Understanding vegetation phenology responses to easily ignored climate factors in china's mid-high latitudes.

Previous studies have primarily focused on the influence of temperature and precipitation on phenology. It is unclear if the easily ignored climate factors with drivers of vegetation growth can effect on vegetation phenology. In this research, we conducted an analysis of the start (SOS) and end (EOS) of the growing seasons in the northern region of China above 30°N from 1982 to 2014, focusing on two-season vegetation phenology. We examined the response of vegetation phenology of different vegetation types to preseason climatic factors, including relative humidity (RH), shortwave radiation (SR), maximum temperature (Tmax), and minimum temperature (Tmin). Our findings reveal that the optimal preseason influencing vegetation phenology length fell within the range of 0-60 days in most areas. Specifically, SOS exhibited a significant negative correlation with Tmax and Tmin in 44.15% and 42.25% of the areas, respectively, while EOS displayed a significant negative correlation with SR in 49.03% of the areas. Additionally, we identified that RH emerged as the dominant climatic factor influencing the phenology of savanna (SA), whereas temperature strongly controlled the SOS of deciduous needleleaf forest (DNF) and deciduous broadleaf forest (DBF). Meanwhile, the EOS of DNF was primarily influenced by Tmax. In conclusion, this study provides valuable insights into how various vegetation types adapt to climate change, offering a scientific basis for implementing effective vegetation adaptation measures.

Vegetation drought risk assessment based on the multi-weight methods in Northwest China.

Vegetation makes an outstanding contribution to the stability of ecosystems and to a certain extent reflects the state of the terrestrial ecosystem. Drought conditions greatly affect the growth and development process of vegetation due to its remarkable stochasticity and complexity. Due to the complex coupling mechanism between vegetation and drought, the research on vegetation drought risk is still limited. In this work, we focus on Northwest China and use the improved vegetation health index (VHI) and other multi-source data. We selected indicator factors based on both hazard and vulnerability, and adopt three weight determination methods, namely entropy method, critic method, and coefficient of variation method, to construct the corresponding index model, and also to establish a vegetation drought risk assessment model to quantitatively evaluate the drought risk of vegetation in northwest China. Results show that the percentage of each drought category remarkably changed during the period encompassing 1981-2020, and the vegetation drought shows deterioration in more areas of northwest China. The vegetation drought risks derived from the three weight determination methods were generally consistent, but differed for a particular vegetation type. The overall spatial distribution pattern of vegetation drought risk in Northwest China is higher in the west and lower in the east, and the vegetation in southern Qinghai and northwestern Xinjiang presents higher drought risk. This study may be used as a tool to provide quantitative basis for vegetation protection and vegetation drought management.

Drought characteristics and dominant factors across China: Insights from high-resolution daily SPEI dataset between 1979 and 2018.

Drought, a complex phenomenon exacerbated by climate change, is influenced by various climate factors. The escalating global temperatures associated with climate change, impact precipitation patterns and water cycle processes, consequently intensifying the occurrence and severity of droughts. To effectively address and adapt to these challenges, it is crucial to identify the dominant climate factors driving drought events. In this study, we utilized the 1979-2018 Chinese meteorological forcing dataset to calculate the daily Standardized Precipitation Evapotranspiration Index (SPEI). The Theil-Sen and Mann-Kendall (M-K) tests were employed to analyze the spatial and temporal trends of drought severity and duration. Additionally, partial correlation analysis was conducted to examine the relationship between climate factors (precipitation and potential evapotranspiration (PET)) and drought characteristic (drought severity and duration). Through this comprehensive analysis, we aimed to identify the primary factors influencing drought severity and duration. The findings revealed the following key results: (1) Over the 40-year period from 1979 to 2018, drought trends in China and its seven climate divisions exhibited an increasing pattern. (2) During drought periods, most regions exhibited a positive correlation between PET and drought severity and duration, while precipitation demonstrated a negative correlation. However, certain areas experiencing severe drought displayed a negative correlation between PET and drought severity and duration, precipitation demonstrated a positive correlation with drought severity and duration. (3) PET emerged as the dominant climatic factor for meteorological drought in the majority of China. These findings contribute valuable insights for policymakers in the development of climate change adaptation and mitigation strategies. By understanding the dominant climate factors driving drought events, policymakers can implement effective measures to mitigate the adverse socioeconomic and environmental impacts associated with climate change.

An improved global vegetation health index dataset in detecting vegetation drought.

Due to global warming, drought events have become more frequent, which resulted in aggravated crop failures, food shortage, larger and more energetic wildfires, and have seriously affected socio-economic development and agricultural production. In this study, a global long-term (1981-2021), high-resolution (4 km) improved vegetation health index (VHI) dataset integrating climate, vegetation and soil moisture was developed. Based on drought records from the Emergency Event Database, we compared the detection efficiency of the VHI before and after its improvement in the occurrence and scope of observed drought events. The global drought detection efficiency of the improved high-resolution VHI dataset reached values as high as 85%, which is 14% higher than the original VHI dataset. The improved VHI dataset was also more sensitive to mild droughts and more accurate regarding the extent of droughts. This improved dataset can play an important role in long-term drought monitoring but also has the potential to assess the impact of drought on the agricultural, forestry, ecological and environmental sectors.

Persistence versus dynamical seasonal forecasts of cereal crop yields.

Climate change is expected to have impacts on the balance of global food trade networks and food security. Thus, seasonal forecasts of precipitation and temperature are an essential tool for stakeholders to make timely choices regarding the strategies required to maximize their expected cereal yield outcomes. The availability of state-of-the-art seasonal forecasts such as the European Centre for Medium-Range Weather Forecasts (ECMWF) system 5 (SEAS5) may be an asset to help decision making. However, uncertainties and reduced skill may hamper the use of seasonal forecasts in several applications. Hence, in this work, we aim to understand the added value of such dynamical forecasts when compared to persistent anomalies of climate conditions used to predict the production of wheat and barley yields. With that in mind, empirical models relating annual wheat and barley yields in Spain to monthly values of precipitation and temperature are developed by taking advantage of ECMWF ERA5 reanalysis. Then, dynamical and persistence forecasts are issued at different lead times, and the skill of the subsequent forecasted yield is verified through probabilistic metrics. The results presented in this study demonstrate two different outcomes: (1) wheat and barley yield anomaly forecasts (dynamical and persistent) start to gain skill later in the season (typically from April onwards); and (2) the added value of using the SEAS5 forecast as an alternative to persistence ranges from 6 to 16%, with better results in the southern Spanish regions.

The impact of climate change in wheat and barley yields in the Iberian Peninsula.

The impact of climate change on wheat and barley yields in two regions of the Iberian Peninsula is here examined. Regression models are developed by using EURO-CORDEX regional climate model (RCM) simulations, forced by ERA-Interim, with monthly maximum and minimum air temperatures and monthly accumulated precipitation as predictors. Additionally, RCM simulations forced by different global climate models for the historical period (1972-2000) and mid-of-century (2042-2070; under the two emission scenarios RCP4.5 and RCP8.5) are analysed. Results point to different regional responses of wheat and barley. In the southernmost regions, results indicate that the main yield driver is spring maximum temperature, while further north a larger dependence on spring precipitation and early winter maximum temperature is observed. Climate change seems to induce severe yield losses in the southern region, mainly due to an increase in spring maximum temperature. On the contrary, a yield increase is projected in the northern regions, with the main driver being early winter warming that stimulates earlier growth. These results warn on the need to implement sustainable agriculture policies, and on the necessity of regional adaptation strategies.