Vegetation phenology and its ecohydrological implications from individual to global scales

The Earth is experiencing unprecedented climate change.Vegetation phenology has already showed strong response to the global warming,which alters mass and energy fluxes on terrestrial ecosystems.With technology and method developments in remote sensing,computer science and citizen science,many recent phenology-related studies have been focused on macrophenology.In this perspective,we 1)reviewed the responses of vegetation phenology to climate change and its impacts on carbon cycling,and reported that the effect of shifted phenology on the terrestrial carbon fluxes is substantially different between spring and autumn;2)elaborated how vegetation phenology affects ecohydrological processes at different scales,and further listed the key issues for each scale,i.e.,focusing on seasonal effect,local feedbacks and regional vapor transport for individual,watershed and global respectively);3)envisioned the potentials to improve current hydrological models by coupling vegetation phenology-related processes,in combining with machine learning,deep learning and scale transformation methods.We propose that comprehensive understanding of climate-macrophenology-hydrology interactions are essential and urgently needed for enhancing our understanding of the ecosystem response and its role in hydrological cycle under future climate change.

Soil erosion assessment by RUSLE with improved P factor and its validation:Case study on mountainous and hilly areas of Hubei Province,China

The Revised Universal Soil Loss Equation(RUSLE)is widely used to estimate regional soil erosion.However,quantitative impacts of soil and water conservation(SWC)measures on conservation practice factor(P)of the RUSLE remain largely unclear,especially for the mountainous and hilly areas.In this study,we improved the RUSLE by considering quantitative impacts of different SWC measures on the P factor value.The improved RUSLE was validated against the long-term(2000-2015)soil erosion monitoring data obtained from 96 runoff plots(15—35°)in mountainous and hilly areas of Hubei Province,China;the result presented a high accuracy with the determination coefficient of 0.89.Based on the erosion monitoring data of 2018 and 2019,the Root Mean Square Error of the result by the improved RUSLE was 28.0%smaller than that by the original RUSLE with decrement of 19.6%—24.0%in the average P factor values,indicating that the soil erosion modelling accuracy was significantly enhanced by the improved RUSLE.Relatively low P factor values appeared for farmlands with tillage measures(P<0.53),grasslands with engineering measures(P<0.23),woodlands with biological measures(P<0.28),and other land use types with biological measures(P<0.51).The soil erosion modulus showed a downward trend with the corresponding values of 1681.21,1673.14,1594.70,1482.40 and 1437.50 t km^(-2)a-1 in 2000,2005,2010,2015 and 2019,respectively.The applicability of the improved RUSLE was verified by the measurements in typical mountainous and hilly areas of Hubei Province,China,and arrangements of SWC measures of this area were proposed.

Simulation analysis of environmental risk accident and management of high-sulfur gas field development in complex terrain

Environmental risk of high sulfur gas field exploitation has become one of the hot spots of environmental management studies.Severe gas H_(2)S blowout accidents in recent years have shown that poor understanding and estimates of the poisonous gas movement could lead to dangerous evacuation delays.It is important to evaluate the real concentration of H_(2)S,especially in complex terrain.Traditional experiential models are not valid in the case of rough terrain,especially in low-lying areas where the gas accumulates.This study,using high sulfur content gas field of Sichuan“Pu Guang gas field”as study object and adopting objective diagnosis of wind field of land following coordinate three dimensions,applied Lagrangian Puff Model and breaking up technique of puffs to simulate the H_(2)S diffusion condition of blowout accidents produced in the high sulfur content gas field of complex terrain area.The results showed that the H_(2)S distribution did not occur mainly in low wind direction,and due to the obstruction of the mountain’s body,it accumulated in front of mountain on produced turn over,flowed around submitted jumping type distribution.The mountain waist near the hilltop and low hollow river valley site rapture points simulating contrast showed that the higher the rapture point,the better the diffusing condition of pollutant,the distribution of risk sensitive point decided piping rupture environmental risk size combining the H_(2)S diffusion result and residential area dispersing in the study area,synthetic judge located in the high rapture point environmental risk was smaller than the low hollow point,thus it was suggested to carryout laying of lining build of equal high line of higher terrain.According to simulation results,the environmental risk management measures aimed at putting down adverse effects were worked out.

Scenario analysis of energy-based low-carbon development in China

China＇s increasing energy consumption and coal-dominant energy structure have contributed not only to severe environmental pollution,but also to global climate change. This article begins with a brief review of China＇s primary energy use and associated environmental problems and health risks. To analyze the potential of China＇s transition to low-carbon development,three scenarios are constructed to simulate energy demand and CO2 emission trends in China up to 2050 by using the Long-range Energy Alternatives Planning System（LEAP） model. Simulation results show that with the assumption of an average annual Gross Domestic Product（GDP） growth rate of 6.45%,total primary energy demand is expected to increase by 63.4%,48.8% and 12.2% under the Business as Usual（BaU）,Carbon Reduction（CR）and Integrated Low Carbon Economy（ILCE） scenarios in 2050 from the 2009 levels. Total energy-related CO2 emissions will increase from 6.7 billion tons in 2009 to 9.5,11,11.6 and11.2 billion tons; 8.2,9.2,9.6 and 9 billion tons; 7.1,7.4,7.2 and 6.4 billion tons in 2020,2030,2040 and 2050 under the BaU,CR and ILCE scenarios,respectively. Total CO2 emission will drop by 19.6% and 42.9% under the CR and ILCE scenarios in 2050,compared with the BaU scenario.To realize a substantial cut in energy consumption and carbon emissions,China needs to make a long-term low-carbon development strategy targeting further improvement of energy efficiency,optimization of energy structure,deployment of clean coal technology and use of market-based economic instruments like energy/carbon taxation.

Characterization of phosphorus species and modeling for its organic forms in eutrophic shallow lake sediments, North China

Variations of phosphorus （P） and its species in surface sediment of Baiyangdian Lake, a eutrophic shallow lake located in North China, were investigated through combination of field survey and numerical calculation based on cluster analysis. P fractionation was performed by a sequential extraction scheme, categorized as loosely bound P （NH4Cl-P）, reductant soluble P （BD-P）, metallic oxide bound P （NaOH-P）, calcium bound P （HCl-P） and organic P （Org-P）. P concentrations exhibited regional similarities and a total of four sub-areas were identified in which the same rank was HCl-P 〉 Org-P 〉 BD-P ,=NaOH-P 〉NH4Cl-E NH4Cl-P, BD-P and Org-P were found to contribute to P enrichment in overlying water column. Specifically, labile Org-P acted as a potential pool with a greater contribution in aerobic layer compared to anaerobic layer. A hysteresis （lag = 4 months） existed when labile Org-P concentration was negatively correlated with aerobic layer thickness. In view of magnitude of identified P contributors in sub-areas, higher potential of P release was present in Fuhe River and Tang River estuary areas. On the basis of calibration and verification, the mathematical model with parameter settings applied in this study was improved to serve as a tool for limnology management and eutrophic control.

Coupling the Xinanjiang model with geomorphologic instantaneous unit hydrograph for flood forecasting in northeast China

The Xinanjiang(XAJ)model has been successfully applied in humid and semi-humid regions.Considering the geomorphologic factors to accurately estimate floods,this study adopted the geomorphologic instantaneous unit hydrograph(GIUH)method to calculate the surface runoff instead of the experience unit hydrograph(EUH)in the original model.The geomorphologic factors of the case study basin were obtained by using a digital elevation model(DEM)and the Terrain analysis using Digital Elevation Models(TauDEM).Furthermore,the dynamic Muskingum model was used for the channel flood routing.This study focused on the simulation of heavy precipitation and floods over the Chong River,which is a tributary river to the Songhua River on the right bank in northeast China.The detailed steps of the method were shown,up to the estimated value of flood runoff discharges and flood peaks and their comparison with observed values.The average deterministic coefficients(DCs)of model calibration and validation were 0.89 and 0.83,respectively.The results show that the model precision is high and the model is feasible for flood forecasting.Lastly,some methodological perspectives to enhance the method are presented.

Spatial impacts of climate factors on regional agricultural and forestry biomass resources in north-eastern province of China

The dynamics of agricultural and forestry biomass are highly sensitive to climate change, particularly in high latitude regions. Heilongjiang Province was selected as research area in North-east China. We explored the trend of regional climate warming and distribution feature of biomass resources, and then analyzed on the spatial relationship between climate factors and biomass resources. Net primary productivity （NPP） is one of the key indicators of vegetation productivity, and was simulated as base data to calculate the distribution of agricultural and forestry biomass. The results show that temperatures rose by up to 0.37℃/10a from 1961 to 2013. Spatially, the variation of agricultural biomass per unit area changed from -1.93 to 5.85 t.km^-2.a^-1 during 2000,2013. More than 85% of farmland areas showed a positive relationship be.tween agricultural biomass and precipitation. The results suggest that precipitation exerts an overwhelming climate influence on agricultural biomass. The mean density of forestry biomass varied from 10 to 30 t·km^-2. Temperature had a significant negative effect on forestry biomass in Lesser Khingan and northern Changbai Mountain, because increased temperature leads to decreased Rubisco activity and increased respiration in these areas. Precipitation had a significant positive relationship with forestry biomass in south-western Changbai Mountain, because this area had a wanner climate and stress from insufficient precipitation may induce xylem cavitation. Understanding the effects of climate factors on regional biomass resources is of great significance in improving environmental management and promoting sustainable development of further biomass resource use.

Loss coefficient of nitrogenous non-point source pollution under various precipitation conditions

In this study,calibrations of non-point source(NPS)pollution models are performed based on Black River basin historical real-time runoff data,sedimentation record data,and NPS sources survey information.The concept of NPS loss coefficient for the watershed or the loss coefficients(LC)for simplicity is brought up by examining NPS build-up and migration processes along riverbanks in natural river systems.The historical data is used for determining the nitrogenous NPS loss coefficient for five land use types including farmland,urban land,grassland,shrub land,and forest under different precipitation conditions.The comparison of outputs from Soil and Water Assessment Tool(SWAT)model and coefficient export method showed that both methods could obtain reasonable LC.The high Pearson correlation coefficient(0.94722)between those two sets of calculation results justified the consistency of those two models.Another result in the study is that different combinations of precipitation condition and land use types could significantly affect the calculated loss coefficient.As for the adsorptive nitrogen,the order of impact on LC for different land use types can be sorted as:farm land.urban land.grassland.shrub land.forest while the order was farmland.grass land.shrub land.forest.urban land for soluble nitrogen.

Contrasting phenology responses to climate warming across the northern extra-tropics

Climate warming has substantially advanced the timing of spring leaf-out of woody species at middle and high latitudes,albeit with large differences.Insights in the spatial variation of this climate warming response may therefore help to constrain future trends in leaf-out and its impact on energy,water and carbon balances at global scales.In this study,we used in situ phenology observations of 38 species from 2067 study sites,distributed across the northern hemisphere in China,Europe and the United States,to investigate the latitudinal patterns of spring leaf-out and its sensitivity(S T,advance of leaf-out dates per degree of warming)and correlation(R_(T),partial correlation coefficient)to temperature during the period 1980-2016.Across all species and sites,we found that S_(T) decreased significantly by 0.15±0.02 d℃^(-1)°N^(-1),and R_(T) increased by 0.02±0.001°N^(-1)(both at P<0.001).The latitudinal patterns in R_(T) and S_(T) were explained by the differences in requirements of chilling and thermal forcing that evolved to maximize tree fitness under local climate,particularly climate predictability and summed precipitation during the pre-leaf-out season.Our results thus showed complicated spatial differences in leaf-out responses to ongoing climate warming and indicated that spatial differences in the interactions among environmental cues need to be embedded into large-scale phenology models to improve the simulation accuracy.