Modeling the effects of precipitation and temperature patterns on agricultural drought in China from 1949 to 2015

Dynamic process of ecosystem water use efficiency and response to drought in the Yellow River Basin, China

Ecosystem water use efficiency (WUE) is a crucial indicator of the impact of climate change on terrestrial ecosystems, reflecting the balance between biological processes (photosynthesis and transpiration) and physical processes (evapotranspiration). However, the response mechanisms and driving processes of WUE to drought remain to be further understood. In this study, we analyzed the spatial and temporal dynamics and response mechanisms of WUE in the Yellow River Basin (YRB) using data on Gross Primary Productivity (GPP), Evapotranspiration (ET) and Standardized Precipitation Evapotranspiration Index (SPEI), which revealed the cumulative effect of drought on WUE and assessed the ecosystem's resilience. The study results showed that (1) GPP, ET and WUE in the YRB exhibited a significant increasing trend, with 63.04 % of the area showing a marked increase in WUE. (2) GPP was the dominant factor influencing WUE in 65.36 % of the area, particularly in cropland and grassland, while ET was more influential in forested areas. Vapor pressure deficit (VPD) was identified as the principal driver affecting vegetation GPP in semi-arid and semi-humid regions of the YRB. In contrast, soil moisture (SM) was the limiting factor in arid areas. (3) 71.00 % of the WUE in the basin was affected by drought cumulative effects, with an average cumulative duration of 4.5 months. Arid regions experienced the most extended duration of 7.29 months, compared to 3.05 months in semi-humid regions. (4) 74.85 % of the regional ecosystems exhibited ecological resilience to drought, particularly in the source areas of the western basin of the YRB. Shrublands have the highest drought resilience among vegetation types, while grasslands have the lowest. The resilience of each climatic zone was in the order of semi-humid, semi-arid, and arid order. This study comprehensively analyzed of the spatial and temporal dynamics and response mechanisms of WUE in the YRB, offering a new perspective and scientific basis for understanding and predicting the ecosystem response to climate change.

Morphological Structure and Physiological and Biochemical Responses to Drought Stress of Iris japonica

Drought is among the most important abiotic stresses on plants, so research on the physiological regulation mechanisms of plants under drought stress can critically increase the economic and ecological value of plants in arid regions. In this study, the effects of drought stress on the growth status and biochemical indicators of Iris japonica were explored. Under drought stress, the root system, leaves, rhizomes, and terrestrial stems of plants were sequentially affected; the root system was sparse and slender; and the leaves lost their luster and gradually wilted. Among the physiological changes, the increase in the proline and soluble protein content of Iris japonica enhanced the cellular osmotic pressure and reduced the water loss. In anatomical structures, I. japonica chloroplasts were deformed after drought treatment, whereas the anatomical structures of roots did not substantially change. Plant antioxidant systems play an important role in maintaining cellular homeostasis; but, as drought stress intensified, the soluble sugar content of terrestrial stems was reduced by 55%, and the ascorbate peroxidase, glutathione reductase, and monodehydroascorbate reductase (MDHAR) activities of leaves and the MDHAR activity of roots were reduced by 29%, 40%, 22%, and 77%, respectively. Overall, I. japonica was resistant to 63 days of severe drought stress and resisted drought through various physiological responses. These findings provide a basis for the application of I. japonica in water-scarce areas.

Molecular mechanism of thiamine in mitigating drought stress in Chinese wingnut (Pterocarya stenoptera): Insights from transcriptomics

Urban garden plants are frequently affected by drought, which can hinder their growth, development, and greening effect. Previous studies have indicated that Chinese wingnut (Pterocarya stenoptera) responds to drought stress by increasing the expression of thiamine synthesis genes. In this study, it was found that exogenous thiamine can effectively alleviate the negative effects of drought stress on plants. Forward transcriptome sequencing and physiological tests were further conducted to reveal the molecular mechanism of thiamine in alleviating drought stress. Results showed that exogenous thiamine activated the expression of eight chlorophyll synthesis genes in Chinese wingnut under drought stress. Moreover, physiological indicators proved that chlorophyll content increased in leaves of Chinese wingnut with thiamine treatment under drought stress. Photosynthesis genes were also activated in Chinese wingnut treated with exogenous thiamine under drought stress, as supported by photosynthetic indicators PIabs and PItotal. Additionally, exogenous thiamine stimulated the expression of genes in the auxin-activated signaling pathway, thus attenuating the effects of drought stress. This study demonstrates the molecular mechanism of thiamine in mitigating the effects of drought stress on non-model woody plants lacking transgenic systems. This study also provides an effective method to mitigate the negative impacts of drought stress on plants.

Metabolomic and transcriptomic analyses reveal that sucrose synthase regulates maize pollen viability under heat and drought stress

Maize pollen is highly sensitive to heat and drought, but few studies have investigated the combined effects of heat and drought on pollen viability. In this study, pollen's structural and physiological characteristics were determined after heat, drought, and combined stressors. Furthermore, integrated metabolomic and transcriptomic analyses of maize pollen were conducted to identify potential mechanisms of stress responses. Tassel growth and spikelet development were considerably suppressed, pollen viability was negatively impacted, and pollen starch granules were depleted during anthesis under stress. The inhibitory effects were more significant due to combined stresses than to heat or drought individually. The metabolic analysis identified 71 important metabolites in the combined stress compared to the other treatments, including sugars and their derivatives related to pollen viability. Transcriptomics also revealed that carbohydrate metabolism was significantly altered under stress. Moreover, a comprehensive metabolome-transcriptome analysis identified a central mechanism in the biosynthesis of UDP-glucose involved in reducing the activity of sucrose synthase SH-1 (shrunken 1) and sus1 (sucrose synthase 1) that suppressed sucrose transfer to UDP-glucose, leading to pollen viability exhaustion under stress. In conclusion, the lower pollen viability after heat and drought stress was associated with poor sucrose synthase activity due to the stress treatments.

Can ecological landscape pattern influence dry-wet dynamics? A national scale assessment in China from 1980 to 2018

Land cover has been demonstrated to have substantial impacts on climate and dry-wet environment, but potential influence of landscape pattern dynamics accompanying land cover change on drought remains unclear. In this study, response of dry-wet dynamics to landscape pattern in China was examined. Results suggest that landscape pattern in China's nine agricultural districts had transformed to varying extents and showed spatiotemporal heterogeneity from 1980 to 2018. For forest landscape, the highest annual average Percentage of Landscape (PLAND) was recorded in SC, reaching 62.26%; and the highest Largest Patch Index (LPI) was presented in YGP, followed by SC, with annual values of 53.79% and 46.26% respectively. The QTP has the most prominent forest connectivity in spite of its lower abundance. For grass landscape, the highest abundance and dominance were recorded in QTP, with annual PLAND fluctuation range of 49.66%-63.52% and annual LPI variation range of 34.10%-58.46%, which is associated with its climate and altitude. The most prominent crop landscape abundance and dominance were recorded in HHHP, with annual PLAND fluctuating interval of 56.53%-60.64%, indicating the highest agricultural development level in this district. At landscape level, dry-wet circumstance could be improved with enhancements in the largest patch percentage, patch density and spatial connectivity, while worsen with increases of landscape fragmentation and separated degree. At class level, increases in abundance and dominance of forest and crop landscapes would reduce drought risk, while it was opposite for grass landscape. Improved forest connectedness would optimize dry-wet environment and reduce drought risk. The PLAND of forest and crop landscapes contributed the most prominent effect to alleviate drought intensity. Compared with forestland and grassland, determining suitable crop landscape configuration to reduce drought risk is more complex because the balance between agricultural economic benefits and ecological landscape effects should be taken into account.

Impacts of Drought and Climatic Factors on Vegetation Dynamics in the Yellow River Basin and Yangtze River Basin, China

Understanding the impacts of drought and climate change on vegetation dynamics is of great significance in terms of formulating vegetation management strategies and predicting future vegetation growth. In this study, Pearson correlation analysis was used to investigate the correlations between drought, climatic factors and vegetation conditions, and linear regression analysis was adopted to investigate the time-lag and time-accumulation effects of climatic factors on vegetation coverage based on the standardized evapotranspiration deficit index (SEDI), normalized difference vegetation index (NDVI), and gridded meteorological dataset in the Yellow River Basin (YLRB) and Yangtze River Basin (YTRB), China. The results showed that (1) the SEDI in the YLRB showed no significant change over time and space during the growing season from 1982 to 2015, whereas it increased significantly in the YTRB (slope = 0.013/year, p < 0.01), and more than 40% of the area showed a significant trend of wetness. The NDVI of the two basins, YLRB and YTRB, increased significantly at rate of 0.011/decade and 0.016/decade, respectively (p < 0.01). (2) Drought had a significant impact on vegetation in 49% of the YLRB area, which was mainly located in the northern region. In the YTRB, the area significantly affected by drought accounted for 21% of the total area, which was mainly distributed in the Sichuan Basin. (3) In the YLRB, both temperature and precipitation generally had a one-month accumulated effect on vegetation conditions, while in the YTRB, temperature was the major factor leading to changes in vegetation. In most of the area of the YTRB, the effect of temperature on vegetation was also a one-month accumulated effect, but there was no time effect in the Sichuan Basin. Considering the time effects, the contribution of climatic factors to vegetation change in the YLRB and YTRB was 76.7% and 63.2%, respectively. The explanatory power of different vegetation types in the two basins both increased by 2% to 6%. The time-accumulation effect of climatic factors had a stronger explanatory power for vegetation growth than the time-lag effect.

Spatiotemporal variations in damages to cropland from agrometeorological disasters in mainland China during 1978-2018

Drought, flood, hail, low temperature, and frost (LTF) are the main agrometeorological disasters (AMDs) in China; however, comprehensive and quantitative studies on cropland damage induced by AMDs across the whole country in terms of long-term trends are still lacking and urgently needed. Based on historical statistical data from yearbooks and bulletins, the overall characteristics of cropland damage by AMDs during 1978-2018 were analyzed using a pre-whitening procedure and a Mann-Kendall trend test at yearly and provincial scales in China. The results showed that drought was the most severe, with an average covered area of 22.2 million ha and an affected area of 11.2 million ha every year during 1978-2018, followed by flood, hail, and LTF. A decreasing trend was observed in covered area and affected area by drought, flood, and hail, while only LTF showed an increasing trend. On provincial scale, more than 70% of the covered area by AMDs was induced by drought and flood in most provincial districts. In all provincial districts of northern China, more than 50% of the covered area was induced by drought. In most provincial districts of southern China, more than 40% of the covered area was induced by flood. Hail disasters were prominent in Xinjiang, with significant increasing trends among all parameters. Compared with the other three AMDs, LTF covered and affected the smallest cropland area, but significant increasing trends were observed in the northwest and middle parts of China. The results of this study systematically display the characteristics of damage to cropland by four main AMDs, which are critical and necessary for disaster risk reduction and adaptive strategy development.

Analysis of Drought Characteristics in Northern Shaanxi Based on Copula Function

Precipitation is low and drought occurs frequently in Northern Shaanxi. To study the characteristics and occurrence and development of drought events in Northern Shaanxi is beneficial to the prevention and control of drought disasters. Based on the monthly rainfall data of 10 meteorological stations in Northern Shaanxi from 1960 to 2019, the characteristic variables of drought events at each meteorological station in Northern Shaanxi were extracted by using run theory and copula function. The joint probability distribution and recurrence period were obtained by combining the duration and intensity of drought, and the relationship between drought characteristics and crop drought affected area was studied. The results show that (1) from 1960 to 2019, drought events mainly occurred in Northern Shaanxi with long duration and low severity, short duration and high severity, or short duration and low severity, among which the frequency of drought events that occurred in Yuyang and Baota districts was higher. The frequency of light drought and extreme drought was more in the south and less in the north, while the frequency of moderate drought and severe drought was more in the north and less in the south. (2) The optimal edge distribution of drought intensity and drought duration in Northern Shaanxi is generalized Pareto distribution, and the optimal fitting function is Frank copula function. The greater the duration and intensity of drought, the greater the cumulative probability and return period. (3) The actual recurrence interval and the theoretical recurrence interval of drought events in Northern Shaanxi were close, and the error was only 0.1–0.3a. The results of the joint return period can accurately reflect the actual situation, and this study can provide effective guidance for the prevention and management of agricultural dryland in Northern Shaanxi.

Establishment of the Baseline for the IWRM in the Ecuadorian Andean Basins: Land Use Change, Water Recharge, Meteorological Forecast and Hydrological Modeling

This study was conducted in the Zamora Huayco (ZH) river basin, located in the inter-Andean region of southern Ecuador. The objective was to describe, through land use/land cover change (LUCC), the natural physical processes under current conditions and to project them to 2029. Moreover, temperature and precipitation forecasts were estimated to detail possible effects of climate change. Using remote sensing techniques, satellite images were processed to prepare a projection to 2029. Water recharge was estimated considering the effects of slope, groundcover, and soil texture. Flash floods were estimated using lumped models, concatenating the information to HEC RAS. Water availability was estimated with a semi-distributed hydrological model (SWAT). Precipitation and temperature data were forecasted using autoregressive and exponential smoothing models. Under the forecast, forest and shrub covers show a growth of 6.6%, water recharge projects an increase of 7.16%. Flood flows suffer a reduction of up to 16.54%, and the flow regime with a 90% of probability of exceedance is 1.85% (7.72 l/s) higher for 2029 than for the 2019 scenario, so an improvement in flow regulation is evident. Forecasts show an increase in average temperature of 0.11 °C and 15.63% in extreme rainfall by 2029. Therefore, intervention strategies in Andean basins should be supported by prospective studies that use these key variables of the system for an integrated management of water resources.

Machine learning for predicting greenhouse gas emissions from agricultural soils

Machine learning (ML) models are increasingly used to study complex environmental phenomena with high variability in time and space. In this study, the potential of exploiting three categories of ML regression models, including classical regression, shallow learning and deep learning for predicting soil greenhouse gas (GHG) emissions from an agricultural field was explored. Carbon dioxide (CO₂) and nitrous oxide (N₂O) fluxes, as well as various environmental, agronomic and soil data were measured at the site over a five-year period in Quebec, Canada. The rigorous analysis, which included statistical comparison and cross-validation for the prediction of CO₂ and N₂O fluxes, confirmed that the LSTM model performed the best among the considered ML models with the highest R coefficient and the lowest root mean squared error (RMSE) values (R = 0.87 and RMSE = 30.3 mg·m^-2·hr^-1 for CO₂ flux prediction and R = 0.86 and RMSE = 0.19 mg·m^-2·hr^-1 for N₂O flux prediction). The predictive performances of LSTM were more accurate than those simulated in a previous study conducted by a biophysical-based Root Zone Water Quality Model (RZWQM2). The classical regression models (namely RF, SVM and LASSO) satisfactorily simulated cyclical and seasonal variations of CO₂ fluxes (R = 0.75, 0.71 and 0.68, respectively); however, they failed to reasonably predict the peak values of N₂O fluxes (R < 0.25). Shallow ML was found to be less effective in predicting GHG fluxes than other considered ML models (R < 0.7 for CO₂ flux and R < 0.3 for estimating N₂O fluxes) and was the most sensitive to hyperparameter tuning. Based on this comprehensive comparison study, it was elicited that the LSTM model can be employed successfully in simulating GHG emissions from agricultural soils, providing a new perspective on the application of machine learning modeling for predicting GHG emissions to the environment.