The summer rainfall over the middle-lower reaches of the Yangtze River valley (YRSR) has been estimated with a multi-linear regression model using principal atmospheric modes derived from a 500 hPa geopotential height...The summer rainfall over the middle-lower reaches of the Yangtze River valley (YRSR) has been estimated with a multi-linear regression model using principal atmospheric modes derived from a 500 hPa geopotential height and a 700 hPa zonal vapor flux over the domain of East Asia and the West Pacific.The model was developed using data from 1958 92 and validated with an independent prediction from 1993 2008.The independent prediction was efficient in predicting the YRSR with a correlation coefficient of 0.72 and a relative root mean square error of 18%.The downscaling model was applied to two general circulation models (GCMs) of Flexible Global Ocean-Atmosphere-Land System Model (FGOALS) and Geophysical Fluid Dynamics Laboratory coupled climate model version 2.1 (GFDL-CM2.1) to project rainfall for present and future climate under B1 and A1B emission scenarios.The downscaled results pro-vided a closer representation of the observation compared to the raw models in the present climate.In addition,compared to the inconsistent prediction directly from dif-ferent GCMs,the downscaled results provided a consistent projection for this half-century,which indicated a clear increase in the YRSR.Under the B1 emission scenario,the rainfall could increase by an average of 11.9% until 2011 25 and 17.2% until 2036 50 from the current state;under the A1B emission scenario,rainfall could increase by an average of 15.5% until 2011 25 and 25.3% until 2036 50 from the current state.Moreover,the increased rate was faster in the following decade (2011 25) than the latter of this half-century (2036 50) under both emissions.展开更多
Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screen...Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screening procedure is used for selecting the skilful PCs as predictors used in the regression equation. The predictors include temperature at 850 hPa （7）, the combination of sea-level pressure and temperature at 850 hPa （P＋T） and the combination of geo-potential height and temperature at 850 hPa （H＋T）. The downscaling procedure is tested with the three predictors over three predictor domains. The optimum statistical model is obtained for each station and month by finding the predictor and predictor domain corresponding to the highest correlation. Finally, the optimum statistical downscaling models are applied to the Hadley Centre Coupled Model, version 3 （HadCM3） outputs under the Special Report on Emission Scenarios （SRES） A2 and B2 scenarios to construct local future temperature change scenarios for each station and month, The results show that （1） statistical downscaling produces less warming than the HadCM3 output itself; （2） the downscaled annual cycles of temperature differ from the HadCM3 output, but are similar to the observation; （3） the downscaled temperature scenarios show more warming in the north than in the south; （4） the downscaled temperature scenarios vary with emission scenarios, and the A2 scenario produces more warming than the B2, especially in the north of China.展开更多
Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome th...Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome this issue,we propose a convolutional graph neural network(CGNN)model,which we enhance with multilayer feature fusion and a squeeze-and-excitation block.Additionally,we introduce a spatially balanced mean squared error(SBMSE)loss function to address the imbalanced distribution and spatial variability of meteorological variables.The CGNN is capable of extracting essential spatial features and aggregating them from a global perspective,thereby improving the accuracy of prediction and enhancing the model's generalization ability.Based on the experimental results,CGNN has certain advantages in terms of bias distribution,exhibiting a smaller variance.When it comes to precipitation,both UNet and AE also demonstrate relatively small biases.As for temperature,AE and CNNdense perform outstandingly during the winter.The time correlation coefficients show an improvement of at least 10%at daily and monthly scales for both temperature and precipitation.Furthermore,the SBMSE loss function displays an advantage over existing loss functions in predicting the98th percentile and identifying areas where extreme events occur.However,the SBMSE tends to overestimate the distribution of extreme precipitation,which may be due to the theoretical assumptions about the posterior distribution of data that partially limit the effectiveness of the loss function.In future work,we will further optimize the SBMSE to improve prediction accuracy.展开更多
The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in ni...The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.展开更多
There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties...There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.展开更多
Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960-2008. One w...Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960-2008. One was to downscale daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly downseale the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross- validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical downsealing to Hadley Centre Coupled Model Version 3 （HadCM3） simulations under the Representative Concentration Pathways 4.5 （RCP 4.5） scenario of the Fifth Coupled Model Inter-comparison Project （CMIP5）. The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5℃, and the 10th of daily minimum temperatures will increase by about 2℃ during the period 2011- 35 relative to 1980-99.展开更多
A statistical downscaling approach based on multiple-linear-regression(MLR) for the prediction of summer precipitation anomaly in southeastern China was established,which was based on the outputs of seven operational ...A statistical downscaling approach based on multiple-linear-regression(MLR) for the prediction of summer precipitation anomaly in southeastern China was established,which was based on the outputs of seven operational dynamical models of Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction(DEMETER) and observed data.It was found that the anomaly correlation coefficients(ACCs) spatial pattern of June-July-August(JJA) precipitation over southeastern China between the seven models and the observation were increased significantly;especially in the central and the northeastern areas,the ACCs were all larger than 0.42(above 95% level) and 0.53(above 99% level).Meanwhile,the root-mean-square errors(RMSE) were reduced in each model along with the multi-model ensemble(MME) for some of the stations in the northeastern area;additionally,the value of RMSE difference between before and after downscaling at some stations were larger than 1 mm d-1.Regionally averaged JJA rainfall anomaly temporal series of the downscaling scheme can capture the main characteristics of observation,while the correlation coefficients(CCs) between the temporal variations of the observation and downscaling results varied from 0.52 to 0.69 with corresponding variations from-0.27 to 0.22 for CCs between the observation and outputs of the models.展开更多
A combination of the optimal subset regression (OSR) approach,the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to cons...A combination of the optimal subset regression (OSR) approach,the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer.Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009.The results show a poor simulation for summer precipitation by the NCCCGCM for China,and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period.The forecast skill can be improved by OSR statistical downscaling,and the OSR forecast performs better than the NCC-CGCM in most years except 2003.The spatial ACC is more than 0.2 in the years 2008 and 2009,which proves to be relatively skillful.Moreover,the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years,including 2005,2006,2008,and 2009.However,the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCCCGCM.展开更多
An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 50...An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 500 hPa(GH5)over East Asia,which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System(NCEP CFS),was used as one predictor for the scheme.The preceding sea ice concentration(SIC)signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor.This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model(GCM)output in cross validation.The multi-year average spatial anomaly correlation coefficient increased from–0.03 to 0.31,and the downscaling temporal root-mean-square-error(RMSE)decreased significantly over that of the original CFS GCM for most China stations.Furthermore,large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM,and the anomaly correlation coefficient between the observation and downscaling results reached～0.6 in the winter of 2008.展开更多
Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied ...Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society （IRI）. In conjunction with the GLM （generalized linear modeling） weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts （the IRI format only specifies probabilities for three categories： below normal, near normal, and above normal） into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.展开更多
Convolutional neural networks(CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation mod...Convolutional neural networks(CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation models(GCMs).However, there is a lack of research exploring the predictor selection for CNN modeling. This paper presents an effective and efficient greedy elimination algorithm to address this problem. The algorithm has three main steps: predictor importance attribution, predictor removal, and CNN retraining, which are performed sequentially and iteratively. The importance of individual predictors is measured by a gradient-based importance metric computed by a CNN backpropagation technique, which was initially proposed for CNN interpretation. The algorithm is tested on the CNN-based statistical downscaling of monthly precipitation with 20 candidate predictors and compared with a correlation analysisbased approach. Linear models are implemented as benchmarks. The experiments illustrate that the predictor selection solution can reduce the number of input predictors by more than half, improve the accuracy of both linear and CNN models,and outperform the correlation analysis method. Although the RMSE(root-mean-square error) is reduced by only 0.8%,only 9 out of 20 predictors are used to build the CNN, and the FLOPs(Floating Point Operations) decrease by 20.4%. The results imply that the algorithm can find subset predictors that correlate more to the monthly precipitation of the target area and seasons in a nonlinear way. It is worth mentioning that the algorithm is compatible with other CNN models with stacked variables as input and has the potential for nonlinear correlation predictor selection.展开更多
The performances of various dynamical models from the Asia-Pacific Economic Cooperation（APEC） Climate Center（APCC） multi-model ensemble（MME） in predicting station-scale rainfall in South China（SC） in June were...The performances of various dynamical models from the Asia-Pacific Economic Cooperation（APEC） Climate Center（APCC） multi-model ensemble（MME） in predicting station-scale rainfall in South China（SC） in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME＇s ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output（DMO） over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.展开更多
The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal r...The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal resolutions in extracting LST from satellite remote sensing(RS)data,the areas with complex landforms of the Eastern Qinling Mountains were selected as the research targets to establish the correlation between the normalized difference vegetation index(NDVI)and LST.Detailed information on the surface features and temporal changes in the land surface was provided by Sentinel-2 and Sentinel-3,respectively.Based on the statistically downscaling method,the spatial scale could be decreased from 1000 m to 10 m,and LST with a Sentinel-3 temporal resolution and a 10 m spatial resolution could be retrieved.Comparing the 1 km resolution Sentinel-3 LST with the downscaling results,the 10 m LST downscaling data could accurately reflect the spatial distribution of the thermal characteristics of the original LST image.Moreover,the surface temperature data with a 10 m high spatial resolution had clear texture and obvious geomorphic features that could depict the detailed information of the ground features.The results showed that the average error was 5 K on April 16,2019 and 2.6 K on July 15,2019.The smaller error values indicated the higher vegetation coverage of summer downscaling result with the highest level on July 15.展开更多
An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dyna...An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).展开更多
General circulation models （GCMs） are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller ...General circulation models （GCMs） are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller scales, such as individual river basins. In this study, a statistical downscaling approach based on the Smooth Support Vector Machine （SSVM） method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the statistical relationship between the larger scale climate predictors and observed precipitation. The relationship obtained was used to project future precipitation from two GCMs （CGCM2 and HadCM3） for the A2 emission scenario. The results obtained using SSVM were compared with those from an artificial neural network （ANN）. The comparisons showed that SSVM is suitable for conducting climate impact studies as a statistical downscaling tool in this region. The temporal trends projected by SSVM based on the A2 emission scenario for CGCM2 and HadCM3 were for rainfall to decrease during the period 2011–2040 in the upper basin and to increase after 2071 in the whole of Hanjiang Basin.展开更多
Reference evapotranspiration （ETo） is often used to estimate actual evapotranspiration in water balance studies. In this study, the present and future spatial distributions and temporal trends of ETo in the Xiangjia...Reference evapotranspiration （ETo） is often used to estimate actual evapotranspiration in water balance studies. In this study, the present and future spatial distributions and temporal trends of ETo in the Xiangjiang River Basin （XJRB） in China were analyzed. ETo during the period from 1961 to 2010 was calculated with historical meteorological data using the FAO Penman-Monteith （FAO P-M） method, while ETo during the period from 2011 to 2100 was downscaled from the Coupled Model Intercomparison Project Phase 5 （CMIP5） outputs under two emission scenarios, representative concentration pathway 4.5 and representative concentration pathway 8.5 （RCP45 and RCP85）, using the statistical downscaling model （SDSM）. The spatial distribution and temporal trend of ETo were interpreted with the inverse distance weighted （IDW） method and Mann-Kendall test method, respectively. Results show that： （1） the mean annual ETo of the XJRB is 1 006.3 mm during the period from 1961 to 2010, and the lowest and highest values are found in the northeast and northwest parts due to the high latitude and spatial distribution of climatic factors, respectively; （2） the SDSM performs well in simulating the present ETo and can be used to predict the future ETo in the XJRB; and （3） CMIP5 predicts upward trends in annual ETo under the RCP45 and RCP85 scenarios during the period from 2011 to 2100. Compared with the reference period （1961-1990）, ETo increases by 9.8%, 12.6%, and 15.6% under the RCP45 scenario and 10.2%, 19.1%, and 27.3% under the RCP85 scenario during the periods from 2011 to 2040, from 2041 to 2070, and from 2071 to 2100, respectively. The predicted increasing ETo under the RCP85 scenario is greater than that under the RCP45 scenario during the period from 2011 to 2100.展开更多
Before 2008,the number of surface observation stations in China was small.Thus,the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration’s Land Assimi...Before 2008,the number of surface observation stations in China was small.Thus,the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration’s Land Assimilation System(HRCLDAS)which ultimately inhibited the output of high-resolution and high-quality gridded products.This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem.Specifically,we take temperature as an example.The model is used to downscale the 0.0625°×0.0625°,2-m temperature data from the China Meteorological Administration’s Land Data Assimilation System(CLDAS)to 0.01°×0.01°,named CLDASSD.We performed quality control on the paired data from CLDAS and HRCLDAS,using data from 2018 and 2019.CLDASSD was trained on the data from 31 March 2018 to 28 February 2019,and then tested with the remaining data.Finally,extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology.Taking the HRCLDAS product and surface observation data as the"true values"and comparing them with the results of bilinear interpolation,especially in complex terrain such as mountains,the root mean square error(RMSE)of the CLDASSD output can be reduced by approximately 0.1℃,and its structural similarity(SSIM)was approximately 0.2 higher.CLDASSD can estimate detailed textures,in terms of spatial distribution,with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks.展开更多
The impacts of future climate change on streamflow of the Dongliao River Watershed located in Jilin Prov-ince, China have been evaluated quantitatively by using a general circulation model （HadCM3） coupled with the ...The impacts of future climate change on streamflow of the Dongliao River Watershed located in Jilin Prov-ince, China have been evaluated quantitatively by using a general circulation model （HadCM3） coupled with the Soil and Water Assessment Tool （SWAT） hydrological model. The model was calibrated and validated against the historical monitored data from 2005 to 2009. The streamflow was estimated by downscaling HadCM3 outputs to the daily mean temperature and precipitation series, derived for three 30-year time slices, 2020s, 2050s and 2080s. Results suggest that daily mean temperature increases with a changing rate of 0.435~C per decade, and precipitation decreases with a changing rate of 0.761 mm per decade. Compared with other seasons, the precipitation in summer shows significant downward trend, while a significant upward trend in autumn. The annual streamflow demonstrates a general down-ward trend with a decreasing rate of 0.405 m^3/s per decade. The streamflow shows significant downward and upward trends in summer and in autumn, respectively. The decreasing rate of streamflow in summer reaches 1.97 m^3/s per decade, which contributes primarily to the decrease of streamflow. The results of this work would be of great benifit to the design of economic and social development planning in the study area.展开更多
Recently, study in past trends of climate variables gained significant consideration because of its contribution in adaptions and mitigation strategies for potential future changes in climate, primarily in the area of...Recently, study in past trends of climate variables gained significant consideration because of its contribution in adaptions and mitigation strategies for potential future changes in climate, primarily in the area of water resource management. Future interannual and inter-seasonal variations in maximum and minimum temperature may bring significant changes in hydrological systems and affect regional water resources. The present study has been performed to observe past(1970-2010) as well as future(2011-2100)spatial and temporal variability in temperature(maximum and minimum) over selected stations of Sutlej basin located in North-Western Himalayan region in India. The generation of future time series of temperature data at different stations is done using statistical downscaling technique. The nonparametric test methods, modified Mann-Kendall test and Cumulative Sum chart are used for detecting monotonic trend and sequential shift in time series of maximum and minimum temperature. Sen's slope estimator test is used to detect the magnitude of change over a period of time on annual and seasonal basis. The cooling experienced in annual TMax and TMin at Kasol in past(1970-2010) would be replaced by warming in future as increasing trends are detected in TMax during 2020 s and 2050 s and in TMin during 2020 s, 2050 s and 2080 s under A1 B and A2 scenarios. Similar results of warming are also predicted at Sunnifor annual TMin in future under both scenarios which witnessed cooling during 1970-2010. The rise in TMin at Rampur is predicted to be continued in future as increasing trends are obtained under both the scenarios. Seasonal trend analysis reveals large variability in trends of TMax and TMin over these stations for the future periods.展开更多
Danghara,a major food production area in southern Tajikistan,is currently suffering from the impact of rapid climate change and intensive human activities.Assessing the future impact of climate change on crop water re...Danghara,a major food production area in southern Tajikistan,is currently suffering from the impact of rapid climate change and intensive human activities.Assessing the future impact of climate change on crop water requirements(CWRs)for the current growing period and defining the optimal sowing date to reduce future crop water demand are essential for local/regional water and food planning.Therefore,this study attempted to analyze possible future climate change effects on the water requirements of major crops using the statistical downscaling method in the Danghara District to simulate the future temperature and precipitation for two future periods(2021-2050 and 2051-2080),under three representative concentration pathways(RCP2.6,RCP4.5,and RCP8.5)according to the CanESM2 global climate model.The water footprint(WFP)of major crops was calculated as a measure of their CWRs.The increased projection of precipitation and temperature probably caused an increase in the main crop’s WFP for the current growing period,which was mainly due to the green water(GW)component in the long term and a decrease in the blue water(BW)component during the second future period,except for cotton,where all components were predicted to remain stable.Under three scenarios for the two future potato and winter wheat decreased from 5.7%to 4.8%and 3.4%to 2.2%,respectively.Although the WFP of cotton demonstrated a stable increase,according to the optimal sowing date,adecrease in irrigation demand or Bw was expected.The results of our study might be useful fordeveloping a new strategy related to irrigation systems and could help to find a balance betweenwater and food for environmental water demands and human use.展开更多
基金supported by the National Basic Research Program of China (Grant No.2010CB950400)the National Natural Science Foundation of China (Key Project,Grant No.41030961)the Australia-China Bilateral Climate Change Partnerships Program of the Australian Department of Climate Change
文摘The summer rainfall over the middle-lower reaches of the Yangtze River valley (YRSR) has been estimated with a multi-linear regression model using principal atmospheric modes derived from a 500 hPa geopotential height and a 700 hPa zonal vapor flux over the domain of East Asia and the West Pacific.The model was developed using data from 1958 92 and validated with an independent prediction from 1993 2008.The independent prediction was efficient in predicting the YRSR with a correlation coefficient of 0.72 and a relative root mean square error of 18%.The downscaling model was applied to two general circulation models (GCMs) of Flexible Global Ocean-Atmosphere-Land System Model (FGOALS) and Geophysical Fluid Dynamics Laboratory coupled climate model version 2.1 (GFDL-CM2.1) to project rainfall for present and future climate under B1 and A1B emission scenarios.The downscaled results pro-vided a closer representation of the observation compared to the raw models in the present climate.In addition,compared to the inconsistent prediction directly from dif-ferent GCMs,the downscaled results provided a consistent projection for this half-century,which indicated a clear increase in the YRSR.Under the B1 emission scenario,the rainfall could increase by an average of 11.9% until 2011 25 and 17.2% until 2036 50 from the current state;under the A1B emission scenario,rainfall could increase by an average of 15.5% until 2011 25 and 25.3% until 2036 50 from the current state.Moreover,the increased rate was faster in the following decade (2011 25) than the latter of this half-century (2036 50) under both emissions.
基金supported by the National Natural Science Foundation of China under grant No.40705030the National Basic Research Program of China (Grant No.2006CB400504)
文摘Monthly mean temperatures at 562 stations in China are estimated using a statistical downscaling technique. The technique used is multiple linear regressions （MLRs） of principal components （PCs）. A stepwise screening procedure is used for selecting the skilful PCs as predictors used in the regression equation. The predictors include temperature at 850 hPa （7）, the combination of sea-level pressure and temperature at 850 hPa （P＋T） and the combination of geo-potential height and temperature at 850 hPa （H＋T）. The downscaling procedure is tested with the three predictors over three predictor domains. The optimum statistical model is obtained for each station and month by finding the predictor and predictor domain corresponding to the highest correlation. Finally, the optimum statistical downscaling models are applied to the Hadley Centre Coupled Model, version 3 （HadCM3） outputs under the Special Report on Emission Scenarios （SRES） A2 and B2 scenarios to construct local future temperature change scenarios for each station and month, The results show that （1） statistical downscaling produces less warming than the HadCM3 output itself; （2） the downscaled annual cycles of temperature differ from the HadCM3 output, but are similar to the observation; （3） the downscaled temperature scenarios show more warming in the north than in the south; （4） the downscaled temperature scenarios vary with emission scenarios, and the A2 scenario produces more warming than the B2, especially in the north of China.
基金partially funded by the National Natural Science Foundation of China(U2142205)the Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)+1 种基金the Special Fund for Forecasters of China Meteorological Administration(CMAYBY2020-094)the Graduate Student Research and Innovation Program of Central South University(2023ZZTS0347)。
文摘Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome this issue,we propose a convolutional graph neural network(CGNN)model,which we enhance with multilayer feature fusion and a squeeze-and-excitation block.Additionally,we introduce a spatially balanced mean squared error(SBMSE)loss function to address the imbalanced distribution and spatial variability of meteorological variables.The CGNN is capable of extracting essential spatial features and aggregating them from a global perspective,thereby improving the accuracy of prediction and enhancing the model's generalization ability.Based on the experimental results,CGNN has certain advantages in terms of bias distribution,exhibiting a smaller variance.When it comes to precipitation,both UNet and AE also demonstrate relatively small biases.As for temperature,AE and CNNdense perform outstandingly during the winter.The time correlation coefficients show an improvement of at least 10%at daily and monthly scales for both temperature and precipitation.Furthermore,the SBMSE loss function displays an advantage over existing loss functions in predicting the98th percentile and identifying areas where extreme events occur.However,the SBMSE tends to overestimate the distribution of extreme precipitation,which may be due to the theoretical assumptions about the posterior distribution of data that partially limit the effectiveness of the loss function.In future work,we will further optimize the SBMSE to improve prediction accuracy.
基金The National Nat-ural Science Foundation of China (NSFC), Grant Nos.90711003, 40375014the program of GYHY200706005, and the APCC Visiting Scientist Program jointly supportedthis work.
文摘The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.
文摘There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.
基金jointly sponsored by the National Basic Research Program of China "973" Program (Grant No. 2012CB956200)the Knowledge Innovation Project (Grant No. KZCX2-EW-202)the Strategic Priority Research Program (Grant No. XDA05090103) of the Chinese Academy of Sciences
文摘Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960-2008. One was to downscale daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly downseale the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross- validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical downsealing to Hadley Centre Coupled Model Version 3 （HadCM3） simulations under the Representative Concentration Pathways 4.5 （RCP 4.5） scenario of the Fifth Coupled Model Inter-comparison Project （CMIP5）. The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5℃, and the 10th of daily minimum temperatures will increase by about 2℃ during the period 2011- 35 relative to 1980-99.
基金supported by the special Fund for Public Welfare Industry (Meteorology) (Grant No. GYHY200906018)the National Basic Research Program of China (Grant Nos. 2010CB950304 and 2009CB421406)the Knowl-edge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-QN202)
文摘A statistical downscaling approach based on multiple-linear-regression(MLR) for the prediction of summer precipitation anomaly in southeastern China was established,which was based on the outputs of seven operational dynamical models of Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction(DEMETER) and observed data.It was found that the anomaly correlation coefficients(ACCs) spatial pattern of June-July-August(JJA) precipitation over southeastern China between the seven models and the observation were increased significantly;especially in the central and the northeastern areas,the ACCs were all larger than 0.42(above 95% level) and 0.53(above 99% level).Meanwhile,the root-mean-square errors(RMSE) were reduced in each model along with the multi-model ensemble(MME) for some of the stations in the northeastern area;additionally,the value of RMSE difference between before and after downscaling at some stations were larger than 1 mm d-1.Regionally averaged JJA rainfall anomaly temporal series of the downscaling scheme can capture the main characteristics of observation,while the correlation coefficients(CCs) between the temporal variations of the observation and downscaling results varied from 0.52 to 0.69 with corresponding variations from-0.27 to 0.22 for CCs between the observation and outputs of the models.
基金supported by China Meteorological Administration R & D Special Fund for Public Welfare (Meteorology) (Grant Nos. GYHY200906018 and GYHY200906015)the National Natural Science Foundation of China (Grant No.41005051)the National Key Technologies R & D Program of China (Grant No. 2009BAC51B05)
文摘A combination of the optimal subset regression (OSR) approach,the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer.Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009.The results show a poor simulation for summer precipitation by the NCCCGCM for China,and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period.The forecast skill can be improved by OSR statistical downscaling,and the OSR forecast performs better than the NCC-CGCM in most years except 2003.The spatial ACC is more than 0.2 in the years 2008 and 2009,which proves to be relatively skillful.Moreover,the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years,including 2005,2006,2008,and 2009.However,the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCCCGCM.
基金supported by the China Meteorological Special Project(GYHY201206016)the National Basic Research Program of China(2010CB950304)the Innovation Key Program of the Chinese Academy of Sciences(KZCX2-YW-QN202)
文摘An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 500 hPa(GH5)over East Asia,which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System(NCEP CFS),was used as one predictor for the scheme.The preceding sea ice concentration(SIC)signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor.This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model(GCM)output in cross validation.The multi-year average spatial anomaly correlation coefficient increased from–0.03 to 0.31,and the downscaling temporal root-mean-square-error(RMSE)decreased significantly over that of the original CFS GCM for most China stations.Furthermore,large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM,and the anomaly correlation coefficient between the observation and downscaling results reached～0.6 in the winter of 2008.
基金supported by Korea Institute of Civil Engineering and Building Technology (Project name: 2015 Development of a micro raingauge using electromagnetic wave)
文摘Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society （IRI）. In conjunction with the GLM （generalized linear modeling） weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts （the IRI format only specifies probabilities for three categories： below normal, near normal, and above normal） into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.
基金supported by the following grants: National Basic R&D Program of China (2018YFA0606203)Strategic Priority Research Program of Chinese Academy of Sciences (XDA23090102 and XDA20060501)+2 种基金Guangdong Major Project of Basic and Applied Basic Research (2020B0301030004)Special Fund of China Meteorological Administration for Innovation and Development (CXFZ2021J026)Special Fund for Forecasters of China Meteorological Administration (CMAYBY2020094)。
文摘Convolutional neural networks(CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation models(GCMs).However, there is a lack of research exploring the predictor selection for CNN modeling. This paper presents an effective and efficient greedy elimination algorithm to address this problem. The algorithm has three main steps: predictor importance attribution, predictor removal, and CNN retraining, which are performed sequentially and iteratively. The importance of individual predictors is measured by a gradient-based importance metric computed by a CNN backpropagation technique, which was initially proposed for CNN interpretation. The algorithm is tested on the CNN-based statistical downscaling of monthly precipitation with 20 candidate predictors and compared with a correlation analysisbased approach. Linear models are implemented as benchmarks. The experiments illustrate that the predictor selection solution can reduce the number of input predictors by more than half, improve the accuracy of both linear and CNN models,and outperform the correlation analysis method. Although the RMSE(root-mean-square error) is reduced by only 0.8%,only 9 out of 20 predictors are used to build the CNN, and the FLOPs(Floating Point Operations) decrease by 20.4%. The results imply that the algorithm can find subset predictors that correlate more to the monthly precipitation of the target area and seasons in a nonlinear way. It is worth mentioning that the algorithm is compatible with other CNN models with stacked variables as input and has the potential for nonlinear correlation predictor selection.
基金supported by the City University of Hong Kong(Grant No.9360126)
文摘The performances of various dynamical models from the Asia-Pacific Economic Cooperation（APEC） Climate Center（APCC） multi-model ensemble（MME） in predicting station-scale rainfall in South China（SC） in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME＇s ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output（DMO） over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.
基金Supported by the National Key R&D Plan(2018YFC1506500)Open Research Fund Project of Key Laboratory of Ecological Environment Meteorology of Qinling Mountains and Loess Plateau of Shaanxi Provincial Meteorological Bureau(2020Y-13)+1 种基金Open Research Fund of Shangluo Key Laboratory of Climate Adaptable City(SLSYS2022007)Shangluo Demonstration Project of Qinling Ecological Monitoring Service System(2020-611002-74-01-006200)。
文摘The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal resolutions in extracting LST from satellite remote sensing(RS)data,the areas with complex landforms of the Eastern Qinling Mountains were selected as the research targets to establish the correlation between the normalized difference vegetation index(NDVI)and LST.Detailed information on the surface features and temporal changes in the land surface was provided by Sentinel-2 and Sentinel-3,respectively.Based on the statistically downscaling method,the spatial scale could be decreased from 1000 m to 10 m,and LST with a Sentinel-3 temporal resolution and a 10 m spatial resolution could be retrieved.Comparing the 1 km resolution Sentinel-3 LST with the downscaling results,the 10 m LST downscaling data could accurately reflect the spatial distribution of the thermal characteristics of the original LST image.Moreover,the surface temperature data with a 10 m high spatial resolution had clear texture and obvious geomorphic features that could depict the detailed information of the ground features.The results showed that the average error was 5 K on April 16,2019 and 2.6 K on July 15,2019.The smaller error values indicated the higher vegetation coverage of summer downscaling result with the highest level on July 15.
基金Botnia-Atlantica, an EU-programme financing cross border cooperation projects in Sweden, Finland and Norway, for their support of this work through the WindCoE project
文摘An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).
基金supported financially by the National Natural Science Foundation of China (Grant Nos.50679063 and 50809049)the International CooperationResearch Fund of China (2005DFA20520)the Re-search Fund for the Doctoral Program of Higher Education(200804861062)
文摘General circulation models （GCMs） are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller scales, such as individual river basins. In this study, a statistical downscaling approach based on the Smooth Support Vector Machine （SSVM） method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the statistical relationship between the larger scale climate predictors and observed precipitation. The relationship obtained was used to project future precipitation from two GCMs （CGCM2 and HadCM3） for the A2 emission scenario. The results obtained using SSVM were compared with those from an artificial neural network （ANN）. The comparisons showed that SSVM is suitable for conducting climate impact studies as a statistical downscaling tool in this region. The temporal trends projected by SSVM based on the A2 emission scenario for CGCM2 and HadCM3 were for rainfall to decrease during the period 2011–2040 in the upper basin and to increase after 2071 in the whole of Hanjiang Basin.
基金supported by the National Natural Science Foundation of China(Grants No.51339004 and 51279138)
文摘Reference evapotranspiration （ETo） is often used to estimate actual evapotranspiration in water balance studies. In this study, the present and future spatial distributions and temporal trends of ETo in the Xiangjiang River Basin （XJRB） in China were analyzed. ETo during the period from 1961 to 2010 was calculated with historical meteorological data using the FAO Penman-Monteith （FAO P-M） method, while ETo during the period from 2011 to 2100 was downscaled from the Coupled Model Intercomparison Project Phase 5 （CMIP5） outputs under two emission scenarios, representative concentration pathway 4.5 and representative concentration pathway 8.5 （RCP45 and RCP85）, using the statistical downscaling model （SDSM）. The spatial distribution and temporal trend of ETo were interpreted with the inverse distance weighted （IDW） method and Mann-Kendall test method, respectively. Results show that： （1） the mean annual ETo of the XJRB is 1 006.3 mm during the period from 1961 to 2010, and the lowest and highest values are found in the northeast and northwest parts due to the high latitude and spatial distribution of climatic factors, respectively; （2） the SDSM performs well in simulating the present ETo and can be used to predict the future ETo in the XJRB; and （3） CMIP5 predicts upward trends in annual ETo under the RCP45 and RCP85 scenarios during the period from 2011 to 2100. Compared with the reference period （1961-1990）, ETo increases by 9.8%, 12.6%, and 15.6% under the RCP45 scenario and 10.2%, 19.1%, and 27.3% under the RCP85 scenario during the periods from 2011 to 2040, from 2041 to 2070, and from 2071 to 2100, respectively. The predicted increasing ETo under the RCP85 scenario is greater than that under the RCP45 scenario during the period from 2011 to 2100.
基金the National Key Research and Development Program of China(Grant No.2018YFC1506604)the National Natural Science Foundation of China(Grant No.91437220)。
文摘Before 2008,the number of surface observation stations in China was small.Thus,the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration’s Land Assimilation System(HRCLDAS)which ultimately inhibited the output of high-resolution and high-quality gridded products.This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem.Specifically,we take temperature as an example.The model is used to downscale the 0.0625°×0.0625°,2-m temperature data from the China Meteorological Administration’s Land Data Assimilation System(CLDAS)to 0.01°×0.01°,named CLDASSD.We performed quality control on the paired data from CLDAS and HRCLDAS,using data from 2018 and 2019.CLDASSD was trained on the data from 31 March 2018 to 28 February 2019,and then tested with the remaining data.Finally,extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology.Taking the HRCLDAS product and surface observation data as the"true values"and comparing them with the results of bilinear interpolation,especially in complex terrain such as mountains,the root mean square error(RMSE)of the CLDASSD output can be reduced by approximately 0.1℃,and its structural similarity(SSIM)was approximately 0.2 higher.CLDASSD can estimate detailed textures,in terms of spatial distribution,with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks.
基金Under the auspices of Major Science and Technology Program for Water Pollution Control and Treatment(No.2009ZX07526-006-04-01)
文摘The impacts of future climate change on streamflow of the Dongliao River Watershed located in Jilin Prov-ince, China have been evaluated quantitatively by using a general circulation model （HadCM3） coupled with the Soil and Water Assessment Tool （SWAT） hydrological model. The model was calibrated and validated against the historical monitored data from 2005 to 2009. The streamflow was estimated by downscaling HadCM3 outputs to the daily mean temperature and precipitation series, derived for three 30-year time slices, 2020s, 2050s and 2080s. Results suggest that daily mean temperature increases with a changing rate of 0.435~C per decade, and precipitation decreases with a changing rate of 0.761 mm per decade. Compared with other seasons, the precipitation in summer shows significant downward trend, while a significant upward trend in autumn. The annual streamflow demonstrates a general down-ward trend with a decreasing rate of 0.405 m^3/s per decade. The streamflow shows significant downward and upward trends in summer and in autumn, respectively. The decreasing rate of streamflow in summer reaches 1.97 m^3/s per decade, which contributes primarily to the decrease of streamflow. The results of this work would be of great benifit to the design of economic and social development planning in the study area.
基金financial support in the form of fellowship provided by University Grant Commission (UGC), Government of India to Mr. Dharmaveer Singh as Research Fellow for carrying out the research
文摘Recently, study in past trends of climate variables gained significant consideration because of its contribution in adaptions and mitigation strategies for potential future changes in climate, primarily in the area of water resource management. Future interannual and inter-seasonal variations in maximum and minimum temperature may bring significant changes in hydrological systems and affect regional water resources. The present study has been performed to observe past(1970-2010) as well as future(2011-2100)spatial and temporal variability in temperature(maximum and minimum) over selected stations of Sutlej basin located in North-Western Himalayan region in India. The generation of future time series of temperature data at different stations is done using statistical downscaling technique. The nonparametric test methods, modified Mann-Kendall test and Cumulative Sum chart are used for detecting monotonic trend and sequential shift in time series of maximum and minimum temperature. Sen's slope estimator test is used to detect the magnitude of change over a period of time on annual and seasonal basis. The cooling experienced in annual TMax and TMin at Kasol in past(1970-2010) would be replaced by warming in future as increasing trends are detected in TMax during 2020 s and 2050 s and in TMin during 2020 s, 2050 s and 2080 s under A1 B and A2 scenarios. Similar results of warming are also predicted at Sunnifor annual TMin in future under both scenarios which witnessed cooling during 1970-2010. The rise in TMin at Rampur is predicted to be continued in future as increasing trends are obtained under both the scenarios. Seasonal trend analysis reveals large variability in trends of TMax and TMin over these stations for the future periods.
基金supported by the National Natural Science Foundation of China(41761144079)the State's Key Project of Researchand Development Plan(2017YFC404501)+4 种基金the CAS Interdisplinary Imnnovation Team(JCTD201920)the Strategic Priority Research Program of the Chinese Academy of Sciences,the Pan-Third Pole Environment Study for a Green Silk Road(XDA20060303)the International Partneship Program of the Chinese Aademy of Sciences(131551KYSB20160002)the CAS Research Center for Ecologyand Environment of Central Asia(Y934031)the Regional Collaborative Innovation Project of Xinjiang Uygur Autonomous Region(2020E01010).
文摘Danghara,a major food production area in southern Tajikistan,is currently suffering from the impact of rapid climate change and intensive human activities.Assessing the future impact of climate change on crop water requirements(CWRs)for the current growing period and defining the optimal sowing date to reduce future crop water demand are essential for local/regional water and food planning.Therefore,this study attempted to analyze possible future climate change effects on the water requirements of major crops using the statistical downscaling method in the Danghara District to simulate the future temperature and precipitation for two future periods(2021-2050 and 2051-2080),under three representative concentration pathways(RCP2.6,RCP4.5,and RCP8.5)according to the CanESM2 global climate model.The water footprint(WFP)of major crops was calculated as a measure of their CWRs.The increased projection of precipitation and temperature probably caused an increase in the main crop’s WFP for the current growing period,which was mainly due to the green water(GW)component in the long term and a decrease in the blue water(BW)component during the second future period,except for cotton,where all components were predicted to remain stable.Under three scenarios for the two future potato and winter wheat decreased from 5.7%to 4.8%and 3.4%to 2.2%,respectively.Although the WFP of cotton demonstrated a stable increase,according to the optimal sowing date,adecrease in irrigation demand or Bw was expected.The results of our study might be useful fordeveloping a new strategy related to irrigation systems and could help to find a balance betweenwater and food for environmental water demands and human use.
