High Interannual Variability Research Articles

Predicting Australian energy demand variability using weather data and machine learning

Abstract Managing energy systems requires understanding the variability of energy demand, which on daily timescales is driven primarily by the weather. Historical records of demand typically cover 1-2 decades which may be too short to capture the range of possible demand, particularly for a climate with high interannual variability such as that of Australia. Predicting demand using long records of weather data opens the possibility of more robustly estimating true demand variability. We estimate daily energy demand between 2010 and 2019 for Australian states in the National Electricity Market using machine learning with reanalysis weather variables as predictors. We assess the performance of these models and examine their behaviour to identify which weather variables are most important for predicting demand. We then use the models to estimate demand for the period 1959-2022. We use this 63-year record to quantify how the probability of high demand days can change compared to individual 10-year periods and when conditioned by the phase of the El Niño Southern Oscillation (ENSO). Energy demand can be accurately predicted with weather, with median errors of 2-4% on years omitted from the training. We show that the probability of extreme demand over different 10-year periods can vary from half to twice as likely, depending on the decade. When further conditioned on ENSO phase, the probabilities can be up to 7 times higher than when using the 63-year period, implying a risk of overestimating weather-related energy demand if shorter records are used. We conclude that machine learning methods can accurately predict energy demand using only weather data, enabling us to estimate demand variability over longer time horizons than is possible with demand observations. These longer records are important when attempting to quantify tail risks of demand, and so can help to inform the design of energy systems.

Positive and Negative Effects of Inter‐Annual Climate Variability on Rice (Oryza sativa L.) Crop in Agro‐Climatic Zones of Punjab

ABSTRACTGlobally, climate changes have significantly shifted the phenological phases and stages of rice, altered the duration of the growing season and negatively affected rice productivity due to flooding and drought. However, in the present study, the positive and negative impacts of inter‐annual climate variability on rice crops during phenological stages in agro‐climatic zones of Punjab for the period from 1989 to 2018. Initially, first difference approach was applied to minimise the impact of technological factors. Then, skewness and kurtosis tests were used to check the normalisation of the data. The standardisation method was used to normalise the data. Pearson correlation was used to determine the significant effects of climate variables on rice yield. The residuals were formed to confirm the effects of inter‐annual climate variability on rice yield in the phenological phases. The analysis revealed that a high variability of rice yields was investigated in the western region compared to the southern and western regions. The results showed a negative impact of heavy rainfall (flooding) on the years with low yields (2010, 2013 and 2016) in the Central region. Similarly, the years with low rice yields (1996, 2010, 2013 and 2014) in the Southern region were negatively affected by flooding at the time of sowing. A positive effect of rainfall was observed in the years with high rice yields (1995, 2002, 2009 and 2018) in the Western region. In contrast, the low‐yielding years 1994, 2003 and 2010 were negatively affected by flooding in the same years during the tillering stage. A high interannual maximum temperature variability was analysed in the Southern > Western > Central regions, leading to yield losses due to biotic stress during tillering and stem elongation stages. This is due to the immense reason of drought stress. The minimum temperature negatively affects the low‐yield years (2001, 2008, 2013 and 2016) in the central zone and the low‐yield years (2014, 2015 and 2016) in the southern zone during the reproductive stage. This research will help to develop new rice varieties that are more productive at high temperatures and require less water, leading to sustainable development in arid and semi‐arid regions.

Assessing the Climatological Characteristics of Observed and Simulated Seasonal Onset of Precipitation Over Southern and Eastern Africa

ABSTRACTPrediction of seasonal onset is crucial to agriculture in southern and eastern Africa. Here, we applied two definitions of onset, namely meteorological and agricultural (crop‐germination), to evaluate CMIP6 models through the lens of rainfall onset over representative maize agricultural regions of South Africa, Tanzania, Malawi and Zambia. We use the ERA5 reanalysis as a proxy for observations, and robust regression to calculate a statistical comparison of the onset definitions for the period 1979–2021. Evaluation of ERA5 reanalysis shows similar magnitude and pattern as gauge based MSWEP. Our results show that, for meteorological onset, Johannesburg, with a subtropical highland climate, experienced earliest onset after 23 December; and an increasing trend (later onset) but not statistically significant (p = 0.2). Over Bethlehem, which has continental climate, the earliest onset date was after October 9 and an increasing interannual variability since 2000 is noted. The standard deviation of onset dates across the regions shows an East‐Central‐South gradient. We also found that the crop‐germination onset definition shows earlier onset of seasonal rains, it differs considerably across regions, and has higher interannual variability, in comparison with the meteorological definition. Over Lilongwe, Mbeya and Lusaka, late meteorological onset with a weak positive and insignificant trend is observed. The CMIP6 model's representation of onset trend differs from reanalysis data, with inter‐model differences. Late meteorological onset is underestimated by GFDL‐CM4 and MPI while INM5, MPI and NorESM overestimate the observed earliest onset. The largest bias is shown by INM and MPI which simulate earliest and latest onset as 190 (07 January) and 206 (23 January) respectively. In addition, models often fail to simulate sufficient precipitation to produce onset for seed germination and crop development. The ACCESS model showed an insignificant trend (p value = 2) and later onset over Lilongwe, an insignificant trend (p value = 0.9) over Lusaka, and an earlier onset over Mbeya. Using the agricultural onset definition, over Bethlehem, all the models and the ERA5 reanalysis did not produce enough precipitation to meet onset conditions. We suggest that rainfall onset studies use several definitions or metrics of onset and that the choice of metric be informed by the research question. Using such an ensemble of onset metrics contributes to a better understanding of variability and uncertainties in agricultural productivity.

Genetic and QTL analysis of sugars and acids content in sweet cherry (Prunus avium L.)

Abstract Sweet cherry is very appreciated by consumers because of its attractive appearance and taste, which is determined by the balanced sweet-sour flavor. In this work, the genetics of soluble solid content (SSC), titratable acidity (TA), sugars and organic acids was investigated in sweet cherry to facilitate breeding improvement for fruit quality. The fruits of five sweet cherry populations (N = 372), three F1 and two F2, were sampled over two years to evaluate SSC, TA, and the content of individual sugars (glucose, fructose, sorbitol, and sucrose) and organic acids (malic, quinic, oxalic, citric and shikimic) by ultra-performance liquid chromatography (UPLC). Glucose, followed by fructose, was the most abundant sugar, while malic acid was the predominant acid. Sorbitol and malic acid were the most stable compounds between years, and had the highest heritability, being also the best correlated to SSC and TA respectively, revealing their relevance for breeding. Significantly positive correlations were observed among sugars and SSC, and acids and TA, but high interannual variability between years was observed for all traits. QTL mapping for SSC, sugars, TA, and organic acids was performed using a multi-family approach with FlexQTL™. Twenty QTLs were detected consistently during the two phenotyped years, and several relevant regions with overlapping QTLs for sugars and acids were also identified. The results confirmed major stable SSC and TA QTLs on the linkage groups 4 and 6, respectively. Within the main LG4 SSC QTL region, where maturity and fruit development time QTLs have been previously detected, three stable sugar (glucose, sorbitol, and sucrose) and two acid (quinic, shikimic) QTLs were also identified, suggesting a pleiotropic effect of ripening date on the content of these compounds. The major malic acid QTL overlapped with TA QTL on LG6, thus TA QTL mapping in LG6 may correspond to malic acid QTLs. Haplotype analyses of major SSC and sugars QTL in LG4, and TA and malic acid in LG6 revealed haplotypes of breeding interest. Several candidate genes previously identified in other Prunus fruit species, like peach, were found to collocate with the QTLs detected herein. This work reports QTLs regions and haplotypes of sugar and acid content in a Prunus non-climacteric stone fruit for the first time.

Effect of Solar Irradiation Inter-Annual Variability on PV and CSP Power Plants Production Capacity: Portugal Case-Study

The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at the time of the supply agreement. For that reason, this study aims to fill the gap in the existing literature and analyse the impact that solar resource variability has on solar power plant production as applied to the case of Portugal (southern Europe). To that end, 17 years (2003–2019) of meteorological data from a network of 90 ground stations hosted by the Portuguese Meteorological Service is examined. Annual capacity factor regarding photovoltaic (PV) and concentrating solar power (CSP) plants is computed using the System Advisor Model, used here for solar power performance simulations. In terms of results, while a long-term trend for increase in annual irradiation is found for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), 0.4148 and 3.2711 kWh/m2/year, respectively, consistent with a solar brightening period, no corresponding trend is found for PV or CSP production. The latter is attributed to the long-term upward trend of 0.0231 °C/year in annual average ambient temperature, which contributes to PV and CSP efficiency reduction. Spatial analysis of inter-annual relative variability for GHI and DNI shows a reduction in variability from the north to the south of the country, as well as for the respective power plant productions. Particularly, for PV, inter-annual variability ranges between 2.45% and 12.07% in Faro and Santarém, respectively, while higher values are generally found for CSP, 3.71% in Faro and 16.04% in São Pedro de Moel. These results are a contribution to future instalments of PV and CSP systems in southern Portugal, a region with very favourable conditions for solar energy harvesting, due to the combination of high production capacity and low inter-annual variability.

Strengthening of the hydrological cycle in the Lake Chad Basin under current climate change

Central Sahel is affected by a reinforcement of rainfall since the beginning of 1990s. This increase in rainfall is affected by high inter-annual variability and is characterized by extreme rain events causing floods of unprecedented magnitude. However, few studies have been carried out on these extreme events. Moreover, with current climate change expected to strengthen the hydrological cycle, we don’t know if these events could become more frequent. Here, we report the hydrological changes that currently occur in the Lake Chad basin. Based on ground observations and satellite data, we focused on the 2022 flood event, demonstrating that it was the most important event from the last 60 years, comparable to what occurred during the last wet period between the 1950s and the 1960s. We showed that under this precipitation regime and if warming is not regulated at a global scale, the return period of the 2022 major riverine flood is expected to be between 2 and 5 years. By using modelling experiments, our study also suggested that in the next decade, future flow rates of the main rivers draining the Lake Chad basin could reach the values observed in the 1950s. These results strongly suggest anticipating water management in a context of poor infrastructural development.

Recent Patterns and Trends of Snow Cover (2000–2023) in the Cantabrian Mountains (Spain) from Satellite Imagery Using Google Earth Engine

Snow cover is a relevant component of the Earth’s climate system, influencing water supply, ecosystem health, and natural hazard management. This study aims to monitor daily snow cover in the Cantabrian Mountains using Sentinel-2, Landsat (5–8), and MODIS data processed in Google Earth Engine (GEE). The main purpose is to extract metrics on snow cover extent, duration, frequency, and trends. Key findings reveal significant spatial and temporal variability in Snow-Cover Days (SCDs) across the region. Over the past 23 years, there has been a notable overall decrease in snow-cover days (−0.26 days per year, and −0.92 days per year in areas with a significant trend). Altitudes between 1000–2000 m a.s.l. showed marked decreases. The analysis of Snow-Cover Fraction (SCF) indicates high interannual variability and records the highest values at the end of January and the beginning of February. The effectiveness of satellite data and GEE is highlighted in providing detailed, long-term snow cover analysis, despite some limitations in steep slopes, forests, and prolonged cloud-cover areas. These results underscore the capacity for continuous monitoring with satellite imagery, especially in areas with sparse snow observation networks, where studies could be enhanced with more localized studies or additional ground-based observations.

Seed size and dispersal mode select mast seeding in perennial plants.

Reproduction by perennial plants varies from being relatively constant over years to the production of massive and synchronous seed crops at irregular intervals, a reproductive strategy called mast seeding. The sources of interspecific differences in the extent of interannual variation in seed production are largely unknown. We conducted a global meta-analysis of animal-dispersed species to quantify how the interannual variability in seed crops produced by plants can be explained by the seed mass, dispersal mode, phylogeny, and climate. Phylogenetic analysis indicated that the interannual variations in seed production and seed mass tended to be similar in related species due to their shared evolution. The interannual variation in seed production was 1.22 times higher in synzoochorous species dispersed by scatter-hoarders compared with endozoochorous species dispersed by frugivores. Furthermore, the production of small seeds was associated with higher interannual variation in seed production, although synzoochorous species produced larger seeds than endozoochorous species. Precipitation rather than temperature had a significant positive effect on the interannual variation in seed production. The seed mass and dispersal mode contributed more to the interannual variation in seed production than phylogeny, climate, and fruit type. Our findings support a long-standing hypothesis that interspecific variation in the masting intensity is largely shaped by interactions between plants and animals.

The impact of grazing on biodiversity and forest succession in the Brazilian dry forest region is constrained by non-equilibrium dynamics

The impacts of grazing on rangelands have historically been studied within the framework of the equilibrium model, which predicts significant impacts of grazing on ecosystems. However, in recent decades, studies have observed a non-equilibrium pattern, suggesting that abiotic factors play a primary role compared to grazing. These studies are primarily focused on rangelands, despite animal husbandry occurring in other biomes, such as seasonally dry tropical forests. Our study examines the influence of goat grazing on biodiversity and forest succession in the Brazilian dry forest (Caatinga). Considering its high interannual precipitation variability, we hypothesize a response that aligns with the non-equilibrium paradigm. We established a gradient of grazing intensity and history in areas at different stages of vegetation succession. A survey of tree - shrub and herbaceous species was conducted at each site and the biomass of both strata was quantified. Linear mixed models and Permanova were employed to assess differences in richness, composition, structure, and biomass among the areas. Our results suggest that grazing (history and intensity) and forest fallow age did not affect species richness, but only species composition. Low and high grazing intensity drive ecosystems toward similar compositions, which align with the non-equilibrium model predictions. Biomass in the herbaceous layer remained unaffected by grazing history, intensity, or forest fallow age, whereas woody biomass was influenced by grazing intensity in older forest fallows. Although trees in low-intensity grazing sites were significantly taller compared to those in other levels, overall, grazing did not disrupt the natural succession process. Older forest fallows exhibited greater diversity and higher basal area compared to new forest fallows, irrespective of grazing intensity. Our findings suggest that: a) grazing has minimal effects on biodiversity and biomass due to non-equilibrium dynamics, and b) with appropriate management, grazing can coexist with the conservation of the Caatinga.

Net Community Production and Inorganic Carbon Cycling in the Central Irminger Sea

AbstractThe subpolar North Atlantic plays an outsized role in the atmosphere‐to‐ocean carbon sink. The central Irminger Sea is home to well‐documented deep winter convection and high phytoplankton production, which drive strong seasonal and interannual variability in regional carbon cycling. We use observational data from moored carbonate chemistry system sensors and annual turn‐around cruise samples at the Ocean Observatories Initiative's Irminger Sea Array to construct a near‐continuous time series of mixed layer total dissolved inorganic carbon (DIC), pCO2, and total alkalinity from summer 2015 to summer 2022. We use these carbonate chemistry system time series to deconvolve the physical and biological drivers of surface ocean carbon cycling in this region on seasonal, annual, and interannual time scales. We find high annual net community production within the seasonally varying mixed layer, averaging 9.8 ± 1.6 mol m−2 yr−1 with high interannual variability (range of 6.0–13.9 mol m−2 yr−1). The highest daily net community production rates occur during the late winter and early spring, prior to the observed high chlorophyll concentrations associated with the spring phytoplankton bloom. As a result, the winter and early spring play a much larger role in biological carbon export from the mixed layer than traditionally thought.

Parenting in a changing environment: A long-term study of prolactin, parental effort and reproductive success in common eiders

Parental care is regulated by multiple endocrine mechanisms. Among these hormones, prolactin (PRL) is involved in the expression of parental behaviors. Despite the consensus that PRL mediates variation in parental effort with age and body condition, its role in the adjustment of parental effort to fluctuating environmental conditions, including changing predation pressure, still awaits further investigation. To shed light on this knowledge gap, we relied on a long-term monitoring of female common eiders Somateria mollissima (n = 1277 breeding attempts, 2012–2022) incubating under fluctuating predation risk to investigate the link between baseline PRL levels and female minimum age, body condition, clutch size, environmental parameters (predation pressure, climate, nest microhabitat) and hatching success. We predicted that PRL would be higher in older females, those in better condition or incubating larger clutches. We also predicted that females would reduce parental effort when nesting under challenging environmental conditions (high predation pressure or poor climatic conditions), translated into reduced baseline PRL levels. We also explored how variation in PRL levels, female characteristics and environmental parameters were related to hatching success. Following our predictions, PRL levels were positively associated with body condition and female age (before showing a senescent decline in the oldest breeders). However, we did not observe any population-level or individual-level reduction in PRL levels in response to increasing predation pressure. Population-level baseline PRL levels instead increased over the study period, coincident with rising predation threat, but also increasing female body condition and age. While we did not provide evidence for a direct association between baseline PRL levels and predation risk, our results support the idea that elevated baseline PRL levels promote hatching success under internal constraints (in young, inexperienced, breeders or those incubating a large clutch) or constraining environmental conditions (during years of high predation pressure or poor climatic and foraging conditions). Finally, the low repeatability of baseline PRL levels and high interannual variability highlight considerable within-individual flexibility in baseline PRL levels. Further research should explore flexibility in parental effort to changing environmental conditions, focusing on both baseline and stress-induced PRL levels.

Three years of CO2, CH4 and N2O fluxes from different sheepfolds in a semiarid steppe region, China

To better assess greenhouse gas (GHG) emissions from livestock folds in semi-arid steppe zones and reduce uncertainties in regional and national GHG emission inventories, we measured the fluxes of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) from sheepfolds under contrasting management regimes (i.e., summer sheepfolds under continuous and rotational grazing strategies and the winter sheepfold) for 3 consecutive years. Our results showed that these GHG fluxes had high intra-annual and interannual variations, emphasizing the importance of multi-year measurement for achieving temporally representative annual budgets. Sheep presence and temperature appeared to be the key factors driving CH4, CO2 and N2O fluxes from sheepfolds, e.g., higher GHG emissions usually occurred in seasons with sheep presence. However, the sheepfold type exerted a distinct influence on the temperature sensitivity of GHG fluxes, i.e., the Q10 values for GHG fluxes were generally higher in summer sheepfolds than in winter sheepfold. The annual CH4, CO2 and N2O emissions for the 3 sheepfolds were estimated to be 1.5–16.5 kg C ha−1 yr−1 (or 1.9–2.6 g C yr−1sheep−1), 8.6–16.0 t C ha−1 yr−1 (or 5.1–6.6 kg C yr−1sheep−1) and 28.3–41.9 kg N ha−1 yr−1 (or 19.0–26.8 g N yr−1sheep−1), respectively. Averaging across the 3 years, the annual net GHG emissions (CH4 + CO2 + N2O) for all sheepfolds ranged from 47 to 71 t CO2-eq ha−1 yr−1 (or 27–36 kg CO2-eq yr−1 sheep−1), of which CO2 and N2O emissions contributed the most; moreover, the annual net GHG emissions had no significant differences between sheepfold types or grazing strategies. Given that local steppe soils have a lower magnitude of soil respiration (CO2) and N2O emissions and are also net sink for atmospheric CH4, the sheepfold sites in this region are undoubtedly one of the significant hotspots for GHG emissions and could be key areas to focus mitigation action.

Interannual variability of summertime formaldehyde (HCHO) vertical column density and its main drivers at northern high latitudes

Abstract. The northern high latitudes (50–90° N, mostly including boreal-forest and tundra ecosystems) have been undergoing rapid climate and ecological changes over recent decades, leading to significant variations in volatile organic compounds (VOC) emissions from biogenic and biomass burning sources. Formaldehyde (HCHO) is an indicator of VOC emissions, but the interannual variability of HCHO and its main drivers over the region remains unclear. In this study, we use the GEOS-Chem chemical transport model and satellite retrievals from the Ozone Monitoring Instrument (OMI) and the Ozone Mapping and Profiler Suite (OMPS) to examine the interannual variability of HCHO vertical column density (VCD) during the summer seasons spanning from 2005 to 2019. Our results show that, in 2005–2019 summers, wildfires contributed 75 %–90 % of the interannual variability of HCHO VCD over Siberia, Alaska and northern Canada, while biogenic emissions and background methane oxidation account for ∼ 90 % of HCHO interannual variability over eastern Europe. We find that monthly solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2), an efficient proxy for plant photosynthesis, shows a good linear relationship (R= 0.6–0.7) with the modeled biogenic HCHO column (dVCDBio,GC) in eastern Europe, Siberia, Alaska and northern Canada, indicating the coupling between SIF and biogenic VOC emissions over the four domains on a monthly scale. In Alaska, Siberia and northern Canada, SIF and dVCDBio,GC both show relatively lower interannual variabilities (SIF: CV = 1 %–9 %, dVCDBio,GC: CV = 1 %–2 %; note that CV stands for coefficient of variation) in comparison to wildfire-induced HCHO (CV = 8 %–13 %), suggesting that the high interannual variabilities of OMI HCHO VCD (CV = 10 %–16 %) in these domains are likely driven by wildfires instead of biogenic emissions.

Spatiotemporal variability of the rainy season in the Yucatan Peninsula

AbstractThe rainfall regime is a critical factor in the Yucatan Peninsula, as the spatial and multiannual variability of rainfall is a major concern, particularly for crops. Variability in the rainy season was examined considering the onset and demise of the annual rainy season, the total rain volume, the rainfall season duration and the intense precipitation events recorded in meteorological stations (1978–2020). We analysed individual time series and calculated the long‐term trend. Additionally, we explored the relationship between each summer rainfall characteristic and several oceanographic indices using multivariate techniques. We also developed a Trans‐Isthmic Index from the relationship between the El Niño–Southern Oscillation and the Atlantic Multidecadal Oscillation. This index allows for determining the effect of the overall influence of the ocean on climate. The timeseries analysis revealed a high interannual variability and long‐term positive trends concerning the duration of the rainy season with earlier onset and later demise, and the total rainfall volume and also a positive trend for the occurrence of heavy precipitation suggesting a shift in intra‐annual patterns. Spatially, the analysis revealed clusters of stations with a similar variation, probably related to the AMO, NIÑO3.4 or TII indices. The spatial pattern was confirmed by analysing CHIRPS gridded precipitation data. Our results show that wetter conditions are associated with lower temperatures in the equatorial Pacific and warmer conditions in the Atlantic.

Spatial and temporal variability in larval connectivity of North Sea plaice Pleuronectes platessa between spawning grounds and coastal European nurseries

Annual year-class strength in North Sea plaice Pleuronectes platessa Linnaeus, 1758 appears to be determined by the connectivity of eggs and larvae between open-sea spawning grounds and coastal nursery areas. Hydrodynamic modelling studies indicate that coastal nurseries can be supplied with larvae from multiple offshore spawning locations. To verify this, we compared relative size-frequency distributions of demersal juvenile plaice just after larval immigration between coastal nurseries as a proxy for larval origin. During 12 yr, up to 21 stations in the Dutch coastal zone and international Wadden Sea were visited and sampled at the beginning of May at the end of larval immigration. The results of the size-frequency analysis showed 2 distinct clusters of stations. The first cluster included all stations along the Dutch coast and Wadden Sea up to Wangerooge in the German East Frisian Wadden Sea, most likely dominated by supply from spawning grounds in the Southern Bight, with contributions from the English Channel and the Dogger Bank. The second cluster included all stations in the North Frisian Wadden Sea, most likely supplied from spawning grounds in the German Bight and near the Dogger Bank. The higher interannual variability in the first cluster might be caused by a larger variability in the relative contributions from multiple spawning areas (English Channel, Southern Bight, Dogger Bank). The comparison of the relative size-frequency distributions at the end of larval immigration introduced in this study may also be a useful tool for the validation of connectivity modelling studies in other areas and for other species.

Regionalization of the Onset and Offset of the Rainy Season in Senegal Using Kohonen Self-Organizing Maps

This study explores the spatiotemporal variability of the onset, end, and duration of the rainy season in Senegal. These phenological parameters, crucial for agricultural planning in West Africa, exhibit high interannual and spatial variability linked to precipitation. The objective is to detect and spatially classify these indices across Senegal using different approaches. Daily precipitation data and ERA5 reanalyses from 1981 to 2018 were utilized. The employed method enables the detection of key dates. Subsequently, the Kohonen algorithm spatially classifies these indices on topological maps. The results indicate a meridional gradient of the onset, progressively later from the southeast to the northwest, whereas the end follows a north–south gradient. The duration varies from 45 days in the north to 150 days in the south. The use of self-organizing maps allows for classifying the onset, end, and duration of the season into four zones for the onset and end, and three zones for the duration of the season. They highlight the interannual irregularity of transitions, with both early and late years. The dynamic analysis underscores the complex influence of atmospheric circulation fields, notably emphasizing the importance of low-level monsoon flux. These findings have tangible implications for improving seasonal forecasts and agricultural activity planning in Senegal. They provide information on the onset, end, and duration classes for each specific zone, which can be valuable for planning crops adapted to each region.

United States crop conditions: 1986–2022

AbstractUSDA National Agricultural Statistics Service general crop condition data are used to quantify the relationship between crop conditions and yield, condition tendencies during the growing season, how conditions vary across the United States, and how conditions have trended for the 1986–2022 period for a variety of crops: barley (Hordeum vulgare L.), corn (Zea mays L.), cotton (Gossypium hirsutum L.), oats (Avena sativa L.), peanuts (Arachis hypogaea L.), rice (Oryza sativa L.), sorghum (Sorghum bicolor L.), soybeans (Glycine max L.), and spring and winter wheat (Triticum aestivum L.). Crop conditions are a statistically significant covariate (90% significance level) for yield and can be used in practice on a weekly basis. This becomes increasingly important as, historically, crop conditions not only deteriorated on average, but also became more variable throughout the growing season. Spatial differences in conditions, variability, and trends in condition ratings and variability are caused by crop selection, regional climate, and management practices such as irrigation. Crop conditions were poorest and had the highest interannual variability across the southern Great Plains region. Since 1986, crop conditions in the Great Plains region have deteriorated significantly, while variability has increased. Meanwhile, crop conditions across the southwestern and southeastern United States have significantly improved, while interannual variability in conditions has experienced robust declines. Given climatic and hydrologic trends, to maintain a positive trend in yield, continued advancements in production management and technology are required. Critical decisions regarding real‐time or future management can be supplemented by using crop condition risk assessment information along with actively monitoring weekly conditions.

Surface and subsurface flow of a glacierised catchment in the cold-arid region of Ladakh, Trans-Himalaya

Hydrological assessments of high-altitude catchments in Trans-Himalayan Ladakh are necessary for a better understanding of water availability in the context of irrigated cultivation under conditions of insufficient quantitative information on cryospheric meltwater discharge. In this study, an integrated spatially distributed temperature index model and a coupled surface/subsurface flow model were used to simulate daily, seasonal, and annual surface and subsurface flows to assess the proportion of corresponding source contributors from the Stok catchment. Snow and glacier meltwater discharge secures irrigated agriculture of more than 300 households in this catchment. The models were forced by temperature, precipitation, ice- and snow-covered areas at daily time steps with calibration (2019; 108 days) and validation (2018; 93 days) against the observed discharge. The simulated discharge shows a good agreement with the observed discharge with R2 and RMSE of 0.8 (p < 0.01) and 0.6 m3/s, respectively. The results between 2003 and 2019 show that the snowmelt contribution to the total annual discharge is largest with 65 %, followed by glacier melt and rainfall contributions of approximately 19 % and 16 %, respectively. A reduction in glacierised areas by 4.2 % was observed while snow-covered areas showed high inter-annual variation. Simulated subsurface flow makes up 62 % (mean = 37.2 × 106 m3) of the total discharge with less inter-annual variation. The simulation suggests that while surface flow ceases during the winter period and peaks in August, the annualized mean flow amounts to ∼23.7 × 106 m3. More than 50 % of the melt occurs in the summer months of June, July and August, when the intensity of snowmelt, ice melt, and rainfall reach its maximum. The findings of this study on meltwater availability and surface/subsurface flow is important for irrigated agriculture of Stok village on a local scale, and it might also help to better understand socio-hydrological dynamics and situations of water scarcity in the wider cold-arid region of Ladakh.

A Multi-Sensor Approach to Characterize Winter Water-Level Drawdown Patterns in Lakes

Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.

Summer solstice orchestrates the subcontinental-scale synchrony of mast seeding.

High interannual variation in seed production in perennial plants can be synchronized at subcontinental scales with wide consequences for ecosystem functioning, but how such synchrony is generated is unclear1-3. We investigated the factors contributing to masting synchrony in European beech (Fagus sylvatica), which extends to a geographic range of 2,000 km. Maximizing masting synchrony via spatial weather coordination, known as the Moran effect, requires a simultaneous response to weather conditions across distant populations. A celestial cue that occurs simultaneously across the entire hemisphere is the longest day (the summer solstice). We show that European beech abruptly opens its temperature-sensing window on the solstice, and hence widely separated populations all start responding to weather signals in the same week. This celestial 'starting gun' generates ecological events with high spatial synchrony across the continent.