Review of coastal land transformation:Factors,impacts,adaptation strategies,and future scopes

Coastal land transformation has been identified as a topic of research in many countries around the world.Several studies have been conducted to determine the causes and impacts of land transformation.However,much less is understood about coupling change detection,factors,impacts,and adaptation strategies for coastal land transformation at a global scale.This review aims to present a systematic review of global coastal land transformation and its leading research areas.From 1,741 documents of Scopus and Web of Science,60 studies have been selected using the PRISMA-2020 guideline.Results revealed that existing literature included four leading focus areas regarding coastal land transformation:change detection,driving factors,impacts,and adaptation measures.These focus areas were further analyzed,and it was found that more than 80%of studies used Landsat imagery to detect land transformation.Population growth and urbanization were among the major driving factors identified.This review further identified that about 37%of studies included impact analysis.These studies identified impacts on ecosystems,land surface temperature,migration,water quality,and occupational effects as significant impacts.However,only four studies included adaptation strategies.This review explored the scope of comprehensive research in coastal land transformation,addressing change detection,factor and impact analysis,and mitigation-adaptation strategies.The research also proposes a conceptual framework for comprehensive coastal land transformation analysis.The framework can provide potential decision-making guidance for future studies in coastal land transformation.

Lead, Zinc and Iron Pollutants Load Assessment in Selected Rivers in Southern Nigeria: Implications for Domestic Uses

The aim of the study is to comparatively assess the concentrations of lead, zinc and iron in Rivers Ase, Warri and Ethiope, in Nigeria. Monthly water samples were collected from six randomly selected sites along the rivers course. 72 water samples were collected from each river at 0 - 15 cm depths. Samples were analysed based on the standard methods recommended by the WHO for testing lead, zinc and iron. The assessment of the water quality was done using the Water Quality Index (WQI) of the Canadian Council of Ministers of the Environment (CCME-WQI). While hypotheses were tested using ANOVA. Findings indicated that CCME-WQI values were 47.3, 66.52 and 78.7. This meant that the water quality of River Ase is impaired and departed from desirable levels, while that of Warri and Ethiope were considered to occasionally be impaired and depart from desirable levels. The ANOVA model showed that there is a significant variation in heavy metal load in the selected rivers at P < 0.05. River water was put to domestic uses such as drinking (20.5%) preparing food (17.8%), bathing (19.8%), washing clothes and dishes (21.3%), brushing teeth (13.3%), and catering for domestic animals (7.5%). Poverty (49.5%) was the major reason for the use of river water for domestic purposes. The locals highlighted that they usually suffer from cholera (26.8%), diarrhoea (25.8%), dysentery (24%) and typhoid (23.5%) as a result of using the river water. The study recommended routine monitoring of anthropogenic and geologic activities, testing of the water regularly amongst others.

Quantification of submarine groundwater discharge(SGD) using radon,radium tracers and nutrient inputs in Punnakayal, south coast of India

The present study focused on the estimation of submarine groundwater discharge(SGD)and the effects of nutrient fluxes due to the SGD process.The parameters of SGD such as magnitude,character,and nutrient flux in Punnakayal region of South East coast of India were evaluated using multiple tracers of groundwater inputs in 2019.It was found that the elevated values for the tracers in the study area,displayed a gradational change in the values as move from estuarine part to the offshore.Simultaneous occurrence of fresh and saline SGD is observed on the study sites.Also,indicated that the SGD fluxes ranged from 0.04 to 0.12 m^3 m^-2d^-1 at the estuary and0.03-0.15 m^3 m^-2d^-1at the groundwater site.A substantially increased value for 222 Rn activities is distinguished in the estuary to values over 312 dpm L^-1.Nutrient embellishments were generally greatest at locations with substantial meteoric elements in groundwater;however,the recirculation of saltwater through the geological formation could provide a way of transferring terrestrially-derived nutrients to the coastal zone at many places.

Estimating daily actual evapotranspiration of a rice–wheat rotation system in typical farmland in the Huai River Basin using a two-step model and two one-step models

The objective of this study is to evaluate the performance of three models for estimating daily evapotranspiration(ET) by employing flux observation data from three years(2007, 2008 and 2009) during the growing seasons of winter wheat and rice crops cultivated in a farmland ecosystem(Shouxian County) located in the Huai River Basin(HRB), China. The first model is a two-step model(PM-Kc);the other two are one-step models(e.g., Rana-Katerji(R-K) and advection-aridity(AA)). The results showed that the energy closure degrees of eddy covariance(EC) data during winter wheat and rice-growing seasons were reasonable in the HRB, with values ranging from 0.84 to 0.91 and R2 of approximately 0.80. Daily ET of winter wheat showed a slow decreasing trend followed by a rapid increase, while that of rice presented a decreasing trend after an increase. After calibrating the crop coefficient(Kc), the PM–Kc model performed better than the model using the Kc recommended by the Food and Agricultural Organization(FAO). The calibrated key parameters of the R-K model and AA model showed better universality. After calibration, the simulation performance of the PM-Kc model was satisfactory. Both the R-K model and AA model underestimated the daily ET of winter wheat and rice. Compared with that of the R-K model, the simulation result of the AA model was better, especially in the simulation of daily ET of rice. Overall, this research highlighted the consistency of the PM-Kc model to estimate the water demand for rice and wheat crops in the HRB and in similar climatic regions in the world.

Machine Learning Based Analysis of Real-Time Geographical of RS Spatio-Temporal Data

Flood disasters can be reliablymonitored using remote sensing photos with great spatiotemporal resolution.However,satellite revisit periods and extreme weather limit the use of high spatial resolution images.As a result,this research provides a method for combining Landsat and MODIS pictures to produce high spatiotemporal imagery for flood disaster monitoring.Using the spatial and temporal adaptive reflectance fusion model(STARFM),the spatial and temporal reflectance unmixingmodel(STRUM),and three prominent algorithms of flexible spatiotemporal data fusion(FSDAF),Landsat fusion images are created by fusing MODIS and Landsat images.Then,to extract flood information,utilize a support vector machine(SVM)to classify the fusion images.Assess the accuracy of your work.Experimental results suggest that the three spatio-temporal fusion algorithms may be used to effectively monitor floods,with FSDAF’s fusion results outperforming STARFM and STRUM in both study areas.The overall flood classification accuracy of the three STARFM,STRUM,and FSDAF algorithms in the Gwydir research region is 0.89,0.90,and 0.91,respectively,with Kappa coefficients of 0.63,0.64,and 0.67.The flood classification accuracy of the three fusion algorithms in the New Orleans research region is 0.90,0.89,and 0.91,with Kappa values of 0.77,0.76,and 0.81,respectively.The spatio-temporal fusion technique can be used to successfully monitor floods,according to this study.

Identification of Groundwater in Hard Rock Terrain Using 2D Electrical Resistivity Tomography Imaging Technique: Securing Water Scarcity at the Time of Seasonal Rainfall Failure, South Andaman

Like many of the tropical islands, the population of Andaman and Nicobar Islands, though not directly, relies predominantly upon rain water harvesting to quench their need and also depends on the groundwater sources. In the background of climate change, severity of hydrological cycle is much anticipated which may cause more extreme and unusual precipitation. It is quite essential to have other alternatives. Accordingly, groundwater could be exploited as a potential alternative. The present study intends to find out the potential groundwater source and estimate aquifer parameters in Kodiyaghat (KD) and Burmanallah (BN). As these areas are composed of very hard rock, Wenner-Schlumberger array has been used to carry out a 2D Electrical Resistivity Tomography survey to find out the fracture zone as well as to delineate the aquifer. KD and BN show maximum resistivity of 25,416 Ωm and 5985 Ωm indicate very hard rock terrain. Similarly, the minimum values of resistivity (21.6 Ωm and 30.4 Ωm) were observed at KD and BN define the presence of freshwater aquifers respectively. The aquifer identified was found to be at a depth of 5 m to 19.9 m at KD and 2.5 m to 20 m at BN. The calculated Hydraulic conductivity (14.85 m/day and 30.14 m/day), transmissivity (86.25 m2/day and 271.27 m2/day) and porosity (28.7% and 31.24%) values at KD and BN confirmed that, the located aquifer was of fresh ground water quality and can be utilized for drinking and house hold purposes. According to the results, almost 70% of the study area is hard rock terrain and 30% comes under potential aquifer zone. The results also show that, both the areas were characterized by Horst and Graben topography and suggest possible groundwater sources for future exploration.

Correlation Between Coastal Geomorphology and Tsunami Inundation Along the Coast of Kanyakumari,India

An investigation has been carried out in the vicinity of the coastal villages of Kanyakumari District,India to decode the influence of coastal geomorphology on inundation degree and run-up level.Even though the tsunami waves approach the study area in different patterns,the consequences are found to be mainly dependent upon the coastal configuration and local geographic setting,the study area are considered to be of three types based upon the geomorphic arrangement,namely shallow coast,elevated coast and estuarine coast.The inundation and run-up level vary from coast to coast even though there is no remarkable variation in the intensity of the approaching tsunami surge.The inundation extent ranges from to 54 m to 413 m with maximum along estuarine coast and minimum along elevated coast.Estuarine coast recorded the maximum run-up level of about 6 m and the minimum of about 1 m along the elevated coast.The percentage of inundated area in the total coastal area varies between 19% to 10% along estuarine coast and elevated coast respectively.Inundation and run-up level cannot be appreciable in the inland along the elevated coast.The beaches of elevated coast are less affected whereas those of estuarine coast are highly affected.Inundation is limited in the elevated beaches along the study area.

Spatial Rainfall Variability and an Increasing Threat of Drought, According to Climate Change in Uttaradit Province, Thailand

This study presents the work commenced in northern Thailand on spatial and temporal variability of rainfall. Thirty years (1988-2017) rainfall data of eight meteorological stations were used for assessing temporal variability and trend analysis. The results showed decreasing trend in rainfall from its first half of the observed study period (1988-2002) to last half of the time period (2003-2017) in total average annual as well as monsoonal average rainfall by 14.92% and 15.50% respectively. It was predicted from linear regression results that by 2030 the average annual and monsoonal rainfall will drop by 35% and 34.10% respectively. All stations showed negative trend except Fakara met-station in annual rainfall. In the seasonal trend analysis, the results showed decreasing trend almost in all met-stations. Mann-Kendall trend test was applied to assess the trend. All met-stations show significant negative trend. To assess drought in the study area, Standardized Precipitation Index (SPI) was applied to 12-month temporal time period. The results predicted meteorological drought in the near future. The spatial distribution of rainfall presented changing phenomena in average annual, monsoonal, winter, and summer seasons in both analyzed periods.

Extreme exposure of fluoride and arsenic contamination in shallow coastal aquifers of the Ganges delta,transboundary of the Indo-Bangladesh region

Globally,shallow aquifer groundwater(GW)has been severely affected in recent decades for both geogenic and anthropogenic reasons.The hydro-geochemical characteristics of the GW change inconsistently with the addition of unwanted inorganic trace elements into the GW aquifer of the Indo-Bangladesh delta region(IBDR),such as arsenic(As)along with fluoride(F-)contamination.Contaminated GW can have a negative impact on drinking water supplies and agricultural output.GW pollution can have serious adverse effects on the environment and human health.Thus,the GW quality of this region is deteriorating progressively,and human health threatening by various life-threatening disorders.Hence,the current study concentrated on the GW quality evaluation and prediction of possible health issues in the IBDR due to elevated contamination of As along with F-within GW aquifers by considering sixteen causative.Field survey-based statistical methods such as entropy quality index(EWQI)combined with health risk index(HRI)was implemented for evaluating the As and F-sensitivity with the help of correlation testing and principal component analysis.The study's outcome explains that a substantial portion of the IBDR has been vastly experiencing inferior GW quality,environmental issues,and health-related problems in dry and wet seasons,correspondingly for As and F-exposure.Piper diagram verified the suitability of water that almost 55%of GW across the study area’s aquifers are unfit for drinking as well as cultivation of crops.Sensitivity analysis and the Monte Carlo simulation method were also applied to assess the contaminant's concentration level and probable health risk appraisal.The present study concludes that the elevated exposure of As and F-pollution has to be monitored regularly and prevent unwanted GW contamination through implementing sustainable approaches and policies to fulfil the sustainable development goal 6(SDG-6)till 2030,ensuring the most basic human right of clean,safe,and hygienic water.

Flood susceptibility modelling using advanced ensemble machine learning models

Floods are one of nature's most destructive disasters because of the immense damage to land,buildings,and human fatalities.It is difficult to forecast the areas that are vulnerable to flash flooding due to the dynamic and complex nature of the flash floods.Therefore,earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters.In this study,we applied and assessed two new hybrid ensemble models,namely Dagging and Random Subspace(RS)coupled with Artificial Neural Network(ANN),Random Forest(RF),and Support Vector Machine(SVM)which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin,the northern region of Bangladesh.The application of these models includes twelve flood influencing factors with 413 current and former flooding points,which were transferred in a GIS environment.The information gain ratio,the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors.For the validation and the comparison of these models,for the ability to predict the statistical appraisal measures such as Freidman,Wilcoxon signed-rank,and t-paired tests and Receiver Operating Characteristic Curve(ROC)were employed.The value of the Area Under the Curve(AUC)of ROC was above 0.80 for all models.For flood susceptibility modelling,the Dagging model performs superior,followed by RF,the ANN,the SVM,and the RS,then the several benchmark models.The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.

An Optimal Method for High-Resolution Population Geo-Spatial Data

China' Mainland has a poor distribution of meteorological stations.Existing models’estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature,and low for relative humidity and wind speed(few studies reported).This study compared the typical generalized additive model(GAM)and autoencoder-based residual neural network(hereafter,residual network for short)in terms of predicting three meteorological parameters,namely air temperature,relative humidity,and wind speed,using data from 824 monitoring stations across China’s mainland in 2015.The performance of the two models was assessed using a 10-fold cross-validation procedure.The air temperature models employ basic variables such as latitude,longitude,elevation,and the day of the year.The relative humidity models employ air temperature and ozone concentration as covariates,while the wind speed models use wind speed coarse-resolution reanalysis data as covariates,in addition to the fundamental variables.Spatial coordinates represent spatial variation,while the time index of the day captures time variation in our spatiotemporal models.In comparison to GAM,the residual network considerably improved prediction accuracy:on average,the coefficient of variation(CV)R2 of the three meteorological parameters rose by 0.21,CV root-mean square(RMSE)fell by 37%,and the relative humidity model improved the most.The accuracy of relative humidity models was considerably improved once the monthly index was included,demonstrating that varied amounts of temporal variables are crucial for relative humidity models.We also spoke about the benefits and drawbacks of using coarse resolution reanalysis data and closest neighbor values as variables.In comparison to classic GAMs,this study indicates that the residual network model may considerably increase the accuracy of national high spatial(1 km)and temporal(daily)resolution meteorological data.Our findings have implications for high-resolution and high-accuracy meteorological parameter mapping in China.

Assessing Water and Soil Pollution Due to Uttara EPZ, Nilphamari, Bangladesh

The Uttara Export Processing Zone (UEPZ) is being the important industrial belt of the northern region. It is an important issue to find out the environmental impact of UEPZ. Water is the most important source of domestic, irrigation and industrial purpose in both rural and urban regions. The present study was carried out to find out the water and soil quality of UEPZ. Five heavy metals were selected (Fe, Cu, Mn, Pb, Cr) to assess water quality of UEPZ and two indices such as heavy metal pollution index (HPI) and contamination index (Cd) were selected to evaluate the impact on water. The results showed that the concentrations of heavy metals in water samples were within the permissible limits of WHO drinking water quality. HPI of water samples in three sites was 20.57 which was lower than 100 the critical value for drinking water. Both results show the region is moderately or slightly polluted. Pollution risks of heavy metal in the soil were evaluated by method of geological acumination index (Igeo) and Pollution load index (PLI) for seven soil samples. The geological evaluation of the cumulative index results showed that the contamination degree of 4 heavy metals follows the sequence of Mn > Zn > Fe > Cu. Then the results of PLI of seven soil sample are 1.474 > 1.398 > 1.372 > 1.308 > 1.302 > 1.290 > 1.289. Both results show the soil sample area were unpolluted to moderately polluted. Finally, an overall impact of UEPZs environment is also discussed in this paper.

Multi-media compartments for assessing ecological and health risks from concurrent exposure to multiple contaminants on Bhola Island,Bangladesh

In this study,a set of coupled multi-media compartments(i.e.,sediment,soil,water and vegetable)was used to assess ecological and health risks from the ingestion of 11 PTEs(Pb,Cd,Cr,As,Hg,Cu,Zn,Ni,Co,Fe,and Mn)and their transportation routes in the water-soil-plant system from the coastal Bhola Island,Bangladesh.The mean concentrations of Cd,Pb,and Co for soil and Cd,Co,and As for sediment were higher than their reference values.In contrast,Cd,Pb,and Ni concentrations in water surpassed the acceptable limits set by national and international laws and were considered unsuitable for drinking purposes.Vegetables demonstrated high Pb and Cd contents,demonstrating a potential food safety risk to the inhabitants.Results of principal component analysis(PCA)revealed that Cd,Pb,Hg,Cu,Ni and Zn sources were likely to be anthropogenic,especially agro-farming inputs,whereas the Fe,As,Cr,Mn,and Co sources were similar to natural origin.So,Cd,Pb and Co are the key contaminants in the study area and pose the elevated health and ecological risks in the coastal area.Cd and Pb exhibited higher ecological risks in soils and sediments,as Pb had the highest bio-accessibility(BA;0.02±0.003)and Cd possessed a high bioaccumulation factor(BCF;0.004±0.006).The self-organizing map analysis recognized three spatial patterns which are good agreement with PCA.The average hazard index(HI)values for soil were above the permissible level(HI>1)set by the respective agency;two times higher HI values were noticed for children than adults,suggesting children are highly susceptible to health risk.Continuous monitoring and source controls for Cd and Pb,along with agro-farming management practices,need to be implemented to reduce the risk of PTE contamination to the aquatic ecosystem and its inhabitants.