Precipitation Scenarios Research Articles

Neural Network Downscaling to Obtain Local Precipitation Scenarios in the Italian Alps: A Case Study

Neural Network Downscaling to Obtain Local Precipitation Scenarios in the Italian Alps: A Case Study

Changes and driving factors of soil water in mixed-species plantations and different precipitation scenarios on the Loess Plateau of China

The implementation of the “Grain-for-Green” program in China led to the extensive introduction of artificial vegetation, thereby intensifying evapotranspiration and the overuse of water resources. Thus, selecting suitable ecological vegetation and planting patterns to save water is a major challenge in the region. Three typical plants were selected in the Loess Plateau of China. The plants had been growing for 13 years. The soil water dynamics were studied based on measurements and Hydrus-1D modeling simulations under different precipitation scenarios (wet, normal, and dry years) and planting patterns (Stipa monoculture (S), a mixture of Stipa and Medicago (SM), and a mixture of Stipa, Medicago, and Caragana (SMC)). The results indicated that the soil water contents with S and SM were 25 % and 8 % higher than the stable field water capacity (SFC), respectively, whereas the SMC was 33 % lower than the SFC in the 0–100 cm depth in relatively normal precipitation years. The evapotranspiration was lowest under S (296–524 mm), followed by SM (351–628 mm) and SMC (368–669 mm). The soil water storage were 19–91 % and 12–58 % higher under S and SM than SMC. The soil desiccation intensity was more severe under SMC (0.3–0.5) than SM (0.5–1.1) and S (0.6–1.4). The soil water storage increased significantly with the annual precipitation (P<0.05), and increased by 54–85 % in relatively abundant water scenarios compared with scarce water scenarios according to Hydrus-1D model simulations. Partial least squares path modeling demonstrated that the soil saturated hydraulic conductivity (path coefficient = 0.478) and soil porosity (–0.703) had significant effects on soil water storage (P<0.01). The results further suggested that using shallow-rooted grass as the dominant plant would reduce water usage in areas with water scarcity, and mixed-species plantations (i.e., a mixture of shallow-rooted and deep-rooted grass) could reduce the loss of sediment in regions.

Assessment of the Restoration Potential of Forest Vegetation Coverage in the Alxa Desert Region of China.

To scientifically evaluate the sustainability of tree planting and afforestation in the Alxa Desert region, this study, grounded in the principles of water balance within the natural water cycle, employed multi-source remote sensing products and ground-based measurements to construct a quantitative response relationship model. This model links evapotranspiration (ET) with meteorological variables and the Enhanced Vegetation Index (EVI). Furthermore, the study estimated the recovery thresholds and potential of forest and grassland vegetation coverage in the Alxa Desert region under various precipitation scenarios. The findings reveal that ET exhibited an increasing trend in 84.17% of the Alxa Desert region, with a significant increase observed in 61.53% of the area, indicating positive outcomes from the implementation of the Three-North Shelterbelt Forest Program. Notably, however, ET in the southeastern plain region demonstrated a decreasing trend, which is strongly associated with human activities. The response relationship model demonstrated that linear relationship areas constituted 47.52%, while nonlinear relationship areas accounted for 45.51% of the total. The overall model exhibited an R2 value of 0.69, indicating a high level of predictive accuracy. Analysis of forest and grassland coverage revealed that, under wet year scenarios, the vegetation coverage showed a significant trend of recovery, with an average recovery threshold of (75.4 ± 12.5)% and an average recovery potential of (8.5 ± 3.6)%. It is noteworthy that the vegetation coverage in 31.25% of the area had already surpassed the recovery threshold. The outcomes of this study provide a theoretical foundation for the formulation of more scientifically rigorous ecological restoration strategies in the future.

Seasonal and vertical patterns of water availability and variability determine plant reproductive phenology.

Changing precipitation regimes can influence terrestrial plants and ecosystems. However, plant phenological responses to changing precipitation temporal patterns and the underlying mechanisms are largely unclear. This study was conducted to explore the effects of seasonal precipitation redistribution on plant reproductive phenology in a temperate steppe. A field experiment with control (C), advanced (AP) and delayed (DP) growing-season precipitation peaks, and the combination of AP and DP (ADP) were employed. Seven dominant plant species were selected and divided into two functional groups (early- vs. middle-flowering species, shallow- vs. deep-rooted species) to monitor reproductive phenology including budding, flowering, and fruiting date, as well as reproductive duration for four growing seasons from 2015 to 2017, and 2022. The AP, but not DP treatment advanced the phenological (i.e., budding, flowering, and fruiting) dates and lengthened the reproductive duration across the 4 growing seasons and 7 monitored species. In addition, the phenological responses showed divergent patterns among different plant functional groups, which could be attributed to shifts in soil moisture and its variability in different months and soil depths. Moreover, species with lengthened reproductive duration increased phenological overlap with other species, which could have a negative impact on their dominance under the AP treatment. Our findings reveal that changing precipitation seasonality could have considerable impacts on plant phenology by affecting soil water availability and variability. Incorporating these two factors simultaneously in the phenology models will help us understand the response of plant phenology under intensified changing precipitation scenarios. In addition, the observations of decreased dominance for the species with lengthened reproductive duration suggest that changing reproductive phenology can have a potential to affect community composition in grasslands under global change.

Effect of Water Tank Size and Supply on Greenhouse-Grown Kidney Beans Irrigated by Rainwater in Cold and Arid Regions of North China

In response to water scarcity in the Bashang area of northwest Hebei Province, a cold and arid region in north China, and to address the diminishing groundwater levels caused by pumping irrigation, this study investigated the impact of rainwater tank size and water supply on kidney beans production in greenhouses under various precipitation scenarios to determine the production potential and development strategies for regional precipitation resources. Under the background of average annual precipitation, kidney bean yield increased with increasing reservoir volume and shorter irrigation cycles. Under a 4-day irrigation cycle, the water demand satisfaction rate of kidney beans reached 100% water demand when the rainwater tank size was 15.7 m3. Against the wide variation in multi-year regional precipitation from 1992 to 2023, the annual effect of rainwater harvest was simulated using precipitation data collected 20 years with an 80% precipitation guarantee rate. The average minimum yield reduction rate obtained was 9.4%, and the corresponding minimum rainwater tank size was 29.5 m3. By superimposing the rainwater harvested in the shed and nonshed areas, the volume of the reservoir without yield reduction could be reduced to 20.0 m3. The sum of discharged and inventory water was much greater than the water scarcity in each water supply situation. Simulating and analyzing the effect of the relationship between rainwater tank size and water supply on rainwater harvesting in regional farmland by year provides important data affecting the construction of regional rainwater storage facilities and water supply efficiency. To achieve a high, stable yield of kidney beans grown in a greenhouse with shed film and shed area rainwater harvesting in north China, 2.6 m3 supplementary groundwater irrigation is still needed during the annual growing season.

Water yield forecast in Shandong Province based on InVEST model

A better understanding of the effects of land use change and climate change on water yield is highly important for water resource planning and sustainable management. Land use changes in Shandong Province were analyzed over recent years. The PLUS Model was applied to forecast the land use layout for 2032, with adjustments made based on actual conditions and utilizing various CMIP6 scenarios for precipitation and actual evapotranspiration. The 2032 Water Yield was quantified using the InVEST Model at both grid and administrative scales, and its temporal-spatial characteristics and spatial correlation were analyzed. The findings revealed several key points: first, a decrease of 16.92% in arable land area, along with diminishing trends in grassland and unused land, and an increase of 33.92% in built-up land area, accompanied by growth in forest and water area. Second, the water yield decreases from southeast to northwest in Shandong Province, with significant inter-annual variations. Third, future predictions suggest that the annual water yield generally increases during flat water years, with offshore areas exhibiting greater water yield depth compared to inland areas. Rizhao is projected to have the highest water yield depth, while Binzhou and Dongying are expected to have the lowest depths, with a spatially positive correlation between them.

Climate-informed flood risk mapping using a GAN-based approach (ExGAN)

This study develops a class of robust models for flood risk mapping in highly vulnerable regions by focusing on accurately depicting extreme precipitation patterns aligned with regional climates. By implementing sophisticated hydrodynamics modeling and advanced probabilistic approaches, the present work underscores the efficacy of physical-based methodologies in the flood risk assessment. We propose a machine learning based ExGAN to address the challenge of synthesizing extreme precipitation scenarios which faithfully capture the nuances of local climatology. It is expected that through refined temporal disaggregation, the ExGAN approach exhibits exceptional proficiency in replicating a diverse spectrum of extreme precipitation patterns specific to the vulnerable region under scrutiny. Therefore, using these synthesized scenarios as inputs in a meticulously calibrated hydrological model would enable a comprehensive and detailed flood risk mapping exercise. To demonstrate the robustness of the developed mode, we perform a rigorous testing and validation within the highly susceptible Martil river basin, situated in the northern Mediterranean region of Morocco. The obtained results confirm that extending return periods would provide invaluable insights into the expanding geographical expanse of at-risk areas, clarifying the evolving landscape of vulnerability rather than merely amplifying inherent risk levels. Comparisons against the conventional Monte-Carlo sampling are also carried out in this study and the obtained results highlight significant overestimations within the latter, emphasizing the imperative need to account for diverse uncertainties beyond the basic sampling strategies within the realm of hydrodynamic modeling.

Optimizing food crop layout considering precipitation uncertainty: Balancing regional water, carbon, and economic pressures with food security

As the global food supply chain becomes increasingly unstable, food security is gaining importance in China. Nevertheless, factors such as water scarcity, limited land resources, and global climate change pose great challenges to increasing food production. Therefore, it is crucial to explore sustainable development models for food systems. In this paper, a multi-objective crop layout model considering crop rotation is constructed and solved using the INSGA2-DS algorithm. Various precipitation scenarios were established for climate change. Data analysis and optimization were conducted under different precipitation scenarios. Applied in the plain area of southwestern Shandong Province, the model can increase soybean yield by 6.78%, net carbon sequestration intensity by 0.04%, net benefit by 2.09%, and crop water productivity by 3.91% during a median flow year. Optimization tends to increase the total area planted with food crops during wet years and decrease it during dry years. The optimization model can enhance water productivity and carbon sequestration intensity of crops, as well as prevent unreasonable fluctuations in the area planted with food crops. This enables the benefits of water conservation and yield increase to be fully utilized, and reduces yield and economic losses due to dry years. The models help to rationally allocate soil and water resources, effectively respond to climate change risks, and provide robust decision support for regional food crop planning.

Modeling phase separation and composition patterning in FeCrAl alloys at neutron irradiation

Possible scenarios of the microstructural evolution of FeCrAl alloys under neutron irradiation are studied by using the phase-field method. The exploited approach is generalized by considering ballistic mixing of atoms, spatial rearrangement of point defects, and dynamics of their sinks (dislocation loops), self-consistently. Stability diagrams manifesting domains of main control parameters (irradiation temperature, damage rate, atomic relocation distance) governing precipitation are obtained. It is shown that depending on irradiation conditions a precipitation of the Cr-rich α′ -phase proceeds by either phase decomposition or composition patterning mechanism. Statistical analysis of precipitation kinetics is provided by studying precipitate number density, mean precipitate size and Chromium content in α′ -phase for a model alloy Fe-30%Cr-5%Al. A locality of point defects distribution with high concentration and dislocation loops growth are discussed. The hardening change is estimated for two possible scenarios of precipitation. Obtained results are verified with experimental observations. This study provides a deep insight into details of microstructural transformation in FeCrAl alloys during neutron irradiation.

The comprehensive effects of future multi‐scenario land use change and climate change on water conservation in Northwest China

AbstractWater conservation is an important ecosystem service, which is crucial to the fragile ecological environment and serious soil erosion in Northwest China. However, water conservation has been affected by climate change and human disturbance, and its future spatial and temporal changes are still unclear. This study employed the Integrated Valuation of Ecosystem Services and Trade‐offs (InVEST) model to assess the water conservation capacity of Northwest China between 1990 and 2020. Subsequently, the Patch‐generating Land Use Simulation (PLUS) model and the NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP‐CMIP6) were integrated to examine changes in water conservation from 2030 to 2050. The water conservation quantity in Northwest China had a fluctuating growth tendency between 1990 and 2020, with an average of 816.61 × 108 m3. The water conservation capacity's regional distribution pattern shows a steady increase from northwest to southeast. Northwest China's forest land had the greatest water conservation capacity between 1990 and 2020, at 66 mm, while the water land had the lowest, at 1.46 mm. The water conservation capacity in the 2030–2050 future scenario is less than that in 2020 when the regional distribution pattern is comparable to 2020. Natural development scenario (ND) in 2030 has the lowest water conservation capacity (13.41 mm) under ssp1‐2.6 precipitation scenario, whereas in 2050, the highest water conservation capacity (17.47 mm) is found in same scenario. The ecological protection scenario (EP) has the greatest water retention capacity (18.15 mm in 2040) and the lowest (14.38 mm in 2050) in the ssp5‐8.5 scenario. Our research can help restore and rebuild Northwest China's natural ecosystem and offer a theoretical foundation for the protection of ecological environment and the rational utilization of water resources in the region.

Assessing downscaling methods to simulate hydrologically relevant weather scenarios from a global atmospheric reanalysis: case study of the upper Rhône River (1902–2009)

Abstract. We assess the ability of two modelling chains to reproduce, over the last century (1902–2009) and from large-scale atmospheric information only, the temporal variations in river discharges, low-flow sequences and flood events observed at different locations of the upper Rhône River catchment, an alpine river straddling France and Switzerland (10 900 km2). The two modelling chains are made up of a downscaling model, either statistical (Sequential Constructive Atmospheric Analogues for Multivariate weather Predictions – SCAMP) or dynamical (Modèle Atmosphérique Régional – MAR), and the Glacier and SnowMelt SOil CONTribution (GSM-SOCONT) model. Both downscaling models, forced by atmospheric information from the global atmospheric reanalysis ERA-20C, provide time series of daily scenarios of precipitation and temperature used as inputs to the hydrological model. With hydrological regimes ranging from highly glaciated ones in its upper part to mixed ones dominated by snow and rain downstream, the upper Rhône River catchment is ideal for evaluating the different downscaling models in contrasting and demanding hydro-meteorological configurations where the interplay between weather variables in both space and time is determinant. Whatever the river sub-basin considered, the simulated discharges are in good agreement with the reference ones, provided that the weather scenarios are bias-corrected. The observed multi-scale variations in discharges (daily, seasonal, and interannual) are reproduced well. The low-frequency hydrological situations, such as annual monthly discharge minima (used as low-flow proxy indicators) and annual daily discharge maxima (used as flood proxy indicators), are reproduced reasonably well. The observed increase in flood activity over the last century is also reproduced rather well. The observed low-flow activity is conversely overestimated, and its variations from one sub-period to another are only partially reproduced. Bias correction is crucial for both precipitation and temperature and for both downscaling models. For the dynamical one, a bias correction is also essential for getting realistic daily temperature lapse rates. Uncorrected scenarios lead to irrelevant hydrological simulations, especially for the sub-basins at high elevation, due mainly to irrelevant snowpack dynamic simulations. The simulations also highlight the difficulty in simulating precipitation dependency on elevation over mountainous areas.

Long-term drought effects on landscape water storage and recovery under contrasting landuses

Recent extreme droughts in Europe have highlighted the urgent need to quantify their effects on ecohydrological fluxes (evapotranspiration, groundwater recharge) and water storage (mainly soil moisture) in the landscape. In response, we combined process-based (EcH2O-iso) and machine learning (NARX) models to estimate the enduring effects of long-term drought on water fluxes and storage and to project future short-term groundwater levels and recovery potential under various precipitation scenarios. The work was undertaken at the Demnitz Mill Creek (DMC), a 70 km2 mixed land use (arable crops and forestry) catchment in northern Germany. Our simulations indicated that the extreme drought years of 2018 and 2022 had the most marked impacts, leading to substantial declines in groundwater recharge (>40 %), evapotranspiration (up to 16 %) and soil moisture (up to 6 %). Simulations indicated that groundwater levels may not recover in the next 15 years if recent precipitation anomalies persist. These findings underscore the urgent requirement for enhancing resilience and promoting integrated strategies in managing land and water resources to optimise water retention in landscapes and to better respond to drought.

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.

Which contributes more in predictor of desert shrubs traits: Intra-annual or inter-annual precipitation change?

Considering the implications of future climatic shifts, mid-latitude desert ecosystems will experience pronounced changes in precipitation dynamics, as evidenced by the elevated inter-annual variability and escalated annual incidence of extreme rainfall events. Despite considerable research on vegetation response to intricate precipitation patterns at the community level, the physiological adaptations of individual plants within these assemblages are not well defined. This investigation employed simulations of diverse precipitation scenarios, utilising varying precipitation amounts (to simulate inter-annual variability) and intervals (to simulate intra-annual variability). A field-manipulated experiment was conducted to assess the responses of the leaf and stem hydraulic functional traits of Artemisia ordosica under different precipitation regimes. The results demonstrated that 1) the magnitude of inter-annual precipitation, rather than the intra-annual redistribution of precipitation, dominated the variability in the functional traits of A. ordosica, with diminished inter-annual precipitation inducing an array of functional trait alterations (e.g., specific leaf area, stomatal conductance, water use efficiency, and conduit diameter) to enhance water-use safety. Reallocating precipitation to low-frequency but high-intensity events during years with less inter-annual precipitation was more advantageous for the survival of A. ordosica. 2) A reduction in annual precipitation fostered a correlation between stem and leaf functional traits in A. ordosica (e.g., hydraulic conductance with photosynthetic rate, stomatal conductance, conduit diameter with transpiration rate, and water use efficiency), suggesting that under decreased precipitation, A. ordosica strengthens the coupling between hydraulics and photosynthesis to prevent hydraulic failure, demonstrating a safer survival strategy. Overall, the impact of global changes in precipitation patterns on the individual survival of A. ordosica primarily depended on the variation in inter-annual precipitation rather than the within-year redistribution. This study enhanced our understanding of the adaptability of dominant shrub species within desert shrub communities to altered precipitation patterns under climate change.

Rock moisture reinforces belowground water storage under different precipitation scenarios and vegetation coverage

Rock moisture is rarely deliberated but crucial for hydrologic regulation in semiarid loess hilly regions. In this study, using in-situ monitoring observation and simulation, soil and rock moisture spatiotemporal variations under planting alfalfa and caragana were analyzed and sketched, and the water storage contributions by weathered bedrock layers (WBLs) under different rainwater scenarios were then simulated and estimated. The results showed that WBLs had apparent spatiotemporal changes in water distribution and storage with rainfall events. Under water scarcity scenarios, WBLs increased the variabilities of water storage, with an average increase of 89.9 % in alfalfa and 14.2 % in caragana, providing wider water supply boundaries for vegetation. However, higher volumetric water content was presented under relatively abundant water scenarios. The average soil water content was 0.08 cm3 cm−3 in alfalfa and 0.09 cm3 cm−3 in caragana, while the average rock water content was 0.12 cm3 cm−3 in alfalfa and 0.13 cm3 cm−3 in caragana. Profile water consumption goes to surface soil evaporation and plant transpiration over an entire growing season, while extrawater would be stored in WBLs to maintain vegetation growth during the water-scarce seasons. Moreover, water storage had a positive linear relationship with the thickness of WBLs. Rock water storage increased by 6.9 % in alfalfa and 3.7 % in caragana after adding 20 cm WBLs, while it increased by 23.6 % in alfalfa and 9.9 % in caragana after adding 50 cm WBLs. Furthermore, various exogenous rainwater simulations showed that differences in water storage were significantly increased with WBLs thickness. This study would provide new insights into the deep-rooted plant distribution planning in semiarid loess hilly regions, and could also be helpful for the rational water resources utilization and management in shallow soil areas.

A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins

AbstractExisting stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6‐hr and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by water vapor transport from the Gulf of Mexico—the key flood‐generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large‐scale atmospheric fields from a climate model, reflecting spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine‐scale precipitation arising from a specific large‐scale atmospheric environment. Model parameters were fitted monthly from December–May, based on storms identified from 1979 to 2021 ERA5 reanalysis data and Analysis of Record for Calibration (AORC) precipitation. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agree well with AORC data and show an overall increase in 1‐ to 500‐year events in the future period (2022–2050). To our knowledge, this is the first SRG simulating nonstationary, anisotropic high‐resolution precipitation over continental‐scale river basins, demonstrating the value of conditioning such stochastic models on large‐scale atmospheric variables. StormLab provides a wide range of extreme precipitation scenarios for design floods in the MRB and can be further extended to other large river basins.

Self-clustered GAN for precipitation nowcasting

This paper proposes a novel GAN framework with self-clustering approach for precipitation nowcasting (ClusterCast). Previous studies have primarily captured the motion vector using only a single latent space, making the models difficult to adapt to disparate space-time distribution of precipitation. Environmental factors (e.g., regional characteristics and precipitation scale) have an impact on precipitation systems and can cause non-stationary distribution. To tackle this problem, our key idea is to train a generator network to predict future radar frames by learning a sub-network that automatically labels precipitation types from a generative model. The training process consists of (i) clustering the hierarchical features derived from the generator stem using a sub-network and (ii) predicting future radar frames according to the self-supervised labels, enabling heterogeneous latent representation. Additionally, we attempt an ensemble forecast that prescribes random perturbations to improve performance. With the flexibility of representation learning, ClusterCast enables the model to learn precipitation distribution more accurately. Results indicate that our method generates non-blurry future frames by preventing mode collapse, and the proposed method demonstrates robustness across various precipitation scenarios. Extensive experiments demonstrate that our method outperforms four benchmarks on a 2-h prediction basis with a mean squared error (MSE) of 8.9% on unseen datasets.

The influence of microstructural evolutions on electrochemical corrosion and passive behavior in precipitation-strengthened high-entropy alloys

Effects of microstructural evolutions on electrochemical corrosion properties and passive film characteristics of precipitation-strengthened (Ni2Co2FeCr)92Al4Nb4 high-entropy alloys (HEAs) were investigated in a saline environment. Compared with single-phase FCC alloys, the appearance of nano-sized L12 particles increases the metastable pitting activities and slightly reduces the pitting potential, while the lamellar D019 phase further deteriorates the localized corrosion resistance. The chemical composition and oxide state of passive films are closely related to the different precipitation scenarios. The corrosion morphologies of the single-phase and L12-strengthened alloys are dominated by pitting, whereas the intergranular corrosion (IGC) and intragranular dealloying-like corrosion occur in the D019-strengthened alloy.

The fate of leaked heavy metals in the urban environment under different persistence and precipitation scenarios

The use of tobacco will lead to the littering of a large number of filters, and the leakage of pollutants from them into the urban environment is a serious concern. The aim of this study was to analyze the leakage of heavy metals from filter and estimate the annual concentration of pollution leakage in different waste routes and different climatic conditions. The results showed that the highest and lowest density of filter in the studied urban environment were 0.51 and 0.01 number/m2, respectively. According to the estimated annual production of 306 million cigarette butts in the studied area, the leakage of the studied metals was estimated to be 401 g. The share of copper, chromium, and cadmium from the total leakage was 67%, 8.3%, and 1.88%, respectively. The leakage of studied metals in rainy conditions was 2.86 times more than sunny conditions. In different scenarios, the minimum and maximum annual leakage of metals were estimated 23,043 and 350,419 mg/year, respectively. Filters are a little but important source of heavy metal emission into the urban environment, the amount of pollution from which is affected by the consumers’ behavior and the efficiency of the urban cleaning system. Education on the correct disposal of filters and increasing the efficiency of the urban cleaning system will lead to a reduction in pollution caused by tobacco consumption.

Modeling Shallow Landslide Runout Distance in Eocene Flysch Facies Using Empirical–Statistical Models (Western Black Sea Region of Türkiye)

Uncertainties related to runout distances in shallow landslide analyses may not only affect lives but may also result in economic losses. Owing to the increase in shallow landslides, which are especially triggered by heavy rainfall, runout distances have been investigated to decipher whether applications of a functional runout distance are feasible. This paper aims to give insights into the modeling of the shallow landslide runout probability in Eocene flysch facies in the Western Black Sea region of Türkiye. There are two main stages in this study—which are dominated by empirical models, the detection of initiation points, and propagation—which help us to understand and visualize the possible runout distances in the study area. Shallow landslide initiation point determination using machine learning has a critical role in the ordered tasks in this study. Modified Holmgren and simplified friction-limited model (SFLM) parameters were applied to provide a good approximation of runout distances during the propagation stage using Flow-R software. The empirical model parameters suggested for debris flows and shallow landslides were investigated comparatively. The runout distance models had approximately the same performance depending on the debris flow and shallow landslide parameters. While the impacted total runout areas for the debris flow parameters were predicted to amount to approximately 146 km2, the impacted total runout areas for the shallow landslide parameters were estimated to be about 101 km2. Considering the inclusion of the RCP 4.5 and RCP 8.5 precipitation scenarios in the analyses, this also shows that the shallow landslide and debris flow runout distance impact areas will decrease. The investigation of runout distance analyses and the inclusion of the RCP scenarios in the runout analyses are highly intriguing for landslide researchers.