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  Subjects -> METEOROLOGY (Total: 106 journals)
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Modeling Earth Systems and Environment
Number of Followers: 1  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2363-6203 - ISSN (Online) 2363-6211
Published by Springer-Verlag Homepage  [2467 journals]
  • Numerical modeling of one-dimensional variably saturated flow in a
           homogeneous and layered soil–water system via mixed form Richards
           equation with Picard iterative scheme

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      Abstract: Abstract Variably saturated flow in an unsaturated zone is a critical subject due to its diverse applications in earth science, waste management, agriculture, and geotechnical problems. The governing Richards equation (RE) of the mathematical models for unsaturated flow is very nonlinear, attracting researchers to solve it quickly and accurately. In this paper, a numerical model is proposed to solve mixed form of RE. In time, backward Euler method is implemented to discrete the equation, and in space, the equation is discretized by the finite difference method. Because of the dependence of both water content and unsaturated hydraulic conductivity on soil water pressure head, the equation must be solved iteratively using the Picard scheme. It is assumed that initial suction head is 1000 cm and suction head at top boundary is 75 cm throughout the simulation. The model is first tested for a homogenous soil water system using numerical simulation data from the literature. The comparison between simulated and observed value demonstrate that the new algorithm is robust and practical with fast convergence. Later, model is extended for layered soil water system assuming a constant pressure head surface boundary condition and van Genuchten parameter is used to define the soil constitutive relationship for each soil layer. The model shows promising results, allowing it to be used for further applications, such as managed aquifer recharge, plant water uptake, and coupled with the advection–dispersion transport equation to predict solute movement.
      PubDate: 2022-11-25
       
  • Managing groundwater demand through surface water and reuse strategies in
           an overexploited aquifer of Indian Punjab

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      Abstract: Groundwater sustainability is one of the most critical issues to the State of Punjab, India. In this research, a numerical groundwater flow model (MODFLOW) was employed to simulate flow and groundwater levels in the Sirhind Canal Tract of Punjab between 1998 and 2030. Historical groundwater patterns were calibrated using reported groundwater data from 1998 to 2013 for aquifer parameters viz. hydraulic conductivity and specific yield. Thereafter, calibrated flow simulated model was validated for the years 2013–2018. Twelve possible strategies, including three irrigation conditions and four pumping scenarios, were postulated to evaluate the performance of groundwater resources through to 2030. During the study, it was found that if current groundwater abstraction continues there will be further steep decline of 21.49 m in groundwater level by 2030. Findings also suggest that canal water supplies will be beneficial to reverse groundwater level decline and help to increase the water level by 11% above that in year 2018. The projected increases in water level will reduce energy demand leading to reduced CO2 emissions of approximately 966.6 thousand tonnes by 2030.
      PubDate: 2022-11-23
       
  • Recent advances in 3D slope stability analysis: a detailed review

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      Abstract: Abstract Regarding the geotechnical aspects, it is very important to correctly estimate a slope’s safety factor against failure. Slope failures, in general, cost a lot of money, hinder transportation, and can even kill people in the worst cases. To successfully prevent such failures, it is required to find proper understanding of the failure mechanisms such that proper analysis techniques may be chosen to appropriately determine the stability of any soil/rock slope. Slope stability analyses in two dimensions have been very popular among the practicing engineers because of the relative simplicity of the underlying concepts, as well as because it also provides conservative estimates of the FOS against failure. However, in many situations where the slope geometry and the loading conditions do not conform the plane strain idealization, a two-dimensional slope analysis can yield results which are far from accurate. In such situations, the only viable option is to study the slope failure analysis in three dimensions. Stability status of a slope in three dimensions is essential when the failure mass and slope geometry alter laterally, the soil/rock properties are anisotropic and not homogeneous, and the local surcharges are applied to the slope. Based upon finite element analysis, limit analysis, and limit equilibrium, stability status in three dimensions has been determined since the 1970s. Many of them are only applicable in specific circumstances. The present paper focuses on presenting an overall picture of different aspects of slope stability analysis in a comprehensive manner.
      PubDate: 2022-11-23
       
  • Modeling and optimal control of monkeypox with cost-effective strategies

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      Abstract: Abstract In this work, we develop and analyze a deterministic mathematical model to investigate the dynamics of monkeypox. We examine the local and global stability of the basic model without control variables. The outcome demonstrates that when the reproduction number \({\mathcal {R}}_{0}<1\) , the model’s disease-free equilibrium would be locally and globally asymptotically stable. We further analyze the effective control of monkeypox in a given population by formulating and analyzing an optimal control problem. We extend the basic model to include four control variables, namely preventive strategies for transmission from rodents to humans, prevention of infection from human to human, isolation of infected individuals, and treatment of isolated individuals. We established the necessary conditions for the existence of optimal control using Pontryagin’s maximal principle. To illustrate the impact of different control combinations on the spread of monkeypox, we use the fourth-order Runge–Kutta forward–backward sweep approach to simulate the optimality system. A cost-effectiveness study is conducted to educate the public about the most cost-effective method among various control combinations. The results suggest that, of all the combinations considered in this study, implementing preventive strategies for transmission from rodents to humans is the most economical and effective among all competing strategies.
      PubDate: 2022-11-22
       
  • A comparative modeling of landslides susceptibility at a meso-scale using
           frequency ratio and analytic hierarchy process models in geographic
           information system: the case of African Alpine Mountains (Rif, Morocco)

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      Abstract: Abstract Landslides represent a major natural hazard for all countries in the world. The Rif mountains in Morocco suffer from different types of landslides. Some of them are very active and present a significant risk to urban areas and transport systems. Consequently, in terms of sustainable development, landslide susceptibility mapping is essential to assess the levels of danger posed by these phenomena. This study aims at evaluating landslide susceptibility using two different approaches based on a statistical method (Frequency Ratio, FR) and on a heuristic method (Analytic Hierarchy Process, AHP). The second purpose is to compare them to select the most relevant and reproducible one with a view to applying it to areas having a similar geomorphological context. This study includes a precise inventory map representing the spatial distribution of three landslide categories within 892 sites. Rock falls, flows and landslides were studied using field survey and satellite imagery. Nine thematic layers of predisposing factors controlling landslides occurrence were prepared. The final result is presented in the form of six susceptibility maps of rockfalls, flows and landslides for FR and AHP models. The result of the success rates (AUC) indicates that the FR method is better with an AUC of 88% for rock fall, 89% for flows and 87% for landslides, while the AUC is 83%, 84% and 76%, respectively for the AHP method. Moreover, the results indicate which method to use for similar regions to produce indicative mapping and help users select priority areas prone to landslides.
      PubDate: 2022-11-22
       
  • Crop type classification with hyperspectral images using deep learning : a
           transfer learning approach

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      Abstract: Abstract Crop classification plays a vital role in felicitating agriculture statistics to the state and national government in decision-making. In recent years, due to advancements in remote sensing, high-resolution hyperspectral images (HSIs) are available for land cover classification. HSIs can classify the different crop categories precisely due to their narrow and continuous spectral band reflection. With improvements in computing power and evolution in deep learning technology, Deep learning is rapidly being used for HSIs classification. However, to train deep neural networks, many labeled samples are needed. The labeling of HSIs is time-consuming and costly. A transfer learning approach is used in many applications where a labeled dataset is challenging. This paper opts for the heterogeneous transfer learning models on benchmark HSIs datasets to discuss the performance accuracy of well-defined deep learning models—VGG16, VGG19, ResNet, and DenseNet for crop classification. Also, it discusses the performance accuracy of customized 2-dimensional Convolutional neural network (2DCNN) and 3-dimensional Convolutional neural network (3DCNN) deep learning models using homogeneous transfer learning models on benchmark HSIs datasets for crop classification. The results show that although HSIs datasets contain few samples, the transfer learning models perform better with limited labeled samples. The results achieved 99% of accuracy for the Indian Pines and Pavia University dataset with 15% of labeled training samples with heterogeneous transfer learning. As per the overall accuracy, homogeneous transfer learning with 2DCNN and 3DCNN models pre-trained on the Indian Pines dataset and adjusted on the Salinas scene dataset performs far better than heterogeneous transfer learning.
      PubDate: 2022-11-22
       
  • Optimizing the dynamics of bone turnover with genetic algorithm

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      Abstract: Abstract Control systems and the modeling strategies are not only limited to engineering problems. These approaches can be used in the field of bio-mathematics as well and modern studies have promoted this approach to a great extent. The computational modeling and simulation of bone metastasis is painful yet critical after cancer invades the body. This vicious cycle is complex, and several research centers worldwide are devoted to understanding the dynamics and setting up a treatment strategy for this life-threatening behavior of cancer. Cancerous cells activation and the corresponding process of metastasis is reported to boost during the periodic waves of COVID-19, due to the inflammatory nature of the infection associated with SARS-2 and its variants. The bone cells are comprised of two types of cells responsible for bone formation and resorption. The computational framework of such cells, in spatial form, can help the researchers forecast the bone dynamics in a robust manner where the impact of cancer is incorporated into the computational model as a source of perturbation. A series of computational models are presented to explore the complex behavior of bone metastasis with COVID-19 induced infection. The finite difference algorithm is used to simulate the nonlinear computational model. The results obtained are in close agreement with the experimental findings. The computational results can help explore the vicious cycle’s fate and help set up control strategies through drug therapies.
      PubDate: 2022-11-21
       
  • Spatiotemporal modelling for assessing the impacts of land use/land cover
           on Idku lake, Egypt

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      Abstract: Abstract Sustainable water resource management requires a knowledge of the spatiotemporal changes of land use and land cover (LULC). This is an essential preparatory stage in identifying the natural or anthropogenic activities that had a negative impact on it. This research used the spatiotemporal modelling approach in Idku Lake, Egypt between 1973 and 2021. Various image processing such as vector analysis, spectral indices and supervised classification were used to classify the satellite images into the identified LULC features. Water, hydrophytes, and fish farms were recognized using multi-temporal Landsat images. The findings indicated that the hydrophytes growth is a minor component that has in the past caused a decrease in the lake’s water area, particularly in the years 1973, 1985, and 1990. This hydrophyte cover is caused by both the drainage system and the nutrients such as nitrogen and phosphorus. But from 2003 to 2021, this factor started to decline due to the government’s efforts to protect the lake. An additional issue that started to appear, particularly from 1985, is human activities including the establishment of fish farms. This led to the loss of 72.89 km2 (57%) over a 48-year period. In other words, human encroachments on Idku lake are the primary cause of its deformation, and this needs to be taken into account to ensure that it is preserved as an aspect of sustainable development of water sources. The methodology and results of this research will assist the government and stockholders in developing a future plan for protecting the environment and ecosystems not only in Idku lake but also in other lakes with similar environmental conditions.
      PubDate: 2022-11-19
       
  • Linear regression model for noise pollution over central Delhi to
           highlight the alarming threat for the environment

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      Abstract: Abstract Noise pollution is the most ignored and underappreciated problem in the world. Even though scientists all over the world have done a lot of research on noise mapping and possible solutions, these solutions are still a long way from being put into practice. Noise reduction is an important step toward making a community that can last for a long time. Without systematic noise mapping, it is hard to figure out how noise changes in space and time. Using the Norsonic sound level meter, this research provides a novel methodological framework to integrate linear regression models with acoustic propagation for dynamic noise maps in Central Delhi. The 17 most sensitive receptors are also located in the study area. The noise mapping has been performed with the help of Dhwani pro and Arc-GIS software. The results from the noise mapping shows that the study area has noise at hazardous level. The second order linear regression noise prediction model has also been used for prediction of noise levels with taking parameters vehicle flow, % of heavy motor vehicle and light motor vehicle as inputs. The prediction performance is ascertained using the statistical test. The predicted noise values show good correlation with the observed noise levels i.e., R of 0.90. The isolation barriers of 5 m height are also introduced in the noise mapping analysis using Dhwani pro. These barriers represent substantial improvement in the noise level. The overall scenario of noise pollution in the study area is at alarming level and requires immediate planning to control the situation.
      PubDate: 2022-11-18
       
  • Deterministic and stochastic optimal control models for plant growth using
           locust fertilizer

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      Abstract: Abstract This paper discusses the problem of locust attack which causes severe damage to crops every year. We developed a mathematical model by considering the fact that neem oil prevents the crop from locust attack and the crop population will be increased when locust fertilizer is incorporated. The possible existence of equilibria and its basic reproduction number ( \(R_0\) ) are found and analyzed. Stability and emergence of bifurcation ( \(R_*\) ) of the model are analyzed. It is observed that the model exhibits bifurcation which results in the existence of locust free equilibrium and interior equilibrium for \(R_{0}< 1\) . It emphasizes the fact that \(R_{0}< 1\) is not sufficient to safeguard the plant from locust population. The proposed model is developed into stochastic model, and the simulation results of both deterministic and stochastic models are depicted using Matlab. In addition to this, we extended the proposed model to deterministic optimal control model using the cost effective time-dependent neem oil strategy to increase the crop production in a desired interval of time. Further, we developed stochastic optimal control model and the numerical results of both the deterministic optimal control and stochastic optimal control models are compared and discussed. Effectiveness of parameters is also performed for our proposed model. Numerical findings show that most of the gregarious locust population fall into solitarization due to the effect of neem oil while applying optimal control. From the model results, it has been observed that stochastic optimal control is effective in increasing the crop production during the locust attack.
      PubDate: 2022-11-16
       
  • Estimation of soil moisture and soil temperature over India using the Noah
           multi-parameterisation land surface model

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      Abstract: Abstract Soil moisture (SM) and soil temperature (ST) are critical state variables for characterizing the land surface, among which SM is recognized as an Essential Climate Variable necessary to understand changes to the Earth's systems. Remote sensing-based maps of SM and ST over India lack in temporal and spatial scales, which can be addressed through Land Surface Models (LSMs). This study examines the performance of the Noah Multi-Parameterisation LSM to estimate multi-level SM and ST within domains at 5 and 10 km spatial resolutions and 3-hourly frequency over India. Results indicate that among the inputs for precipitation forcing, viz. CHIRPS, GDAS and IMERG, the best performance is obtained with CHIRPS and IMERG for the 5 and 10 km resolutions, respectively. Incorporating a dynamic Greenness Vegetation Fraction (GVF) along with IMERG intensified post-precipitation dry downs in predicted SM and improved the accuracy of SM and ST by up to 25.21% (0.029 m3/m3) and 8.36% (0.2 K), respectively. Better performance was also observed over Clay, Loam and Sandy Clay Loam soils, which extend over 67% of India’s land area, compared to other soil types. The accuracy of model predictions at 10 km resolution is about 0.095 m3/m3 for surface-level SM and about 4.22 K for ST. Performance metrics indicate a correlation of 0.74; a root mean square error of 0.048 m3/m3 and a bias of 0.004 m3/m3 in surface-level SM against the satellite-based SM product from ESA C3S. These results indicate the potential for LSMs to obtain information on SM and ST over India.
      PubDate: 2022-11-15
       
  • Predicting the potential geographic distribution of Camellia sinensis var.
           

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      Abstract: Abstract The study focuses on the potential geographic distribution of a valuable green tea plant for sustainable agriculture development in the mountainous northwestern region of Vietnam, called Camellia sinensis var. shan (Css). It also discusses how the spatial simulation of species distribution through environmental variables and Maximum Entropy (MaxEnt) model at a local scale. In the context of climate change, its potential distribution was modeled under current and future scenarios of 04 representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) for 2050. From the field survey, 70 sample points were used for the procedure, including 75% and 25% of total for training and validation, respectively. Understanding the demand of downscaled CMIP5 bioclimatic data to higher resolution, the prediction-mapping results of Css meet the accuracy requirement of Area Under Curve, which ranges from 0.9016 ± 0.0147. In which, the mean diurnal range (Bio 2) was contributed significantly (85.5%) to prediction results. The highest increase of Css was observed from prediction map, which has included 24.87% for RCP 2.6 (at high suitable degree), 2.11% for RCP 4.5 (at medium suitable degree), 1.85% (at very less suitable degree) and more than 3.19 times (at very high suitable degree) for RCP 6.0, and 32.03% for RCP 8.5 (at less suitable degree). Prediction results in 2050 revealed that the slight gain at high and very high suitable degree couldn’t compensate for the “less suitable” expansion of Css in north-western Vietnam. This research could provide useful information about potential distribution assessment under multiple climate change scenarios for local development strategies as well as conservation management.
      PubDate: 2022-11-14
       
  • Environmental monitoring of heavy metals distribution in the agricultural
           soil profile and soil column irrigated with sewage from the Day River,
           Beni-Mellal City (Morocco)

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      Abstract: Abstract This paper examines the vertical distribution of heavy metals (As, Cr, Cu, Cd, Pb, Zn, and Fe) in the soil profiles irrigated with sewage from Day River and explores the impact that this sewage has on agricultural soil profile. To this end, three soil profile samples were taken from three unirrigated sites except for rain, and three soil profile samples are collected from sewage irrigated sites in June 2017. Each soil profile is divided into three horizons H1, H2, and H3 at the depth of 0–30, 30–60, and 60–90 cm, respectively. The results of physio-chemical characteristics of the soil show that excessive irrigation with sewage increases organic matter (OM), electrical conductivity (EC), pH, carbonates (CaCO3), and the concentration of all evaluated heavy metals in the soil. In addition, the concentration of all evaluated heavy metals stayed within permissible limits proposed by the World Health Organization (WHO) for unirrigated soil profiles and irrigated soil profiles except for Cd (4.58 mg/kg), Pb (106.26 mg/kg), and As (39.67 mg/kg) in the latter. The vertical distribution of heavy metals demonstrates that the concentration of As, Cr, and Fe increases with the depth, while Pb and Cu decrease downward. In addition, the Cd and Zn have a random distribution. The concentration of heavy metals in the discharged water reveals that the Cd, Pb, and As are gradually decreasing with the depth which confirms that the studied soil retains the heavy metals. The study concludes that sewage irrigation from the Day River contributes to the accumulation of heavy metals and has a dangerous impact on agricultural soil. It is then advisable that local authorities come up with an action plan to treat sewage before discharging it into the Day River.
      PubDate: 2022-11-14
       
  • Development of ANN model for the prediction of discharge coefficient of an
           arced labyrinth side weir

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      Abstract: Abstract Side weirs, referred to as lateral weirs, are flow diversion hydraulic structures frequently used in canal systems, irrigation-drainage systems, and urban sewage systems as a head regulator of distributaries and escapes. Previous studies have mainly focused on side weirs that are rectangular or triangular shape. The present study investigated the hydraulic effects of an arced labyrinth side weir with triangular keys. It is necessary to establish the discharge coefficient equation for the side weir in order to estimate the outflow over an arced labyrinth. To estimate the discharge coefficient of the arced labyrinth side weir, a thorough laboratory investigation was carried out. Non-linear regression and artificial neural network (ANN) approaches have been used to analyse the data and create new models. Based on a number of performance metrics, it was shown that the suggested ANN model (R = 0.9235 and RMSE = 0.0451) has a greater accuracy than the non-linear regression model (R = 0.7206 and RMSE = 0.0521). Additionally, it was found that the discharge coefficient calculated using ANN is more precise than the results of regression equation.
      PubDate: 2022-11-11
       
  • Effects of dust and meteorological variables on temperature inversion over
           Kuwait

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      Abstract: Abstract Temperature inversion plays an essential role in the atmospheric thermal stability that affects the transportation and dispersion of pollutants. Little is known about the relationship between temperature inversion and meteorology in hot and dry arid climates. The relationship between temperature inversion with dust storms and meteorology is investigated in this study, in which the State of Kuwait has a hot arid environment. Hourly data on temperature inversion, dust storms, and meteorological variables (temperature, relative humidity, wind speed, visibility, and solar radiation) were collected for 5 years (2013–2017). The Meteorological Temperature Profiler (MTP-5) model was used to calculate temperature inversions. Spearman’s non-parametric correlations (rs) were used to determine the relationship between temperature inversions and the five meteorological variables, while Fisher’s Exact Chi-square tests (χ2) were used to determine the relationship between temperature inversions and dust storms. The results showed that temperature inversions are very common in Kuwait. Temperature inversions generally develop after sunset and diminish after sunrise and are significantly correlated with high temperature and low relative humidity (p ≤ 0.01). Temperature inversions were shown to be poorly correlated with dust storms (χ2 = 0.794, rs = 0.016). These findings indicate that nighttime, particularly during the spring and summer, creates good conditions for temperature inversions in arid regions and can lead to higher pollutant concentrations. These findings have implications for improving our understanding of air pollution and the contributing factors in hot arid environments.
      PubDate: 2022-11-09
       
  • Advance mathematical modeling for the delineation of the groundwater
           potential zone in Guna district, India

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      Abstract: Abstract The study area is in a subtropical semi-arid climatic zone, where people suffer due to the low availability of surface and subsurface water. Hence, advanced mathematical modeling for the delineation of the groundwater potential (GWP) zone was carried out through the geospatial technique, the multicriteria decision analysis model, and the analytical hierarchy process (AHP) model. The thematic layers of the geology, geomorphology, soil types, lineament density, the inclination of topography, drainage density, land use, and land cover were developed and created by utilizing available supplementary data and a digital elevation model. GWP zone had classified into five classes excellent, good, moderate, poor, and very poor. It had observed that the consistency index was 0.0106, while the random index (RI) for seven variables considered from standard tables is 1.32. This study’s calculated consistency ratio (CR) was 0.0080, lower than 0.1000. This low CR indicated moderate consistency in the results of pairwise comparisons among the thematic layers assigned weight for analysis. Hence, the AHP model used for this research has shown reasonably good accuracy. The statistics of the GWP zone indicated that ~ 3.97% of the study area possessed an excellent GWP zone, ~ 37.09% good, ~ 41.72% moderate, ~ 15.23% poor, and ~ 1.99% very poor. This model of the GWP map shows that the area under the curve of the receiver operating characteristic curve is ~ 0.726. It indicated good agreement between experimental results and predicted results.
      PubDate: 2022-11-09
       
  • Geostatistical modelling of groundwater quality for irrigation: a case
           study of Mayiladuthurai district, Tamil Nadu

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      Abstract: Abstract The present work was carried out in the district of Mayiladuthurai, Tamil Nadu, India, which forms the delta part of the river Cauvery. Groundwater quality parameters of pre-monsoon and post-monsoon seasons of nine consecutive years were analysed to evaluate the status of groundwater quality for irrigation. Water quality data were initially subjected to cation–anion balance error analysis. Cluster analysis was then performed on data sets using SPSS software from which three clusters were identified based on the level of water contamination. Principal component analysis of water quality showed that factors such as seawater intrusion, anthropogenic activity and natural geology lead to deterioration of water quality in that region. Using Schoeller diagram, major ions in the water were found to be Na+, HCO3−, Cl−, Mg2+ and SO42−. From the Piper diagram, water type was predicted to be a mixed water type indicating that freshening of aquifer is essential. Weighted overlay analysis was performed using seven selective parameters that affected irrigation water quality using QGIS; it was found that groundwater quality falls under suitable zone in 53% of agricultural area and moderately suitable zone in 47% of agricultural area. Major factor contributing to decline in water quality was salinity, followed by magnesium hazard, as inferred from sensitivity analysis performed on the different water quality parameters.
      PubDate: 2022-11-09
       
  • Comparative study of two drought description models in Central-Africa: the
           revisited effective drought index and the standardized precipitation index
           

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      Abstract: Abstract In this paper, computation of the Effective Drought Index (EDI) is revisited and the performance of the derived model (mEDI) is compared to Standardized Precipitation Index (SPI) using Self-Calibrating Palmer Drought Severity Index (scPDSI) as the benchmark. Four monthly data sets over Central-Africa were used: precipitation from ten observation stations, gridded precipitation from Global Precipitation Climatology Centre (GPCC) and Climatic Research Unit (CRU), and gridded scPDSI. Station data span the periods 1951–2005 and 1971–2013 for Cameroon and Democratic Republic of Congo (DRC), respectively, and 1950–2019 for all gridded data. SPI was computed based on gamma fitting function and EDI was revised so that mEDI uses monthly precipitation as input data and can quantify multi-scalar droughts. As results, the performances of both indices generally increase with the time scale (TS) and decrease as total annual precipitation increases. The mEDI model outperforms SPI for precipitation above 2288.91 mm/2444.02 mm at TS < 12-month, and below 3233.87 mm/2366.42 mm at 12-month TS, for GPCC/CRU data, while SPI performs better for precipitation out of these intervals, except at the particular TSs of 12, 24, 36 and 48 months where mEDI regains the advantage. At 15-month TS and over, both indices show substantially equal performances. If spatial average of precipitation is used as input for each of the four defined climatic zones, the performances of both indices are improved. SPI best describes drought on short TSs while on medium and long TSs (> 9-month TS), mEDI shows the best performance.
      PubDate: 2022-11-08
       
  • Simulation analysis of thermal performance of the pond roof in
           sustainability and optimization of energy consumption and compare it
           with green roof by design builder software (case study: Yazd City)

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      Abstract: Abstract Pond roof system is one of the best ways to save energy. This article has been done to evaluate the thermal behavior of the pond roof in optimizing the energy consumption of residential buildings in Yazd with hot and dry climate and also compared the results of other studies on the proper height of the water layer on the pond roof and its comparison with green roof. The research method is quantitative, and library and documentary studies have been used to complete the literature. This study was aimed at investigating the effect of factors affecting the thermal performance of roof pond systems, such as water height on the annual energy consumption, and evaluating the sustainability and efficiency of using roof pond systems in the hot and dry climate of Yazd City. Design Builder software is the energy simulator program used in this study. First, using energy simulator software (Design Builder), the effect of green roof with different bed layers and pond roof with different water heights on the annual energy consumption of a residential building in Yazd w investigated. It also calculates the cooling and heating load based on the ASHRAE standard using the heat balance used in Energy Plus. The results show that with increasing water layer height, the building better performance in energy efficiency. Evaluating the height of the water layer 5–15–25–35–45–55–65 cm respectively 7.77%, 8.11%, 8.54%, 8.91%, 9.15%, 9.53%, 9.86% in annual energy consumption in the simulated residential sample has been saved and also compared to conventional roof has about 86% less heat transfer and compared to green roof about 11 times in optimizing annual heating energy and about 1.4 times in optimizing annual cooling energy and about. It has 1.8 times better thermal performance in annual energy consumption, and by examining the amount of water consumption for storage with gray water regeneration and also the optimal thermal performance of the closed roof pond with fixed insulation cover, the appropriate height of the pond was determined to be about 30 cm.
      PubDate: 2022-11-08
       
  • An Integrated Approach of Soil–Erosion Modeling for Soil and Water
           Conservation Planning in a Degraded Semi–Arid Environment of Tigray
           Region, Northern Ethiopian Highlands

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      Abstract: Abstract Soil erosion by water is a severe problem in the Sub-Saharan African countries (e.g., Ethiopia) due to poor soil and water conservation (SWC) planning and implementation. Through the Revised Universal Soil Loss Equation (RUSLE) and Soil Loss Tolerance (SLT) index supported with remote sensing, Geographic Information System (GIS) and expert knowledge, we investigated the net mean annual soil loss (NSL) and SLT for planning SWC along different agro-climatic zones, and geomorphic features in Seharti–Samre district, Tigray–northern Ethiopian Highlands. Satellite and ground observed data sets, such as Digital Elevation Model, Landsat Operational Land Imager (OLI), soil, and climate were collected and processed using geo-spatial softwares. The findings revealed that the estimated mean NSL and SLT in the study area are 86.8 t ha–1 y–1 and 6.8 t ha–1 y–1, respectively. Besides, the study found that the NSL and SLT rates for lowland, midland and highland areas are 178.4 and 3.5 t ha–1 y–1; 72.8 and 7.4 t ha–1 y–1; and 44.1 and 7.5 t ha–1 y–1, respectively. The statistical relationship between NSL and SLT at pixel level in the study area is negative (r = – 0.038) at p  <  0.001. The quantitative analysis and modeling of NSL and SLT have a paramount significance at district or watershed level to implement better SWC practices for reducing soil erosion and maximizing land productivity in a degraded semi-arid environment.
      PubDate: 2022-11-07
       
 
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