Climate change tendencies observable in the rainfall measurements since 1950 in the Federal Land of North Rhine-Westphalia and their consequences for urban hydrology

2011 ◽  
Vol 63 (11) ◽  
pp. 2633-2640
Author(s):  
Th. Einfalt ◽  
M. Quirmbach ◽  
G. Langstädtler ◽  
B. Mehlig
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Values ◽  
Measurement Data ◽  
Urban Hydrology ◽  
Flexible Design ◽  
Precipitation Regime ◽  
The Past ◽  
Federal Land ◽  
Standard Values

Climate change is present in climatological models – but did we already observe changes in the past measurement data? For the state of North Rhine Westphalia, the rainfall measurements since 1950 have been systematically analysed in order to find out whether there have already been trends and whether the behaviour of rainfall has changed in time. More than 600 station series have been screened for use in the project and quality controlled. Implausible data were discarded. For the analysis, standard values such as yearly sums, half-yearly sums, monthly sums, number of dry days, number of days with precipitation above a threshold, partial time series and extreme values statistics have been calculated and evaluated. Results show that also in the past 50 years, changes in precipitation regime could be observed. These changes have been regionally different. Consequences for urban hydrology include a development of more flexible design approaches.

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2016 ◽  
Vol 20 (4) ◽  
pp. 1387-1403 ◽  
Author(s):  
Hjalte Jomo Danielsen Sørup ◽  
Ole Bøssing Christensen ◽  
Karsten Arnbjerg-Nielsen ◽  
Peter Steen Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a spatio-temporal Neyman–Scott rectangular pulses weather generator (WG). Precipitation time series used as input to the WG are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 km  ×  60 km model domain. The WG simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from regional climate models (RCMs) with spatial resolutions of 25 and 8 km, respectively. Six different RCM simulation pairs are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

scholarly journals Method Consideration of Variation Diagnosis and Design Value Calculation of Flood Sequence in Yiluo River Basin, China

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2722
Author(s):  
Xinxin Li ◽  
Xixia Ma ◽  
Xiaodong Li ◽  
Wenjiang Zhang
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Human Activities ◽  
Extreme Values ◽  
Frequency Estimation ◽  
Synthesis Method ◽  
Estimation Method ◽  
Stationary Time Series ◽  
Distribution Model ◽  
Design Flood

The conventional approaches of the design flood calculation are based on the assumption that the hydrological time series is subject to the same distribution in the past, present, and future, i.e., the series should be consistent. However, the traditional methods may result in overdesign in the water conservancy project since the series has non-stationary variations due to climate change and human activities. Therefore, it is necessary to develop a new approach for frequency estimation of non-stationary time series of extreme values. This study used four kinds of mutation test methods (the linear trend correlation coefficient, Mann–Kendall test, sliding t-test, and Pettitt test) to identify the trend and mutation of the annual maximum flow series (1950–2006) of three hydrological stations in the Yiluo River Basin. Then we evaluated the performance of two types of design flood methods (the time series decomposition-synthesis method, the mixed distribution model) under the impacts of climate change and human activities on hydro-meteorological conditions. The results showed that (a) the design flood value obtained by the time series decomposition-synthesis method based on the series of the backward restore is larger than that obtained by the decomposition synthesis method based on the series of the forward restore; (b) when the return period is 100 years or less, the design flood value obtained by the mixed distribution model using the capacity ratio parameter estimation method is less than that obtained by the hybrid distribution model with simulated annealing parameter estimation method; and (c) both methods can overcome sequence inconsistency in design frequencies. This study provides insight into the frequency estimation of non-stationary time series of extreme values under the impacts of climate change and human activities on hydro-meteorological conditions.

Relationship between heating/cooling period and changing temperature conditions in the urban areas of Hungary

2020 ◽  
Author(s):  
Csenge Dian ◽  
Attila Talamon ◽  
Rita Pongrácz ◽  
Judit Bartholy
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Energy Consumption ◽  
Extreme Values ◽  
Building Design ◽  
Temperature Time Series ◽  
Maximum Temperature ◽  
Design Parameters ◽  
Daily Average ◽  
Temperature Time

<p>Climate change, extreme weather conditions, and local scale urban heat island (UHI) effect altogether have substantial impacts on people’s health and comfort. The urban population spends most of its time in buildings, therefore, it is important to examine the relationship between weather/climate conditions and indoor environment. The role of buildings is complex in this context. On the one hand UHI effect is mostly created by buildings and artificial surfaces. On the other hand they account for about 40% of energy consumption on European average. Since environmental protection requires increased energy efficiency, the ultimate goal from this perspective is to achieve nearly zero-energy buildings. When estimating energy consumption, daily average temperatures are taken into account. The design parameters (e.g. for heating systems) are determined using temperature-based criteria. However, due to climate change, these critical values are likely to change as well. Therefore, it is important to examine the temperature time series affecting the energy consumption of buildings. For the analysis focusing on the Carpathian region within central/eastern Europe, we used the daily average, minimum and maximum temperature time series of five Hungarian cities (i.e. Budapest, Debrecen, Szeged, Pécs and Szombathely). The main aim of this study is to investigate the effect of changing daily average temperatures and the rising extreme values on building design parameters, especially heating and cooling periods (including the length and average temperatures of such periods).</p>

scholarly journals Multidimensional extremes: joint study of precipitation and temperature time series

2021 ◽  
Author(s):  
Beatrix Izsák ◽  
Tamás Szentimrey ◽  
Mónika Lakatos ◽  
Rita Pongrácz
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Values ◽  
Standardized Precipitation Index ◽  
Single Element ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
The Standardized Precipitation Index ◽  
Climatic Time Series ◽  
Precipitation And Temperature

<p>To study climate change, it is essential to analyze extremes as well. The study of extremes can be done on the one hand by examining the time series of extreme climatic events and on the other hand by examining the extremes of climatic time series. In the latter case, if we analyze a single element, the extreme is the maximum or minimum of the given time series. In the present study, we determine the extreme values of climatic time series by examining several meteorological elements together and thus determining the extremes. In general, the main difficulties are connected with the different probability distribution of the variables and the handling of the stochastic connection between them. The first issue can be solved by the standardization procedures, i.e. to transform the variables into standard normal ones. For example, the Standardized Precipitation Index (SPI) uses precipitation sums assuming gamma distribution, or the standardization of temperature series assumes normal distribution. In case of more variables, the problem of stochastic connection can be solved on the basis of the vector norm of the variables defined by their covariance matrix. According to this methodology we have developed a new index in order to examine the precipitation and temperature variables jointly. We present the new index with the mathematical background, furthermore some examples for spatio-temporal examination of these indices using our software MASH (Multiple Analysis of Series for Homogenization; Szentimrey) and MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey, Bihari). For our study, we used the daily average temperature and precipitation time series in Hungary for the period 1870-2020. First of all, our analyses indicate that even though some years may not be considered extreme if only either precipitation or average temperature is taken in to account, but examining the two elements together these years were extreme years indeed. Based on these, therefore, the study of the extremes of multidimensional climate time series complements and thus makes the study of climate change more efficient compared to examining only one-dimensional time series.</p>

scholarly journals Downscaling future precipitation extremes to urban hydrology scales using a spatio-temporal Neyman–Scott weather generator

2015 ◽  
Vol 12 (2) ◽  
pp. 2561-2605 ◽  
Author(s):  
H. J. D. Sørup ◽  
O. B. Christensen ◽  
K. Arnbjerg-Nielsen ◽  
P. S. Mikkelsen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Regional Climate ◽  
Weather Generator ◽  
Spatial Extent ◽  
Urban Hydrology ◽  
Precipitation Time Series ◽  
Spatio Temporal

Abstract. Spatio-temporal precipitation is modelled for urban application at 1 h temporal resolution on a 2 km grid using a Spatio-Temporal Neyman–Scott Rectangular Pulses weather generator (WG). Precipitation time series for fitting the model are obtained from a network of 60 tipping-bucket rain gauges irregularly placed in a 40 by 60 km model domain. The model simulates precipitation time series that are comparable to the observations with respect to extreme precipitation statistics. The WG is used for downscaling climate change signals from Regional Climate Models (RCMs) with spatial resolutions of 25 and 8 km respectively. Six different RCM simulations are used to perturb the WG with climate change signals resulting in six very different perturbation schemes. All perturbed WGs result in more extreme precipitation at the sub-daily to multi-daily level and these extremes exhibit a much more realistic spatial pattern than what is observed in RCM precipitation output. The WG seems to correlate increased extreme intensities with an increased spatial extent of the extremes meaning that the climate-change-perturbed extremes have a larger spatial extent than those of the present climate. Overall, the WG produces robust results and is seen as a reliable procedure for downscaling RCM precipitation output for use in urban hydrology.

scholarly journals Scenarios of extreme daily precipitation for Norway under climate change

2004 ◽  
Vol 35 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Thomas Skaugen ◽  
Marit Astrup ◽  
Lars A. Roald ◽  
Eirik Førland
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Model ◽  
Daily Precipitation ◽  
Extreme Values ◽  
Global Climate Model ◽  
Extreme Value ◽  
Training Data ◽  
Max Planck ◽  
Control Data

Based on downscaled daily precipitation values from the global climate model of the Max Planck Institute in Hamburg, time series of 20 years have been generated to describe the current climate of 1980–1999 (control data) and the future climate of 2030–2049 (scenario data) for Norway. These time series serve as training data for the Randomised Bartlett-Lewis Rectangular Pulse Model (RBLRPM), a precipitation simulation model, and time series of 1000 years length have been generated to assess possible changes in the extreme precipitation regime due to climate change. The analysis of changes in extreme value patterns for annual and seasonal values in the scenario and control data sets shows tendencies towards increased extreme values and seasonal shifts for the scenario period. A general increase in mean and standard deviation of the extreme value sample and for values of 10 and 100 years return period is found, although the regional variability is significant. For some regions the increase is in the order of 10 to 50 % for both annual and seasonal values.

Weather Generators

2020 ◽  
Author(s):  
Shuiqing Yin ◽  
Deliang Chen
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Large Scale ◽  
Extreme Values ◽  
Future Climate Change ◽  
Continuous Simulation ◽  
Weather Generators ◽  
Change Impact ◽  
Multiple Variables ◽  
Climate Time Series

Weather generators (WGs) are stochastic models that can generate synthetic climate time series of unlimited length and having statistical properties similar to those of observed time series for a location or an area. WGs can infill missing data, extend the length of climate time series, and generate meteorological conditions for unobserved locations. Since the 1990s WGs have become an important spatial-temporal statistical downscaling methodology and have been playing an increasingly important role in climate-change impact assessment. Although the majority of the existing WGs have focused on simulation of precipitation for a single site, more and more WGs considering correlations among multiple sites, and multiple variables, including precipitation and nonprecipitation variables such as temperature, solar radiation, wind, humidity, and cloud cover have been developed for daily and sub-daily scales. Various parametric, semi-parametric and nonparametric WGs have shown the ability to represent the mean, variance, and autocorrelation characteristics of climate variables at different scales. Two main methodologies including change factor and conditional WGs on large-scale dynamical and thermal dynamical weather states have been developed for applications under a changing climate. However, rationality and validity of assumptions underlining both methodologies need to be carefully checked before they can be used to project future climate change at local scale. Further, simulation of extreme values by the existing WGs needs to be further improved. WGs assimilating multisource observations from ground observations, reanalysis, satellite remote sensing, and weather radar for the continuous simulation of two-dimensional climate fields based on the mixed physics-based and stochastic approaches deserve further efforts. An inter-comparison project on a large ensemble of WG methods may be helpful for the improvement of WGs. Due to the applied nature of WGs, their future development also requires inputs from decision-makers and other relevant stakeholders.

scholarly journals Forecasting the orbital-climate diagram dynamics based on the wavelet analysis and fuzzy neural networks

2018 ◽  
Vol 14 (1) ◽  
pp. 13-21
Author(s):  
Valery I. Alekseev
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Fuzzy Inference ◽  
Fuzzy Neural Networks ◽  
Time Interval ◽  
Orbital Theory ◽  
Time Series Forecast ◽  
The Past ◽  
Fuzzy Neural ◽  
Orbit Inclination

Based on the new concept of the V. Bolshakov's orbital theory of paleoclimate, as well as the multiscale time-series wavelet decomposition method, and neural network fuzzy inference rules, the paper derives a predicted curve for the so-called orbital-climate diagram (OCD) in the ratio of  (eccentricity, orbit inclination, precession) within the time interval from -1000 kyr in the past to 100 kyr in the future since modern times. This diagram features the Earth climate change caused by an insolation change to be the principal factor of the climate change, driven by Earth's orbital elements changes. Efficiency of the time-series forecast method is proved by the obtained predicted OCD trajectory verification within the past 100 kyr period and other paleoclimate data with a correlation coefficient 0.93.

Temperature and Rainfall Partial Trends in Oxford, 1870-2019

2021 ◽  
Author(s):  
Eyüp Şişman ◽  
Burak KIZILÖZ
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Trend Analysis ◽  
Temperature Increase ◽  
Innovative Design ◽  
Monotonic Trend ◽  
Climate Change Effects ◽  
The Past ◽  
Temperature And Precipitation ◽  
Average Precipitation

Abstract In this study, the trends and stabilities of temperature and precipitation hydro-meteorology time series recorded since 1870 in Oxford city of England were analyzed in detail. The Innovative Triangular Trend Analysis (ITTA) method has been inspired to identify and analyze the trends and stabilities of the selected time series. To compare the results obtained by the above-mentioned method, the Classical Mann Kendall (MK) method has been applied to each series determined for ITTA design. Thanks to the innovative design of ITTA which is preferred by the Classic MK and Sen slope methods, the trends of time series could be analyzed in detail. In this study, the first draft structure has been improved with the help of ± 5-±10 % percentage change levels which were added to the ITTA method, and thus more objective evaluations about the trend magnitudes in time series is possible. For the same draft, the monotonic trend slopes which were found by the classical MK were also calculated through the Sen slope method. The data trends could explain in more detail with the help of the draft used in this study, compared to the studies in the literature. Climate change, which has been the most important factor in trend formation in recent years, has been taken into consideration while determining the design series. The thirty-year period up to 2019, a year in which the climate change was felt much more, constitutes the most important reference years for the analysis beginning from 1990, a year in which the climate change effects started to emerge. When the data trends of one hundred fifty years are examined for the different sub-time series, it is seen that the temperature increase in during1990-2019 period is much higher than the past hundred and twenty years, according to the analysis results. The highest average precipitation occurred in the 1990–2019 and 1900–1929 periods, and their amounts and patterns are nearly similar.

scholarly journals Characteristics of Spring Phenological Changes in China over the Past 50 Years

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Junhu Dai ◽  
Huanjiong Wang ◽  
Quansheng Ge
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Woody Plants ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Observation Data ◽  
Phenological Data ◽  
The Past ◽  
The North ◽  
Spatial Differences

In order to understand past plant phenological responses to climate change in China (1963–2009), we conducted trends analysis of spring phenophases based on observation data at 33 sites from the Chinese Phenological Observation Network (CPON). The phenological data on first leaf date (FLD) and first flowering date (FFD) for five broad-leaved woody plants from 1963 to 2009 were analyzed. Since most phenological time series are discontinuous because of observation interruptions at certain period, we first interpolated phenological time series by using the optimal model between the spring warming (SW) model and the UniChill model to form continuous time series. Subsequently, by using regression analysis, we found that the spring phenophases of woody plants in China advanced at a mean rate of 0.18 days/year over the past 50 years. Changes of spring phenophases exhibited strong regional difference. The linear trends in spring phenophases were −0.18, −0.28, −0.21, −0.04, and −0.14 days/year for the Northeast China Plain, the North China Plain, the Middle-Lower Yangtze Plain, the Yunnan-Guizhou Plateau, and South China, respectively. The spatial differences in phenological trends can be attributed to regional climate change patterns in China.

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