JournalTOCs

Modeling Earth Systems and Environment
Number of Followers: 1

Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2363-6203 - ISSN (Online) 2363-6211
Published by Springer-Verlag

[2468 journals]

Landslide spatial prediction utilizing fuzzy unordered rules induction
ensemble models: a case study in Thai Nguyen, Vietnam
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  Abstract: Abstract Landslides cause yearly devastating disasters in mountainous areas that cost lives, property, and money. Risk management and mitigation may benefit from the geographic information provided by landslide susceptibility maps, which show locations more likely to have landslides. The development of landslide-predictive maps requires the use of state-of-the-art machine learning models. This research aims to develop advanced hybrid ML models for mapping landslide susceptibility in the Thai Nguyen province of Vietnam. We used the Fuzzy Unordered Rules Induction Algorithm (FURIA) as a base classifier model and developed ensemble models of Bagging-FURIA (BA-FURIA), Dagging-FURIA (DA-FURIA), and Decorate-FURIA (DE-FURIA). Seventeen landslide-affecting factors were gathered for the modeling by considering the connection between previous landslide disasters and specific geo-environmental characteristics. Several statistical indicators were applied to verify the performance of predictive models. The analysis results of the ROC curve of the testing dataset indicated that all predictive models performed well, with the DA-FURIA model having the best precision, with an AUC value of 0.941, closely followed by the BA-FURIA model with an AUC of 0.930, the DE-FURIA model with an AUC of 0.926, and the FURIA model with an AUC of 0.910. The findings of this research may help with land-use planning and infrastructure building to ensure the long-term economic development of the area.
  PubDate: 2023-12-02
Exploring the pre-stack LSRTM methods: wavefield solutions in the
pseudodepth domain
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  Abstract: Abstract The Least-Squares Reverse-Time Migration (LSRTM) framework comprises two key steps. Firstly, it involves generating a reflectivity image through Reverse-Time Migration (RTM), followed by the application of the Least-Squares Migration (LSM) scheme to iteratively update this image. However, the convergence of both operations is computationally intensive and memory-consuming, particularly when simulating RTM data in deeper zones. The presence of anisotropic media, where velocity increases with depth, significantly distorts the RTM results. This issue can be addressed by employing Least-Squares Reverse-Time Migration (LSRTM) in the pseudodepth domain algorithm, which involves projecting the migration velocity and utilizing a weighted wavefield extrapolator. Each point in Cartesian space \((x,y,z)\) corresponds to a vertical-time-point with coordinates \(\left( {\zeta_{1} ,\zeta_{2} ,\zeta_{3} } \right)\) enabling interpolation of the reconstructed source wavefield through a Cartesian-to-Pseudodepth mapping function. Extrapolation of the LSRTM reconstructed wavefield in the pseudodepth domain mitigates aliasing of seismic signals, ensuring even sampling and facilitating the recovery of original amplitudes in the final migrated image. This approach offers cost reduction, as the finite-difference pseudodepth wavefield extrapolator operates on Born-modeled seismic data, yielding results similar to classical RTM, albeit with slight amplitude variations due to implementation nuances and oversampling effects. The pseudodepth domain LSRTM introduces advanced optimizations, including a reduction in the number of vertical samples and minimized memory allocation. We have successfully obtained J. Claerbout’s principle of underground reflector positioning in synthetic examples and extended its application to 2D field data, optimizing both the number of vertical samples and memory usage for the extraction of the pre-stack LSRTM section.
  PubDate: 2023-12-01
Probabilistic slope stability analysis using subset simulation enhanced by
ensemble machine learning techniques
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  Abstract: Abstract Within the field of geotechnical engineering, complex challenges arise due to uncertainties associated with variable loads, soil properties, ground stratification, and other related factors. In order to effectively address these uncertainties, it is recommended to employ reliability analysis based on probabilistic methodologies. The objective of this study is to investigate the effectiveness of ensemble machine learning methods in predicting the factor of safety (FOS) for railway embankments. The FOS is a critical indicator of the stability of cohesive slopes. By utilizing the recently developed Subset Simulation (SS) method for reliability analysis, we aim to investigate the potential of machine learning in improving predictions of FOS (Factor of Safety). We have obtained a comprehensive dataset consisting of 1400 instances from the subset simulation evaluation. This dataset serves as the foundation for our investigation. In the context of machine learning, we employ six commonly used methodologies, namely decision tree regression (DTR), multiple linear regression (MLR), K nearest neighbor regression (KNN), random forest regression (RF), extreme gradient boosting regression (XGB), and support vector regression (SVR), to develop predictive models for extrapolating FOS values. Afterwards, we utilize ensemble machine learning techniques to combine the outputs of these individual predictive models. Among the various ensemble strategies, the voting ensemble (VO-ENSM) stands out as a strong candidate, demonstrating significant proficiency in predicting the Factor of Safety (FOS) for the complex terrain of railway embankments.
  PubDate: 2023-11-30
Experimental and numerical modeling of diaphragm grouting in earth dams
considering construction defects
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  Abstract: Abstract Grouting is a common technique to rehabilitate old dams that started to leak. Nevertheless, grouted diaphragms are subject to construction defects which cause further seepage issues. In this study, a permeability tank experimental model and a finite element numerical model are used to simulate seepage through earth dams. The effect of using a grouted diaphragm on the dam’s seepage and stability is evaluated. The paper also investigates the negative effects of diaphragm construction defects, namely, thickness deficiency, cracking, and high permeability of concrete. Different scenarios are modeled for the grouted diaphragm without and with defects. Additionally, a toe drain is added in some scenarios to investigate if it enhances the stability of the dam. The seepage discharge, velocity, hydraulic gradient, pore water pressure, and downstream slope stability are evaluated. The grouted diaphragm reduced the seepage discharge by 20% in the case of partial grouting and up to 100% for full-height grouting, enhancing the stability of the dam. Yet, partial grouting increased the velocity at the diaphragm’s free end by 2–3 times its value in a homogeneous dam. Cracking of the diaphragm’s lower end was the most detrimental to the dam’s safety, while the high hydraulic conductivity of concrete came in second place. Using a toe drain increased the seepage discharge by 12–20%, but greatly lowered the pore water pressure, and enhanced the downstream slope stability by 20–23%. Hence, adding a grouted diaphragm that penetrates the full height of the dam combined with a toe drain is recommended to rehabilitate earth dams.
  PubDate: 2023-11-30
Modeling of water scarcity for spatial analysis using Water Poverty Index
and fuzzy-MCDM technique
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  Abstract: Abstract The study focuses on the spatial distribution of water scarcity and its influencing factors that affect sustainable livelihoods at the provincial scale. However, the limitation of spatial heterogeneity representation makes it difficult to reveal the complexity of phenomena in practice. Therefore, to solve this problem, this study aims to develop a Water Poverty Index (WPI) from a set of water-related indicators as a comprehensive quantitative approach that integrates the expert knowledge and physical data of water scarcity assessment. With the support of the Fuzzy-Analytic Hierarchy Process (Fuzzy-AHP), GIS-based modeling in WPI measurement for Nghe An province, Vietnam, is done from 05 components with 15 corresponding criteria. The weighted result with high accuracy illustrates that annual precipitation plays the highest role in the hierarchy level (WR1 = 0.279); by contrast, the weight of criterion for children mortality under 05 years is the lowest role (WC2 = 0.005). The spatial analysis results show that 13.34% (2135.68 km2) of the study area is at very high risk of water poverty, while most of them (26.28%) are located in the east and southeast regions with a high-risk level (4221.67 km2). The medium, low, and very low-risk levels correspond to 19.8% (3168.47 km2), 22.13% (3540.98 km2), and 18.329% (2932.47 km2). The differences in the level of WPI values form a spatial divergence for water scarcity with different dominant factors. This approach could provide a useful multi-level risk assessment for local planning as well as water resource management in the future.
  PubDate: 2023-11-29
Integrated isotope-geochemical and microbiological studies of groundwaters
in oilfields (the Southern Part of the West Siberian Basin)
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  Abstract: Abstract The results of integrated isotope-geochemical and microbiological studies of reservoir waters from oilfields under development in the southern part of the West Siberian petroleum province are reported for the first time. The waters have mainly Cl Na chemistry, with salinity 13–72 g/L TDS, pH between 6.9 and 8.5, and reductive geochemical environment with Eh ranging from − 166.3 to − 90.1 mV. Salinity variations are due to the injection of waters from the Aptian–Albian–Cenomanian aquifer to maintain the reservoir pressure early during the development, and subsequent use of closed-cycle produced water, at increased water cutting of the production well stock. The presence of two main genetic types of groundwaters in the hydrogeological section is revealed: the ancient waters of infiltration genesis, and the waters of sediment genesis. In most of the sampled waters, the δD–δ18O pair indicates climate-induced 4–5‰ 16O depletion prior to burial, combined with later 2–3‰ 18O enrichment during prolonged water–rock interaction. In some water samples, dissolved inorganic carbon is of bacterial origin, as suggested by its isotope composition (δ13CDIC). The waters hotter than 90°Cshow an evident positive δ13CDIC shift to a range of − 8 to + 4‰ VPDB, which is common to thermal water values. The analyzed waters are characterized by lower 87Sr/86Sr ratios than those in the present-day seawater. A substantial Sr input from mantle sources is assumed, manifested up to different degrees in the waters of the studied oilfields. Microorganisms involved in the nitrogen, sulfur, iron, and carbon cycles are detected in the studied water samples. The products of the vital activities of nitrifying, thionic, and sulfate-reducing bacteria are nitric and sulfuric acids, and hydrogen sulfide. The joint action of these microbial groups poses large-scale corrosion risks to the oilfield facilities.
  PubDate: 2023-11-29
Glacial lakes mapping using satellite images and deep learning algorithms
in Northwestern Indian Himalayas
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  Abstract: Abstract Global climate change's influence on the Himalayan glaciers has initiated glacier retreats, resulting in the development and evolution of glacial lakes, alterations in river flow dynamics, and changes in glacier boundaries. These changes have led to a rise in the occurrence of glacial lake outburst floods (GLOFs), causing substantial socio-economic damages. Therefore, monitoring and studying these glacial lakes is imperative to comprehend the impacts of climate change on the cryosphere. Automatic and Semi-automatic methods for mapping glacial lakes from remote sensing satellite data have been developed and extensively used in mapping and monitoring glacial lakes. Artificial intelligence (AI) based machine learning and deep learning algorithms have been successfully used for feature extraction and image segmentation in recent years. Fully convolutional neural (FCN) networks-based U-Net architecture, which involves a gradual integration of superficial visual characteristics and semantic information extracted from images to segment small objects effectively, is used in the present study to extract glacial lakes pixel-by-pixel from satellite data, providing a more efficient and accurate method for identifying and mapping these lakes. The Landsat and Indian Remote Sensing imagery were utilized to train, test and map glacial lakes in the Chandra-Bhaga basin of Himachal Pradesh in North Western Indian Himalaya. A total of 134 glacial lakes were mapped in the basin for 2021, covering approximately 4 km2 area, yielding an aggregate accuracy score of 0.90 with a recall of 0.95, an F1-score of 0.96, and an intersection over union (IoU) value of 0.94. The digital elevation model (DEM) was used to remove the mountain shadows identified and extracted as false glacial lakes during the post-processing phase. The mapped glacial lakes were verified and validated using high-resolution satellite imagery from Google Earth Pro. The fully automatic Deep learning-based glacial lake extraction is effective and efficient for developing glacial lake inventory and continuous monitoring to assess the associated glacial lake outburst hazard.
  PubDate: 2023-11-29
Using geostatistics and machine learning models to analyze the influence
of soil nutrients and terrain attributes on lead prediction in forest
soils
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  Abstract: Abstract The study aimed at investigating the possibility of predicting lead (Pb) in forest soils by combining terrain attributes and soil nutrients using geostatistics and machine learning algorithms (MLAs). The study was partitioned into three categories: predicting Pb in forest soil using terrain attributes and RK (Context 1); predicting Pb in forest soil using soil nutrients and RK (Context 2); and lastly predicting Pb in forest soils using a combination of soil nutrients, terrain attributes, and RK (Context 3). Stochastic Gradient Boosting-regression kriging (SGB-RK), cubist regression kriging (CUB_RK), quantile regression forest kriging(QRF_RK) and k nearest neighbour regression kriging (KNN_RK) were the modeling approaches used in the estimation of lead (Pb) concentration in forest soil. The results showed that combining the terrain attribute as an auxiliary dataset with QRF_RK proved to be the most effective method for predicting Pb in forest soil (context 1). The most effective method for predicting Pb in forest soil was to combine soil nutrients as an auxiliary dataset with SGB_RK (context 2). Combining cubist_RK with an ancillary dataset of soil nutrients and terrain attributes is the most effective method for predicting Pb in forest soils (context 3). In addition, combining ancillary variables such as soil nutrients and terrain attributes with cubist_RK generated the best results for estimating Pb concentration in forest soils. It was found that applying a robust digital soil mapping (DSM) model in combination with terrain attributes and soil nutrients is efficient in predicting the spatial distribution and estimation of uncertainty levels of lead (Pb) in forest soils based on the model’s accuracy parameters.
  PubDate: 2023-11-29
Climate change projections in Guatemala: temperature and precipitation
changes according to CMIP6 models
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  Abstract: Abstract Projected changes in precipitation and temperature for Guatemala were examined using the phase 6 dataset of the Coupled Model Intercomparison Project (CMIP6). CMIP6 models project alterations in annual mean temperature and precipitation in Guatemala relative to the current climate. A set of 25 CMIP6 models project a continuous increase in annual mean temperature over Guatemala during the twenty-first century under four future scenarios. The data provided by WorldClim has a spatial resolution of 2.5 min (of a longitude/latitude degree) this means a 4.5 km × 4.5 km of area of each pixel approximately. for the climate horizons of 2021–2040, 2041–2060, 2061–2080, and 2081–2100, these were adjusted based on the average of 38 local stations in Guatemala from the period (1970–2000). The projected temperature shows a large increase over 5 °C under the SSP5-8.5 scenario, over the northern parts of Guatemala and the northwest. By the end of the twenty-first century, the annual mean temperature in Guatemala is projected to increase by on average 1.8 °C, 2.9 °C, 4.3 °C, and 5.4 °C under the SSP1_2.6, SSP2_4.5, SSP3_7.0, and SSP5_8.5 scenarios, respectively, relative to current climate (1990–2020). The warming is differentiated on a monthly time scale, with CMIP6 models projecting greater warming in July, August, and September, part of the summer and autumn season. Annual precipitation is projected to decrease in Guatemala during the twenty-first century under all scenarios. The rate of change in projected mean annual precipitation varies considerably among scenarios; − 5%, − 9%, − 18%, and − 22% under the SSP1_2.6, SSP2_4.5, SSP3_7.0, and SSP5_8.5 scenarios, respectively. Monthly precipitation projections show great variability, with projected precipitation for the months of May, June, and July, part of the spring and summer, showing a greater decrease than other months and specifically in the northern part of the country. On the other hand, mid-summer precipitation (July and August) shows a decrease in the central and eastern part of the country. The results presented in this study provide baseline information on CMIP6 models for Guatemala, which serve as a basis for developing climate change adaptation and mitigation strategies.
  PubDate: 2023-11-28
Assessing the potential impact of climate change on Kobus megaceros in
South Sudan: a combination of geostatistical and species distribution
modelling
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  Abstract: Abstract Kobus megaceros is a wetland antelope listed as endangered by United Nations Educational, Scientific and Cultural Organization (UNESCO) in its natural habitat in South Sudan. The population of the species in South Sudan’s wetlands remains unknown. Climate change is expected to have a significant impact on the species population in a variety of ways. This paper aims to estimate the current population density and investigate the impact of climate change on K. megaceros by the end of the century. Bayesian Maximum Entropy (BME) and species distribution modelling (SDM) were used to estimate spatial density and predict habitat suitability for Kobus megacero in RCP4.5 and RCP8.5 pathways. The observed occurrences and abundances of Kobus megacero were downloaded from the global biodiversity information facility (GBIF) website. The Africlim online database was used to gather environmental predictors for current and future scenarios. We implemented SDM in R biomod2 package with Maxent algorithm to determine the geographical extent of habitat suitability for RCP4.5 and RCP8.5. The area under the ROC curve (AUC) and true skill statistics (TSS) were used to evaluate the model. The findings revealed that the current population density of Nile lechwe is too small; hence, this could accelerate the extinction of Nile lechwe. Although 4.97% of the country is currently highly suitable, future scenarios show that about 79–83% of the current suitable habitat will be lost due to climate change in the mid-2055s and mid-2085s. This implies that a proactive conservation strategy should be implemented to reduce the species’ chances of extinction.
  PubDate: 2023-11-28
Habitat quality assessment based on local expert knowledge and landscape
patterns for bird of prey species in Hamadan, Iran
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  Abstract: Abstract Habitat quality assessment provides crucial information that can benefit species protection by concentrating conservation resources on specific areas. Unfortunately, such assessments are often rare for avian species in Iran, especially for birds of prey. Here, we modeled the habitat quality of several birds of prey species, including the Egyptian vulture (Neophron percnopterus), Eastern imperial eagle (Aquila heliaca), Saker falcon (Falco cherrug), and Eurasian hobby (Falco subbuteo) in Hamadan province, Iran, based on land use and land cover (LULC) data, distribution of anthropogenic threats, and expert knowledge. Habitat quality maps were then evaluated by applying landscape composition and configuration metrics, which indicated that a large portion of the 3500 km2 study area is highly suitable for A. heliacal (2115.5 km2) and F. cherrug (1658.13 km2) while a much smaller fraction was highly suitable for N. percnopterus (59 km2) and F. subbuteo (90 km2). Landscape metrics analysis also revealed the importance of patch contiguity, density, and fractal dimension for conservation value. Landscape pattern dynamics play a significant role in determining species’ habitat quality and can be informative for biodiversity conservation. Based on our findings, we recommend that the suitable habitats identified in this study be further assessed for ecological protection.
  PubDate: 2023-11-28
Quantifying LULC changes in Urmia Lake Basin using machine learning
techniques, intensity analysis and a combined method of cellular automata
(CA) and artificial neural networks (ANN) (CA-ANN)
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  Abstract: Abstract The land use and land cover (LULC) classification accuracy of six machine learning models were compared in Urmia Lake Basin using Landsat images. The overall accuracy confirms that the random forest (RF) (0.957), regularized random forest (RRF) (0.957), the combined method of genetic algorithm and random forest (GA-RF) (0.959) and the combined method of simulated annealing and random forest (SA-RF) (0.957) perform slightly better than the support vector machine (SVM) (0.946) and conditional inference random forest (CIRF) (0.947) though this difference was negligible. The worst classifier was the CIRF with only 43.8% of the grassland pixels correctly assigned to the respective class whereas the GA-RF, SA-RF and RRF performed significantly better with 60.4% of correct classification. Except for the grassland class, the performance of the GA-RF and the SA-RF for the rest of LULC classes were similar (greater than 90%). The magnitude and extent of LULC change was examined using intensity analysis including the interval, category, and transition levels of change. The maximum intensity was from 2006 to 2013, with an annual change in area of 5% which is attributed to the building of the Shahrchay Dam in 2006. The LULC predicted using the combined method of cellular automata (CA) and artificial neural networks (ANN) model (CA-ANN) indicated the soil and rangeland classes are estimated to experience the largest decrease (-5.48%) and increase (7.21%) by year 2035.
  PubDate: 2023-11-26
Lineament extraction and paleostress analysis in the Bikélélé iron
deposit (the Chaillu Massif, Republic of Congo): integration of
ALOS-PALSAR DEM and field investigation data
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  Abstract: Abstract In this study, ALOS-PALSAR DEM has been used along with field investigation data to map geological lineaments and analyze the paleostress in the Bikélélé iron deposit. The methodology involved three approaches: manual extraction of lineaments based on visual interpretation by using four shaded relief images through ArcMap v.10.8 software, and stress inversion and slip tendency methods computed in the Win-Tensor program. The resulting lineament map from manual extraction showed that NW–SE, NE–SW, E–W, and N–S trending are the dominant directions of geological structures. These results were confirmed by field investigation data and were well correlated with previous regional studies. The paleostress reconstruction and analysis indicated the existence of two stress fields that were linked to two deformation phases. The first stress field corresponds to the first deformation phase, D1, which involved NW–SW compression and NE–SW extension. This first deformation, D1, resulted in the formation of NW–SE to N–S sinistral fractures and E–W dextral fractures. This phase also developed N–S to E–W foliations and F1 folds. The second stress field was linked to the second phase of deformation, D2. This latter is characterized by NNE–SSW compression and WNW–ESE extension, that formed N–S dextral fractures, NE–SW to E–W sinistral fractures as well as F2 folds. The deformation phase D2 is associated with the Eburnean orogeny, which affected the Archean basement across the Central Africa. The model summarizing these geological structures and the associated deformation phases has been presented.
  PubDate: 2023-11-25
Modelling storm event-based sediment yield and assessing its heavy metal
loading: case of Lake Victoria's Inner Murchison Bay catchment in Uganda
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  Abstract: Abstract Information on catchment sediment yield and its heavy metal content is crucial to understanding of the transport mechanisms and the potential ecological threats of this sediment. This research aimed at modelling sediment yield and quantifying heavy metals bound to the sediment. The study was conducted in the Lake Victoria's Inner Murchison Bay catchment in Uganda. Depth integrated suspended sediment sampling and discharge measurements were done at the outlets of Nakivubo and Ggaba sub-catchments for ten storm events during a wet season between March and May 2022. The sediment yields for the storms were computed using average suspended sediment concentrations and discharge retrieved from hydrographs. Corresponding event-based sediment yields were modelled using the Modified Universal Soil Loss Equation (MUSLE). MUSLE model was calibrated and validated with observed sediment yields from Nakivubo and Ggaba sub-catchments respectively. Eighteen suspended sediment samples from the two sub-catchments were analysed for contamination by eight heavy metals. Results showed that the mean discharge and Suspened Sediment Concentration (SSC) were: 3.08 ± 1.66 m3/s and 1238 ± 665.6 mg/L; 0.495 ± 0.41 m3/s and 1102 ± 843.7 mg/L for Nakivubo and Ggaba respectively. MUSLE model performance indicators for calibration were: R2 of 0.94, NSE of 0.936 and PBIAS of -7.7 and for validation, R2 of 0.9, NSE of 0.57 and a PBIAS of -15.5. Thus, MUSLE proved a reliable tool for simulating event-wise sediment yield. Cadmium, Lead, and Zinc with contamination factors between 7–11, 1.9–4.1, and 0.9–1.7, respectively were the most prevalent heavy metals from both sub-catchments. Heavy metal pollution exhibited a linear relationship with suspended sediment concentration, with R2 values up to 0.958 and 0.82 in Nakivubo and Ggaba, respectively. The metal pollution in sediment carried into the bay, poses grave ecological and human health risks. Particularly, drinking water and fish sourced from the lake are susceptible to heavy metal contamination. Integrated catchment management practices that reduce sediment and heavy metal transport into Lake Victoria are an urgent requirement.
  PubDate: 2023-11-13
An overview of causal factors in fluctuations of some economic indices in
Iran using impulse response analysis (1990–2022)
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  Abstract: Abstract In recent decades, the Iranian economy has experienced unprecedented financial challenges, resulting in fluctuations in some economic indices. In this study, the impulse response analysis was conducted to identify the causal factors, which are responsible for fluctuating two main indices of gold and land prices during 1990–2022. For this purpose, a vector autoregression model (VAR), with 12 endogenous variables, was constructed, using EViews software. The results revealed that the shock of the inflation rate, market capitalization, and gasoline prices will not significantly fluctuate gold and land prices in Iran. Besides, the results revealed that some variables, such as GDP per capita, stock traded value, the exchange rate, global gold price, and global oil price may fluctuate national gold and land indices in Iran during the observation periods. Among these causal factors, only the shock of exchange rate, with high decomposition variance (> 78%), will immediately fluctuate national gold and land prices. Hence, the co-movement of gold and land price toward the signals of the exchange rate is obvious and could be forecasted for future periods. An important managerial implication is to focus on the controlling approaches of the exchange rate, which is the main driving power of economic fluctuations and instabilities in Iran.
  PubDate: 2023-11-10
Development and application of modeling techniques to estimate the
unsaturated hydraulic conductivity
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  Abstract: Abstract The knowledge of the unsaturated hydraulic conductivity (K) is very essential for the various fields of water resources, irrigation, and hydrology. It is also important to know the phenomena of water movement on the ground constantly. In order to better forecast unsaturated hydraulic conductivity, this article reports the comparison of efficacy of five distinct soft computing approaches: support vector machine (SVM), random forest, Gaussian process (GP), gene expression techniques, and multivariate adaptive regression spline. Three kernels function (Poly, RBF, and PUK) were used in SVM and GP modeling techniques. For fulfill this aim, experimentation has been performed using mini-disc infiltrometer in 20 locations in Ghaggar basin. Total 240 observations were collected, and out of which, 70% were used for training the model and remaining for testing. The input variables of this investigation were sand, clay, silt, bulk density (ρ) and moisture content and output variable was K. The result of modeling techniques suggests that PUK kernel with SVM was superior to the other modeling techniques. This implies that these computational methods can be used to make estimates about the values of K at any time.
  PubDate: 2023-11-01
Modeling the resilient modulus of subgrade soils with a four-parameter
constitutive equation
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  Abstract: Abstract A new constitutive model for the resilient modulus (MR) of subgrade soils was developed in this study based on existing models. The general form of three-parameter model which is common in many constitutive equations for MR was extended here to have four parameters. The proposed model relates MR with the bulk stress, confining pressure and deviatoric stress of the soil using four coefficients. The performance of the model was tested by fitting it to an MR test data obtained from the long-term pavement performance database. A multi-variable nonlinear curve fitting was done using the SciPy library. The results showed that the model has a very good fit to the data with coefficient of determination, root mean square error and mean absolute error values of 0.94, 2.20 and 1.76, respectively. The results of the proposed model were also compared with the bulk stress model and the universal model, which is currently used by the mechanistic-empirical pavement design guide (MEPDG), and obtained to be generally better than both models. The proposed model could potentially be a good alternative to the existing constitutive models if methods for the determination of the k-coefficients could be developed.
  PubDate: 2023-11-01
Instream constructed wetland capacity at controlling phosphorus outflow
under a long‐term nutrient loading scenario: approach using SWAT model
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  Abstract: Abstract At the watershed scale, sustained applications of phosphorus (P) fertilizers on croplands cause unwanted P losses into aquatic systems and subsequent risks of water eutrophication. Nevertheless, instream constructed wetlands (ICWs) offer the possibility to control P transfer from land to water and maintain P concentration in water below levels that negatively affect aquatic life. However, including ICWs in long-term water quality conservation plans is arguable because their long-term functionality is still less known. To better understand this long-term functionality, this study used the soil and water assessment tool (SWAT) model to portend an ICW’s hydrological behavior and its capacity at controlling P release under exceptional climate conditions in the Southeastern Coastal Plain of the United States. Specifically, the model was calibrated and validated for stream and P flow using experimental ICW data and an assumption of a continuous corn-soybean rotation across an agricultural watershed. A multi-decadal simulation was used to evaluate monthly balances of dissolved P (ΔDP) and total P (ΔTP) under a variable climate spectrum and a continuous nutrient loading scenario. Analyses of monthly ΔDP and ΔTP time series over consecutive decadal periods 2001–2010 and 2011–2020 showed signals of negative P balances at a probability of 0.18. Point biserial correlations analyses unveiled a significant relationship between monthly ICW’s P balances and precipitation variability at the watershed scale. The P releases were under control during low to moderate precipitation conditions, but extreme precipitation events caused abnormal P outflows. Hence, ICWs could be a sustainable option for long-term P outflow control under low to moderate hydrologic regimes.
  PubDate: 2023-11-01
Spatiotemporal trend analysis of groundwater level changes, rainfall, and
runoff generated over the Notwane Catchment in Botswana between 2009 and
2019
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  Abstract: Abstract The semi-arid south-eastern part of Botswana has recently been experiencing severe water shortages, and the demand currently surpasses the supply in the greater Gaborone area. Within the context of increased stormwater runoff generated over the area and the potential for groundwater recharge, this study aims to investigate the relationships between groundwater depths and rainfall amounts and identify their patterns and significance or lack thereof over Botswana’s largest water demand centre that falls within the data-scarce Notwane catchment area (NCA). Trend analysis of monthly rainfall and groundwater levels between 2012 and 2019 and their homogeneity were undertaken using the Mann-Kendal test, followed by the application of the water balance method to estimate runoff over the catchment between 2009 and 2019. Runoff and precipitation between the two periods were compared using paired t-tests. Investigations revealed that rainfall increased insignificantly, whereas groundwater depth generally increased significantly. The homogeneity test revealed a general insignificant increase in rainfall over NCA. No catchment-wide conclusions were obtained regarding groundwater depth increases. Water-balance computed runoff in 2019 was an increase of 13.7% from that computed in 2009, despite the conservative 3% increase in rainfall between the two periods. Increase in runoff could even be higher if land use changes were incorporated. This study revealed that there is groundwater recharge over the catchment, particularly after heavy rainfall events. The results of this study offer insights for identifying groundwater recharge potential zones, which could inform decision making with regard to strategies for induced groundwater recharge to replenish groundwater resources that can conjunctively be used with surface water resources.
  PubDate: 2023-11-01
Integrating 2D hydrodynamic, SWAT, GIS and satellite remote sensing models
in open channel design to control flooding within road service areas in
the Odaw river basin of Accra, Ghana
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  Abstract: Abstract Infrastructure network security and resiliency have become major problems in recent years as a result of an increasing number of catastrophic weather-related occurrences around the world. Major catastrophic events in Accra within the Odaw river basin began in 1959, with the flood claiming the lives of roughly 154 Ghanaians and causing varying levels of burns and injuries. The Odaw river basin has a total catchment area estimated to be about 275 km2 which drains the major urbanised areas of Accra including Ga East, Ga west, Accra metropolitan assembly and Adenta Municipal Assembly further upstream. These areas mostly get affected by flash floods. This study used integrated 2D hydrodynamic, SWAT, Geographic information system (GIS) and Remote Sensing (RS) models to estimate flood depth and the extent to map out inundated areas for effective open channel design to control flooding within the Odaw river basin. Landsat images were classified using a Random Forest algorithm to produce a LULC map for the SWAT model. SWAT model was used to delineate the basin with its channels, sub-basins, outlet points, flow length, area and runoff depth of the Odaw river basin using GIS. A 2D hydrodynamic model was used to estimate the flood depth (0–0.3 m, 0.3–5.0 m and > 5.0 m) and extent within the Odaw river basin using HER-RAS 6.0 software. Flood depth greater than 0.3 m was identified to be dangerous because it causes vehicles to float and submerge. George Bush Motorway, Kwame Nkrumah Motorway, Tetteh Quarshie Interchange, Obetsebi Lamptey Circle, Graphic Road, Black Meteors Lane, Ring Road Central, Afram Road, Guggisberg Avenue and Hall Street were identified road networks within this flood depth (> 0.3 m). Achimota, Asofan, Ashouman, Akokome, Adenkrebi, Kweman, Kokomlemle, Alajo and Tesano were also road service areas identified within this flood depth greater than 0.3 m. A 50-year peak flow of 447.801 m3/s which occurred in 2016 within the basin was obtained from field measurement by Edward in The Kwame Nkrumah University of Science and Technology. This peak flow was used to propose an open channel design to control flooding within the basin using Manning’s equation. The results of the study show that the integrated 2D hydrodynamic, SWAT, GIS and RS models have better performance in open channel design.
  PubDate: 2023-11-01