Spatial variability of precipitation regimes over Turkey

Abstract

This study aims to predict the daily precipitation from meteorological data from Turkey using the wavelet—neural network method, which combines two methods: discrete wavelet transform (DWT) and artificial neural networks (ANN). The wavelet—ANN model provides a good fit with the observed data, in particular for zero precipitation in the summer months, and for the peaks in the testing period. The results indicate that wavelet—ANN model estimations are significantly superior to those obtained by either a conventional ANN model or a multi linear regression model. In particular, the improvement provided by the new approach in estimating the peak values had a noticeably high positive effect on the performance evaluation criteria. Inclusion of the summed sub-series in the ANN input layer brings a new perspective to the discussions related to the physics involved in the ANN structure.     

A spatial disaggregation procedure for precipitation

Abstract

A disaggregation procedure is presented to render forecast values of precipitation from an atmospheric model with spatial resolution of 11 × 11 km suitable as input for a distributed hydrological model with spatial resolution of 1.1 × 1.1 km. Statistical and morphological properties of the input field, such as spatial mean, variance, correlation structure and intermittency, are respected in the disaggregated field. The adopted approach is a combination of interpolation and simulation. The four nodal points of the atmospheric model grid cell are used both for determining the parameters of the exponential distribution for simulating precipitation values, and in a simple interpolation procedure to determine the spatial location of the precipitation values. A shifted distribution with two parameters is used in the case of full coverage of the grid cell, and a one-parameter distribution with a theoretically derived intermittency parameter is used if intermittency is present. The results are promising with respect to the statistical and morphological properties of the disaggregated field.     

Analysis and modeling of spatial correlation structure in small-scale rainfall in Central Oklahoma

Spatial correlation structure in small-scale rainfall is analyzed based on a dense cluster of raingauges in Central Oklahoma. This cluster, called the EVAC PicoNet, consists of 53 gauges installed in 25 measurement stations covering an area of about 3 km by 3 km. Two raingauges are placed in 24 stations and five in the central station. Three aspects of the estimated spatial correlation functions are discussed: dependence on time-scale ranging from 1 min to 24 h, inter-storm variability, and dependence on rainfall intensity. The results show a regular dependence of the correlogram parameters on the averaging time-scale, large differences of the correlograms in the individual storms, and the dominance of storms with high spatial variability on the average large sample characteristics. The authors also demonstrate and discuss the ambiguities in correlation estimates conditioned on rainfall intensities. The findings of this study have implications for raingauge network design, rainfall modeling, and conclusive evaluation of radar and satellite estimates of rainfall.     

Probabilistic rainfall thresholds for flood forecasting: evaluating different methodologies for modelling rainfall spatial correlation (or dependence)

Rainfall threshold (RT) method is one of the evolving flood forecasting approaches. When the cumulative rainfall depth for a given initial soil moisture condition intersects the threshold rainfall curve, the peak discharge is expected to be equal or greater than the threshold discharge for flooding at the target site. Besides the total rainfall depth, spatial and temporal distribution of rainfall impacts the flood peak discharge and the time to peak. To revisit a previous study conducted by the authors, in which spatially independent rainfall pattern was assumed, the spatial distribution of rainfall was simulated following a Monte Carlo approach. The structure of the spatial dependence among sub‐watersheds' rainfalls was taken into account under three different scenarios, namely independent, bivariate copula (2copula) and multivariate Gaussian copula (MGC). For each set of generated random dimensionless rainfalls, the probabilistic RT curves were derived for dry moisture condition. Results were evaluated with both historical and simulated events. For the simulated events, threshold curves were assessed by means of categorical statistics, such as hit rate, false rate and critical success index (CSI). Results revealed that the best performance based on the CSI criterion corresponded to 50% curve in 2copula and MGC scenarios as well as 90% curve in the independent scenario. The recognition of 50% curve in 2copula and MGC scenarios is in agreement with our expectations that the mean probable curve should have the best performance. Moreover, the proposed inclusion of spatially dependent rainfall scenario improved the performance of RT curves by about 25% in comparison with the presumed spatially uniform rainfall scenario. Copyright © 2011 John Wiley & Sons, Ltd.     

Effect of zero measurements on the spatial correlation structure of rainfall

In this study, the effect of zero measurements on the spatial correlation function of rainfall is analyzed for the quantification of a rainfall field. The use of a bivariate mixed distribution function made it possible to analyze and compare the spatial correlation functions for these three different data sets: only the positive measurements at both gauge locations, positive measurements at either one or both gauge locations, and all measurements including zero at both locations. As an example, the spatial correlation functions are derived for the Geum River Basin, Korea and evaluated for the wet and dry seasons, respectively. Results show that the effect of zero measurements on spatial correlation structures is significant during the wet season, when the inter-station correlations were estimated significantly lower than those during the dry season. It was also found that only the case considering positive measurements are valid for the quantification of rainfall field. Even during the wet season, the inter-station correlation coefficients derived by considering the zero measurements show their high variability along with many abnormally looking high estimates, which made the quantification of the spatial correlation function become very ambiguous.     

Ground motion spatial correlation fitting methods and estimation uncertainty

Ground shaking intensity varies spatially in earthquakes, and many studies have estimated correlations of intensity from past earthquake data. This paper presents a framework for quantifying uncertainty in the estimation of correlations and true variability in correlations from earthquake to earthquake. A procedure for evaluating estimation uncertainty is proposed and used to evaluate several methods that have been used in past studies to estimate correlations. The results indicate that a weighted least squares algorithm is most effective in estimating spatial correlation models and that earthquakes with at least 100 recordings are needed to produce informative earthquake-specific estimates of spatial correlations. The proposed procedure is also used to distinguish between estimation uncertainty and the true variability in model parameters that exist in a given data set. The estimation uncertainty is seen to vary between well-recorded and poorly recorded earthquakes, whereas the true variability is more stable.     

Detecting the effects of spatial variability of rainfall on hydrological modelling within an uncertainty analysis framework

Spatial patterns of rainfall are known to cause differences in observed flow. In this paper, the effects of perturbations in rainfall patterns on changes in parameter sets as well as model output are explored using the hydrological model Dynamic TOPMODEL for the Brue catchment (135 km²) in southwest England. Overall rainfall amount remains the same at each time step so the perturbations act as effectively treated errors in the spatial pattern. The errors were analysed with particular emphasis on when they could be detected under an uncertainty framework. Higher rainfall perturbations (multipliers of × 4 and greater) in the low lying and high areas of the catchment resulted in changes to event peaks and accompanying compensation in the baseflow. More significantly, changes in the effective model parameter values required by the best models to take account of the more extreme patterns were able to be detected by noting when distributions of parameters change under uncertainty. Copyright © 2009 John Wiley & Sons, Ltd.     

The links between the categorised Southern Oscillation indicators and climate and hydrologic variables in Turkey

Some previous global and regional studies have indicated teleconnection between the extreme phases of the Southern Oscillation (SO) and Turkish climate and hydrologic variables; however, they failed to suggest a strong correlation structure. In this study, categorised Southern Oscillation index (SOI) and Multivariate ENSO (El Nino Southern Oscillation) index (MEI) series were used to examine the far‐reaching effects of the SO on temperature, precipitation and streamflow patterns in Turkey. These SO indicators were categorised into five subgroups according to their empirical distributions. Correlations between the categorised SO indicators and three analysis variables were computed using the Spearman's rho from lag‐0 to lag‐4. Significance of calculated correlations was tested at the 0·01 level for station‐based analysis and at the 0·05 level for regional analysis. Temperature records demonstrated significant correlations with the categorised SOI and MEI in nearly half of the entire stations. For some categories, precipitation and streamflow were found to be correlated with the SO indicators in some stations mainly in western Turkey. Regional analyses of temperature and precipitation revealed a clear and strong correlation structure with the categorised SO indicators on a large portion of Turkey. This was not concluded by the earlier pertinent studies. Besides, this study showed that significant correlations were obtained not only for the SO extreme phases (namely, El Nino and La Nina) but also for neutral and moderate phases of the SO. Plausible explanations for the observed teleconnection are presented. Copyright © 2009 John Wiley & Sons, Ltd.     

张北地震前地磁空间相关异常特征

应用地磁空间相关方法对华北地区１９９６年１月至１９９８年１月地磁核旋定点（北京时２１ｈ）观测数据的分析研究表明,１９９８年１月１０日张北ＭＳ６．２地震和１９９６年５月３日内蒙古包头ＭＳ６．４地震前存在地磁空间相关低值异常。     

The influences of the Southern and North Atlantic Oscillations on climatic surface variables in Turkey

In this study, Turkish climatic variables (precipitation, stream flow and maximum and minimum temperatures) were first analysed in association with both the Southern Oscillation (SO) and the North Atlantic Oscillation (NAO). The relationships between Turkish maximum and minimum monthly temperatures and the extreme phases of the SO (El Niño and La Niña events) were examined. The results of this analysis showed that relationships between Turkish monthly maximum temperatures and El Niño and La Niña contain some complexity still to be identified, because both events produce a signal indicating a correspondence with cold anomalies in the aggregate composites. A relationship between turkish minimum temperatures and El Niño was detected in western Anatolia, whereas there was no significant and consistent signal associated with La Niña. Moreover a series of cross‐correlation analyses was carried out to demonstrate the teleconnections between the climatic variables and both the NAO and SO. The NAO during winter was found to influence precipitation and stream‐flow patterns. In contrast temperature patterns appeared to be less sensitive to the NAO. Furthermore, lag‐correlation results indicated a prediction potential for both precipitation and stream‐flow variables in connection with the NAO. Simultaneous and time‐lag correlations between the climatic variables and the SO index, in general, indicated weaker relationships in comparison with those for the NAO. These analyses also showed that the influences of the SO on Turkish temperature data are negligible. The outcomes were presented in conjunction with an explanation regarding physical mechanisms behind the implied teleconnections. Copyright © 2004 John Wiley & Sons, Ltd.     

Influence of spatial correlation of strong ground motion on uncertainty in earthquake loss estimation

In addition to the mean values of possible loss during an earthquake, parameters of the probability distribution function for the loss to a portfolio (e.g. fractiles and standard deviation) are very important. Recent studies have shown that the proper treatment of ground‐motion variability and, particularly, the correlation of ground motion are essential for the estimation of the seismic hazard, damage and loss for distributed portfolios. In this study, we compared the effects of variations in the between‐earthquake correlation and in the site‐to‐site correlation on seismic loss and damage estimations for the extended objects (hypothetical portfolio) and critical elements (e.g. bridges) of a network. A scenario earthquake approach and a portfolio containing a set of hypothetical building and bridges were used for the purpose. We showed that the relative influences of the types of correlation on characteristics of loss distribution and the probability of damage are not equal. In some cases, when the median values of loss distribution or the probability that at least one critical element of a lifeline will be damaged are considered and when the spatial correlation of ground motion is used, the possible variations in the between‐earthquake correlation may be neglected. The shape of the site‐to‐site correlation function (i.e. the rate of decrease of the coefficient of spatial correlation with separation distance) seems also to be important when modelling spatially correlated ground‐motion fields. Copyright © 2010 John Wiley & Sons, Ltd.     

Analyses of the warm season rainfall climatology of the northeastern US using regional climate model simulations and radar rainfall fields

We examine the warm season (April-September) rainfall climatology of the northeastern US through analyses of high-resolution radar rainfall fields from the Hydro-NEXRAD system and regional climate model simulations using the weather research and forecasting (WRF) model. Analyses center on the 5-year period from 2003 to 2007 and the study area includes the New York-New Jersey metropolitan region covered by radar rainfall fields from the Fort Dix, NJ WSR-88D. The objective of this study is to develop and test tools for examining rainfall climatology, with a special focus on heavy rainfall. An additional emphasis is on rainfall climatology in regions of complex terrain, like the northeastern US, which is characterized by land-water boundaries, large heterogeneity in land use and cover, and mountainous terrain in the western portion of the region. We develop a 5-year record of warm season radar rainfall fields for the study region using the Hydro-NEXRAD system. We perform regional downscaling simulations for the 5-year study period using the WRF model. Radar rainfall fields are used to characterize the interannual, seasonal and diurnal variation of rainfall over the study region and to examine spatial heterogeneity of rainfall. Regional climate model simulations are characterized by a wet bias in the rainfall fields, with the largest bias in the high-elevation regions of the model domain. We show that model simulations capture broad features of the interannual, seasonal, and diurnal variation of rainfall. Model simulations do not capture spatial gradients in radar rainfall fields around the New York metropolitan region and land-water boundaries to the east. The model climatology of convective available potential energy (CAPE) is used to interpret the regional distribution of warm season rainfall and the seasonal and diurnal variability of rainfall. We use hydrologic and meteorological observations from July 2007 to examine the interactions of land surface processes and rainfall from a regional perspective.     

Spatial variability and interpolation of daily precipitation amount

Daily precipitation amounts show spatial variation over sub-continential regions. Point measurements, representative for regions of land, have to be interpolated towards unobserved locations. In this study four days in 1984 were selected to investigate the spatial variability of daily precipitation amount in North-western Europe in relation to the meteorological conditions. Data were interpolated using Kriging. Crossvalidation was used to compare interpolated values with measured values. Large differences in the spatial structure of daily precipitation amount are obsered as a result of different meterological conditions. Stratification of the study area into a coastal, a mountainous and an interior stratum proved to be successful, reducing the Mean Squared Error of Prediction with up to 55%.     

Wavelet combined innovative trend analysis for precipitation data in the Euphrates-Tigris basin,Turkey

ABSTRACT

In this study, annual and seasonal precipitation trend analysis was performed in the Euphrates-Tigris basin, Turkey, using innovative trend analysis (ITA) and discrete wavelet transformation. In this context, it was seen that there is a downward trend in winter, spring and annual precipitation, whereas precipitation has an increasing tendency in summer and autumn seasons, in the greater part of the basin. When annual and seasonal data were decomposed into wavelet components, the most significant trends were observed for high-periodic wavelet components, such as D3 (8-year), D4 (16-year) and D5 (32-year), where these components represent the periods of the precipitation data. Then, the relationship between North Atlantic Oscillation (NAO) and trend in precipitation was investigated. In this regard, it was found that there could be a significant relationship between the NAO and precipitation trends of the Euphrates-Tigris basin, especially in winter, based on the wavelet ITA.     

The effects of climate change on historical and future extreme rainfall in Antalya,Turkey

Abstract

There is increasing concern that flood risk will be exacerbated in Antalya, Turkey as a result of global-warming-induced, more frequent and intensive, heavy rainfalls. In this paper, first, trends in extreme rainfall indices in the Antalya region were analysed using daily rainfall data. All stations in the study area showed statistically significant increasing trends for at least one extreme rainfall index. Extreme rainfall datasets for current (1970–1989) and future periods (2080–2099) were then constructed for frequency analysis using the peaks-over-threshold method. Frequency analysis of extreme rainfall data was performed using generalized Pareto distribution for current and future periods in order to estimate rainfall intensities for various return periods. Rainfall intensities for the future period were found to increase by up to 23% more than the current period. This study contributed to better understanding of climate change effects on extreme rainfalls in Antalya, Turkey.     

Spatial variability and interpolation of daily precipitation amount

Daily precipitation amounts show spatial variation over sub-continential regions. Point measurements, represntative for regions of land, have to be interpolated towards unobserved locations. In this study four days in 1984 were selected to investigate the spatial variability of daily precipitation amount in north-western Europe in relation to the meteorological conditions. Data were interpolated using kriging. Crossvalidation was used to compare interpolated values with measured values. Large differences in the spatial structure of daily precipitation amount are observed as a result of different meteorological conditions. Stratification of the study area into a coast, a mountain and an interior stratum proved to be successful, reducing the Mean Squared Error of Prediction with up to 55%.This article was inadvertently printed in SHH 6(3) 1992 without figures and figure legends. The article is being reprinted in this issue in complete form. The editor apologizes for this error in publication.     

A spatial cross‐correlation model of spectral accelerations at multiple periods

Many seismic loss problems (such as disruption of distributed infrastructure and losses to portfolios of structures) are dependent upon the regional distribution of ground‐motion intensity, rather than intensity at only a single site. Quantifying ground‐motion over a spatially‐distributed region therefore requires information on the correlation between the ground‐motion intensities at different sites during a single event. The focus of the present study is to assess the spatial correlation between ground‐motion spectral accelerations at different periods. Ground motions from eight well‐recorded earthquakes were used to study the spatial correlations. On the basis of obtained empirical correlation estimates, we propose a geostatistics‐based method to formulate a predictive model that is suitable for simulation of spectral accelerations at multiple sites and multiple periods, in the case of crustal earthquakes in active seismic regions. While the calibration of this model and investigation of its implications were somewhat complex, the model itself is very simple to use for making correlation predictions. A user only needs to evaluate a simple equation relying on three sets of coefficients provided here to compute a correlation coefficient for spectral values at two periods and at a specified separation distance. These results may then be used in evaluating the seismic risk of portfolios of structures with differing fundamental periods. Copyright © 2012 John Wiley & Sons, Ltd.     

Rainfall spatial variability observed by X-band weather radar and its implication for the accuracy of rainfall estimates

The main objective of this paper is to estimate the error in the rainfall derived from a polarimetric X-band radar, by comparison with the corresponding estimate of a rain gauge network. However the present analysis also considers the errors inherent to rain gauge, in particular instrumental and representativeness errors. A special emphasis is addressed to the spatial variability of the rainfall in order to appreciate the representativeness error of the rain gauge with respect to the 1 km square average, typical of the radar derived estimate. For this purpose the spatial correlation function of the rainfall is analyzed.     

A model to consider the spatial variability of rainfall partitioning within deciduous canopy. I. Model description

In this first paper of two, a numerical simulation model capable of simulating the spatial variability of rainfall partitioning within a canopy is presented. The second paper details how the model is parameterized, and some testing of its capabilities. The first stage of the model is to derive the mature canopy structure. This is achieved through simplified individual tree structures and a random placement routine based on a modified Poisson distribution. Following this the spatial discretization for throughfall is attained as a series of layers of triangles with a tree trunk at every apex. The number of layers is derived from the leaf area index through a modified Poisson distribution. Seasonal variation in the deciduous canopy is simulated through a time vector routine. The rainfall partitioning section of the model is based upon the Rutter model which has been modified to each individual triangle layer. The main feature of this model is that it offers a method of simulating rainfall partitioning at a scale that is a function of the size of the elementary physical components (tree and leaf scale). This can be used to investigate soil moisture variations under a canopy, or to study the variations within the forest hydrological processes themselves. © 1997 John Wiley & Sons, Ltd.     

Empirical nonergodic shaking scenarios based on spatial correlation models: An application to central Italy

This paper provides a new methodological framework to generate empirical ground shaking scenarios, designed for engineering applications and civil protection planning. The methodology is useful both to reconstruct the ground motion pattern of past events and to generate future shaking scenarios, in regions where strong‐motion datasets from multiple events and multiple stations are available. The proposed methodology combines (1) an ad‐hoc nonergodic ground motion model (GMM) with (2) a spatial correlation model for the source region‐, site‐, and path‐systematic residual terms, and (3) a model of the remaining aleatory error to take into account for directivity effects. The associated variability is a function of the type of scenario generated (bedrock or site, past or future event) and it is minimal for source areas where several events have occurred and for sites where recordings are available. In order to develop the region‐specific fully nonergodic GMM and to compute robust estimation of the residual terms, the approach is calibrated on a highly dense dataset compiled for the area of central Italy. Example tests demonstrate the validity of the approach, which allows to simulate acceleration response spectra at unsampled sites, as well as to capture peculiar physical features of ground motion patterns in the region. The proposed approach could be usefully adopted for data‐driven simulations of ground shaking maps, as alternative or complementary tool to physic‐based and stochastic‐based approaches.