Regionalisation of precipitation for the Iberian Peninsula and climate change

Temporal variability of precipitation over the Iberian Peninsula (IP) has high spatial gradients. Therefore, statistics of the temporal behaviour of precipitation and derived quantities over the IP must be estimated taking into account these spatial gradients. Some statistics can be displayed over a map. However there are statistics, such as Probability Density Functions at each location of the IP, that are impossible to display in a map. Because of this, it is mandatory to reduce the number of degrees of freedom which, in this case, consists of a reduction of the time series representative of the IP domain. In this work, we present a spatial partition of the IP region into areas of similar precipitation. For that, an observed dataset of daily-total precipitation for the years between 1951 and 2003 was used. The land-only high resolution data was obtained on a regular grid with 0.2° resolution in the IP domain. This data was subjected to a k-means Cluster Analysis in order to divide the IP into K regions. The clustering was performed using the squared Euclidean distance. Four clusters of IP grid points, defining 4 IP regions, were identified. The grid points in each region share the same time-varying behaviour which is different from region to region. The annual precipitation discriminates the following regions: (1) north Iberia, (2) a large region extending from the centre to the Mediterranean shores of the IP, (3) a large region ranging from the centre to the western and southwestern shores of the Iberia, and (4) northwest Iberia. The regions obtained for the four seasons of the year are similar. These results are consistent with the thermodynamic characteristics described in the available literature. These Iberian regions were used to assess climate change of seasonal precipitation from the multi-model ensemble of the fifteen simulations provided by the European project ENSEMBLES. Probability Density Functions of annual- and seasonal-total precipitation, consecutive dry days, and total precipitation above the 95th percentile, averaged in each region were estimated for a reference climate (1961–1960), a near-future climate (2021–2050), and a distant-future climate (2069–2098). Climate change projections are based on comparisons of these functions between each future climate and the reference climate.Finally, we emphasize that: (i) the methodology used here, based on Cluster Analysis, can be used to regionalise other areas of the world, and (ii) the identified regions of the IP can be used to represent the Iberian precipitation by four time series that can be subjected to further analysis, whose results can be presented in a concise manner.     

The implications of bias correction methods and climate model ensembles on soil erosion projections under climate change

Climate change will most likely cause an increase in extreme precipitation and consequently an increase in soil erosion in many locations worldwide. In most cases, climate model output is used to assess the impact of climate change on soil erosion; however, there is little knowledge of the implications of bias correction methods and climate model ensembles on projected soil erosion rates. Using a soil erosion model, we evaluated the implications of three bias correction methods (delta change, quantile mapping and scaled distribution mapping) and climate model selection on regional soil erosion projections in two contrasting Mediterranean catchments. Depending on the bias correction method, soil erosion is projected to decrease or increase. Scaled distribution mapping best projects the changes in extreme precipitation. While an increase in extreme precipitation does not always result in increased soil loss, it is an important soil erosion indicator. We suggest first establishing the deviation of the bias-corrected climate signal with respect to the raw climate signal, in particular for extreme precipitation. Furthermore, individual climate models may project opposite changes with respect to the ensemble average; hence climate model ensembles are essential in soil erosion impact assessments to account for climate model uncertainty. We conclude that the impact of climate change on soil erosion can only accurately be assessed with a bias correction method that best reproduces the projected climate change signal, in combination with a representative ensemble of climate models. © 2018 John Wiley & Sons, Ltd.     

中国平均降水和极端降水对气候变暖的响应:CMIP5模式模拟评估和预估

基于24个CMIP5全球耦合模式模拟结果,分析了中国区域年平均降水和ETCCDI强降水量(R95p)、极端强降水量(R99p)对增暖的响应.定量分析结果显示,CMIP5集合模拟的当代中国区域平均降水对增温的响应较观测偏弱,而极端降水的响应则偏强.对各子区域气温与平均降水、极端降水的关系均有一定的模拟能力,并且极端降水的模拟好于平均降水.RCP4.5和RCP8.5情景下,随着气温的升高,中国区域平均降水和极端降水均呈现一致增加的趋势,中国区域平均气温每升高1℃,平均降水增加的百分率分别为3.5%和2.4%,R95p增加百分率为11.9%和11.0%,R99p更加敏感,分别增加21.6%和22.4%.就各分区来看,当代的区域性差异较大,未来则普遍增强,并且区域性差异减小,在持续增暖背景下,中国及各分区极端降水对增暖的响应比平均降水更强,并且越强的极端降水敏感性越大.未来北方地区平均降水对增暖的响应比南方地区的要大,青藏高原和西南地区的R95p和R99p增加最显著,表明未来这些区域发生暴雨和洪涝的风险将增大.     

Possible climate change/variability and human impacts,vulnerability of drought-prone regions,water resources and capacity building for Africa

ABSTRACT

This review article discusses the climate, water resources and historical droughts of Africa, drought indices, vulnerability, impact of global warming and land use for drought-prone regions in West, southern and the Greater Horn of Africa, which have suffered recurrent severe droughts in the past. Recent studies detected warming and drying trends in Africa since the mid 20th century. Based on the Fourth Assessment Report of the Intergovernmental Panel on Climate Change and the Coupled Model Intercomparison Project Phase 5 (CMIP5), both northern and southern Africa are projected to experience drying, such as decreasing precipitation, runoff and soil moisture in the 21st century and could become more vulnerable to the impact of droughts. The daily maximum temperature is projected to increase by up to 8°C (RCP8.5 of CMIP5), precipitation indices such as total wet day precipitation (PRCPTOT) and heavy precipitation days (R10 mm) could decrease, while warm spell duration (WSDI) and consecutive dry days (CDD) could increase. Uncertainties of the above long-term projections, teleconnections to climate anomalies such as ENSO and the Madden-Julian Oscillation, which could also affect the water resources of Africa, and capacity building in terms of physical infrastructure and non-structural solutions are also discussed. Given that traditional climate and hydrological data observed in Africa are generally limited, satellite data should also be exploited to fill the data gap for Africa in the future.

Editor D. Koutsoyiannis; Associate editor N. Ilich     

The impact of climate change on groundwater recharge and runoff in a humid,equatorial catchment: sensitivity of projections to rainfall intensity

Abstract

Projected warming in equatorial Africa, accompanied by greater evaporation and more frequent heavy precipitation events, may have substantial but uncertain impacts on terrestrial hydrology. Quantitative analyses of climate change impacts on catchment hydrology require high-resolution (<50 km) climate data provided by regional climate models (RCMs). We apply validated precipitation and temperature data from the RCM PRECIS (Providing Regional Climates for Impact Studies) to a semi-distributed soil moisture balance model (SMBM) in order to quantify the impacts of climate change on groundwater recharge and runoff in a medium-sized catchment (2098 km²) in the humid tropics of southwestern Uganda. The SMBM explicitly accounts for changes in soil moisture, and partitions effective precipitation into groundwater recharge and runoff. Under the A2 emissions scenario (2070–2100), climate projections from PRECIS feature not only rises in catchment precipitation and modelled potential evapotranspiration by 14% and 53%, respectively, but also increases in rainfall intensity. We show that the common application of the historical rainfall distribution using delta factors to the SMBM grossly underestimates groundwater recharge (i.e. 55% decrease relative to the baseline period of 1961–1990). By transforming the rainfall distribution to account for changes in rainfall intensity, we project increases in recharge and runoff of 53% and 137%, respectively, relative to the baseline period.     

Simulation of future climate scenarios with a weather generator

Numerous studies across multiple disciplines search for insights on the effects of climate change at local spatial scales and at fine time resolutions. This study presents an overall methodology of using a weather generator for downscaling an ensemble of climate model outputs. The downscaled predictions can explicitly include climate model uncertainty, which offers valuable information for making probabilistic inferences about climate impacts. The hourly weather generator that serves as the downscaling tool is briefly presented. The generator is designed to reproduce a set of meteorological variables that can serve as input to hydrological, ecological, geomorphological, and agricultural models. The generator is capable of reproducing a wide set of climate statistics over a range of temporal scales, from extremes, to low-frequency interannual variability; its performance for many climate variables and their statistics over different aggregation periods is highly satisfactory. The use of the weather generator in simulations of future climate scenarios, as inferred from climate models, is described in detail. Using a previously developed methodology based on a Bayesian approach, the stochastic downscaling procedure derives the frequency distribution functions of factors of change for several climate statistics from a multi-model ensemble of outputs of General Circulation Models. The factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. Using embedded causal and statistical relationships, the generator simulates future realizations of climate for a specific point location at the hourly scale. Uncertainties present in the climate model realizations and the multi-model ensemble predictions are discussed. An application of the weather generator in reproducing present (1961-2000) and forecasting future (2081-2100) climate conditions is illustrated for the location of Tucson (AZ). The stochastic downscaling is carried out using simulations of eight General Circulation Models adopted in the IPCC 4AR, A1B emission scenario.     

Assessing groundwater sustainability under changing climate using isotopic tracers and climate modelling,southwest Ohio,USA

Groundwater, an essential resource, is likely to change with global warming because of changes in the CO₂ levels, temperature and precipitation. Here, we combine water isotope geochemistry with climate modelling to examine future groundwater recharge in southwest Ohio, USA. We first establish the stable isotope profiles of oxygen and deuterium in precipitation and groundwater. We then use an isotope mass balance model to determine seasonal groundwater recharge from precipitation. Climate model output is used to project future changes in precipitation and its seasonal distribution under medium and high climate change scenarios. Finally, these results are combined to examine future changes in groundwater recharge. We find that 76% of the groundwater recharge occurs in the cool season. Climate models project precipitation increase in the cool season and decrease in the warm season. The total groundwater recharge is expected to increase by 3.2% (8.8%) under the medium (high) climate change scenarios.     

How the performance of hydrological models relates to credibility of projections under climate change

Two approaches can be distinguished in studies of climate change impacts on water resources when accounting for issues related to impact model performance: (1) using a multi-model ensemble disregarding model performance, and (2) using models after their evaluation and considering model performance. We discuss the implications of both approaches in terms of credibility of simulated hydrological indicators for climate change adaptation. For that, we discuss and confirm the hypothesis that a good performance of hydrological models in the historical period increases confidence in projected impacts under climate change, and decreases uncertainty of projections related to hydrological models. Based on this, we find the second approach more trustworthy and recommend using it for impact assessment, especially if results are intended to support adaptation strategies. Guidelines for evaluation of global- and basin-scale models in the historical period, as well as criteria for model rejection from an ensemble as an outlier, are also suggested.     

Deep-learning based projection of change in irrigation water-use under RCP 8.5

Stream water-use is essential for both agricultural and hydrological management and yet not many studies have explored its non-stationarity and nonlinearity with meteorological variables. This study proposed a deep-learning based model to estimate agricultural water withdrawal using hydro-meteorological variables, which projected the changes of agricultural water withdrawal influenced by climate change of future. The relationships between meteorological variables and stream water-use rate (WUR) were quantified using a deep belief network (DBN). The influences of precipitation, potential evapotranspiration, and monthly averaged WUR on the performance of the developed DBN model were tested. As a result, this DBN with potential evapotranspiration (PET) provided better performances than precipitation to estimate the WUR. The PET of multi-model scenarios for Representative Concentration Pathways 8.5 would be increased as time goes by, and thus leads to increase WUR estimated by DBN in three basins, located in South Korea during the future period. On the contrary, water availability expected to decrease compared to the current. Therefore, managing water-uses and improving efficiencies can be prepared for the change in agricultural water-use by climate change in the future.     

A multi-model ensemble approach for the assessment of climatic and anthropogenic impacts on river flow change

ABSTRACT

Climate variability and human activities are considered to be the most likely reasons for negative trends in river inflow and the water level of some lakes and wetlands in the world. To quantify the uncertain impacts of climate variations and anthropogenic activities on Ajichay River flow in Iran, a multi-model ensemble approach based on the Bayesian model averaging (BMA) method is applied. Several statistical and simulation-based methods are used to distinguish the impacts of climatic and anthropogenic factors on river flow. The results show that almost all the methods identified human activities as the dominant impact on streamflow (about 73–85% of the change). The between-model and within-model uncertainty analyses using BMA showed that the 95% uncertainty intervals of the individual approaches have relatively large deviation ranges. The BMA mean prediction could reduce the range of between-model uncertainties to 14–27% for climate impacts and 74–80% for human impacts. This approach provides a way to better understand the contributions of climatic and anthropogenic impacts on river flow change.     

Climate models and hydrological parameter uncertainties in climate change impacts on monthly runoff and daily flow duration curve of a Mediterranean catchment

ABSTRACT

Climate models and hydrological parameter uncertainties were quantified and compared while assessing climate change impacts on monthly runoff and daily flow duration curve (FDC) in a Mediterranean catchment. Simulations of the Soil and Water Assessment Tool (SWAT) model using an ensemble of behavioural parameter sets derived from the Generalized Likelihood Uncertainty Estimation (GLUE) method were approximated by feed-forward artificial neural networks (FF-NN). Then, outputs of climate models were used as inputs to the FF-NN models. Subsequently, projected changes in runoff and FDC were calculated and their associated uncertainty was partitioned into climate model and hydrological parameter uncertainties. Runoff and daily discharge of the Chiba catchment were expected to decrease in response to drier and warmer climatic conditions in the 2050s. For both hydrological indicators, uncertainty magnitude increased when moving from dry to wet periods. The decomposition of uncertainty demonstrated that climate model uncertainty dominated hydrological parameter uncertainty in wet periods, whereas in dry periods hydrological parametric uncertainty became more important.

Editor M.C. Acreman; Associate editor S. Kanae     

Climate impacts on global irrigation requirements under 19 GCMs,simulated with a vegetation and hydrology model

Abstract

This study quantifies global changes in irrigation requirements for areas presently equipped for irrigation of major crop types, using climate projections from 19 GCMs up to the 2080s. Analysis is based on results from the global eco-hydrological model LPJmL that simulates the complex and dynamic interplay of direct and indirect climate change effects upon irrigation requirements. We find a decrease in global irrigation demand by ～17% in the ensemble median, due to a combination of beneficial CO₂ effects on plants, shorter growing periods and regional precipitation increases. In contrast, increases of >20% are projected with a high likelihood (i.e. in more than two thirds of the climate change scenarios) for some regions, including southern Europe, and, with a lower likelihood, for parts of Asia and North America as well. If CO₂ effects were not accounted for, however, global irrigation demand would hardly change, and increases would prevail in most regions except for southern Asia (where higher precipitation is projected). We stress that the CO₂ effects may not be realized everywhere, that irrigation requirements will probably increase further due to growing global food demand (not considered here), and that a significant amount of water to meet future irrigation requirements will have to be taken from fossil groundwater, environmental flow reserves or diverted rivers.

Editor D. Koutsoyiannis; Associate editor A. Montanari

Citation Konzmann, M., Gerten, D., and Heinke, J., 2013. Climate impacts on global irrigation requirements under 19 GCMs, simulated with a vegetation and hydrology model. Hydrological Sciences Journal, 58 (1), 1–18.     

Future hydro-meteorological drought of the Johor River Basin,Malaysia, based on CORDEX-SEA projections

Water scarcity issues in the Johor River Basin (JRB) could affect the populations of Malaysia and Singapore. This study provides an overview of future hydro-meteorological droughts using climate projections from an ensemble of four Coordinated Regional Climate Downscaling Experiments – Southeast Asia (CORDEX-SEA) domain outputs under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios for the 2021–2050 and 2071–2100 periods. The climate projections were bias corrected using the quantile mapping approach before being incorporated into the Soil and Water Assessment Tool (SWAT) hydrological model. The Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were used to examine the meteorological and hydrological droughts, respectively. Overall, future annual precipitation, streamflow, and maximum and minimum temperatures are projected to change by about ?44.2 to 24.3%, ?88.7 to 42.2%, 0.8 to 3.7ºC and 0.7 to 4.7ºC, respectively. The results show that the JRB is likely to receive more frequent meteorological droughts in the future.     

Climate change sensitivity assessment of streamflow and agricultural pollutant transport in California's Central Valley using Latin hypercube sampling

Bracketing the uncertainty of streamflow and agricultural runoff under climate change is critical for proper future water resource management in agricultural watersheds. This study used the Soil and Water Assessment Tool (SWAT) in conjunction with a Latin hypercube climate change sampling algorithm to construct a 95% confidence interval (95CI) around streamflow, sediment load, and nitrate load predictions under changes in climate for the Sacramento and San Joaquin River watersheds in California's Central Valley. The Latin hypercube algorithm sampled 2000 combinations of precipitation and temperature changes based on Intergovernmental Panel on Climate Change projections from multiple General Circulation Models. Average monthly percent changes of the upper and lower 95CI limits compared to the present‐day simulation and a statistic termed the “r‐factor” (average width of the 95CI band divided by the standard deviation of the 95CI bandwidth) were used to assess watershed sensitivities. 95CI results indicate that streamflow and sediment runoff in the Sacramento River watershed are more likely to decrease under climate change compared to present‐day conditions, whereas the increase and decrease for nitrate runoff were found to be equal. For the San Joaquin River watershed, streamflow slightly decreased under climate change, whereas sediment and nitrate runoff increased compared to present‐day climate. Comparisons of watershed sensitivities indicate that the San Joaquin River watershed is more sensitive to climate changes than the Sacramento River watershed, which is largely caused by the high density of agricultural land. Copyright © 2012 John Wiley & Sons, Ltd.     

Projection of surface water resources in the context of climate change in typical regions of China

Climate change and its impact on hydrological processes are overarching issues that have brought challenges for sustainable water resources management. In this study, surface water resources in typical regions of China are projected in the context of climate change. A water balance model based on the Fu rational function equation is established to quantify future natural runoff. The model is calibrated using data from 13 hydrological stations in 10 first-class water resources zones of China. The future precipitation and temperature series come from the ISI-MIP (Inter-Sectoral Impact Model Intercomparison Project) climate dataset. Taking natural runoff for 1961–1990 as a baseline, the impacts of climate change on natural runoff are studied under three emissions scenarios: RCP2.6, RCP4.5 and RCP8.5. Simulated results indicate that the arid and semi-arid region in the northern part of China is more sensitive to climate change compared to the humid and semi-humid region in the south. In the near future (2011–2050), surface water resources will decrease in most parts of China (except for the Liaozhong and Daojieba catchments), especially in the Haihe River Basin and the middle reaches of the Yangtze River Basin. The decrement of surface water resources in the northern part of China is more than that in the southern part. For the periods 2011–2030 and 2031–2050, surface water resources are expected to decrease by 12–13% in the northern part of China, while those in the southern part will decrease by 7–10%.

EDITOR D. Koutsoyiannis

ASSOCIATE EDITOR R. Hirsch     

The impact of climate change on extreme precipitation in Sicily,Italy

Increasing precipitation extremes are one of the possible consequences of a warmer climate. These may exceed the capacity of urban drainage systems, and thus impact the urban environment. Because short‐duration precipitation events are primarily responsible for flooding in urban systems, it is important to assess the response of extreme precipitation at hourly (or sub‐hourly) scales to a warming climate. This study aims to evaluate the projected changes in extreme rainfall events across the region of Sicily (Italy) and, for two urban areas, to assess possible changes in Depth‐Duration‐Frequency (DDF) curves. We used Regional Climate Model outputs from Coordinated Regional Climate Downscaling Experiment for Europe area ensemble simulations at a ~12 km spatial resolution, for the current period and 2 future horizons under the Representative Concentration Pathways 8.5 scenario. Extreme events at the daily scale were first investigated by comparing the quantiles estimated from rain gauge observations and Regional Climate Model outputs. Second, we implemented a temporal downscaling approach to estimate rainfall for sub‐daily durations from the modelled daily precipitation, and, lastly, we analysed future projections at daily and sub‐daily scales. A frequency distribution was fitted to annual maxima time series for the sub‐daily durations to derive the DDF curves for 2 future time horizons and the 2 urban areas. The overall results showed a raising of the growth curves for the future horizons, indicating an increase in the intensity of extreme precipitation, especially for the shortest durations. The DDF curves highlight a general increase of extreme quantiles for the 2 urban areas, thus underlining the risk of failure of the existing urban drainage systems under more severe events.     

Impact of human activities on stream flow in the Biliu River basin,China

The obvious decline in stream flow to the Biliu River reservoir over the period 1990–2005 has raised increasing concerns. Climate change and human activities, which mainly include land use changes, hydraulic constructions and artificial water consumption, are considered to be the most likely reasons for the decline in stream flow. This study centres on a detailed analysis of the runoff response to changes in human activities. Using a distributed hydrological model, (Soil and Water Assessment Tool), we simulated runoffs under different human activity and climate scenarios to understand how each scenario impacts stream flow. The results show that artificial water consumption correlates with the precipitation (wet, normal and dry) of the year in question and is responsible for most of the decrease in runoff during each period and for each different wetness year. A Fuzzy Inference Model is also used to find the relationship between the precipitation and artificial water consumption for different years, as well as to make inferences regarding the future average impact on runoff. Land use changes in the past have increased the runoff by only a small amount, while another middle reservoir (Yunshi) has been responsible for a decrease in runoff since operation began in 2001. We generalized the characteristics of the human activities to predict future runoff using climate change scenarios. The future annual flow will increase by approximately 10% from 2011 to 2030 under normal human activities and future climate change scenarios, as indicated by climate scenarios with a particularly wet year in the next 20 years. This study could serve as a framework to analyse and predict the potential impacts of changes both in the climate and human activities on runoff, which can be used to inform the decision making on the river basin planning and management. Copyright © 2012 John Wiley & Sons, Ltd.     

Changes in hydrology and streamflow as predicted by a modelling experiment forced with climate models

Changes in precipitation and temperature have direct effects on crop water use, water stress, crop yield, evapotranspiration, water nutrient dynamics and other indicators. This study, built on a modelling framework with the Soil and Watershed Assessment Tool (SWAT) model for the Raccoon River Watershed in central Iowa, a typical US Midwestern agricultural watershed, examines the watershed response to changes in meteorological inputs from an ensemble of ten global climate models under the A1B scenario. Changes in climate were directly applied to observations (the delta change method) assuming that the estimates of climate change are reliable even if the simulated current climate may be biased. The ensemble average for the mid‐century (1946–1965) predicted 0.7% increase in daily precipitation (monthly variation from ?11.3% to +19.5%) and 2.78 °C increase in average temperature over the entire watershed. These predictions were translated through a well‐calibrated SWAT modelling setup into 22% decrease in snowfall, 16% decrease in surface runoff, 18% decrease in baseflow, 8% increase in evapotranspiration and 17% decrease in total water yield. The spatial impact at the subwatershed level revealed a wide variation (but no defined trend) with decrease in water yield that ranged from 10% to 23%. Flow near the watershed outlet (Van Meter, Iowa) is expected to decline by 17% on an average annual basis with the highest impact occurring during summer months with a maximum 39% reduction in August. Changes in climate were found to have a clear and significant impact signal of decreasing streamflow at the watershed outlet with far‐reaching implication for drinking water supplies for the central Iowa communities. Copyright © 2013 John Wiley & Sons, Ltd.     

Assessment of future water provisioning and sediment load under climate and LULC change scenarios in a peninsular river basin,India

Assessment of the impact of changes in climate and land use and land cover (LULC) on ecosystem services (ES) is important for planning regional-scale strategies for sustainability and restoration of ES. The Upper Narmada River Basin (UNRB) in peninsular India has undergone rapid LULC change due to recent agricultural expansion. The impact of future climate and LULC change on ES in the UNRB is quantified and mapped using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST 3.3.0) tool. Our results show that water yield is projected to increase under climate change (about 43% for representative concentration pathway 4.5 for 2031–2040), whereas it is projected to decrease under the LULC change scenario. Sediment export is projected to increase (by 54.53%) under LULC change for 2031–2040. Under the combined effect of climate and LULC change, both water yield and sediment export are expected to increase. Climate change has a greater impact on projected water yield than LULC change, whereas LULC has greater impact on sediment export. Spatial analysis suggests a similar trend of variation in relative difference (RD) of ES in adjacent sub-basins. The quantified changes in ES provisioning will benefit future land management, particularly for operation of the Rani Avanti Bai Sagar Reservoir downstream of the UNRB.     

CMIP5多模式集合对东北三省未来气候变化的预估研究

应用CN05观测资料,以及参与国际耦合模式比较计划第5阶段(CMIP5)中的26个模式,评估了新一代全球气候模式对东北三省气候变化模拟能力并选出4个较优模式,发现经过筛选得出的较优模式集合平均模拟结果的可靠性得到进一步加强,尤其体现在对气温的模拟上.在此基础上着重分析了多模式集合在不同典型浓度路径(RCPs)下对未来气候变化特征的预估.结果表明:21世纪的未来阶段,东北三省将处于显著增温的状态,且RCP8.5情景下的增温速率(0.53℃/10a)明显高于RCP4.5情景下的速率(0.22℃/10a);空间上,北部地区将成为增温幅度最大、增温速率最高的区域.未来降水将会相对增加,但波动较大,21世纪末期RCP4.5和RCP8.5情景下的降水增加幅度分别为11.24%和15.95%;空间上,辽宁省西部地区将成为降水增加最为显著的区域.根据水分盈亏量,21世纪未来阶段,RCP4.5情景下的东北三省绝大多数地区未来将相对变湿,尤其到了中后期;RCP8.5情景下则是中西部地区将相对变干,其余地区则会相对变湿.