexity of the pedological processes involved in their formation, tropical soils present variability in their parameters. This results in uncertainties regarding its behaviour in geotechnical structures. The most common promotion agent for deformation and loss of strength in residual soils in an unsaturated condition is the advancing of the wetting front due to rainfalls. A tropical climate with high levels of rainfall makes landslides a constant and dangerous phenomenon, leading to loss of human life and material. Thus, it is essential to verify the influence of precipitation on slope behaviour in residual conditions. This study analyzes the behaviour of a slope formed by a residual soil by determining the soil properties followed by a numerical analysis. Laboratory tests were performed to characterize the soil and determine the strength, hydraulic, and unsaturated condition parameters. In the numerical analysis, it was intended to visualize the influence of rainfall and hydraulic parameters on the pore pressure distribution inside the slope and in its stability. After the numerical step, it was found that the different rainfall characteristics (intensity and duration) increased the pore pressures and decreased the strength of the material. However, it was insufficient to trigger any failure mechanisms. The hydraulic parameters’ critical roles at seepage through the soil and how this is reflected in the calculations of safety factors were verified.

FOREST SEMANTIC SEGMENTATION BASED ON DEEP LEARNING USING SENTINEL-2 IMAGES

Abstract. Forests are invaluable for maintaining biodiversity, watersheds, rainfall levels, bioclimatic stability, carbon sequestration and climate change mitigation, and the sustainability of large-scale climate regimes. In other words, forests provide a wide range of ecosystem services and livelihoods for the people and play a critical role in influencing global atmospheric cycles. Providing sustainable, reliable, and accurate information on forest cover change is essential for an holistic forest management, efficient use of resources, neutralizing the effects of global warming and better monitoring of deforestation activities. Within the scope of this study, it is aimed to perform semantic segmentation of 5 different tree species (larch, red pine, yellow pine, oak, spruce) from Sentinel-2 satellite images. For this purpose, the regions where these tree species are densely populated in Turkey (Marmara, Aegean, Eastern Black Sea) were selected as pilot regions. A unique data set was created using the data of the selected pilot regions. As a result of the study, it was possible to determine the forest types temporally for the selected classes with more than 90% Intersection over Union score for all classes. The developed deep learning model with the created forest data set can be implemented to the other forests areas with same species in other parts of the world.

An appraisal of the severity of the 2022 drought and its impacts

AbstractThe summer of 2022 was particularly notable for the prolonged and extensive hot and dry conditions experienced across the UK. The resultant 2022 drought had widespread impacts and prompted much discussion about the continuing vulnerability of the UK to drought, particularly in the context of anthropogenic warming. Here, we briefly describe the evolution of the drought before focusing on the severity of rainfall, river flow, soil moisture and groundwater level deficits from summer 2022, placing the event into a historical context and addressing the impacts of the drought. We also consider whether the 2022 drought is part of a long‐term trend towards increasing drought severity.

Assimilation of the deep learning-corrected global forecast system fields into the regional model for improving medium-range persistent precipitation forecasts

The error of regional models gradually increases during long-term integration, and it can be reduced with the constraint of large-scale circulation by assimilating global forecast system (GFS) fields to improve the forecast performance. However, with increasing forecast time, the GFS forecast error also significantly increases and varies widely. Directly assimilating GFS fields into the regional model without processing may generate negative impacts, whereas employing postprocessing correction methods can effectively mitigate GFS forecast errors. In this study, the GFS data of European Centre for Medium-Range Weather Forecasts (ECMWF) are first corrected using a convolutional long- and short-term memory network method. The correction model can correct multiple forecasts simultaneously by training only one model, and the correction results are less erroneous than those obtained by training multiple correction models for the different forecast times and pressure layers. The corrected ECMWF forecast fields are then assimilated into Weather Research and Forecasting Model (WRF) using the nudging method. This assimilation method generally improves the forecasts for precipitation above the moderate rainfall level at forecast times of 4–7 days, and reduces the errors of surface wind speed forecasts. This study improves the accuracy of correction models and the forecasts of persistent precipitation weather event with regional model.