Quantifying uncertainty in future sea level projections downscaled from CMIP5 global climate models

Abstract

Sea level projections for the future indicate a likely increase, raising concerns in the community due to its detrimental consequences. It has been reported by several researchers that there is considerable uncertainty in the future climate projections of Global Climate Models (GCMs), the primary tools for projecting future climate. In this study, the support vector machine (SVM) was employed to downscale sea level projections for the future from the projections of CMIP5 (Coupled Model Intercomparison Project Phase 5) GCMs. Quantile regression was employed to examine the predictors’ uncertainty, and it was found that sea surface salinity (halosteric component of sea level change) is the highly uncertain variable among the three predictors, followed by sea surface temperature and mean sea level pressure. The uncertainty associated with downscaled future sea level projections under Representative Concentration Pathways (RCPs) 4.5 and 8.5, stemming from GCM structure, was investigated using Normal distribution and non-parametric kernel density estimation. Kolmogorov–Smirnov (KS) test was performed to assess the goodness of fit and found that both normal distribution and kernel density estimation satisfactorily represent the probability density function (PDF) of sea level projections for the future. The uncertainty bounds in the sea level projections under both RCPs were estimated using the bias-corrected and accelerated bootstrap algorithm and found that the lower and upper bounds of sea level projection during December 2050 are 0.529 m and 0.604 m under RCP 4.5 and 0.535 m and 0.700 m under RCP 8.5. Results of the study revealed that uncertainty is comparatively high under RCP 8.5.

Defluorination Techniques: Past, Present and Future Prospective

Abstract

Fluoride ions are commonly found in minerals like mica, fluorapatite, muscovite, topaz, biotite, sellaite, cryolite, muscovite, and fluorspar. However, the presence of fluoride in groundwater has become a concern due to its contamination by wastewater from coal thermal power stations, aluminium smelters, electroplating-based industries, and glass and ceramic manufacturing facilities. While low fluoride consumption has some health benefits, excessive intake can lead to serious health issues such as crippling skeletal fluorosis, Alzheimer’s syndrome, carcinogenic effects, infertility, and thyroid disorders. To address these chronic health impacts, there has been significant research to find out sustainable and highly efficient methods for fluoride removal. This review paper overviews various defluorination techniques, such as precipitation and coagulation, ion exchange, electrodialysis or reverse osmosis (RO), nano-filtration, adsorption, with their various advantages and drawbacks. The present review aims to provide insight knowledge of importance of fluoride, its toxicity issues and their available removal strategies and their limitations. This will help the researchers in developing cost-effective, environmentally friendly, and convenient techniques for defluorination in different matrices. The paper encourages scientists to work towards achieving reliable and eco-friendly defluorination methods for future application.

Graphical Abstract

Geomorphological assessment as basic complement of InSAR analysis for landslide processes understanding

Abstract

Landslide research has benefited greatly from advances in remote sensing techniques. However, the recent increase in available data on land surface movement provided by InSAR techniques can lead to identifying only those areas that were active during data acquisition as hazardous, overlooking other potentially unsafe areas or neglecting landslide-specific geological settings in hazard assessments. Here, we present a case study that serves as a reminder for landslide researchers to carefully consider the geology and geomorphology of study areas where complex active movements are detected using InSAR technology. In an area extensively studied using InSAR and UAV-related techniques, we provide new insights by applying classical approaches. The area is the coastal stretch of La Herradura, and its importance lies in the fact that it has served as an illustrative example in the Product User Manual of the European Ground Motion Service, a platform that provides ground motion data on a European scale. Our approach is to revisit the area and carry out qualitative geological and geomorphological assessments supported by UAV surveys and GIS spatial analysis on a broader scale than previously published investigations. Our classical approach has yielded the following new observations, crucial for risk assessment and land management: active landslides identified by InSAR techniques since 2015 are bodies nested within large mass movements that affect entire slopes. A variety of processes contribute to slope dynamics, such as large slumps, marble rock spreading and block sliding, and surface rock falls and topples. The revised delineation of the landslide bodies reveals an area almost five times larger than previously mapped. These new findings in a well-known area highlight (1) the importance of updating and downscaling previous maps and (2) the ongoing importance of classical fieldwork and desk studies as basic complements to modern InSAR analyses.

Assessment of current and future trends in water resources in the Gambia River Basin in a context of climate change

Abstract

Accurate assessment of water resources at the watershed level is crucial for effective integrated watershed management. While semi-distributed/distributed models require complex structures and large amounts of input data, conceptual models have gained attention as an alternative to watershed modeling. In this paper, the performance of the GR4J conceptual model for runoff simulation in the Gambia watershed at Simenti station is analyzed over the calibration (1981–1990) and validation period (1991–2000 and 2001–2010). The main inputs to conceptual models like GR4J are daily precipitation data and potential evapotranspiration (PET) measured from the same catchment or a nearby location. Calibration of these models is typically performed using the Nash–Sutcliffe daily efficiency with a bias penalty as the objective function. In this case, the GR4J model is calibrated using four optimization parameters. To evaluate the effectiveness of the model's runoff predictions, various statistical measures such as Nash–Sutcliffe efficiency, coefficient of determination, bias, and linear correlation coefficient are calculated. The results obtained in the Gambia watershed at Simenti station indicate satisfactory performance of the GR4J model in terms of forecast accuracy and computational efficiency. The Nash–Sutcliffe (Q) values are 0.623 and 0.711 during the calibration period (1981–1990) and the validation period (1991–2000), respectively. The average annual flow observed during the calibration period is 0.385 mm while it increases with a value of 0.603 mm during the validation period. As for the average flow simulated by the model, it is 0.142 mm during the calibration period (i.e., a delay of 0.142 mm compared to the observed flow), 0.626 mm in the validation period (i.e., an excess of 0.023 mm compared to the observed flow). However, this study is significant because it shows significant changes in all metrics in the watershed sample under different scenarios, especially the SSP245 and SSP585 scenarios over the period 2021–2100. These changes suggest a downward trend in flows, which would pose significant challenges for water management. Therefore, it is clear that sustainable water management would require substantial adaptation measures to cope with these changes.

Urban expansion in Greater Irbid Municipality, Jordan: the spatial patterns and the driving factors

Abstract

Urban expansion within Greater Irbid Municipality (GIM) witnessed an extraordinary rise, expanding approximately ninefold between 1967 and 2020. Recent trends revealed a shift in urban growth towards southern and eastern regions. These dynamics carry critical implications for urban planners and environmental managers, urging a comprehensive understanding of the driving factors behind this expansion to anticipate future challenges. Employing logistic regression (LR) and geographically weighted logistic regression (GWLR) analyses using remote sensing data and GIS, spatially variant coefficients for driving factors emerged, illuminating the evolving landscape of urban development drivers within GIM. Yarmouk University historically promoted urban expansion, but recent proximity to Yarmouk University and JUST University, coupled with higher existing building percentages, inhibited further urbanization. The analysis also revealed that elevation and slope had negligible impacts on urban expansion. These findings underline the evolving dynamics of urban development drivers within the study region. The local perspective depicted significant spatial disparities in coefficients, highlighting variations in magnitude and direction. GWLR emerged as a more robust methodology, effectively capturing regional variations and enhancing model reliability. These findings hold immense value for informing current and future urban planning practices in Greater Irbid Municipality. Proactively addressing identified challenges and understanding the intricate dynamics of urban expansion can assist Irbid in shaping a sustainable and resilient future, avoiding potential pitfalls in its urban development endeavors.

Uncertainty-based analysis of water balance components: a semi-arid groundwater-dependent and data-scarce area, Iran

Abstract

In regions where gauging is lacking or limited, the development of a methodology for water balance assessment is a key challenge. Water balance assessment is a significant task in managing the sustainable use of water resources. This study aims to improve the accuracy of water balance components including actual evapotranspiration and groundwater recharge rate in the Hashtgerd study area, Iran. Groundwater extraction volumes are determined based on two-period data collection; while precipitation, evapotranspiration, and groundwater storage change are calculated from annual datasets. To address the data-scarce problem, as an additional measure of actual evapotranspiration, satellite measurements are used to improve recharge rate accuracy. To understand the impact of satellite data uncertainties on water resources studies, each estimated water balance component is compared to observation data, and the uncertainty of these components is quantified using statistical methods, variability, and standard error. The results show that the Hashtgerd aquifer receives 199 MCM from the main drains which is a major part of the water inflow. The extraction of wells is one of the most significant outflow components of the aquifer (i.e., 284 MCM); while water loss by evaporation is estimated to be 207 MCM of the outflow. The finding shows that satellite-based evapotranspiration can reduce recharge uncertainty which can improve the resolution of groundwater balance. This study supports that the aquifer is under severe environmental pressure. One of those concerns is that groundwater levels decreased by 0.92 m per year between 2000 and 2019.

DGFormer: a physics-guided station level weather forecasting model with dynamic spatial-temporal graph neural network

Abstract

In recent years, there has been an increased interest in understanding and predicting the weather using weather station data with Spatial-Temporal Graph Neural Networks (STGNN). However, it has large prediction errors as a result of the inherent non-linearities and the influence of dynamic spatio-temporal auto-correlation. Using a continuously-varying graph topology chronologically, while embedding domain knowledge to enforce validity, can effectively resolve the issue, but the implementation of such concept constitutes an interdisciplinary challenge for researchers. A Dynamic Graph Former (DGFormer) model is proposed to address this challenge. It combines a topology learner through a deep generative layer with domain knowledge enhancement inserted into the STGNN structure, where the derived physics-guided method allows for an efficient integration with the earth system. For capture of the optimal topology, we merge a node-embedding-based similarity metric learning and the superposition principle as physical assistants into the dynamic graph module. We evaluate our model with a real-world weather dataset on short-term (12 hours) and medium-range (360 hours) prediction tasks. DGFormer achieves outstanding performance with obvious improvements by up to 34.84% at short-term prediction and by up to 23.25% at medium-range prediction compared with the state-of-the-art methods. We also conducted detailed analyses for cities in three regions and visualized the dynamic graph, revealing the characteristics, advantages, and graph visualization of our model.

DGFormer: a physics-guided station level weather forecasting model with dynamic spatial-temporal graph neural network

Abstract

In recent years, there has been an increased interest in understanding and predicting the weather using weather station data with Spatial-Temporal Graph Neural Networks (STGNN). However, it has large prediction errors as a result of the inherent non-linearities and the influence of dynamic spatio-temporal auto-correlation. Using a continuously-varying graph topology chronologically, while embedding domain knowledge to enforce validity, can effectively resolve the issue, but the implementation of such concept constitutes an interdisciplinary challenge for researchers. A Dynamic Graph Former (DGFormer) model is proposed to address this challenge. It combines a topology learner through a deep generative layer with domain knowledge enhancement inserted into the STGNN structure, where the derived physics-guided method allows for an efficient integration with the earth system. For capture of the optimal topology, we merge a node-embedding-based similarity metric learning and the superposition principle as physical assistants into the dynamic graph module. We evaluate our model with a real-world weather dataset on short-term (12 hours) and medium-range (360 hours) prediction tasks. DGFormer achieves outstanding performance with obvious improvements by up to 34.84% at short-term prediction and by up to 23.25% at medium-range prediction compared with the state-of-the-art methods. We also conducted detailed analyses for cities in three regions and visualized the dynamic graph, revealing the characteristics, advantages, and graph visualization of our model.

Uncertainty-based analysis of water balance components: a semi-arid groundwater-dependent and data-scarce area, Iran

Abstract

In regions where gauging is lacking or limited, the development of a methodology for water balance assessment is a key challenge. Water balance assessment is a significant task in managing the sustainable use of water resources. This study aims to improve the accuracy of water balance components including actual evapotranspiration and groundwater recharge rate in the Hashtgerd study area, Iran. Groundwater extraction volumes are determined based on two-period data collection; while precipitation, evapotranspiration, and groundwater storage change are calculated from annual datasets. To address the data-scarce problem, as an additional measure of actual evapotranspiration, satellite measurements are used to improve recharge rate accuracy. To understand the impact of satellite data uncertainties on water resources studies, each estimated water balance component is compared to observation data, and the uncertainty of these components is quantified using statistical methods, variability, and standard error. The results show that the Hashtgerd aquifer receives 199 MCM from the main drains which is a major part of the water inflow. The extraction of wells is one of the most significant outflow components of the aquifer (i.e., 284 MCM); while water loss by evaporation is estimated to be 207 MCM of the outflow. The finding shows that satellite-based evapotranspiration can reduce recharge uncertainty which can improve the resolution of groundwater balance. This study supports that the aquifer is under severe environmental pressure. One of those concerns is that groundwater levels decreased by 0.92 m per year between 2000 and 2019.

Impact of climate change and land cover dynamics on nitrate transport to surface waters

Abstract

The study investigated the impact of climate and land cover change on water quality. The novel contribution of the study was to investigate the individual and combined impacts of climate and land cover change on water quality with high spatial and temporal resolution in a basin in Turkey. The global circulation model MPI-ESM-MR was dynamically downscaled to 10-km resolution under the RCP8.5 emission scenario. The Soil and Water Assessment Tool (SWAT) was used to model stream flow and nitrate loads. The land cover model outputs that were produced by the Land Change Modeler (LCM) were used for these simulation studies. Results revealed that decreasing precipitation intensity driven by climate change could significantly reduce nitrate transport to surface waters. In the 2075–2100 period, nitrate-nitrogen (NO3-N) loads transported to surface water decreased by more than 75%. Furthermore, the transition predominantly from forestry to pastoral farming systems increased loads by about 6%. The study results indicated that fine-resolution land use and climate data lead to better model performance. Environmental managers can also benefit greatly from the LCM-based forecast of land use changes and the SWAT model’s attribution of changes in water quality to land use changes.

Graphical abstract