Future malaria environmental suitability in Africa is sensitive to hydrology.

Changes in climate shift the geographic locations that are suitable for malaria transmission because of the thermal constraints on vector Anopheles mosquitos and Plasmodium spp. malaria parasites and the lack of availability of surface water for vector breeding. Previous Africa-wide assessments have tended to solely represent surface water using precipitation, ignoring many important hydrological processes. Here, we applied a validated and weighted ensemble of global hydrological and climate models to estimate present and future areas of hydroclimatic suitability for malaria transmission. With explicit surface water representation, we predict a net decrease in areas suitable for malaria transmission from 2025 onward, greater sensitivity to future greenhouse gas emissions, and different, more complex, malaria transmission patterns. Areas of malaria transmission that are projected to change are smaller than those estimated by precipitation-based estimates but are associated with greater changes in transmission season lengths.

Groundwater recharge estimation using WetSpass-M and MTBS leveraging from HydroOffice and WHAT tools for baseflow in Weyib watershed, Ethiopia.

WetSpass-M model and multi-technique baseflow separation (MTBS) were applied to estimate spatio-temporal groundwater recharge (GWR) to be used to comprehend and enhance sustainable water resource development in the data-scarce region. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) techniques outperform the existing 13 MTBS techniques to separate baseflow depending on the correlation matrix; mean baseflow was 5.128 m3/s. The WetSpass-M model performance evaluated by Nash-Sutcliff Efficiency (NSE) was 0.95 and 0.89; R2 was 0.90 and 0.85 in comparison to observed and simulated mean monthly baseflow and runoff (m3/s), respectively. The estimated mean annual water balance was 608.2 mm for actual evapotranspiration, 221.42 mm for the surface runoff, 87.42 mm for interception rate, and 177.66 mm for GWR, with an error of - 3.29 mm/year. The highest annual actual evapotranspiration was depicted in areas covered by vegetation, whereas lower in the settlement. The peak annual interception rates have been noticed in areas covered with forests and shrublands, whereas the lowest in settlement and bare land. The maximum annual runoff was depicted in settlement and bare land, while the lowest was in forest-covered areas. The annual recharge rates were low in bare land due to high runoff and maximum in forest-covered areas due to low surface runoff. The watershed's downstream areas receive scanty annual rainfall, which causes low recharge and drought. The findings point the way ahead in terms of selecting the best approach across multi-technique baseflow separations.

Expression responses of XTH genes in tomato and potato to environmental mechanical forces: focus on behavior in response to rainfall, wind and touch.

Rainfall, wind and touch, as mechanical forces, were mimicked on 6-week-old soil-grown tomato and potato under controlled conditions. Expression level changes of xyloglucan endotransglucosylase/hydrolase genes (XTHs) of tomato (Solanum lycopersicum L. cv. Micro Tom; SlXTHs) and potato (Solanum tuberosum L. cv. Desirée; StXTHs) were analyzed in response to these mechanical forces. Transcription intensity of every SlXTHs of tomato was altered in response to rainfall, while the expression intensity of 72% and 64% of SlXTHs was modified by wind and touch, respectively. Ninety-one percent of StXTHs (32 out of 35) in potato responded to the rainfall, while 49% and 66% of the StXTHs were responsive to the wind and touch treatments, respectively. As previously demonstrated, all StXTHs were responsive to ultrasound treatment, and all were sensitive to one or more of the environmental mechanical factors examined in the current study. To our best knowledge, this is the first study to demonstrate that these ubiquitous mechanical environmental cues, such as rainfall, wind and touch, influence the transcription of most XTHs examined in both species.

Longitudinal assessment of extreme climate events in Kinnaur district, Himachal Pradesh, north-western Himalaya, India.

It is vital to keep an eye on changes in climatic extremes because they set the stage for current and potential future climate, which usually have a reasonable adverse impact on ecosystems and society. The present study examines the variability and trends in precipitation and temperature across seasons in the Kinnaur district, offering valuable insights into the complex dynamics of the Himalayan climate. Using Climatic Research Unit gridded Time Series (CRU TS) datasets from 1951 to 2021, the study analyzes the data to produce 28 climate indices based on India Meteorological Department (IMD) convention indices and Expert Team on Climate Change Detection and Indices (ETCCDI). Although there may be considerable variation in climate indices in terms of absolute values within different products, there is consensus in both long-term trends and inter-annual variability. Analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Initially, the data were subjected to rigorous quality control procedures, which involved identifying anomalies. Statistical analysis like trend analysis, employing Mann-Kendall test and Sen's slope estimator, reveal significant (p < 0.05) increase in consecutive dry days (CDD) at 0.03 days/year and decrease in consecutive wet days (CWD) at 0.02 days/year. Notably, the frequency of heavy precipitation occurrences showed an increasing trend. Changes in precipitation in the Western Himalaya are driven by a complex interplay of orographic effects, monsoonal dynamics, atmospheric circulation patterns, climate change, and localized factors such as topography, atmospheric circulation patterns, moisture sources, land-sea temperature contrasts, and anthropogenic influences. Moreover, in case of temperature indices, there is significant increasing trend observed. Temperature indices indicate a significant annual increase in warm nights (TN90p) at 0.06%/year and warm days (TX90p) at 0.11%/year. Extreme temperature events have been trending upward, with monthly daily maximum temperature (TXx) increasing by 1.5 °C yearly. This study enhances our comprehension of the global warming phenomenon and underscores the importance of acknowledging alterations in the water cycle and their repercussions on hydrologic resources, agriculture, and livelihoods in the cold desert of the northwestern Indian Himalaya.

Uncertainty reduction for precipitation prediction in North America.

Large differences in projected future annual precipitation increases in North America exists across 27 CMIP6 models under four emission scenarios. These differences partly arise from weak representations of land-atmosphere interactions. Here we demonstrate an emergent constraint relationship between annual growth rates of future precipitation and growth rates of historical temperature. The original CMIP6 projections show 0.49% (SSP126), 0.98% (SSP245), 1.45% (SSP370) and 1.92% (SSP585) increases in precipitation per decade. Combining observed warming trends, the constrained results show that the best estimates of future precipitation increases are more likely to reach 0.40-0.48%, 0.83-0.93%, 1.29-1.45% and 1.70-1.87% respectively, implying an overestimated future precipitation increases across North America. The constrained results also are narrow the corresponding uncertainties (standard deviations) by 13.8-31.1%. The overestimated precipitation growth rates also reveal an overvalued annual growth rates in temperature (6.0-13.2% or 0.12-0.37°C) and in total evaporation (4.8-14.5%) by the original models' predictions. These findings highlight the important role of temperature for accurate climate predictions, which is important as temperature from current climate models' simulations often still have systematic errors.

Frequency-oriented hierarchical fusion network for single image raindrop removal.

Single image raindrop removal aims at recovering high-resolution images from degraded ones. However, existing methods primarily employ pixel-level supervision between image pairs to learn spatial features, thus ignoring the more discriminative frequency information. This drawback results in the loss of high-frequency structures and the generation of diverse artifacts in the restored image. To ameliorate this deficiency, we propose a novel frequency-oriented Hierarchical Fusion Network (HFNet) for raindrop image restoration. Specifically, to compensate for spatial representation deficiencies, we design a dynamic adaptive frequency loss (DAFL), which allows the model to adaptively handle the high-frequency components that are difficult to recover. To handle spatially diverse raindrops, we propose a hierarchical fusion network to efficiently learn both contextual information and spatial features. Meanwhile, a calibrated attention mechanism is proposed to facilitate the transfer of valuable information. Comparative experiments with existing methods indicate the advantages of the proposed algorithm.

Rain, rain, go away, come again another day: do climate variations enhance the spread of COVID-19?

The spread of infectious diseases was further promoted due to busy cities, increased travel, and climate change, which led to outbreaks, epidemics, and even pandemics. The world experienced the severity of the 125 nm virus called the coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization (WHO) in 2019. Many investigations revealed a strong correlation between humidity and temperature relative to the kinetics of the virus's spread into the hosts. This study aimed to solve the riddle of the correlation between environmental factors and COVID-19 by applying RepOrting standards for Systematic Evidence Syntheses (ROSES) with the designed research question. Five temperature and humidity-related themes were deduced via the review processes, namely 1) The link between solar activity and pandemic outbreaks, 2) Regional area, 3) Climate and weather, 4) Relationship between temperature and humidity, and 5) the Governmental disinfection actions and guidelines. A significant relationship between solar activities and pandemic outbreaks was reported throughout the review of past studies. The grand solar minima (1450-1830) and solar minima (1975-2020) coincided with the global pandemic. Meanwhile, the cooler, lower humidity, and low wind movement environment reported higher severity of cases. Moreover, COVID-19 confirmed cases and death cases were higher in countries located within the Northern Hemisphere. The Blackbox of COVID-19 was revealed through the work conducted in this paper that the virus thrives in cooler and low-humidity environments, with emphasis on potential treatments and government measures relative to temperature and humidity. HIGHLIGHTS: • The coronavirus disease 2019 (COIVD-19) is spreading faster in low temperatures and humid area. • Weather and climate serve as environmental drivers in propagating COVID-19. • Solar radiation influences the spreading of COVID-19. • The correlation between weather and population as the factor in spreading of COVID-19.

Effects of seasonal precipitation regimes on microbial biomass and extracellular enzyme activity during shrub foliar litter decomposition in a subtropical forest.

Elucidating the mechanisms underlying microbial biomass and extracellular enzyme activity responses to the seasonal precipitation regime during foliar litter decomposition is highly important for understanding the material cycle of forest ecosystems in the context of global climate change; however, the specific underlying mechanisms remain unclear. Hence, a precipitation manipulation experiment involving a control (CK) and treatments with decreased precipitation in the dry season and extremely increased precipitation in the wet season (IE) and decreased precipitation in the dry season and proportionally increased precipitation in the wet season (IP) was conducted in a subtropical evergreen broad-leaved forest in China from October 2020 to October 2021. The moisture, microbial biomass, and extracellular enzyme activities of foliar litter from two dominant shrub species, Phyllostachys violascens and Alangium chinense, were measured at six stages during the dry and wet seasons. The results showed that (1) both IE and IP significantly decreased the microbial biomass carbon and microbial biomass nitrogen content and the activities of ß-1,4-glucosidase, ß-1,4-N-acetylglucosaminidase, acid phosphatase and cellulase in the dry season, while the opposite effects were observed in the wet season. (2) Compared with those of IE, the effects of IP on foliar litter microbial biomass and extracellular enzyme activity were more significant. (3) The results from the partial least squares model indicated that extracellular enzyme activity during foliar litter decomposition was strongly controlled by the foliar litter water content, microbial biomass nitrogen, the ratio of total carbon to total phosphorus, foliar litter total carbon, and foliar litter total nitrogen. These results provide an important theoretical basis for elucidating the microbial mechanisms driving litter decomposition in a subtropical forest under global climate change scenarios.

Meta-analysis reveals that the effects of precipitation change on soil and litter fauna in forests depend on body size.

Anthropogenic climate change is altering precipitation regimes at a global scale. While precipitation changes have been linked to changes in the abundance and diversity of soil and litter invertebrate fauna in forests, general trends have remained elusive due to mixed results from primary studies. We used a meta-analysis based on 430 comparisons from 38 primary studies to address associated knowledge gaps, (i) quantifying impacts of precipitation change on forest soil and litter fauna abundance and diversity, (ii) exploring reasons for variation in impacts and (iii) examining biases affecting the realism and accuracy of experimental studies. Precipitation reductions led to a decrease of 39% in soil and litter fauna abundance, with a 35% increase in abundance under precipitation increases, while diversity impacts were smaller. A statistical model containing an interaction between body size and the magnitude of precipitation change showed that mesofauna (e.g. mites, collembola) responded most to changes in precipitation. Changes in taxonomic richness were related solely to the magnitude of precipitation change. Our results suggest that body size is related to the ability of a taxon to survive under drought conditions, or to benefit from high precipitation. We also found that most experiments manipulated precipitation in a way that aligns better with predicted extreme climatic events than with predicted average annual changes in precipitation and that the experimental plots used in experiments were likely too small to accurately capture changes for mobile taxa. The relationship between body size and response to precipitation found here has far-reaching implications for our ability to predict future responses of soil biodiversity to climate change and will help to produce more realistic mechanistic soil models which aim to simulate the responses of soils to global change.

Repurposing spent biomass of vetiver grass used for stormwater treatment to generate biochar and ethanol.

Stormwater pollution is a key factor contributing to water quality degradation, posing substantial environmental and human health risks. Although stormwater retention ponds, also referred to as wet ponds, are commonly implemented to alleviate stormwater challenges by reducing peak flow and removing suspended solids, their effectiveness in removing heavy metals and nutrients is limited. This study evaluated the performance of floating treatment platforms (FTPs) featuring vetiver grass (Chrysopogon zizanioides), a non-invasive, nutrient- and metal-accumulating perennial grass, in removing heavy metals (Cu, Pb, and Zn) and nutrients (P and N) in stormwater retention ponds. Furthermore, the potential for utilizing the spent vetiver biomass for generating biochar and bioethanol was investigated. The study was conducted in a greenhouse setup under simulated wet and dry weather conditions using pond water collected from a retention pond in Stafford Township, New Jersey, USA. Two FTPs with vetiver (vegetated FTPs) were compared with two FTPs without vetiver (non-vegetated FTPs), which served as controls. Results showed that the removal of heavy metals and nutrients by the FTPs with vetiver was significantly higher (p < 0.05) than the FTPs without vetiver. Notably, vetiver showed resilience to stormwater pollutants and hydroponic conditions, displaying no visible stress symptoms. The biochar and bioethanol generated from the spent vetiver exhibited desirable yield and quality, without raising concerns regarding pollutant leaching, indicated by very low TCLP and SPLP concentrations. This study provides compelling evidence that the implementation of vetiver-based FTPs offers a cost-effective and environment-friendly solution for mitigating stormwater pollution in retention ponds. Furthermore, the utilization of vetiver biomass for biofuel and biochar production supports clean production and fostering circular economy efforts.

Changes in fronts regulate nitrate cycling in Zhanjiang Bay: A comparative study during the normal wet season, rainstorm, and typhoon periods.

Typhoons and rainstorms (>250 mm/day) are extreme weather events changing hydrological characteristics and thus nitrogen (N) cycle in coastal waters. However, responses of N cycle to rainstorms and typhoons and their underlying mechanisms need to be elucidated. In this study, we conducted an analysis of a comparative dataset encompassing concentrations of nitrate (NO3-), ammonium (NH4+), dissolved oxygen (DO), chlorophyll a (Chl a), hydrological parameters, dual isotopic composition of NO3- (δ15N-NO3- and δ18O-NO3-) in Zhanjiang Bay during three distinct periods: the normal wet season, rainstorm, and typhoon periods. After the rainstorm, the salinity front in Zhanjiang Bay was more weakened and steadier than that during the normal wet season, mainly because onshore wind and a large amount of freshwater was inputted into the ocean surface. This weakened and steady salinity front strengthened water stratification and provided a favorable condition for phytoplankton blooms. Correspondingly, evident NO3- deficits coincided with elevated δ15N-NO3- and δ18O-NO3- values indicated that sufficient NO3- sustained phytoplankton blooms, leading to NO3- assimilation during the rainstorm period. By contrast, due to the onshore wind induced by the typhoon, the salinity front in Zhanjiang Bay was more intensified and unsteady after the typhoon than the normal wet season. The salinity front after the typhoon was unsteady enough to enhance vertical mixing in the water column. Relatively high DO concentrations suggested that enhanced vertical mixing after the typhoon support freshly organic matter decomposition and nitrification via oxygen injection from the air into the water column. In addition, NO3- deficits coincided with elevated δ15N-NO3- values and δ18O-NO3- values demonstrated the coexistence of NO3- assimilation during the typhoon period. This study suggests that the changing processes involved in NO3- cycling after typhoons and rainstorms are associated with the stability and intensity of the salinity front altered by these weather events.

Microplastic contamination in urban aquatic environments: Occurrence characteristics in urban streams and stormwater runoff from urban surfaces.

This study investigates microplastics in urban environments, focusing on their abundance, types, and relationships with hydrological parameters. Microplastic analyses encompassed two steps: (1) examining urban streams including discharges from a wastewater treatment plant (WWTP) during non-rainy seasons, and (2) analyzing stormwater runoff from urban surfaces for microplastic content during rainy seasons. In urban streams, WWTP discharge exhibited higher microplastic concentrations compared to other streams, indicating WWTP discharge is a dominant source of microplastic pollution. The most prevalent microplastics detected were polypropylene, polyethylene, and their copolymer, although a variety of other types were also found. Concentrations of microplastics were notably influenced by specific urban land uses, as evidenced by a strong correlation (0.95) between microplastic concentrations and areas characterized by industrial and transportation activities. During rainy seasons, microplastics followed the pattern of stormwater runoff, but the highest concentrations, significantly exceeding those in urban streams, were observed before the peak runoff. These maximum concentrations and their timing of occurrence were linked to antecedent dry days, rain intensity, and runoff rate, showing significant statistical correlations. Regardless of their sizes, a diverse range of microplastic types was identified in these conditions, with no consistent pattern across different rain events. This highlights the complex nature of urban microplastic pollution. This study reveals that aquatic ecosystems are significantly affected by two primary factors: (1) the consistent contribution of microplastics from WWTP discharges, and (2) the short-term, but severe, impacts of microplastic pollution associated with stormwater runoff. Furthermore, it suggests the development of alternative strategies to mitigate microplastic pollution in aquatic ecosystems, informed by the findings on the characteristics of microplastics in urban environments. This research underscores the urgent need for integrated urban environmental management strategies, paving the way for future studies to further explore and address the multifaceted challenges posed by microplastic pollution in aquatic ecosystems.

Rainfall analysis and deriving design storm for hydrological modelling of Koyna dam catchment area in Maharashtra state of India.

Rainfall is a key hydro meteorological variable. Climate change is disrupting the hydrological cycle and altering the usual cycle of rainfall, which frequently results in long-lasting storms with significant rainfall. A first step in hydrologic design of project is to determine the design storm or rainfall events to be used. For deriving design storm, researchers concluded that instead of using generalized readily available curves or maps, it is better to estimate design storm based on site specific historical rainfall data. The objective of the study is to analyze the rainfall data in the koyna watershed area in order to evaluate the design storm, which will be further used as an input data for HEC-HMS event based hydrological modelling of flood peak attenuation of design storm flow at koyna dam during extreme rainfall event. In this study, 40 years (1982-2021) of rainfall data from 8 rain gauge stations in Koyna Dam Catchment area is used initially for performing trend analysis through statistical and graphical techniques and then for Isopluvial analysis. The Sen's slope test and the Mann-Kendall test are the statistical techniques employed, and Innovative Trend Analysis is the graphical technique used. IDF approach is used for deriving design storm, and using Gumbel's frequency distribution method Isohyetal maps, IDF tables and curves are prepared for 2,10,25,50,75 and 100 year return periods and 6,12,24,48 and 96 h durations. Results obtained from statistical and graphical trend analysis of annual rainfall series are consistent. No statistically significant trend in annual rainfall series is observed, however there is rising and falling trend was observed in annual as well as monthly rainfall series. From the results of design storm study, the design storm hyetograph of 10 years return period and 96 h duration is selected, which gives the rainfall intensity of 10.88 mm/h for the koyna catchment. There are various dams nearby koyna catchment, The Isohyet maps, IDF curves and table output available from this study can be more reliably used during planning and design of hydraulic structure for other areas near by koyna catchment.

Exploring the Relationship between Melioidosis Morbidity Rate and Local Environmental Indicators Using Remotely Sensed Data.

Melioidosis is an endemic infectious disease caused by Burkholderia pseudomallei bacteria, which contaminates soil and water. To better understand the environmental changes that have contributed to melioidosis outbreaks, this study used spatiotemporal analyses to clarify the distribution pattern of melioidosis and the relationship between melioidosis morbidity rate and local environmental indicators (land surface temperature, normalised difference vegetation index, normalised difference water index) and rainfall. A retrospective study was conducted from January 2013 to December 2022, covering data from 219 sub-districts in Northeast Thailand, with each exhibiting a varying morbidity rate of melioidosis on a monthly basis. Spatial autocorrelation was determined using local Moran's I, and the relationship between the melioidosis morbidity rate and the environmental indicators was evaluated using a geographically weighted Poisson regression. The results revealed clustered spatiotemporal patterns of melioidosis morbidity rate across sub-districts, with hotspots predominantly observed in the northern region. Furthermore, we observed a range of coefficients for the environmental indicators, varying from negative to positive, which provided insights into their relative contributions to melioidosis in each local area and month. These findings highlight the presence of spatial heterogeneity driven by environmental indicators and underscore the importance of public health offices implementing targeted monitoring and surveillance strategies for melioidosis in different locations.

Trend analysis of extreme rainfall indices from CHIRPS precipitation estimates over the Lake Tana sub-basin, Abbay Basin of Ethiopia.

Ethiopia is among the African nations most susceptible to climate change because of its frequent droughts and heavy rainfall. Therefore, hydrological and water management problems require an investigation of regional variability and extreme rainfall patterns. This study analyzed the spatiotemporal trends of extreme rainfall in the Lake Tana sub-basin (LTSB) of Ethiopia's upper Blue Nile basin (UBNB) between 1981 and 2019. The trend and geographic patterns of ten extreme rainfall indices are evaluated using high-resolution data from Climate Hazards Group InfraRed Precipitation Stations (CHIRPS). The researcher used RClimDex, an R software tool, to analyze the ten severe rainfall indices. The variability of the extreme rain indices was also assessed by applying the standard anomaly index (SAI). The trend analysis shows that the majority of rainfall indices decreased in the majority of station locations. Among the rainfall locations, the decreasing trend was only significant in 40% consecutive wet days (CWD), 13.33% (R95p and R99p), and 6.66% highest rainfall amount in a 1-day period (RX1day). In contrast, significant positive patterns were revealed in the incidence of rainfall events of number of heavy precipitation days (R10mm), annual total wet day rainfall (PRCPTOT), and consecutive dry days (CDD), with significant positive trends of 26.66% (R10mm) and 40% (PRCPTOT). Furthermore, a spatial distribution result of extreme rainfall trends reveals considerable variations between stations location. Thus, these findings point to the necessity of creating adaptation and mitigation plans for climate change variability within the sub-basin.

Enhancing bioretention efficiency for pollutant mitigation in stormwater runoff: Exploring ecosystem cycling dynamics amidst temporal variability.

In this study, three distinct bioretention setups incorporating fillers, plants, and earthworms were established to evaluate the operational efficiency under an ecosystem concept across varying time scales. The results revealed that under short-term operating conditions, extending the drying period led to a notable increase in the removal of NO3--N, total phosphorus (TP), and chemical oxygen demand (COD) by 5 %-7%, 4 %-12 %, and 5 %-10 %, respectively. Conversely, under long-time operating conditions, the introduction of plants resulted in a significant boost in COD removal by 10 %-20 %, while the inclusion of earthworms improved NH4+-N and NO3--N removal, especially TP removal by 9 %-16 %. Microbial community analysis further indicated the favorable impact of the bioretention system on biological nitrogen and phosphorus metabolism, particularly with the incorporation of plants and earthworms. This study provides a reference for the operational performance of bioretention systems on different time scales.

A Coupled Statistical and Deterministic Model for Forecasting Climate-Driven Dengue Incidence in Selangor, Malaysia.

The mosquito-borne dengue virus remains a major public health concern in Malaysia. Despite various control efforts and measures introduced by the Malaysian Government to combat dengue, the increasing trend of dengue cases persists and shows no sign of decreasing. Currently, early detection and vector control are the main methods employed to curb dengue outbreaks. In this study, a coupled model consisting of the statistical ARIMAX model and the deterministic SI-SIR model was developed and validated using the weekly reported dengue data from year 2014 to 2019 for Selangor, Malaysia. Previous studies have shown that climate variables, especially temperature, humidity, and precipitation, were able to influence dengue incidence and transmission dynamics through their effect on the vector. In this coupled model, climate is linked to dengue disease through mosquito biting rate, allowing real-time forecast of dengue cases using climate variables, namely temperature, rainfall and humidity. For the period chosen for model validation, the coupled model can forecast 1-2 weeks in advance with an average error of less than 6%, three weeks in advance with an average error of 7.06% and four weeks in advance with an average error of 8.01%. Further model simulation analysis suggests that the coupled model generally provides better forecast than the stand-alone ARIMAX model, especially at the onset of the outbreak. Moreover, the coupled model is more robust in the sense that it can be further adapted for investigating the effectiveness of various dengue mitigation measures subject to the changing climate.

Rain use efficiency gradients across Australian ecosystems.

Rain use efficiency (RUE) quantifies the ecosystem's capacity to use precipitation water to assimilate atmospheric CO2. The spatial distribution of RUE and its drivers across the Australian continent is largely unknown. This knowledge gap limits our understanding of the possible contribution of Australian ecosystems to global carbon assimilation. This study investigates the spatial distribution of RUE across diverse terrestrial ecosystems in Australia. The results show that RUE ranges from 0.43 (1st percentile) to 3.10 (99th percentile) g C m-2 mm-1 with a continental mean of 1.19 g C m-2 mm-1. About 68 % of the spatiotemporal variability of RUE can be explained by a multiple linear regression model primarily contributed by climatic predictors. Benchmarked by the model estimation, drainage-diverging/converging landscapes tend to have reduced/increased RUE. The model also revealed the impact of increasing atmospheric CO2 concentration on RUE. The continental mean RUE would increase by between 29.3 and 64.8 % by the end of this century under the SSP5-8.5 scenario in which the CO2 concentration is projected to double from the present level. This increase in projected RUE is attributed to the assumed greening effect of increasing CO2 concentration, which does not consider the saturation of CO2 fertilisation effect and the warming effect on increasing wildfire occurrence. Under the SSP1-2.6 scenario, RUE would decrease by about 7 %. This study provides baseline RUEs of various ecosystems in Australia for investigating the impacts of human interferences and climate change on the capacity of Australian vegetation to assimilate atmospheric CO2 under given precipitation.

Above- and belowground phenology responses of subtropical Chinese fir (Cunninghamia lanceolata) to soil warming, precipitation exclusion and their interaction.

Plant phenology plays an important role in nutrient cycling and carbon balance in forest ecosystems, but its response to the interaction of global warming and precipitation reduction remains unclear. In this study, an experiment with factorial soil warming (ambient, ambient +5 °C) and precipitation exclusion (ambient, ambient -50 %) was conducted in a subtropical Chinese fir (Cunninghamia lanceolata) plantation. We investigated the effects of soil warming, precipitation exclusion, and their interactions on Chinese fir phenology involving tree height and fine root growth. In the meantime, the impact of tree height growth and related climatic factors on fine root production was also assessed. The results showed that: (1) more variable phenology responses were observed in fine root growth than in tree height growth to the climatic treatments; the duration of fine root growth and tree height growth was significantly reduced by the precipitation exclusion and warming treatment, respectively; phenology differences of fine root and tree height growth caused by the solo warming and precipitation exclusion treatment were further enhanced by the combined treatment; and despite the greater inter-annual phenology stability of tree height growth than that of fine root growth, both of them showed insignificant response to all the climate treatments; (2) asynchrony of phenology between tree height and fine root growth was significantly enlarged by solo warming and precipitation exclusion treatments, and further enlarged by the combined treatment; (3) fine root production was significantly and positively correlated with air, and soil temperature, and tree height growth as well, which was altered by warming and precipitation exclusion treatments. Our results demonstrated that climatic changes significantly and differently alter phenology of, and extend the phenology asynchrony between, above and below ground plant components, and also highlight the climate-sensitive and variable nature of root phenology. Overall, these phenology responses to climatic change may weaken the close link between fine root production and tree height growth, which may result in temporal mismatch between nutrient demand and supply in Chinese fir plantation.