Rainfall Zone Research Articles

What drives the overall technical efficiency of farm businesses in the Wheatbelt of Western Australia? Analysis of persistent and transient efficiencies in the presence of firm effects

AbstractThis study enhances our comprehension of the performance of farm businesses in Western Australia by estimating persistent and transient technical efficiency measures in the presence of firm‐level effects. The analysis is based on a balanced panel data set of 54 farm businesses from the years 2002 to 2011. We employ Kumbhakar et al.'s (2014, Journal of Productivity Analysis, 41, 321) model to estimate persistent efficiency and firm effects separately, as well as Kumbhakar's and Heshmati's (1995, American Journal of Agricultural Economics, 77, 660) model where these two factors are confounded. Furthermore, we investigate the factors that influence transient and persistent technical efficiency. Our findings reveal that failing to differentiate between persistent technical efficiency and firm effects underestimates the estimates of persistent and overall technical efficiency. This underestimation may result in misguided policy recommendations for improving the technical performance of farm businesses. We also find that persistent efficiency dominates overall technical efficiency. The significant determinants of persistent overall technical efficiency include the regional rainfall zone where a farm business is located, the managerial competency of the farm operator denoted by their age and off‐farm activities. Off‐farm income plays a crucial role in determining transient technical efficiency.

Rain event detection and magnitude estimation during Indian summer monsoon: Comprehensive assessment of gridded precipitation datasets across hydroclimatically diverse regions

Accurate precipitation estimates are quintessential for hydrologic modeling and climate studies. Different gridded precipitation products are available in any region, and selecting the best one is essential for hydroclimatic modeling and analysis. In the current study, observation- (APHRODITE), reanalysis- (IMDAA, ERA5-Land, PGF), satellite-based (IMERG, CHIRPS, PERSIANN-CDR), and hybrid (MSWEP) gridded precipitation products with different spatial and temporal resolutions are evaluated using several continuous, categorical, graphical, and interval-based performance measures towards detecting Indian Summer Monsoon Rainfall (ISMR) events and estimating their magnitudes for the subcontinent of India, considering IMD gauge-based gridded as reference product. We confine our analysis to the monsoon season (i.e., June to September), the principal rainy season in the Indian sub-continent. The dearth of data and limited rain gauge-based observations from non-uniform sparse monitoring networks across India necessitated grid-to-grid comparative evaluations instead of point-to-grid assessments. We propose a new ranking framework to determine the suitability of precipitation datasets for twenty-seven hydroclimatic regions comprising homogeneous rainfall zones, Köppen-Geiger climate zones, and major river basins. Results from the comprehensive evaluation suggest that (1) APHRODITE, MSWEP, and ERA5-Land best approximate precipitation event occurrences across India, (2) MSWEP and ERA5-Land are most suitable (highest rank) alternatives at the regional level, while APHRODITE is found to be next suitable dataset owing to its persistent dry bias, (3) performances of CHIRPS and IMERG have reasonably lower rank score across India, (4) close agreement of examined datasets is noted over semi-arid and sub-humid regions (e.g., peninsular and central India), whereas ERA5-Land, IMDAA, and APHRODITE fail to detect and reproduce the intensity of the events along the west coast and northeastern India, (5) PGF and PERSIANN-CDR are the least situated datasets. Moreover, the present study provides a unique and innovative perspective to characterise the precipitation over a vast topographic, ecologic, and climatic gradient region like India.

Phytolith spectra of the monotypic grass Danthonidium gammiei (Bhide) Hubb in Hook (Danthonioideae) from Northern Western Ghats, India: Taxonomic and environmental significance of its distinctive Bilobates

Grass Silica Short Cell Phytoliths (GSSCPs) are regarded as a potential adjunctive tool for grass subfamily identification owing to their definite morphology and affinity towards certain grass subfamilies. In the present study phytolith spectra of the monotypic genus Danthonidium gammiei (Bhide) Hubb in Hook, collected from northern Western Ghats, Maharashtra is studied in detail for the first time. This grass profusely produces Bilobates along with other GSSCPs and Elongate types. A thorough study is undertaken to find out potential of the Danthonidium gammiei Bilobates in distinguishing this grass from other major Bilobate producing grass sub-families like Panicoideae and Aristidoideae. The possible relationship between Bilobates specific for Danthonidium gammiei and the intensity of rainfall in northern Western Ghats is also discussed here. Statistical analyses (PCA, CA, and CCA) also establish the potential of Bilobate phytoliths in identifying Danthonioideae subfamily and discriminating different rainfall zones of the region.

The phylogenetically isolated South African endemic diving beetle Caperhantus cicurius (Fabricius, 1787): redescription and range extensions (Coleoptera, Dytiscidae, Colymbetinae)

New distribution records and a redescription are presented for Caperhantus cicurius (Fabricius, 1787), a diving beetle endemic to the Cape region of South Africa, which is only distantly related to other Afrotropical dytiscids. We show that the species is much more widely distributed than previously recognized and present details of occupied habitats for the first time. Caperhantus cicurius occurs throughout the winter rainfall zone in South Africa, from Namaqualand to the Eastern Cape Province, particularly in temporary waters in the Fynbos and Succulent Karoo biomes.

Integrated Geospatial and Analytical Hierarchy Process Approach for Assessing Sustainable Management of Groundwater Recharge Potential in Barind Tract

Groundwater depletion in Bangladesh’s Barind tract poses significant challenges for sustainable water management. This study aims to delineate groundwater recharge potential zones in this region using an integrated geospatial and Analytical Hierarchy Process (AHP) approach. The methodology combines remote-sensing data with GIS analysis, considering seven factors influencing groundwater recharge: rainfall, soil type, geology, slope, lineament density, land use/land cover, and drainage density. The AHP method was employed to assess the variability of groundwater recharge potential within the 7586 km2 study area. Thematic maps of relevant factors were processed using ArcGIS software. Results indicate that 9.23% (700.22 km2), 47.68% (3617.13 km2), 37.12% (2816.13 km2), and 5.97% (452.70 km2) of the study area exhibit poor, moderate, good, and very good recharge potential, respectively. The annual recharge volume is estimated at 2554 × 106 m3/year, constituting 22.7% of the total precipitation volume (11,227 × 106 m3/year). Analysis of individual factors revealed that geology has the highest influence (33.57%) on recharge potential, followed by land use/land cover (17.74%), soil type (17.25%), and rainfall (12.25%). The consistency ratio of the pairwise comparison matrix was 0.0904, indicating acceptable reliability of the AHP results. The spatial distribution of recharge zones shows a concentration of poor recharge potential in areas with low rainfall (1200–1400 mm/year) and high slope (6–40%). Conversely, very good recharge potential is associated with high rainfall zones (1800–2200 mm/year) and areas with favorable geology (sedimentary deposits). This study provides a quantitative framework for assessing groundwater recharge potential in the Barind tract. The resulting maps and data offer valuable insights for policymakers and water resource managers to develop targeted groundwater management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to agricultural water demand and climate change adaptation.

Large herbivore δ18O as a proxy for aridity in the South African winter and year-round rainfall zone

Abstract This study explores patterning in δ18O values of tooth enamel in contemporary African herbivores from mainly C3-dominated ecosystems. Evapotranspiration causes plants to lose H216O to a greater extent than H218O, leaving leaves enriched in 18O. In eastern Africa, ES species (evaporation-sensitive species: those obtaining water from food) tend to have more positive δ18Oenamel values than EI species (evaporation-insensitive species: those heavily dependent on drinking water); the magnitude of the difference increases with increasing aridity. We find the same pattern applies in the winter and year-round rainfall region of southern Africa, allowing us to use δ18Oenamel in fossil animals to examine paleo-aridity. We apply this approach to infer aridity at Quaternary fossil assemblages from present-day winter and year-round rainfall zones, including Elandsfontein (ca. 1–0.6 Ma), Hoedjiespunt (ca. 300–130 ka), and Nelson Bay Cave (23.5–3 ka). This analysis suggests that (1) at various times during the Pleistocene, Elandsfontein and Hoedjiespunt environments were wetter than last glacial maximum (LGM) to Holocene environments at Nelson Bay Cave (year-round rainfall zone); and (2) considered alongside other evidence from the year-round rainfall zone, wetter conditions across the Pleistocene–Holocene transition at Nelson Bay Cave suggests that climate changes at near-coastal sites may be out of phase with the adjacent interior.

Comparative performance of integrated farming system models in scarce rainfall zone under rainfed and irrigated conditions

Comparative performance of integrated farming system models in scarce rainfall zone under rainfed and irrigated conditions

Economic analysis of crop protection strategies: comparing the value of increased fungicide inputs and crop genetic improvement in managing Ascochyta blight in Australian chickpeas.

Genetic improvement of crop varieties requires significant investment. Therefore, varieties must be developed to suit a broad range of breeding targets, such as yield and suitability to rainfall zones, farm management practices and quality traits. In the case of breeding for disease resistance, breeders need to consider the value of genetic improvement relative to other disease management strategies and the dynamics of pathogen genetic and phenotypic diversity. This study uses a benefit-cost analysis framework to assess the economic value of fungicide management and crop genetic improvement in disease resistance for Australian chickpea varieties. When assessing the likelihood of growers switching to new crop varieties with improved genetic resistance to disease, the simulation results reveal that adopting these varieties yielded higher net benefit values compared to implementing current fungicide strategies across all rainfall zones. On average, the increase in net benefit varied between 2.6% and 3.5%. Conversely, when we examined the scenario involving modifying the current fungicide strategy, we observed that, on average, switching from the current fungicide management strategy to one which involved additional fungicides was beneficial in about 73% of the cases. Our analysis reveals the importance of factors such as commodity prices, production costs, disease-related variables and risk aversion in determining the economic benefits of adopting new crop protection strategies. Furthermore, the research reveals the need for accessible information and reliable data sources when evaluating the benefits of new agricultural technologies. This would assist growers in making informed and sustainable disease management decisions. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Crop planning using innovative trend analysis of 62-years rainfall data

The success of climate-smart agriculture in high rainfall zone lies in understanding the rainfall trend and planning or modifying the cropping system for maximum yield. Moisture stress in critical crop growth stages is detrimental to the crop and drastically reduces the yield. Udhagamandalam region in Western Ghats is a high rainfall area and is largely cultivated by vegetable crops. Rainfall trend based crop planning would enhance the crop yield without water stress. A study was carried out at ICAR-Indian Agricultural Research Institute, Dhemaji, Assam focused on assessing the long-term seasonal and monthly rainfall trends of Udhagamandalam region, Tamil Nadu using non-parametric tests and Innovative Trend Analysis (ITA). Daily rainfall of 62 years from 1960–2021 was analyzed with non-parametric tests, viz. Mann-Kendall and modified Mann-Kendall and ITA to find the seasonal rainfall characteristics. Mann- Kendall (3.055) and modified Mann-Kendall (3.055) tests showed a significantly increasing trend in the annual and seasonal monsoonal rainfall. ITA revealed either a significant positive or a negative trend in all the months except February, with the highest trend in June (2.625). In contrast to standard non-parametric tests, ITA detected a significant positive trend in all seasons and annual rainfall, except in cold winters where the trend is negative. The long-term trend analysis results suggest that the ITA is more precise for rainfall trend analysis than standard non-parametric tests and can be used to evaluate hidden variations of rainfall trends. Hence, ITA is recommended for analyzing rainfall trends for crop planning in high-rainfall regions. IT analysis of 62 years of rainfall data of Udhagamandalam suggested that vegetable crop planning can be done by farmers from August–November months as the rainfall trend during this period is assured as an increasing trend of rainfall pattern was observed.

Effect of nitrogen management practices on growth and yield of maize (Zea mays L.) under scarce rainfall zone of Andhra Pradesh

Effect of nitrogen management practices on growth and yield of maize (Zea mays L.) under scarce rainfall zone of Andhra Pradesh

Is human activity driving climate change? Perspectives from Australian landholders

Australian agriculture and the rural communities that depend upon it are expected to experience significant impacts from climate change. The recognition of the human role in climate change is central in the design and implementation of effective strategies to mitigate and adapt to its impacts. Understanding the extent to which members of the public, such as private landholders, acknowledge human-caused climate change is critical, given their role as custodians of large tracts of natural resources. Rural social benchmarking studies are a useful tool for understanding landholder values and beliefs. Here, we use a rural social benchmarking survey to examine landholder agreement regarding the extent to which humans contribute to climate change across four Australian agricultural regions. We perform hierarchical clustering analysis to determine subgroups of landholders with similar patterns of survey responses. We then evaluate this effect of cluster membership and demographic characteristics using Bayesian ordinal regression on levels of agreement with the statement “that human activities are influencing climate change.” Our findings reveal three distinct clusters based on patterns of responses to survey questions eliciting participants values beliefs and norms. Cluster membership exhibits the strongest positive influence on agreement (0.52, 95% CI: 0.37 to 0.67). This was followed by higher education levels (0.32, 95% CI: 0.22 to 0.41). Gender showed a moderately uncertain but positive influence. Years residing on the property, participant age, and property size showed very little influence, while rainfall zones showed a negative influence of-0.29 (95% CI: −0.47 to-0.12). Our results underscore the need for extension programs to consider landholder typologies based on a combination of lived experience and demographics.

An insight from homogeneity testing of long-term rainfall datasets over East Java, Indonesia

Robust, reliable, and trustworthy ground observation datasets are the preliminary requirement for assessing the impact of climate change over regions. Principal testing to assess the quality of ground observation rely on the missing data and homogeneity result. The study used 40 years of monthly rainfall documented from different topographical features in the monsoonal region of East Java, Indonesia. The test included annual rainfall, early rainy season (October-November-December), and primary rain season (January-February-March). The homogeneity of rainfall determined by absolute technique: Pettitt’s test, the Standard Normal Homogeneity Test, the Buishand Rank Test, and the von Neumann Ratio. Among the time series, October-November-December observation results in better homogeneity. However, the rainfall datasets during primary rainy season showed the worst homogeneity. By performing annual and seasonal homogeneity test from 67 rainfall stations: 5 stations out of data length required, 5% stations ‘rejected’, 11% ‘suspect’, 11% ‘doubtful’, and 73% were ‘trusted’. Therefore, a total of 45 stations can be used as metadata for relative comparison and 7 stations can be considered to be useful for analysis despite ‘doubtful’. The remaining 10 stations need careful consideration to be used for future water management. Change point detected particularly between the year of 1997 through 2000. Pettitt’s test has outstanding results in the case of extreme climatic anomaly, but less sensitive of continuous abrupt change. The von Neumann test could detect abnormal data, but was not suitable for datasets containing few extreme values. The insights from homogeneity testing were: a) it is important to remove any outliers in the datasets before conducting homogeneity testing, b) both parametric and nonparametric homogeneity tests should be performed, and c) comparisons should be made with surrounding rainfall stations. Comparison with trusted long-term rainfall data is valuable for stations labeled as ‘doubtful’ or ‘suspect’ to mitigate false detections in individual homogeneity tests. The identified ‘useful’ rainfall data can then serve as reference stations for relative homogeneity tests. These findings suggest that reference stations should be assessed within similar rainfall zones.

The impact of repayment obligations arising as a by‐product of input use on partial inefficiency: Evidence from Western Australian farm businesses

AbstractFarm businesses often use debt to finance the purchase inputs and meet operational costs. Selected studies have investigated the impact of debt use on farm performance as measured using technical efficiency. However, no prior studies in agriculture have considered treating the debt repayment obligation created as a by‐product of production when benchmarking farm performance. This study employs nonparametric directional distance function models to quantify the impact of debt repayment as a by‐product of input use purchased on credit on farm‐level partial inefficiency using a panel data of 54 mixed enterprise broadacre farms in Western Australia from 2002 to 2011. The study finds that omitting repayment obligations created in the production process in analysis results in underestimating partial inefficiency scores. Farm size, production diversification and rainfall zone are significant drivers of partial inefficiency. The implication of the study's findings is that failure to account for repayment obligations created in the production process may result in erroneous policy suggestions that undermine the efforts of farm businesses to minimise or avoid financial stress.

Impacts of Fengyun-4A and Ground-Based Observation Data Assimilation on the Forecast of Kaifeng’s Heavy Rainfall (2022) and Mechanism Analysis of the Event

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model’s humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm’s water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm’s development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain’s terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment.

Examining the effects of climate change and human impacts on a high-resolution, late Holocene paleofire record from South Africa's winter rainfall zone

Fire is central to the Cape Floristic Region's highly biodiverse and disturbance-adapted Fynbos Biome. However, prehistoric fire regimes, their ecological consequences, and their relationships with large-scale climate drivers and human activities remain poorly understood. Here, we use a high-resolution sedimentary charcoal record from Verlorenvlei, a coastal lake situated on the west coast, to interrogate links between fire, climate, and pastoralism in the Fynbos Biome. Our record has a robust chronology supported by 24 radiocarbon dates and provides a continuous sedimentary sequence spanning the last 4200 years, documenting fire activity before and after the local arrival of pastoralists in the Verlorenvlei area ∼1500 cal years BP. Fire at Verlorenvlei over the last 4200 years is variable, with relatively low activity until ∼2000 cal years BP, after which variable but generally higher fire activity occurs until the highest period of fire activity from ∼1450 to 1800 CE (∼500–150 cal years BP). The increase in fire activity ∼2000 years ago corresponds with a shift in the diatom assemblage at Verlorenvlei from marine towards brackish and freshwater species, reflecting increased precipitation derived from a strengthening of the southern westerly winds. The peak in fire activity beginning ∼1450 CE (∼500 cal years BP), near the onset of the Little Ice Age, tracks a second diatom-inferred strengthening of the westerly winds. Other southern hemisphere and Antarctic records further corroborate this increased westerly influence after ∼2000 years. Linear regression modeling on the fire record indicates that moisture availability is the primary driver of fire at Verlorenvlei, with little evidence that human populations influenced fire. Our reconstruction suggests that fire activity at Verlorenvlei is limited by moisture availability and that wetter conditions facilitate increased vegetation (i.e., fuel) and intensified fire at this otherwise fuel-limited site. This work has implications for management and conservation decisions in response to future predictions of a warmer and drier climate along South Africa's west coast.

APSIM-based modeling approach to understand sorghum production environments in Mali

Sorghum production system in the semi-arid region of Africa is characterized by low yields which are generally attributed to high rainfall variability, poor soil fertility, and biotic factors. Production constraints must be well understood and quantified to design effective sorghum-system improvements. This study uses the state-of-the-art in silico methods and focuses on characterizing the sorghum production regions in Mali for drought occurrence and its effects on sorghum productivity. For this purpose, we adapted the APSIM-sorghum module to reproduce two cultivated photoperiod-sensitive sorghum types across a latitude of major sorghum production regions in Western Africa. We used the simulation outputs to characterize drought stress scenarios. We identified three main drought scenarios: (i) no-stress; (ii) early pre-flowering drought stress; and (iii) drought stress onset around flowering. The frequency of drought stress scenarios experienced by the two sorghum types across rainfall zones and soil types differed. As expected, the early pre-flowering and flowering drought stress occurred more frequently in isohyets < 600 mm, for the photoperiod-sensitive, late-flowering sorghum type. In isohyets above 600 mm, the frequency of drought stress was very low for both cultivars. We quantified the consequences of these drought scenarios on grain and biomass productivity. The yields of the highly-photoperiod-sensitive sorghum type were quite stable across the higher rainfall zones > 600 mm, but was affected by the drought stress in the lower rainfall zones < 600 mm. Comparatively, the less photoperiod-sensitive cultivar had notable yield gain in the driest regions < 600 mm. The results suggest that, at least for the tested crop types, drought stress might not be the major constraint to sorghum production in isohyets > 600 mm. The findings from this study provide the entry point for further quantitative testing of the Genotype × Environment × Management options required to optimize sorghum production in Mali.

Maize Aspergillus section Flavi isolate diversity may be distinct from that of soil and subsequently the source of aflatoxin contamination

Aspergillus section Flavi (Flavi) is a diverse group of fungal species whose common members include A. flavus and A. parasiticus. These are well-known for the production of aflatoxin (AF) B and G and other toxic metabolites, like cyclopiazonic acid (CPA). They are saprophytic soil dwellers and also become crop opportunistic epiphytes. The consequence is contamination of the crop with mycotoxins, such as carcinogenic AF. We investigated the Flavi community structure of maize and that of their surrounding soil, including their mycotoxigenicity. Furthermore, we investigated the link of the maize Flavi diversity with preharvest maize AF levels. The study was carried out in four selected districts of Zambia, in a low rainfall zone. The Flavi characterisation was triphasic, involving morphological (colony colour and sclerotia formation), metabolic (AF and CPA production) and genetic (calmodulin gene polymorphism) analyses. Flavi abundance was determined by dilution plate technique on modified rose Bengal agar. Results showed that Flavi communities on maize and in soil differed. Maize had a higher Flavi species diversity than soil. A. parasiticus dominated the soil community by frequency of field appearance (85%), while maize was dominated by A. minisclerotigenes (45%). CPA-producers with or without AF production dominated the maize (65%) while producers of only AF (B/G) dominated the soil (88%). The ratio between maize A. parasiticus and A. minisclerotigenes abundance seemed to have had a bearing on the levels of AF in maize, with a ratio close to 1:1 having higher levels than a pure community of either A. parasiticus or A. minisclerotigenes.

Evaluation of Integrated Pest Management Module against Pests of Ground Nut and Productivity, Profitability Analysis under Open Field Conditions

Background: Groundnut is an annual oilseed crop that is cultivated in many tropical and sub-tropical countries for its seed purpose. The productivity of groundnut has drastically reduced since 2015 due to an array of insect pests especially sucking pests have emerged as a prominent factor contributing to decreased yields in groundnut by direct damage as well as vectors of virus diseases. Methods: The integrated package of practices including seed treatment with fungicide (mancozeb @ 3 g/kg seed) followed by insecticide (imidacloprid @ 2 mL/kg seed) and new generation insecticides (imidacloprid, chlorpyriphos and flubendiamide) were evaluated against sucking pests (aphid, leaf hopper and thrips) and leaf feeders (Spodoptera litura) under open field conditions in scarce rainfall zone of Andhra Pradesh, India during 2020-21, 2021-22 and 2022-23. The parameters regarding incidence of pests, population reduction after treatment imposition, safety to natural enemies, phytotoxicity effects, extension gap, technology gap and technology index, were worked out in farmer’s field and demonstration field. Result: In this present study imidacloprid 17.8 SL was performed well in controlling aphids, leaf hoppers and thrips in all the three seasons and flubendiamide 39.35 SC was concluded as best treatment against Spodoptera litura in all seasons. During all the study years, the benefic cost ratio of demo practice indicates (1.26, 1.01 and 1.04) when compared to farmers practice (0.71, 0.69 and 0.76), respectively for 2020, 2021 and 2022. Similarly, the average extension gap, technology gap and yield gap were worked out and ranged from (3.4, 3.1 and 5.3), (1.82, 4.23 and 1.82), (12.72, 11.36 and 19.46) for the years 2020, 2021 and 2022, respectively.

Evolution of winter precipitation in the Nile river watershed since the last glacial

Abstract. Between 14.5 and 5 ka, the Sahara was vegetated owing to a wet climate during the African humid period. However, the climatic factors sustaining the “green Sahara” are still a matter of debate. Particularly the role of winter precipitation is poorly understood. Using the stable hydrogen isotopic composition (δD, where D stands for deuterium) of high molecular weight (HMW) n-alkanoic acids in a marine sediment core from the eastern Mediterranean, we provide a continuous record for winter precipitation in the Nile river delta spanning the past 18 kyr. Pairing the data with δD records from HMW n-alkanes from the same core, we show that HMW n-alkanoic acids constantly derived from the delta, while the HMW n-alkanes also received significant contributions from the headwaters between ∼ 15–1 ka when fluvial runoff enhanced. This enables us to reconstruct the evolution of Mediterranean (winter) and monsoonal (summer) rainfall in the Nile river watershed in parallel. In the delta, the Heinrich stadial 1 (HS1) evolved in two phases, with a dry episode between ∼ 17.5–16.0 ka, followed by wet conditions between ∼ 16–14.5 ka. Winter rainfall enhanced substantially between 11–6 ka, lagging behind the intensification of the summer monsoon by ca. 3 kyr. Heavy winter rainfall resulted from a southern position of the Atlantic storm track combined with elevated sea surface temperatures in the eastern Mediterranean, reinforcing local cyclogenesis. We show that during the green Sahara, monsoon precipitation and Mediterranean winter rainfall were both enhanced and infer that the winter rainfall zone extended southwards, delivering moisture to the Sahara. Our findings corroborate recent hypotheses suggesting that winter rains that extended southward were a crucial addition to the northward displacement of the summer monsoon in helping to sustain a green Sahara.

Spatially analysed expansion of individual street tree crowns enables species-specific crown expansion predictions in different rainfall zones

Many cities are developing ambitious future canopy cover targets in recognition of urban trees' numerous benefits. Selecting fast-growing and climate-adapted tree species is important to help achieve these targets. The assessment of growth performance of tree species is challenging, but essential for selecting species that grow in different environments. Often, remote sensing is used to measure canopy cover change at a landscape or neighbourhood scale in urban forests, but rarely at individual tree or species scales. In this study, we developed a novel spatial analysis method combining remotely sensed canopy cover mapping with georeferenced urban tree inventory data to identify individual tree crowns and measure crown expansion rates. We developed species-specific models of crown expansion rates for 20 most common street tree species growing in two rainfall zones (478–665 mm). Predicted crown areas at 10 years after planting ranged from 6.6 m2 to 43.7 m2. The species showed four different crown expansion responses at 10 years after planting: Fast and consistent growth; Fast but sensitive growth; Slow and consistent growth; Slow and sensitive growth. This study demonstrates a simple, but robust method to delineateindividual tree crowns that can be used to develop species-specific crown expansion models. It also shows the importance of developing species specific crown expansion models in different rainfall zones, as some tree species were clearly sensitive to rainfall differences. Using this spatial analysis method, urban forest managers can make informed decisions regarding tree species selection, considering rainfall zone specific environmental growth conditions, as well as space constraints and water availability.