scholarly journals STATISTICAL PROPERTIES OF THE ANNUAL MAXIMUM SNOW DEPTH AND A NEW APPROACH TO ESTIMATE THE RETURN PERIOD VALUES

Author(s):  
MASANORI IZUMI ◽  
HIROZO MIHASHI ◽  
TORU TAKAHASHI
2016 ◽  
Vol 43 (1) ◽  
pp. 9-17 ◽  
Author(s):  
Li Qin ◽  
Yujiang Yuan ◽  
Ruibo Zhang ◽  
Wenshou Wei ◽  
Shulong Yu ◽  
...  

Abstract Heavy snowfall and extreme snow depth cause serious losses of human life and property in the northern Tianshan Mountains almost every winter. Snow cover is an important indicator of climate change. In this study, we developed five tree-ring-width chronologies of Schrenk spruce (Picea schrenkiana Fisch. et Mey) from the northern Tianshan Mountains using standard dendrochronological methods. Correlation analyses indicated that radial growth of trees in the northern Tianshan Mountains is positively affected by annual maximum snow depth. This relationship was validated and models of annual maximum snow depth back to the 18th century were developed. The reconstruction explains 48.3% of the variance in the instrumental temperature records during the 1958/59–2003/04 calibration periods. It indicates that quasi-periodic changes exist on 2.0–4.0-yr, 5.3-yr, 14.0-yr, and 36.0-yr scales. The reconstructed series shows that maximum snow depth exhibits obvious stages change, the periods characterized by lower maximum snow depth were 1809/10–1840/41, 1873/74–1893/94, 1909/10–1929/30, 1964/65–1981/82, and the periods characterized by higher maximum snow depth were 1841/42–1872/73, 1894/95–1908/09, 1930/31–1963/64, and 1982/83–present. The lower period of annual maximum snow depth during the 1920s–1930s is consistent with the severe drought that occurred at this time in northern China. From the 1970s to the present, the maximum snow depth has increased clearly with the change to a warmer and wetter climate in Xinjiang. The reconstruction sheds new light on snow cover variability and change in a region where the climate history for the past several centuries is poorly understood.


2018 ◽  
Vol 246 ◽  
pp. 01105
Author(s):  
Shuang-yan Jin ◽  
Wen-yong Gao ◽  
Si-wu Luo ◽  
Ya-jun Gao

The return period of "7.26" rainstorm flood in 2017 in Wudinghe basin is analyzed by the method of P-III distribution. The Lijiahe and Dingjiagou stations with long rainfall observation data in the rainstorm area are selected, and the frequency curve of the annual maximum 24 hours rainfall are established, and the recurrence period of rainfall stations in rainstorm area are estimated according to the parameters determined by the curve fitting method. The frequency curve of the annual maximum peak discharge of Baijiachuan hydrological stations and so on are established, and the return period are analyzed in combination with the historical survey floods. The results show that the return period of Zhaojiabian of heavy rainfall center is about 100 years, and which of the other stations over than 200mm in Wudinghe basin is about 30~90 years; while the return period of the peak discharge of Baijiachuan and Suide hydrological station is about 30 and 20 years respectively.


2015 ◽  
Vol 9 (1) ◽  
pp. 1-44
Author(s):  
E. Trujillo ◽  
M. Lehning

Abstract. In recent years, marked improvements in our knowledge of the statistical properties of the spatial distribution of snow properties have been achieved thanks to improvements in measuring technologies (e.g. LIDAR, TLS, and GPR). Despite of this, objective and quantitative frameworks for the evaluation of errors and extrapolations in snow measurements have been lacking. Here, we present a theoretical framework for quantitative evaluations of the uncertainty of point measurements of snow depth when used to represent the average depth over a profile section or an area. The error is defined as the expected value of the squared difference between the real mean of the profile/field and the sample mean from a limited number of measurements. The model is tested for one and two dimensional survey designs that range from a single measurement to an increasing number of regularly-spaced measurements. Using high-resolution (~1 m) LIDAR snow depths at two locations in Colorado, we show that the sample errors follow the theoretical behavior. Furthermore, we show how the determination of the spatial location of the measurements can be reduced to an optimization problem for the case of the predefined number of measurements, or to the designation of an acceptable uncertainty level to determine the total number of regularly-spaced measurements required to achieve such error. On this basis, a series of figures are presented that can be used to aid in the determination of the survey design under the conditions described, and under the assumption of prior knowledge of the spatial covariance/correlation properties. With this methodology, better objective survey designs can be accomplished, tailored to the specific applications for which the measurements are going to be used. The theoretical framework can be extended to other spatially distributed snow variables (e.g. SWE) whose statistical properties are comparable to those of snow depth.


2006 ◽  
Vol 10 (2) ◽  
pp. 233-243 ◽  
Author(s):  
E. Gaume

Abstract. This paper presents some analytical results and numerical illustrations on the asymptotic properties of flood peak distributions obtained through derived flood frequency approaches. It confirms and extends the results of previous works: i.e. the shape of the flood peak distributions are asymptotically controlled by the rainfall statistical properties, given limited and reasonable assumptions concerning the rainfall-runoff process. This result is partial so far: the impact of the rainfall spatial heterogeneity has not been studied for instance. From a practical point of view, it provides a general framework for analysis of the outcomes of previous works based on derived flood frequency approaches and leads to some proposals for the estimation of very large return-period flood quantiles. This paper, focussed on asymptotic distribution properties, does not propose any new approach for the extrapolation of flood frequency distribution to estimate intermediate return period flood quantiles. Nevertheless, the large distance between frequent flood peak values and the asymptotic values as well as the simulations conducted in this paper help quantifying the ill condition of the problem of flood frequency distribution extrapolation: it illustrates how large the range of possibilities for the shapes of flood peak distributions is.


2019 ◽  
Vol 79 ◽  
pp. 03022
Author(s):  
Shangwen Jiang ◽  
Ling Kang

Under changing environment, the streamflow series in the Yangtze River have undergone great changes and it has raised widespread concerns. In this study, the annual maximum flow (AMF) series at the Yichang station were used for flood frequency analysis, in which a time varying model was constructed to account for non-stationarity. The generalized extreme value (GEV) distribution was adopted to fit the AMF series, and the Generalized Additive Models for Location, Scale and Shape (GAMLSS) framework was applied for parameter estimation. The non-stationary return period and risk of failure were calculated and compared for flood risk assessment between stationary and non-stationary models. The results demonstrated that the flow regime at the Yichang station has changed over time and a decreasing trend was detected in the AMF series. The design flood peak given a return period decreased in the non-stationary model, and the risk of failure is also smaller given a design life, which indicated a safer flood condition in the future compared with the stationary model. The conclusions in this study may contribute to long-term decision making in the Yangtze River basin under non-stationary conditions.


1996 ◽  
Vol 42 (140) ◽  
pp. 136-140 ◽  
Author(s):  
Tsutomu Nakamura ◽  
Masujiro Shimizu

AbstractReduced amounts of snow in the eight winters from 1986-87 to 1993-94 at Nagaoka, Japan, seem to be due to a winter air-temprature rise. The winter air temprature has shown cyclic varition gradual increase in the past 100years. The linear rate of the temperature rise in the past century was calculated as 1.35°C per 100 years. Both the maximum Snow depth and winter precipitation showed an inversely positive correlation with winter mean air temperature, The square of the statistical correlation coefficient r2was calculated as 0.321 and 0.107. respectively. Statistically smoothed curves or the maximum snow depth and winter precipitation showed maximum values in 1940, Fluctuations in deviation of the maximum Snow depth showed smaller values than in precipitation. The minimum winter mean air temperature obtained from a 10 year moving average curve was found in 1942, and the deviation fom the climatic mean changed from negative to positive in 1949. The change in sign or the temperature deviation and the increase of the deviation may be attributable to global warming.


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