Impacts of climate and land use change on groundwater recharge under shared socioeconomic pathways: A case of Siem Reap, Cambodia

The rapid pace of urbanization blended with climate change has significantly altered surface and groundwater flows. In the context of tourism-driven economic potential areas, these drivers have greater effects, including threatening groundwater availability. This study assessed the combined impacts of climate and land use changes on the groundwater recharge (GWR) in Siem Reap, Cambodia utilizing Phase Six of the Coupled Model Intercomparison Project (CMIP6) global climate models (GCMs), DynaCLUE land-use model, and Soil Water Assessment Tool (SWAT). Three climate models CanESM5, EC_Earth3, and MIROC6, out of seven, best captured the observed data after performance evaluation through the entropy method, were bias-corrected linearly for two shared socioeconomic pathways (SSPs) - SSP2-4.5 and SSP5-8.5. The results indicate a general increase in precipitation under both SSPs, while the average annual maximum temperature is likely to increase by 0.024 °C/year and 0.049 °C/year under SSP2-4.5 and SSP5-8.5, respectively. A similar trend but relatively higher increase is expected for the minimum temperature. Furthermore, the historical land use change showed the expansion of urban settlement by 373% between 2004 and 2019 at the expense of forest and shrubland. Future land use projections from the DynaCLUE model show that the urban settlements in the study area are likely to expand, from their 2019 condition, by 55% in 2030, 209% in 2060, and 369% in 2090 under SSP2 and at double of these rates under SSP5 scenario. The GWR is expected to rise by 39-53% during the wet season and decrease by 13-29% during the dry season under both scenarios. Meanwhile, under constant land use, the GWR is likely to increase more compared to other scenarios, highlighting the importance of land use planning to policymakers and planners. Additionally, the study shall also be important to practitioners and researchers in understanding, planning, and evaluating the performance of multiple climate models in groundwater assessment.

Impact of dissolved organic matter and environmental factors on methylmercury concentrations across aquatic ecosystems inferred from a global dataset

Mercury (Hg) input into ecosystems is estimated to have increased by twofold to fivefold since the industrial revolution. In aquatic ecosystems, methylmercury (MeHg) receives the most attentions of all the Hg species due to its neurotoxicity and strong bioaccumulation capacity in food chain. Dissolved organic matter (DOM) is crucial in impacting aquatic Hg transformation. However, only few spatially constrained studies have attempted to quantify the relative importance of DOM and other factors (e.g., Hg availability, temperature, pH, and land-use type) on MeHg concentration. In this study, we collected data of 585 water samples at 373 sites globally, including lakes, rivers, estuaries, and wetlands, and characterized the global pattern of MeHg distribution and environmental drivers of aquatic MeHg concentration. Our results showed that MeHg concentrations ranged from detection limits to 11 (geometric mean 0.11 and average 0.29) ng/L, and the highest MeHg concentration and Hg methylation potential were observed in wetlands. A positive relationship was observed between MeHg fraction in the total mercury (THg) and DOM for all the aquatic ecosystems. Using the structural equation modeling, we found that Hg availability was a dominant factor in impacting water MeHg concentration followed by DOM. According to 129 samples of specific DOM source information, we found that the percentage of THg as MeHg (%MeHg) in water dominated by the autochthonous DOM was higher than that dominated by the allochthonous DOM. Our results could advance understanding of aquatic Hg cycling and their environmental drivers, which are fundamental for predicting and mitigating MeHg productions and its potential health risks for humans.

Evaluation of global historical land use scenarios based on regional datasets on the Qinghai-Tibet Area

Global historical land use scenarios are widely used to model human-induced climate change from the regional to global scales. It is necessary to conduct regional scale assessments of these global scenarios, identifying their uncertainties and pointing out directions for improvement. Based on the regional reconstruction Li-dataset, remotely sensed dataset, and grazing intensity dataset, the uncertainties of land use area and geographical distribution in HYDE3.1, HYDE3.2, and SAGE (a global land dataset from the Center for Sustainability and the Global Environment) scenarios for the Qinghai-Tibet Area (QTA) are evaluated. The comparisons show that the cropland areas on the QTA in HYDE3.2 for 1900-2000 are close to those of the Li-dataset, whereas HYDE3.1 underestimated and SAGE overestimated the cropland areas significantly. Spatially, HYDE3.1, HYDE3.2, and SAGE have large uncertainties, which cannot reflect the distribution of cropland on the QTA and its changes for 1900-2000 well, and too much cropland is allocated to southeastern Tibet. HYDE3.1 and HYDE3.2 overestimated the pasture area and its distribution on the QTA significantly. The distribution of pasture in SAGE showed overall an agreement with the spatial pattern for grazing intensity, but changes in grazing intensity for 2000-2010 was not reflected in SAGE. The FAO pasture definition and estimates and the method of using population as a proxy for pasture area are not appropriate for the QTA. Methodology which uses the pasture inventory data to calibrate satellite-based grassland maps to obtain the current pasture maps may also not be appropriate because of the lacking differentiation between natural and anthropogenic grasslands in remotely sensed data. More regional level land use estimates with concise definitions, define the land use more clearly, and stratification reconstruction based on differences in agro-climatic conditions and resource endowments may be used to improve global maps.

Spatial assessment of land degradation through key ecosystem services: The role of globally available data

Land degradation is a serious issue especially in dry and developing countries leading to ecosystem services (ESS) degradation due to soil functions' depletion. Reliably mapping land degradation spatial distribution is therefore important for policy decisions. The main objectives of this paper were to infer land degradation through ESS assessment and compare the modelling results obtained using different sets of data. We modelled important physical processes (sediment erosion and nutrient export) and the equivalent ecosystem services (sediment and nutrient retention) to infer land degradation in an area in the Ethiopian Great Rift Valley. To model soil erosion/retention capability, and nitrogen export/retention capability, two datasets were used: a 'global' dataset derived from existing global-coverage data and a hybrid dataset where global data were integrated with data from local surveys. The results showed that ESS assessments can be used to infer land degradation and identify priority areas for interventions. The comparison between the modelling results of the two different input datasets showed that caution is necessary if only global-coverage data are used at a local scale. In remote and data-poor areas, an approach that integrates global data with targeted local sampling campaigns might be a good compromise to use ecosystem services in decision-making.