We report on the effects of forest management practices of understory removal and N-fixing species（Cassia alata） addition on soil CO2 fluxes in an Eucalyptus urophylla plantation（EUp）,Acacia crassicarpa plantation...We report on the effects of forest management practices of understory removal and N-fixing species（Cassia alata） addition on soil CO2 fluxes in an Eucalyptus urophylla plantation（EUp）,Acacia crassicarpa plantation（ACp）,10-species-mixed plantation（Tp）,and 30-species-mixed plantation（THp） using the static chamber method in southern China.Four forest management treatments,including（1） understory removal（UR）;（2） C.alata addition（CA）;（3） understory removal and replacement with C.alata（UR＋CA）;and（4） control without any disturbances（CK）,were applied in the above four forest plantations with three replications for each treatment.The results showed that soil CO2 fluxes rates remained at a high level during the rainy season（from April to September）,followed by a rapid decrease after October reaching a minimum in February.Soil CO2 fluxes were significantly higher（P 〈 0.01） in EUp（132.6 mg/（m2.hr）） and ACp（139.8 mg/（m2.hr）） than in Tp（94.0 mg/（m2.hr）） and THp（102.9 mg/（m2.hr））.Soil CO2 fluxes in UR and CA were significantly higher（P 〈 0.01） among the four treatments,with values of 105.7,120.4,133.6 and 112.2 mg/（m2.hr） for UR＋CA,UR,CA and CK,respectively.Soil CO2 fluxes were positively correlated with soil temperature（P 〈 0.01）,soil moisture（P 〈 0.01）,NO3？-N（P 〈 0.05）,and litterfall（P 〈 0.01）,indicating that all these factors might be important controlling variables for soil CO2 fluxes.This study sheds some light on our understanding of soil CO2 flux dynamics in forest plantations under various management practices.展开更多
Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast.The quality of rainfall data in terms of both intensity and dis...Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast.The quality of rainfall data in terms of both intensity and distribution is very important for establishing a reliable meteo-hydrological forecasting model.To improve the accuracy of rainfall data,the successive correction method is introduced to correct the bias of rainfall,and a meteo-hydrological forecasting model based on WRF and WRF-Hydro is applied for streamflow forecast over the Zhanghe River catchment in China.The performance of WRF rainfall is compared with the China Meteorological Administration Multi-source Precipitation Analysis System(CMPAS),and the simulated streamflow from the model is further studied.It shows that the corrected WRF rainfall is more similar to the CMPAS in both temporal and spatial distribution than the original WRF rainfall.By contrast,the statistical metrics of the corrected WRF rainfall are better.When the corrected WRF rainfall is used to drive the WRF-Hydro model,the simulated streamflow of most events is significantly improved in both hydrographs and volume than that of using the original WRF rainfall.Among the studied events,the largest improvement of the NSE is from-0.68 to 0.67.It proves that correcting the bias of WRF rainfall with the successive correction method can greatly improve the performance of streamflow forecast.In general,the WRF/WRF-Hydro meteo-hydrological forecasting model based on the successive correction method has the potential to provide better streamflow forecast in the Zhanghe River catchment.展开更多
Weather manifests in spatiotemporally coherent structures.Weather forecasts hence are affected by both positional and structural or amplitude errors.This has been long recognized by practicing forecasters(cf.,e.g.,Tro...Weather manifests in spatiotemporally coherent structures.Weather forecasts hence are affected by both positional and structural or amplitude errors.This has been long recognized by practicing forecasters(cf.,e.g.,Tropical Cyclone track and intensity errors).Despite the emergence in recent decades of various objective methods for the diagnosis of positional forecast errors,most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error.The Forecast Error Decomposition(FED)method proposed in this study uses the Field Alignment technique which aligns a gridded forecast with its verifying analysis field.The total error is then partitioned into three orthogonal components:(a)large scale positional,(b)large scale structural,and(c)small scale error variance.The use of FED is demonstrated over a month-long MSLP data set.As expected,positional errors are often characterized by dipole patterns related to the displacement of features,while structural errors appear with single extrema,indicative of magnitude problems.The most important result of this study is that over the test period,more than 50%of the total mean sea level pressure forecast error variance is associated with large scale positional error.The importance of positional error in forecasts of other variables and over different time periods remain to be explored.展开更多
The Climate Change Conference of Parties(COP)21 in December 2015 established Nationally Determined Contributions toward reduction of greenhouse gas emissions.In the years since COP21,it has become increasingly evident...The Climate Change Conference of Parties(COP)21 in December 2015 established Nationally Determined Contributions toward reduction of greenhouse gas emissions.In the years since COP21,it has become increasingly evident that carbon dioxide removal(CDR)technologies must be deployed immediately to stabilize concentration of atmospheric greenhouse gases and avoid major climate change impacts.Biochar is a carbon-rich material formed by high-temperature conversion of biomass under reduced oxygen conditions,and its production is one of few established CDR methods that can be deployed at a scale large enough to counteract effects of climate change within the next decade.Here we provide a generalized framework for quantifying the potential contribution biochar can make toward achieving national carbon emissions reduction goals,assuming use of only sustainably supplied biomass,i.e.,residues from existing agricultural,livestock,forestry and wastewater treatment operations.Our results illustrate the significant role biochar can play in world-wide CDR strategies,with carbon dioxide removal potential of 6.23±0.24%of total GHG emissions in the 155 countries covered based on 2020 data over a 100-year timeframe,and more than 10%of national emissions in 28 countries.Concentrated regions of high biochar carbon dioxide removal potential relative to national emissions were identified in South America,northwestern Africa and eastern Europe.展开更多
基金supported by the National Natural Science Foundation of China (No. 30630015,30771704)
文摘We report on the effects of forest management practices of understory removal and N-fixing species（Cassia alata） addition on soil CO2 fluxes in an Eucalyptus urophylla plantation（EUp）,Acacia crassicarpa plantation（ACp）,10-species-mixed plantation（Tp）,and 30-species-mixed plantation（THp） using the static chamber method in southern China.Four forest management treatments,including（1） understory removal（UR）;（2） C.alata addition（CA）;（3） understory removal and replacement with C.alata（UR＋CA）;and（4） control without any disturbances（CK）,were applied in the above four forest plantations with three replications for each treatment.The results showed that soil CO2 fluxes rates remained at a high level during the rainy season（from April to September）,followed by a rapid decrease after October reaching a minimum in February.Soil CO2 fluxes were significantly higher（P 〈 0.01） in EUp（132.6 mg/（m2.hr）） and ACp（139.8 mg/（m2.hr）） than in Tp（94.0 mg/（m2.hr）） and THp（102.9 mg/（m2.hr））.Soil CO2 fluxes in UR and CA were significantly higher（P 〈 0.01） among the four treatments,with values of 105.7,120.4,133.6 and 112.2 mg/（m2.hr） for UR＋CA,UR,CA and CK,respectively.Soil CO2 fluxes were positively correlated with soil temperature（P 〈 0.01）,soil moisture（P 〈 0.01）,NO3？-N（P 〈 0.05）,and litterfall（P 〈 0.01）,indicating that all these factors might be important controlling variables for soil CO2 fluxes.This study sheds some light on our understanding of soil CO2 flux dynamics in forest plantations under various management practices.
基金Program of Key Laboratory of Meteorological Disaster(KLME202209)National Key R&D Program of China(2017YFC1502102)。
文摘Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast.The quality of rainfall data in terms of both intensity and distribution is very important for establishing a reliable meteo-hydrological forecasting model.To improve the accuracy of rainfall data,the successive correction method is introduced to correct the bias of rainfall,and a meteo-hydrological forecasting model based on WRF and WRF-Hydro is applied for streamflow forecast over the Zhanghe River catchment in China.The performance of WRF rainfall is compared with the China Meteorological Administration Multi-source Precipitation Analysis System(CMPAS),and the simulated streamflow from the model is further studied.It shows that the corrected WRF rainfall is more similar to the CMPAS in both temporal and spatial distribution than the original WRF rainfall.By contrast,the statistical metrics of the corrected WRF rainfall are better.When the corrected WRF rainfall is used to drive the WRF-Hydro model,the simulated streamflow of most events is significantly improved in both hydrographs and volume than that of using the original WRF rainfall.Among the studied events,the largest improvement of the NSE is from-0.68 to 0.67.It proves that correcting the bias of WRF rainfall with the successive correction method can greatly improve the performance of streamflow forecast.In general,the WRF/WRF-Hydro meteo-hydrological forecasting model based on the successive correction method has the potential to provide better streamflow forecast in the Zhanghe River catchment.
文摘Weather manifests in spatiotemporally coherent structures.Weather forecasts hence are affected by both positional and structural or amplitude errors.This has been long recognized by practicing forecasters(cf.,e.g.,Tropical Cyclone track and intensity errors).Despite the emergence in recent decades of various objective methods for the diagnosis of positional forecast errors,most routine verification or statistical post-processing methods implicitly assume that forecasts have no positional error.The Forecast Error Decomposition(FED)method proposed in this study uses the Field Alignment technique which aligns a gridded forecast with its verifying analysis field.The total error is then partitioned into three orthogonal components:(a)large scale positional,(b)large scale structural,and(c)small scale error variance.The use of FED is demonstrated over a month-long MSLP data set.As expected,positional errors are often characterized by dipole patterns related to the displacement of features,while structural errors appear with single extrema,indicative of magnitude problems.The most important result of this study is that over the test period,more than 50%of the total mean sea level pressure forecast error variance is associated with large scale positional error.The importance of positional error in forecasts of other variables and over different time periods remain to be explored.
文摘The Climate Change Conference of Parties(COP)21 in December 2015 established Nationally Determined Contributions toward reduction of greenhouse gas emissions.In the years since COP21,it has become increasingly evident that carbon dioxide removal(CDR)technologies must be deployed immediately to stabilize concentration of atmospheric greenhouse gases and avoid major climate change impacts.Biochar is a carbon-rich material formed by high-temperature conversion of biomass under reduced oxygen conditions,and its production is one of few established CDR methods that can be deployed at a scale large enough to counteract effects of climate change within the next decade.Here we provide a generalized framework for quantifying the potential contribution biochar can make toward achieving national carbon emissions reduction goals,assuming use of only sustainably supplied biomass,i.e.,residues from existing agricultural,livestock,forestry and wastewater treatment operations.Our results illustrate the significant role biochar can play in world-wide CDR strategies,with carbon dioxide removal potential of 6.23±0.24%of total GHG emissions in the 155 countries covered based on 2020 data over a 100-year timeframe,and more than 10%of national emissions in 28 countries.Concentrated regions of high biochar carbon dioxide removal potential relative to national emissions were identified in South America,northwestern Africa and eastern Europe.
