Rainfall Scenarios Research Articles

Cleaning scheduling in photovoltaic solar farms with deterministic and stochastic optimization

Soiling in solar panels causes a decrease in their ability to capturing solar irradiance, thus reducing the module’s power output. To reduce losses due to soiling, the panels are cleaned. This cleaning represents a relevant share of the operation and maintenance cost for solar farms, for which there are different types of technologies available with different costs and duration. In this context, this paper proposes a method that allows scheduling the dates on which cleaning generates greater utility in terms of income from energy sales and costs associated with cleaning. For this, two optimization models that deliver a schedule of dates where the best income-cost balance is obtained, are proposed and compared: a deterministic Mixed Integer Linear Problem and a stochastic Markov Decision Process. Numerical results show that both models outperform the baseline case by ∼ 4.6%. A simulator was built and both models were compared to the baseline case for 10,000 rainfall and irradiance scenarios. The stochastic model outperformed both models for all scenarios, thus proving that modeling rainfalls increases profitability in the face of uncertainty.

Evaluation of sealants to mitigate the release of per- and polyfluoroalkyl substances (PFAS) from AFFF-impacted concrete: Characterization and forecasting

Per- and polyfluoroalkyl substances (PFAS) within concrete pads impacted by historical firefighting training using aqueous film-forming foam (AFFF) may be potential secondary sources of PFAS due to surficial leaching. This study aimed to (i) characterize the effectiveness of two commercially available sealants (Product A and Product B) in mitigating leaching of five PFAS (e.g., PFOS, PFOA, PFHxS, PFHxA, 6:2 FTS) from concrete surfaces at the laboratory-scale, and (ii) develop a model to forecast cumulative leaching of the same five PFAS over 20 years from sealed and unsealed concrete surfaces. Laboratory trials demonstrated that both sealants reduced the surficial leaching of the five PFAS studied, and Product B demonstrated a comparatively greater reduction in surface leaching than Product A as measured against unsealed controls. The cumulative PFOS leaching from an unsealed concrete surface is estimated by the model to be about 400 mg/m2 over 20 years and reached asymptotic conditions after 15 years. In contrast, the model output suggests asymptotic conditions were not achieved within the modeled time of 20 years after sealing with Product A and 85% of PFOS was predicted to have leached (∼340 mg/m2). Negligible leaching of PFOS after sealing with Product B was observed ( < 5 × 10−9 mg/m2). Results from modeled rainfall scenarios suggest PFAS leachability is reduced from sealed versus unsealed AFFF-impacted concrete surfaces.

Performance assessment for the integrated green-gray-blue infrastructure under extreme rainfall scenarios

Synergistic allocation of urban stormwater infrastructure is critical to flood risk prevention and control under extreme rainfall events. This study focuses on the interaction regularity of green, gray, and blue infrastructure in the process of runoff retention and discharge under extreme rainfall scenarios. Three strategies, namely, gray infrastructure, green-gray infrastructure, and green-gray-blue infrastructure, are proposed to analyze the effectiveness of different scenarios on flood risk control capabilities of urban drainage systems and to determine the key influencing factors of the three strategies. The results show that green-gray-blue infrastructure demonstrates synergy in improving the efficiency of urban drainage systems. Under gravity drainage conditions, the backwater jacking caused by high river water levels has a certain impact on the smooth discharge of stormwater runoff in urban drainage systems. This case study identified a sensitive range in the influence of water level on runoff control and drainage function and an adaptive range in which the flood increases slightly with the water level increase. These present notable rules in improving the synergistic effect of blue infrastructure in the integrated urban green-gray-blue infrastructure system.

Unpaved road characterization during rainfall scenario: Electromagnetic wave and cone penetration assessment

This study aims to examine the effect of rainfall on integrity assessment on unpaved roads using electromagnetic waves and cone penetration. An unpaved road testbed is prepared with sandy soil in densely- and loosely-packed conditions. Site investigations using a time domain reflectometry (TDR), ground penetrating radar (GPR), and dynamic cone penetrometer (DCP) are performed on the testbed under before and after rainfall scenarios, and an additional laboratory compaction test using the soil samples is conducted. The results show that the spatially scaled GPR signal converted by the TDR signal can accurately estimate the interface depth. In addition, rainfall may reduce the accuracy of GPR signal polarity-based analyses for anomaly detection on unpaved roads. This study reveals that TDR, GPR, and DCP with simple laboratory compaction tests and physics-inspired effective stress-depth models can be a powerful tool for underground mapping when subjected to rainfall season.

Surveillance audio-based rainfall observation: An enhanced strategy for extreme rainfall observation

The increased frequency of extreme rainfall events (EREs) causing disastrous effects on society has become an indisputable fact in recent years. The weak performance of the current observation network in terms of accuracy and spatiotemporal resolution makes the development of new rainfall estimation techniques essential to reduce the impact of ERE-driven disasters. The rainfall sounds collected by widespread surveillance cameras provide an opportunity to produce high-resolution, all-weather rainfall data. However, the complex structure of the ground surface and the random background noise make building an effective surveillance audio-based ERE estimation system challenging. In this study, from the viewpoint of surveillance sound space, a 3D printer was used to create a shelter for the surveillance camera to define the underlying surface of falling raindrops artificially. Combining the knowledge of meteorology, micro-physics, and acoustics of rainfall, the shelter structure was designed to standardize the acoustical behavior while enhancing the consistency and specificity of raindrop sound, especially in complex scenarios such as those disturbed by different levels of wind. After that, convolutional neural network-based deep learning algorithms were used to classify ERE levels, and an audio-based ERE classification system was built. The experimental results show that the shelter facilitates audio-based rainfall representation; moreover, with the help of shelter, our proposed system achieved performance with about 93.4% accuracy in complex rainfall scenarios. Our study supports high-resolution rainfall data production on existing surveillance resources, developing a novel and reliable alternative for the perception of ERE and the calibration of observations from current rainfall networks.

Precipitation timing and soil substrate drive phenology and fitness of Arabidopsis thaliana in a Mediterranean environment

Abstract In Mediterranean climates, the timing of seasonal rains determines germination, flowering phenology and fitness. As climate change alters seasonal precipitation patterns, it is important to ask how these changes will affect the phenology and fitness of plant populations. We addressed this question experimentally with the annual plant species Arabidopsis thaliana. In a first experiment, we manipulated the date of rainfall onset and recorded germination phenology on sand and soil substrates. In a second experiment, we manipulated germination date, growing season length and mid‐season drought to measure their effects on flowering time and fitness. Within each experiment, we manipulated seed dormancy and flowering time using multilocus near‐isogenic lines segregating strong and weak alleles of the seed dormancy gene DOG1 and the flowering time gene FRI. We synthesized germination phenology data from the first experiment with fitness functions from the second experiment to project population fitness under different seasonal rainfall scenarios. Germination phenology tracked rainfall onset but was slower and more variable on sand than on soil. Many seeds dispersed on sand in spring and summer delayed germination until the cooler temperatures of autumn. The high‐dormancy DOG1 allele also prevented immediate germination in spring and summer. Germination timing strongly affected plant fitness. Fecundity was highest in the October germination cohort and declined in spring germinants. The late flowering FRI allele had lower fecundity, especially in early fall and spring cohorts. Projections of population fitness revealed that: (1) Later onset of autumn rains will negatively affect population fitness. (2) Slow, variable germination on sand buffers populations against fitness impacts of variable spring and summer rainfall. (3) Seasonal selection favours high dormancy and early flowering genotypes in a Mediterranean climate with hot dry summers. The high‐dormancy DOG1 allele delayed germination of spring‐dispersed fresh seeds until more favourable early fall conditions, resulting in higher projected population fitness. These findings suggest that Mediterranean annual plant populations are vulnerable to changes in seasonal precipitation, especially in California where rainfall onset is already occurring later. The fitness advantage of highly dormant, early flowering genotypes helps explain the prevalence of this strategy in Mediterranean populations. Read the free Plain Language Summary for this article on the Journal blog.

Past and future freshwater availability scenarios and their impact on the Indian Sundarbans ecosystem and fisheries

Abstract The Indian Sundarbans is considered one of the zones of highest vulnerability in the world in terms of climate change. About 4.43 million people living in the Indian Sundarbans face a lack of freshwater availability due to the erratic behaviour of monsoon rains, frequent cyclonic storms, intrusion of saline water, and other factors, all of which affect the fisheries and agriculture activities of this area. In this study, estimates of freshwater availability through past and predicted future rainfall and evapotranspiration change scenarios in the Sundarbans are presented. Due to the lack of high-quality in-situ data, various sources of gridded rainfall and evapotranspiration data were used. Between 1948 and 2010, half of the 19 administrative blocks showed a decreasing trend of monsoonal rainfall while the rest showed an increasing trend. Freshwater availability showed a decreasing trend during the monsoon season over different blocks of the Sundarbans, which is a matter of great concern for fisheries and agricultural activities. Statistical downscaling was used to generate future rainfall and evapotranspiration scenarios, using coarse resolution Global Climate Models from the Coupled Model Intercomparison Project phase five for a smaller area like the Sundarbans. Downscaled Global Climate Models project an increasing trend in future monsoon rainfall in both RCP 4.5 and RCP 8.5 emission scenarios. The increasing rainfall can trigger excessive run-off and flooding, which would in turn affect aquaculture infrastructure and damage lentic aquaculture productions across the Sundarbans. However, increased rainfall may expand the flood plain area and extend the feeding grounds of fish. Hence, the impact of rainfall change is quite unpredictable. Proper adaptation techniques may be required to harness the positive impacts while preventing negative effects.

A Study of Aerosol–Cloud Variability under Different Rainfall Scenarios over Western India and the Arabian Sea

A Study of Aerosol–Cloud Variability under Different Rainfall Scenarios over Western India and the Arabian Sea

Urban flood regulating ecosystem services under climate change: how can Nature-based Solutions contribute?

Urban areas are mostly highly sealed spaces, which often leads to large proportions of surface runoff. At the same time, heavy rainfall events are projected to increase in frequency and intensity with anthropogenic climate change. Consequently, higher risks and damages from pluvial flooding are expected. The analysis of Flood Regulating Ecosystem Services (FRES) can help to determine the benefits from nature to people by reducing surface runoff and runoff peaks. However, urban FRES are rarely studied for heavy rainfall events under changing climate conditions. Therefore, we first estimate the functionality of current urban FRES-supply and demand under changing climate conditions. Second, we identify the effects of Nature-based Solutions (NbS) on FRES-supply and demand and their potential future functionality and benefits concerning more intensive rainfall events. A district of the city of Rostock in northeastern Germany serves as the case study area. In addition to the reference conditions based on the current land use, we investigate two potential NbS: (1) increasing the number of trees; and (2) unsealing and soil improvement. Both NbS and a combination of both are applied for three heavy rainfall scenarios. In addition to a reference scenario, two future scenarios were developed to investigate the FRES functionality, based on 21 and 28% more intense rainfall. While the potential FRES-demand was held constant, we assessed the FRES-supply and actual demand for all scenario combinations, using the hydrological model LEAFlood. The comparison between the actual demand and supply indicates the changes in FRES-supply surplus and unmet demand increase. Existing land use structures reached a FRES capacity and cannot buffer more intense rainfall events. Whereas, the NbS serve FRES benefits by increasing the supply and reducing the actual demand. Using FRES indicators, based on hydrological models to estimate future functionality under changing climate conditions and the benefits of NbS, can serve as an analysis and decision-support tool for decision-makers to reduce future urban flood risk.

A methodological framework for the evaluation of short-range flash-flood hydrometeorological forecasts at the event scale

Abstract. This paper presents a methodological framework designed for the event-based evaluation of short-range hydrometeorological ensemble forecasts, in the specific context of an intense flash-flood event characterized by high spatiotemporal variability. The proposed evaluation adopts the point of view of end users in charge of the organization of evacuations and rescue operations at a regional scale. Therefore, the local exceedance of discharge thresholds should be anticipated in time and accurately localized. A step-by-step approach is proposed, including first an evaluation of the rainfall forecasts. This first step helps us to define appropriate spatial and temporal scales for the evaluation of flood forecasts. The anticipation of the flood rising limb (discharge thresholds) is then analyzed at a large number of ungauged sub-catchments using simulated flows and zero-future rainfall forecasts as references. Based on this second step, several gauged sub-catchments are selected, at which a detailed evaluation of the forecast hydrographs is finally achieved. This methodology is tested and illustrated for the October 2018 flash flood which affected part of the Aude River basin (southeastern France). Three ensemble rainfall nowcasting research products recently proposed by Météo-France are evaluated and compared. The results show that, provided that the larger ensemble percentiles are considered (75th percentile for instance), these products correctly retrieve the area where the larger rainfall accumulations were observed but have a tendency to overestimate its spatial extent. The hydrological evaluation indicates that the discharge threshold exceedances are better localized and anticipated if compared to a naive zero-future rainfall scenario but at the price of a significant increase in false alarms. Some differences in the performances between the three ensemble rainfall forecast products are also identified. Finally, even if the evaluation of ensemble hydrometeorological forecasts based on a low number of documented flood events remains challenging due to the limited statistical representation of the available data, the evaluation framework proposed herein should contribute to draw first conclusions about the usefulness of newly developed rainfall forecast ensembles for flash-flood forecasting purpose and about their limits and possible improvements.

Using SWMM for emergency response planning: A case study evaluating biological agent transport under various rainfall scenarios and urban surfaces

To assist in emergency preparedness for a biological agent terrorist attack or accidental pathogen release, potential contaminant levels and migration pathways of spores spread by urban stormwater were evaluated using a Storm Water Management Model (SWMM) of U.S. Coast Guard Base Elizabeth City, North Carolina. The high temporal-spatial resolution SWMM model was built using spore concentrations in stormwater runoff from asphalt, grass, and concrete collected from a point-scale field study. The subsequent modeled contamination scenarios included a notional plume release and point releases mimicking the field study under three rainfall conditions. The rainfall scenarios included a 6-hour natural rainfall event on Dec. 8, 2021 and two design storms (2-year and 100-year events). The observed spore concentrations from asphalt and concrete from the actual field experiment were applied to calibrate the washoff parameters in the SWMM model, using an exponential washoff function. The calibrated washoff coefficient (c1) and exponent (c2) were 0.01 and 1.00 for asphalt, 0.05 and 1.45 for grass, and 2.45 and 1.00 for concrete, respectively. The calibrated SWMM model simulated spore concentrations in runoff at times and magnitudes similar to the field study data. In the point release modeled scenario, the concrete surface generated 55.6% higher average spore concentrations than asphalt. Similarly, in the field experiment, a 175% (p < 0.05) higher average spore concentration in surface runoff was observed from concrete than from asphalt. This study demonstrates how SWMM may be used to evaluate spore washoff from urban surfaces under different precipitation amounts, intensities, and durations, and how visualized spatial migration pathways in stormwater runoff may be used for emergency planning and remediation.

Sustainable irrigation management in tropical lowland rice in Brazil

Drought events and water use conflicts drive the need for more efficient water management in rice-growing areas of the Brazilian Cerrado. Recent studies have shown the advantages of adopting water-saving irrigation in the region but a comprehensive assessment is needed. This study aims to model the performance of rice cultivation and water productivity in the tropical floodplains of the Cerrado biome of northern Brazil in response to irrigation management under contrasting seasonal rainfall levels. Twenty-seven scenarios of rice cultivation, resulting from the combination of three sites, three irrigation treatments, and three rainfall regimes, were simulated with the calibrated and validated hydrological model SWAP/WOFOST. The rainfall levels high (1501 mm), intermediate (952 mm), and low (510 mm) were relative to 120 days and obtained from weather stations located in the region. Two irrigation methods (flooding and water-saving irrigation) were compared against rainfed cultivation. Actual transpiration of the flooding and water-saving irrigation was 9 % and 4 % higher in the intermediate and high rainfall scenarios while it was 30 % and 20 % higher in the low rainfall scenario compared to the rainfed treatment. The largest deep percolation loss was 12700 mm per season for flood irrigation in the low rainfall scenario, whereas the lowest one was 349 mm for the rainfed treatment in the low rainfall scenario. Changing from flooding to water-saving irrigation increases water productivity by an average of 9 % and decreases relative grain yield by 5–12 %. Water productivity based on bottom flux increased on average by about five times (high rainfall scenario) to ten times (low rainfall scenario) when comparing flooding with water-saving irrigation. Results suggest that saving irrigation based on crop transpiration can reduce deep percolation losses and increase water productivity in the rice-growing areas of the Brazilian Cerrado.

Modelling the impacts of climate change and management options on sustainable groundwater use in an irrigated agriculture landscape

Declining groundwater resources are a big concern for irrigated agriculture worldwide. This study investigated the causes of declining groundwater and how future climate and management measures impact the groundwater resource in Bangladesh. A conceptual hydrological model (NAM) was used to quantify catchment runoff, groundwater recharge and evapotranspiration loss and a hydraulic model (MIKE11) was used to quantify changes in river flow. The models were calibrated using observed water level, discharge, groundwater level (GWL) and actual evapotranspiration (ET). The modelling encompassed five contrasting climate scenarios of rainfall and potential evapotranspiration (PET) and a management scenario of additional surface water irrigation. The results show that GWL is declining across 16 administrative districts in the northwest region of Bangladesh. On average, the annual decline varies from 20 to 30 mm except for two southern districts where the annual decline was more than 80 mm. Declining rainfall and increasing use of groundwater for irrigation were the principal causes of declining GWL across the region. Under the climate change scenarios, modelled groundwater conditions improve due to increased rainfall. The increase in river water extraction for irrigation also produced positive outcomes for GWL (i.e., lessening the declining rate).

From empirically to physically based early warning predictions of rainfall-induced landslides in silty volcanic soils: the Lattari Mountains case study

The work proposes a procedure to build an early warning predictive tool to assess the occurrence of rainfall-induced landslides in silty volcanic covers. The procedure combines both an empirically and a physically based tool used sequentially: the former is designed to be calibrated using older, highly sized and coarser rainfall data, and the latter to interpret recent and finer weather data. Both approaches need to be informed by a common experimental reference summarising the rainfall history, the rainfall point, defined as the couple made of antecedent 4-month rainfall cumulative value (C4m) and last-persistent event (CPLE). The empirical approach aims to identify if, in the (C4m–CPLE) plane, the rainfall point falls in a ‘safe’ or ‘potentially unsafe’ zone where the two distinct regions are built by interpreting rainfall data associated or not with landslide events. In the physically based approach, evaporation and runoff are estimated to refine the assessment of ‘effective’ rainfall points. The resulting transformed rainfall point (C′4 m, C′PLE) is turned into a prediction of the suction level at the mid-depth assumed as a ‘reference’ for the entire cover. Such value is compared with a suction threshold empirically defined. Suction levels prediction is developed by computing in the C′4 m–C′PLE plane the iso-suction lines generated by several rainfall scenarios. The accuracy of the developed procedure is comparable with state-of-the-art literature or operational approaches, properly identifying landslide case events and minimising the number of false alarms. Furthermore, it can inform the preparedness stages more effectively, explicitly accounting for the antecedent slope wetness stage and how it could be far from the incipient slope failure conditions. The developed procedure takes into account the effects of evaporation and antecedent rainfalls that, in dry periods, lead to very dry conditions in the subsoil, making even significant rainfall events inconsequential. Conversely, other procedures already operating in LEWS or highly considered literature background overestimate the effects of rainfalls during dry periods. The developed procedure delivers a simple but robust way to derive landslide thresholds based on the interpretation of past rainfall histories. At the same time, literature methods often require sophisticated approaches to retrieve thresholds.

Deep learning enables super-resolution hydrodynamic flooding process modeling under spatiotemporally varying rainstorms

Real-time information on flooding extent, severity, and duration is necessary for effective metropolitan flood emergency management. Existing pluvial flood analysis methods are unable to simulate real-time regional flooding processes under spatiotemporally varying rainstorms. This paper presents a deep learning-enabled super-resolution hydrodynamic flood analysis method to simulate the real-time pluvial flooding process over a large area under spatiotemporally varying rainstorms. Compared with existing flood downscaling techniques, which are limited to flow depth, the proposed method produces high-resolution flow depth and velocity predictions, providing more comprehensive information for flood emergency management. The proposed method adopts a coarse-grid hydrodynamic model to generate a low-resolution flood map time series, which is subsequently converted to high-resolution flood maps by a deep learning model. The deep learning model can be trained using a limited number of assumed rainfall scenarios, which greatly reduces data preparation effort. The proposed method is applied to a complex terrain of 352 km2 in Hong Kong that covers both mountainous and urban areas. Results show that the proposed method simulates the spatiotemporal variations of flood depth and velocity with root mean square errors as low as 0.082 m and 0.088 m/s, respectively, and correlation coefficients of 0.962 and 0.921, respectively. The computation time for a 48-h rainfall event in the study area is less than 30 s, which is 2690 times faster than the direct fine-grid hydrodynamic analysis. The deep learning-enabled super-resolution hydrodynamic flood analysis method provides a promising computational tool for emergency flood risk management.

Roof Runoff Analysis: A Case for Cebu Technological University - Main Campus

The volume of runoff varies depending on the surface on which it flows. Cebu Technological University - Main Campus, which is situated in Cebu City at the intersection of M.J. Cuenco Avenue and R. Palma Street, has been selected as location for the investigation of the effect of roofing properties on roof runoff. The average runoff coefficient was calculated under various rainfall scenarios. These were also related to the roofing structure, design patterns and materials whether these are sloping roofs or flat roofs. This investigation aims to enhance efficiency in rainwater harvesting systems. The calculated values of runoff coefficient in various roofs in the campus range from 0.78 to 0.92. A comparative analysis was used to differentiate the gap between the calculated runoff coefficient and the established values from literature. The recommended size for the rainwater tanks were calculated by taking into consideration the amount of volume that can be collected in the roofs in the campus.

Modeling of Gully Erosion in Ethiopia as Influenced by Changes in Rainfall and Land Use Management Practices

Gully erosion is one of the most extreme land degradation processes that exhibit spatial and temporal variation depending on topography, soil, climate, and land use and management characteristics. This study investigated the impact of changes in rainfall, land use/land cover (LULC), and land use management (LUM) practices on gully erosion in two midland watersheds (treated Kecha and untreated Laguna) in the Upper Blue Nile basin of Ethiopia by using the LANDPLANER model in combination with intensive field measurements and remote sensing products. We simulated gully erosion under past (in 2005), present (in 2021), and three potential future curve number conditions, each time under four rainfall scenarios (10, 30, 60, and 100 mm) using the dynamic erosion index (e), static topographic (esp), and erosion channel (esp_channel) thresholds. Density plot analyses showed that gullies frequently occur in low-lying gentle slope areas with relatively higher curve number values. The best predictions of gullies identified through true positive rates (TPR) and true negative rates (TNR) were achieved considering the static esp_channel &gt; 1 for Kecha (TPR = 0.667 and TNR = 0.544) and the dynamic e &gt; 0.1 for 60 mm of rainfall in Laguna (TPR = 0.769 and TNR = 0.516). Despite the 10 mm rainfall having negligible erosion-triggering potential in both watersheds, the 60 and 100 mm rainfall scenarios were 4–5 and 10–17 times, respectively, higher than the 30 mm rainfall scenario. While the LULC change in the untreated Laguna watershed increased the impact of rainfall on gully initiation by only 0–2% between 2005 and 2021, the combination of LULC and LUM significantly reduced the impact of rainfall in the treated Kecha watershed by 64–79%. Similarly, the gully initiation area in Kecha was reduced by 28% (from 33% in 2005 to 5% in 2021) due to changes in LULC and LUM practices, whereas Laguna showed little increment by only 1% (from 42% in 2005 to 43% in 2021) due to LULC change. In addition, the future predicted alternative land use planning options showed that gully initiation areas in Laguna could be reduced by 1% with only LULC conversion; 39% when only LUM practices were implemented; and 37% when both were combined. These results indicate that LUM practices outweigh the impact of LULC on gully erosion in the studied paired watersheds. Overall, LANDPLANER successfully simulated the spatio-temporal variation of gully erosion with scenario-based analyses and hence can be used to predict gullies in the study area and other data-scarce regions with similar agro-ecological settings.

Geophysical Surveys for Geotechnical Model Reconstruction and Slope Stability Modelling

Performing a reliable stability analysis of a landslide slope requires a good understanding of the internal geometries and an accurate characterisation of the geotechnical parameters of the identified strata. Geotechnical models are commonly based on geomorphological data combined with direct and intrusive geotechnical investigations. However, the existence of numerous empirical correlations between seismic parameters (e.g., S-wave velocity) and geotechnical parameters in the literature has made it possible to investigate areas that are difficult to reach with direct instrumentation. These correlations are often overlooked even though they enable a reduction in investigation costs and time. By means of geophysical tests, it is in fact possible to estimate the N-SPT value and derive the friction angle from results obtained from environmental seismic noise measurements. Despite the empirical character and a certain level of uncertainty derived from the estimation of geotechnical parameters, these are particularly useful in the preliminary stages of an emergency, when straight data are not available and on all those soils where other direct in situ tests are not reliable. These correlations were successfully applied to the Theilly landslide (Western Alps, Italy), where the geotechnical model was obtained by integrating the results of a multi-parameter geophysical survey (H/V seismic noise and ground-penetrating radar) with stratigraphic and geomorphological observations, digital terrain model and field survey data. The analysis of the triggering conditions of the landslide was conducted by means of hydrological–geotechnical modelling, evaluating the behaviour of the slope under different rainfall scenarios and considering (or not) the stabilisation interventions present on the slope. The results of the filtration analyses for all events showed a top-down saturation mechanism, which led to the formation of a saturated face with a maximum thickness of 5 m. Stability analyses conducted for the same events showed the development of a shallow landslide in the first few metres of saturated soil. The modelling results are compatible with the actual evolution of the phenomenon and allow us to understand the triggering mechanism, providing models to support future interventions.

FORECASTING RAINFALL WITH MACHINE LEARNING

Rainfall is one of the most destructive natural disasters and is extremely difficult to model. As a result, a numerical analysis model based on a hydraulic theory and a machine learning algorithm were combined to produce a classification-based real-time rainfall prediction for the ground truth model. Using a two- dimensional inundation model and the environmental protection agency's storm model, the rainfall dataset is built in advance for various rainfall scenarios. The rainfall depth data for each map grid are divided by year based on the average rainfall prediction based on ground truth. If the observed rainfall is entered, a model is built to predict the representative cumulative volume. Rainfall-accuracy based on the actual situation in the proposed ADA SVM, ADA NB, MLP, and J48 algorithm. Keywords: ADA SVM,ADA NB,MLP, AND J48 ALGORITHM

Rainwater harvesting systems: An urban flood risk mitigation measure in arid areas

Rainwater harvesting (RWH) systems have been developed to compensate for shortage in the water supply worldwide. Such systems are not very common in arid areas, particularly in the Gulf Region, due to the scarcity of rainfall and their reduced efficiency in covering water demand and reducing water consumption rates. In spite of this, RWH systems have the potential to reduce urban flood risks, particularly in densely populated areas. This study aimed to assess the potential use of RWH systems as urban flood mitigation measures in arid areas. Their utility in the retention of stormwater runoff and the reduction of water depth and extent were evaluated. The study was conducted in a residential area in Bahrain that experienced waterlogging after heavy rainfall events. The water demand patterns of housing units were analyzed, and the daily water balance for RWH tanks was evaluated. The effect of the implementation of RWH systems on the flood volume was evaluated with a two-dimensional hydrodynamic model. Flood simulations were conducted in several rainfall scenarios with different probabilities of occurrence. The results showed significant reductions in the flood depth and flood extent, but these effects were highly dependent on the rainfall intensity of the event. RWH systems are effective flood mitigation measures, particularly in urban arid regions short of proper stormwater control infrastructure, and they enhance the resilience of the built environment to urban floods.