Projections of actual evapotranspiration under the 1.5 °C and 2.0 °C global warming scenarios in sandy areas in northern China

A framework for assessing the impacts of land-use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale

BACKGROUND

Irrigation is used extensively to enhance grain production and ensure food security. Many studies have used crop models and global climate models to study the variation of irrigated crop yield in the context of climate change. But most considered the influence of direct climate change but neglected the influence of irrigation water availability, which is affected by land-use/cover change (LUCC) and indirect climate change, on irrigated crop yield. This study therefore developed a framework including Patch-generating Land Use Simulation model, Soil and Water Assessment Tool, Agricultural Production Systems sImulator Model, and global climate models for exploring the impacts of LUCC, direct climate change, and indirect climate change on wheat yield in a typical watershed.

RESULTS

Both LUCC and climate change caused increased runoff from October to May, and thus increased the irrigation water availability, by 51.6 and 30.7 mm per growing season under 1.5 and 2.0 °C warming, respectively. The combined influence of LUCC, direct, and indirect climate change increased wheat yield by about 18.5% and 15.5% in the context of 1.5 and 2.0 °C warming, respectively. The relative contribution of LUCC, indirect climate change and direct climate change to yield was 4.7%, 41.2%, and 54.1% under 1.5 °C warming, and 13.1%, 28.7%, and 58.2% under 2.0 °C warming, respectively.

CONCLUSION

We suggest that changes in irrigation water availability should be considered from a watershed perspective when simulating the influence of climate change on crop yield, especially regional crop production estimation. © 2023 Society of Chemical Industry.

Shifts in biochemical and physiological responses by the inoculation of arbuscular mycorrhizal fungi in Triticum aestivum growing under drought conditions

BACKGROUND

A decrease in rainfall is one of the main constraints on wheat production, although the association of wheat with arbuscular mycorrhizal fungi (AMF) may be an alternative for crop production under drought conditions. In the present study, we used three wheat cultivars (Purple, Ilustre and Maxi Baer) inoculated with two AMF strains, one obtained from the hyperarid Atacama Desert (northern Chile; Fm) and the other obtained from southern Chile (Cc). Plants were maintained under two irrigation conditions (normal irrigation and drought) and the physiological behaviour and enzymatic and nonenzymatic antioxidant activities in the shoots were determined. In addition, the phenolic compounds were identified by high-performance liquid chromatography-dioide array detection-electrospray ionization-tandem mass spectrometry and quantified.

RESULTS

AMF colonization produced higher levels of efficiency of photosystem II and photosynthetic pigments. High values of catalase in Purple-Cc, ascorbate peroxidase in Purple-Cc, glutathione reductase in Maxi-Cc and superoxide dismutase in Purple-Cc, all under stress, were registered. Of the inoculated cultivars, Purple-Cc showed the highest flavonoid levels, whereas hydroxycinnamic acids were higher in Maxi-Fm without drought, with apigenin and luteolin being the most abundant. High levels of phenols were present in the Ilustre-Fm plants without drought. Under normal irrigation, high levels of antioxidant activity were registered in the AMF treatments, whereas, under stress conditions, in general, high values were observed under the Fm inoculation.

CONCLUSION

Our results showed that the greatest antioxidant activity and phenolic content occurred in wheat plants inoculated with AMF, indicating their influence on coping with water stress, which is of importance in vast areas where global climate change is resulting in diminished rainfall. © 2021 Society of Chemical Industry.

Leaf Physiological Responses of Three Psammophytes to Combined Effects of Warming and Precipitation Reduction in Horqin Sandy Land, Northeast China

The decreasing precipitation with global climate warming is the main climatic condition in some sandy grassland ecosystems. The understanding of physiological responses of psammophytes in relation to warming and precipitation is a possible way to estimate the response of plant community stability to climate change. We selected Lespedeza davurica, Artemisia scoparia, and Cleistogenes squarrosa in sandy grassland to examine the effect of a combination of climate warming and decreasing precipitation on relative water content (RWC), chlorophyll, proline, and antioxidant enzyme activities. We found that all experimental treatments have influenced RWC, chlorophyll, proline, and antioxidant enzyme activities of three psammophytes. L. davurica has the highest leaf RWC among the three psammophytes. With the intensification of precipitation reduction, the decreasing amplitude of chlorophyll from three psammophytes was L. davurica > C. squarrosa > A. scoparia. At the natural temperature, the malondialdehyde (MDA) content of the three psammophytes under severe drought treatment was much higher than other treatments, and their increasing degree was as follows: A. scoparia > C. squarrosa > L. davurica. At the same precipitation gradient, the proline of three psammophytes under warming was higher than the natural temperature. The differences in superoxide dismutase (SOD) among the three psammophytes were A. scoparia > L. davurica > C. squarrosa. Moreover, at natural temperature, more than 40% of precipitation reduction was most significant. Regardless of warming or not, the catalase (CAT) activity of A. scoparia under reduced precipitation treatments was higher than natural temperature, while the response of L. davurica was opposite. Correlation analyses evidenced that warming (T) was significant in L. davurica and precipitation (W) was significant in A. scoparia and C. squarrosa according to the Monte-Carlo permutation test (p = 0.002, 0.004, and 0.004). The study is important in predicting how local plants will respond to future climate change and assessing the possible effects of climate change on sandy grassland ecosystems.

Global predictions of primary soil salinization under changing climate in the 21st century

Soil salinization has become one of the major environmental and socioeconomic issues globally and this is expected to be exacerbated further with projected climatic change. Determining how climate change influences the dynamics of naturally-occurring soil salinization has scarcely been addressed due to highly complex processes influencing salinization. This paper sets out to address this long-standing challenge by developing data-driven models capable of predicting primary (naturally-occurring) soil salinity and its variations in the world's drylands up to the year 2100 under changing climate. Analysis of the future predictions made here identifies the dryland areas of South America, southern and western Australia, Mexico, southwest United States, and South Africa as the salinization hotspots. Conversely, we project a decrease in the soil salinity of the drylands in the northwest United States, the Horn of Africa, Eastern Europe, Turkmenistan, and west Kazakhstan in response to climate change over the same period.

Using AI-MCDM Model to Boost Sustainable Energy System Development: A Case Study on Solar Energy and Rainwater Collection in Guangdong Province

Rural areas in southern China receive ample rainfall annually as well as over 1600 h of annual sunshine. Despite a generally severe urban–rural development imbalance, these rural areas feature well-developed basic infrastructure and diverse economic activities. Rural revitalization policies in these areas have emphasized the development of cultural and ecological tourism, which has spurred economic development and given rise to a trend of villa construction. Residential buildings sit on large areas where natural resources are abundant. These advantages are conducive to the development and use of sustainable resources. This study proposes an incentive policy encouraging rural residents to renovate their buildings to include rainwater conservation and solar power generation. The Delphi method, an analytic hierarchy process, and fuzzy logic theory were combined to establish an AI-MCDM model, with applications of artificial intelligence and multiple-criteria decision making. Using Conghua District, Guangdong Province as an example, the study suggested that the model is beneficial to increasing the willingness of rural residents to reconstruct and renovate their residences, promoting the development of a low-carbon ecological region, Wenquan Township. We conducted the Delphi process twice to assess and validate incentives for installing natural resource conservation structures in agricultural areas. Nine criteria were identified, which can be divided into three main dimensions of participation situation, generating capacity, and storage facilities. The proposed AI-MCDM model developed using the Delphi–Fuzzy Analytic Hierarchy Process Model has high objectivity and can support rural areas in developing low-carbon, sustainable characteristics. The findings can serve as a reference for governments formulating incentives to encourage the installation of rainwater conservation and solar energy generation structures by rural households.

Long-Term Projection of Water Cycle Changes over China Using RegCM

The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture.

Projections of desertification trends in Central Asia under global warming scenarios

Central Asia (CA) is a core area of global desertification, but the effect of the intensifying "global greening" policy on the desertification process under global warming scenarios in CA remains unclear. Based on multi-source remote sensing data and Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) 2b climate data, this study investigated desertification in CA using actual evapotranspiration (ETa), temperature and precipitation as driving factors. Coupling with the CA-Markov model, the inversion method of desertification was improved, and the evolution normal form of desertification in CA was proposed. Finally, spatio-temporal variations of desertification in CA were quantified. The results indicate that temperature, precipitation, and normalized difference vegetation index (NDVI) in CA increased during the historical period (1980-2015), with sudden changes in 1994. In contrast, although ETa exhibited fluctuating increases (7.41 mm/10 yr) during this period, no sudden changes were observed in 1994. In the future (2006-2099), the climate of CA will become warmer and wetter. With reference to 1980-2005, precipitation under global warming of 2.0 °C (GW2.0) will be higher than that under global warming of 1.5 °C (GW1.5) by 10.3 mm, and ETa will increase by 20.88 mm and 27.54 mm under GW1.5 and GW2.0, respectively. Although the area of desert lands has decreased (5.94 × 10⁴ km²/10 yr), the area of potential desert lands has increased (0.17 × 10⁴ km²/10 yr). With global warming, this situation will continue to intensify, mainly in Xinjiang of China, and Kazakhstan. The Aral Sea plays an important role in the desertification of CA. The potential increase in desert land under GW2.0 is equivalent to the current water area of the Aral Sea. The findings could provide policy support for combating desertification in CA and promoting the achievement of the Sustainable Development Goals.

Projected Spatiotemporal Dynamics of Drought under Global Warming in Central Asia

Drought, one of the most common natural disasters that have the greatest impact on human social life, has been extremely challenging to accurately assess and predict. With global warming, it has become more important to make accurate drought predictions and assessments. In this study, based on climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we used the Palmer Drought Severity Index (PDSI) to analyze and project drought characteristics and their trends under two global warming scenarios—1.5 °C and 2.0 °C—in Central Asia. The results showed a marked decline in the PDSI in Central Asia under the influence of global warming, indicating that the drought situation in Central Asia would further worsen under both warming scenarios. Under the 1.5 °C warming scenario, the PDSI in Central Asia decreased first and then increased, and the change time was around 2080, while the PDSI values showed a continuous decline after 2025 in the 2.0 °C warming scenario. Under the two warming scenarios, the spatial characteristics of dry and wet areas in Central Asia are projected to change significantly in the future. In the 1.5 °C warming scenario, the frequency of drought and the proportion of arid areas in Central Asia were significantly higher than those under the 2.0 °C warming scenario. Using the Thornthwaite (TH) formula to calculate the PDSI produced an overestimation of drought, and the Penman–Monteith (PM) formula is therefore recommended to calculate the index.

Influences of 1.5 °C and 2.0 °C global warming scenarios on water use efficiency dynamics in the sandy areas of northern China

Water use efficiency (WUE) is an important variable used in hydrometeorology study to reveal the links between carbon-water cycles in sandy ecosystems which are highly sensitive to climate change and can readily reflect the effects of it. In light of the Paris Agreement, it is essential to identify the regional impacts of 0.5 °C of additional global warming to inform climate adaptation and mitigation strategies. Using the modified Carnegie-Ames-Stanford Approach (CASA) and Advection-Aridity (AA) models with global warming values of 1.5 °C and 2.0 °C above preindustrial levels from Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b) datasets, we conducted a new set of climate simulations to assess the effects of climate on WUE (the ratio of net primary productivity (NPP) to actual evapotranspiration (ETa)) in different sandy land types (mobile sandy land, MSL; semimobile/semifixed sandy land, SMSF; and fixed sandy land, FSL) during the period of baseline (1986-2005) and future (2006-2100). The spatiotemporal patterns of ETa, NPP, and WUE mostly showed increasing trends; the value of WUE decreased (6.40%) only in MSL with an additional 0.5 °C of warming. Meteorological and vegetation factors determined the variations in WUE. With warming, only the correlation between precipitation and WUE decreased in the three sandy land types, and the leaf area index (LAI) increased with an additional 0.5 °C of warming. The desertification degree comprehensively reflects the linkages among the standardized precipitation evapotranspiration index (SPEI), LAI and WUE. Simulation results indicated the sandy area extent could potential increase by 20 × 10⁴ km² per decade on average during 2016-2047 and that the increase could be gradual (2.60 × 10⁴ km² per decade) after 2050 (2050-2100). These results highlight the benefits of limiting the global mean temperature change to 1.5 °C above preindustrial levels and can help identify the risk of desertification with an additional 0.5 °C of warming.