Tag: Climate Change Downscaling Techniques

Abstract

The present study has shown the changes in climatic variables and land use/land cover to observe the long-term changes in the hydrological characteristics of the Kulsi River. Mann–Kendall test, Expert Team on Climate Change Detection and Indices Index, t tests, and Analysis of variation were used to analyze the change and variation. The Mann–Kendall test shows a significant declining trend of rainfall at Chamaria (− 26.76 mm/year) annually, the seasonally significant declining trend was noticed in the winter season at the rate of (− 1.47 mm/year) at Ukium, whereas Chamaria showed a declining trend of rainfall in the post-monsoon season at the rate of (− 5.62 mm/year). Among all stations, maximum temperature showed a significant increasing trend at the rate of (0.03 °C/year) at Chaygaon, while minimum temperature showed a significant increasing trend at the rate of (0.02 °C/year) at Ukium and Chamaria, respectively. Consecutive dry days have shown significant positive trends at the rate of (0.85 days/year), (0.67 days/year) and (1.35 days/year) at Chaygaon, Ukium, and Chamaria. The areas under forest cover and water body have decreased, whereas cropland and transition areas have increased. In the case of the hydrological variables, only the water level has shown significant annual variation (p value is 0.002). However, both water level and discharge show seasonal variation significantly. The water level has shown a significant negative relation with maximum temperature (R and p values are − 0.78 and 0.00 respectively) and a significant positive relation with forest cover (R and p values are 0.6 and 0.01 respectively). On the other hand, water discharge has a significant positive relation with annual rainfall (R and p values are 0.5 and 0.03, respectively). The findings of the research show that increasing maximum temperature and decreasing forest cover are responsible for the decline of water level in the Kulsi River. Whereas, a decrease in rainfall leads to a decrease in discharge and vice versa. The findings of the present research will be helpful to the policymakers and government in preparing a suitable sustainable river management plan to address the declining water resources to mitigate water scarcity issues.

Abstract

Flood is one of the most frightening dangers in the world, which can cause a lot of human and financial losses. In this study, an attempt has been made to create a flood risk map with higher accuracy by using the combination of SWAT, SDSM, AHP, and TOPSIS models. The flood risk map helps to identify areas that have flood potential. Managers and officials can control and reduce human and financial losses caused by floods by using such maps and adopting correct policies. In this study, using the SWAT and SDSM models, the future runoff of the Kashkan basin of Lorestan Province in Iran was simulated for the period from 2049 to 2073. Simulated runoff with different return periods of 2, 5, 10, 25, 50, and 100 years was investigated. According to the obtained results, RCP2.6 was introduced as the most dangerous scenario of this watershed with a runoff forecast of 7715 cubic meters per second. With the help of the obtained flood risk map, sub-basins 22, 24, 28, and 32 representing Khorram Abad and Poldakhter cities were introduced as flood-prone areas of the study area. The simulation of the precipitation, maximum and minimum temperature of the studied basin in the period from 2006 to 2100 showed that the maximum and minimum temperatures can get warmer by 1.3–3  C, and 1 to 2  C can get colder. On the other hand, the rainfall of the entire basin will be able to decrease between 54 and 120 mm. The methods used in this study can also be used to detect flood-prone areas for other parts of the world that have been exposed to sudden floods due to climate change.

Graphical abstract

Abstract

In the context of river basins, the threat of climate change has been extensively studied. However, many of these studies centred on hazard analysis while neglecting the need for comprehensive risk assessments that account for exposure and vulnerability. Hazard analysis alone is not adequate for making adaptive decisions. Thus, to effectively manage flood risk, it is essential to understand the elements that contribute to vulnerability and exposure in addition to hazard analysis. This study aims to assess flood risk (in space and time until the year 2100) for the agricultural system, in the White Volta Basin in northern Ghana. Employing the impact chain methodology, a mix of quantitative and qualitative data and techniques were used to assess hazard, exposure, and vulnerability. Multi-model climate change data (RCP 8.5) from CORDEX and observation data from the Ghana Meteorological Agency were used for hazard analysis. Data on exposure, vulnerability, and adaptation were collected through structured interviews. Results indicate that flood hazard will increase by 79.1% with high spatial variability of wet periods but the flood risk of the catchment will increase by 19.3% by the end of the twenty-first century. The highest flood risk is found in the Upper East region, followed by North East, Northern, Savannah, and Upper West for all four analysed periods. Adaptive capacity, sensitivity, and exposure factors are driven by poverty, ineffective institutional governance, and a lack of livelihood alternatives. We conclude that the region is highly susceptible and vulnerable to floods, and that shifting from isolated hazard analysis to a comprehensive assessment that considers exposure and vulnerability reveals the underlying root causes of the risk. Also, the impact chain is useful in generating insight into flood risk for policymakers and researchers. We recommend the need to enhance local capacity and foster social transformation in the region.

Abstract

Precipitation is a key component in hydrologic processes. It plays an important role in hydrological modeling and water resource management. However, many regions suffer from limited and data scarcity due to the lack of ground-based rain gauge networks. The main objective of this study is to evaluate other source of rainfall data such as remote sensing data (three different satellite-based precipitation products (CHIRPS, PERSIANN, and GPM) and a reanalysis (ERA5) against ground-based data, which could provide complementary rainfall information in semiarid catchment of Tunisia (Haffouz catchment), for the period between September 2000 and August 2018. These remotely sensed-data are compared for the first time with observations in a semiarid catchment in Tunisia.

Twelve rain gauges and two different interpolation methods (inverse distance weight and ordinary kriging) were used to compute a set of interpolated precipitation reference fields. The evaluation was performed at daily, monthly, and yearly time scales and at different spatial scales, using several statistical metrics. The results showed that the two interpolation methods give similar precipitation estimates at the catchment scale. According to the different statistical metrics, CHIRPS showed the most satisfactory results followed by PERSIANN which performed well in terms of correlation but overestimated precipitations spatially over the catchment. GPM underestimates the precipitation considerably, but it gives a satisfactory performance temporally. ERA5 shows a very good performance at daily, monthly, and yearly timescale, but it is unable to represent the spatial variability distribution of precipitation for this catchment. This study concluded that satellite-based precipitation products or reanalysis data can be useful in semiarid regions and data-scarce catchments, and it may provide less costly alternatives for data-poor regions.

Abstract

Precipitation is a key component in hydrologic processes. It plays an important role in hydrological modeling and water resource management. However, many regions suffer from limited and data scarcity due to the lack of ground-based rain gauge networks. The main objective of this study is to evaluate other source of rainfall data such as remote sensing data (three different satellite-based precipitation products (CHIRPS, PERSIANN, and GPM) and a reanalysis (ERA5) against ground-based data, which could provide complementary rainfall information in semiarid catchment of Tunisia (Haffouz catchment), for the period between September 2000 and August 2018. These remotely sensed-data are compared for the first time with observations in a semiarid catchment in Tunisia.

Twelve rain gauges and two different interpolation methods (inverse distance weight and ordinary kriging) were used to compute a set of interpolated precipitation reference fields. The evaluation was performed at daily, monthly, and yearly time scales and at different spatial scales, using several statistical metrics. The results showed that the two interpolation methods give similar precipitation estimates at the catchment scale. According to the different statistical metrics, CHIRPS showed the most satisfactory results followed by PERSIANN which performed well in terms of correlation but overestimated precipitations spatially over the catchment. GPM underestimates the precipitation considerably, but it gives a satisfactory performance temporally. ERA5 shows a very good performance at daily, monthly, and yearly timescale, but it is unable to represent the spatial variability distribution of precipitation for this catchment. This study concluded that satellite-based precipitation products or reanalysis data can be useful in semiarid regions and data-scarce catchments, and it may provide less costly alternatives for data-poor regions.

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.

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

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.

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.

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.