Mean Daily Air Temperature Research Articles

Airborne fungal spore load and season timing in the Central and Eastern Black Sea region of Turkey explained by climate conditions and land use

The widespread fungal aeroplankton comprises numerous plant pathogens and allergenic components. Here, we present the first study describing the airborne spore composition in the Central and Eastern Black Sea region of Turkey against the background of meteorological variables and land use. This region is climatically diverse and there are large differences in altitude and land cover. Using multivariate statistical techniques, we assessed the combined effects of the main weather factors on the airborne spore count and temporal patterns of spore season for 30 fungal taxa in five provinces with three different climate types. Moreover, we combined meteorological and land use data to search for potential source areas of airborne spores recorded at the study sites.Spore season start and peak dates substantially varied between sites (maximally 130 days between western and eastern part of the study area – for Boletus mean start date), however for most of the taxa investigated the season ended at a similar time at different sites. All the meteorological variables included in redundancy analysis accounted for 10.8–48.9% of the total variance in the fungal spore data, with the highest value in sites with continental climate. Daily mean air temperature was the most important variable and significantly correlated with the daily count of all the spore types (0.11 ≤ rs ≤ 0.84). However, when temperature range was calculated for three large, percentile-based spore count clusters it turned out that between 17% (subtropical climate) and 56% (continental climate) of the taxa showed no difference in temperature between low and high counts. Finally, based on combination of wind conditions and land use data we identified grasslands, croplands and coniferous forests as the main potential sources of fungal spores in the study area, and suggested that spores from the forests may be transported over longer distances than from open areas.

Estimation of the influences of air density on wind energy assessment: A case study from China

The distribution characteristics of air density in China were modelled on a high-spatial-resolution scale (4000 m × 4000 m) and the absolute error was calculated by comparing with constant air density. Daily mean air pressure and air temperature time series from 2403 meteorological stations for the period 1998–2018 were used to compute air density at the hub height of a typical wind turbine. The statistical air density distributions were applied in the study area and the influence of elevation on changes in air density was quantified. Mean annual energy production in the study area was estimated using extrapolated parameters of the Weibull distribution and adjusted power curves. Compared with energy production under the assumption of constant air density, the results demonstrate that over 33% of the study area had a mean air density less than 1.000 kg/m3 due to the effect of elevation. Values as low as 0.347 kg/m3 were observed in the Tibetan Plateau. Energy production was underestimated (0.04–1.76%) in the north-east and north when the variability of air density was considered, and wind resources were overestimated by more than 20% in approximately 13.1% of the study area. The results of this study will contribute to identifying suitable locations for developing the wind energy industry. The influence of air density on wind energy production at different elevations can now be quantified, allowing more efficient utilization of wind resources.

Salinity has little effect on photosynthetic and respiratory responses to seasonal temperature changes in black mangrove (Avicennia germinans) seedlings.

Temperature and salinity are important regulators of mangrove range limits and productivity, but the physiological responses of mangroves to the interactive effects of temperature and salinity remain uncertain. We tested the hypothesis that salinity alters photosynthetic responses to seasonal changes in temperature and vapor pressure deficit (D), as well as thermal acclimation _of leaf respiration in black mangrove (Avicennia germinans). To test this hypothesis, we grew seedlings of A. germinans in an outdoor experiment for ~ 12months under four treatments spanning 0 to 55ppt porewater salinity. We repeatedly measured seedling growth and in situ rates of leaf net photosynthesis (Asat) and stomatal conductance to water vapor (gs) at prevailing leaf temperatures, along with estimated rates of Rubisco carboxylation (Vcmax) and electron transport for RuBP regeneration (Jmax), and measured rates of leaf respiration at 25°C (Rarea25). We developed empirical models describing the seasonal response of leaf gas exchange and photosynthetic capacity to leaf temperature and D, and the response of Rarea25 to changes in mean daily air temperature. We tested the effect of salinity on model parameters. Over time, salinity had weak or inconsistent effects on Asat, gs and Rarea25. Salinity also had little effect on the biochemical parameters of photosynthesis (Vcmax, Jmax) and individual measurements of Asat, gs, Vcmax and Jmax showed a similar response to seasonal changes in temperature and D across all salinity treatments. Individual measurements of Rarea25 showed a similar inverse relationship with mean daily air temperature across all salinity treatments. We conclude that photosynthetic responses to seasonal changes in temperature and D, as well as seasonal temperature acclimation of leaf R, are largely consistent across a range of salinities in A. germinans. These results might simplify predictions of photosynthetic and respiratory responses to temperature in young mangroves.

Analysis of changes in thermal growing season and their relationships with atmospheric teleconnection patterns for the Yellow River basin in China

ABSTRACT The thermal growing season (TGS) that strongly correlates with changes in temperature refers to the whole period during which vegetation growth can occur. The daily mean air temperature data of 66meteorological stations located in the Yellow River basin (YRB) from 1960 to 2017 were used to investigate the inter-annual variations in the TGS indices and their relationships with atmospheric teleconnection patterns (ATPs). The thermal growing season start (GSS), thermal growing season end (GSE), and the length of thermal growing season (GSL) were considered. Our results suggested a significant negative trend in GSS, together with a significant positive trend in GSE, leading to a significant extension of GSL by 2.4 day/decade (p<0.01). The spatial patterns in the trends between GSS and GSE were distinct in this study, indicating that the changes in temperature in spring were considerably different from those in autumn. The mutation analysis presented in this study showed that mutation points in GSS, GSE, and GSL overall were observed in 1998, 1996, and 2001, respectively. Furthermore, the TGS indices were influenced by the ATPs discussed in this studyat certain time scales, and the entire research area was affected by multiple climate indices, indicating that other climate indexes always adjusted the influences of one climate index.

Assessment of Soybean Evapotranspiration and Controlled Water Stress Using Traditional and Converted Evapotranspirometers

Evapotranspiration (ETR), reference evapotranspiration (ET0), and seasonal ETR totals were determined for soybean over two growing seasons, at Keszthely, Hungary, using traditionally operated and converted evapotranspirometers. The study aimed to document the plant–water response of two soybean varieties (Sinara: Sin; Sigalia: Sig) which have different water demands. Three water supply treatments were tested: unlimited (WW) watering, 50% of crop water requirement (RO), and rainfed (P). Reconstructed evapotranspirometers allowed crop water deprivation to be simulated under field conditions. ETR sums were higher during the cooler 2017 than in the warmer 2018, calling attention to the importance of being informed about more detailed meteorological variables other than monthly (seasonal) means. In addition to variation in daily mean air temperatures (Ta), maximum Ta played a key role in determining ETR under naturally occurring extreme weather conditions in 2018. Irrespective of the variety, daily mean ETR was on average 65–75% greater than in the water-stress treatment. Unexpectedly, water stress-tolerant Sin used slightly more water than Sig, which was bred for standard weather conditions. Measured mean ETR was as much as 10% higher than derived ET0 rates, causing crop coefficient to exceed 1.0 during flowering. Careful selection of the soybean variety when practicing water-saving management may lead to more efficient variety improvement in a breeding program. It may also be important for soybean producers and farmers to adopt the best variety, aiming to decrease the use of irrigation water to increase seed yield.

The Interplay between Atmospheric Conditions and Grape Berry Quality Parameters in Portugal

The atmospheric conditions are a strong modulator of grape berry composition, but further research is required to better understand this relationship, which is particularly pertinent under the context of climate change. The present study assesses the relationship between interannual variability in atmospheric conditions (mean, maximum and minimum air temperatures and precipitation totals) on grape berry quality attributes in three main Portuguese wine regions—Douro, Dão and Alentejo—and targets two major varieties growing in Portugal (cv. Touriga Nacional and cv. Aragonez/Tempranillo). Berry weight, titratable acidity (TA), pH, potential alcohol (PA), anthocyanins and total phenols index (TPI) data, collected two to three weeks after the end of the veraison until technological maturity, since 1999 in Douro, 2004 in Alentejo and 2008 in Dão, were selected. Meteorological data were obtained from both automatic weather stations and a climatic database defined at a very-high-resolution grid (&lt;1 km) (PTHRES). The influence of daily mean, maximum and minimum air temperatures (November–October) and precipitation totals (April to June and July to September) on the above-mentioned berry quality parameters were first explored to identify the months/periods more influential to grape berry composition. Different statistical approaches were subsequently carried out to explore in greater detail these relationships. At technological maturity, temperature was negatively correlated to berry weight, titratable acidity, anthocyanins and TPI, but was positively correlated to pH and potential alcohol. Moreover, lowest levels of berry weight and TA (and highest levels of pH) were more frequent in warmer regions, while the opposite was seen in the cooler regions. PA, TPI and anthocyanins at maturity did not show a clear trend across regions. In addition, the maturation parameters of each site were grouped into two clusters—years where the maturation parameter is higher (cluster 1) and years where it is lower (cluster 2)—and significant differences in monthly mean temperatures between clusters were found. Overall, temperatures at veraison and maturation periods (June–August) were more influential in determining grape berry composition at harvest. The influence of precipitation was dependent on location and variety. The results also suggested that berry composition in Alentejo is more sensitive to atmospheric variability, while Aragonez seems more resilient than Touriga Nacional. These outcomes are based on a systematized and unprecedentedly large grape berry quality database in Portugal and provided the grounds for the development of grape quality forecast models, either to be used operationally in each vintage or for assessing potential modifications in berry composition in response to changing climates.

Breaking rice yield barrier with the ratooning method under changing climatic conditions: A paradigm shift in rice‐cropping systems in southwestern Japan

AbstractRice (Oryza sativa L.) ratooning has recently attracted attention since it possesses higher yield potential compared to conventional rice‐growing methods; however, it requires a longer growing season. Field experiments were conducted in southwestern Japan in 2017 and 2018 to determine the effect of harvest time (early and normal) and cutting height (high and low) of the first crop on grain yield of the first and second crops using a high‐yielding line under high N application and high air temperature conditions in rice ratooning. The highest total yield of the first and second crops was produced when the first crop was harvested at the normal time with the high cutting height in 2018. This yield reached 14.4 t ha−1, which is equivalent to threefold the average yield achieved by Japanese farmers. Plants harvested at the high cutting height produced higher grain yield of the second crop because of the larger spikelet number m−2 and better grain filling using abundant leaf area index (LAI) and nonstructural carbohydrate (NSC) in the stubble. In southwestern Japan, the daily mean air temperatures during the early growth stage of the first crop and the late ripening stage of the second crop have been increasing over time. Our results suggest that increasing LAI and NSC in the stubble by harvesting at the normal time harvest with the high cutting height under high level N application conditions may provide an adaptive strategy to increase yield with a rice‐ratooning system as growing seasons extend due to global climate change.

Studies on Determination of Critical Growing Degree Days, Base Temperature (Tb) and Response of Temperature Variations on Flowering and Yield per se in Cowpea [Vigna unguiculata (L.) Walp.

Growing degree days (GDD) or heat units accumulation is the major factor that affects the dry matter production in the plants. In the present investigation eleven genotypes were used to screen for temperature insensitivity through staggered plantings across the seasons in a year. Days to flowering initiation was recorded and base temperature (Tb) was determined using mean daily air temperature (MAT). GDD of individual genotypes was estimated using base temperatures of particular genotypes. It was observed that the GDD, days to flowering initiation and yield were exhibited high variation across the seasons, the flowering time from days to planting (FTDAP) registered significant negative correlation with GDD and MAT and positively correlated with yield. Whereas GDD is positively correlated with MAT and negatively correlated with yield. Here three genotypes namely, IC202926, IC198326 and IC257428 were identified as temperature insensitive genotypes as their performances were comparable across the seasons without much fluctuations.

Detection of year-to-year spring and autumn bio-meteorological variations in siberian ecosystems

Detecting year-to-year variability of the timing of the start of the growing season (SGS) and the end of the growing season (EGS) is an important task in accurately evaluating ecosystem functions and services under climate change in vulnerable ecosystems in Siberia. We constructed a degree-day model for estimating the SGS and EGS dates at a deciduous coniferous forest site in Siberia, based on the relationship between daily phenology images and daily mean air temperature between 2013 and 2017. We tested the model by applying it to another similar site in Siberia. The model successfully estimated the SGS and EGS dates from the cumulative effective temperature, derived from daily mean air temperatures exceeding a best-fit threshold value of 2 °C (root mean square error: RMSE = 1.00) and falling below a best-fit threshold value of 1 °C, 0 °C or −1 °C (RMSE = 2.29), respectively. The modelled SGS and EGS dates closely matched the observed dates of leaf flush and leaf fall, respectively, in the larch overstory.

AIR TEMPERATURE AND OZONE CONCENTRATION AS RISK FACTORS FOR LIFE-THREATENING CARDIOVASCULAR CONDITIONS IN SOUTHERN RUSSIA

Heat waves and increasing tropospheric ozone concentration accompanying global warming are recognized as risk factors for public health. The aim of the study was to assess the associations between atmospheric air temperature, ozone concentration as risk factors for life-threatening cardiovascular conditions in Southern Russia. Methods. An ecological study was performed. The number of ambulance calls (the monthly average for 2017 and daily for January, April, July and October) for acute cerebrovascular conditions, hypertensive crises, and acute myocardial infarction were analyzed. Data on mean daily air temperature was obtained from the weather reports (https://rp5. ru). The ozone concentration was determined by the optical method using an automatic gas analyzer at the environmental monitoring station in the Karadagsky State Nature Reserve. Spearman nonparametric correlation analysis was applied to study associations between the outcomes and the selected environmental conditions. Results. During 2017, the average daily concentration of ozone in the surface layer of the atmosphere exceeded the norm by 50-150 %s. The largest number of significant correlations between all recorded cardiovascular life-threatening conditions and air temperature and ozone concentration was detected in July (0,38 &lt; rs &lt; 0,79; all p-values &lt; 0,05). In other seasons of the year, this correlation was somewhat weaker (0,43 &lt; rs &lt; 0,47; all p-values &lt; 0,05). Associations between hypertensive crises and myocardial infarction and average daily temperature were found in January. An association between myocardial infarction and the surface ozone concentration was observed in October. Conclusion. The maximum number of emergency conditions occurring in the summer months may be associated with the synergistic effects of of high levels of ozone and air temperature.

The influence of an in-stream thermal gradient on chironomid emergence during winter

The River Continuum Concept (RCC) predicts that aquatic insect communities undergo predictable changes in composition from headwaters to downstream regions relative to changes in habitat. An important aspect of this change is stream temperature. Studies have been conducted on thermal partitioning of chironomids within the RCC and across streams with different thermal regimes; however, few have focused on effects of fine-scale within-stream temperature variability on winter chironomid composition. Our objectives were to characterize thermal heterogeneity within a small headwater trout stream during winter using slope and Y-intercepts from linear regressions of air–water temperatures and to document patterns of chironomid emergence and taxonomic composition relative to mean water temperatures, regression slopes, and Y-intercepts along stream length. We developed air–water temperature regressions using mean daily air and water temperatures for three headwater trout streams. We assessed similarity in taxonomic composition across sites by collecting and analyzing chironomid surface floating pupal exuviae. We demonstrate that regressions of mean daily air and water temperatures can characterize fine-scale thermal heterogeneity within and among headwater trout streams during winter. We also demonstrate that chironomid taxonomic composition varied at spatial scales substantially less than a kilometer within a stream that possessed significant thermal heterogeneity.

The effect of environmental drivers on summer spatial variability of water temperature in Polish lowland watercourses

Thermal properties of streams and rivers, due to significant impact on biota and other physico-chemical water properties, were broadly recognized in hydrological literature last years. Nevertheless, water temperature of temperate lowland watercourses has received relatively small attention, despite the development of new measurement devices and techniques. Thus, the current study attempt to document spatial water temperature variability of lowland watercourses and examined the effects of environmental drivers on their thermal heterogeneity. For this purpose, water temperature was monitored from May to October 2017 with the use of digital data loggers in 20 sites located in central Poland, representing two spatial scales—main rivers (MR) and tributaries (TR). On the basis of the measurement data, statistical distribution of magnitude and variability water temperature parameters was presented, whereas cluster analysis (Ward method) was used to group sites similar in their thermal properties. Furthermore, selected catchment and channel metrics were computed using GIS software for each site, which in combine with the principal component analysis allowed to assess the effect of such metrics on thermal parameters. Then, to support the findings of PCA and assess meteorological dependence of the water temperature, linear regression between daily mean water and air temperatures was performed. The results indicate that in terms of magnitude and variability parameters TR scale sites demonstrated clear thermal heterogeneity, particularly in comparison to MR sites, characterized by similar thermal properties even between separate, independent catchments; in such sites the highest thermal contrast were related to anthropogenic impacts, such as reservoir releases and sewage inflows. Clear longitudinal zonation of water temperature parameters was found as presented by first two principal components, which was related to transition from small headwater sites to the largest, downstream catchments, driven mainly by changes of catchment area, mean slope, and width:depth ratio. The increase of the catchment area also resulted in a shift in linear regression parameters, which suggested higher meteorological control in the downstream direction and simultaneously, lower impacts of groundwater inflows. The obtained results provide new insight into lowland watercourses temperature behavior, being of primary significance in the context of fisheries and environmental management, particularly in the face of climate warming and increasing anthropopressure.

Causes of leaf reddening in subterranean clover cultivars

ABSTRACT Subterranean clover (Trifolium subterraneum L.) is an important legume in dryland hill pastures in New Zealand. In this environment plants adapt to low temperatures (<10°C) through a series of physiological mechanisms, such as the foliar biosynthesis of pigments, to protect vulnerable tissues. The biosynthesis of anthocyanins is one of the most important physiological responses for cold acclimation in plants. In this study we investigated anthocyanin concentration by accessing leaf reddening in 14 cultivars from three subspecies (yanninicum, brachycalycinum or subterraneum) during winter growth. The effect of anthocyanin production on spring herbage productivity was further quantified in the field and in the glasshouse. Leaf concentration of anthocyanins increased 10-fold as a consequence of cold exposure (mean daily air temperature was below ∼7°C) in red leaves compared with green leaves. There were large cultivar differences with yanninicum ssp. cultivars having the highest leaf reddening at >60%. Lowest values were observed in ‘Antas’ (brachycalycinum ssp.) and ‘Denmark’ (subterraneum spp.) at <20% of leaf reddening. Australian subterraneum ssp. cultivars had 13% higher leaf reddening values than Italian cultivars. Leaf reddening was a temporary, cold induced, anthocyanin response that did not affect subsequent subterranean clover plant growth.

On the Analysis of the Performance of WRF and NICAM in a Hyperarid Environment

Abstract The Weather Research and Forecasting (WRF) Model and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) are forced with the Global Forecast System (GFS) data and run over the United Arab Emirates (UAE) for two 4-day periods: one in the cold season (16–18 December 2017) and another in the warm season (13–15 April 2018). The models’ performance is evaluated against four observational datasets: weather station observations, eddy-covariance flux measurements at Al Ain, microwave radiometer–derived temperature profile, and twice-daily radiosonde measurements at Abu Dhabi. An overestimation of the daily mean air temperature by 1°–3°C is noticed for both models and periods. This warm bias is attributed to the reduced cloud cover and resulting increased surface downward shortwave radiation flux. A comparison with the eddy-covariance data suggested that both models also underestimate the observed albedo. However, when the models predict heavier amounts of precipitation, they tend to be colder than observations, typically by 2°–3°C. NICAM and WRF overpredict the strength of the near-surface wind speed at all weather stations by roughly 1–3 m s−1, which has been attributed to a poor representation of its subgrid-scale fluctuations and surface drag parameterization. WRF tends to be wetter and NICAM drier than the station observations, possibly because of differences in the cloud microphysics schemes. While the performance of both models for the near-surface fields is comparable, NICAM outperforms WRF in the simulation of vertical profiles of temperature, relative humidity, and wind speed, being able to partially correct some of the biases in the GFS data.

A Synergistic Effect of Blockings on a Persistent Strong Cold Surge in East Asia in January 2018

A persistent strong cold surge occurred in East Asia in late January 2018, causing mean near-surface air temperature in China to hit the second lowest since 1984. Moreover, the daily mean air temperature remained persistently negative for more than 20 days. Here, we find that a synergistic effect of double blockings in Western Europe and North America plays an important accelerating role in the rapid phase transition of Arctic Oscillation and an amplifying role in the strength of cold air preceding to the cold surge outbreaks by the use of an isentropic potential vorticity analysis. In mid-January, an Atlantic mid-latitude anticyclone merged with Western Europe blocking, which led to a strengthening of the blocking. Simultaneously, the Pacific-North American blocking was also significantly strengthened. The two blockings synchronously deeply stretched towards the Arctic, which resulted in, on the one hand, warm and moist air of the Pacific and the Atlantic being excessively transported into the Arctic, and on the other hand, the polar vortex being split and cold air being squeezed southwards and accumulating extensively on the West Siberian Plain. After the breakdown of the double blocking pattern, which lasted for about 10 days, the record-breaking cold surge broke out in East Asia. It was discovered that the synergistic effect of double blockings extending into the Arctic, which is conducive to extreme cold events, has been rapidly increasing in recent years.

Multidecadal water quality deterioration in the largest freshwater lake in China (Poyang Lake): Implications on eutrophication management

Poyang Lake is the largest freshwater lake in China and a globally important wetland with various functions. Exploring the multidecadal trend of water quality and hydroclimatic conditions is important for understanding the adaption of the lake system under the pressure from multiple anthropogenic and meteorological stressors. The present study applied the Mann–Kendall trend analysis and Pettitt test to detect the trend and breakpoints of hydroclimatic, and water quality parameters (from the 1980s to 2018) and the trend of monthly–seasonal ammonia (NH4-N) and total phosphorus (TP)concentrations (from 2002 to 2018) in Poyang Lake. Results showed that Poyang Lake had undergone a highly significant warming trend from 1980 to 2018, with a warming rate of 0.44 °C/decade in terms of annual daily mean air temperature. The wind speed and water level of the lake presented a highly significant decreasing trend, whereas no notable trend was detected for precipitation variations. The annual mean total nitrogen (TN), NH4-N, TP, and permanganate index (CODMn) concentrations showed significant upward trends from the 1980s to 2018. Remarkable abrupt shifts were detected for TN, NH4-N, and CODMn in around 2003. They were in accordance with the water level breakpoint of the lake, thus implying the important role of hydrological conditions in water quality variations in floodplain lakes. A significant increasing trend has been detected for Chl-a variations during wet season from 2008 to 2018, which could be attributed to the increasing trend of nutrient concentration during the nutrient-limited phase of Poyang Lake. These hydroclimatic and water quality trends suggest a high risk of increasing phytoplankton growth in Poyang Lake. This study thus emphasizes the need for adaptive lake eutrophication management for floodplain lakes, particularly the consideration of the strong trade-off and synergies between hydroclimatic conditions and water quality variations.

Sezonowe zróżnicowanie temperatury wody na przykładzie wybranych rzek nizinnych Mazowsza = Seasonal differentiation of water temperature on the example of lowland Mazovian rivers

Thermal regime has a critical impact on the lotic environment, as maximum temperature determines the boundaries of the occurrence of aquatic species, seasonal and diurnal water temperature variations affect their bioenergetics, while the timing of specific water temperature values during the year is important in the context of spawning and migrations. However, despite the great importance of water temperature studies in the context of environmental management and fisheries, as well as the development of accurate measurement techniques, such investigations have received relatively limited attention in Poland. The current study attempted to examine the seasonal differentiation of water temperature in lowland rivers. For this purpose, water temperature was recorded from the 1st of May 2015 to the 30th of April 2019 with a temporal resolution of 30-minutes. Digital temperature reorders used to make the measurements were distributed across six sites in Jeziorka, Świder and Utrata catchments located on the Mazovian Lowland and the Southern Podlachia Lowland near Warsaw. The hydrometeorological background of the water temperature monitoring was determined on the basis of data from the Warszawa-Okęcie station and water gauging stations. On the basis of the measurement data, mean, maximum, and minimum monthly water temperatures were calculated and presented on the background of the appropriate air temperature data, while statistical distribution of the 30-minute water temperature, aggregated in a monthly timescale, was presented on the box and whiskers plots. The Ward method was used to group months similar in terms of their thermal conditions, while the Pearson correlation coefficient was applied to evaluate the strength of the relationship between water and air temperature. The results indicate that the seasonal course of water temperature follows the course of air temperature, with the highest mean monthly water temperatures recorded in July, while the lowest in January. Statistical distribution analysis of water temperature in individual months and its grouping by the Ward method allowed to identify two periods characterized by relatively stable thermal conditions and two periods of dynamic changes of water temperature. In contrast to the maximum values of water temperature, which were observed in the summer as a result of intensive solar radiation and low streamflow rates, the greatest variability of water temperature, as indicated by reference to mean daily range and standard deviation, was found in the spring months, i.e. in April and May, while the lowest in winter, from December to February. The relationship between daily mean water temperature and air temperature, established with the use of the Pearson correlation coefficient on a monthly basis, was clearly stronger during the spring increase and the autumn fall of the water temperature, which can be linked with greater vulnerability to atmospheric heat fluxes. A definitely weaker relationship was found in the winter and summer months, when greater importance can be attached to other drivers of stream temperature, like the presence of ice cover, cloudiness, riparian shading, and groundwater inflows.

Seasonal forecasting of daily mean air temperatures using a coupled global climate model and machine learning algorithm for field-scale agricultural management

Seasonal climate forecasting can facilitate sound agricultural decision-making. However, agricultural applications, such as yield prediction and sowing timing, require seasonal forecast data at high temporal (daily) and spatial (field-scale) resolutions. In this study, a hybrid model was proposed that coupled a global climate model (GloSea5GC2) with a regularized extreme learning machine (RELM) to perform seasonal forecasting (up to 90 days) of daily mean air temperatures at the field scale. Twenty candidate frameworks for the hybrid model were developed by employing different combinations of the transformation scheme, numbers of climate model ensemble members, and learning algorithms. The output blending method was also evaluated. Results showed that the hybrid models demonstrated a good capacity for long-range prediction at the field scale. In particular, the hybrid model (root mean square error, RMSE, 1.02–3.35) demonstrated better predictive skill compared to the climatology model (RMSE 1.61–3.37) at all lead times. When the hybrid model frameworks were compared, the framework which combined centered data with hindcast data, ensemble learning, and the member mean method was the most appropriate. Adopting the centering scheme with hindcast data as RELM input data had the largest influence on hybrid model predictive skill, improving model performance by 3–20% based on RMSE. The output blending method was found to be a suitable alternative when hindcast data were unavailable. The results of this study provide insight into the development of seasonal forecast hybrid models for agro-climatic variables such as minimum and maximum air temperature, relative humidity, and specific humidity at the field scale.

Influence of El Niño and La Niña on coffee yield in the main coffee-producing regions of Brazil

Coffee is the most consumed beverage and one of the most valuable commodities worldwide. The crop is very sensitive to meteorological elements, mainly at the microclimatic scale, such as solar radiation, air temperature, and precipitation. El Nino and La Nina are atmospheric phenomena characterized by temperature anomalies in tropical areas of the Pacific Ocean, and changing wind and precipitation patterns in tropical and mid-latitude regions. For these reasons, the objective of this study was to evaluate the influence of El Nino and La Nina on Coffea arabica and canephora yields in the states and localities of the main Brazilian coffee-producing regions. We considered the states of Minas Gerais, Sao Paulo, Parana, Bahia, Espirito Santo, and Rondonia. The yield data were obtained from 79 localities for the period 2002–2017 from the Brazilian Institute of Geography. Daily mean air temperature and precipitation were obtained from the NASA-POWER platform and the quarterly registry of the value of the ONI index was obtained from the National Oceanic and Atmospheric Administration. El Nino tended to decrease precipitation, and increase temperature and water deficits for all Brazilian coffee regions. Neutral and La Nina years were climatically similar. Climatic elements tended to be less variable and favor high coffee yields during El Nino years at national, regional, and local scales. The yield increase occurred because the hottest days of EN happen in the first quarter of the phenological cycle, when the crop is normally in the vegetative phase, favoring the generation of new branches. The highest historical yields were generally correlated with the occurrence of El Nino.

Evaluation and machine learning improvement of global hydrological model-based flood simulations

A warmer climate is expected to accelerate global hydrological cycle, causing more intense precipitation and floods. Despite recent progress in global flood risk assessment, the accuracy and improvement of global hydrological models (GHMs)-based flood simulation is insufficient for most applications. Here we compared flood simulations from five GHMs under the Inter-Sectoral Impact Model Intercomparison Project 2a (ISIMIP2a) protocol, against those calculated from 1032 gauging stations in the Global Streamflow Indices and Metadata Archive for the historical period 1971–2010. A machine learning approach, namely the long short-term memory units (LSTM) was adopted to improve the GHMs-based flood simulations within a hybrid physics- machine learning approach (using basin-averaged daily mean air temperature, precipitation, wind speed and the simulated daily discharge from GHMs-CaMa-Flood model chain as the inputs of LSTM, and observed daily discharge as the output value). We found that the GHMs perform reasonably well in terms of amplitude of peak discharge but are relatively poor in terms of their timing. The performance indicated great discrepancy under different climate zones. The large difference in performance between GHMs and observations reflected that those simulations require improvements. The LSTM used in combination with those GHMs was then shown to drastically improve the performance of global flood simulations (especially in terms of amplitude of peak discharge), suggesting that the combination of classical flood simulation and machine learning techniques might be a way forward for more robust and confident flood risk assessment.