Future Climate Trends Research Articles

Climatic Influence on the Carotenoids Concentration in a Mediterranean Coastal Lagoon Through Remote Sensing

The Albufera of Valencia, a Mediterranean coastal lagoon, has experienced a shift to hypertrophic conditions over the past 40 years due to agricultural and urban-industrial pollution. From August 2023 to early 2024, the water of the lagoon turned reddish-brown. This change has been observed in the past, but never with this intensity or duration, which typically occurs during periods of drought. In this study, carotenoid concentrations were analyzed in relation to precipitation and temperature using field and remote sensing data from February 2016 to December 2023. In November 2023, samples showed unusually high concentrations of carotenoids. The study confirmed the effectiveness of a new algorithm for estimating carotenoids using Sentinel-2 imagery to complement chlorophyll-a data. Results showed that temperature and precipitation significantly influenced carotenoid/chlorophyll-a ratio, highlighting a climatic control of phytoplankton community structure. These results highlight the importance of long-term monitoring and conservation efforts to address climate change and human impacts. This research is a first step in using optical properties of lakes as an indicator of phytoplankton dynamics under environmental stress and warns of the potential for increased occurrence or persistence of such phenomena with future climate trends.

Temperature and precipitation projection in the lower Mahanadi Basin through machine learning methods

This study examined climate change dynamics in the lower Mahanadi River basin by integrating observed and climate model data. Historical precipitation and temperature data (1979–2020) from the India Meteorological Department (IMD) and monthly climate model data from the CORDEX-SMHI-MIROC model via the Earth System Grid Federation (ESGF) are utilized. Four machine learning models (Fbprophet, Holt-Winters, LSTM RNN, and SARIMAX) are applied to forecast precipitation, Tmax, and Tmin, and are compared across different representative concentration pathway (RCP 2.6, 4.5, and 8.5) scenarios. Diverse trajectories emerge, highlighting potential shifts in precipitation and temperature dynamics over near, mid, and far-term intervals. Fbprophet and SARIMAX are identified as superior models through performance evaluation metrics (R2, RMSE, r, P-bias, and NSE). Spatial analysis using ArcGIS and IDW interpolation reveals spatial variations in climate projections, aiding in visualizing future climate trends within the Mahanadi Basin. This study acknowledges limitations such as historical data uncertainties, socio-economic indicators, and unpredictable RCP trajectories, introducing a novel method to integrate machine learning with climate model data for assessing reliability. It also explores anticipated shifts in monthly precipitation and temperature patterns, providing insights into future climate variations.

Assessing the influence of climate change on multiple climate indices in Nepal using CMIP6 global climate models

Global climate models (GCMs) serve as essential tools for projecting future climate trends, but their coarse resolution limits localized impact assessments in sectors like hydrology, agriculture, and biodiversity. Observation data with a spatial resolution of a few kilometers are crucial for downscaling and bias-correcting GCMs at finer resolutions. However, Nepal's extreme topography and organizational challenges have led to uneven distribution of meteorological stations and inconsistent data quality. Moreover, CMIP6-based climate extremes projections for the entire country are currently unavailable. To tackle these challenges, we developed a comprehensive national database for Nepal, offering high-resolution historical and projected precipitation and temperature data analyzed through 25 climate extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Initially, observation grid data were prepared at a daily timescale with a spatial resolution of 0.05° × 0.05° for baseline period (1981–2010) using the Asian Precipitation High-Resolved Observational Data Integration Toward Evaluation (APHRODITE), the fifth generation of the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and available good quality observed climate data. This data was then utilized to downscale and bias-correct 18 CMIP6 GCMs for 2015–2100 under four SSPs (1–2.6, 2–4.5, 3–7.0, 5–8.5). Quantile mapping was employed for the bias correction of the CMIP6 GCMs. The performance of the multimodal ensemble (MME) indicated better Nash-Sutcliffe Efficiency (NSE), root mean square error ratio (RSR), and Percent Bias (PBIAS) of climate extreme indices for the historical period. A comparative analysis was conducted across Nepal's major geographic regions to account for spatial variability in regional climate systems. The finer-resolution dataset can be crucial to deepen our understanding of climate impacts, and climate change, and eventually informing the policy-making in Nepal. Moreover, the methodology can be effectively replicated in data-scarce developing nations to promote climate research and adaptation efforts.

Modeling Spatio-Temporal Rainfall Distribution in Beni–Irumu, Democratic Republic of Congo: Insights from CHIRPS and CMIP6 under the SSP5-8.5 Scenario

In light of the lack of ground-based observations, this study utilizes reanalysis data from the CHIRPS database and CMIP6 models under the SSP5-8.5 scenario to predict future rainfall in the Beni–Irumu region of eastern DR Congo. The use of reanalysis data offers a viable method for understanding historical and future climate trends in regions with limited ground data. Using a spatial resolution of 0.05°, selected general circulation models (GCMs) were downscaled to CHIRPS data. Analysis of historical rainfall data over 32 years reveals spatial disparities, with high-altitude regions like Mount Stanley experiencing higher annual mean rainfall (1767.87 ± 174.41 mm) compared to lower areas like Kasenyi (863.65 ± 81.85 mm), in line with orographic effects. Future projections under the SSP5-8.5 scenario indicate significant decreases in rainfall for areas such as Oicha (−565.55 mm) in the near term, while regions like Kasindi/Yihunga exhibit moderate decrease (−58.5 mm). In the mid-term, some areas show signs of recovery, with Bulongo experiencing a minor decrease (−21.67 mm), and Kasindi/Yihunga (+152.95 mm) and Kyavinyonge (+71.11 mm) showing increases. Long-term projections suggest overall improvements, with most areas experiencing positive rainfall differences; however, persistent challenges remain in Oicha (−313.82 mm). These findings highlight the dynamic impacts of climate change on rainfall distribution in the Beni–Irumu region, underscoring the need for targeted interventions to address the varied impacts, especially in vulnerable regions like Oicha.

The historical ecological background of West Nile virus in Portugal indicates One Health opportunities

West Nile (WNV) is a zoonotic arbovirus with an expanding geographical range and epidemic activity in Europe. Not having yet experienced a human-associated epidemic, Portugal remains an outlier in the Mediterranean basin. In this study, we apply ecological niche modelling informed by WNV historical evidence and a multitude of environmental variables from across Portugal. We identify that ecological backgrounds compatible with WNV historical circulation are mostly restricted to the south, characterized by a warmer and drier climate, high avian diversity, specific avian species and land types. We estimate WNV ecological suitability across the country, identifying overlaps with the distributions of the three relevant hosts (humans, birds, equines) for public and animal health. From this, we propose a category-based spatial framework providing first of a kind valuable insights for WNV surveillance in Portugal under the One Health nexus. We forecast that near future climate trends alone will contribute to pushing adequate WNV ecological suitability northwards, towards regions with higher human density. This unique perspective on the past, present and future ecology of WNV addresses existing national knowledge gaps, enhances our understanding of the evolving emergence of WNV, and offers opportunities to prepare and respond to the first human-associated epidemic in Portugal.

Human movement and environmental barriers shape the emergence of dengue

Understanding how emerging infectious diseases spread within and between countries is essential to contain future pandemics. Spread to new areas requires connectivity between one or more sources and a suitable local environment, but how these two factors interact at different stages of disease emergence remains largely unknown. Further, no analytical framework exists to examine their roles. Here we develop a dynamic modelling approach for infectious diseases that explicitly models both connectivity via human movement and environmental suitability interactions. We apply it to better understand recently observed (1995-2019) patterns as well as predict past unobserved (1983-2000) and future (2020-2039) spread of dengue in Mexico and Brazil. We find that these models can accurately reconstruct long-term spread pathways, determine historical origins, and identify specific routes of invasion. We find early dengue invasion is more heavily influenced by environmental factors, resulting in patchy non-contiguous spread, while short and long-distance connectivity becomes more important in later stages. Our results have immediate practical applications for forecasting and containing the spread of dengue and emergence of new serotypes. Given current and future trends in human mobility, climate, and zoonotic spillover, understanding the interplay between connectivity and environmental suitability will be increasingly necessary to contain emerging and re-emerging pathogens.

A novel data-driven dynamical model for predicting future climate trends

A novel data-driven dynamical model for predicting future climate trends

Assessing inconsistencies in historical land-use reconstructions for Africa at 1800

Reconstructing historical land-use and land-cover change (LULCC) inevitably involves interpolation across regions for which there is limited data to support reconstructions. Here, we evaluate the extent to which the global land-use model HYDE v3.2.1 reflects historical land use in sub-Saharan Africa (including Madagascar) when compared to archaeological and historical reconstructions. Both the HYDE models and Widgren’s archaeological and historical reconstructions estimate that agriculture was widely scattered across sub-Saharan Africa in 1800 CE. However, in steep terrain, at slopes greater than 10°, the HYDE model estimated 0% cropland allocation while archaeological and historical observations suggest intensive, mixed and extensive farming was common in these areas. In addition, while HYDE allocates farmland primarily into high-quality soils, archaeological reconstructions suggest that intensive farming occurred across a wide range of soil qualities. Gaps in our knowledge of cropland distribution have significant impacts on models of biodiversity change since understanding biodiversity in the Anthropocene is reliant on our understanding of past land-use changes. While HYDE’s mismatches are known, the research presented here provides an important resource in identifying where these allocation rules fail. These mismatches in global land-use models such as HYDE might also be replicated for other regions of the world, such as South America. These mismatches also need to be accounted for when generating model projections that use historical land-use models to impute present and future trends in land-use, climate and biodiversity change. Localised archaeological and historical data can therefore be used to support historical global land-use reconstructions for Africa and other regions across the world.

Vegetation cover change in Ugam Chatkal National Park, Uzbekistan, in relation to climate variables during the post-Soviet period (1991-2022)

This paper presents a comprehensive study relating to the vegetation cover change in Ugam Chatkal National Park (Uzbekistan) and its relation to climate change during the post-Soviet period (1991-2022). The study utilises remote sensing technology, specifically the Normalised Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI), to monitor spatio-temporal changes in vegetation. Landsat satellite imagery and meteorological data, including temperature and precipitation records form the basis of the analysis. The research aims to understand the impact of climatic factors, such as air temperature, soil temperature and precipitation on vegetation cover. Statistical methods, for example Pearson’s correlation analysis are employed to determine the strength and direction of relationships between these variables. The study reveals that both NDVI and SAVI are strongly correlated with air and soil temperatures, indicating the significant influence of these climatic factors on vegetation health and growth. The findings suggest that changes in vegetation cover in the Ugam Chatkal National Park are closely tied to climate change, with air temperature revealing a substantial correlation with time, indicating a trend towards increasing temperatures. The study also forecasts future climatic and vegetation trends, predicting an increase in air temperature, precipitation and vegetation cover over the next four decades. In particular, the research highlights the magnitude of monitoring and understanding the complex interactions between climate change and vegetation dynamics, which are crucial for environmental management and policy-making in the region.

"Intelligent Firmament - An Overview on Crop Classification and Recommendation System Using Machine"

Abstract: The agriculture industry is essential to maintaining human existence and guaranteeing the availability of food worldwide. As the global population continues to rise, there is an urgent need for novel technology that will improve agricultural efficiency and output. The "Intelligent Firmament" is a cutting-edge system that combines machine learning methods for crop classification with individualized recommendations to transform agriculture. Utilizing proficient machine learning algorithms, the system categorizes crops according to meteorological conditions, soil properties, and satellite photos. The Crop Classification Module is the first part of the Intelligent Firmament. This module extracts useful data about crop varieties, development stages, and general health by analyzing high-resolution satellite photos using deep learning models. To deliver dynamic recommendations, the Intelligent Firmament also makes use of climate models and real-time weather forecasts. The system can adjust and recommend appropriate crops that are in line with expected environmental conditions favorably to machine learning models that have been trained on past weather data to predict future climate trends. By taking a proactive stance, farmers may reduce the hazards brought on by erratic weather patterns and adjust their farming methods as necessary. The Intelligent Firmament, which uses machine learning to classify crops and provide customized recommendations, is a paradigm change in agriculture

Modelled Climate Change Impacts on Spring Canola Production Across British Columbia, Canada

This paper investigates the impact of projected near future climate trends on the production of spring planted canola (Brassica napus, Brassica rapa, and Brassica juncea of canola classification) across British Columbia, Canada. Analysis of historic climate trends from 2001-2020, informed by the Global Historical Climatology Network daily (GHCNd) database, establishes patterns of warming temperatures and increasing precipitation values across the province over the early 21st century. Near future climate trends were modelled using CMIP6 climate models, from 2021-2040 under SSP1-2.6, SSP2-4.5 and SSP5-8.5, with the projections downscaled using ClimateBC. The projected trends mirrored those of the observed historic record, while an observable relationship between rising levels of climate change and increasing projected annual precipitation and temperature is recorded. The subsequent crop modelling using the CSM-CROPGRO-Canola and DNDC models, fed with the modelled climate trends, highlighted the expectation for near future climate change to cause significant decreases in spring canola production across British Columbia.

Linking Climate Change Information with Crop Growing Seasons in the Northwest Ethiopian Highlands

In Ethiopia, the impacts of climate change are expected to have significant consequences for agriculture and food security. This study investigates both past (1981–2010) and future (2041–2070) climate trends and their influence on the length of the growing season (LGS) in the North-Western Ethiopian highlands. Climate observations were obtained from the National Meteorological Agency of Ethiopia, while the best performing and highest resolution models from the CMIP5 experiment and RCP6 (Coupled Models Intercomparison Project and representative concentration pathway 6) were used for the analysis. Standard statistical methods were applied to compute soil water content, evaluate climate variability and trends, and assess their impact on the length of the growing season. Maximum temperature (tasmax) and minimum temperature (tasmin) inter-annual variability anomalies show that the region has experienced cooler years than hotter years in the past. However, in the future, the coolest years are expected to decrease by −1.2 °C, while the hottest years will increase by +1.3 °C. During the major rainfall season (JJAS), the area has received an adequate amount of rainfall in the past and is very likely to receive similar rainfall in the future. On the other hand, the rainfall amount in the season February to May (FMAM) is expected to assist only with early planting. For the season October to January (ONDJ), the rainfall amount may help lengthen the growing season of JJAS if properly utilized; otherwise, the season has the potential to destroy crops before and during the harvesting time. The soil water content changes in the future remain close to those of the past period. The length of growing seasons has less variable onset and cessation dates, while in the future, the length of the growing period (LGP) of 174 to 177 days will be suitable for short- and long-cycle crops, as well as double cropping, benefiting crop production yield in the North-Western Ethiopian highlands in the future.

Hydrological sustainability of international virtual water trade

International virtual water (VW) trade helps to balance water stress across regions. However, it can be questioned whether such trade can remain sustainable as water resources are redistributed across regions resulting from changes in our climate. A conceptual framework to compare VW trade volumes with water fluxes within the water cycle is introduced. We evaluate the distribution of traded water surpluses and deficits associated with crop, animal, and industrial products over 157 countries and 182 global watersheds. About 7% of the countries are identified to conduct VW trade unsustainably. Regions within Africa, North America, central Asia, and Europe exhibit unfeasible VW trading resulting from higher appropriation of freshwater resources than availability influenced by precipitation and evaporation. Assessment at the watershed scale captures overexploitation at finer resolution, generally overlooked in country level analysis. An evaluation into the future reveals more watersheds becoming vulnerable to water storage depletion under future climate trends.

Winter determines the annual:seasonal temperature change of Asia temperate monsoon region since mid-Holocene

The evolution of global temperature during Holocene is crucial for understanding the mechanisms of climate change and future climate trends. However, there is a discrepancy between the temperature trends reconstructed from proxy and model, which is attributed to the seasonal biases and the appearance of the Holocene thermal maximum was dominated by the temperature of the warm season. In this paper, fossil charcoal assemblages from the southern Chinese Loess Plateau were analysed to provide quantitative mean annual temperatures (MAT) and four-season temperature records during 6.55–3.45 cal ka BP. The results indicate that both the seasonal temperatures and MAT during the middle Holocene reached warm peaks between 5.55 and 5.35 cal ka BP. The similar trend between the winter temperature (WIT) and MAT denotes the highest contribution of WIT changes to the MAT in the study area since the mid-Holocene.

Surface soil pollen and climate in northern Xinjiang, China: Implications for paleoclimate reconstruction

Quantitative paleoclimate reconstruction based on surface soil pollen and climate records on a regional scale is widely applied as a standard approach to investigating past climate changes. This study aimed to explore the quantitative relationships between the distribution of pollen taxa in 438 pollen samples from surface soils at altitudes ranging from 190 m to 3235 m and 19 environmental parameters in northern Xinjiang, China, using ordination and correlation analyses. The results showed that mean annual precipitation (MAP), precipitation in July, mean annual temperature (MAT), mean temperature in July and mean annual wind speed were the main environmental factors influencing the surface pollen distribution. Pinus pollen dispersal and transportation were primarily affected by spring wind, suggesting that exotic pollen played an essential role in surface pollen distribution. Picea pollen, with a percentage and concentration of over 30% and 30000 grains/g, was distributed at sites with MAT and MAP of −5.1–1.2 °C and 212–459 mm, respectively. Artemisia and Chenopodiaceae, with percentages and concentrations of over 50% and 50,000 grains/g and 50% and 10,000 grains/g, respectively, were mainly distributed at sites with MAT and MAP of −1.9–9.1 °C and 145–416 mm, and −3.2–8.9 °C and 129–449 mm, respectively, providing an accurate reference for paleoclimates reconstruction. This study presents information regarding the relationship between surface soil pollen and climate records, which may contribute to the reconstruction of historical climates, promote ecological development in Xinjiang, China, and assist in predicting future climate trends.

Assessing future climate trends and implications for managed forests across Canadian ecozones

Climate change interacts with ecological processes leading to changes in tree and forest growth rate, biome shifts and species composition, all of which are influenced by disturbances. This study explores future overarching climate trends of eight of Canada's ecozones containing managed forests. For the 2071 to 2100 period, climate projections indicate a warming trend of up to an additional 5.5 °C and an overall increase in annual precipitation. Future trends suggest marked contrast between coastal and interior forests and polarization between western and eastern forests. Warmer temperatures, accumulating degree-days above 5 °C and frost-free days suggest longer and drier growing seasons and greater risk of drought particularly in moisture-limited areas such as montane cordillera, taiga shield and boreal shield ecozones. Warmer temperatures and rising precipitation combined with less snow suggest shorter and wetter future winters. This indicates greater risk of rain-on-snow and freeze-thaw events, flooding and landslides particularly in coastal ecozones. We discuss how these projections are likely to result in shifts in dominant species and abundance, which when coupled with the cumulative effects of future disturbances, is likely to alter future forest dynamics and impact harvestable wood volumes for Canada's forestry industry.

Exacerbated climate risks induced by precipitation extremes in the Yangtze River basin under warming scenarios

The Yangtze River basin is a typical region of the world that has a well-developed economy but is also greatly affected by multiple climate extremes. An improved understanding of future climate trends and associated exposures in this region is urgent needed to address socioeconomic risks. This research aims to quantify historical and future projected population exposure to precipitation extremes in the Yangtze basin using meteorological records and downscaled climate models. The study found that the hazard zone for precipitation extremes during baseline period was primarily located in the mid-lower Yangtze basin, particularly around the Poyang Lake watershed. Climate projections for 2050 indicate a further increase in the occurrence of precipitation extremes in this hazard zone, while a decrease in extreme events is detectable in the upper Yangtze basin under higher radiative forcing. Future socioeconomic scenarios suggest a tendency for population growth and migration towards the lower Yangtze basin, resulting in aggravated climate risks in megacities. Multi-model simulations indicate that population exposure to precipitation extremes in the lower Yangtze basin will increase by 9–22% around 2050, with both climate and population factors contributing positively. Shanghai, Changsha, Hangzhou, Ganzhou, and Huanggang are identified as hotspot cities facing the highest foreseeable risks of precipitation extremes in the Yangtze basin.

Geochemical characterization evidence for the climate variability of the Mid-Pliocene warm period in the Nihewan Basin, North China

The mid-Pliocene warm period (mPWP) is regarded as an important geological analogue for predicting future climate characteristics and trends, due to its similar atmospheric CO2 concentration to that of the modern period. Within a chronologic framework provided by magnetostratigraphy, we analyzed the carbonate δ18O and geochemical element contents of 243 sediment samples from the NHA drill core in the Nihewan Basin, and combined them with grain size and magnetic susceptibility records. Our results revealed the paleoclimatic characteristics in the Nihewan Basin during 3.66–2.89 Ma for the first time. The interval of 3.58–3.31 Ma had the warmest and most stable climate during this interval, with low-amplitude fluctuations in all proxies, and with the following general characteristics: δ18O < −8‰, CIA > 40%, Rb/Sr > 0.3, ∑REE > 180 μg/g, and TiO2 > 0.7%). The climate remained warm overall during 3.31–3.10 Ma, but there were three pronounced cold events (at ∼3.30 Ma, ∼3.23 Ma, and ∼ 3.14 Ma) with the following general characteristics: δ18O > −7‰, CIA < 35%, Rb/Sr < 0.2, ∑REE < 160 μg/g, TiO2 < 0.6%). These events are correlative with increases in global ice volume, corresponding respectively to Marine Isotope Stage (MIS) M2, KM6, and KM2. The climate cooled substantially after 3.10 Ma, and most of the proxy index values were close to those during the three previous cold events. We ascribe this to increases in global ice volume and the strengthening of the East Asian winter monsoon.

Global and regional soil organic carbon estimates: Magnitudes and uncertainties

Globally, soil is the largest terrestrial carbon (C) reservoir. Robust quantification of soil organic C (SOC) stocks in existing global observation-based estimates avails accurate predictions in carbon-climate feedbacks and future climate trends. We investigated the magnitudes and distributions of global and regional SOC estimates (i.e., density and stocks) based on five widely used global gridded SOC datasets, a regional permafrost dataset developed in 2021 (UM2021), and a global-scale soil profile database (World Soil Information Service) reporting measurements of a series of physical and chemical edaphic attributes. The five global gridded SOC datasets were the Harmonized World Soil Database (HWSD), World Inventory of Soil Emission Potentials at 30 arc-second resolution (WISE30sec), Global Soil Dataset for Earth System Models (GSDE), Global Gridded Soil Information at 250-m resolution (SoilGrids250m), and Global Soil Organic Carbon Map (GSOCmap). Our analyses showed that the magnitude and distribution of SOC varied widely among datasets, with certain datasets showing region-specific robustness. At the global scale, SOC stocks at the top 30 and 100 cm were estimated to be 828 (range: 577–1 171) and 1 873 (range: 1 086–2 678) Pg C, respectively. The estimates from GSDE, GSOCmap, and WISE30sec were comparable, and those of SoilGrids250m and HWSD were at the upper and lower ends. The spatial SOC distribution varied greatly among datasets, especially in the northern circumpolar and Tibetan Plateau permafrost regions. Regionally, UM2021 and WISE30sec performed well in the northern circumpolar permafrost regions, and GSDE performed well in China. The estimates of SOC by different datasets also showed large variabilities across different soil layers and biomes. The discrepancies were generally smaller for the 0–30 cm soil than the 0–100 cm soil. The datasets demonstrated relatively higher agreement in grasslands, croplands, and shrublands/savannas than in other biomes (e.g., wetlands). The users should be mindful of the gaps between regions and biomes while choosing the most appropriate SOC dataset for specific uses. Large uncertainties in existing global gridded SOC estimates were generally derived from soil sampling density, different sources, and various mapping methods for soil datasets. We call for future efforts for standardizing soil sampling efforts, cross-dataset comparison, proper validation, and overall global collaboration to improve SOC estimates.

Assessing future climate trends and their impact on different fractured rock aquifers in Northern Togo

Abstract Water resources in the West African Sudan Savannah are concentrated in fractured crystalline and sedimentary rock aquifers. Due to their low storage, these aquifers are vulnerable to changes in climate. This study assesses future temperature and precipitation trends in the Koumfab watershed of northern Togo. The impact of these future climate trends on surface water and the two types of fractured rock aquifers is also investigated. A fully integrated hydrologic model of the Koumfab watershed in Northern Togo is used to study the impact of climate change on water resources. Bias-corrected temperature and precipitation from four regional climate models (RCMs) and two greenhouse emissions scenarios are used as climate forcings for the integrated model. Climate projections from the four RCMs indicate a rise (1.4 −5.5 °C) in average temperature, from the control (1971–2000) to the future period (2031–2060). Projected precipitation follows two contrasting trends with some RCMs predicting an increase (by up to 14%) in mean annual precipitation, while others predict a decline (by up to −14%). These changes in precipitation and temperature will have a noticeable effect on groundwater levels and stream flow. The hydrologic model indicates a decline in groundwater levels for all future climate projections by 1.4−5.7 m. The decline in groundwater levels is not uniformly distributed in the two types of aquifers. The decline in the fractured sandstone aquifer will be four times greater than that of the fractured granodiorite/migmatite aquifer. Projected stream flow at the watershed outlet shows increasing (59 -332%) and decreasing trends (−39 −100%).