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Stochastic Environmental Research and Risk Assessment
Journal Prestige (SJR): 1.096  Citation Impact (citeScore): 3 Number of Followers: 4  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1436-3259 - ISSN (Online) 1436-3240 Published by Springer-Verlag  [2468 journals]
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- Spatiotemporal variations, photochemical characteristics, health risk
assessment and mid pandemic changes of ambient BTEX in a west Asian
metropolis-
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Abstract: Abstract This study examined the concentration of BTEX in Tehran from 2018 to 2020 in five monitoring stations with different backgrounds, which has been accomplished using the combination of passive sampling and GC-FID method. The total concentration of BTEX was estimated to be 65.39 (µg/m3), with a higher average concentration in 2019–2020 (77.79 µg/m3) compared to 2018–2019 (53.48 µg/m3) due to the leaping concentration of Toluene in the pandemic era. Despite a Benzene concentration decline in recent years, the average annual concentration of Benzene (5.66 µg/m3) at five stations remained higher than the EU commission and India standards (5 µg/m3) as well as Japan and Iraq thresholds (3 µg/m3). Toluene dominated other species in terms of concentrations, mass distribution (~0.6%), followed by m,p–Xylene (~0.2%), Benzene (~0.05–0.1) and Ethylbenzene (< 0.05). The evidence regarding seasonal changes of BTEX in 2019 shows the maximum concentration of these compounds in autumn, which is probably due to heavier traffic compared to other seasons. In contrast, in the first half of 2020 (which encompasses the start of the pandemic period and urban lockdown), point sources seem to play a prominent role in concentration fluctuations, as confirmed by changes in interspecies relationships and lower traffic congestion. The highest mean concentrations were observed in high-traffic, residential and suburban sites, respectively. The study reveals that m,p-Xylene possess the highest Ozone formation potential (~109.46), followed by Toluene (~85.34), o-Xylene (~46.87), Ethylbenzene (~13.52) and Benzene (~2.61). Health risk assessment results indicated the high carcinogenic risk of Benzene (mean = 3.6 × 10–6) and the acceptable non-carcinogenic risk of BTEX (hazard index~0.03 < specified limit of 1). Finally, the estimated weighted exposures of BTEX emphasized that residents near the high-traffic districts are more exposed to BTEX.
PubDate: 2023-10-01
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- Comparison of on-site versus NOAA’s extreme precipitation
intensity-duration-frequency estimates for six forest headwater catchments
across the continental United States-
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Abstract: Abstract Urgency of Precipitation Intensity-Duration-Frequency (IDF) estimation using the most recent data has grown significantly due to recent intense precipitation and cloud burst circumstances impacting infrastructure caused by climate change. Given the continually available digitized up-to-date, long-term, and fine resolution precipitation dataset from the United States Department of Agriculture Forest Service’s (USDAFS) Experimental Forests and Ranges (EF) rain gauge stations, it is both important and relevant to develop precipitation IDF from onsite dataset (Onsite-IDF) that incorporates the most recent time period, aiding in the design, and planning of forest road-stream crossing structures (RSCS) in headwaters to maintain resilient forest ecosystems. Here we developed Onsite-IDFs for hourly and sub-hourly duration, and 25-yr, 50-yr, and 100-yr design return intervals (RIs) from annual maxima series (AMS) of precipitation intensities (PIs) modeled by applying Generalized Extreme Value (GEV) analysis and L-moment based parameter estimation methodology at six USDAFS EFs and compared them with precipitation IDFs obtained from the National Oceanic and Atmospheric Administration Atlas 14 (NOAA-Atlas14). A regional frequency analysis (RFA) was performed for EFs where data from multiple precipitation gauges are available. NOAA’s station-based precipitation IDFs were estimated for comparison using RFA (NOAA-RFA) at one of the EFs where NOAA-Atlas14 precipitation IDFs are unavailable. Onsite-IDFs were then evaluated against the PIs from NOAA-Atlas14 and NOAA-RFA by comparing their relative differences and storm frequencies. Results show considerable relative differences between the Onsite- and NOAA-Atlas14 (or NOAA-RFA) IDFs at these EFs, some of which are strongly dependent on the storm durations and elevation of precipitation gauges, particularly in steep, forested sites of H. J. Andrews (HJA) and Coweeta Hydrological Laboratory (CHL) EFs. At the higher elevation gauge of HJA EF, NOAA-RFA based precipitation IDFs underestimate PI of 25-yr, 50-yr, and 100-yr RIs by considerable amounts for 12-h and 24-h duration storm events relative to the Onsite-IDFs. At the low-gradient Santee (SAN) EF, the PIs of 3- to 24-h storm events with 100-yr frequency (or RI) from NOAA-Atlas14 gauges are found to be equivalent to PIs of more frequent storm events (25–50-yr RI) as estimated from the onsite dataset. Our results recommend use of the Onsite-IDF estimates for the estimation of design storm peak discharge rates at the higher elevation catchments of HJA, CHL, and SAN EF locations, particularly for longer duration events, where NOAA-based precipitation IDFs underestimate the PIs relative to the Onsite-IDFs. This underscores the importance of long-term high resolution EF data for new applications including ecological restorations and indicates that planning and design teams should use as much local data as possible or account for potential PI inconsistencies or underestimations if local data are unavailable.
PubDate: 2023-10-01
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- Analyzing the long-term variability and trend of aridity in India using
non-parametric approach-
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Abstract: Abstract Aridity is a climatic phenomenon characterized by shortage of water availability in a given time and space resulting in low moisture and reduced carrying capacity of ecosystems. It is represented by a numerical indicator known as Aridity Index (AI), a function of rainfall and temperature. Aridification is a slow and steady effect of climate change and assessing its spread and change is vital in context of global climatic variations. Aridity is predominantly significant for agrarian countries like India, where a slight rise in drylands area can have a significant impact on the economy and community sustenance. AI is an inclusive indicator of climatic conditions in most arid and semi-arid regions. It helps in identifying and interpreting large scale trend in temperature and precipitation; and thus, classifying region into different climatic classes. The present study assessed long-term AI based on precipitation and temperature data obtained from the India Meteorological Department at the resolution of 1 × 1 degree for years 1969–2017. AI is estimated as a ratio of mean precipitation to mean potential evapotranspiration, calculated using Thornthwaite method. The results highlight the trend of aridity over pan-India with Innovative Trend Analysis and Mann–Kendall test. The study concludes that there is a relatively slow, however steadily progressive drier conditions being established in most of the regions. A shift from ‘Semi-arid’ towards ‘Arid’ class appeared in central mainland. The north-eastern Himalaya showed decrease in humid conditions (‘Humid’ to ‘Sub-humid’). The study implies that there is a rising aridity trend over the years due to changing climatic conditions. The shifts in aridity can have serious implications on agriculture, long-term water resource utilization and land use management plans. Our results have scope for future landscape management studies in drylands and better adaptation methods in arid regions.
PubDate: 2023-10-01
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- Quantifying the effect of climate variability on seasonal precipitation
using Bayesian clustering approach in Kebir Rhumel Basin, Algeria-
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Abstract: Abstract This paper presents a Bayesian clustering approach that allows quantification of the effect of climate variability on seasonal precipitation data in Kebir Rhumel Basin (KRB). We applied this approach to simultaneously identify clusters of stations with similar characteristics and the climate variability associated with each cluster and for the individual stations within each cluster. Both full pooling Bayesian clustering (FPBC) and partial pooling Bayesian clustering (PPBC) models with nonstationary generalized extreme value (GEV) distribution are applied to each season. In these models, a climate index variable, namely the El Niño Southern Oscillation (ENSO), is included as a time-varying covariate with an appropriate basis function to potentially explain the temporal variation of one or more of the parameters of the distribution. Results reveal that the partial pooling Bayesian clustering model provided the best fit for the seasonal precipitation data. The significant effect of ENSO differs from one season to another. During spring and autumn, ENSO significantly affects precipitation across large parts of KRB. Furthermore, the southern part and northern part of KRB are positively and negatively influenced by ENSO during winter and summer, respectively. Moreover, almost all stations during spring and autumn are negatively and positively influenced by ENSO, respectively. Finally, we demonstrated that the proposed model helps to reduce the uncertainty in the parameter estimation and provides more robust results.
PubDate: 2023-10-01
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- Saharan dust contributions to high hourly PM10 concentrations at a
background station in Southwestern Europe-
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Abstract: Abstract This paper presents a long-term analysis of ambient PM10 concentrations measured at a background station in Spain, using backward trajectories generated with the HYSPLIT model, together with the identification of synoptic patterns and a fuzzy clustering analysis, to identify the sources present and evaluate their relationship with meteorological variables such as wind speed and direction, temperature, and atmospheric pressure. Ambient PM10 presented maximum values during the summer with mean concentrations of approximately 14 µg m−3 for August, and minimum values during the winter with concentrations of 6 µg m−3 for January. The daily cycle presented two peaks—one in the morning and another in the afternoon—with this variability being associated with transport emissions. African air masses reached the study site extending vertically at least up to 2500 m.a.g.l, with a frequency of 43.5%, and are associated to a mean ground PM10 concentration of 41.3 µg m−3. During the episodes of Saharan intrusions, high pressures with unclassified synoptic patterns (U) prevailed over the Iberian Peninsula. Local, European, maritime and intermittent contributions were the four main sources of pollutants identified through fuzzy clustering analysis. These intermittent contributions are associated with Saharan dust intrusions, with a long-term average PM10 concentration of 1.7 µg m−3, which represents an important contribution of 11.2%. These sources affect seasonal variations of PM10 background concentrations, and reach their maximum when the greatest contributions of desert dust occur—mainly during the spring–summer months. These results provide useful information for future comparisons and environmental monitoring of PM10 levels.
PubDate: 2023-10-01
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- Dryland farming wheat yield prediction using the Lasso regression model
and meteorological variables in dry and semi-dry region-
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Abstract: Abstract The risk of climate change and international market fluctuations complicate crop production. Wheat is considered one of the most strategic products in food security. Dryland farming of wheat is prevalent in many parts of the world, and it occupies a large part of the cultivated land. However, its performance is highly dependent on weather conditions and changes. Yield prediction models could be used for planning purposes when dealing with changes in yield. This study analyzed the effectiveness of the least absolute shrinkage and selection operator (LASSO) model in selecting variables for predicting dryland wheat yield in southwestern Iran. The model was used with 45 weather-based variables across annual, seasonal, and monthly time frames. The results showed that temperature, evaporation, and extreme temperatures followed by radiation and precipitation variables categories, are effective meteorological variables in estimating dryland farming wheat yield in the study area. Monthly timescale could estimate yield with minimum error compared to other timescales. However, considering all selected variables regardless of their timescale (total) results, the best estimation in most districts with the model’s R2 and normalized root mean square error (NRMSE) varied between 57.98–99.50 and 1.46–21.94, respectively. Therefore, the LASSO regression could be used reliably for each district considering the most effective meteorological parameters in that region for accurate decision-makers policies.
PubDate: 2023-10-01
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- Heavy metal pollution and ecological risk under different land use types:
based on the similarity of pollution sources and comparing the results of
three evaluation models-
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Abstract: In key areas of ecological protection, it is significant to consider the similarity of pollution sources among heavy metals and the interaction between different sources, especially the ecological risk areas caused by heavy metal pollution. We collected 51 soil samples from five land use types with different soil depths in an industrial area on the Qinghai-Tibet Plateau. Two and three major heavy metal combination types of Cd, Cu, Cr, Pb and Zn in different soil layers were identified using absolute principal component score-multiple linear regression models, and the potential pollution sources corresponding to the different types were quantified using Geo-Detector models. Factor-detector explanatory power of the land use type (q = 0.66) was much higher than that of the other factors of APCS1 in soil layer A, which was the most likely potential sources of Cd and Pb with high levels in urban land and irrigated land. Industrial activities, especially metallurgy and mining, are the most likely potential sources of Cd, Cu and Pb pollution. The downward migration of heavy metals in the study area was inferred from the similar trends of several indicators between soil layers A and B. The new model Nemerow Integrated Risk Index (NIRI) was used to analyse the integrated ecological risk across the study area and under different land use types by comparing with the pollution load index and Nemerow Integrated Pollution Index, and it was found that the risk level was lower in grassland and forest land than under other land use types, while it was higher in urban land and irrigated land. The contribution rate of Cd to NIRI values exceeded 80%, while the contribution rates of the 5 heavy metals to NIPI and PLI values are not significantly different, indicating that NIRI can highlight the impact of high cadmium toxicity factors on the overall risk level and is more accurate and flexible in identifying risk areas. Graphical abstract
PubDate: 2023-10-01
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- Exploring the environmental sustainability potential of the China-Pakistan
economic corridor for Pakistan-
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Abstract: Abstract In 2013, China and Pakistan agreed to jointly build infrastructure and to generate power in order to support the development of Pakistan alongside the successful implementation of Belt and Road Initiative. The projects carried out under this agreement are collectively called the China-Pakistan Economic Corridor (CPEC), and the CPEC is anticipated to bring prosperity and peace to South Asia. Notably, the CPEC will improve the respective economies of both countries and the trade between them, establish a regional connection between these countries, solve their energy issues, increase their people’s interactions, and develop their infrastructure, by the establishment of 2000 km-transportation infrastructure (pipelines, railways, and roads) between China (Kashgar) and Pakistan (Gwadar port) on the Arabian Sea, improving the China-Pakistan connectivity. However, Pakistan faces numerous potential eco-environmental vulnerabilities in implementing this multibillion-dollar CPEC. Although, the CPEC is a collection of game-changing infrastructure projects that will improve Pakistan’s destiny and modernize it. The study also confirms that the people serving in different sectors of Pakistan have sufficient awareness and understanding of CPEC project. They are hopeful towards the advantages and execution of the CPEC project, indicating the effective strategies for supporting the sustainable development of Pakistan.
PubDate: 2023-10-01
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- Joint modeling of drought and dust hazards using copula- based model over
Iran from 1988 to 2018-
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Abstract: Abstract This study aims to employ joint modeling for drought and dust hazards. Long-term monthly precipitation and temperature were used to calculate drought characteristics based on the standardized precipitation- evapotranspiration index. Three-hourly horizontal visibility and wind erosion event codes were also obtained to detect dust events. The results of drought hazard assessment indicated that about 52% of the total area of Iran was affected by moderate hazard. Other classes of drought hazard i.e., severe, low, and very severe, covered approximately 24, 18, and 6%, respectively. The assessment of dust hazard was conducted by weighting the frequency of dust events based on the horizontal visibility. The findings on dust hazard showed that approximately 18% of the total area of Iran was affected by the more intense hazard, which covered the southeastern sectors. Bivariate copulas were considered for joint modeling of drought and dust hazards. The copulas of Student’s t, Gaussian, Gumbel, and others were applied to construct a joint model. Bivariate assessment of drought and dust hazard illustrated that the eastern part of Iran, which covers around 47% of the country, exposed at greater hazard than other parts. The copula-based conditional probability was also obtained to quantify the probability of dust occurrence under different drought hazard levels. The conditional probability values showed that by intensification of drought conditions the probability of dust hazard increased. In addition the frequency of dust occurrences increased when the region faced very severe droughts. Generally, the prolonged and severe droughts were identified as the most important reasons for the increased hazard of dust and drought in the study region. This study results indicated that the joint risk of drought and dust hazard along with vulnerability factors, can be used for possible mitigation of the risks of these natural hazards.
PubDate: 2023-10-01
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- Master equation model for solute transport in river basins: part I channel
fluvial scale-
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Abstract: Abstract Normal and anomalous diffusion are ubiquitous in many physical complex systems. Here we define a system of diffusion equations generalized in time and space, using the conservation principles of mass and momentum at channel scale by a master equation. A numerical model for describing the steady one-dimensional advection-dispersion equation for solute transport in streams and channels imposed with point-loading is presented. We find the numerical model parameter as the solution of this system by estimating the transition probability that characterizes the physical phenomenon in the diffusion regime. The results presented (Part I) refer to the channel scale and represent the first part of a research project that has been extended to the basin scale.
PubDate: 2023-10-01
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- An analytical approach to evaluate the impact of age demographics in a
pandemic-
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Abstract: Abstract The time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process. This study introduces a six-state compartmental model to explain and assess the impact of age demographics by designing a dynamic, explainable analytics model of the SARS-CoV-2 coronavirus. An age-stratified mathematical model taking the form of a deterministic system of ordinary differential equations divides the population into different age groups to better understand and assess the impact of age on mortality. It also provides a more accurate and effective interpretation of the disease evolution, specifically in terms of the cumulative numbers of infected cases and deaths. The proposed Kermack-Mckendrick model is incorporated into a non-linear least-squares optimization curve-fitting problem whose optimized parameters are numerically obtained using the Levenberg-Marquard algorithm. The curve-fitting model’s efficiency is proved by testing the age-stratified model’s performance on three U.S. states: Connecticut, North Dakota, and South Dakota. Our results confirm that splitting the population into different age groups leads to better fitting and forecasting results overall as compared to those achieved by the traditional method, i.e., without age groups. By using comprehensive models that account for age, gender, and ethnicity, regional public health authorities may be able to avoid future epidemics from inflicting more fatalities and establish a public health policy that reduces the burden on the elderly population.
PubDate: 2023-10-01
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- Trivariate frequency analysis of droughts using copulas under future
climate change over Vidarbha region in India-
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Abstract: Abstract Mankind is currently facing a crucial challenge in terms of climate change which may result in severe drought in many areas across the world. The present study analysed the joint return periods of meteorological drought events for historical and future projections across the Vidarbha region of India. To describe the drought occurrences in the area, the study used the standardized precipitation evapotranspiration index. The properties of each drought event, viz., duration, severity, and peak, were identified to model the multivariate drought risks. Several copula families were evaluated using statistical tests for joint dependency modelling of drought properties. The obtained joint distribution and selected univariate marginals are further used for estimating trivariate return periods for the historical and future climate change scenarios. The drought properties exhibited good interdependency justifying the use of a trivariate framework. The observed historical data of rainfall, maximum and minimum temperature were collected from the India Meteorological Department. The latest future projections of climate variables from coupled model intercomparison project phase 6 (CMIP6) datasets were acquired from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) for six general circulation models. The copula-based trivariate frequency analysis was performed to compare the return periods associated with ‘moderate’ and ‘above moderate drought’ (i.e., combination of severe and extreme drought) events for the reference period (1981–2020) and future periods (2021–2100) under two SSP scenarios, SSP2-4.5 and SSP5-8.5. The ‘AND’ condition return period showed high drought risk during historical period covering a larger area. The findings of the study suggested that for ‘AND’ condition the trivariate frequency of above moderate droughts would increase in the future across Vidarbha region. The analysis showed that during near future more area will be under the risk of above moderate drought conditions compared to the far future, and the high-risk zones will be more under SSP5-8.5 than SSP2-4.5 scenario. The frequency of moderate drought episodes will be higher than the above moderate drought events, but the percentage area in the high-risk zone will be lower. The ‘OR’ condition return period projected alleviation of drought risk in the future, while the far future projected more area under drought risk compared to near future for the SSP scenarios.
PubDate: 2023-10-01
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- Near-real-time forecasting of reservoir inflows using explainable machine
learning and short-term weather forecasts-
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Abstract: Abstract Reservoirs play a crucial role in flood control by storing and regulating the water. Inflow forecasting in real-time is essential for the effective management of reservoir water. The emergence of high spatial resolution weather forecasts and explainable Machine Learning (eXML) algorithms remove the barriers to accurate forecasting and provide a better understanding of Machine Learning (ML) models. In this context, reservoir inflows are forecasted using eXML models and Global Forecasting System (GFS) data: precipitation, minimum and maximum temperature. Popular ML methods like Long Short Term Memory (LSTM), Multilayer Perceptron, Support Vectors Machine and Random Forest are used in this study. Further, these models' explainability is assessed using the Shapley additive explanations method. The ML models are trained and tested using the observed data from 2000 to 2018 in the Tenughat catchment, Damodar river basin, India. Among all the ML models, the LSTM model better predicted the inflows with an NSE value of 0.938. The eXML revealed that inflow and precipitation variables of 1-day lag significantly impact the models’ prediction in all models. The GFS data of 2017–2018 was bias-corrected using the Scaled Distribution Mapping method, and the method improved the GFS data significantly. The inflow forecasting using the LSTM model with the bias-corrected GFS data revealed that the LSTM model performed well up to 3-day lead time (NSE = 0.908, 0.888, 0.876 for 1–3 day lead). Thus, the LSTM model with the GFS forecasts has substantial potential for real-time forecasting of reservoir inflows.
PubDate: 2023-10-01
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- Multimodel classification and regression technique for the statistical
downscaling of temperature-
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Abstract: Abstract Human activity has increased the amount of carbon dioxide and other greenhouse gases emitted into the atmosphere, causing climate change. As a result, rising temperatures have wide-ranging consequences on water management. This study proposes downscaling daily temperature based on a modified Classification and Regression Technique with an ensemble machine learning (EML) approach at the Woodstock station in the Upper Thames River basin. The GCM Canadian Earth System Model (CanESM5) from Coupled Model Intercomparison Project-6 is used. The CanESM5 model simulated variables are used as predictors and observed baseline daily temperature as predictands. The Regression-based single machine learning (Support Vector, Tree-based and Gaussian Process Regression) and EML based statistical downscaling are applied and compared. The variable temperature states are determined using Gaussian Mixture Model clustering, and the Light Gradient Boosting Model (LightGBM) is used to classify future temperature states. Results showed that applying the EML boosted the performance by 2–25% compared to single models. The temperature states for the two projected climate scenarios (SSP126 and SSP585) were simulated by selected best-performing single and EML model combinations for the near (2026–2050) and far future (2076–2100). The findings demonstrate that the future projected temperatures may rise 1–3 °C for both scenarios and are less volatile than the observed baseline temperature. Overall, the study indicates that the ensemble approach-based downscaling combining several single models have considerably improved the performance and was more reliable.
PubDate: 2023-10-01
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- Assessment of climate change impacts on the construction of homogeneous
climate zones and climate projections during the twenty first century over
Pakistan-
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Abstract: Abstract The effects of climate change have posed several threats and far more than the effects on Pakistan as the temperature is constantly rising and this could lead to an increase in the severity of extreme events. This study analyzed the climate change’s impact on climatic regions that are established by using the data of precipitation and temperature for 55 meteorological stations in Pakistan based on Reconnaissance Drought Index. For the construction of homogeneous climatic regions (HCRs), two statistical methods were integrated. First, cluster analysis was employed to identify climatic regions (CRs) using site characteristics of the stations, which gave five climatic regions (CRs). Second, heterogeneity measure and discordancy measure were used based on L-moment approach and regional adjustments to satisfy the homogeneity of HCRs. These HCRs were evaluated on REMO model (a regional climate model), for the baseline period of 1976–2005. To assess the impacts of climate change, HCRs were constructed, and climate projections were developed under the RCP 4.5 and RCP 8.5 for twenty first century. The results of HCRs show that some stations interchanged with other groups under both future scenarios due to changes in temperature and precipitation. It is observed that the maximum and minimum temperature will highly increase in the future and major increase in temperature is projected in 2071–2100 with RCP 4.5 and 8.5. This study will assist in the short and long-term planning of climate change, irrigation, water management, hydropower etc. in the country.
PubDate: 2023-10-01
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- Identifying non-stationarity in the dependence structures of
meteorological factors within and across seasons and exploring possible
causes-
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Abstract: Abstract Precipitation (P) and temperature (T) are key components of the hydrometeorological system, and their dependence structures exhibit significant dynamic changes, including non-stationary behavior, in response to environmental variations. These changes affect local hydrological processes and impact the predictability of the hydrometeorological system. However, the dynamics of dependence structures among meteorological factors during corresponding and adjacent seasons, as well as their underlying causes, have not been fully revealed. Therefore, this study comprehensively explored the dynamics of the precipitation-temperature dependence structure (PTDS) and temperature-temperature dependence structure (TTDS), and their possible causes. Firstly, non-stationary of PTDS was identified using a copula model. Then the main drivers of PTDS were determined by the random forest (RF) model and variable projection importance (VIP) criteria. These drivers include both conventional factors such as local meteorological factors (e.g., P, T, wind speed (WS), vapor pressure, relative humidity and sunshine duration (SD)) and teleconnection factors (e.g., Sunspots, the Arctic Oscillation, Pacific Decadal Oscillation (PDO), El Niño-Southern Oscillation (ENSO)). Additionally, the normalized difference vegetation index (NDVI) was used to investigate the response of dependence structure to vegetation dynamics. Finally, the ridge regression model was applied to construct driver models for the dynamics of dependence structures. The Loess Plateau was selected as the study area because of its high ecological sensitivity and typical human afforestation activities. The results show that: (1) non-stationarity in the PTDS occurred in different seasons and at various stations; (2) the primary drivers of PTDS and TTDS dynamics are predominantly local meteorological factors; (3) there is a strong correlation between SD and ENSO, and the impacts of PDO on local meteorological factors (WS and T) play a crucial role in driving the PTDS dynamics; and (4) NDVI is the main driver, primarily influencing T and ultimately affecting the dynamics of PTDS and TTDS. These findings suggest that there are significant ecological impacts through radiative or non-radiative feedback mechanisms under warming scenarios. Overall, this study provides new insights into the drivers and mechanisms behind the dynamics of dependence structures among meteorological elements. It contributes to a deeper understanding of the changing local hydrometeorological processes.
PubDate: 2023-10-01
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- A meta-Gaussian distribution for sub-hourly rainfall
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Abstract: Abstract Meta-Gaussian models are ubiquitous in the statistical literature. They provide a flexible building block to represent non-Gaussian distributions which inherit modeling and inference methods available in the Gaussian framework. In particular they have been widely used for modeling rainfall distributions. The first step when working with meta-Gaussian models consists in choosing an appropriate transformation which allows to map the Gaussian distribution to the target rainfall distribution. Many transfer functions have been proposed in the literature but most of them are not appropriate to describe heavy-tailed distributions, which is known to be a usual feature for rainfall at sub-daily scales. In this context, we propose and study a new meta-Gaussian model that can handle heavy-tailed observations. It leads to a four parameter model for which each parameter is linked to a different part of the distribution: a first one describes the probability of rainfall occurrence, two of them are related to the lower and upper tailed features of the distribution, and the last one is just a scaling parameter. Theoretical arguments are given to justify the proposed model. A statistical analysis of seven French rain gauges indicates the flexibility of our approach under different climatological regions and different aggregation times, here from 6 min to 24 h. Our distribution outperforms other meta-Gaussian models that have been proposed in the literature and, in particular, it captures well heavier tail behaviours below the hourly scale.
PubDate: 2023-10-01
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- Non-linear granger causality approach for non-stationary modelling of
extreme precipitation-
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Abstract: Abstract Interactions and feedback between the atmosphere, oceans, and land lead to natural climatic variations responsible for severe weather events such as Extreme Precipitation (EP). Understanding the lagged/delayed effect of teleconnections would improve the predictive capabilities, especially in a complex, non-linear, and non-stationary hydro-climatic system. The objective of the study is two-fold: (i) selection of lagged climate indices to improve the efficacy of the NS model using Granger Causality and (ii) non-stationary modeling of extreme precipitation. This study proposes a new Generalized Regression Neural Network based Non-linear Granger (NL-GRNN) causality approach. The climate indices with their optimal time-lags having significant Granger causality are selected for modeling EP in a Non-Stationary Generalized Extreme Value (NSGEV) framework. Seventeen climate indices are considered and applied for two regions of different climatic conditions, namely Chennai, India, and San Diego, North America. Results show that NL-GRNN based NSGEV models are found to be computationally efficient and show better performance compared to the linear- and artificial neural network based Granger causality approaches. It is observed that the ENSO-related indices and the northern hemisphere climate variability modes such as AO, PNA, and NAO dominantly influence EP over Chennai and San Diego, respectively. The outcomes of the study will be useful in planning and design of water infrastructure, disaster management and disease outbreak.
PubDate: 2023-10-01
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- Road slope monitoring and early warning system integrating numerical
simulation and image recognition: a case study of Nanping, Fujian, China-
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Abstract: Abstract A novel road slope monitoring and early warning system was developed by integrating 3D image recognition technology and 3D numerical modeling technology for monitoring and predicting slope deformation. It was applied for monitoring and early warning for a road slope in Nanping, Fujian, China. The system consists of equipment information management, data management, forecast and early warning, information release and model visualization modules. It can carry out point-surface-body monitoring, 3D model visualization, damage trend prediction and early warning of landslides. From year 2022, three rainfall events (January 22:19.8 mm/day, April 30:29.2 mm/day, and May 27:31.8 mm/day) were predicted and verified using this system. The results show that: (1) The displacement results of the severely deformed region predicted by numerical simulation are similar to the displacement results of image recognition. With the increase in rainfall intensity, the surface layer of some areas shed 0.18–1.1 m, and the error was within 15%; (2) The predicted position of the deformation area is consistent with the position identified by the image, all of which are at the top of the slope, and a small part is on the right side of the middle of the slope; (3) The fluctuation range of the displacement tangent angle of the three rainfall events is 0–44.32°, the slope is relatively stable as a whole, and it is in the stage of no warning. The successful implementation could provide a reference for slope disaster monitoring and early warning.
PubDate: 2023-10-01
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- A sensitivity study of surface wind simulations to PBL schemes for the
southern part of Andhra Pradesh, India-
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Abstract: Abstract Wind energy has the potential to play a vital role in fulfilling the world's rising energy needs. Due to the cubic relationship between wind speed and power, slight wind speed variations substantially impact power generation. Various wind speed assessment methods are available in the literature, and the Numerical Weather Prediction (NWP) methods are among them. However, its sensitivity analysis is required under the NWP model to estimate wind speed effectively. The planetary boundary layer (PBL) scheme is crucial in the sensitivity analysis. This work used a strategic approach with sensitivity analysis to estimate wind fields in the southern part of Andhra Pradesh, India, using the NWP model. The wind field is simulated using the WRF model, and the best configuration is determined by simulating nine distinct PBL parameterization combinations for four locations. Despite overestimation from 3 am to 8 pm and underestimation from 8 pm to 3 am, the mean absolute percentage errors are determined to be 6%, 9%, 14%, and 16%, respectively. The Weibull distribution mean, shape parameter, and scale parameter all have relative errors of 9.2%, 8.9%, and 15.36%, respectively. The developed methodology would be valuable in assessing wind resources.
PubDate: 2023-10-01
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