Snow Cover Monitoring from Remote-Sensing Satellites: Possibilities for Drought Assessment

Snow cover is an important earth surface characteristic because it influences partitioning of the surface radiation, energy, and hydrologic budgets. Snow is also an important source of moisture for agricultural crops and water supply in many higher latitude or mountainous areas. For instance, snowmelt provides approximately 50%–80% of the annual runoff in the western United States (Pagano and Garen, 2006) and Canadian Prairies (Gray et al., 1989; Fang and Pomeroy, 2007), which substantially impacts warm season hydrology. Limited soil moisture reserves from the winter period can result in agricultural drought (i.e., severe early growing season vegetation stress if rainfall deficits occur during that period), which can be prolonged or intensified well into the growing season if relatively dry conditions persist. Snow cover deficits can also result in hydrological drought (i.e., severe deficits in surface and subsurface water reserves including soil moisture, streamflow, reservoir and lake levels, and groundwater) since snowmelt runoff is the primary source of moisture to recharge these reserves for a wide range of agricultural, commercial, ecological, and municipal purposes. Semiarid regions that rely on snowmelt are especially vulnerable to winter moisture shortfalls since these areas are more likely to experience frequent droughts. In the Canadian Prairies, more than half the years of three decades (1910–1920, 1930–1939, and 1980–1989) were in drought. Wheaton et al. (2005) reported exceptionally low precipitation and low snow cover in the winter of 2000–2001, with the greatest anomalies of precipitation in Alberta and western Saskatchewan along with near-normal temperature in most of southern Canada. The reduced snowfall led to lower snow accumulation. A loss in agricultural production over Canada by an estimated $3.6 billion in 2001–2002 was attributed to this drought. Fang and Pomeroy (2008) analyzed the impacts of the most recent and severe drought of 1999/2004–2005 for part of the Canadian Prairies on the water supply of a wetland basin by using a physically based cold region hydrologic modeling system. Simulation results showed that much lower winter precipitation, less snow accumulation, and shorter snow cover duration were associated with much lower discharge from snowmelt runoff to the wetland area during much of the drought period of 1999/2004–2005 than during the nondrought period of 2005/2006

Snow Cover Monitoring from Remote-Sensing Satellites: Possibilities for Drought Assessment

Snow cover is an important earth surface characteristic because it influences partitioning of the surface radiation, energy, and hydrologic budgets. Snow is also an important source of moisture for agricultural crops and water supply in many higher latitude or mountainous areas. For instance, snowmelt provides approximately 50%–80% of the annual runoff in the western United States (Pagano and Garen, 2006) and Canadian Prairies (Gray et al., 1989; Fang and Pomeroy, 2007), which substantially impacts warm season hydrology. Limited soil moisture reserves from the winter period can result in agricultural drought (i.e., severe early growing season vegetation stress if rainfall deficits occur during that period), which can be prolonged or intensified well into the growing season if relatively dry conditions persist. Snow cover deficits can also result in hydrological drought (i.e., severe deficits in surface and subsurface water reserves including soil moisture, streamflow, reservoir and lake levels, and groundwater) since snowmelt runoff is the primary source of moisture to recharge these reserves for a wide range of agricultural, commercial, ecological, and municipal purposes. Semiarid regions that rely on snowmelt are especially vulnerable to winter moisture shortfalls since these areas are more likely to experience frequent droughts. In the Canadian Prairies, more than half the years of three decades (1910–1920, 1930–1939, and 1980–1989) were in drought. Wheaton et al. (2005) reported exceptionally low precipitation and low snow cover in the winter of 2000–2001, with the greatest anomalies of precipitation in Alberta and western Saskatchewan along with near-normal temperature in most of southern Canada. The reduced snowfall led to lower snow accumulation. A loss in agricultural production over Canada by an estimated $3.6 billion in 2001–2002 was attributed to this drought. Fang and Pomeroy (2008) analyzed the impacts of the most recent and severe drought of 1999/2004–2005 for part of the Canadian Prairies on the water supply of a wetland basin by using a physically based cold region hydrologic modeling system. Simulation results showed that much lower winter precipitation, less snow accumulation, and shorter snow cover duration were associated with much lower discharge from snowmelt runoff to the wetland area during much of the drought period of 1999/2004–2005 than during the nondrought period of 2005/2006

Modeling and estimation of snow depth spatial correlation structure from observations over North America

Estimation of spatial correlations should be an integral part of objective analysis of geophysical variables. However, a statistical assessment of spatial correlations has been absent from studies of objective analysis of snow depth since its debut over 2 decades ago. We show a method for computing regional spatial correlations of observed snow depth and the daily snow depth increment and fitting them to correlation functions to estimate the correlation scale parameters. Both horizontal and vertical distance correlations are computed from station observations over a well sampled part of North America. The vertical and horizontal distance correlations are fitted to exponential functions using the least square method to estimate the correlation scale parameters including the amplitude, which represents short distance correlation. Our assessment suggests a large horizontal e-folding correlation scale for both the observed snow depth and the daily increment, with implications for improving predictions in poorly monitored areas with relatively flat topography. Over mountainous terrain, vertical e-folding correlation scale for observed snow depth is much smaller than that for the daily snow depth increment and for the snow depth increment used in operational snow analyses. That means that optimal interpolation-based analysis of the increments may be more accurate than the interpolation of snow depth data

Control some quality parameters of the natural juice of blueberry with different fruit content

During the study, were analyzed two natural juice with blueberry juice concentrate, packed in Tetra Pak. At the juice were studied these parameters: the technological process of juice production, the sensory characteristics (color, aroma, taste and homogeneity), physical and chemical (pH degree, refraction index, soluble solids degree ° Brix and turbidity expressed with NTU ). The parameters were analyzed in the juice with fruit content of 50% and 25%. From the sensoric aspect the most qualitative fluid resulted with a 50% fruit concentration, from the refractive index content and the degree of ° Brix higher results had 25% fruit juice concentrate, the pH rate was of approximate values while turbidity as a quality juice resulted liquid with fruit percentage 25%. In conclusion we can conclude that the two fluids studied result within the norms for quality regulation, but the above-mentioned changes come from the fruit juice content

Influence of Growing Region in the Quality of Olive Oils Produced in Albania in 2012-2013

Albania as part of the Mediterranean basin, with a favourable geographic position has been part of the zone where olive growing varieties are autochthonous. The growing regions of olives in Albania have a possibility to develop the olive oil industry, a possibility to develop the economy. This study aims the evaluation of chemical and sensorial criteria in extra virgin olive oils of autochthonous cultivars examined during the harvesting period 2012-2013 in Berat, Lezhe, Elbasan and Vlore regions. Each sample of extra virgin olive oil was representative of olive trees from one variety. The extraction was made using continuous extraction systems (Olerina 40, capacity 5-10 kg), at the Oil Laboratory of the Faculty of Biotechnology and Food, Agricultural University of Tirana. The determination of chemical and sensorial criteria were done according to the European Commission Regulation EEC/2565/91. The characteristics of the cultivar are very important in the quality and the components of the olive oil. One of the factors which influence this characteristics is the region. The data of all parameters analyzed demonstrated that all olive oils produced by authochthonous cultivar belonging to each region are extra virgin. Riche in polyphenols Kalinjot 280 (mg/l caffeic acid).Keywords: extra olive oil, region, phenol compounds, pigments, autochthonous varieties

Snowfall Rate Retrieval Using Passive Microwave Measurements and Its Applications in Weather Forecast and Hydrology

(AMSU), Microwave Humidity Sounder (MHS) and Advance Technology Microwave Sounder (ATMS). ATMS is the followon sensor to AMSU and MHS. Currently, an AMSU and MHS based land snowfall rate (SFR) product is running operationally at NOAA/NESDIS. Based on the AMSU/MHS SFR, an ATMS SFR algorithm has also been developed. The algorithm performs retrieval in three steps: snowfall detection, retrieval of cloud properties, and estimation of snow particle terminal velocity and snowfall rate. The snowfall detection component utilizes principal component analysis and a logistic regression model. It employs a combination of temperature and water vapor sounding channels to detect the scattering signal from falling snow and derives the probability of snowfall. Cloud properties are retrieved using an inversion method with an iteration algorithm and a twostream radiative transfer model. A method adopted to calculate snow particle terminal velocity. Finally, snowfall rate is computed by numerically solving a complex integral. The SFR products are being used mainly in two communities: hydrology and weather forecast. Global blended precipitation products traditionally do not include snowfall derived from satellites because such products were not available operationally in the past. The ATMS and AMSU/MHS SFR now provide the winter precipitation information for these blended precipitation products. Weather forecasters mainly rely on radar and station observations for snowfall forecast. The SFR products can fill in gaps where no conventional snowfall data are available to forecasters. The products can also be used to confirm radar and gauge snowfall data and increase forecasters' confidence in their prediction

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future