scholarly journals Toward a practical approach for ergodicity analysis

2015 ◽  
Vol 2 (5) ◽  
pp. 1425-1446 ◽  
Author(s):  
H. Wang ◽  
C. Wang ◽  
Y. Zhao ◽  
X. Lin ◽  
C. Yu
Keyword(s):  
Time Series ◽  
Yellow River ◽  
Rain Gauge ◽  
Ergodic Property ◽  
Practical Approach ◽  
Function Evaluation ◽  
Rain Gauge Station ◽  
Gauge Station ◽  
Mean Ergodicity ◽  
The Mean

Abstract. It is of importance to perform hydrological forecast using a finite hydrological time series. Most time series analysis approaches presume a data series to be ergodic without justifying this assumption. This paper presents a practical approach to analyze the mean ergodic property of hydrological processes by means of autocorrelation function evaluation and Augmented Dickey Fuller test, a radial basis function neural network, and the definition of mean ergodicity. The mean ergodicity of precipitation processes at the Lanzhou Rain Gauge Station in the Yellow River basin, the Ankang Rain Gauge Station in Han River, both in China, and at Newberry, MI, USA are analyzed using the proposed approach. The results indicate that the precipitations of March, July, and August in Lanzhou, and of May, June, and August in Ankang have mean ergodicity, whereas, the precipitation of any other calendar month in these two rain gauge stations do not have mean ergodicity. The precipitation of February, May, July, and December in Newberry show ergodic property, although the precipitation of each month shows a clear increasing or decreasing trend.

scholarly journals Intervention Analysis of Urbanization Effect on Rainfall Data at the Seoul Rain Gauge Station

2007 ◽  
Vol 40 (8) ◽  
pp. 629-641 ◽  
Author(s):  
Chul-Sang Yoo ◽  
Dae-Ha Kim ◽  
Sang-Hyoung Park ◽  
Byung-Su Kim ◽  
Chang-Yeol Park
Keyword(s):  
Rain Gauge ◽  
Rainfall Data ◽  
Intervention Analysis ◽  
Rain Gauge Station ◽  
Urbanization Effect ◽  
Gauge Station

Comparison of TRMM Data with Rain Gauge Observations in the Upper Huaihe River Basin of China

2013 ◽  
Vol 726-731 ◽  
pp. 3385-3390
Author(s):  
Josephine Osei-Kwarteng ◽  
Qiong Fang Li ◽  
Kwaku Amaning Adjei
Keyword(s):  
Hydrological Modeling ◽  
Tropical Rainfall Measuring Mission ◽  
Rain Gauge ◽  
Rain Gauge Station ◽  
Huaihe River ◽  
Resources Management ◽  
Sustainable Water Resources Management ◽  
Rain Gage ◽  
Gauge Station ◽  
Trmm 3B42

In this study, the Tropical Rainfall Measuring Mission (TRMM) version 7 satellite rainfall product, TRMM 3B42 (V7), was validated using rain gauge measurements in the Upper Huaihe Basin, China. This validation was carried out at monthly and annual temporal scales for an 11-year period using four selected grids with six, four, two and one rain gauge station (s) located within the TRMM grid respectively; the rain gage measurements for grids with more than one rain gauge were averaged. This study found that the validation of the TRMM dataset in grids where there were adequate rain gauge were present to capture the distributed and stochastic nature of rainfall with very good correlation (0.87-0.94) and with very little relative bias when the rain gage accumulations were compared with the TRMM estimates. From the study we found that the TRMM dataset can be used as precipitation input for hydrological modeling at monthly and annual scales for sustainable water resources management in the Upper Huaihe River and even in un-gaged or sparsely gaged basins in other parts of the world.

scholarly journals Contamination Vulnerability Analysis of Watershed for Water Quality Monitoring

2016 ◽  
Vol 20 (1) ◽  
Author(s):  
Widyastuti Widyastuti ◽  
Slamet Suprayogi
Keyword(s):  
Water Quality ◽  
Land Surface ◽  
Water Quality Monitoring ◽  
Vulnerability Index ◽  
Rain Gauge ◽  
Vulnerability Analysis ◽  
Quality Monitoring ◽  
Neural Network Modeling ◽  
Rain Gauge Station ◽  
Gauge Station

This research is an early step to determine the location of rain gauge station for artificial neural network modeling. The implementation of this model is very useful for water quality monitoring. The objectives of this study are: 1) to study the distribution of watershed parameter, that are average annual precipitation, land use and land-surface slope, 2) to conduct vulnerability analysis of watershed contamination, 3) to determine the location of rain gauge station. The study was performed by weighing and rating method of watershed parameters. The vulnerability degree of watershedtocontaminationispresentedasvulnerabilityindex.Thisindexisdeterminedbyoverallsumofallmultiplication between score and weigh number of each parameter. All data manipulation and data analysis were performed by using Geographic Information System (ArcView version by 3.2). The vulnerability of watershed contamination map had been generated using overlay operation of parameters. The results show that vulnerability index are varies between 10 up to 40 intervals. Hence, the indexes were categorized into three levels of watershed vulnerability, namely low (10 – 20), moderate (20 – 30) and high (30 – 40). It is found that the study area covered more by high vulnerability of watershed to contamination. The zoning of watershed vulnerability meant to determine the rain gauge location. There are three rain gauge stations on the area that they are in a high vulnerability level, whereas the other vulnerability level area has one rain gauge station. Each level of vulnerability area is able to represent the source of contaminant that it maybe influence the water quality of Gajahwong river.

scholarly journals Accuracy assessment and error cause analysis of GPM (V06) in Xiangjiang river catchment

2021 ◽  
Author(s):  
Bingru Tian ◽  
Hua Chen ◽  
Jialing Wang ◽  
Chong-Yu Xu
Keyword(s):  
Rain Gauge ◽  
Precipitation Intensity ◽  
Intensity Level ◽  
Total Precipitation ◽  
Rain Gauge Station ◽  
Xiangjiang River ◽  
River Catchment ◽  
Satellite Precipitation ◽  
Gauge Station ◽  
The Impact

Abstract Application potential and development prospect of satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) have promising implications. This study discusses causes of spatiotemporal differences on GPM data through the following steps: Initially, calculate bias between satellite-based data and rain gauge data of Xiangjiang river catchment to assess the accuracy of GPM (06E, 06 L, and 06F) products. Second, total errors of satellite precipitation data are divided into hit bias (HBIAS: precipitation detected by both GPM and rain gauge station), missed precipitation (MBIAS: precipitation detected only by rain gauge station), and false precipitation (FBIAS: precipitation detected only by GPM). Third, evaluate the impact of precipitation intensity and total precipitation on accuracy of GPM data and their influence on three error components. Several conclusions are drawn from the results above: (1) Satellite-based precipitation measurements perform better on a larger temporal-spatial scale. (2) The accuracy of TRMM and GPM data displays significant variances on space and time. Season, precipitation intensity, and total precipitation are main factors influencing the accuracy of TRMM and GPM data. (3) The detection capability of satellite products change with seasonal variation and different precipitation intensity level.

scholarly journals Does More Moisture in the Atmosphere Lead to More Intense Rains?

2021 ◽  
Author(s):  
Agostino Manzato
Keyword(s):  
Precipitable Water ◽  
Rain Gauge ◽  
Rain Gauge Station ◽  
Scatter Plot ◽  
North East ◽  
Station Data ◽  
Gauge Station ◽  
Friuli Venezia Giulia

Abstract It is typically interpreted that more moisture in the atmosphere leads to more intense rains. This notion may be supported, for example, by taking a scatter plot between rain and column precipitable water. The present paper suggests, however, that the main consequence of intense rains with more moistures in the atmosphere is that there is a more chance to happen, rather than of an increase in the expected magnitude. This tendency equally applies to any rains above 1 mm/6h to a lesser extent. The result is derived from an analysis of 33 local rain–gauge station data and a shared sounding over Friuli Venezia Giulia, North–East Italy.

Der Sommer und Herbst 2003 aus phänologischer Sicht | Summer and autumn 2003 from a phenological point of view

2004 ◽  
Vol 155 (5) ◽  
pp. 142-145 ◽  
Author(s):  
Claudio Defila
Keyword(s):  
Time Series ◽  
New Record ◽  
Point Of View ◽  
Late Spring ◽  
Mean Deviation ◽  
Leaf Shedding ◽  
The Mean ◽  
Very High ◽  
Selection Of

The record-breaking heatwave of 2003 also had an impact on the vegetation in Switzerland. To examine its influences seven phenological late spring and summer phases were evaluated together with six phases in the autumn from a selection of stations. 30% of the 122 chosen phenological time series in late spring and summer phases set a new record (earliest arrival). The proportion of very early arrivals is very high and the mean deviation from the norm is between 10 and 20 days. The situation was less extreme in autumn, where 20% of the 103 time series chosen set a new record. The majority of the phenological arrivals were found in the class «normal» but the class«very early» is still well represented. The mean precocity lies between five and twenty days. As far as the leaf shedding of the beech is concerned, there was even a slight delay of around six days. The evaluation serves to show that the heatwave of 2003 strongly influenced the phenological events of summer and spring.

scholarly journals The influence of solar wind on extratropical cyclones – Part 1: Wilcox effect revisited

2009 ◽  
Vol 27 (1) ◽  
pp. 1-30 ◽  
Author(s):  
P. Prikryl ◽  
V. Rušin ◽  
M. Rybanský
Keyword(s):  
Time Series ◽  
Solar Wind ◽  
Northern Hemisphere ◽  
High Speed ◽  
Coronal Holes ◽  
Extratropical Cyclones ◽  
Sector Boundary ◽  
Speed Solar Wind ◽  
The Mean ◽  
High Speed Solar Wind

Abstract. A sun-weather correlation, namely the link between solar magnetic sector boundary passage (SBP) by the Earth and upper-level tropospheric vorticity area index (VAI), that was found by Wilcox et al. (1974) and shown to be statistically significant by Hines and Halevy (1977) is revisited. A minimum in the VAI one day after SBP followed by an increase a few days later was observed. Using the ECMWF ERA-40 re-analysis dataset for the original period from 1963 to 1973 and extending it to 2002, we have verified what has become known as the "Wilcox effect" for the Northern as well as the Southern Hemisphere winters. The effect persists through years of high and low volcanic aerosol loading except for the Northern Hemisphere at 500 mb, when the VAI minimum is weak during the low aerosol years after 1973, particularly for sector boundaries associated with south-to-north reversals of the interplanetary magnetic field (IMF) BZ component. The "disappearance" of the Wilcox effect was found previously by Tinsley et al. (1994) who suggested that enhanced stratospheric volcanic aerosols and changes in air-earth current density are necessary conditions for the effect. The present results indicate that the Wilcox effect does not require high aerosol loading to be detected. The results are corroborated by a correlation with coronal holes where the fast solar wind originates. Ground-based measurements of the green coronal emission line (Fe XIV, 530.3 nm) are used in the superposed epoch analysis keyed by the times of sector boundary passage to show a one-to-one correspondence between the mean VAI variations and coronal holes. The VAI is modulated by high-speed solar wind streams with a delay of 1–2 days. The Fourier spectra of VAI time series show peaks at periods similar to those found in the solar corona and solar wind time series. In the modulation of VAI by solar wind the IMF BZ seems to control the phase of the Wilcox effect and the depth of the VAI minimum. The mean VAI response to SBP associated with the north-to-south reversal of BZ is leading by up to 2 days the mean VAI response to SBP associated with the south-to-north reversal of BZ. For the latter, less geoeffective events, the VAI minimum deepens (with the above exception of the Northern Hemisphere low-aerosol 500-mb VAI) and the VAI maximum is delayed. The phase shift between the mean VAI responses obtained for these two subsets of SBP events may explain the reduced amplitude of the overall Wilcox effect. In a companion paper, Prikryl et al. (2009) propose a new mechanism to explain the Wilcox effect, namely that solar-wind-generated auroral atmospheric gravity waves (AGWs) influence the growth of extratropical cyclones. It is also observed that severe extratropical storms, explosive cyclogenesis and significant sea level pressure deepenings of extratropical storms tend to occur within a few days of the arrival of high-speed solar wind. These observations are discussed in the context of the proposed AGW mechanism as well as the previously suggested atmospheric electrical current (AEC) model (Tinsley et al., 1994), which requires the presence of stratospheric aerosols for a significant (Wilcox) effect.

scholarly journals Technical note: Inherent benchmark or not? Comparing Nash–Sutcliffe and Kling–Gupta efficiency scores

2019 ◽  
Vol 23 (10) ◽  
pp. 4323-4331 ◽  
Author(s):  
Wouter J. M. Knoben ◽  
Jim E. Freer ◽  
Ross A. Woods
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Ad Hoc ◽  
Model Performance ◽  
Technical Note ◽  
Mean Flow ◽  
Model Adequacy ◽  
Robust Model ◽  
The Mean ◽  
Adequacy Assessment

Abstract. A traditional metric used in hydrology to summarize model performance is the Nash–Sutcliffe efficiency (NSE). Increasingly an alternative metric, the Kling–Gupta efficiency (KGE), is used instead. When NSE is used, NSE = 0 corresponds to using the mean flow as a benchmark predictor. The same reasoning is applied in various studies that use KGE as a metric: negative KGE values are viewed as bad model performance, and only positive values are seen as good model performance. Here we show that using the mean flow as a predictor does not result in KGE = 0, but instead KGE =1-√2≈-0.41. Thus, KGE values greater than −0.41 indicate that a model improves upon the mean flow benchmark – even if the model's KGE value is negative. NSE and KGE values cannot be directly compared, because their relationship is non-unique and depends in part on the coefficient of variation of the observed time series. Therefore, modellers who use the KGE metric should not let their understanding of NSE values guide them in interpreting KGE values and instead develop new understanding based on the constitutive parts of the KGE metric and the explicit use of benchmark values to compare KGE scores against. More generally, a strong case can be made for moving away from ad hoc use of aggregated efficiency metrics and towards a framework based on purpose-dependent evaluation metrics and benchmarks that allows for more robust model adequacy assessment.

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