 Subjects -> METEOROLOGY (Total: 106 journals)
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- A Monte Carlo simulation approach for the assessment of health risk from
NO $$_{3}^{-}$$ -N perturbation in groundwater-
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Abstract: Abstract As a prerequisite towards sustainable management of an aquifer system, it is critical to reveal and quantify the relationship between NO \(_{3}^{-}\) –N and human health in order to delineate health-risk zones. The various sources of NO \(_{3}^{-}\) –N in an environment as well as the interaction of natural and anthropogenic processes, present considerable obstacles when considering a technique to estimate health risk. Another constraint on health risk estimation is choosing right technique. This research applied deterministic and MC approaches coupled with finite mixture model to evaluate the sources and concentrations of NO \(_{3}^{-}\) –N in groundwater, and appraise the hazard risks associated with various exposure groups in the Densu Basin in Southern Ghana. The Monte Carlo approach was applied to the data with due cognizance of the various identified sources of NO \(_{3}^{-}\) –N, and a hypothetical single source (HSS). The results suggest that the probability of risk for identified NO \(_{3}^{-}\) –N sources (human-induced) fall within the ranges of 0.16- \(-\) 0.39, 0.15- \(-\) 0.30, and 0.12- \(-\) 0.24 respectively for infants, children, and adults. The MC technique applied to the HSS concentration recorded a relatively low probability of risk, presenting 0.07- \(-\) 0.17, 0.02- \(-\) 0.05, and 0.01- \(-\) 0.04 for infants, children, and adults respectively. It, therefore, goes without saying that the health hazard caused by the human-induced sources of NO \(_{3}^{-}\) –N on exposure groups is comparatively higher than the single sources. The MC simulation based on identified NO \(_{3}^{-}\) –N sources appears to have performed better compared to the HSS and the deterministic approach.
PubDate: 2023-03-21
- Delineation of groundwater potential zones using the AHP technique: a case
study of Alipurduar district, West Bengal-
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Abstract: Abstract Increasing population with increasing demand of groundwater affects the level of groundwater. In the context of considerable change in the use of groundwater pattern, particularly with continuous increase in demand for groundwater due to many reasons, the present paper attempts to delineate groundwater potential zones (GWPZ) using integrated remote sensing, geographic information systems (GIS) and analytic hierarchy process (AHP) methods. To transform and harmonize geographic data and weightage ranking to get reliable information, geographic information systems are combined with analytical hierarchical processes. The current study has been done in the district where many areas are under tea garden and cultivated land. The use of excess of groundwater results in a drop in the water level. The mapping and the identification of groundwater potential zones were done for the Ganga alluvial plain of Alipurduar District of India. The groundwater potential index (GPI) was computed based on several factors (e.g., land use–land cover, soil type, geology, elevation, slope, rainfall, normalized difference vegetation index, drainage density, pre- and post-monsoon groundwater depth, etc.). To generate the groundwater potential zone map of the study area, an overlay weighted sum method was applied to integrate all thematic criteria. Groundwater potential index maps have been classified into five zones. The excellent potential zone comprise 50.5% (1583.68 km2), good 27.4% (859.26 km2), moderate11.3% (354.37 km2), poor 7.1% (222.66 km2) and very poor 3.7% (116.03 km2), respectively. After that, the maps were verified with groundwater-level fluctuation data of 30 observed wells through the ROC (receivers operating characteristic) curve. This paper has important implications for planning the sustainable groundwater plan and also different purposes, such as natural and artificial recharge, watershed delineation and proper water usage, can be effectively implemented in this agriculture-dominated areas in the district.
PubDate: 2023-03-20
- Prediction and mapping of land degradation in the Batanghari watershed,
Sumatra, Indonesia: utilizing multi-source geospatial data and machine
learning modeling techniques-
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Abstract: Abstract In the present study, the Geospatial Artificial Intelligence (Geo-AI) is proposed to overcome challenges and phenomena related to land degradation identification in the field by integrating multi-source geospatial data and machine learning modeling techniques. The study area is located in the Batanghari watershed, Sumatra, Indonesia, which is a tropical environment. The existence of environmental problems in the study area can be identified based on land degradation. This study’s novelty model is that it is the first to integrate the six main variables of multi-source geospatial data—topographical, biophysical, bioclimatic, geo-environmental, global human modification, and accessibility—in predicting and mapping the potential of land degradation in Indonesia’s tropical environment. Support Vector Machine (SVM), Minimum Distance (MD), Classification and Regression Trees (CART), Gradient Tree Boost (GTB), Naïve Bayes (NB), Random Forest (RF) are machine learning modeling algorithms used to predic and map land degradation in the study area. The prediction results from these modeling algorithms can be compared and evaluated to get the most optimal performance and accuracy. During the modeling phase, 70% of the reference data were divided into training and 30% into validation. The performance of the model was compared using three accuracy assessment processes (producer accuracy, user accuracy, and overall accuracy). The overall accuracy of the results of the comparison and evaluation of machine learning modeling on the SVM, MD, CART, GTB, NB, and RF algorithms in the study area are 52.8, 34.5, 81.2, 85.8, 36.3, and 86.2%, respectively. Therefore, the study concluded that the RF, CART, and GTB are machine learning modeling algorithms that were proposed to be applied and to produce a land degradation map in the study area. The results of this study can be used by policymakers in making decisions related to environmental management, ecosystem rehabilitation, and restoration. Technically, this can also be applied to other watersheds with similar characteristics. In addition, the results of this study can also be used to develop an intelligent watershed monitoring system to effectively monitor land cover change.
PubDate: 2023-03-19
- Contribution of the enhancement methods and 2D modeling to the evaluation
of sedimentary cover thicknesses of the Bahira basin (Morocco) using
aeromagnetic data-
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Abstract: Abstract The Bahira Basin, located in Central Morocco, is a vast synclinal basin inserted between two Paleozoic basement. This basin is known for its large reserves of phosphate (Ganntour plateau), consequently it presents a regional economic interest. This study focuses on the use of aeromagnetic data to evaluate the thicknesses of sedimentary cover of the Bahira basin using enhancement methods, depth estimation techniques and 2D forward modeling. The enhancement methods were enabled to highlight the location and edge of the magnetic sources. The results show that most of the magnetic sources have hercynian orogeny trends. Combined 3D Euler Deconvolution and Source parameter imaging (SPI) methods indicate that depth to basement underlying the Cretaceous-Quaternary sedimentary cover exceed 7 km. The maximum depth values obtained from the various depth estimation methods correlated perfectly with each other. Generally, it was observed that the western part of the study area is characterized by thick sedimentation. The magnetic 2D forward modeling basement structure suggest the uplift of the Paleozoic basement at the south and north part of the Bahira basin. The results of this study can be used to better understand the basement structure of the Bahira basin for forward exploration interest.
PubDate: 2023-03-18
- Water quality index forecast using artificial neural network techniques
optimized with different metaheuristic algorithms-
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Abstract: Abstract An accurate water quality index (WQI) forecast is essential for freshwater resources management due to providing early warnings to prevent environmental disasters. This research provides a novel procedure to simulate monthly WQI considering water quality parameters and rainfall. The methodology includes data pre-processing and an artificial neural network (ANN) model integrated with the constraint coefficient-based particle swarm optimization and chaotic gravitational search algorithm (CPSOCGSA). The CPSOCGSA technique was compared with the marine predator's optimization algorithm (MPA) and particle swarm optimization (PSO) to increase the model's reliability. The Yesilirmak River data from 1995 to 2014 was considered to build and inspect the suggested strategy. The outcomes show the pre-processing data methods enhance the quality of the original dataset and identify the optimal predictors' scenario. The CPSOCGSA-ANN algorithm delivers the best performance compared with MPA-ANN and PSO-ANN considering multiple statistical indicators. Overall, the methodology shows good performance with R2 = 0.965, MAE = 0.01627, and RMSE = 0.0187.
PubDate: 2023-03-17
- Modeling and analysis of novel COVID-19 outbreak under fractal-fractional
derivative in Caputo sense with power-law: a case study of Pakistan-
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Abstract: Abstract In this paper, a five-compartment model is used to explore the dynamics of the COVID-19 pandemic, taking the vaccination campaign into account. The present model consists of five components that lead to a system of five ordinary differential equations. In this paper, we examined the disease from the perspective of a fractal fractional derivative in the Caputo sense with a power law type kernal. The model is also fitted with real data for Pakistan between June 1, 2020, and March 8, 2021. The fundamental mathematical characteristics of the model have been investigated thoroughly. We have calculated the equilibrium points and the reproduction number for the model and obtained the feasible region for the system. The existence and stability criteria of the model have been validated using the Banach fixed point theory and the Picard successive approximation technique. Furthermore, we have conducted stability analysis for both the disease-free and endemic equilibrium states. On the basis of sensitivity analysis and the dynamics of the threshold parameter, we have estimated the effectiveness of vaccination and identified potential control strategies for the disease using the proposed model outbreaks. The stability of the concerned solution in Ulam-Hyers and Ulam-Hyers-Rassias sense is also investigated. For the proposed problem, some results regarding basic reproduction numbers and stability analysis for various parameters are represented graphically. Matlab software is used for numerical illustrations. Graphical representations are given for different fractional orders and for various parametric values.
PubDate: 2023-03-16
- Modeling predictive changes of carbon storage using invest model in the
Beht watershed (Morocco)-
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Abstract: Abstract Carbon sequestration and storage is considered one of the world's most recognized and vital ecosystem services, as it reduces atmospheric CO2, accelerating climate change. It refers to the capacity of terrestrial ecosystems to capture and store carbon within one of the 4 carbon pools. Although the diversity of land uses at the Beht watershed level, the spatial distribution and quantification of carbon storage are never studied. Thus, the objective of the current study aims to model and evaluate the link between the different types of land use/land cover changes (LULC) and the carbon sequestration service in the Beht watershed over 20 years, and estimate the economic value of the carbon sequestered in the remaining stock. For the processing of spatial data, we applied the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST 3.7.0) carbon storage and sequestration modeling software. The results show that built-up areas, agriculture, and forests increased by 136, 86, and 0.34% respectively, whereas rangeland and water bodies decreased by 18 and 81%, respectively. This change of LULC had the greatest effect on carbon storage passing from 10.76106tC to 13.15 106tC between 2000 and 2020, which is more important in forests contributing by 141.4tC per ha. Based on the social cost of carbone, we estimated the economic value of carbon sequestration service between 17,548,000 and 35,096,000 $/year, or 104 and 208 $/ha/year. The results confirm the importance of using other management strategies such as REED + , or payment for ES that will lead environmental policies to adopt sustainable LULCs that support livelihoods and management choices.
PubDate: 2023-03-16
- Relative humidity prediction with covariates and error correction based on
SARIMA-EG-ECM model-
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Abstract: Abstract RH is a physical quantity measuring atmospheric water vapor content. Predicting RH is of great importance in weather, climate, industrial production, crops, human health, and disease transmission, since it is helpful in making critical decisions. In this paper, the effects of covariates and error correction on relative humidity (RH) prediction have been studied, and a hybrid model based on seasonal autoregressive integrated moving average (SARIMA) model, cointegration (EG), and error correction model (ECM) named SARIMA-EG-ECM (SEE) has been proposed. The prediction model was performed in the meteorological observations of Hailun Agricultural Ecology Experimental Station, China. Based on the SARIMA model, the meteorological variables that interact with RH were used as covariates to perform EG tests. A cointegration model has been constructed. It revealed that RH had a cointegration relationship with air temperature (TEMP), dew point temperature (DEWP), precipitation (PRCP), atmospheric pressure (ATMO), sea-level pressure (SLP), and 40 cm soil temperature (40ST), which revealed the long-term equilibrium relationship between series. An ECM was established which indicated that the current fluctuations of DEWP, ATMO, and SLP have a significant impact on the current fluctuations of RH. The established ECM describes the short-term fluctuation relationship between the series. With the increase of the forecast horizon from 6 to 12 months, the prediction performance of the SEE model decreased slightly. A comparative study has also been introduced, indicating that the SEE performs superior to SARIMA and Long Short-Term Memory (LSTM) network.
PubDate: 2023-03-14
- Modeling of rain erosivity employing simulated rainfall and laser
precipitation monitor-
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Abstract: Abstract In the absence of natural rain, a rainfall simulator offers an excellent opportunity to characterize and correlate the raindrop parameters, which are essential in studying the soil erosivity potential. However, their estimation requires precise instrumentation, which is seldom available. The technique of physically based modeling through empirical and conceptual relationships helps to correlate these rain parameters. The present study engaged six nozzles of different capacities and laser precipitation monitor (LPM) in obtaining the empirical relationships between different erosivity parameters. The simulator was calibrated to simulate natural rainfall conditions in the laboratory, and the performance was evaluated based on rain granulometry, drop size distribution, terminal velocity, and kinetic energy of raindrops. Different linear and non-linear regression relationships were developed and tested statistically to correlate the pressure, median volume drop diameters (D50) of rain, the kinetic energy of raindrop per unit area per unit time (KEtime,), and kinetic energy expended per unit rain quantity (KEvol) with the rain intensity (I). The estimated KEtime and KEvol ranged from 10.384 to 572.273 Jm−2 h−1 and 0.57 to 17.51 Jm−2 mm−1, respectively, comparable to the natural rain at specified rain intensities. The present study also developed a generalized exponential equation to correlate D50–I and a power law-based equation for erosivity and rainfall depth. The adequacy of the developed relationships was verified with MAE, MSE, and RMSE indicating the significance of the relationship. The developed correlations shall be helpful in the estimation of various rainfall parameters with the simple measurement of the most common parameters such as rain intensity and depth. The results of the present study will enable researchers to develop events-scale physically based models on soil erosion.
PubDate: 2023-03-14
- Appraisal of subsurface structural model, a tool for understanding the
influence of geodynamics in base metal occurrence within the Southern
Benue Trough, southeastern Nigeria-
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Abstract: Abstract Airborne magnetic datasets over Southern Benue Trough were collected, filtered, enhanced, and subsequently employed to generate a model of the subsurface basement topography, interpret the structural framework in connection to geodynamic processes, lithology distributions, and estimate the depth of the magnetic basement which invariably reveal the sedimentary thickness in the study area. The data processing includes analytic signal, tilt gradient, and source parameter imaging (SPI) were applied. The residual magnetic intensity ranges from less than − 60.5 to slightly above 120 nT while the magnetic analytical signal values range from 0.002 to 0.053 nT/km. The maps indicated contrasting subsurface field magnetic intensities for which the study area was divided into three basic magnetic zones (zones characterized by high, intermediate, and low magnetic intensities respectively). The results of analytical signal analysis and tilt derivative gave the foresight of subsurface structural morphology (regional and local) and their orientations, which indicates that the area was subjugated by NE–SW trending structures with minor NW–SE, E–W, and N–S regional lineaments. These structures indicated cross-cutting relationships which designated phases of generic events. The result of the SPI shows the maximum sedimentary thickness of about 3 and 4 km around the Nkalagu and Afikpo areas respectively. Shallow sedimentary thickness was observed within the basement complex areas around Ugep, Uban Hills, Obokpa, Okokori, as well as the central and northern areas (Abakaliki, Ishiagu, and Afikpo) with outcropping intrusive/volcanic rocks. Afikpo area has a sediment thickness of above 3 km which can be highlighted and recommended for hydrocarbon evaluation while considering other major petroleum play factors, not forgetting the influence of activities that brought the emplacement of the intrusive found within the basin. The structural concept defined here holds lots of promise for favorable mapping of base metal deposits and their structural control.
PubDate: 2023-03-14
- “Evaluations of regional climate models for simulating precipitation and
temperature over the Guder sub-basin of Upper Blue Nile Basin, Ethiopia”
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Abstract: Abstract This study evaluates the performance of five Regional Climate Models (RCMs) (CCLM4-8-17, HIRHAM5, RACMO22T, RCA4 and REMO2009) driven by a Global Climate Model) (GCM) (ICHEC-EC-EARTH) for simulating rainfall and temperature in the Guder sub-basin of the Upper Blue Nile Basin in Ethiopia. The RCMs data were downloaded from the Earth System Grid Federation (ESGF) website and observational data were obtained from the National Meteorology Agency (NMA). The RCMs were evaluated against observed data on the basis of how they produce the monthly average, wet season (June-September), and annual average rainfall and temperature during the 1986–2005 periods. The result showed that in most stations RCA4, HIRHAM5, and CCLM4-8-17 models overestimate the rainfall with a minimum bias of 0.01 mm at Jeldu station and a maximum bias of 6.48 mm at Ambo station. On the other hand, all the models underestimate the maximum temperature between 0.46 and 10.04 °C and overestimate the minimum temperature in most climate stations within the ranges 0.14 °C–5.03 °C. In most of the statistical metrics, RACMO22T was superior while the RCA4 and HIRHAM5 RCMs models show the poorest performance in terms of capturing monthly and annual cycles of rainfall. But, for temperature simulation, HIRHAM5 simulation was relatively the best in simulating both the annual and wet season maximum and minimum temperature over the Guder sub-basin. This finding indicated that the best model for rainfall simulation had a poor performance for temperature. Therefore, this justifies the need for RCMs model evaluation in order to choose the most realistic model for a localized climate impact study.
PubDate: 2023-03-14
- A spatiotemporal classification approach to evaluate the impacts of land
use and land cover changes before and after the Três Irmãos reservoir
formation in the Tietê River, Brazil-
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Abstract: Abstract Land use and land cover (LULC) maps are relevant tools to recognize deep changes in the natural landscape and direct strategies to minimize environmental impacts, being an important apparatus to obtain useful information in making decision by managers from watershed committees, political leaders and environmental agencies. Using Landsat multispectral images, this study aimed to investigate the changes in LULC of a watershed, influenced by the construction of the dam of the Três Irmãos hydroelectric plant, located in the low course of the Tietê River. The images were acquired in 1990, 2000, 2010 and 2018, which covers the periods before and after the flood caused by the dam build. For each date, LULC maps were generated using the supervised classification Maximum Likelihood (ML) algorithm. Those analysis has allowed identify possible influences of the reservoir formation over LULC in the watershed. A deep analysis showed a significant change of natural vegetation were flooded due to the damming, being partially recovered, in percentage terms, over time around the new watercourse. This study presents a reduction of 67% in the natural vegetation area from 1990 to 2000, while there was an increase of 35,36% between 2000 and 2018, when compared to the area occupied by natural vegetation existing in 1990. In addition, areas of natural vegetation also have been replaced by cultivated areas, which are predominant in the watershed.
PubDate: 2023-03-13
- 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: 2023-03-11
- Numerical simulation using the finite element method to investigate the
effect of internal cutoff walls on seepage and hydraulic gradients in
homogeneous earth dams-
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Abstract: Abstract The seepage reduction in embankment dams is essential to their stability and safety. This research aims to evaluate the effects of penetration depth (16, 18, 20, 22, 24 m), distance from the beginning of the crest width (0, 2, 4, 6, 8 m), and inclination angle (45°, 90°, 135°) of the internal cutoff wall on some of the design parameters in an embankment dam using the Finite Element Method. It was found that the existence of an internal cutoff wall leads to a significant decline in the phreatic line level. Increasing the internal cutoff wall penetration depth decreases seepage flow so that the maximum decline in seepage flow rate is 39.7% for a penetration depth of 24 m. In contrast, by increasing the internal cutoff wall penetration depth, hydraulic gradient and relative drop in total head increases. In addition, as the distance of the internal cutoff wall from the upstream end of the crest width increases, the seepage discharge and hydraulic gradient increase. For a penetration depth of 22 m, the maximum decrease in seepage discharge was about 31.23% for x = 0 m. It was seen that by increasing the inclination angle of the internal cutoff wall, the maximum hydraulic gradient occurred in more downstream locations. An internal cutoff wall with inclination angle of 45° and penetration depth of 24 m was found to have the best performance at reducing seepage by 49.7%. The relative drop in the total head reaches the maximum value with a cutoff inclination angle of 135°.
PubDate: 2023-03-11
- Correction to: Earlier green-up and senescence of temperate United States
rangelands under future climate-
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PubDate: 2023-03-09
- SDIQR mathematical modelling for COVID-19 of Odisha associated with influx
of migrants based on Laplace Adomian decomposition technique-
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Abstract: Abstract In this study, the Laplace Adomian decomposition technique (LADT) is employed to analyse a numerical study with the SDIQR mathematical model of COVID-19 for infected migrants in Odisha. The analytical power series and LADT are applied to the Covid-19 model to estimate the solution profiles of the dynamical variables. We proposed a mathematical model that incorporates both the resistive class and the quarantine class of COVID-19. We also introduce a procedure to evaluate and control the infectious disease of COVID-19 through the SDIQR pandemic model. Five compartments like susceptible ( \(S\) ), diagnosed ( \(D\) ), infected ( \(I\) ), quarantined ( \(Q\) ) and recovered ( \(R\) ) population are found in our model. The model can only be solved approximately rather than analytically as it contains a system of nonlinear differential equations with reaction rates. To demonstrate and validate our model, the numerical simulations for infected migrants are plotted with suitable parameters.
PubDate: 2023-03-09
- Desertification modeling in the Moroccan Middle Atlas using Sentinel-2A
images and TCT indexes (case of the Ain Nokra Forest)-
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Abstract: Abstract This paper focuses on the quantitative modeling of desertification in Morocco’s Middle Atlas regions, Ain Nokra Forest. In order to map the degree of desertification in the forest in 2021, this study used Sentinel-2A images. More precisely, the spectral indices, such as the NDVI, albedo, and TCT indices, have first been calculated. After putting different combinations through a linear regression analysis, the feature space was created using the correlation ratio that is the most negative of these indices (NDVI–albedo, TCG–TCB, and TCW–TCB). With a correlation ratio of − 0.80, the TCG–TCB combination is the best, followed by the NDVI–albedo ratio, which has a correlation ratio of − 0.63. A desertification map for the entire forest has been created as a result of the first combination being used to propose the Desertification Degree Index (DDI). There are currently five categories of desertification (extreme, severe, moderate, low, and non-desertification). according to our model, the forest is progressively desertifying. Typically, 40% of the forest area is classified as having severe to intense desertification, while 51% is classified as having weak to moderate desertification. However, only 8.52% of the region is deemed to be “non-desertified”. Finally, due to its incredibly high overall accuracy of 88.32%, the model is almost optimal for quantitative analysis and monitoring of desertification in the Moroccan Middle Atlas.
PubDate: 2023-03-09
- A hybrid deep learning model for rainfall in the wetlands of southern Iraq
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Abstract: Abstract Machine learning is being used by researchers and computer scientists to develop a new method for predicting rainfall. Due to the non-linear relationship between input data and output conditions, rainfall prediction is hard, so deep neural network (DNN) models substitute for costly, complex systems. Deep neural network-based weather forecasting models can be designed quickly and cheaply to predict rainfall. On the other hand, water levels depend on rainfall. Unpredictable rainfall due to climate change might cause floods or droughts. Many individuals, especially farmers, rely on rain forecasts. In our study, we focus on the area of marshes in southern Iraq, some of the most famous landmarks in the area (and the world), where the Shatt al-Arab flows into the Arabic Gulf and the Tigris and Euphrates rivers developed within the Mesopotamian plain to create a natural balance. Since the beginning of the 1980s, the wetlands, sometimes known as "the marshes," have experienced droughts. And by the late 1990s, a sizable portion of the marshes had dried up, leaving the arid and salty Sabkha lands void of life, particularly lands with vast bodies of water and high levels of human activity. Moreover, the corresponding regions have undergone visible hydrological and climatic changes. In this study focuses on the marshes of southern Iraq and aims to develop a rainfall forecasting model. We propose a novel approach based on optimized LSTM and hybrid deep learning algorithms to improve the forecasting of average monthly rainfall. To evaluate the efficiency of the predictions, a comparison of the predicted rainfall and the actual recorded rainfall is made, and the performance and accuracy of the models are examined. The hybrid convolutional stacked bidirectional long-short term memory (CNN-BDLSTMs) outperformed the other models.
PubDate: 2023-03-07
- Assessing the change of water quality and quantity in the upper basin of
Thac Ba reservoir under the impacts of future land-use scenarios-
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Abstract: Abstract Variability in land use is anticipated to affect regional hydrologic conditions and have a wide range of implications on water resources and human health across the world, particularly in developing countries like Vietnam. Land-use/land-cover change has been a significant difficulty for the social–economic development of upland areas in northern Vietnam in recent years, owing to its relationship with downstream environmental concerns such as water quantity and quality. The Geographical Information System (GIS) and SWAT model (Soil and Water Assessment Tools) were used in this study to assess the impacts of land-use change on streamflow and sediment runoff under various future development scenarios based on economic circumstances. The Thac Ba reservoir watershed was chosen for examination because it is vital for agriculture, hydropower generation, and household water supply to communities living in the watershed’s proximity and downstream. It is estimated that 75–85% of the total yearly rainfall falls between May and October. When it comes to sediment runoff in the Thac Ba watershed, increasing monthly flow discharge increases mean monthly sediment runoff during the rainy season in all situations, but decreasing monthly flow discharge decreases mean monthly sediment runoff during the dry season in all scenarios. The findings demonstrated that the seasonal streamflow had been affected in a more sophisticated manner than had been observed in earlier forest conversion scenarios. Both the rainy and dry seasons saw an increase in average streamflow, which is a beneficial thing in terms of streamflow. When compared to the baseline scenario, average values in the rainy season climb by 4.72 and 8.04 percent, respectively, in scenarios 2.3 and 2.4 (extreme situations) (1.9 percent in extreme scenario). An in-depth investigation indicates that the average flow rises throughout the year, but continues to fall in November, December, and January. According to the scenarios, the most significant changes are shown in May (ranging from 2.77 percent to 15 percent) and June (ranging from 2.11 percent to 14.7 percent). Furthermore, in scenario 2.4, during dry months, the average maximum streamflow is reduced by approximately 3.5 percent in November and December, but increases in March and April.
PubDate: 2023-03-04
- Patterns of coral diseases linked to the impact of climate change: a case
study of scleractinia corals in Southeast Sulawesi, Indonesia’s coral
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Abstract: Abstract Climate change in the seawater of Southeast Sulawesi affects the health of coral reef ecosystems, causing disease infection at several observation sites. Therefore, this study examined the spread of coral diseases in the Southeast Sulawesi Sea to understand the environmental impact, especially the impact of climate change and global warming. The methods included data sampling techniques such as observing coral and disease conditions, collecting water quality measurements, and processing satellite imagery data. The results showed that the number of colonies at the sites varied. The Wanci site (Wakatobi Regency) had the highest number of colonies in the healthy and infected categories (627 colonies), followed by the Padamarang site (Kolaka Regency) and Pasijambe site (Kendari City), with 572 and 340 colonies, respectively. The number of infected colonies also fluctuated, with 46 in Wanci, 27 in Pasijambe, and 13 in Padamarang, while the respective prevalence rates were 7%, 8%, and 2%. Black band disease predominated at the three observation sites: the Wanci site (19 infected colonies), the Pasijambe site (4.5 infected colonies), and the Padamarang site (three infected colonies). Several climate change factors, including sea surface temperature, pH, salinity, dissolved oxygen, ocean currents, and nitrate and phosphate concentration, had positive correlations. These were confirmed to determine the number of infected coral colonies. The prevalence of coral disease showed a good correlation with the nitrate content (r = 0.844; p < 0.05) and phosphate content (r = 0.611; p < 0.05). Climate change pressure was thus found to affect the condition of the Southeast Sulawesi marine environment. In addition to reducing anthropogenic pressure, parties must use coastal management to reduce the various environmental pressures that affect corals. Finally, education is required to enable local people and industries to identify the challenges that corals face and propose solutions.
PubDate: 2023-03-02
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