Climate Change Impacts on Groundwater Recharge in Cold and Humid Climates: Controlling Processes and Thresholds

Long-term changes in precipitation and temperature indirectly impact aquifers through groundwater recharge (GWR). Although estimates of future GWR are needed for water resource management, they are uncertain in cold and humid climates due to the wide range in possible future climatic conditions. This work aims to (1) simulate the impacts of climate change on regional GWR for a cold and humid climate and (2) identify precipitation and temperature changes leading to significant long-term changes in GWR. Spatially distributed GWR is simulated in a case study for the southern Province of Quebec (Canada, 36,000 km2) using a water budget model. Climate scenarios from global climate models indicate warming temperatures and wetter conditions (RCP4.5 and RCP8.5; 1951–2100). The results show that annual precipitation increases of >+150 mm/yr or winter precipitation increases of >+25 mm will lead to significantly higher GWR. GWR is expected to decrease if the precipitation changes are lower than these thresholds. Significant GWR changes are produced only when the temperature change exceeds +2 °C. Temperature changes of >+4.5 °C limit the GWR increase to +30 mm/yr. This work provides useful insights into the regional assessment of future GWR in cold and humid climates, thus helping in planning decisions as climate change unfolds. The results are expected to be comparable to those in other regions with similar climates in post-glacial geological environments and future climate change conditions.

A Meta-Analysis on Degraded Alpine Grassland Mediated by Climate Factors: Enlightenment for Ecological Restoration

Alpine grassland is the main ecosystem on the Qinghai-Tibet Plateau (QTP). Degradation and restoration of alpine grassland are related to ecosystem function and production, livelihood, and wellbeing of local people. Although a large number of studies research degraded alpine grassland, there are debates about degradation patterns of alpine grassland in different areas and widely applicable ecological restoration schemes due to the huge area of the QTP. In this study, we used the meta-analysis method to synthesize 80 individual published studies which were conducted to examine aboveground and underground characteristics in non-degradation (ND), light degradation (LD), moderate degradation (MD), heavy degradation (HD), and extreme degradation (ED) of alpine grassland on the QTP. Results showed that aboveground biomass (AGB), belowground biomass (BGB), Shannon-Wiener index (H′), soil moisture (SM), soil organic carbon (SOC), soil total nitrogen (TN), and available nitrogen (AN) gradually decreased along the degradation gradient, whereas soil bulk density (BD) and soil pH gradually increased. In spite of a tendency to soil desertification, losses of other soil nutrients and reduction of enzymes, there was no linear relationship between the variations with degradation gradient. Moreover, the decreasing extent of TN was smaller in areas with higher precipitation and temperature, and the decreasing extent of AGB, SOC, and TN was larger in areas with a higher extent of corresponding variables in the stage of ND during alpine grassland degradation. These findings suggest that in areas with higher precipitation and temperature, reseeding and sward cleavage can be used for restoration on degraded alpine grassland. Fencing and fertilization can be used for alpine grassland restoration in areas with lower precipitation and temperature. Microbial enzymes should not be used to restore degraded alpine grassland on a large scale on the QTP without detailed investigation and analysis. Future studies should pay more attention to the effects of climate factors on degradation processes and specific ecological restoration strategies in different regions of the QTP.

Climate Projections Over Different Climatic Regions of Afghanistan for Shared Socioeconomic Scenarios

The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2-4.5 and 5-8.5) and two future time horizons, early (2020-2059) and late (2060-2099). The Compromise Programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975-2014). Three models, namely ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2-0, showed the highest skill in simulating all three variables, and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5-2.5oC, 2.7-4.3 oC, and 4.5-5.3 oC and minimum temperature by 1.3-1.8 oC, 2.2-3.5 oC, and 4.6-5.2 oC for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively in the later period. Meanwhile, the changes in precipitation in the range of -15-18%, -36-47% and -40-68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.

Climate Change Outcomes on the Environmental Ecotoxicology

Climate change is a daunting problem and has only recently attracted attention. This chapter presents a review on the implications of climate change on the regulation, and modelling of toxic pollutants. Also, it identifies relationships between climate fluctuations and changes in some polluants distribution (heavy metals, hydrocarbons, and pesticides). Moreover, the influence of climate change on polluant environmental behavior is explored by studying polluants response to inter-annual climate fluctuations such as precipitation and temperature. Therefore, it will be important to monitor strategies taking into account climate change and new regulatory plans should be devised in toxics polluant management.

Waterborne Diseases Arising From Climate Change

As a result of increased frequency and intensity of heat waves, increased floods and droughts, change in climate will affect biological, physical, and chemical components of water through different paths thus enhancing the risk of waterborne diseases. Identifying the role of weather in waterborne infection is a priority public health research issue as climate change is predicted to increase the frequency of extreme precipitation and temperature events. This chapter provides evidence that precipitation and temperature can affect directly or indirectly water quality and consequently affect the health human. This chapter also highlights the complex relationship between precipitation or temperature and transmission of waterborne disease such as diarrheal disease, gastroenteritis, cryptosporidiosis, giardiasis, and cholera.

Spatial analysis and model construction of NDVI Based on meteorological data

NDVI (Normalized Vegetation Index) is an important characteristic index to study regional vegetation change, which is greatly influenced by meteorological data. Based on the analysis of the trend change and correlation between NDVI and PWV (Precipitable Water Vapor), precipitation and temperature in four geographical regions of China, this paper constructs a model between NDVI and PWV, precipitation and temperature in each geographical region according to multiple regression, and predicts NDVI through meteorological data. The results show that:(1) NDVI and meteorological factors have the same changing trend, and the maximum value appears in every region from June to September, and the value of NDVI in southern region is relatively large. (2) The correlation between rainfall and NDVI is the highest in Qinghai-Tibet region, the correlation between temperature, PWV and NDVI is the highest in northern region, the correlation between NDVI and rainfall, temperature and PWV is the lowest in southern region. (3)According to the meteorological data ,NDVI prediction can be achieved better, and the prediction effect in southern region is the best and the model accuracy is the highest. (4) NDVI is negatively related to El Niño event, positively related to La Nina event, and the stronger El Niño and La Nina events are, the higher the correlation is.