Regional Climate Change Impact Research Articles

Major distribution shifts are projected for key rangeland grasses under a high-emission scenario in East Africa at the end of the 21st century

Grassland landscapes are important ecosystems in East Africa, providing habitat and grazing grounds for wildlife and livestock and supporting pastoralism, an essential part of the agricultural sector. Since future grassland availability directly affects the future mobility needs of pastoralists and wildlife, we aim to model changes in the distribution of key grassland species under climate change. Here we combine a global and regional climate model with a machine learning-based species distribution model to understand the impact of regional climate change on different key grass species. The application of a dynamical downscaling step allows us to capture the fine-scale effects of the region’s complex climate, its variability and future changes. We show that the co-occurrence of the analysed grass species is reduced in large parts of eastern Africa, and particularly in the Turkana region, under the high-emission RCP8.5 scenario for the last 30 years of the 21st century. Our results suggest that future climate change will alter the natural resource base, with potentially negative impacts on pastoralism and wildlife in East Africa.

Impact of regional climate change on apple yield in Himachal Pradesh, India

Climate change has become a major global as well as regional challenge because of its adverse effects on biodiversity and agriculture. Himachal Pradesh, a state in India, is situated in the western Indian Himalayan region, which predominantly features an agricultural economy, with apple being the major cash crop of the region. This paper presents the trend analysis of climatic parameters to delineate the effect of climate change on the apple yield in the region from 1991 to 2018. The temperature and rainfall data were obtained from the Indian metrological department, while snowfall data and ambient air quality data were collected from the database of NASA. The chill unit was calculated using the UTAH model. The climatic parameters were compared with apple yield to identify the impact of climate change on the crop. Therefore, apple yield reduction risk was calculated. The trend analysis indicated a temperature decrease during the monsoon months and a temperature increase during winters. The same analysis also indicated decreased snowfall by 0.375 kg/m2 and increased rainfall during the spring season by 23.2 mm, 19.6 mm, 10.4 mm (between mid-February and mid-April from 1985 to 2020). The chill units and apple production showed a positive correlation. In summary, our results indicated that climate change has reduced the apple yield by 11.48%.

Climate trend analysis in the ramis catchment, upper wabi shebelle basin, Ethiopia, using the CMIP6 dataset

Climate change, a global concern, has significant implications for rainfall and temperature patterns. A critical knowledge gap exists in understanding these implications for the Ramis catchment of the Upper Wabi-Shebelle Basin. This research projects future rainfall and temperature patterns in the Ramis catchment of the Upper Wabe-Shebelle basin. The projection utilized a multimodel comprising eight distinct general circulation models from coupled model intercomparison phase six (CMIP6) data simulation. These models were selected based on their performance, considering two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). The Quantile Mapping (QM) bias correction technique, implemented in R, was used to enhance the reliability of extracted data. The Mann‒Kendall (MK) trend test method was employed to analyze temperature and precipitation trends for two future periods, 2030–2060 and 2061–2090, annually and seasonally. The results suggest a decrease in rainfall during the spring and Winter seasons for 2030 to 2060 and 2061 to 2090 respectively, potentially leading to water scarcity that could impact crop growth and water supply. A decreasing trend in annual rainfall was observed from 2030 to 2060 under SSP2-4.5 and SSP5-8.5 scenarios. The study also uncovered significant and consistent warming trends in maximum and minimum temperatures across the Ramis catchment under both the SSP2-4.5 and SSP5-8.5 scenarios. Rainfall Anomaly Index (RAI) analysis predicts a minor increase in rainfall in the Ramis catchment from 2030 to 2090 under both scenarios, with a notable RAI decrease expected in 2043–2071, indicating potential drought conditions. These findings offer critical insights for regional climate change impact assessments across the Ramis catchment.

Enzymes and biosurfactants of industrial interest produced by culturable fungi present in sediments of BoeckellaLake, Hope Bay, north-east Antarctic Peninsula.

This study characterized cultivable fungi present in sediments obtained from BoeckellaLake, Hope Bay, in the north-east of the Antarctic Peninsula, and evaluated their production of enzymes and biosurfactants of potential industrial interest. A total of 116 fungal isolates were obtained, which were classified into 16 genera within the phyla Ascomycota, Basidiomycota and Mortierellomycota, in rank. The most abundant genera of filamentous fungi included Pseudogymnoascus, Pseudeurotium and Antarctomyces; for yeasts, Thelebolales and Naganishia taxa were dominant. Overall, the lake sediments exhibited high fungal diversity and moderate richness and dominance. The enzymes esterase, cellulase and protease were the most abundantly produced by these fungi. Ramgea cf. ozimecii, Holtermanniella wattica, Leucosporidium creatinivorum, Leucosporidium sp., Mrakia blollopis, Naganishia sp. and Phenoliferia sp. displayed enzymatic index > 2. Fourteen isolates of filamentous fungi demonstrated an Emulsification Index 24% (EI24%) ≥ 50%; among them, three isolates of A. psychrotrophicus showed an EI24% > 80%. BoeckellaLake itself is in the process of drying out due to the impact of regional climate change, and may be lost completely in approaching decades, therefore hosts a threatened community of cultivable fungi that produce important biomolecules with potential application in biotechnological processes.

Examining current bias and future projection consistency of globally downscaled climate projections commonly used in climate impact studies

The associated uncertainties of future climate projections are one of the biggest obstacles to overcome in studies exploring the potential regional impacts of future climate shifts. In remote and climatically complex regions, the limited number of available downscaled projections may not provide an accurate representation of the underlying uncertainty in future climate or the possible range of potential scenarios. Consequently, global downscaled projections are now some of the most widely used climate datasets in the world. However, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we explore the utility of two such global datasets (CHELSA and WorldClim2) in providing plausible future climate scenarios for regional climate change impact studies. Our analysis was based on three steps: (1) standardizing a baseline period to compare available global downscaled projections with regional observation-based datasets and regional downscaled datasets; (2) bias correcting projections using a single observation-based baseline; and (3) having controlled differences in baselines between datasets, exploring the patterns and magnitude of projected climate shifts from these datasets to determine their plausibility as future climate scenarios, using Hawaiʻi as an example region. Focusing on mean annual temperature and precipitation, we show projected climate shifts from these commonly used global datasets not only may vary significantly from one another but may also fall well outside the range of future scenarios derived from regional downscaling efforts. As species distribution models are commonly created from these datasets, we further illustrate how a substantial portion of variability in future species distribution shifts can arise from the choice of global dataset used. Hence, projected shifts between baseline and future scenarios from these global downscaled projections warrant careful evaluation before use in climate impact studies, something rarely done in the existing literature.

Measuring the impact of regional climate change on heating and cooling demand for the Chilean energy transition

The regional impact of climate change on heating and cooling demand is important to consider when designing optimal long-term energy policies. Several studies have addressed this issue, but either at a very aggregated level or without optimizing the whole energy system. The aims of this paper are to fill this gap in a generic way and to assess the impact of climate change on heating and cooling energy demands for residential and commercial sectors at the regional and nodal levels in the context of Chile’s energy transition. We propose a methodology based on high resolution climate simulations for the Representative Concentration Pathways (RCP) RCP 2.6 and RCP 8.5 scenarios. First, a statistical analysis is performed to estimate the long-term trends of so-called heating and cooling degree-days and their impact on final regional energy demands. Then, demand pathways in the energy transition are assessed using a multi-sectoral energy planning model. Numerical experiments using data from Chile show an overall positive economic impact of climate change (limited to heating and cooling demands) for the energy system, with a significant decrease in heating demand compared to a limited increase in cooling requirements. For the RCP 8.5 scenario, cost reductions reach 2.1% of the total discounted system cost on the 2020-2050 period mainly due to a significant decrease of gas consumption for heating. This research highlights the importance for policymakers to consider climate change in efficient energy policies.

A dataset of monthly and yearly litterfall production in different succession stages of evergreen broad-leaved forests in Tiantong (2008 – 2018)

Litter production is an important part of the matter cycle of forest ecosystems, which plays an important role in maintaining forest soil fertility and promoting ecosystem nutrient cycling. Additionally, it serves as an important indicator for the quantification of ecosystem function such as forest productivity and carbon balance. The data in the dataset are from two observation plots in Zhejiang Tiantong National Forest Ecosystem National Observation and Research Station. The community succession types in the above two pilots belong to the middle-late stage and mature stage of evergreen broad-leaved forest succession, respectively. The projected area of each plot is 50 m × 50 m. There are 27 litter traps of 0.5 m2 in each plot. We collected and counted the data in terms of litter components, i.e. branches, leaves, flowers (fruits), bark and debris). The dataset includes the monthly and annual production of each component and the total amount of litter in the two plots from 2008 to 2018. The data have been checked against the original paper records, as well as the dynamic trend of data in the same period, so as to ensure data quality control and accuracy assessment. This dataset can provide basic information for understanding the changes of ecosystem structure and function during the succession process of evergreen broad-leaved forests. Moreover, it can also provide data support for studying the impact of regional climate change on the forest ecosystem and the assessment of carbon balance in the zone of evergreen broad-leaved forests.

Evolutionary Characteristics of Daytime and Nocturnal Precipitation Heterogeneity in Gansu Province, Northwest China

The diurnal variation in precipitation and its evolution are important foundations for understanding the regional impact of climate change and the parameterization of the model. Based on the daily precipitation data set of 23 national meteorological stations during 1970–2019, the spatial and temporal distribution characteristics of precipitation concentration degree (PCD) and precipitation concentration period (PCP) in Gansu province were evaluated on daytime and nocturnal scales. The results show the following: (1) Annual precipitation ranges from 69.1 ± 24.7 mm to 578.3 ± 96.6 mm, mainly (54.1 ± 2.6%) occurring at night, and the spatial distribution of the nocturnal precipitation rate is positively (r = 0.53, p < 0.01) correlated with annual precipitation; the wetting trend (12.7 mm/10 a, p < 0.01) in the past 50 years is obvious, and is mainly dominated by the frequency of precipitation (r = 0.58, p < 0.001), with both performing better during the day. (2) Most PCD is located between 0.55 and 0.75, showing a basic distribution pattern for daytime greater than nocturnal, higher values, and stronger interannual fluctuations in arid areas; the significant decreasing trend (p < 0.05) of PCD is very clear and highly consistent, especially in the high-altitude area, and the increase in precipitation in the dry season and the improvement in precipitation uniformity in the wet season play a key role. (3) PCP often fluctuates slightly around the 39th–41st pentad, but the general rule that daytime values are smaller than night values and the interannual variability is larger in arid areas also requires special attention; PCP has shown a relatively obvious advance trend in a few regions, but this is because the prominent and complex changes in the monthly precipitation distribution pattern have not been fully reflected. Along with continuous humidification, the decrease in PCD and the advance of PCP are likely to be the priority direction of precipitation evolution in the arid region of Northwest China, especially during the day. These findings provide a new perspective for understanding regional climate change.

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

Abstract. As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

Localized Strong Warming and Humidification Over Winter Japan Tied to Sea Ice Retreat

AbstractEast Asian winter weather has altered dramatically in response to progressing climate change. However, analysis of time‐averaged atmospheric fields might obscure climate change signals that could relate to specific types of events. This study aimed to detect climate change signals for various background atmospheric fields by applying classification to daily surface pressure patterns. We found a pronounced trend of increase in surface air temperature over Japan since the 1980s, stronger than the seasonal mean trend, when dominating cold air advection from the Sea of Okhotsk. This cold air was warmed and humidified by the retreat of sea ice in the Sea of Okhotsk. In contrast, the warming trend was mitigated when warm air advection was dominant. These results highlight the importance of daily background atmospheric conditions in amplifying and localizing the impact of regional climate change.

A regional scale impact and uncertainty assessment of climate change in the Western Ghats in India.

The general circulation models (GCMs) and emission scenarios (RCP 4.5 and 8.5) have proven to be significantly functional in evaluating the impacts of climate change (CC) on hydrology, although their performance and accuracy varies on a regional scale. The objective of the present study is to evaluate the performance of five CMIP5 GCMs (CanESM2, BNU-ESM, CNRM-CM5, MPI-ESM-LR and MPI-ESM-MR) on a regional scale in the West Flowing River Basins-2 (WFRB-2) in India to model the impact of CC and its scenario uncertainty using reliability ensemble average (REA) method. For quantifying the results, the upper, middle and lower regions of WFRB-2 are separately analysed. The MPIMR and MPILR GCM model shows highest reliability factor range (0.3-0.6) in predicting the annual mean and annual maximum rainfall for most of the grids in the region. The GCM-simulated runoff using VIC (variable infiltration capacity) model is evaluated using statistical parameters such as root mean square error (RMSE), percentage bias (Pbias) and standard deviation (Std). The annual mean (maximum) runoff obtained using REA ensemble shows least RMSE, Pbias and Std values, i.e. 21.08%, 9.10mm and 8.9mm (6%, 39.1mm, 39.1mm), respectively for the middle region, which demonstrates higher reliability of GCM outputs in the flood-prone regions of WFRB-2. Furthermore, the future projection of annual maximum rainfall/runoff shows an increase of 50mm/15mm in the near future (2011-2040) for lower and 20mm/6mm for middle regions, which may cause flooding activities in the lower and middle region of WFRB-2.

Impact of regional climate change on the mosquito vector Aedes albopictus in a tropical island environment: La Réunion

The recent expansion of Aedes albopictus across continents in both tropical and temperate regions and the exponential growth of dengue cases over the past 50 years represent a significant risk to human health. Although climate change is not the only factor responsible for the increase and spread of dengue cases worldwide, it might increase the risk of disease transmission at global and regional scale. Here we show that regional and local variations in climate can induce differential impacts on the abundance of Ae. albopictus. We use the instructive example of Réunion Island with its varied climatic and environmental conditions and benefiting from the availability of meteorological, climatic, entomological and epidemiological data. Temperature and precipitation data based on regional climate model simulations (3 km × 3 km) are used as inputs to a mosquito population model for three different climate emission scenarios. Our objective is to study the impact of climate change on the life cycle dynamics of Ae. albopictus in the 2070–2100 time horizon. Our results show the joint influence of temperature and precipitation on Ae. albopictus abundance as a function of elevation and geographical subregion. At low-elevations areas, decreasing precipitation is expected to have a negative impact on environmental carrying capacity and, consequently, on Ae. albopictus abundance. At mid- and high-elevations, decreasing precipitation is expected to be counterbalanced by a significant warming, leading to faster development rates at all life stages, and consequently increasing the abundance of this important dengue vector in 2070–2100.

A comparison of multiple statistically downscaled climate change datasets for the conterminous USA

Climate change projections provided by global climate models (GCM) are generally too coarse for local and regional applications. Local and regional climate change impact studies therefore use downscaled datasets. While there are studies that evaluate downscaling methodologies, there is no study comparing the downscaled datasets that are actually distributed and used in climate change impact studies, and there is no guidance for selecting a published downscaled dataset. We compare five widely used statistically downscaled climate change projection datasets that cover the conterminous USA (CONUS): ClimateNA, LOCA, MACAv2-LIVNEH, MACAv2-METDATA, and NEX-DCP30. All of the datasets are derived from CMIP5 GCMs and are publicly distributed. The five datasets generally have good agreement across CONUS for Representative Concentration Pathways (RCP) 4.5 and 8.5, although the agreement among the datasets vary greatly depending on the GCM, and there are many localized areas of sharp disagreements. Areas of higher dataset disagreement emerge over time, and their importance relative to differences among GCMs is comparable between RCP4.5 and RCP8.5. Dataset disagreement displays distinct regional patterns, with greater disagreement in △Tmax and △Tmin in the interior West and in the North, and disagreement in △P in California and the Southeast. LOCA and ClimateNA are often the outlier dataset, while the seasonal timing of ClimateNA is somewhat shifted from the others. To easily identify regional study areas with high disagreement, we generated maps of dataset disagreement aggregated to states, ecoregions, watersheds, and forests. Climate change assessment studies can use the maps to evaluate and select one or more downscaled datasets for their study area.

Quantitative assessment of the contributions of climate change and human activities on vegetation degradation and restoration in typical ecologically fragile areas of China

Vegetation is a key component of terrestrial ecosystems, and its changes are very sensitive to climate change (CC) and human activities (HA), especially in ecologically fragile areas (EFA). However, the mechanism of relative contribution to vegetation degradation and restoration in EFA under the influence of CC and HA is still unclear. Based on the Carnegie-Ames-Stanford Approach (CASA) model and the Miami model, we estimated three key parameters of vegetation in China’s EFA: actual net primary productivity (ANPP), potential net primary productivity (PNPP), and human activity net primary productivity (HNPP). Using these variables, we quantitatively analyzed the relative contribution of CC and HA to vegetation restoration and degradation from 1982 to 2018 by the residual trend method. The results showed that the area ratio of vegetation restoration in China’s EFA was close to 71.6%, and the total ANPP increased by 174 Tg C, mainly concentrated in the Southwest Karst area and the Loess Plateau, but the Qinghai-Tibet Plateau showing a trend of degradation during 1982 to 2018. The climate background of each region was a key factor that can never be ignored to determine the positive or negative impact of regional CC and HA on vegetation activities. The CC and HA played a positive role in vegetation restoration areas, with a relative contribution of 59.1% and 16.4%, regional warming and humidification caused by the increase of temperature and precipitation was the main factor driving vegetation restoration. In vegetation degradation areas, The HA was the main driving force, especially the relative contribution of the Qinghai-Tibet Plateau, the arid and semi-arid areas and the Loess Plateau, which were as high as 96.8%, 83.3% and 80.2%, respectively. Finally, the annual average temperature (TEMP) and the annual solar radiation (SRAD) were relatively important and sensitive for the CASA model in the input environmental variables. This study provided a way to quantitatively understand the mechanism of climate change and human activities on the dynamic evolution of vegetation in EFA. In the future, the implementation of ecological protection and restoration projects need to fully consider the differences in climate background and strengthen the monitoring of the ecological environment.

Gridded value-added of primary, secondary and tertiary industries in China under Shard Socioeconomic Pathways

Gridded distribution of future economy plays an important role in climate change impact assessment. The trend of the output values of different industries is crucial for a variety of planning and design processes. Under the Shared Socioeconomic Pathways (SSPs) global framework, the multidimensional model and Cobb-Douglas production model with localized population and economic parameters are used to develop the annual provincial population and value-added of primary, secondary and tertiary industries in China from 2020 to 2100. The most recently implemented fertility-promoting and industrial planning policies in China are considered in our projections. We build multiple models to evaluate the impact of different types of land use on the value-added of primary, secondary and tertiary industries and then gridded the projected value-added to a 5′ × 5′ resolution, based on recorded county-level economic statistics and gridded land use. The reliability of estimations is verified against 2011–2019 statistical data and multiple published datasets. The high-resolution economic dataset is expected to contribute greatly to national and regional climate change impact, adaptation, and vulnerability studies.

Climate monitoring as an indicator of the hydrological condition of the Siversky Donets river basin

Formulation of the problem. Today, in changing climate conditions, it is very relevant to study the impact of regional climate change on the regime of hydrological indicators and ecological status of the Siversky Donets river basin within Kharkiv region. Analysis of recent research and publications. Hydrometeorological studies are complex and large-scale. In the late 20th - early 21st centuries, a number of articles studied annual runoff of the rivers of Ukraine under the influence of atmospheric processes. Present-day changes in temperature and humidity of the territory affect the hydrological conditions of the rivers. The aim of the work is to assess the relationship between climatic and hydrological indicators (environmental dynamics) of the Siversky Donets basin against the background of regional climate change. In these conditions, monitoring of climate, hydrological and environmental indicators, allows us to make further management decisions on water resources management. Research methods are presented by statistical and cartographic analysis. The source data are a number of climatological, hydrological and environmental observations within the state network of the Ukrainian Hydrometeorological Center of the SES of Ukraine. Problems of further research. Modern changes in climatic conditions in Ukraine are characterized by locality and rapidity. Considering the volume of water use from the river Siversky Donets, the question arises about the water supply of the region, optimization of its use and further rational management. Presentation of the main research material. The dates of the ice cover have changed in recent years, and often ice phenomena may not occur at all. We can see significant warming in winter on the example of January air temperatures. Summer temperatures are growing the fastest (by 0.37°C every 10 years), autumn temperatures are in the second place in terms of growth rate. which means that stable ice cover on most rivers of the Donets basin has been absent in recent decades, which clearly indicates a warming trend. Practical value. Based on the main provisions of the national environmental policy of Ukraine on the use of water resources the study of changes in hydrological regime of rivers is of practical importance for sustainable management. Calculation of the IWP has revealed that most rivers belong to the third and fourth categories - "moderately polluted" and "polluted", but there are also absolutely catastrophic cases. Research results. Heavy economic burden on the waterway will increase its over-regulation. In future, comprehensive assessment of climate change impact on the hydrological conditions of the rivers will determine the degree of change in the ecological state of the waterways, their rational use and protect.

Bias Correction of Climate Model Outputs Using IMD Srinagar Data for Climate Change Impact Assessment

Regional climate models (RCMs) give more credible findings for a regional climate change impact assessment, but they still have a bias that must be rectified. Two correction functions using two methods, the modified difference approach and linear scaling method, were utilized for local bias correction of Tmax, Tmin, and precipitation data at monthly scales and validated to minimize the bias between modelled (HAD GEM2-ES-GCM) and observed climate data at IMD Srinagar Station, J&K. Linear scaling technique at monthly time scale for Tmax, Tmin, and precipitation was superior to modified difference approach for bias correction of modelled data to close it to observed data.

Bias‐adjustment of high‐resolution temperature CORDEX data over the Carpathian region: Expected changes including the number of summer and frost days

AbstractClimate model simulations' outputs are prone to biases compared to observations; furthermore, climate projections can be very different in modelling future temperature characteristics. One possible solution for reducing uncertainty and eliminating possible systematic errors within climate projections is the bias‐adjustment of the raw climate model data. We used the quantile mapping method for bias‐adjustment of a mini ensemble consisting of 8‐member high‐resolution (0.11°) regional climate model simulations provided by the CORDEX community. As the method requires a reliable observational dataset serving as reference data, we used the quality controlled and homogenized observational dataset: CARPATCLIM. Quantile mapping bias‐adjustment technique was applied on the following variables: daily mean, minimum and maximum temperature. We analysed changes in mean temperature characteristics and of climate indices for future periods of 2021–2050 and 2070–2099 with respect to the reference period 1976–2005 for the Carpathian Region. The selected climate indices are based on minimum (frost days, FD) and maximum daily temperature data (summer days, SU). Our bias‐adjusted RCM results suggest a similar degree of mean temperature change as the raw RCMs' data. Bias‐adjusted and raw RCM data project a remarkable annual temperature increase on average under the RCP8.5 scenario (1.4°C and 3.9°C by 2021–2050 and 2070–2099, respectively). The highest temperature increase is likely to occur in summer: it is 4.3°C on average by the end of the 21st century. More pronounced differences were found for the projected changes of the number of summer and frost days based on the bias‐adjusted and the raw RCM data. Our results draw attention to the fact that bias‐adjusted RCM data are crucial for the provision of regional climate change impact and adaptation studies for the Carpathian Region.

Impact of Regional Climate Change on the Development of Lithium Resources in Zabuye Salt Lake, Tibet

Climate change has important implication for the ecological environment and human social activities. The Qinghai-Tibet Plateau is a sensitive area for climate change, and the lakes on the Qinghai-Tibet Plateau are known as the “guardians” of climate change. However, there are few studies on the impact of climate variations on the exploitation of lithium resources in salt lakes. In this work, the collected data from Zabuye Salt Lake Observation Station and the experimental data of brine at different temperatures were employed as the research objects to investigate the relationship between regional climate change and lithium resources in salt lakes. The results indicated that Zabuye Salt Lake had abundant solar energy resources from 1991 to 2020 with the annual average sunshine duration of 3,122.4 h, the average annual evaporation of 2,579.07 mm/yr, and the annual average precipitation of 168.65 mm/yr. The average annual evaporation is around 15.3 times the average annual precipitation, owing to the short rainy season and the low rainfall. The phase diagram displayed that two lithium carbonate precipitation peaks clearly appeared at 25°C, and no obvious precipitation peak appeared at 15°C, indicating that a high-grade lithium carbonate mixed salt was prone to be formed at a higher temperature while lithium ions was more inclined to enrich in brine at a lower temperature. Therefore, Zabuye Salt Lake with the characteristics of low temperature, large temperature difference, less precipitation, strong radiation and large evaporation could be applied as unique lithium resources in the salt gradient solar pond, which would play a positive role in the development of salt lake resources.

Groundwater recharge over the past 100 years: Regional spatiotemporal assessment and climate change impact over the Saguenay‐Lac‐Saint‐Jean region, Canada

AbstractProper knowledge of potential groundwater recharge (PGR) and its spatiotemporal distribution are essential for sustainable groundwater management, especially within the context of climate change. Here, a robust GIS‐based water budget framework was developed to estimate PGR at a regional scale and map its spatial distribution. This framework is demonstrated over the Saguenay‐Lac‐Saint‐Jean region (13 200 km2) of Quebec (Canada). The PGR mapping process was based on a model incorporating water budget components. The vertical inflows (VI) include water amounts from rainfall and snowmelt, whereby the latter was assessed using HYDROTEL model. VI were combined with the maximum and minimum temperatures to estimate actual evapotranspiration (AET), while the surface runoff (RuS) was assessed using the curve number method. Field observations of annual variation in temperatures and the water budget components, over a period of 100 years (1910–2009), were used to provide a comprehensive overview of the effects of climate change on PGR. The last 10 years of the observation period (i.e., 2000–2009) indicate that 6% of the study area has PGR rates of 35%–50%. PGR rates of 20%–35% occur in 58% of the study area, while 36% have PGR of 5%–20%. The trend analysis of temperature time series reveals an average of 1.1 ± 0.6°C increase over 100 years. Also, an increase in the water budget components is observed. Despite the increasing trends of RuS and AET, PGR still showed an increasing trend with an average increase of 0.7 ± 0.4 mm/year over the past 100 years. This observation indicates that the increase in VI was enough to compensate for the increases in AET and RuS. This finding of an increasing PGR in the study area provides useful information for future studies focusing on predicting long‐term PGR evolution and for the development of efficient long‐term groundwater management strategies.