Future Precipitation Projections Research Articles

Multi-Model Ensemble Projection of Precipitation Changes over China under Global Warming of 1.5 and 2°C with Consideration of Model Performance and Independence

A weighting scheme jointly considering model performance and independence (PI-based weighting scheme) is employed to deal with multi-model ensemble prediction of precipitation over China from 17 global climate models. Four precipitation metrics on mean and extremes are used to evaluate the model performance and independence. The PI-based scheme is also compared with a rank-based weighting scheme and the simple arithmetic mean (AM) scheme. It is shown that the PI-based scheme achieves notable improvements in western China, with biases decreasing for all parameters. However, improvements are small and almost insignificant in eastern China. After calibration and validation, the scheme is used for future precipitation projection under the 1.5 and 2°C global warming targets (above preindustrial level). There is a general tendency to wetness for most regions in China, especially in terms of extreme precipitation. The PI scheme shows larger inhomogeneity in spatial distribution. For the total precipitation PRCPTOT (95th percentile extreme precipitation R95P), the land fraction for a change larger than 10% (20%) is 22.8% (53.4%) in PI, while 13.3% (36.8%) in AM, under 2°C global warming. Most noticeable increase exists in central and east parts of western China.

Combating food insecurity in a rapidly changing mountain climate environment: insights from Lesotho

This paper assesses the options that developing countries have in ensuring food security in an environment where key climate parameters are changing rapidly. Based on a case study of Lesotho, the paper utilizes the Global Climate Model ensemble to determine future precipitation and temperature projections using data from Climate Explorer. The results indicate that in Lesotho, maximum temperature is likely to continue to increase. Coupled with a significant increase in precipitation under both Representative Concentration Pathway (RCP) 4.5 (p = 0.0008) and RCP 8.5 (p = 0.0001) scenarios and a significant increase of evaporation under the two scenarios for RCP 4.5 (p = 0.0008) and RCP 8.5 (p = 0.0103), the country’s preparedness for hazards arising from climate change is rendered uncertain. Despite this reality, we suggest that uncertainty could be reduced by reinforcing existing innovative measures that could improve the productive capacity of subsistence farmers, so that they meet their own food requirements, while preventing further environmental deterioration. While some measures will be based on the intensification of government-led social support mechanisms, others will depend on the support rendered to “tried and tested” traditional practices such as machobane and fato-fato, which have a long tradition in the country. However, on their own, these measures are insufficient to cope with rapidly changing climatic conditions, unless they are coupled with national research development initiatives, improved early warning systems, and enhancement of environmental monitoring capabilities, the implementation of which requires careful land use planning.

Future climatic changes, extreme events, related uncertainties, and policy recommendations in the Hindu Kush sub-regions of Pakistan

This study explores the relative changes and future projections of temperature and precipitation for baseline (1976–2005) and future (2006–2040, 2041–2070, and 2071–2100) periods for the Bajaur, Mohmand, and Khyber districts of Pakistan situated in the Hindu Kush region. Data from 14 GCMs (out of which five GCMs were selected based on evaluation and validation) and three RCMs were downscaled and bias corrected using the quantile delta mapping (QDM) method for GCMs and the best easy systemic (BES) method for RCMs. The future extremes were projected by using the standard indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Uncertainty in the data was analysed by a probabilistic distribution function (PDF), boxplots, and standard deviation. For RCP4.5, the GCM and RCM projections show an increase in the average maximum temperature of 0.98 °C for 2006–2040, 1.89 °C and 2.04 °C for 2041–2070, and 2.25 °C and 2.56 °C for 2071–2100, respectively, while the increase is almost double for RCP8.5 during the last period (2071–2099) over the whole study area. The percent increase in precipitation over the whole study area from GCMs and RCMs for RCP4.5 is 10.00–17.00% and 21.14–34.47%, while for RCP8.5, it is 11.73–22.12% and 16.17–31.50%, respectively. In RCM projections for RCP4.5, the Khyber district will experience drier conditions in mid-century, while for GCMs (RCP4.5), the same conditions are projected through the end of the century. In terms of extreme events, warm temperature extremes and extreme precipitation events show an increasing trend accompanied by a decrease in cold extremes over all regions. Hence, it is concluded that warm and wet conditions are projected to prevail in all regions. However, GCM data depict less uncertainty compared with RCM data, and while models show less error for the baseline period, the error increases slightly for the future periods. Thus, this study provides research findings along with policy recommendations essential to combat the potential impacts of projected changes in the regional climate.

Refining projected multidecadal hydroclimate uncertainty in East-Central Europe using CMIP5 and single-model large ensemble simulations

Future hydroclimate projections of global climate models for East-Central Europe diverge to a great extent, thus, constrain adaptation strategies. To reach a more comprehensive understanding of this regional spread in model projections, we make use of the CMIP5 multi-model ensemble and six single-model initial condition large ensemble (SMILE) simulations to separate the effects of model structural differences and internal variability, respectively, on future hydroclimate projection uncertainty. To account for model uncertainty, we rank 32 CMIP5 models based on their predictive skill in reproducing multidecadal past hydroclimate variability. Specifically, we compare historical model simulations to long instrumental and reanalysis surface temperature and precipitation records. The top 3–ranked models—that best reproduce regional past multidecadal temperature and precipitation variability—show reduced spread in their projected future precipitation variability indicating less dry summer and wetter winter conditions in part at odds with previous expectations for Central Europe. Furthermore, not only does the regionally best performing CMIP5 models belong to the previously identified group of models with more realistic land-atmosphere interactions, their future summer precipitation projections also emerge from the range of six SMILEs’ future simulations. This suggests an important role for land-atmosphere coupling in regulating hydroclimate uncertainty on top of internal variability in the upcoming decades. Our results help refine the relative contribution of structural differences between models in affecting future hydroclimate uncertainty in the presence of irreducible internal variability in East-Central Europe.

Projected precipitation changes over China for global warming levels at 1.5 °C and 2 °C in an ensemble of regional climate simulations: impact of bias correction methods

Four bias correction methods, i.e., gamma cumulative distribution function (GamCDF), quantile–quantile adjustment (QQadj), equidistant cumulative probability distribution function (CDF) matching (EDCDF), and transform CDF (CDF-t), to read are applied to five daily precipitation datasets over China produced by LMDZ4-regional that was nested into five global climate models (GCMs), BCC-CSM1-1m, CNRM-CM5, FGOALS-g2, IPSL-CM5A-MR, and MPI-ESM-MR, respectively. A unified mathematical framework can be used to define the four bias correction methods, which helps understanding their natures and essences for identifying the most reliable probability distributions of projected climate. CDF-t is shown to be the best bias correction method based on a comprehensive evaluation of different precipitation indices. Future precipitation projections corresponding to the global warming levels of 1.5 °C and 2 °C under RCP8.5 were obtained using the bias correction methods. The multi-method and multi-model ensemble characteristics allow to explore the spreading of projections, considered a surrogate of climate projection uncertainty, and to attribute such uncertainties to different sources. It was found that the spread among bias correction methods is smaller than that among dynamical downscaling simulations. The four bias correction methods, with CDF-t at the top, all reduce the spread among the downscaled results. Future projection using CDF-t is thus considered having higher credibility.

Statistically downscaled precipitation sensitivity to gridded observation data and downscaling technique

AbstractFuture climate projections illuminate our understanding of the climate system and generate data products often used in climate impact assessments. Statistical downscaling (SD) is commonly used to address biases in global climate models (GCM) and to translate large‐scale projected changes to the higher spatial resolutions desired for regional and local scale studies. However, downscaled climate projections are sensitive to method configuration and input data source choices made during the downscaling process that can affect a projection's ultimate suitability for particular impact assessments. Quantifying how changes in inputs or parameters affect SD‐generated projections of precipitation is critical for improving these datasets and their use by impacts researchers. Through analysis of a systematically designed set of 18 statistically downscaled future daily precipitation projections for the south‐central United States, this study aims to improve the guidance available to impacts researchers. Two statistical processing techniques are examined: a ratio delta downscaling technique and an equi‐ratio quantile mapping method. The projections are generated using as input results from three GCMs forced with representative concentration pathway (RCP) 8.5 and three gridded observation‐based data products. Sensitivity analyses identify differences in the values of precipitation variables among the projections and the underlying reasons for the differences.Results indicate that differences in how observational station data are converted to gridded daily observational products can markedly affect statistically downscaled future projections of wet‐day frequency, intensity of precipitation extremes, and the length of multi‐day wet and dry periods. The choice of downscaling technique also can affect the climate change signal for variables of interest, in some cases causing change signals to reverse sign. Hence, this study provides illustrations and explanations for some downscaled precipitation projection differences that users may encounter, as well as evidence of symptoms that can affect user decisions.

Future changes in precipitation-caused landslide frequency in British Columbia

Landslide hazards in British Columbia are mainly caused by precipitation and can result in significant damage and fatalities. Anthropogenic climate change is expected to increase precipitation frequency and intensity in the winter, spring, and fall in British Columbia (BC), potentially resulting in increased frequency of landslide hazard. Quantifying the effect of changing precipitation on future landslide hazard across the varying topographic and climatic conditions in BC requires detailed projections of future precipitation. Here, the operational Landslide Hazard Assessment for Situational Awareness (LHASA) model is used with high-resolution, statistically downscaled daily precipitation to generate detailed simulations of landslide hazard in BC over the twenty-first century. Historical evaluation of the LHASA model is performed using a station-based, gridded observational precipitation dataset. Classification of observed landslide dates and locations as hazard events occurs as successfully as, or slightly better than, when LHASA is applied globally with satellite precipitation. Using the LHASA model with precipitation projections from 12 downscaled global climate models following RCP8.5 indicates that future landslide hazard frequency will increase from 16 days per year to 21 days per year (32%) on average by the 2050s for landslide susceptible regions in the province. Areas of the province currently with the most frequent landslide hazards (18 to 21 days per year), including the west coast and northern Rocky Mountains, are expected to see between 8 and 11 additional hazardous days (49 to 61% increases) per year. Most of the increased hazard frequency occurs during winter and fall, reflecting those seasons with the largest projected increases in single and multi-day precipitation. Risk assessments for regions in British Columbia vulnerable to landslides will need to account for increasing hazard due to climate change altered precipitation.

Characteristics and future projections of summer extreme precipitation in Sichuan Province, China

Based on the precipitation data observed by stations and data simulated by 23 CMIP5 models, the features and future changes of summer (Jun-Jul-Aug) extreme precipitation events in Sichuan Province of China were analysed. We found that the total precipitation (RSum), extreme precipitation threshold (Threshold90), extreme precipitation (TR90), extreme precipitation percentage (TR90pct) and extreme precipitation intensity (TR90str) decreased from the southeast to the northwest in Sichuan Province, reflecting the differences between eastern Sichuan (ESC, basins) and western Sichuan (WSC, mountains). Compared with the observations, most of the CMIP5 models showed that there were wet biases in WSC and an unclear bias pattern in ESC for the RSum, Threshold90, TR90, and TR90str. However, the extreme precipitation days (ND90) and TR90pct values simulated by the models were generally overestimated and underestimated, respectively. Compared with the historical period, most models showed obvious increases in the TR90 and TR90pct in the 21 century, while the characteristics of Rsum, ND90, and TR90str were inconspicuous. Compared with the mid-21st century, the extreme precipitation in the late-21st century exhibited a certain degree of increase. Even during the same period, the results of RCP8.5 were higher than those of RCP4.5, especially for the ND90, TR90, and TR90pct.

Application of artificial neural network and predictor screening method for downscaling climatic parameters

In this paper, artificial neural network (ANN) was used for downscaling the outputs of general circulation models (GCMs) to evaluate changes in precipitation and mean temperature for a future period in Urmia at the north-west of Iran. MIROC-ESM-CHEM from IPCC AR5 was selected as an acceptable model based on correlation coefficient (CC) values, which is calculated between precipitation of GCM models and precipitation data prepared by Urmia Meteorological Organization for 1951-2000. As a first step, the most important parameters of the MIROC-ESM-CHEM were selected before the downscaling process by ANN in the base period (1951-2000). Afterward, the future projections of precipitation and mean temperature during 2020-2060 were applied using ANN-based simulation according to the CC method. By comparing the results, the MIROC-ESM-CHEM showed a 2.01% increase under RCP4.5 and a 0.16% decrease under RCP8.5 in annual precipitation. Also, the temperature projection outputs showed the annual mean temperature would increase in the future period in this area, and it is likely to get warmer.

Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments

Abstract. Assessing impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control, and ecosystem conservation practices. Such assessments are commonly accomplished using outputs from a hydrologic model forced with future precipitation and temperature projections. The algorithms used for the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties into the simulated hydrologic variables. Here, a modeling framework was developed that integrates multiple runoff generation algorithms with a routing model and associated parameter optimizations. This framework is able to identify uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Three fundamentally different runoff generation approaches, runoff coefficient method (RCM, conceptual), variable infiltration capacity (VIC, physically based, infiltration excess), and simple-TOPMODEL (STP, physically based, saturation excess), were coupled with the Hillslope River Routing model to simulate surface/subsurface runoff and streamflow. A case study conducted in Santa Barbara County, California, reveals increased surface runoff in February and March but decreased runoff in other months, a delayed (3 d, median) and shortened (6 d, median) wet season, and increased daily discharge especially for the extremes (e.g., 100-year flood discharge, Q100). The Bayesian model averaging analysis indicates that the probability of such an increase can be up to 85 %. For projected changes in runoff and discharge, general circulation models (GCMs) and emission scenarios are two major uncertainty sources, accounting for about half of the total uncertainty. For the changes in seasonality, GCMs and hydrologic models are two major uncertainty contributors (∼35 %). In contrast, the contribution of hydrologic model parameters to the total uncertainty of changes in these hydrologic variables is relatively small (<6 %), limiting the impacts of hydrologic model parameter equifinality in climate change impact analysis. This study provides useful information for practices associated with water resources, risk control, and ecosystem conservation and for studies related to hydrologic model evaluation and climate change impact analysis for the study region as well as other Mediterranean regions.

Evaluation of temporal drought variation and projection in a tropical river basin of Kerala

Abstract Temperature is an indispensable parameter of climate that triggers evapotranspiration and has vital importance in aggravating drought severity. This paper analyses the existence and persistence of drought conditions which are said to prevail in a tropical river basin which was once perennial. Past observed data and future climate projections of precipitation and temperature were used for this purpose. The assessment and projection of this study employ the Standardized Precipitation Evapotranspiration Index (SPEI) compared with that of the Standardized Precipitation Index (SPI). The results indicate the existence of drought in the past and the drought conditions that may persist in the future according to RCP 4.5 and 8.5 scenarios. The past drought years identified in the study were compared with the drought declared years in the state and were found to be matching. The evaluation of the future scenarios unveils the occurrence of drought in the basin ranging from mild to extreme conditions. It has been noted that the number of moderate and severe drought months has increased based on SPEI compared to SPI, indicating the importance of temperature in drought studies. The study can be considered as a plausible scientific remark helpful in risk management and application decisions.

Long-Term Rainfall Trends and Future Projections over Xijiang River Basin, China

Precipitation trend detection is vital for water resources development and decision support systems. This study predicts the climate change impacts on long-term precipitation trends. It deals with the analysis of observed historical (1960–2010) and arithmetic mean method in assembling precipitation from CMIP5 Global Climate Models (GCMs) datasets for a future period (2020–2099) under four emission scenarios. Daily precipitation data of 32 weather stations in the Xijiang River Basin were provided by National Meteorological Information Centre (NMIC) of the China Meteorological Administration (CMA) and Global Climate Models (GCMs) with all four emission scenarios statistically downscaled using Bias Correction Special Disaggregation (BCSD) and applied for bias correction via Climate Change Toolkit (CCT). Nonparametric Mann–Kendall test was applied for statistical significance trend analysis while the magnitude of the trends was determined by nonparametric Sen’s estimator method on a monthly scale to detect monotonic trends in annual and seasonal precipitation time series. The results showed a declined trend was observed for the past 50 years over the basin with negative values of MK test (Z) and Sen’s slope Q. Historical GCMs precipitation detected decreasing trends except for NoerESM1-M which observed slightly increasing trends. The results are further validated by historical precipitation recorded by the Climate Research Unit (CRU-TS-3.1). The future scenarios will likely be positive trends for annual rainfall. Significant positive trends were observed in monsoon and winter seasons while premonsoon and postmonsoon seasons will likely be slightly downward trends. The 2040s will likely observe the lowest increase of 6.6% while the 2050s will observe the highest increase of 11.5% over the 21st century under future scenarios. However, due to the uncertainties in CMIP5, the future precipitation projections should be interpreted with caution. Thus, it could be concluded that the trend of change in precipitation around the Xijiang River Basin is on the increase under future scenarios. The results can be valuable to water resources and agriculture management policies, as well as the approach for managing floods and droughts under the perspective of global climate change.

Estimation of the Future Probable Precipitation based on the Assumption of Non-Stationarity in Seoul

In this study, an estimation of the future probable rainfall in Seoul, Korea, was performed, using non-stationary frequency analysis according to climate change and it was compared with the current probable rainfall. Hourly rainfall data provided by the Korea Meteorological Administration with durations of 1, 2, 3, 6, 12, 24, and 48-h were used as input. For the future projection of precipitation, the RCP 8.5 scenario was selected with the same durations. Moreover, the future hourly rainfall was extracted from using the daily precipitation from 29 Global Climate Models (GCMs), based on the statistical temporal down-scaling method and their corresponding bias corrections. Subsequently, the annual maximum precipitation was extracted for each year. In this study, both stationary and non-stationary frequency analysis was applied based on the observed and predicted time series data sets. In particular, for the non-stationary frequency analysis, the Differential Evolution Markov Chain technique, which combines the Bayesian-based Differential Evolution and Markov chain Monte Carlo methods, was adopted. Finally, the current and future intensity-duration-frequency curves were derived from the optimal probability distribution, and each probable rainfall was estimated. The results of the 29-scenario are presented with quantile estimations. The non-stationary frequency analysis results for Seoul revealed rainfalls of 94.4 mm/h for 30 y, 101.7 mm/h for 50 y, and 111.5 mm/h for 100 y return periods. The average value of the 29-GCM model ensemble was estimated to be approximately 5 mm/h higher than that obtained from the stationary frequency analysis. Considering the changes in hydrological characteristics due to climate change in Seoul, the results of this study could be utilized to pro-actively respond to natural disasters due to such phenomena.

Future Projections of Heavy Precipitation in Kanto and Associated Weather Patterns Using Large Ensemble High-Resolution Simulations

Future Projections of Heavy Precipitation in Kanto and Associated Weather Patterns Using Large Ensemble High-Resolution Simulations

The seesaw response of the intertropical and South Pacific convergence zones to hemispherically asymmetric thermal forcing

Arguments based on atmospheric energetics and aqua-planet model simulations link the latitudinal position of the intertropical convergence zone (ITCZ) to atmospheric cross-equatorial energy transport—a greater southward transport corresponds to a more northerly position of the ITCZ. This idea is often invoked to explain an interhemispheric dipole pattern of precipitation anomalies in paleoclimates. In contrast, here we demonstrate that in the tropical Pacific the response of the fully coupled ocean-atmosphere system to a hemispherically asymmetric thermal forcing, modulating this energy transport, involves an interplay between the ITCZ and its counterpart in the South Pacific—the Southern Pacific convergence zone (SPCZ). This interplay leads to interhemispheric seesaw changes in tropical precipitation, such that the latitudinal position of each rain band remains largely fixed, but their intensities follow a robust inverse relationship. The seesaw behavior is also evident in the past and future coupled climate simulations of the Climate Model Intercomparison Project Phase 5 (CMIP5). We also show that the tropical Pacific precipitation response to thermal forcing is qualitatively different between the aqua-planet (without ocean heat transport), slab-ocean (with climatological ocean heat transport represented by a “Q-flux”) and fully-coupled model configurations. Specifically, the induced changes in the ITCZ latitudinal position successively decrease, while the seesaw precipitation intensity response becomes more prominent, from the aqua-planet to the slab-ocean to the fully-coupled configuration. Thus, the ITCZ/SPCZ seesaw can explain the paleoclimate precipitation dipole pattern without invoking a too strong climate forcing and is relevant to future projections of tropical precipitation.

Future projection of winter precipitation over northwest India and associated regions using CORDEX-SA experiments

Precipitation during winter months of December, January, and February over northwest India is important for the production of winter crops as well as for glacial accumulation which goes on to feed major rivers in India with fresh water supply. Winter precipitation over this region is due to the movement of synoptic systems approaching from the Mediterranean known as Western Disturbances (WDs). So far, spatial pattern of winter precipitation over this region has not been studied using different RCMs. The present study evaluates the regional climate model employed in Coordinated Regional Climate Downscaling Experiment in South Asia (CORDEX-South Asia) framework to characterize winter precipitation over northwest India and Karakoram region. There are 16 CORDEX-South Asia experiments with 3 regional climate models (RCM) driven with 16 global climate models (GCM). Precipitation climatology is calculated for different RCM along with their ensemble. All the experiments show a wet bias over the region. The selection of best performing RCM is identified using various statistical analyses. The CORDEX experiment run by EC_EARTH RCM with RCA4 GCM developed by Swedish Meteorological and Hydrological Institute (SMHI) is identified as the best performing model when compared with observation. The change in future seasonal mean precipitation is assessed over northwest India and Karakoram region under two representation concentration pathways (RCP4.5 and RCP8.5) scenarios. A positive change in precipitation is observed over Karakoram region under the two RCP scenarios for near future and far future periods. The magnitude of precipitation change is found to be less over the study area under RCP 8.5 scenario for near future period compared to other scenario and time period.

Impact of Reference Dataset Selection on RCM Evaluation, Bias Correction, and Resulting Climate Change Signals of Precipitation

Abstract Gridded datasets of precipitation are of great importance to evaluate recent climate models and are frequently applied to select a subset of available models. As climate models are still prone to biases on the regional scale, gridded datasets are also essential to correct or adjust these biases. Various studies revealed considerable differences, that is, observational uncertainty, in the available gridded datasets of precipitation, especially over complex terrain. This study focuses on the impacts of observational uncertainty on the evaluation, selection, and bias correction of 15 regional climate model (RCM) simulations provided through the EURO-CORDEX initiative over the alpine Adige catchment located in northern Italy. Nine reference datasets originating from observations, reanalysis, and remote sensing are applied to evaluate the performance of RCMs and select a subset based on validity. These reference datasets are then applied to bias correct the RCM ensemble using a standard quantile mapping method, and the resulting changes in the projections are assessed. The presented results show a selection of similar RCMs, indicating that observational uncertainty is lower than model uncertainty. The influence of the choice of the reference dataset on bias correction is negligible for the climate change signals. Small differences in projected change signals can be attributed to model selection. As expected, the choice of the reference dataset strongly influences future projections of precipitation even more pronounced for the extremes. The findings of this study highlight the need to account for observational uncertainty for bias correction of RCM simulations for impact modeling studies.

Projected changes in Brook Trout and Brown Trout distribution in Wisconsin streams in the mid-twenty-first century in response to climate change

Climate warming is a threat to the survival of fishes adapted to cold water. Brook Trout Salvelinus fontinalis and Brown Trout Salmo trutta are two cold-water species occurring in streams in Wisconsin, where climate change may make these species particularly vulnerable. Vulnerable trout populations need to be identified to aid in the development of adaptation strategies. We used web-based stream temperature and fish-distribution models in FishVis to predict current (late twentieth century) and project future (mid-twenty-first century) distributions of Brook Trout and Brown Trout. The models predict the suitability of habitat for trout in individual reaches using environmental variables in a geographic information system, including adjacent and upstream channel characteristics, surficial geology, landcover, and climate. Future projections of air temperature and precipitation were obtained from 13 general circulation models downscaled for Wisconsin. Currently, 34,251 km of streams are suitable for Brook Trout and 20,011 km for Brown Trout. The models project a decline of 68% (10,995 km) in stream habitat for Brook Trout and a decline of 32% (13,668 km) for Brown Trout. These projected declines, while substantial, were lower than earlier estimates because our models account for projected increased precipitation that may enhance groundwater inputs and partially offset higher air temperatures.

Estimating the response of hydrological regimes to future projections of precipitation and temperature over the upper Yangtze River

The upper region of the Yangtze River, as the freshwater source for the middle and lower Yangtze River, is highly vulnerable to climate change. Understanding and projecting the spatiotemporal variability of extreme flows in this region are key to creating appropriate adaptation policies and strategies. In this study, climate change signals were scaled from seven bias-corrected CMIP5 models and were used to force a variable infiltration capacity (VIC) model to quantify and assess the impact of climate change on the hydrology of the region. The respective contribution of precipitation (P) and temperature (T) to evapotranspiration (ET) and hydrological regimes (annual mean runoff, high flow, and low flow) were also quantified using the analysis of variance method (ANOVA). For the scenario period (2031–2060), the projected runoff is projected to increase in vary between 11.33% and 64.08% in winter, while increases in summer range from 8.64% to 40.97%. The changes in ET are different depending on the climate mode used; these increase during winter and decrease in summer under the influence of temperature. In addition, we found that low flows are projected to decrease while the mean annual runoff will increase in the future period. These projected changes demonstrated that temperature effects dominate in the study area for the future period under the representative concentration pathway 8.5 scenario.

Quantifying the range of future glacier mass change projections caused by differences among observed past-climate datasets

Observed past climate data used as input in glacier models are expected to differ among datasets, particularly those for precipitation at high elevations. Differences among observed past climate datasets have not yet been described as a cause of uncertainty in projections of future changes in glacier mass, although uncertainty caused by varying future climate projections among general circulation models (GCMs) has often been discussed. Differences among observed past climate datasets are expected to propagate as uncertainty in future changes in glacier mass due to bias correction of GCMs and calibration of glacier models. We project ensemble future changes in the mass of glaciers in Asia through the year 2100 using a glacier model. A set of 18 combinations of inputs, including two observed past air temperature datasets, three observed past precipitation datasets, and future air temperature and precipitation projections from three GCMs were used. The uncertainty in projected changes in glacier mass was partitioned into three distinct sources: GCM uncertainty, observed past air temperature uncertainty, and observed past-precipitation uncertainty. Our findings indicate that, in addition to the differences in climate projections among GCMs, differences among observed past climate datasets propagate fractional uncertainties of about 15% into projected changes in glacier mass. The fractional uncertainty associated with observed past precipitation was 33–50% that of the observed air temperature. Differences in observed past air temperatures and precipitation did not propagate equally into the ultimate uncertainty of glacier mass projection when ablation was dominant.