Spatial-temporal variations of terrestrial evapotranspiration across China from 2000 to 2019

Global evapotranspiration from high-elevation mountains has decreased significantly at a rate of 3.923 %/a over the last 22 years

Clarifying the responses of human activities and climate change to the water cycle under variable environments is crucial for accurately assessing regional water balance. An analysis of the changes in actual evapotranspiration and its driving factors was conducted in the global high-elevation mountains during the period from 2001 to 2022. Utilizing 18 formulas for calculating evapotranspiration, which are based on comprehensive, temperature, radiation, and mass transfer, and then simulated the variations in reference evapotranspiration. Furthermore, we optimized the ET simulation model based on the most effective simulation results and projected future changes using scenario simulation data. Our findings reveal that: 1) ET at high-elevation mountains has significantly decreased at an average rate of 3.923 %/a, with monthly values ranging from 31.179 to 33.652 mm and an average of 32.646 mm; 2) The radiation-based model of Irmark-Allen is particularly well-suited for simulating ET at high-elevation mountains, with precision analysis and the Taylor diagram confirming its superior simulation performance. After optimizing the model using the method of least squares, the value of R2 before and after the optimization were 0.633 and 0.853, respectively. 3) An upward trend in ET under both SSP245 and SSP585 scenario in future simulation projections. Attribution analysis has identified Vapor Pressure Deficit as the key positive driver influencing the change of ET in global high-elevation mountains. Structural equation modeling further reveals that variations in net radiation and precipitation play a significant role in altering evapotranspiration rates. Meanwhile，The water balance analysis reveals that ET has been declining from 2001 to 2022. This phenomenon can be largely attributed to the substantial decline in vapor pressure deficit, the rise in the Normalized Difference Vegetation Index signifying increased vegetation cover, and the reduction in shallow soil moisture during the same period. These factors collectively explain the notable decrease in ET observed in high-elevation mountains.

Quantifying the policy-driven large scale vegetation restoration effects on evapotranspiration over drylands in China

Evapotranspiration (ET) is a key variable in the water cycle and reflects the ecosystem's feedback into the climate system. However, quantitative studies on the response of ET to large-scale vegetation restoration projects and climate change are still lacking, especially in drylands. To address this deficiency, this research examined the variation in ET since the implementation of restoration projects in the drylands of China in 2000-2018, and utilized quantitative analysis methods to investigate the effects of six environmental factors, including temperature (TEM), precipitation (PRE), solar radiation (RAD), vapour pressure deficit (VPD), soil moisture (SM), and leaf area index (LAI) on ET. Furthermore, a new method was proposed to detect the ET change caused by land use and land cover change (LUCC). The results indicated that ET showed a significant increasing trend (3.54 mm yr-1) during 2000-2018, and PRE was identified as a main influential factor with an ET contribution rate of more than 50%, especially in areas with insignificant vegetation greening. Additionally, the LAI had a major positive impact on ET in the areas of significant vegetation greening, and the contribution rate was nearly 40%. Furthermore, large-scale vegetation restoration expanded the area of high-transpiration vegetation types, and the ΔET (net variable quantity of ET caused by LUCC) increased obviously especially for the changes from cropland and grassland to forest, and barren land to grassland. These findings provide a new perspective for future assessments and further decision making regarding vegetation restoration projects in drylands.

Evapotranspiration responses to CO2 and its driving mechanisms in four ecosystems based on CMIP6 simulations: Forest, shrub, farm and grass

Evapotranspiration (ET) is an essential process of the water cycle through which water is transferred from terrestrial ecosystems to atmosphere. However, in the climate context of increasing CO₂ concentration (also called as a CO₂-enriched climate), the variation of ET and its main drivers among different ecosystems remain unclear. This study analyzed the output data of the CMCC ESM2 model with a ridge regression method, and proposed the trends and drivers of ET in different ecosystems in a CO₂-enriched climate. In particular, the temporal - spatial characteristics of ET and its primary drivers for different periods and wetness levels were revealed. With the rising of CO₂ concentration, the atmospheric evapotranspiration demand increases, and the vegetation grows more luxuriantly. ET shows an overall upward trend, especially in the shrub ecosystems (7.41 mm decade^-1). Our results show that the thermal conditions are the main driving factors for humid forest and shrub ecosystems whereas relative humidity (RH) is the main driving factor for arid farm and grass ecosystems. In terms of the average contribution in all periods, surface solar radiation contributes 26% and 41% to ET variation in forest and shrub ecosystems, and RH contributes 49% and 32% to ET variation in farm and grass ecosystems, respectively. Notably, with the increase of wetness levels, the contribution of water conditions on ET becomes smaller, while that of thermal conditions becomes larger. Correlation analysis shows that LAI impacts on ET are regulated by environmental factors, which reflects the complexity of ET change mechanism. Overall, these findings further provide a reference for rational planning of ecosystems and efficient utilization of water resources.

Spatiotemporal variation of irrigation water requirements for grain crops under climate change in Northwest China

Clarifying the spatiotemporal variation of crop irrigation water requirement (IWR) under the background of climate change is an essential basis for water resource management, determining the irrigation quota and adjusting the planting structure. Using 61 years of climate data from 205 stations in Northwest China, this study investigated the spatiotemporal variations of climatic factors and IWR during the growth period of five main grain crops (spring wheat, winter wheat, spring maize, summer maize, and rice) and explored the dominant climatic driving factors of IWR variation. Results showed that (1) the IWR of grain crops showed distinct differences. Rice was the highest water consumption crop (mean of 753.78 mm), and summer maize was the lowest (mean of 452.90 mm). (2) The variation trends and average values of IWR of different grain crops have spatial heterogeneity across Northwest China. For most crops, high values and increasing trends of IWR were mainly located in eastern Xinjiang, western Gansu, and western Inner Mongolia. (3) T_max (maximum temperature), T_min (minimum temperature), and P_eff (effective precipitation) showed an increasing trend during the growth period of each grain crop, while U₁₀ (wind speed at 10 m height), SD (solar radiation), and RH (relative humidity) presented decreasing trends. (4) SD, T_max, and U₁₀ promoted the increase of grain crops' IWR, while P_eff and RH inhibited it. The impacts of climatic factors on the grain crop IWR differed among different regions. P_eff was the most influential factor to the IWR of all grain crops in most areas. Therefore, under the premise of a significant increase in T and uncertain precipitation mode in the future, it is urgent to take effective water-saving measures according to the irrigation needs of the region. To cope with the adverse impact of climate change on the sustainable development of agriculture in the northwest dry area, to ensure regional and national food security.

Assessing the impact of drought-land cover change on global vegetation greenness and productivity

Drought-land cover change (D-LCC) is considered to be an important stress factor that affects vegetation greenness and productivity (VG&P) in global terrestrial ecosystems. Understanding the effects of D-LCC on VG&P benefits the development of terrestrial ecosystem models and the prediction of ecosystem evolution. However, till today, the mechanism remains underexploited. In this study, based on the Theil-Sen median estimator and Mann-Kendall test, Hurst exponent evaluation and rescaled range analysis (R/S), Pearson and Partial correlation coefficient analyses, we explore the spatiotemporal distribution characteristics and future trends of Leaf area index (LAI), Net primary productivity (NPP), Solar-induced chlorophyll fluorescence (SIF), Standardized precipitation evapotranspiration index (SPEI), Soil moisture (SM), Land cover type (LC), and the impact mechanism of D-LCC on global VG&P. Our results provide four major insights. First, three independent satellite observations consistently indicate that the world is experiencing an increasing trend of VG&P: LAI (17.69 %), NPP (20.32 %) and SIF (16.46 %). Nonetheless, productivity-reducing trends are unfolding in some tropical regions, notably the Amazon rainforest and the Congo basin. Second, from 2001 to 2020, the frequency, severity, duration, and scope of global droughts have been increasing. Third, the impact of land cover change on global VG&P is region-dependent. Finally, our results indicate that the continuous growth of VG&P in the global vegetation area is likely to become more difficult to maintain.

Spatiotemporal Variation in Actual Evapotranspiration and the Influencing Factors in Ningxia from 2001 to 2020

Surface evapotranspiration (ET) is an important part of the hydrological cycle. Based on the MOD16 ET product and the data collected by meteorological stations, this study investigated, for the first time, the characteristics, variation trend and influencing factors of actual ET in Ningxia from 2001 to 2020 along temporal and spatial scales using the Theil-Sen median trend analysis, Mann-Kendall test and Hurst index, and predicted the future trend of ET. The results revealed a strong correlation between the MOD16 ET product and ET data collected at meteorological stations (r = 0.837, R² = 0.701). Over the past 20 years, the annual ET in Ningxia showed an overall increasing trend, and the proportion of the increasing area was 96.58%. Quarterly ET varied over time, with the highest value in the third quarter and the lowest value in the second quarter. Annual ET showed a positive correlation with normalized difference vegetation index (NDVI), surface temperature and precipitation but no correlation with relative humidity. Additionally, the Hurst index revealed areas showing a persistent increase in ET, accounting for 84.91% of the total area, indicating that the future trend of ET in Ningxia is consistent with the past trend.

Spatiotemporal pattern of reference crop evapotranspiration and its response to meteorological factors in Northwest China over years 2000-2019

Agricultural irrigation water in Northwest China accounts for more than 80% of total local water consumption, which is 1.23 times that of China. However, Northwest China is the most water-scarce place in China. Water scarcity in restricts crop growth and production. Reference crop evapotranspiration (ET₀) is important for agricultural water management. Understanding the reason for ET₀ change is helpful to provide a basis for rational planning of agricultural irrigation systems to conserve water. This study investigated the temporal and spatial variation characteristics of ET₀ at 181 meteorological stations in Northwest China from 2000 to 2019. And the sensitive factors and dominant factors affecting ET₀ change were quantitatively identified based on sensitivity analysis and contribution rate evaluation. Results showed that (1) a significant increase in maximum and minimum temperature (T_max and T_min), a significant decrease in sunshine duration (SD) and relative humidity (RH), and a slight increase in wind speed at 10 m height (U₁₀) were observed. (2) Annual ET₀ had an insignificant increasing trend. Spring and autumn ET₀ contributed greatly to the growth of annual ET₀, especially in March, May, September, October, and November. ET₀ in HH (Yellow River Basin area) had decreased at annual scale, while other subregions were the opposite trend. Significant differences in monthly and seasonal changes in the spatial distribution of ET₀. (3) U₁₀ was the dominating contribution factor related to annual ET₀ variability, followed by T_min, RH, T_max, and SD. In seasonal time scale, T_min, SD, U₁₀, and RH were the most dominant factors in spring, summer, autumn, and winter respectively. (4) Spatial distribution for contribution rates of various meteorological factors showed significant diversity among various subregions. The positive contribution of U₁₀ was the major cause of the increase in ET₀ in semi-arid grassland area (BGH), the southwest of "Qice line" (QCXXN), and the southeast of "Qice line" (QCXDN); the significant increase in T_min contributed most in Qaidam Basin (CDM), Hexi inland river basin (HX), the northeast of "Qice line" (QCXDB), and the northwest of "Qice line" (QCXXB), while the contribution of decreasing SD offsets the positive effects of other factors, leading to the decrease in ET₀ in HH. Our work illustrates that water management measures should be different at different spatial and temporal scales. The effect of U₁₀ can be offset by covering, to reduce evaporation and maintain water in BGH, QCXXN, and QCXDN. And high-temperature resistant varieties are planted to adapt to temperature growth in CDM, HX, QCXDB, and QCXXB. Agricultural water management strategies should be formulated and selected according to local conditions.

Trends and Changes in Hydrologic Cycle in the Huanghuaihai River Basin from 1956 to 2018

The Huanghuaihai River Basin (HRB) is one of the most prominent areas of water resource contradiction in China. It is of great significance to explore the relationship between water balance in this area for a deep understanding of the response of the water cycle to climate change. In this study, machine learning methods are used to prolong the actual evapotranspiration (ET) of the basin on the time scale and explore water balances calculated from various sources. The following conclusions are obtained: (1) it is found that the simulation accuracy of Global Land Evaporation Amsterdam Model (GLEAM) products in HRB is good. The annual average ET spatial distribution tends to increase from northwest to southeast; (2) three machine learning algorithms are used to construct the ET calculation model. The correlation coefficients of the three methods are all above 0.9 and the mean relative error values of random forest (RF) are all less than 30%. The RF has the best effect; (3) the relative errors of water balance in HRB from 1956–1979, 1980–2002 and 2003–2018 are less than ±5%, which indicates that the calculation of each element of the water cycle in the study area can well reflect the water balance relationship of the basin.