Annual Cumulative Precipitation Research Articles

Increased precipitation has not enhanced the carbon sequestration of afforestation in Northwest China

Concerns have been raised about the sustainability of large-scale afforestation in semi-arid regions due to potential water constraints. This study investigated whether increased humidity in the semi-arid regions of northwest China could sustain the continued expansion of afforestation efforts. Using multi-source remote sensing data, we found that between 2012 to 2020, annual cumulative precipitation increased by 2.5 millimeters per year, while annual average carbon sequestration in afforested areas declined by 0.002 kilograms of carbon per square meter per year, indicating asynchronous trends. This disparity was primarily attributed to the trade-off between vegetation photosynthesis and transpiration in response to external water conditions, which led to a decline in the water use efficiency of afforested vegetation. The effect of water use efficiency on carbon sequestration was driven by gross primary productivity, rather than evapotranspiration. These findings underscore the importance of targeted afforestation in semi-arid regions, considering local water resource sustainability.

From crisis to opportunity: climate change benefits livestock production in Somalia

Abstract While livelihoods of Somalian livestock smallholders rely heavily on seasonal climate conditions, little is known of long-term implications of the changing climate for this nation. Here, we quantify implications of the changing climate on the productivity and profitability of livestock smallholders across a rainfall gradient in northwestern Somalia. Using the Sustainable Grazing Systems (SGS) model, we explore 80 future climate realisations, with global climate model projections including low- and high-impact socio-economic pathways (SSP245 and SSP585), two climate horizons (2040 and 2080) and four case study farm regions. In general, future seasonal and annual rainfall and temperature relative to the baseline period (1981–2020) increased for most regions. Mean annual temperatures increased by 9%–14%, while cumulative annual precipitation increased by 37%–57% from mid to late century, respectively. Grassland production increased with later climate horizons, as higher average annual rainfall together with elevated atmospheric carbon dioxide drove up growth rates in spring and autumn. Under the low emissions scenario (SSP245), changes in farm profit were modest or positive, ranging from negative 4% in Berbera–20% plus in Sheikh. Under the higher emissions scenario (SSP585), farm profits were higher, ranging from 23% to 42% above baseline profits, largely due to greater pasture production and lower requirements for supplementary feed. We conclude that future climates will benefit the productivity and profitability of smallholder farmers in Somalia, although more agile farm management will be required to cope with increased seasonal climate variability.

Geological hazards susceptibility evaluation based on multi‐year spatial–temporal evolution of assessment factors in Luding area, Sichuan Province, China

More than 50 earthquakes occurred in the Luding region, including a 6.8‐magnitude earthquake on September 5, 2022, which induced huge amounts of geological disasters. As the main triggered factors for the geological disasters, the vegetation, precipitation and temperature show regular changes on the time axis. Thus, this paper based on the spatial–temporal evolution of the precipitation, temperature and normalized difference vegetation index (NDVI), focuses on the susceptibility of the Luding area using the weighted information content method (ICM) and analytic hierarchy process (AHP) for the post‐earthquake regional planning and reconstruction. In the Luding area, it was found that there were 18 debris flow gullies, and 470 geological disasters scattered in the debris flow gullies. The NDVI of the study area has been declining in recent years, with only about 12% of the NDVI increasing from 2015 to 2020. From 2011 to 2015, the annual cumulative precipitation has been dropping overall, while the July average temperature has increasing quadratic functions after decreasing trend. Furthermore, the studied area's geological hazards susceptibility was classified into five categories, with extremely low susceptibility accounting for 5.82%, low susceptibility accounting for 13.61%, moderate susceptibility accounting for 38.54%, high susceptibility accounting for 39.96% and extremely high susceptibility accounting for 2.08%. The geological disasters in moderate, high and extremely high susceptibility accounted for approximately 95.08%, and the covered area of geological disasters in moderate, high and extremely high susceptibility accounted for 88.06%. Through the receiver operating characteristic (ROC) curve of the model verification found the AUC = 0.743. The geological hazards susceptibility study results show that the approach is useful for the analysis and prevention of geological disasters.

Study on spatiotemporal dynamic characteristics of precipitation and causes of waterlogging based on a data-driven framework

The discernible alterations in regional precipitation patterns, influenced by the intersecting factors of urbanization and climate change, exert a substantial impact on urban flood disasters. Based on multi-source precipitation data, a data-driven model fusion framework was constructed to analyze the spatial and temporal dynamic distribution characteristics of precipitation in Beijing. Wavelet analysis method was used to reveal the periodic variation characteristics and multi-scale effects of precipitation, and the machine learning method was used to characterize the spatiotemporal dynamic change pattern of precipitation. Finally, geographical detector was used to explore the causes of waterlogging in Beijing. The research outcomes reveal a disparate distribution of precipitation across the year, with 78 % of the total precipitation occurring during the flood season. The principal periodic cycles observed in annual cumulative precipitation (ACP) were identified at 21, 13, and 9-year intervals. Spatially, while a decreasing trend in precipitation was observed in most areas of Beijing, 63.4 % of the region exhibited an escalating concentration trend, thereby heightening the risk of urban waterlogging. Machine learning model clustering elucidated three predominant spatial dynamic distribution patterns of precipitation in Beijing. The utilization of web crawler technology to acquire water accumulation data addressed challenges in obtaining urban waterlogging data, and validation through Landsat8 images enhanced data reliability and authenticity. Factor detection shows that road network density, topography, and precipitation were the main factors affecting urban waterlogging. These findings hold significant implications for informing flood control strategies and emergency management protocols in urban areas across China.

Spatiotemporal distribution and environmental influences of severe fever with thrombocytopenia syndrome in Shandong Province, China

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in China in 2009. The purpose of this study was to describe the spatiotemporal distribution of SFTS and to identify its environmental influencing factors and potential high-risk areas in Shandong Province, China.MethodsData on the SFTS incidence from 2010 to 2021 were collected. Spatiotemporal scan statistics were used to identify the time and area of SFTS clustering. The maximum entropy (MaxEnt) model was used to analyse environmental influences and predict high-risk areas.ResultsFrom 2010 to 2021, a total of 5705 cases of SFTS were reported in Shandong. The number of SFTS cases increased yearly, with a peak incidence from April to October each year. Spatiotemporal scan statistics showed the existence of one most likely cluster and two secondary likely clusters in Shandong. The most likely cluster was in the eastern region, from May to October 2021. The first secondary cluster was in the central region, from May to October 2021. The second secondary cluster was in the southeastern region, from May to September 2020. The MaxEnt model showed that the mean annual wind speed, NDVI, cattle density and annual cumulative precipitation were the key factors influencing the occurrence of SFTS. The predicted risk map showed that the area of high prevalence was 28,120 km2, accounting for 18.05% of the total area of the province.ConclusionsThe spatiotemporal distribution of SFTS was heterogeneous and influenced by multidimensional environmental factors. This should be considered as a basis for delineating SFTS risk areas and developing SFTS prevention and control measures.

Changes in Temperature and Precipitation Trends in Selected Polish Cities Based on the Results of Regional EURO-CORDEX Climate Models in the 2030–2050 Horizon

This study presents the potential impacts of climate change in 49 Polish cities by analyzing seven climate indicators. The analysis was carried out for the following three horizons: the current climate conditions (an average period spanning 2006 to 2015), near-future conditions (an average period spanning 2026 to 2035), and future conditions (an average period spanning 2046 to 2055). Climate indices were computed with bias-corrected EURO-CORDEX model ensembles from two Representative Concentration Pathway scenarios, RCP4.5 and RCP8.5. The systematic error was reduced using the quantile mapping method with a non-parametric approach of robust empirical quantiles (RQUANT). Data were used as references in the period of current climate conditions, and those required for bias correction consisted of historical ground-based observations provided by the Institute of Meteorology and Water Management. The analysis encompassed various key climate indices, including the annual average temperature, the count of hot days, cold days, and frost days, the cumulative annual precipitation, the frequency of days with precipitation, and instances of extreme precipitation (defined as the days with precipitation exceeding 10 mm/day). These findings reveal a noteworthy rise in the average annual temperature of approximately 1 °C and an uptick in the number of hot days by 3.7 days. Conversely, a decline in the number of cold days by approximately 19 days and frost days by 8 days was observed. Additionally, there was an augmentation in the annual precipitation sum, reaching up to 80 mm in RCP 8.5, accompanied by an increase in the number of days with precipitation (up by 3.3 days) and days with extreme precipitation (up by 2 days).

Glacier Retreat Leads to the Expansion of Alpine Lake Karakul Observed Via Remote Sensing Water Volume Time Series Reconstruction

Due to high altitudes, Central Asian alpine lakes can serve as indicators of localized climate change. This article monitored the water volume time series trends of the ungauged alpine Lake Karakul, which is typical because of the abundance of glaciers in the basin, from 1990 to 2020 via multiple source remote sensing data. The “Global-Local” multi-scale lake extraction method is used to delineate the boundary of Lake Karakul. Consistency analysis was performed on the altimetry data of CryoSat-2, ICESat-1 and ICESat-2, assuming that the lake surface was flat; a threshold value was set to remove gross error, and then 3σ was used to remove the surface elevation anomaly. Based on the pyramid volume model, the lake area and surface elevation information were used to reconstruct the water volume time series of Lake Karakul. The influencing factors of water volume temporal variation were discussed. The results show that Lake Karakul has been on an expansionary trend in recent years: The lake area increased from 394.9 km2 in 1988 to 411.4 km2 in 2020; the rate of increase is 0.74 m/year. The surface elevation increased from 3886.6 m in 2003 to 3888.6 m in 2020; the rate of increase is 0.11 km2/year. The lake water volume accumulated was 0.817 km3 in 2003–2020, with an accumulation rate of 0.059 km3/year. The Lake Karakul basin is developing towards dry heat, with a cumulative temperature variation rate of +0.38 °C/year; the average rate of variation in annual cumulative precipitation is −3.37 mm/year; the average evapotranspiration in the watershed is on a fluctuating increasing trend, with a rate of variation of +0.43 mm/year; glaciers in the lake basin have a retreating trend, with an average annual rate of variation of −0.22 km2/year from 1992 to 2020. Lake Karakul is more sensitive to temperature variations, and the runoff from retreating glaciers in the basin is an important contribution to the expansion of Lake Karakul.

Future climate change impacts on wheat grain yield and protein in the North China Region

The threat of global climate change on wheat production may be underestimated by the limited capacity of many crop models to predict grain quality and protein composition. This study aimed to integrate a wheat quality module of protein components into the CROPSIM-CERES-Wheat model to investigate the impact of climate change on wheat grain yield and protein quality in the North China Region (NCR) using five Global Climate Models (GCMs) from CMIP6 under three shared socioeconomic pathways. The CERES-Wheat model with a quality module was developed and calibrated and validated using data from several sites in the NCR. The results of the calibration and validation showed that the modified CERES-Wheat model can accurately predict grain yield, protein content and its components in field experiments. Compared with the baseline period (1981–2010), the annual mean temperature and annual cumulative precipitation increased in the NCR in the 2030's, 2050's and 2080's. The radiation was higher under the SSP126 and SSP585 scenarios, and lower under the SSP370 scenario compared to the baseline period. The anthesis and maturity date occurred earlier under the three future scenarios. The average grain yield increased by 13.3–30.9 % under three future scenarios. However, the regional average grain protein content of winter wheat in the future decreased by 2.0 %- 3.5 %. The reduction in wheat grain protein at the regional was less pronounced under SSP370 than that under SSP126 and SSP585. The structural protein content of winter wheat decreased under future climate conditions compared with the baseline period, but the storage protein content showed the opposite tendency. The model provided a useful tool to study the effects of future climate on grain quality and protein composition. These findings are important for developing agricultural practices and strategies to mitigate the potential impacts of climate change on wheat production and wheat quality in the future.

The continuing decrease of sandy desert and sandy land in northern China in the latest 10 years

Timely monitoring of the sandy desert and sandy land (SDSL) dynamics and their driving factors is vital for ecological restoration policy-making to combat the expansion of the SDSL. Northern China, being the principal implementation area of ecological restoration projects, is ecologically fragile and encompasses almost all of the SDSL regions in China. To explore and compare the dominant driving factors for SDSL changes by extracting both aeolian activity information and vegetation indices, the SDSL datasets in northern China from 2010 to 2020 were first constructed in this study by the human–computer interactive interpretation method based on the series Landsat image data. The spatiotemporal dynamic characteristics of SDSL were subsequently analyzed, and the dominant driving factors of SDSL changes were further explored via the geodetector model by the fractional vegetation cover trend and the difference of aeolian activity index. The results showed a net decrease of 3,780.25 km2 in the total SDSL area between 2010 and 2020, and the decrease rate was 2.44 times higher in the shorter period of 2015–2020 compared to 2010–2015. The spatial differentiation between the western and eastern parts of the Helan Mountains was prominently exhibited by the relatively higher changed SDSL area in the eastern part (62.74% for aeolian activity weakened and 83.32% for aeolian activity intensified) and the increasingly changed SDSL area in the western part (from 33.09% to 39.60% in aeolian activity weakened and from 13.02% to 19.60% in aeolian activity intensified). Especially, the increased aeolian activity intensified SDSL area has happened on the southern edge of the Gurbantunggut Desert and along both sides of the Tarim River from 2015 to 2020 deserve attention. The co-driving of the multiple driving factors dominated the change of SDSL from 2010 to 2020, with the county mean value of the difference in aeolian activity index (MDAAI) and the county mean value of the trend in fractional vegetation cover (MFVCT) as dependent variables, respectively. Climatic factors of the mean annual average temperature, the mean annual cumulative precipitation, and the mean annual aridity index have a relatively larger explanatory power for the change of SDSL in both MDAAI and MFVCT. Anthropogenic factors of the mean annual afforestation area and the mean annual number of large livestock and sheep and goats (year-end) have a greater explanatory power in MDAAI, while the mean annual number of large livestock and sheep and goats (year-end) and the mean annual population density in MFVCT. In particular, the dependent variable MDAAI is better at identifing the dominant drivers of aeolian activity intensified SDSL area compared to MFVCT, with anthropogenic factors playing a stronger dominant role.

Assessment of Landslide Risk Near Mountain Power Generation Facilities in Korea

Rising greenhouse gases fuel global warming, triggering disasters like droughts, floods, and landslides. Efforts to reduce emissions and combat climate change focus on renewable energy (wind, wave, hydrogen, and solar). However, rapid expansion of solar facilities in mountainous regions, coupled with unchecked logging, intensifies landslides and environmental pollution. Therefore, in this study, the risk of installed solar power generation facilities was evaluated based on the precipitation in a year and rainy season only, and topographical characteristics. The annual cumulative precipitation and rainy season were calculated for 2020, and the risk of landslides caused by rainfall was evaluated through correlation with the annual landslide area. As of 2020, more than 50% of landslides occurred in the areas having a slope range of 30 - 60% and the landslide damage area was also highly correlated with the precipitation during the rainy season (R2 = 0.74). Prior to the installation of solar power generation facilities in the steep mountainous area, more strict and appropriate verification must be done. Additionally, a systematic management plan of solar power generation facilities should be investigated in a permission process.Landslide risk of mountainous solar power generation facilities versus precipitation.

Spatiotemporal evolution and attribution analysis of grassland NPP in the Yellow River source region, China

The protection of the environment and promotion of sustainable development in the Yellow River Basin (YRB) is a major national strategy in China. Madoi County (MC), at the headwaters of the YRB, plays a crucial role in ensuring the ecological security and sustainable development of the middle and lower reaches of the river. Vegetation net primary productivity (NPP) is a critical indicator for assessing the structure and function of the ecosystem. Therefore, studying the spatiotemporal evolutionary characteristics of grassland net primary productivity (GNPP) in MC and its driving mechanisms is of the utmost importance. We employed linear regression analysis, optimal parameters-based geographical detector (OPGD), multiscale geographic weighted regression (MGWR), and residual analysis to investigate the spatiotemporal change of GNPP in MC under the influence of climate change (CC) and policies in different periods based on MOD17A3 NPP data. Additionally, we explored the natural and anthropogenic factors affecting GNPP and their spatiotemporal processes on global and grid scales. Lastly, we quantified the relative contributions of CC and human activity (HA) to the observed changes in GNPP. The results were as follows: 1) GNPP in MC has demonstrated a fluctuating and increasing trend since 2000, with an average annual growth rate of 1.58 gC·m−2·a−1 and a maximum growth rate of 6.29 gC·m−2·a−1 in 2017–2021. 2) Spatial variation in GNPP has been evident since 2000, with a gradual increase in GNPP from the northwest to southeast. Regions with notable increases in grassland were predominantly located in areas with eco-engineering. 3) The OPGD was used to identify key driving factors (DFs), revealing that climate factors had stronger explanatory powers for the GNPP at each phase. MGWR results indicated that the effect of key DFs exhibited clear spatiotemporal heterogeneity. Annual cumulative solar radiation and annual cumulative precipitation had the most significant effect on GNPP. Regions with positive effects (PEs) of grassland area change and grassland cover change were mostly located in eco-engineering zones. The PEs of DFs on GNPP increased. 4) The contribution of CC to GNPP changes and the dominant area share were greater than those of HA. However, areas with PEs of HA increased gradually. The synergies between eco-protection policies and natural regulation are therefore critical to the eco-environmental protection in the YRB.

Insights into the spatiotemporal dynamics of West Nile virus transmission in emerging scenarios

The incidence of West Nile fever (WNF) is highly variable in emerging areas, making it difficult to identify risk periods. Using clinical case records has important biases in understanding the transmission dynamics of West Nile virus (WNV) because asymptomatic infections are frequent. However, estimating virus exposure in sentinel species could help achieve this goal at varying spatiotemporal scales. To identify the determinants of inter-annual variation in WNV transmission rates, we designed a 15-year longitudinal seroepidemiological study (2005–2020) in five environmentally diverse areas of southwestern Spain. We modeled individual annual area-dependent exposure risk based on potential environmental and host predictors using generalized linear mixed models. Further, we analyzed the weight of predictors on exposure probability by variance partitioning of the model components. The analysis of 2418 wild ungulate sera (1168 red deer - Cervus elaphus - and 1250 Eurasian wild boar - Sus scrofa) with a highly sensitive commercial blocking ELISA identified an average seroprevalence of 24.9% (95% confidence interval (CI): 23.2–26.7%). Antibody prevalence was slightly higher in wild boar (27.5%; CI: 25.1–30.1%) than in deer (22.2%; CI: 19.8–24.7%). We observed a spatial trend in exposure, with higher frequency in the southernmost areas and a slight, although area-dependent, increasing temporal trend. Host-related predictors were important drivers of exposure risk. The environmental predictor with the highest weight was annual cumulative precipitation, while temperature variations were also relevant but with less weight. We observed a coincidence of spatiotemporal changes in exposure with the notification of WNF outbreaks in horses and humans. That indicates the usefulness of wild ungulates as sentinels for WNV transmission and as models to understand its spatiotemporal dynamics. These results will allow the development of more accurate predictive models of spatiotemporal variations in transmission risk that can inform health authorities to take appropriate action.

Soil water dynamics and groundwater evolutions of check dams under natural rainfall reduction in semi-arid areas

Stable regional water resource distribution is essential for ecosystems, yet soil water dynamics and groundwater evolutions of check dams remain many uncertainties under natural rainfall reduction in semi-arid areas. To explore these changeable hydrologic processes, continuous fixed-point observation and water balance models were introduced in nearly-four consecutive hydrological years (2018–2021). The results showed that soil water storage presented a downward fluctuation in each soil layer of all land uses. Moreover, the grassland profile moisture descended slowly and had deeper infiltration depths than others, which might be an ideal land-use type for the reservoir’s sustainable utilization in check dams. The results also indicated that surface soil moisture (<50 cm) could rapidly respond to a single rainfall event > 10 mm d−1, whereas deep soil moisture (>50 cm) and shallow groundwater fluctuations were mainly affected by annual cumulative precipitation. Furthermore, a conceptual hydrological model of rainwater falling to the ground, percolating through soil profiles, and reaching groundwater through different vegetation covers in check dams has been proposed. Soil infiltration rates varied under different land use types and depths, ranging from 0.82 to 3.47 cm h−1. The groundwater recharge time from accumulated annual rainfall was 69–250 days, whereas the discharge time due to vegetation water consumption and groundwater lateral movement was 66–774 days. With a decrease in annual rainfall, the recharge time gradually increased, whereas the discharge time gradually decreased. These results inaugurate new perspectives for a deeper understanding of ecohydrological equilibrium in check dams and provide theoretical insights into ecological restoration in semi-arid areas.

Annual cumulative ambient precipitation determines the effects of climate change on biomass and yield of three important field crops

Annual cumulative ambient precipitation determines the effects of climate change on biomass and yield of three important field crops

Quality Control Impacts on Total Precipitation Gauge Records for Montane Valley and Ridge Sites in SW Alberta, Canada

This paper presents adjustment routines for Geonor totalizing precipitation gauge data collected from the headwaters of the Oldman River, within the southwestern Alberta Canadian Rockies. The gauges are situated at mountain valley and alpine ridge locations with varying degrees of canopy cover. These data are prone to sensor noise and environment-induced measurement errors requiring an ordered set of quality control (QC) corrections using nearby weather station data. Sensor noise at valley sites with single-vibrating wire gauges accounted for the removal of 5% to 8% (49–76 mm) of annual precipitation. This was compensated for by an increase of 6% to 8% (50–76 mm) from under-catch. A three-wire ridge gauge did not experience significant sensor noise; however, the under-catch of snow resulted in 42% to 52% (784–1342 mm) increased precipitation. When all QC corrections were applied, the annual cumulative precipitation at the ridge demonstrated increases of 39% to 49% (731–1269 mm), while the valley gauge adjustments were −4% to 1% (−39 mm to 13 mm). Public sector totalizing precipitation gauge records often undergo minimal QC. Care must be exercised to check the corrections applied to such records when used to estimate watershed water balance or precipitation orographic enhancement. Systematic errors at open high-elevation sites may exceed nearby valley or forest sites.

Prediction of global marginal land resources for Pistacia chinensis Bunge by a machine learning method

Biofuel has attracted worldwide attention due to its potential to combat climate change and meet emission reduction targets. Pistacia chinensis Bunge (P. chinensis) is a prospective plant for producing biodiesel. Estimating the global potential marginal land resources for cultivating this species would be conducive to exploiting bioenergy yielded from it. In this study, we applied a machine learning method, boosted regression tree, to estimate the suitable marginal land for growing P. chinensis worldwide. The result indicated that most of the qualified marginal land is found in Southern Africa, the southern part of North America, the western part of South America, Southeast Asia, Southern Europe, and eastern and southwest coasts of Oceania, for a grand total of 1311.85 million hectares. Besides, we evaluated the relative importance of the environmental variables, revealing the major environmental factors that determine the suitability for growing P. chinensis, which include mean annual water vapor pressure, mean annual temperature, mean solar radiation, and annual cumulative precipitation. The potential global distribution of P. chinensis could provide a valuable basis to guide the formulation of P. chinensis-based biodiesel policies.

Warning Models for Landslide and Channelized Debris Flow under Climate Change Conditions in Taiwan

Climate change has caused numerous disasters around the world. It has also influenced the climate of Taiwan, with urban areas exhibiting a temperature increase by 1 °C between 1998 and 2020. In this study, climate change and landslides in Taiwan were statistically analyzed. Cumulative annual precipitation in mountain watersheds in central Taiwan exhibit a declining trend and is lower than that in urban areas. The relatively few typhoons reduced the distribution of rainfall in mountain watersheds and fewer landslides. From 2017 to 2020, typhoon-induced rains caused fewer landslides than did other climate events such as the meiyu front, tropical low pressure, and southwesterly flow events. Three rainfall characteristics of landslide initiation were identified: high rainfall intensity over a short duration (&lt;12 h), high-intensity and prolonged rainfall, and high cumulative rainfall over a long duration (&gt;36 h). Combinations of warning models for landslides in cumulative rainfall–duration plots with rainfall intensity classification and mean rainfall intensity–duration plots with cumulative rainfall classification were presented. In recent (2018–2020) years, climate change has resulted in higher temperatures, less rainfall in mountain watersheds, and a lower rainfall threshold at which landslides are initiated by non-typhoon climate events.

PHENOLOGICAL SENSITIVITIES TO CLIMATE ARE SIMILAR IN TWO CLARKIA CONGENERS: INDIRECT EVIDENCE FOR FACILITATION, CONVERGENCE, NICHE CONSERVATISM, OR GENETIC CONSTRAINTS

To date, most herbarium-based studies of phenological sensitivity to climate and of climate-driven phenological shifts fall into two categories: detailed species-specific studies vs. multi-species investigations designed to explain inter-specific variation in sensitivity to climate and/or the magnitude and direction of their long-term phenological shifts. Few herbarium-based studies, however, have compared the phenological responses of closely related taxa to detect: (1) phenological divergence, which may result from selection for the avoidance of heterospecific pollen transfer or competition for pollinators, or (2) phenological similarity, which may result from phylogenetic niche conservatism, parallel or convergent adaptive evolution, or genetic constraints that prevent divergence. Here, we compare two widespread Clarkia species in California with respect to: the climates that they occupy; mean flowering date, controlling for local climate; the degree and direction of climate change to which they have been exposed over the last ∼115 yr; the sensitivity of flowering date to inter-annual and to long-term mean maximum spring temperature and annual precipitation across their ranges; and their phenological change over time. Specimens of C. cylindrica were sampled from sites that were chronically cooler and drier than those of C. unguiculata, although their climate envelopes broadly overlapped. Clarkia cylindrica flowers ∼ 3.5 d earlier than C. unguiculata when controlling for the effects of local climatic conditions and for quantitative variation in the phenological status of specimens. However, the congeners did not differ in their sensitivities to the climatic variables examined here; cumulative annual precipitation delayed flowering and higher spring temperatures advanced flowering. In spite of significant spring warming over the sampling period, neither species exhibited a long-term phenological shift. Precipitation and spring temperature interacted to influence flowering date: the advancing effect on flowering date of high spring temperatures was greater in dry than in mesic regions, and the delaying effect of high precipitation was greater in warm than in cool regions. The similarities between these species in their phenological sensitivity and behavior are consistent with the interpretation that facilitation by pollinators and/or shared environmental conditions generate similar patterns of selection, or that limited genetic variation in flowering time prevents evolutionary divergence between these species.

The impact of geomorphology on groundwater recharge in a semi-arid mountainous area

The impact of geomorphological structures on rainwater infiltration and groundwater recharge in the mountainous terrain of a semi-arid region was studied in the southern part of the Judean Western Mountain Aquifer (WMA) in Israel and the Palestinian Authority. The study was conducted in two main typical geomorphological landscape units which include exposed rock outcrops on the hillslope, and human-made agricultural soil terraces in a first-order stream channel. We measured rainwater infiltration through the unsaturated zone in the two geomorphological setups using vadose zone monitoring systems (VMS), that enabled continuous tracking of rainwater percolation and tracer transport through the unsaturated zone with respect to the rain patterns.The results from three years of continuous monitoring of water percolation through the unsaturated zone indicated a quick and intense percolation pattern in the rocky terrain compared to that measured in the deep soil of the terrace. The precipitation patterns, as reflected from the rain distribution and intensities, appeared to have a significant impact on deep water percolation and ultimately on groundwater recharge. In the rocky terrain, rain events of high intensity, even during drought years of low cumulative precipitation, resulted in deep water infiltration, which ultimately turned into groundwater recharge. On the other hand, infiltration through deep soils, as prevailed in terraces, valleys, and high plains, was found to be very limited, subjected to intense evaporation and ultimately result by reduced groundwater recharge. It appears that the value of annual cumulative precipitation is of a lower importance to groundwater recharge while high rain intensity is the most dominant factor responsible for deep water infiltration.Tracer migration patterns in the unsaturated zone revealed that precipitation on the rock outcrops promotes the generation of local runoff that rapidly drains into the subsurface through infiltration hot spots. It has been observed that these hot spots are often marked by prickly burnet shrubs (Sarcopoterium spinosum), which thrive year-round on the water abundancy in the soil pockets beneath the impervious rock on land surface. An important outcome of this study is the suggested strong coupling between geomorphological features and groundwater recharge. Application of variable infiltration functions to the different geomorphological characteristics of each terrain may improve groundwater recharge models. Climate changes in recent years, expected to lead to decreased annual precipitations and increased evaporation in parallel with an increase in rainfall intensities in the semi-arid mountainous areas of the eastern Mediterranean. Yet according to our results, the expected increase in rain intensity may actually result in an increase in groundwater recharge.

Biogeographical characteristics of Schistosoma mansoni endemic areas in Ethiopia: a systematic review and meta analysis

BackgroundIn Ethiopia, schistosomiasis is caused by Schistosoma mansoni and S. haematobium with the former being widespread and more than 4 million people are estimated to be infected by S. mansoni annually with 35 million at risk of infection. Although many school- and community-based epidemiological surveys were conducted over the past decades, the national distribution of schistosomiasis endemic areas and associated socio-environmental determinants remain less well understood. In this paper, we review S. mansoni prevalence of infections and describe key biogeographical characteristics in the endemic areas in Ethiopia.MethodsWe developed a database of S. mansoni infection surveys in Ethiopia through a systematic review by searching articles published between 1975 and 2019 on electronic online databases including PubMed, ScienceDirect, and Web of Science. A total of 62 studies involving 95 survey locations were included in the analysis. We estimated adjusted prevalence of infection from each survey by considering sensitivity and specificity of diagnostic tests using Bayesian approach. All survey locations were georeferenced and associated environmental and geographical characteristics (e.g. elevation, normalized difference vegetation index, soil properties, wealth index, and climatic data) were described using descriptive statistics and meta-analysis.ResultsThe results showed that the surveys exhibited a wide range of adjusted prevalence of infections from 0.5% to 99.5%, and 36.8% of the survey sites had adjusted prevalence of infection higher than 50%. S. mansoni endemic areas were distributed in six regional states with the majority of surveys being in Amhara and Oromia. Endemic sites were found at altitudes from 847.6 to 3141.8 m above sea level, annual mean temperatures between 17.9 and 29.8 ℃, annual cumulative precipitation between 1400 and 1898 mm, normalized difference vegetation index between 0.03 and 0.8, wealth index score between –68 857 and 179 756; and sand, silt, and clay fraction in soil between 19.1–47.2, 23.0–36.7, and 20.0–52.8 g/100 g, respectively.ConclusionsThe distribution of S. mansoni endemic areas and prevalence of infections exhibit remarked environmental and ecological heterogeneities. Future research is needed to understand how much these heterogeneities drive the parasite distribution and transmission in the region.Graphic