Results 1 - 10 of 1114
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[en] The duration and extent of snow cover is expected to change rapidly with climate change. Therefore, there is a need for improved monitoring of snow for the benefit of forecasting, impact assessments and the population at large. Remotely sensed techniques prove useful for remote areas where there are few field-based monitoring stations. This paper reports on a study of snow season using snow cover area fraction data from the two northernmost counties in Norway, Troms and Finnmark. The data are derived from the daily 500 m standard snow product (MOD10A1) from the NASA Terra MODerate Resolution Imaging Spectroradiometer (MODIS) sensor for the 2000–2010 period. This dataset has been processed with multi-temporal interpolation to eliminate clouds. The resulting cloud-free daily time series of snow cover fraction maps, have subsequently been used to derive the first and last snow-free day for the entire study area. In spring, the correlation between the first snow-free day mapped by MODIS data and snow data from 40 meteorological stations was highly significant ( p < 0.05) for 36 of the stations, and with a of bias of less than 10 days for 34 of the stations. In autumn, 31 of the stations show highly significant ( p < 0.05) correlation with MODIS data, and the bias was less than 10 days for 27 of the stations. However, in some areas and some years, the start and end of the snow season could not be detected due to long overcast periods. In spring 2002 and 2004 the first snow-free day was early, but arrived late in 2000, 2005 and 2008. In autumn 2009 snowfall arrived more than 7 days earlier in 50% of the study area as compared to the 2000–2010 average. MODIS-based snow season products will be applicable for a wide range of sectors including hydrology, nature-based industries, climate change studies and ecology. Therefore refinement and further testing of this method should be encouraged. (letter)
[en] An agro-climatic study was carried out in eastern Indian state of Bihar (middle Indo-Gangetic Plains) to identify optimum planting schedules and water availability of rainfed crops based on moisture availability index (MAI), i.e., the ratio of weekly assured rainfall and potential evapotranspiration (PET) for delineating safe growing period and crop production potential at micro-level in order to develop climate smart agricultural production system. For this purpose, historical weekly rainfall data for a period ranging from 30 to 55 years of 110 rain-gauge stations and normal weekly PET were employed. The assured weekly rainfall at different probability levels, viz. 25, 50, and 75%, was computed employing incomplete gamma distribution technique. The study revealed that at 50% probability (i.e., 50 out of 100 years), the sowing window of rainfed crops with MAI ≥ 0.33 ranged from 19 to 24 SMW (standard meteorological week) over different districts in Zone I (North west alluvial plains), 18 to 23 SMW in Zone II (North east alluvial plains), 23–24 SMW in Zone IIIA (Part of South Bihar alluvial plains), and 24–25 SMW in Zone IIIB (Part of South Bihar alluvial plains). The districts under Zone II recorded the earliest sowing week for starting sowing of rainfed crops, and the most delayed start of sowing was recorded in the districts under Zone IIIB at all probability levels. Kishanganj District recorded the highest duration of water availability followed by West Chamaparan District at all MAI and probability levels. In terms of longer length of water availability and higher values of MAI, Zone II appeared to be the most potential agroclimatic zone followed by Zone I and Zone IIIA. The Zone IIIB was adjudged as the least potential Zone in terms of shorter water availability period for rainfed crop production.
[en] A new high-resolution regional climate change ensemble has been established for Europe within the World Climate Research Program Coordinated Regional Down-scaling Experiment (EURO-CORDEX) initiative. The first set of simulations with a horizontal resolution of 12.5 km was completed for the new emission scenarios RCP4.5 and RCP8.5 with more simulations expected to follow. The aim of this paper is to present this data set to the different communities active in regional climate modelling, impact assessment and adaptation. The EURO-CORDEX ensemble results have been compared to the SRES A1B simulation results achieved within the ENSEMBLES project. The large-scale patterns of changes in mean temperature and precipitation are similar in all three scenarios, but they differ in regional details, which can partly be related to the higher resolution in EURO-CORDEX. The results strengthen those obtained in ENSEMBLES, but need further investigations. The analysis of impact indices shows that for RCP8.5, there is a substantially larger change projected for temperature-based indices than for RCP4.5. The difference is less pronounced for precipitation-based indices. Two effects of the increased resolution can be regarded as an added value of regional climate simulations. Regional climate model simulations provide higher daily precipitation intensities, which are completely missing in the global climate model simulations, and they provide a significantly different climate change of daily precipitation intensities resulting in a smoother shift from weak to moderate and high intensities. (authors)
[en] The question whether to walk slowly or to run when it starts raining in order to stay as dry as possible has been considered for many years-and with different results, depending on the assumptions made and the mathematical descriptions for the situation. Because of the practical meaning for real life and the inconsistent results depending on the chosen parameters, this problem is well suited to undergraduate students learning to decide which parameters are important and choosing reasonable values to describe a physical problem. Dealing with physical parameters is still useful at university level, as students do not always recognize the connection between pure numbers and their qualitative and quantitative influence on a physical problem. This paper presents an intuitive approach which offers the additional advantage of being more detailed, allowing for more parameters to be tested than the simple models proposed in most other publications.
[en] Rainfall is one of the most important components of the hydrological system and the understanding of its spatio-temporal variability is of fundamental importance for the management of hydrographic basins, especially those located in the semi-arid region of Brazil. In this way, the objective of this work is to analyze and classify the historical series of precipitation as to their profiles and trends in the Watershed of Riacho do Navio (WRN), located in the Pernambuco State. Data from the plains of Betânia and Airi, both with a series of more than 30 years of precipitation records. The total annual, monthly, maximum rainfall in a single day and number of rainy days were organized to fit the Gumbel, in order to classify the years of very dry to very rainy ones by the quantis technique and trend determination by the nonparametric test of Mann-Kendall. The pluviometric stations of Betânia and Airi presented 15.8 and 15.5% of the series considered as very dry years, with an average precipitation of 191.0 and 308.1 mm, respectively. The Mann-Kendall test identified a decrease in total annual rainfall, with a reduction of -52.7 mm (-11%) for the Bethany station and -82.2 mm (-10%) for Airi, in 30 years. The decrease in the number of days with precipitation in 30 years is 6 days for Bethany and 20 days for Airi. The reduction of rainfall and the number of rainy days, will probably imply a reduce the surface water and groundwater in WRN and, consequently, there may be changes in the dynamics of the native vegetation and soil erosion increase, causing difficult in the yield to rainfed agriculture from this region. (author)
[en] In this paper we propose for the first time a multiplicative continuous model for generating multifractal fields with zero values, as a continuous generalization of the intermittent lognormal β -model proposed by Over and Gupta (1996). It is built using infinitely multiplicative random variables, the multiplicative analog to infinitely divisible distributions for addition. The model also needs stochastic multiplicative measures and multiplicative stochastic integrals. It possesses as a special case a continuous generalization of the classical discrete β -model. Applications are numerous in many fields of applied science, including small-scale rainfall and soil science. (paper)
[en] This article presents an algorithm and a structured methodology to address the issue of the optimisation of resources when clearing snow from stretches of the manoeuvring area of an airport. This overall issue is how to best utilise limited resources to remove snow from taxiways and runways so as to leave surfaces in an acceptable state for aircraft operations. To achieve this the airfield is divided into subsets of significant stretches for the purpose of operations and target times are set at which these are to be open to aircraft traffic. The manoeuvring area is also divided into zones, with the condition that the subsets of significant stretches lie within just one of these zones. The mathematical model contains operating restrictions with regard to the fulfilment of partial operational targets applied to the subsets of significant stretches, and also concerning the snow-clearing machines. The problem is solved by an iterative optimisation process based on linear programming applied successively to the zones that make up the manoeuvring area during each iteration. The method is particularised for the case of the manoeuvring area of Adolfo Suarez Madrid - Barajas Airport. (Author)
[en] The Hindu Kush-Himalayan (HKH) region holds the largest mass of ice in Central Asia and is highly vulnerable to global climate change, experiencing significant warming (0.21 ± 0.08 deg. C/decade) over the past few decades. Accurate monitoring of the timing and duration of snowmelt across the HKH region is important, as this region is expected to experience further warming in response to increased greenhouse gas forcing. Despite the many advantages and applications of satellite-derived radar scatterometer data shown for capturing ice and snow melt dynamics at high latitudes, similar comprehensive freeze/thaw detection studies at lower latitudes (including the HKH region) are still absent from the scientific literature. A comprehensive freeze/thaw detection study is utilized on perennial snow/ice and seasonal snow cover for the first time in the Himalayan and Karakoram regions. A dynamic threshold-based method is applied to enhanced QuikSCAT Ku-band backscatter observations from 2000 to 2008 that (a) provides spatial maps of the timing of melt, freeze, and melt season duration, and (b) emphasizes regional variability in freeze/thaw dynamics. The resulting average melt durations for 2000-2008 are 161 ± 11 days (early May-mid-October) for the eastern Himalayas, 130 ± 16 days (late May-early October) for the central Himalayas, 124 ± 13 days (mid-May-mid-September) for the western Himalayas, and 124 ± 12 days (late May-late September) for the Karakoram region. The eastern Himalayan region has on average an earlier melt onset, a later freeze-up, and therefore a longer melt season (∼5 weeks) relative to the central and western Himalayan and the Karakoram regions. Snowmelt dynamics exhibit regional and interannual variability with clear connections to terrain features, in particular elevation and aspect. With respect to ongoing controversies surrounding melt in the Himalayan region, this study provides an overall perspective of regional differences in melt onset, freeze-up, and melt duration that have important implications for glaciological and hydrological processes across the HKH region.
[en] Soil temperature, an important indicator of climate change, has rarely explored due to scarce observations, especially in the Tibetan Plateau (TP) area. In this study, changes observed in five meteorological variables obtained from the TP between 1960 and 2014 were investigated using two non-parametric methods, the modified Mann–Kendall test and Sen’s slope estimator method. Analysis of annual series from 1960 to 2014 has shown that surface (0 cm), shallow (5–20 cm), deep (40–320 cm) soil temperatures (ST), mean air temperature (AT), and precipitation (P) increased with rates of 0.47 °C/decade, 0.36 °C/decade, 0.36 °C/decade, 0.35 °C/decade, and 7.36 mm/decade, respectively, while maximum frozen soil depth (MFD) as well as snow cover depth (MSD) decreased with rates of 5.58 and 0.07 cm/decade. Trends were significant at 99 or 95% confidence level for the variables, with the exception of P and MSD. More impressive rate of the ST at each level than the AT indicates the clear response of soil to climate warming on a regional scale. Monthly changes observed in surface ST in the past decades were consistent with those of AT, indicating a central place of AT in the soil warming. In addition, with the exception of MFD, regional scale increasing trend of P as well as the decreasing MSD also shed light on the mechanisms driving soil trends. Significant negative-dominated correlation coefficients (α = 0.05) between ST and MSD indicate the decreasing MSD trends in TP were attributable to increasing ST, especially in surface layer. Owing to the frozen ground, the relationship between ST and P is complicated in the area. Higher P also induced higher ST, while the inhibition of freeze and thaw process on the ST in summer. With the increasing AT, P accompanied with the decreasing MFD, MSD should be the major factors induced the conspicuous soil warming of the TP in the past decades.
[en] This paper discusses the characteristics of the unique “Regional East Gale (REG) with Blowing Snow” natural disaster that occurs in Jeminay County, Xinjiang Uygur Autonomous Region, China. The damage caused by such REG events is described, followed by discussion on the possible prevention measures for this kind of disaster. This work provides a theoretical basis for continuing development of the service that forecasts REG disasters and thus helps with mitigating against their impacts.