Water resource availability is one of the primary limiting factors with regard to ecosystems in the western China. Having a clear understanding of multi-scale drought patterns in this region is a key step for adaption...Water resource availability is one of the primary limiting factors with regard to ecosystems in the western China. Having a clear understanding of multi-scale drought patterns in this region is a key step for adaption and mitigation to climate change. The Palmer drought severity index （PDSI） is a widely applied index to assess drought conditions. In this study, long-term monthly self-calibrated PDSI data from 1951 to 2012 were examined for drought spatiotemporal variations in the western China. The results clearly indicated that apparent spatial heterogeneities were evidenced between two sub-regions （arid land with annual precipitation less than 200 mm and semiarid land with annual precipitation between 200 to 500 mm） as well as in the entire region of the western China. Ensemble empirical mode decomposition （EEMD） analyses on monthly PDSI and other atmospheric variable time-series obtained from the Department of Civil and Environmental Engineering, Princeton University revealed that all monthly time-series of variables could be completely decomposed into eight intrinsic mode functions （IMFs） and a trend （residual）. This indicates that the monthly PDSI and atmospheric variables of the semiarid area in the western China contain eight quasi-period oscillations on various timescale spanning, seasonal to decadal cycles and a trend of a larger timescale from 1951-2012. The multi-scale drought patterns identified in this research could be powerful supports for decision-making regarding coping with droughts in this region.展开更多
Drought is one of the most significant environmental disasters,especially in arid and semi-arid regions.Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used ar...Drought is one of the most significant environmental disasters,especially in arid and semi-arid regions.Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world.One of these indicators is the Palmer drought severity index(PDSI),which is used in many parts of the world to assess the drought situation and continuation.In this study,the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995-2014 according to meteorological data from six weather stations in the province.A statistical downscaling model(SDSM)was used to apply the output results of the general circulation model in Fars Province.To implement data processing and prediction of climate data,a statistical period 1995-2014 was considered as the monitoring period,and a statistical period 2019-2048 was for the prediction period.The results revealed that there is a good agreement between the simulated precipitation(R2>0.63;R2,determination coefficient;MAE<0.52;MAE,mean absolute error;RMSE<0.56;RMSE,Root Mean Squared Error)and temperature(R2>0.95,MAE<1.74,and RMSE<1.78)with the observed data from the stations.The results of the drought monitoring model presented that dry periods would increase over the next three decades as compared to the historical data.The studies showed the highest drought in the meteorological stations Abadeh and Lar during the prediction period under two future scenarios representative concentration pathways(RCP4.5 and RCP8.5).According to the results of the validation periods and efficiency criteria,we suggest that the SDSM is a proper tool for predicting drought in arid and semi-arid regions.展开更多
基金Project(51301021)supported by the National Natural Science Foundation of ChinaProjects(2013G1311051,CHD2011JC139)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(SKLSP201302)supported by the State Key Laboratory of Solidification Processing in NWPU,China
基金supported by the National Basic Research Program of China(2012CB956204)the National Natural Science Foundation(41371364)
文摘Water resource availability is one of the primary limiting factors with regard to ecosystems in the western China. Having a clear understanding of multi-scale drought patterns in this region is a key step for adaption and mitigation to climate change. The Palmer drought severity index （PDSI） is a widely applied index to assess drought conditions. In this study, long-term monthly self-calibrated PDSI data from 1951 to 2012 were examined for drought spatiotemporal variations in the western China. The results clearly indicated that apparent spatial heterogeneities were evidenced between two sub-regions （arid land with annual precipitation less than 200 mm and semiarid land with annual precipitation between 200 to 500 mm） as well as in the entire region of the western China. Ensemble empirical mode decomposition （EEMD） analyses on monthly PDSI and other atmospheric variable time-series obtained from the Department of Civil and Environmental Engineering, Princeton University revealed that all monthly time-series of variables could be completely decomposed into eight intrinsic mode functions （IMFs） and a trend （residual）. This indicates that the monthly PDSI and atmospheric variables of the semiarid area in the western China contain eight quasi-period oscillations on various timescale spanning, seasonal to decadal cycles and a trend of a larger timescale from 1951-2012. The multi-scale drought patterns identified in this research could be powerful supports for decision-making regarding coping with droughts in this region.
文摘Drought is one of the most significant environmental disasters,especially in arid and semi-arid regions.Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world.One of these indicators is the Palmer drought severity index(PDSI),which is used in many parts of the world to assess the drought situation and continuation.In this study,the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995-2014 according to meteorological data from six weather stations in the province.A statistical downscaling model(SDSM)was used to apply the output results of the general circulation model in Fars Province.To implement data processing and prediction of climate data,a statistical period 1995-2014 was considered as the monitoring period,and a statistical period 2019-2048 was for the prediction period.The results revealed that there is a good agreement between the simulated precipitation(R2>0.63;R2,determination coefficient;MAE<0.52;MAE,mean absolute error;RMSE<0.56;RMSE,Root Mean Squared Error)and temperature(R2>0.95,MAE<1.74,and RMSE<1.78)with the observed data from the stations.The results of the drought monitoring model presented that dry periods would increase over the next three decades as compared to the historical data.The studies showed the highest drought in the meteorological stations Abadeh and Lar during the prediction period under two future scenarios representative concentration pathways(RCP4.5 and RCP8.5).According to the results of the validation periods and efficiency criteria,we suggest that the SDSM is a proper tool for predicting drought in arid and semi-arid regions.
