Spatial and temporal variation in weather events critical for boreal agriculture: III Frost and winter time fluctuation

In the boreal zone of Europe, differences between the four seasons are considerable. Also, the within-season variation in climatic conditions is substantial. This has many impacts on agriculture that are exceptional when compared to any other environmental zone in Europe. All the meteorological data were based on weather observations made by the Finnish Meteorological Institute. Likelihood (%) for soil frost (≤ 0 °C at 20 cm soil depth) at nine weather stations, and late snow cover (> 1 cm) (10 km × 10 km grid) were estimated for late spring. Probabilities (%) of night frost at the ground surface (March-September) were calculated at nine weather stations by frequencies of the lowest observed night-time temperature: a) between –2 and –5 °C (mild), b) ≤ –5 °C (moderate) and c) ≤ –9 °C (severe). Also, the probabilities (%) of night frost in mid-summer were estimated (≤ –1 °C for at least five hours). Furthermore, a significant shift from mild to below-freezing conditions was measured in winter as a period of at least ten days with daily maximum temperatures above 0°C followed by at least a 10-day period with daily mean temperatures below –5°C in order to characterize high fluctuating winter conditions. All these except late snow cover constitute high risks to crop production. Deep soil frost may postpone sowings, while in advanced springs, night frost may cause damage. For winter crops and perennials, shifts from mild to cold spells outside the growing season are particularly detrimental. Again the data may have many other applications beyond the assessments highlighted in this paper.

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Spatial Predictions of Extreme Wind Speeds over Switzerland Using Generalized Additive Models

Journal of Applied Meteorology and Climatology ◽

10.1175/2010jamc2206.1 ◽

2010 ◽

Vol 49 (9) ◽

pp. 1956-1970 ◽

Cited By ~ 20

Author(s):

Christophe Etienne ◽

Anthony Lehmann ◽

Stéphane Goyette ◽

Juan-Ignacio Lopez-Moreno ◽

Martin Beniston

Keyword(s):

Meteorological Data ◽

Generalized Additive Models ◽

Additive Models ◽

Daily Maximum ◽

Landscape Characteristics ◽

Wind Speeds ◽

Extreme Wind ◽

Weather Stations ◽

Extreme Wind Speeds ◽

Wind Statistics

Abstract The purpose of this work is to present a methodology aimed at predicting extreme wind speeds over Switzerland. Generalized additive models are used to regionalize wind statistics for Swiss weather stations using a number of variables that describe the main physiographical features of the country. This procedure enables one to present the results for Switzerland in the form of a map that provides the 98th percentiles of daily maximum wind speeds (W98) at a 10-m anemometer height for cells with a 50-m grid interval. This investigation comprises three major steps. First, meteorological data recorded by the weather stations was gathered to build local wind statistics at each station. Then, data describing the topographic and landscape characteristics of the country were prepared using geographic information systems (GIS). Third, appropriate regression models were selected to make spatially explicit predictions of extreme wind speeds in Switzerland. The predictions undertaken in this study provide realistic values of the W98. The effects of topography on the results are particularly conspicuous. Wind speeds increase with altitude and are greatest on mountain peaks in the Alps, as would be intuitively expected. Relative errors between observations and model results calculated for the meteorological stations do not exceed 30%, and only 12 out of 70 stations exhibit errors that exceed 20%. The combination of GIS techniques and statistical models used to predict a highly uncertain variable, such as extreme wind speed, yields interesting results that can be extended to other fields, such as the assessment of storm damage on infrastructures.

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Model of the influence of snow cover on soil freezing

Annals of Glaciology ◽

10.3189/172756400781820282 ◽

2000 ◽

Vol 31 ◽

pp. 417-421 ◽

Cited By ~ 7

Author(s):

N. I. Osokin ◽

R. S. Samoylov ◽

A.V. Sosnovskiy ◽

S. A. Sokratov ◽

V. A. Zhidkov

Keyword(s):

Snow Cover ◽

Soil Depth ◽

Negative Temperature ◽

Snow Accumulation ◽

Frozen Soil ◽

Soil Freezing ◽

Soil Frost ◽

Wide Range ◽

Freezing Depth ◽

Good Agreement

AbstractA mathematical model of snow-cover influence on soil freezing, taking into account the phase transition layer, water migration in soil, frost heave and ice-layer formation, has been developed. The modeled results are in good agreement with data observed in natural conditions. The influence of a possible delay between the time of negative temperature establishment in the air and the beginning of snow accumulation, and possible variations of the thermophysical properties of snow cover in the wide range previously reported were investigated by numerical experiments. It was found that the delay could change the frozen-soil depth up to 2–3 times, while different thermophysical characteristics of snow changed the resulting freezing depth 4–5 times.

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Data validation procedures in agricultural meteorology – a prerequisite for their use

Advances in Science and Research ◽

10.5194/asr-6-141-2011 ◽

2011 ◽

Vol 6 (1) ◽

pp. 141-146 ◽

Cited By ~ 5

Author(s):

J. Estévez ◽

P. Gavilán ◽

A. P. García-Marín

Keyword(s):

Quality Control ◽

Crop Production ◽

Dynamic Range ◽

Reference Evapotranspiration ◽

Meteorological Data ◽

Irrigated Agriculture ◽

Data Validation ◽

Control Procedures ◽

Weather Stations ◽

Accurate Quantification

Abstract. Quality meteorological data sources are critical to scientists, engineers, climate assessments and to make climate related decisions. Accurate quantification of reference evapotranspiration (ET0) in irrigated agriculture is crucial for optimizing crop production, planning and managing irrigation, and for using water resources efficiently. Validation of data insures that the information needed is been properly generated, identifies incorrect values and detects problems that require immediate maintenance attention. The Agroclimatic Information Network of Andalusia at present provides daily estimations of ET0 using meteorological information collected by nearly of one hundred automatic weather stations. It is currently used for technicians and farmers to generate irrigation schedules. Data validation is essential in this context and then, diverse quality control procedures have been applied for each station. Daily average of several meteorological variables were analysed (air temperature, relative humidity and rainfall). The main objective of this study was to develop a quality control system for daily meteorological data which could be applied on any platform and using open source code. Each procedure will either accept the datum as being true or reject the datum and label it as an outlier. The number of outliers for each variable is related to a dynamic range used on each test. Finally, geographical distribution of the outliers was analysed. The study underscores the fact that it is necessary to use different ranges for each station, variable and test to keep the rate of error uniform across the region.

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Spatial and temporal variation in weather events critical for boreal agriculture: I Elevated temperatures

Agricultural and Food Science ◽

10.23986/afsci.51465 ◽

2016 ◽

Vol 25 (1) ◽

Cited By ~ 1

Author(s):

Pirjo Peltonen-Sainio ◽

Pentti Pirinen ◽

Hanna M Mäkelä ◽

Otto Hyvärinen ◽

Erja Huusela-Veistola ◽

...

Keyword(s):

Production System ◽

Crop Production ◽

Elevated Temperatures ◽

Climatic Variability ◽

Temporal Variations ◽

Maximum Temperature ◽

Spatial And Temporal Variation ◽

Daily Maximum Temperature ◽

Daily Maximum ◽

Animal Farming

Variation in temperature challenges crop production and animal farming. Elevated temperatures are often harmful, though may also open opportunities at high latitudes. Impacts depend on the vulnerability of the object, production system and their resilience to climatic variability. The station-wise temperature observations from the Finnish Meteorological Institute for a time period of 54 years (1961‒2014) were interpolated to a regular 10 km × 10 km grid covering the whole country. Several successive time slices were used to measure the likelihood for: 1) elevated temperatures of a) ≥1 °C above normal for three weeks, b) ≥2 °C above normal for two weeks and c) ≥3 °C above normal for one week, and 2) heatwaves with daily maximum temperature >25 °C for: a) 5 days (short) or b) 14 days (long episode). We also estimated the likelihood of warm winds in the early growing season which may enhance pest migration. We found large spatial and temporal variations in the likelihoods of elevated temperatures with many impacts on crop production, animal farming and welfare. In fact, only 1 °C temperature elevation may already be harmful, though in some cases also beneficial depending on region and vulnerability or adaptation of the object and production system. Though we show only some examples of the potential impacts of temperature variation on high latitude agro-ecosystems, these data are valuable as such for much wider applications in agriculture and beyond that.

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Snow removal reduces annual cellulose decomposition in a riparian boreal forest

Canadian Journal of Soil Science ◽

10.4141/cjss2012-025 ◽

2013 ◽

Vol 93 (4) ◽

pp. 427-433 ◽

Cited By ~ 32

Author(s):

Juergen Kreyling ◽

Mahsa Haei ◽

Hjalmar Laudon

Keyword(s):

Boreal Forest ◽

Snow Cover ◽

Boreal Forests ◽

Soil Depth ◽

Decomposition Rates ◽

Soil Frost ◽

Snow Removal ◽

Cellulose Decomposition ◽

Significant Difference ◽

The Impact

Kreyling, J., Haei, M. and Laudon, H. 2013. Snow removal reduces annual cellulose decomposition in a riparian boreal forest. Can. J. Soil Sci. 93: 427–433. Decomposition is a key process in carbon and nutrient cycling. However, little is known about its response to altered winter soil temperature regimes in boreal forests. Here, the impact of soil frost on cellulose decomposition over 1 yr and soil biotic activity (bait-lamina sticks) over winter, in spring, and in summer was investigated using a long-term (9-yr) snow-cover manipulation experiment in a boreal Picea abies forest. The experiment consisted of the treatments: snow removal, increased insulation, and ambient control. The snow removal treatment caused longer and deeper soil frost (minimum temperature −8.6°C versus −1.4°C) at 10 cm soil depth in comparison with control, while the increased insulation treatment resulted in nearly no soil frost during winter. Annual cellulose decomposition rates were reduced by 46% in the snow removal manipulation in comparison with control conditions. Increased insulation had no significant effect on decomposition. The decomposition was mainly driven by microorganisms, as no significant difference was observed for containers enclosed with a 44-µm and a 1-mm mesh. Soil biotic activity was slightly increased by both the snow removal and the increased insulation treatment in comparison with control conditions over winter. However, this effect disappeared over spring and summer. We conclude that soil frost can have strong effects on decomposition in boreal ecosystems. Further studies should investigate to which degree the observed reduction in decomposition due to reduced snow cover in winter slows or even offsets the expected increase in decomposition rates with global warming.

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Comparison of Drivers of Soil Microbial Communities Developed in Karst Ecosystems with Shallow and Deep Soil Depths

Agronomy ◽

10.3390/agronomy11010173 ◽

2021 ◽

Vol 11 (1) ◽

pp. 173

Author(s):

Huiling Guan ◽

Jiangwen Fan ◽

Haiyan Zhang ◽

Warwick Harris

Keyword(s):

Microbial Community ◽

Microbial Communities ◽

Soil Depth ◽

Phospholipid Fatty Acid ◽

Soil Microbial Communities ◽

Deep Soil ◽

Soil System ◽

Soil Microbial ◽

Total Soil ◽

Karst Ecosystems

Soil erosion is prevalent in karst areas, but few studies have compared the differences in the drivers for soil microbial communities among karst ecosystems with different soil depths, and most studies have focused on the local scale. To fill this research gap, we investigated the upper 20 cm soil layers of 10 shallow–soil depth (shallow–SDC, total soil depth less than 100 cm) and 11 deep–soil depth communities (deep–SDC, total soil depth more than 100 cm), covering a broad range of vegetation types, soils, and climates. The microbial community characteristics of both the shallow–SDC and deep–SDC soils were tested by phospholipid fatty acid (PLFAs) analysis, and the key drivers of the microbial communities were illustrated by forward selection and variance partitioning analysis. Our findings demonstrated that more abundant soil nutrients supported higher fungal PLFA in shallow–SDC than in deep–SDC (p < 0.05). Furthermore, stronger correlation between the microbial community and the plant–soil system was found in shallow–SDC: the pure plant effect explained the 43.2% of variance in microbial biomass and 57.8% of the variance in the ratio of Gram–positive bacteria to Gram–negative bacteria (G+/G−), and the ratio of fungi to total bacteria (F/B); the pure soil effect accounted for 68.6% variance in the microbial diversity. The ratio of microbial PLFA cyclopropyl to precursors (Cy/Pr) and the ratio of saturated PLFA to monounsaturated PLFA (S/M) as indicators of microbial stress were controlled by pH, but high pH was not conducive to microorganisms in this area. Meanwhile, Cy/Pr in all communities was >0.1, indicating that microorganisms were under environmental stress. Therefore, the further ecological restoration of degraded karst communities is needed to improve their microbial communities.

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Climate Variability Since 1970 and Farmers’ Observations in Northern Ghana

Sustainable Agriculture Research ◽

10.5539/sar.v5n2p41 ◽

2016 ◽

Vol 5 (2) ◽

pp. 41 ◽

Cited By ~ 5

Author(s):

Emmanuel Nyadzi

Keyword(s):

Climate Change ◽

Climate Variability ◽

Water Conservation ◽

Crop Production ◽

Meteorological Data ◽

Northern Region ◽

Northern Ghana ◽

Climate Variability And Change ◽

Urban Centers ◽

Access To Credit

<p>The study examines how farmers’ observations of climate variability and change correspond with 42 years (1970-2011) meteorological data of temperature and rainfall. It shows how farmers in the Northern Region of Ghana adjust to the changing climate and explore the various obstacles that hinder the implementation of their adaptation strategies. With the help of an extension officer, 200 farmers from 20 communities were randomly selected based on their farming records. Temperatures over the last four decades (1970-2009) increased at a rate of 0.04 (± 0.41) ˚C and 0.3(± 0.13)˚C from 2010-2011 which is consistent to the farmers (82.5%) observations. Rainfall within the districts are characterised by inter-annual and monthly variability. It experienced an increased rate of 0.66 (± 8.30) mm from 1970-2009, which was inconsistent with the farmers (81.5%) observation. It however decreased from 2010-2011 at a huge rate of -22.49 (±15.90) mm which probably was the reason majority of the respondents claim rainfall was decreasing. Only 64.5% of the respondents had adjusted their farming activities because of climate variability and change. They apply fertilizers and pesticides, practice soil and water conservation, and irrigation for communities close to dams. Respondents desire to continue their current adaptation methods but may in the future consider changing crop variety, water-harvesting techniques, change crop production to livestock keeping, and possibly migrate to urban centers. Lack of climate change education, low access to credit and agricultural inputs are some militating factors crippling the farmers’ effort to adapt to climate change.</p>

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Reconstruction of the annual balance of Vadret da Morteratsch, Switzerland, since 1865

Annals of Glaciology ◽

10.3189/172756409787769609 ◽

2009 ◽

Vol 50 (50) ◽

pp. 126-134 ◽

Cited By ~ 28

Author(s):

Johanna Nemec ◽

Philippe Huybrechts ◽

Oleg Rybak ◽

Johannes Oerlemans

Keyword(s):

Meteorological Data ◽

Balance Model ◽

Surface Energy Fluxes ◽

Temperature And Precipitation ◽

Continuous Mass ◽

Weather Stations ◽

Precipitation Records ◽

High Elevations ◽

Annual Balance ◽

Almost Continuous

AbstractWe have reconstructed the annual balance of Vadret da Morteratsch, Engadine, Switzerland, with a two-dimensional energy-balance model for the period 1865–2005. The model takes into account a parameterization of the surface energy fluxes, an albedo that decreases exponentially with snow depth as well as the shading effect of the surrounding mountains. The model was first calibrated with a 5 year record of annual balance measurements made at 20 different sites on the glacier between 2001 and 2006 using meteorological data from surrounding weather stations as input. To force the model for the period starting in 1865, we employed monthly temperature and precipitation records from nearby valley stations. The model reproduces the observed annual balance reasonably well, except for the lower part during the warmest years. Most crucial to the results is the altitudinal precipitation gradient, but this factor is hard to quantify from the limited precipitation data at high elevations. The simulation shows an almost continuous mass loss since 1865, with short interruptions around 1920, 1935 and 1980. A trend towards a more negative annual balance can be observed since the beginning of the 1980s. The simulated cumulative mass balance for the entire period 1865–2005 was found to be –46mw.e.

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Mapping and verification of the snow cover timing in the Baikal region by remote sensing data MODIS “snow cover”

Geodesy and Cartography ◽

10.22389/0016-7126-2021-973-7-21-31 ◽

2021 ◽

Vol 973 (7) ◽

pp. 21-31

Author(s):

Е.А. Rasputina ◽

A.S. Korepova

Keyword(s):

Remote Sensing ◽

Snow Cover ◽

Spatial Resolution ◽

Baikal Region ◽

Remote Sensing Data ◽

Meteorological Station ◽

Sensing Data ◽

Modis Data ◽

Weather Stations ◽

The Difference

The mapping and analysis of the dates of onset and melting the snow cover in the Baikal region for 2000–2010 based on eight-day MODIS “snow cover” composites with a spatial resolution of 500 m, as well as their verification based on the data of 17 meteorological stations was carried out. For each year of the decennary under study, for each meteorological station, the difference in dates determined from the MODIS data and that of weather stations was calculated. Modulus of deviations vary from 0 to 36 days for onset dates and from 0 to 47 days – for those of stable snow cover melting, the average of the deviation modules for all meteorological stations and years is 9–10 days. It is assumed that 83 % of the cases for the onset dates can be considered admissible (with deviations up to 16 days), and 79 % of them for the end dates. Possible causes of deviations are analyzed. It was revealed that the largest deviations correspond to coastal meteorological stations and are associated with the inhomogeneity of the characteristics of the snow cover inside the pixels containing water and land. The dates of onset and melting of a stable snow cover from the images turned out to be later than those of weather stations for about 10 days. First of all (from the end of August to the middle of September), the snow is established on the tops of the ranges Barguzinsky, Baikalsky, Khamar-Daban, and later (in late November–December) a stable cover appears in the Barguzin valley, in the Selenga lowland, and in Priolkhonye. The predominant part of the Baikal region territory is covered with snow in October, and is released from it in the end of April till the middle of May.

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