Spatiotemporal foresting of soil erosion for SSP-RCP scenarios considering local vegetation restoration project: A case study in the three gorges reservoir (TGR) area, China.

Soil erosion is a common form of land degradation. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides a scenario framework for global socio-economic development and climate change by combining Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). The soil erosion estimation under global climate change and land-use change scenarios provided by CMIP6 is valuable for representing future changes and hotspots. This study estimated the future changes in soil erosion in the Three Gorges Reservoir (TGR) area, China, which has suffered severe soil loss over an extended period, and vegetation restoration projects have been conducted since 1999. The scenarios provided by SSP1-2.6, SSP2-4.5, and SSP5-8.5 were coupled with the scenarios of regional vegetation restoration projects to reflect future land use changes (LUC) and climate change. The results showed that future soil erosion from 2020 to 2100 in the TGR area will experience a non-significant decreasing trend (with trend slopes of -0.013, -0.020, and-0.006 in SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, with p > 0.05). However, with the R factors calculated by different methods, this decreasing trend becomes either insignificant or a significant increasing trend. SSP1-2.6 will experience the lowest soil erosion in 2100 owing to the large amount of forest increase in this scenario. Furthermore, as estimates, the grain-for-green policy (GGP) will reduce 89353.47, 92737.73 and 42916.52 ton soil erosion per year in SSP1-2.6, SSP2-4.5 and SSP3-8.5 by 2100, respectively. In the future, the GGP will become increasingly important for controlling soil loss in the TGR area owing to the increasing precipitation in all scenarios, which increases the risk of soil loss.

Effect of climate change on reservoir water balance and irrigation water demand: a case of Gidabo irrigation project, Rift Valley Basin, Ethiopia.

The consequences of climate change on agriculture water demand are among the current and prospective challenges. The amount of water needed by crops is significantly affected by the regional climate. The influence of climate change on irrigation water demand and reservoir water balance components were examined. The results of seven regional climate models were compared, and the top-performing model was chosen for the study area. After model calibration and validation, the HEC-HMS model was used to forecast future water availability in the reservoir. The results show that under the RCP 4.5 and RCP 8.5 emission scenarios, the reservoir's water availability in the 2050s will decline by approximately 7% and 9%, respectively. The CROPWAT results showed that the required irrigation water might rise by 26 to 39% in the future. However, the water supply for irrigation may be drastically reduced due to the drop in reservoir water storage. As a result, the irrigation command area could drop up to 21% (2878.4 ha) to 33% (4502 ha) in future climatic conditions. Therefore, we recommend alternative watershed management techniques and climate change adaptation measures to endure upcoming water shortages in the area.

Predicting how climate change threatens the prey base of Arctic marine predators.

Arctic sea ice loss has direct consequences for predators. Climate-driven distribution shifts of native and invasive prey species may exacerbate these consequences. We assessed potential changes by modelling the prey base of a widely distributed Arctic predator (ringed seal; Pusa hispida) in a sentinel area for change (Hudson Bay) under high- and low-greenhouse gas emission scenarios from 1950 to 2100. All changes were relatively negligible under the low-emission scenario, but under the high-emission scenario, we projected a 50% decline in the abundance of the well-distributed, ice-adapted and energy-rich Arctic cod (Boreogadus saida) and an increase in the abundance of smaller temperate-associated fish in southern and coastal areas. Furthermore, our model predicted that all fish species declined in mean body size, but a 29% increase in total prey biomass. Declines in energy-rich prey and restrictions in their spatial range are likely to have cascading effects on Arctic predators.

Meeting Paris agreement objectives will temper seabird winter distribution shifts in the North Atlantic Ocean.

We explored the implications of reaching the Paris Agreement Objective of limiting global warming to <2°C for the future winter distribution of the North Atlantic seabird community. We predicted and quantified current and future winter habitats of five North Atlantic Ocean seabird species (Alle alle, Fratercula arctica, Uria aalge, Uria lomvia and Rissa tridactyla) using tracking data for ~1500 individuals through resource selection functions based on mechanistic modeling of seabird energy requirements, and a dynamic bioclimate envelope model of seabird prey. Future winter distributions were predicted to shift with climate change, especially when global warming exceed 2°C under a "no mitigation" scenario, modifying seabird wintering hotspots in the North Atlantic Ocean. Our findings suggest that meeting Paris agreement objectives will limit changes in seabird selected habitat location and size in the North Atlantic Ocean during the 21st century. We thereby provide key information for the design of adaptive marine-protected areas in a changing ocean.

Health impacts and cost-benefit analyses of surface O3 and PM2.5 over the U.S. under future climate and emission scenarios.

Health impacts of surface ozone (O3) and fine particulate matter (PM2.5) are of major concern worldwide. In this work, the Environmental Benefits Mapping and Analysis Program tool is applied to estimate the health and economic impacts of projected changes in O3 and PM2.5 in the U.S. in future (2046-2055) decade relative to current (2001-2010) decade under the Representative Concentration Pathway (RCP) 4.5 and 8.5 climate scenarios. Future annual-mean O3 reductions under RCP 4.5 prevent ~1,800 all-cause mortality, 761 respiratory hospital admissions (HA), and ~1.2 million school loss days annually, and result in economic benefits of ~16 billion, 29 million, and 132 million U.S. dollars (USD), respectively. By contrast, the projected future annual-mean O3 increases under RCP8.5 cause ~2,400 mortality, 941 respiratory HA, and ~1.6 million school loss days annually and result in economic disbenefits of ~21 billion, 36 million, and 175 million USD, respectively. Health benefits of reduced O3 double under RCP4.5 and health dis-benefits of increased O3 increase by 1.5 times under RCP8.5 in future with 2050 population and baseline incidence rate. Because of the reduction in projected future PM2.5 over CONUS under both scenarios, the annual avoided all-cause deaths, cardiovascular HA, respiratory HA, and work loss days are ~63,000 and ~83,000, ~5,300 and ~7,000, ~12,000 and ~15,000, and ~7.8 million and ~10 million, respectively, leading to economic benefits of ~560 and ~740 billion, ~240 and ~320 million, ~450 and ~590 million, and ~1,400 and ~1,900 million USD for RCP4.5 and 8.5, respectively. Health benefits of reduced PM2.5 for future almost double under both scenarios with the largest benefits in urban areas. RCP8.5 projects larger health and economic benefits due to a greater reduction in PM2.5 but with a warmer atmosphere and higher O3 pollution than RCP4.5. RCP4.5 leads to multiple-benefit goals including reduced O3 and PM2.5, reduced mortality and morbidity, and saved costs. Greater reduction in future PM2.5 under RCP4.5 should be considered to achieve larger multi-benefits.

Assessment of Arctic sea ice simulations in cGENIE model and projections under RCP scenarios.

Simulating and predicting Arctic sea ice accurately remains an academic focus due to the complex and unclear mechanisms of Arctic sea ice variability and model biases. Meanwhile, the relevant forecasting and monitoring authorities are searching for models to meet practical needs. Given the previous ideal performance of cGENIE model in other fields and notable features, we evaluated the model's skill in simulating Arctic sea ice using multiple methods and it demonstrates great potential and combined advantages. On this basis, we examined the direct drivers of sea-ice variability and predicted the future spatio-temporal changes of Arctic sea ice using the model under different Representative Concentration Pathways (RCP) scenarios. Further studies also found that Arctic sea ice concentration shows large regional differences under RCP 8.5, while the magnitude of the reduction in Arctic sea ice thickness is generally greater compared to concentration, showing a more uniform consistency of change.

Land subsidence in Bangkok vicinity: Causes and long-term trend analysis using InSAR and machine learning.

Land subsidence in Bangkok, a pressing environmental challenge, demands sustained long-term policy interventions. Although mitigation measures have successfully alleviated subsidence rates within inner Bangkok, neighboring provinces continue to experience escalating rates. Conventional land-based monitoring methods exhibit limitations in coverage, and the anticipated nonlinear contributions of climatic and socioeconomic factors further complicate the spatiotemporal distribution of subsidence. This study aims to provide future subsidence predictions for the near (2023-2048), mid (2049-2074), and far-future (2075-2100), employing Interferometric Synthetic Aperture Radar (InSAR), Random Forest machine learning algorithm, and combined Shared Socioeconomic Pathways-Representative Concentration Pathways (SSP-RCPs) scenarios to address these challenges. The mean Line-of-Sight (LOS) velocity was found to be -7.0 mm/year, with a maximum of -53.5 mm/year recorded in Ayutthaya. The proposed model demonstrated good performance, yielding an R2 value of 0.84 and exhibiting no signs of overfitting. Across all scenarios, subsidence rates tend to increase by more than -9.0 mm/year in the near-future. However, for the mid and far-future, scenarios illustrate varying trends. The 'only-urban-LU change' scenario predicts a gradual recovery, while other change scenarios exhibit different tendencies.

Impacts of climate and land use change on terrestrial carbon storage: A multi-scenario case study in the Yellow River Basin (1992-2050).

Currently, socioeconomic development and climate change pose new challenges to the assessment and management of terrestrial carbon storage (CS). Accurate prediction of future changes in land use and CS under different climate scenarios is of great significance for regional land use decision-making and carbon management. Taking the Yellow River Basin (YRB) in China as the study area, this study proposed a framework integrating the land use harmonization2 (LUH2) dataset, the patch-generating land use simulation (PLUS) model, and the integrated valuation of ecosystem services and trade-offs (InVEST) model. Under this framework, we systematically analyzed the spatiotemporal evolution characteristics of land use and their impact on CS in the YRB from 1992 to 2050. The results showed that (1) CS was highest in forestland and lowest in construction land, with a spatial distribution of high in the south and low in the north. From 1992 to 2020, construction land, forestland, and grassland increased while cropland decreased, reducing the total CS by 74.04 Tg. (2) From 2020 to 2050, under SSP1-2.6 scenario, forestland increased by 158.87 %; under SSP2-4.5 scenario, unused land decreased by 65.55 %; and under SSP5-8.5 scenario, construction land increased by 13.88 %. By 2050, SSP1-2.6 scenario exhibited the highest CS (8105.25 Tg), followed by SSP2-4.5 scenario (7363.61 Tg), and SSP5-8.5 scenario was the lowest (7315.86 Tg). (3) Forestland and construction land were the most critical factors affecting the CS. Shaanxi and Shanxi had the largest CS in all scenarios, and Qinghai had a huge carbon sink potential under SSP1-2.6 scenario. Scenario modeling demonstrated that future climate and land-use changes would have significant impacts on terrestrial CS in the YRB, and green development pathways could strongly contribute to meeting the dual­carbon target. Overall, this study provides a scientific basis for promoting low-carbon development, land-use optimization, and ecological civilization construction in YRB, China.

Biomes of the world under climate change scenarios: increasing aridity and higher temperatures lead to significant shifts in natural vegetation.

The global potential distribution of biomes (natural vegetation) was modelled using 8,959 training points from the BIOME 6000 dataset and a stack of 72 environmental covariates representing terrain and the current climatic conditions based on historical long term averages (1979-2013). An ensemble machine learning model based on stacked regularization was used, with multinomial logistic regression as the meta-learner and spatial blocking (100 km) to deal with spatial autocorrelation of the training points. Results of spatial cross-validation for the BIOME 6000 classes show an overall accuracy of 0.67 and R2logloss of 0.61, with "tropical evergreen broadleaf forest" being the class with highest gain in predictive performances (R2logloss = 0.74) and "prostrate dwarf shrub tundra" the class with the lowest (R2logloss = -0.09) compared to the baseline. Temperature-related covariates were the most important predictors, with the mean diurnal range (BIO2) being shared by all the base-learners (i.e.,random forest, gradient boosted trees and generalized linear models). The model was next used to predict the distribution of future biomes for the periods 2040-2060 and 2061-2080 under three climate change scenarios (RCP 2.6, 4.5 and 8.5). Comparisons of predictions for the three epochs (present, 2040-2060 and 2061-2080) show that increasing aridity and higher temperatures will likely result in significant shifts in natural vegetation in the tropical area (shifts from tropical forests to savannas up to 1.7 ×105 km2 by 2080) and around the Arctic Circle (shifts from tundra to boreal forests up to 2.4 ×105 km2 by 2080). Projected global maps at 1 km spatial resolution are provided as probability and hard classes maps for BIOME 6000 classes and as hard classes maps for the IUCN classes (six aggregated classes). Uncertainty maps (prediction error) are also provided and should be used for careful interpretation of the future projections.

Assessment of Carbon Storage under Different SSP-RCP Scenarios in Terrestrial Ecosystems of Jilin Province, China.

Carbon storage is one of the key factors determining the global carbon balance in the terrestrial ecosystems. Predicting future changes in carbon storage is significant for regional sustainable development in the background of the "dual carbon" objective. This study which coupled the InVEST model and the PLUS model and is based on land use in different future scenarios evaluated the evolution characterization of terrestrial carbon storage in Jilin Province from 2000 to 2040 and explored the impact of related factors on it. The results show that: (1) from 2000 to 2020, the area of cultivated land and built-up areas increased continuously in Jilin Province, while the area of forest land, grassland, and wetland decreased with time; the ecological land has been restored to a certain degree. (2) Due to the continuous reduction in ecological land, the overall carbon storage in Jilin Province from 2000 to 2020 showed a downward trend, with a total reduction of 30.3 Tg, and the carbon storage in the western part of Jilin Province changed significantly. The SSP2-RCP4.5 scenario shows a minimum value of carbon storage in 2030 and a small increase in 2040; the SSP1-RCP2.6 scenario shows an increasing trend in carbon storage from 2020 to 2040; the area of built-up areas and cultivated land increases and the loss in carbon storage is more serious under the SSP5-RCP8.5 scenario. (3) On the whole, with the increase in elevation and slope, the carbon storage showed a trend of increasing first and then decreasing, and the carbon storage of shady and semi-shady slopes was higher than that of sunny and semi-sunny slopes; forest land and cultivated land were the keys to carbon storage changes in Jilin Province.

Future climate change could reduce irrigated and rainfed wheat water footprint in arid environments.

The concept of water footprint (WF) has been used to manage freshwater resources for the past two decades and is considered as indicator of the sustainability of agricultural systems. Accordingly, the current study aimed to quantify WF and its components in the future climate for rainfed and irrigated wheat agro-ecosystems in 17 provinces of Iran located in arid or semi-arid environments. The provinces were divided into five climate classes. The simulations were conducted under current (1980-2010) and future climate (2040-2070) using the Agricultural Production Systems sIMulator (APSIM) crop model, following the Agricultural Model Intercomparison and Improvement Project (AgMIP) protocol. Baseline simulations indicated that the total WF, averaged across all climate classes, was 1148 m3 t-1 for irrigated and 1155 m3 t-1 for rainfed wheat. WF was projected to decline in the future compared to baseline in both irrigated and rainfed systems mostly because of increases in yield of +9% in rainfed systems and 3.5% in irrigated systems, and decreases in water consumption by -5.4% and -10.1%, respectively. However, the share of gray water footprint (WFgray) was projected to increase in the near future for both rainfed (+5.4%) and irrigated (+6.9%) systems. These findings suggest that cleaner and more sustainable production (i.e. obtaining grain yield under optimal water and nitrogen consumption) could be achieved in irrigated and rainfed wheat ago-ecosystems if optimal N fertilizer management is adopted. Additionally, rainfed cultivation can be further expanded in some areas which is expected to result in a substantial reduction in blue water (i.e. less irrigation), especially in sub-humid and semi-arid cool areas.

Regionalized dynamic climate series for ecological climate impact research in modern controlled environment facilities.

Modern controlled environment facilities (CEFs) enable the simulation of dynamic microclimates in controlled ecological experiments through their technical ability to precisely control multiple environmental parameters. However, few CEF studies exploit the technical possibilities of their facilities, as climate change treatments are frequently applied by static manipulation of an inadequate number of climate change drivers, ignoring intra-annual variability and covariation of multiple meteorological variables. We present a method for generating regionalized climate series in high temporal resolution that was developed to force the TUMmesa Model EcoSystem Analyzer with dynamic climate simulations. The climate series represent annual cycles for a reference period (1987-2016) and the climate change scenarios RCP2.6 and RCP8.5 (2071-2100) regionalized for a climate station situated in a forested region of the German Spessart mountains. Based on the EURO-CORDEX and ReKliEs-DE model ensembles, typical annual courses of daily resolved climatologies for the reference period and the RCP scenarios were calculated from multimodel means of temperature (ta), relative humidity (rh), global radiation (Rg), air pressure (P), and ground-level ozone and complemented by CO2. To account for intra-annual variation and the covariability of multiple climate variables, daily values were substituted by hourly resolved data resampled from the historical record. The resulting present climate Test Reference Year (TRY) well represented a possible annual cycle within the reference period, and expected shifts in future mean values (e.g., higher ta) were reproduced within the RCP TRYs. The TRYs were executed in eight climate chambers of the TUMmesa facility and-accounting for the technical boundaries of the facility-reproduced with high precision. Especially, as an alternative to CEF simulations that reproduce mere day/night cycles and static manipulations of climate change drivers, the method presented here proved well suited for simulating regionalized and highly dynamic annual cycles for ecological CEF studies.

An observation-based scaling model for climate sensitivity estimates and global projections to 2100.

We directly exploit the stochasticity of the internal variability, and the linearity of the forced response to make global temperature projections based on historical data and a Green's function, or Climate Response Function (CRF). To make the problem tractable, we take advantage of the temporal scaling symmetry to define a scaling CRF characterized by the scaling exponent H, which controls the long-range memory of the climate, i.e. how fast the system tends toward a steady-state, and an inner scale τ ≈ 2   years below which the higher-frequency response is smoothed out. An aerosol scaling factor and a non-linear volcanic damping exponent were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference which allows us to analytically calculate the transient climate response and the equilibrium climate sensitivity as: 1 . 7 - 0.2 + 0.3   K and 2 . 4 - 0.6 + 1.3   K respectively (likely range). Projections to 2100 according to the RCP 2.6, 4.5 and 8.5 scenarios yield warmings with respect to 1880-1910 of: 1 . 5 - 0.2 + 0.4 K , 2 . 3 - 0.5 + 0.7   K and 4 . 2 - 0.9 + 1.3   K. These projection estimates are lower than the ones based on a Coupled Model Intercomparison Project phase 5 multi-model ensemble; more importantly, their uncertainties are smaller and only depend on historical temperature and forcing series. The key uncertainty is due to aerosol forcings; we find a modern (2005) forcing value of [ - 1.0 , - 0.3 ] Wm - 2 (90 % confidence interval) with median at - 0.7 Wm - 2 . Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to RCP 2.6 for which the probability to remain under 1.5 K is 48 %. RCP 4.5 and RCP 8.5-like futures overshoot with very high probability.

Assessment of Future Water Yield and Water Purification Services in Data Scarce Region of Northwest China.

Water shortage and pollution have become prominent in the arid regions of northwest China, seriously affecting human survival and sustainable development. The Bosten Lake basin has been considered as an example of an arid region in northwest China, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model has been used to quantitatively evaluate the future water yield and water purification services for four representative concentration pathways (RCP) scenarios. The results show that for the four RCP scenarios, the annual average precipitation in 2020-2050 decreases compared to that in 1985-2015; the area of cultivated land and unused land decreases, and the area of other land-use types increases from 2015 to 2050. The water yield service reduces, while the water purification service increases from 2015 to 2050 in the Bosten Lake basin. In 2050, the water yield and water purification services are the best for the RCP6.0 scenario, and are the worse for the RCP4.5 scenario and RCP8.5 scenario, respectively. The distribution of the water yield and water purification services show a gradual decline from northwest to southeast.

Global Socioeconomic Risk of Precipitation Extremes Under Climate Change.

Precipitation extremes are among the most serious consequences of climate change around the world. The observed and projected frequency and intensity of extreme precipitation in some regions will greatly influence the social economy. The frequency of extreme precipitation and the population and economic exposure were quantified for a base period (1986-2005) and future periods (2016-2035 and 2046-2065) based on bias corrected projections of daily precipitation from five global climatic models forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in the shared socioeconomic pathways (SSPs). The RCP8.5-SSP3 scenario produces the highest global population exposure for 2046-2065, with nearly 30% of the global population (2.97 × 109 persons) exposed to precipitation extremes >10 days/a. The RCP2.6-SSP1 scenario produces the highest global GDP exposure for 2046-2065, with a 5.56-fold increase relative to the base period, of up to (2.29 ± 0.20) × 1015 purchasing power parity $-days. Socioeconomic effects are the primary contributor to the exposure changes at the global and continental scales. Population and GDP effects account for 64-77% and 78-91% of the total exposure change, respectively. The inequality of exposure indicates that more attention should be given to Asia and Africa due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries, that is, Luxembourg, Belgium, and the Netherlands, should also commit to effective adaptation measures.

Projections of land use changes under the plant functional type classification in different SSP-RCP scenarios in China.

Land use projections are crucial for climate models to forecast the impacts of land use changes on the Earth's system. However, the spatial resolution of existing global land use projections (e.g., 0.25°×0.25° in the Land-Use Harmonization (LUH2) datasets) is still too coarse to drive regional climate models and assess mitigation effectiveness at regional and local scales. To generate a high-resolution land use product with the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways (SSPs-RCPs) for various regional climate studies in China, here we first conduct land use simulations with a newly developed Future Land Uses Simulation (FLUS) model based on the trajectories of land use demands extracted from the LUH2 datasets. On this basis, a new set of land use projections under the plant functional type (PFT) classification, with a temporal resolution of 5 years and a spatial resolution of 5 km, in eight SSP-RCP scenarios from 2015 to 2100 in China is produced. The results show that differences in land use dynamics under different SSP-RCP scenarios are jointly affected by global assumptions and national policies. Furthermore, with improved spatial resolution, the data produced in this study can sufficiently describe the details of land use distribution and better capture the spatial heterogeneity of different land use types at the regional scale. We highlight that these new land use projections at the PFT level have a strong potential for reducing uncertainty in the simulation of regional climate models with finer spatial resolutions.

Simulating soil organic carbon stock as affected by land cover change and climate change, Hyrcanian forests (northern Iran).

Soil organic carbon (SOC) contains a considerable portion of the world's terrestrial carbon stock, and is affected by changes in land cover and climate. SOC modeling is a useful approach to assess the impact of land use, land use change and climate change on carbon (C) sequestration. This study aimed to: (i) test the performance of RothC model using data measured from different land covers in Hyrcanian forests (northern Iran); and (ii) predict changes in SOC under different climate change scenarios that may occur in the future. The following land covers were considered: Quercus castaneifolia (QC), Acer velutinum (AV), Alnus subcordata (AS), Cupressus sempervirens (CS) plantations and a natural forest (NF). For assessment of future climate change projections the Fifth Assessment IPCC report was used. These projections were generated with nine Global Climate Models (GCMs), for two Representative Concentration Pathways (RCPs) leading to very low and high greenhouse gases concentration levels (RCP 2.6 and RCP 8.5 respectively), and for four 20year-periods up to 2099 (2030s, 2050s, 2070s and 2090s). Simulated values of SOC correlated well with measured data (R2=0.64 to 0.91) indicating a good efficiency of the RothC model. Our results showed an overall decrease in SOC stocks by 2099 under all land covers and climate change scenarios, but the extent of the decrease varied with the climate models, the emissions scenarios, time periods and land covers. Acer velutinum plantation was the most sensitive land cover to future climate change (range of decrease 8.34-21.83tCha-1). Results suggest that modeling techniques can be effectively applied for evaluating SOC stocks, allowing the identification of current patterns in the soil and the prediction of future conditions.

Trend analysis of watershed-scale precipitation over Northern California by means of dynamically-downscaled CMIP5 future climate projections.

The impacts of climate change on watershed-scale precipitation through the 21st century were investigated over eight study watersheds in Northern California based on dynamically downscaled CMIP5 future climate projections from three GCMs (CCSM4, HadGEM2-ES, and MIROC5) under the RCP4.5 and RCP8.5 future climate scenarios. After evaluating the modeling capability of the WRF model, the six future climate projections were dynamically downscaled by means of the WRF model over Northern California at 9km grid resolution and hourly temporal resolution during a 94-year period (2006-2100). The biases in the model simulations were corrected, and basin-average precipitation over the eight study watersheds was calculated from the dynamically downscaled precipitation data. Based on the dynamically downscaled basin-average precipitation, trends in annual depth and annual peaks of basin-average precipitation during the 21st century were analyzed over the eight study watersheds. The analyses in this study indicate that there may be differences between trends of annual depths and annual peaks of watershed-scale precipitation during the 21st century. Furthermore, trends in watershed-scale precipitation under future climate conditions may be different for different watersheds depending on their location and topography even if they are in the same region.