Exposure assessment is critical for hazard risk management.It is important to investigate the cropland exposure to compound drought and heatwave(CDHW)events because of their severe impacts on agriculture.We quantified...Exposure assessment is critical for hazard risk management.It is important to investigate the cropland exposure to compound drought and heatwave(CDHW)events because of their severe impacts on agriculture.We quantified the variations in CDHW characteristics(i.e.,frequency,duration,and magnitude)and the cropland exposure to CDHW events in Northeast China using 20 CMIP6 climate projections for each of the four Shared Socioeconomic Pathways(i.e.,SSP126,SSP245,SSP370,and SSP585).The results indicate that the intensification of CDHW events leading to an anticipated increase in cropland exposure ranges from 1.6-fold to 5.8-fold(the range describes the differences among SSPs),with the west and northeast of the region poised to experience more pronounced increases.Notably,adherence to the SSP126 pathway can reduce both the increase rate of CDHW magnitude and cropland exposure compared to other SSPs.Path analysis demonstrates that cropland exposure is primarily driven by maximum temperature(Tmax).Although precipitation(Pre)increases(0.36-0.75 mm year^(-1)),the rise in potential evapotranspiration(PET)due to global warming is higher than that of Pre(0.26-1.07 mm year^(-1))except for SSP126,resulting in more drought events.Futhermore,elevated Tmax increases the frequency of extreme temperature events.Therefore,increases in Tmax and agricultural land area collectively contribute to exposure rise,with Tmax being the dominant factor in this process.Our findings emphasize the pivotal role of regulating the development pathway into SSP126 for sustainable agriculture,and optimizing crop patterns and planting heat-tolerant crop varieties are recommended for CDHW adaption.展开更多
Marine heatwave(MHW)events refer to periods of significantly elevated sea surface temperatures(SST),persisting from days to months,with significant impacts on marine ecosystems,including increased mortality among mari...Marine heatwave(MHW)events refer to periods of significantly elevated sea surface temperatures(SST),persisting from days to months,with significant impacts on marine ecosystems,including increased mortality among marine life and coral bleaching.Forecasting MHW events are crucial to mitigate their harmful effects.This study presents a twostep forecasting process:short-term SST prediction followed by MHW event detection based on the forecasted SST.Firstly,we developed the“SST-MHW-DL”model using the ConvLSTM architecture,which incorporates an attention mechanism to enhance both SST forecasting and MHW event detection.The model utilizes SST data from the preceding 60 d to forecast SST and detect MHW events for the subsequent 15 d.Verification results for SST forecasting demonstrate a root mean square error(RMSE)of 0.64℃,a mean absolute percentage error(MAPE)of 2.05%,and a coefficient of determination(R^(2))of 0.85,indicating the model’s ability to accurately predict future temperatures by leveraging historical sea temperature information.For MHW event detection using forecasted SST,the evaluation metrics of“accuracy”,“precision”,and“recall”achieved values of 0.77,0.73,and 0.43,respectively,demonstrating the model’s capability to capture the occurrence of MHW events accurately.Furthermore,the attention-enhanced mechanism reveals that recent SST variations within the past 10 days have the most significant impact on forecasting accuracy,while variations in deep-sea regions and along the Taiwan Strait significantly contribute to the model’s efficacy in capturing spatial characteristics.Additionally,the proposed model and temporal mechanism were applied to detect MHWs in the Atlantic Ocean.By inputting 30 d of SST data,the model predicted SST with an RMSE of 1.02℃and an R^(2)of 0.94.The accuracy,precision,and recall for MHW detection were 0.79,0.78,and 0.62,respectively,further demonstrating the model’s robustness and usability.展开更多
基金financially supported by the National Key Research and Development Program of China(Grant No.2023YFF0805704)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA28020503)。
文摘Exposure assessment is critical for hazard risk management.It is important to investigate the cropland exposure to compound drought and heatwave(CDHW)events because of their severe impacts on agriculture.We quantified the variations in CDHW characteristics(i.e.,frequency,duration,and magnitude)and the cropland exposure to CDHW events in Northeast China using 20 CMIP6 climate projections for each of the four Shared Socioeconomic Pathways(i.e.,SSP126,SSP245,SSP370,and SSP585).The results indicate that the intensification of CDHW events leading to an anticipated increase in cropland exposure ranges from 1.6-fold to 5.8-fold(the range describes the differences among SSPs),with the west and northeast of the region poised to experience more pronounced increases.Notably,adherence to the SSP126 pathway can reduce both the increase rate of CDHW magnitude and cropland exposure compared to other SSPs.Path analysis demonstrates that cropland exposure is primarily driven by maximum temperature(Tmax).Although precipitation(Pre)increases(0.36-0.75 mm year^(-1)),the rise in potential evapotranspiration(PET)due to global warming is higher than that of Pre(0.26-1.07 mm year^(-1))except for SSP126,resulting in more drought events.Futhermore,elevated Tmax increases the frequency of extreme temperature events.Therefore,increases in Tmax and agricultural land area collectively contribute to exposure rise,with Tmax being the dominant factor in this process.Our findings emphasize the pivotal role of regulating the development pathway into SSP126 for sustainable agriculture,and optimizing crop patterns and planting heat-tolerant crop varieties are recommended for CDHW adaption.
基金The National Natural Science Foundation of China under contract Nos 42376175,42090044 and U2006211。
文摘Marine heatwave(MHW)events refer to periods of significantly elevated sea surface temperatures(SST),persisting from days to months,with significant impacts on marine ecosystems,including increased mortality among marine life and coral bleaching.Forecasting MHW events are crucial to mitigate their harmful effects.This study presents a twostep forecasting process:short-term SST prediction followed by MHW event detection based on the forecasted SST.Firstly,we developed the“SST-MHW-DL”model using the ConvLSTM architecture,which incorporates an attention mechanism to enhance both SST forecasting and MHW event detection.The model utilizes SST data from the preceding 60 d to forecast SST and detect MHW events for the subsequent 15 d.Verification results for SST forecasting demonstrate a root mean square error(RMSE)of 0.64℃,a mean absolute percentage error(MAPE)of 2.05%,and a coefficient of determination(R^(2))of 0.85,indicating the model’s ability to accurately predict future temperatures by leveraging historical sea temperature information.For MHW event detection using forecasted SST,the evaluation metrics of“accuracy”,“precision”,and“recall”achieved values of 0.77,0.73,and 0.43,respectively,demonstrating the model’s capability to capture the occurrence of MHW events accurately.Furthermore,the attention-enhanced mechanism reveals that recent SST variations within the past 10 days have the most significant impact on forecasting accuracy,while variations in deep-sea regions and along the Taiwan Strait significantly contribute to the model’s efficacy in capturing spatial characteristics.Additionally,the proposed model and temporal mechanism were applied to detect MHWs in the Atlantic Ocean.By inputting 30 d of SST data,the model predicted SST with an RMSE of 1.02℃and an R^(2)of 0.94.The accuracy,precision,and recall for MHW detection were 0.79,0.78,and 0.62,respectively,further demonstrating the model’s robustness and usability.
