Evaporation from a large lowland reservoir – observed dynamics during a warm summer

Evaporation forms a large loss term in the water balance of inland water bodies. During summer seasons, which are projected to become warmer with more severe and prolonged periods of drought, the combination of high evaporation rates and increasing demand on freshwater resources forms a challenge for water managers. Correct parameterisation of open water evaporation is crucial to include in operational hydrological models to make well supported predictions of the loss of water through evaporation. Here, we aim to study the controls on open water evaporation of a large lowland reservoir in the Netherlands. To this end, we analyse the dynamics of open water evaporation at two locations, i.e. Stavoren and Trintelhaven, at the border of Lake IJssel (1100 km2) where eddy covariance systems were installed during the summer seasons of 2019 and 2020. From these measurements we find that wind speed and the vertical vapour pressure gradient, but not available energy, can explain most of the variability of observed hourly open water evaporation. This is in agreement with Dalton's model which is a well-established model often used in oceanographic studies for calculating open water evaporation. At the daily timescale, we find that wind speed and water temperature are the main drivers in Stavoren. These observed driving variables of open water evaporation are used to develop simple data-driven models for both measurement locations. Validation of these models demonstrates that a simple model using only two variables, performs well both at the hourly timescale (R2 = 0.84 in Stavoren, and R2 = 0.67 in Trintelhaven), and at the daily timescale (R2 = 0.72 in Stavoren, and R2 = 0.51 in Trintelhaven). Using only routinely measured meteorological variables leads to well performing simple data-driven models at hourly (R2 = 0.78 in Stavoren, and R2 = 0.51 in Trintelhaven) and daily (R2 = 0.85 in Stavoren, and R2 = 0.43 in Trintelhaven) timescales. These results for the summer periods show that global radiation is not directly coupled to open water evaporation at the hourly or even daily timescale, but rather wind speed and vertical gradient of vapour pressure are variables that explain most of the variance of open water evaporation. However, when we extend the time series to a complete year, we find a distinct yearly cycle reflecting the yearly dynamics of global radiation. We find that the commonly used model of Penman (1948) produces results that resemble the yearly cycle of observed evaporation. However, at the diurnal scale estimated evaporation using Penman’s model disagrees with observed evaporation. Therefore, using the Penman equation to model open water evaporation for shorter periods of time is questioned. We would like to stress the importance of including the correct drivers in the parameterization of open water evaporation in hydrological models to adequately represent the role of evaporation in the surface-atmosphere interaction of inland water bodies.

Review of The Method of Valuation of The SDR—Proposed Extension of The Valuation Of The SDR Basket and Modification of The Date Of Effect of A New Basket

On March 5, 2021, the IMF’s Executive Board approved an extension of the current Special Drawing Right (SDR) valuation basket by ten months from September 30, 2021 to July 31, 2022. The IMF normally reviews the composition and valuation of the SDR basket every five years. The extension effectively resets the five-yearly cycle of SDR valuation reviews, with the next review to be completed by mid-2022 and the new basket becoming effective on August 1, 2022. The ten-month extension contributes to the Fund’s ongoing efforts to prioritize work during the COVID-19 crisis and allows for a more suitable effectiveness date of the new basket, which does not coincide with some major markets being closed. The approved extension, as well as effectiveness date of a new basket, is intended to facilitate the continued smooth functioning of SDR-related operations.

The Importance of Time and Place: Nutrient Composition and Utilization of Seasonal Pollens by European Honey Bees (Apis mellifera L.)

Honey bee colonies have a yearly cycle that is supported nutritionally by the seasonal progression of flowering plants. In the spring, colonies grow by rearing brood, but in the fall, brood rearing declines in preparation for overwintering. Depending on where colonies are located, the yearly cycle can differ especially in overwintering activities. In temperate climates of Europe and North America, colonies reduce or end brood rearing in the fall while in warmer climates bees can rear brood and forage throughout the year. To test the hypothesis that nutrients available in seasonal pollens and honey bee responses to them can differ we analyzed pollen in the spring and fall collected by colonies in environments where brood rearing either stops in the fall (Iowa) or continues through the winter (Arizona). We fed both types of pollen to worker offspring of queens that emerged and open mated in each type of environment. We measured physiological responses to test if they differed depending on the location and season when the pollen was collected and the queen line of the workers that consumed it. Specifically, we measured pollen and protein consumption, gene expression levels (hex 70, hex 110, and vg) and hypopharyngeal gland (HPG) development. We found differences in macronutrient content and amino and fatty acids between spring and fall pollens from the same location and differences in nutrient content between locations during the same season. We also detected queen type and seasonal effects in HPG size and differences in gene expression between bees consuming spring vs. fall pollen with larger HPG and higher gene expression levels in those consuming spring pollen. The effects might have emerged from the seasonal differences in nutritional content of the pollens and genetic factors associated with the queen lines we used.

Kathleen Black, et al., Rhythms of Religious Ritual: The Yearly Cycle of Jews, Christians, and Muslims

Please click the PDF button to access the review.

Australian environmental regulation tensions to worsen

Significance The review runs on a ten-yearly cycle and comes as laws will be drafted to stop environmental activists pressuring firms to boycott mining projects, especially those with large carbon footprints. Impacts Fossil fuel miners will find it increasingly hard to secure project financing, as consumer campaigns target banks. Reforming conservation laws will be frustrated by the widening gap between environmentalists and business groups. Long delays faced by investors for project approvals are partly due to poor governance standards and lack of transparency.

Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia

Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorization (PMF) model. This approach can estimate the factor-related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions, though, can be challenging, and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit suboptimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter samples with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water-soluble organic aerosol mass spectra have provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia: Tallinn (urban), Tartu (suburban) and Kohtla-Järve (KJ; industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu and accounting for 73 % ± 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % ± 8 % and 11 % ± 5 %, respectively), and two other primary OA types lower in mass. A sulfur-containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle, and an oil OA was connected to the oil shale industries in KJ prevailing at this site that comprises 36 % ± 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behavior: a winter oxygenated OA dominated in winter (36 % ± 14 % for KJ, 25 % ± 9 % for Tallinn and 13 % ± 5 % for Tartu) and was correlated with benzoic and phthalic acid, implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % ± 5 % in KJ, 41 % ± 7 % in Tallinn and 35 % ± 7 % in Tartu) and exhibited high correlations with oxidation products of a-pinene-like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA), suggesting a biogenic origin.

Development of a versatile source apportionment analysis based on positive matrix factorization: a case study of the seasonal variation of organic aerosol sources in Estonia

Bootstrap analysis is commonly used to capture the uncertainties of a bilinear receptor model such as the positive matrix factorisation (PMF) model. This approach can estimate the factor related uncertainties and partially assess the rotational ambiguity of the model. The selection of the environmentally plausible solutions though can be challenging and a systematic approach to identify and sort the factors is needed. For this, comparison of the factors between each bootstrap run and the initial PMF output, as well as with externally determined markers, is crucial. As a result, certain solutions that exhibit sub-optimal factor separation should be discarded. The retained solutions would then be used to test the robustness of the PMF output. Meanwhile, analysis of filter sample with the Aerodyne aerosol mass spectrometer and the application of PMF and bootstrap analysis on the bulk water soluble organic aerosol mass spectra has provided insight into the source identification and their uncertainties. Here, we investigated a full yearly cycle of the sources of organic aerosol (OA) at three sites in Estonia, Tallinn (urban), Tartu (suburban) and Kohtla–Järve (KJ, industrial). We identified six OA sources and an inorganic dust factor. The primary OA types included biomass burning, dominant in winter in Tartu accounting for 73 % ± 21 % of the total OA, primary biological OA which was abundant in Tartu and Tallinn in spring (21 % ± 8 % and 11 % ± 5 %, respectively) and two other primary OA types lower in mass. A sulphur containing OA was related to road dust and tire abrasion which exhibited a rather stable yearly cycle and an oil OA was connected to the oil shale industries in KJ prevailing at this site comprising 36 % ± 14 % of the total OA in spring. The secondary OA sources were separated based on their seasonal behaviour: a winter oxygenated OA dominated in winter (36 % ± 14 % for KJ, 25 % ± 9 % for Tallinn and 13 % ± 5 % for Tartu) and was correlated with benzoic and phthalic acid implying an anthropogenic origin. A summer oxygenated OA was the main source of OA in summer at all sites (26 % ± 5 % in KJ, 41 % ± 7 % in Tallinn and 35 % ± 7 % in Tartu) and exhibited high correlations with oxidation products of α-pinene like pinic acid and 3-methyl-1, 2, 3-butanetricarboxylic acid (MBTCA) suggesting a biogenic origin.