Daily Rainfall Time Series Research Articles

Nonstationary Frequency Analysis of Annual Maximum Rainfall Using Climate Covariates

The perception that hydrometeorological processes are non stationary on timescales that are applicable to extreme value analysis is recently well documented due to natural climate variability or human intervention. In this study the generalized extreme value (GEV) distribution is used to assess nonstationarity in annual maximum daily rainfall time series for selected meteorological stations in Greece and Cyprus. The GEV distribution parameters are specified as functions of time-varying covariates and estimated using the conditional density network (CDN) as proposed by Cannon (2010). The CDN is a probabilistic extension of the multilayer perceptron neural network. If one of the covariates is dependent on time, then the GEV-CDN model could perform non stationary extreme value analysis. Model parameters are estimated via the generalized maximum likelihood (GML) approach using the quasi-Newton BFGS optimization algorithm, and the appropriate GEV-CDN model architecture for a selected meteorological station is selected by fitting increasingly complicated models and choosing the one that minimizes the Akaike information criterion with small sample size correction or the Bayesian information criterion. For each meteorological station in Greece and Cyprus different formulations are tested with combinational cases of stationary and non stationary parameters of the GEV distribution, linear and nonlinear architecture of the CDN and combinations of the input climatic covariates. Climatic covariates examined in this study are the Southern Oscillation Index (SOI), which describes atmospheric circulation in the eastern tropical Pacific related to El Nino Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) index that varies on an interdecadal rather than inter annual time scale and atmospheric circulation patterns as expressed by the Mediterranean Oscillation Index (MOI) and North Atlantic Oscillation (NAO) indices.

Analysis of spatial and temporal patterns in onset, cessation and length of growing season in Nigeria

Proper understanding of patterns, distribution and changes in rainfall onset dates (ROD), rainfall cessation dates (RCD) and lengths of growing seasons (LGS) is crucial for agricultural planning, adaptation strategies and food security in Nigeria. This study determines the spatial and temporal changes and examines the relationships among these rainfall attributes in addition to possible synoptic connectivity amongst measurement stations. The data used consist of time series of daily rainfall between 1971 and 2005. ROD and RCD were determined using rainfall-based methods proposed and commonly used in Nigeria. The results show that the mean ROD, RCD and LGS have zonal distributions over Nigeria. Onset dates are delayed from the coast northward leading to shorter LGS in those stations. Variability is generally high below 8°N with coefficient of variation (CV) ranging from 13 to 36% for ROD; 3–12% for RCD and 8–17% for LGS. In the middle belt (8–11°N), spatially CV showed values ranging from 10 to 20% for ROD; 3–8% for RCD and 8–21% for LGS. In the Sahel (above 11°N), rains come later in the year and CV showed values ranging from 2 to 9% for ROD; 2–3% for RCD and 12–30% for LGS. Similarly, of the entire Nigeria, 34.8% and 50.0% exhibit earlier RODs and shorter LGS. Whereas in 40.6% and 68.8% of the stations, delayed RCDs and higher SPI (standardized precipitation index) were observed. The observed association between LGS and ROD was very strong, which may be explored to predict potential LGS. Results from the principal component analysis showed that about ten components contain information on possible geo-environmental processes behind variability in onset and cessation across all the synoptic stations of Nigeria. The signatures of SST, ITD and other features of monsoon systems including large scale forcings of SOI and NAO are discussed together with the implications of the changes in the rainfall attributes examined. This information could provide baseline data for development of appropriate local to zonal adaptation measures as well as future studies related to climate change impacts on rainfall attributes considered in this study.

Multifractal analyses of daily rainfall time series in Pearl River basin of China

The multifractal properties of daily rainfall time series at the stations in Pearl River basin of China over periods of up to 45 years are examined using the universal multifractal approach based on the multiplicative cascade model and the multifractal detrended fluctuation analysis (MF-DFA). The results from these two kinds of multifractal analyses show that the daily rainfall time series in this basin have multifractal behavior in two different time scale ranges. It is found that the empirical multifractal moment function K(q) of the daily rainfall time series can be fitted very well by the universal multifractal model (UMM). The estimated values of the conservation parameter H from UMM for these daily rainfall data are close to zero indicating that they correspond to conserved fields. After removing the seasonal trend in the rainfall data, the estimated values of the exponent h(2) from MF-DFA indicate that the daily rainfall time series in Pearl River basin exhibit no long-term correlations. It is also found that K(2) and elevation series are negatively correlated. It shows a relationship between topography and rainfall variability.

A hidden seasonal switching model for multisite daily rainfall

[1] A hidden seasonal switching model for daily rainfall over a region is proposed where season onset times are stochastic and can vary from year to year. The model allows seasons to occur earlier or later than expected and have varying lengths. This stochastic seasonal variation accommodates considerably more of the observed intraannual rainfall variability than can be represented using seasonal models with standard fixed seasons. In essence, the model dynamically classifies daily rainfall time series into seasons whose onsets vary from year to year and within which the model parameters are assumed to be time homogeneous. A variety of nonseasonal models could have been used to describe daily rainfall within seasons. Here a generalization of the Richardson model is adopted which has rainfall states (dry, light rain, and heavy rain) some of which are hidden or unobserved. It is further assumed that the rainfall states generate rainfall that is independent of season (seasonally invariant), so it is only the dynamics of the rainfall states that vary from season to season. A suitable estimation strategy based on maximum likelihood and the EM algorithm is developed for fitting the model across a region. This strategy is validated on simulated data. Various forms of the model are fitted to daily rainfall measurements from 12 sites in southern New Zealand. These results are discussed and compared to those from fitting standard fixed season models.

Hybrid of ARIMA-GARCH Modeling in Rainfall Time Series

The dependence structure of rainfall is usually very complex both in time and space. It is shown in this paper that the daily rainfall series of Ipoh and Alorsetar are affected by nonlinear characteristics of the variance often referred to as variance clustering or volatility, where large changes tend to follow large changes and small changes tend to follow small changes. In most empirical modeling of hydrological time series, the focus was on modeling and predicting the mean behavior of the time series through conventional methods of an Autoregressive Moving Average (ARMA) modeling proposed by the Box Jenkins methodology. The conventional models operate under the assumption that the series is stationary that is: constant mean and either constant variance or season-dependent variances, however, does not take into account the second order moment or conditional variance, but they form a good starting point for time series analysis. The residuals from preliminary ARIMA models derived from the daily rainfall time series were tested for ARCH behavior. The autocorrelation structure of the residuals and the squared residuals were inspected, the residuals are uncorrelated but the squared residuals show autocorrelation, the Ljung-Box test confirmed the results. McLeod-Li test and a test based on the Lagrange multiplier (LM) principle were applied to the squared residuals from ARIMA models. The results of these auxiliary tests show clear evidence to reject the null hypothesis of no ARCH effect. Hence indicates that GARCH modeling is necessary. Therefore the composite ARIMA-GARCH model captures the dynamics of the daily rainfall series in study areas more precisely. On the other hand, Seasonal ARIMA model became a suitable model for the monthly average rainfall series of the same locations treated.

Application of Multi-site Weather Generators for Investigating Wet and Dry Spell Lengths under Climate Change: A Case Study in Southern Taiwan

The study compared the performances of three weather generators (WGs), including a parametric model and two non-parametric models, in producing synthetic daily rainfall time series for multiple sites. The observed daily rainfalls of six raingauges during 1979~2008 in the catchment of Tseng-Wen Reservoir in Southern Taiwan were used as the data set. The generated results reveal that the k-nearest neighbor WG with a fixed window (i.e., a non-parametric model) is the best for daily rainfall generation at each site and performs well in preserving spatial correlation of rainfall among sites. The best WG was further applied to assess the impact of climate change on rainfall temporal characteristics (i.e., annual number of wet day, annual maximum number of continuous wet days and annual maximum number of continuous dry days) by using the downscaling results of 24 GCMs under the A1B emission scenario during 2020~2039. It is found that the rainfall temporal characteristics will change in the future which may make Southern Taiwan tend to face a longer period with no rain.

Precipitation pattern analysis in the Tiber River basin (central Italy) using standardized indices

ABSTRACTThis study analyses the precipitation pattern in the Tiber River basin (central Italy) to determine trends, possible change points, periodicity and relationships with global climate indices, such as the North Atlantic Oscillation (NAO) index. The dataset is composed of daily rainfall time series, recorded at 21 to 102 different sampling stations, from 1952 to 2007. The following quantities were analysed at both an annual and seasonal scale: cumulative precipitation, number of rainy days, mean intensity, maximum daily precipitation and number of days needed to reach a given percentage of the cumulative precipitation. To compare the time series collected from different stations along with the different quantities, a standardization procedure based on the normal inverse function computed on the best‐fit cumulative probability distribution was applied. The standardized time series from each station was regionalized by kriging. Trends were analysed using the Mann–Kendall test, and possible change points were determined using an algorithm based on the singular spectrum analysis. The periodicity of the signals was evaluated by cross‐wavelet analysis. The results show a decreasing trend in annual precipitation (−8%) mainly because of decreasing winter precipitation (−16%). This trend appears to be related to a decrease in the number of rainy days. A significant change point in precipitation trends was identified in the mid‐1980s. Conversely, those indices related to the intensity of precipitation (mean intensity, maximum daily precipitation) do not show significant trends. The cross‐wavelet analysis performed on the annual/winter precipitation and NAO time series indicates the existence of four coherent periodic signals with periods of approximately 3, 4, 8 and 15 years. Copyright © 2012 Royal Meteorological Society

Comparison of cokriging and adaptive neuro-fuzzy inference system models for suspended sediment load forecasting

The present research was carried out by using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), cokriging (CK) and ordinary kriging (OK) using the rainfall and streamflow data for suspended sediment load forecasting. For this reason, the time series of daily rainfall (mm), streamflow (m3/s), and suspended sediment load (tons/day) data were used from the Kojor forest watershed near the Caspian Sea between 28 October 2007 and 21 September 2010 (776 days). Root mean square error, efficiency coefficient, mean absolute error, and mean relative error statistics are used for evaluating the accuracy of the ANN, ANFIS, CK, and OK models. In the first part of the study, various combinations of current daily rainfall, streamflow and past daily rainfall, streamflow data are used as inputs to the neural network and neuro-fuzzy computing technique so as to estimate current suspended sediment. Also, the accuracy of the ANN and ANFIS models are compared together in suspended sediment load forecasting. Comparison results reveal that the ANFIS model provided better estimation than the ANN model. In the second part of the study, the ANN and ANFIS models are compared with OK and CK. The comparison results reveal that CK was a better estimation than the OK. The ANFIS and ANN models also provided better estimation than the OK and CK models.

Validation of the satellite-derived rainfall estimates over the Tibet

Measuring rainfall from space appears to be the only cost effective and viable means in estimating regional precipitation over the Tibet, and the satellite rainfall products are essential to hydrological and agricultural modeling. A long-standing problem in the meteorological and hydrological studies is that there is only a sparse raingauge network representing the spatial distribution of precipitation and its quantity on small scales over the Tibet. Therefore, satellite derived quantitative precipitation estimates are extremely useful for obtaining rainfall patterns that can be used by hydrological models to produce forecasts of river discharge and to delineate the flood hazard area. In this paper, validation of the US National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) RFE (rainfall estimate) 2.0 data was made by using daily rainfall observations at 11 weather stations over different climate zones from southeast to northwest of the Tibet during the rainy season from 1 June to 30 September 2005 and 2006. Analysis on the time series of daily rainfall of RFE-CPC and observed data in different climate zones reveals that the mean correlation coefficients between satellite estimated and observed rainfall is 0.74. Only at Pali and Nielamu stations located in the southern brink of the Tibet along the Himalayan Mountains, are the correlation coefficients less than 0.62. In addition, continuous validations show that the RFE performed well in different climate zones, with considerably low mean error (ME) and root mean square error (RMSE) scores except at Nielamu station along the Himalayan range. Likewise, for the dichotomous validation, at most stations over the Tibet, the probability of detection (POD) values is above 73% while the false alarm rate (FAR) is between 1% and 12%. Overall, NOAA CPC RFE 2.0 products performed well in the estimation and monitoring of rainfall over the Tibet and can be used to analyze the precipitation pattern, produce discharge forecast, and delineate the flood hazard area.

Detecting changes in streamflow after partial woodland clearing in two large catchments in the seasonal tropics

summary We used daily rainfall and streamflow time series from two large catchments in the seasonal tropics of Queensland, Australia to investigate the hydrological impacts of woodland clearing. The Comet catchment (16,440 km 2 ) had 45% of the native woodland cleared during the mid-1960s. In the Upper Burdekin catchment (17,299 km 2 ) clearing decreased native woodland extent from 83% to 58% between 1998 and 2009. An earlier modelling study concluded that clearing in the Comet catchment increased annual streamflow by more than 40%. Here, several published inference methods to separate land use effects from climate variability were applied. Trend analysis of daily rainfall and streamflow data showed that interannual changes in mean streamflow in the Comet catchment were mostly due to changes in rainfall. In particular, a series of La Nina events after clearing led to an unusual lack of dry periods and an apparently associated temporary increase in runoff coefficient. The overriding importance of climate variability was further confirmed using a conceptual framework that was used to interpret changes in the long-term coupled water–energy budget. Even so, there was some evidence for a slight increase in streamflow for the first few years after clearing. Fitting a Budyko-type model for two climatically similar pre- and postclearing periods (1920–1953 and 1979–2007) did not suggest a considerable change in the catchment water balance after clearing. Analysis of daily streamflow metrics did reveal some changes however, with enhanced peak flows and reduced low flows. In the Upper Burdekin catchment, trend analysis revealed a change in baseflow dynamics after clearing, while event storm flow for large rainfall events increased. In summary, woodland clearing in northern Queensland appears to have had a smaller impact on mean and interannual streamflow than might be expected from studies at sites and in small experimental catchments, but changes in daily streamflow patterns do suggest a modest change in catchment dynamics.

Analyses of annual and seasonal maximum daily rainfall accumulations for Ukraine, Moldova, and Romania

AbstractAnnual and seasonal maximum daily rainfall time series from 44 rain gages in Romania, Ukraine, and Moldova are used to examine questions related to the validity of the stationarity assumption, the dependence of extreme rainfall on elevation, and its upper tail properties during the 20th century. Analyses are performed both at the annual and seasonal scales. The validity of the stationarity assumption is examined by testing the records for abrupt and slowly varying changes. The presence of change‐points in the mean and variance of the daily maximum rainfall distribution is assessed using the nonparametric Pettitt test, while the presence of monotonic patterns is examined using the nonparametric Mann‐Kendall and Spearman tests. These results indicate that most of the violations of the stationarity assumption are related to abrupt rather than slowly varying changes, and that it is difficult to detect an anthropogenic climate change signal in these records. The generalized extreme value (GEV) distribution is used as modelling framework to investigate the dependence of extreme rainfall on elevation and its upper tail properties. Both at the annual and seasonal scales there is no strong dependence of the parameters of the GEV distribution on elevation. These records point to an unbounded‐above and heavy‐tail behaviour, and discussion of these upper tail properties in light of mixtures of different rainfall‐generating mechanisms is provided. Copyright © 2011 Royal Meteorological Society

Conceptual Rainfall–Runoff Model Performance with Different Spatial Rainfall Inputs

AbstractDifferent methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall–runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall–runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the “distributed” application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall–runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required.

Investigation into the impacts of land-use change on sediment yield characteristics in the upper Huaihe River basin, China

The processes of sediment generation in the upper Huaihe River basin have been altered by the intensified human activities over the past decades, particularly land-use change. To investigate the impacts of land-use change on the sediment yield characteristics in the upper Huaihe River, the catchment above the Xixian hydrological controlling station was selected as the case study site. The Soil and Water Assessment Tool (SWAT) model was used to simulate land-use change effects on sediment yield by the use of three-phase (1980s, 1990s and 2000s) land-use maps, soil type map (1:200,000) and 1987–2008 daily time series of rainfall from the upper Huaihe River basin. On the basis of the simulated time series of daily sediment concentration, land-use change effects on spatio-temporal change patterns of soil erosion modulus, rainfall–sediment yield relationship, and the sensitivity of rainfall–sediment yield relationship to rainfall for different types of land use were explored. The results revealed that under the same condition of soil texture and terrain slope the advantage for sediment yield and the sensitivity of rainfall–sediment yield relationship to rainfall descended by woodland, paddy field and farmland. The outputs of the paper could provide references for soil and water conservation and river health protection in the upper stream of Huaihe River.

Multifractal analysis of daily rainfall from a monsoon watershed in Eastern China

The multifractal behavior of daily rainfall was investigated for a watershed in Eastern China to better understand the temporal structure of rainfall under monsoonal climate. In this study, over periods of up to 46 years, daily rainfall recorded in 1962 to 2007 at 10 meteorological stations in the administrative area of Lin-Yi City in Shandong province were analyzed with focus on features of power spectra, standard statistical moments, and exceedance probability tails of these daily rainfall time series. Spectral analysis and study of the moments of the rainfall intensity showed that a scaling range from 1 day to 1 year is present. Empirical moment scaling functions of the rainfall intensity calculated for different moments of order suggested that the values of universal multifractal parameters α and C1 for all stations were approximated to 0.7 and 0.37, respectively. Comparing with the parameters estimated in other literatures, our results showed higher values for α but lower values for C1 in general, which suggested that the rainfall series in the study watershed influenced by the East-Asia monsoon climate have similarities to that in France, but are spikier and smoother than that in the semi-arid region in Portugal. The parameter H values were estimated as vary from −0.18 to −0.22, which is similar to the result obtained by Tessier et al. (J Geophys Res-Atmos 101:26427–26440, 1996).

Regionalization of the probability of wet spells and rainfall persistence in the Basque Country (Northern Spain)

AbstractThe following paper deals with the modelling of a 30‐year‐long daily rainfall time series (1965–1994) from 39 weather stations in the Basque Country region in Northern Spain. Markov chain models are used to explore the temporal rainfall patterns in the area and to characterize the persistence of daily rain in terms of the order of the discrete Markov process. The Markov models—theoretical probabilities—are fitted to the empirical probability of wet spells lasting one more day than a specific length in the time series under the assumption of three different values (1, 5 and 10 mm) of a minimum threshold. Markovian orders that first fit the empirical distributions are used to map the theoretical persistence of daily rainfall in the Basque Country area through the use of geostatistical interpolators. Markovian orders have been confirmed as remarkable stochastic models for fitting empirical distributions of wet spells in this area, ultimately being shown as a spatial representation of theoretical persistence. Copyright © 2011 Royal Meteorological Society

Investigation into the Impacts of Land-Use Change on Runoff Generation Characteristics in the Upper Huaihe River Basin, China

AbstractLand-use change has significant impacts on hydrologic processes at the watershed level; thus, quantitative assessment on the impacts of land-use change is vital for basin environment protection and water resources sustainable development. Owing largely to computer and geographical information system (GIS) technology improvements, the distributed hydrological models, which allow describing the temporal variability and spatial distribution of water-balance components, offer an effective approach to quantify the land-use change effects on watershed water quantity. In this study, a soil and water assessment tool model was used to simulate land-use change effects on water quantity in the upper Huaihe River basin in China above the Xixian hydrological controlling station with a catchment area of 10,190 km2 by the use of temporal three-phase (1980s, 1990s, 2000s) land-use maps, soil type map (1:200,000 scale), and 1980–2008 daily time series of rainfall from the upper Huaihe River basin. Within the mode...

Temporal complexity of daily precipitation records from different atmospheric environments: Chaotic and Lévy stable parameters

Rainfall events are very erratic at short and large temporal and spatial scales. The main objectives of the present study were (i) to describe different time series of daily precipitation records using both chaos theory and stable distribution methods and (ii) to search for potential relationships between chaotic and Lévy-stable parameters. We studied eight time series of daily rainfall from different latitudes around the world. Each rainfall signal spanned nine years (1997–2005). We used methods derived from chaos theory (embedding delays, spectrum of Lyapunov exponents, determinism tests and others) and parameters computed after fitting a stable distribution model to each differenced time series of rainfall data. All the rainfall signals showed chaotic structures with two positive Lyapunov exponents. The stability index was α < 2 which accounts for the scale-free behavior of rainfall dynamics. There were found latent statistical relationships between chaotic and Lévy stable parameters. That represents a potential connection between chaotic behavior, sub-Gaussian statistics and rainfall dynamics. Future research should deal with the connection between chaotic invariants, stable parameters and rainfall phenomenology.

On the frequency of heavy rainfall for the Midwest of the United States

Summary Annual maximum daily rainfall time series from 221 rain gages in the Midwest United States with a record of at least 75 years are used to study extreme rainfall from a regional perspective. The main topics of this study are: (i) seasonality of extreme rainfall; (ii) temporal stationarity and long-term persistence of annual maximum daily rainfall; (iii) frequency analyses of annual maximum daily rainfall based on extreme value theory; and (iv) clustering of heavy rainfall events and impact of climate variables on the frequency of occurrence of heavy rainfall events. Annual maximum daily rainfall in the Midwest US exhibits a marked seasonality, with the largest frequencies concentrated in the period May–August. Non-parametric tests are used to examine the validity of the stationarity assumption in terms of both abrupt and slowly varying temporal changes. About 10% of the stations show a change-point in mean and/or variance. Increasing monotonic patterns are detected at 19 stations. Quantile regression analyses suggest that the number of stations with a significant increasing trend tends to decrease for increasing quantiles. Temporal changes in the annual maximum daily rainfall time series are also examined in terms of long-term persistence. Conclusive statements about the presence of long-term persistence in these records are, however, not possible due to the large uncertainties associated with the estimation of the Hurst exponent from a limited sample. Modeling of annual maximum daily rainfall records with the Generalized Extreme Value (GEV) distribution shows well-defined spatial patterns for the location and scale parameters but not for the shape parameter. Examination of the upper tail properties of the annual maximum daily rainfall records points to a heavy tail behavior for most of the stations considered in this study. The largest values of the 100-year annual maximum daily rainfall are found in the area between eastern Kansas, Iowa, and Missouri. Finally, we use the Poisson regression as a framework for the examination of clustering of heavy rainfall. Our results point to a clustering behavior due to temporal fluctuations in the rate of occurrence of the heavy rainfall events, which is modulated by climatic factors representing the influence of both Atlantic and Pacific Oceans.

A multiple threshold method for fitting the generalized Pareto distribution to rainfall time series

Abstract. Previous studies indicate the generalized Pareto distribution (GPD) as a suitable distribution function to reliably describe the exceedances of daily rainfall records above a proper optimum threshold, which should be selected as small as possible to retain the largest sample while assuring an acceptable fitting. Such an optimum threshold may differ from site to site, affecting consequently not only the GPD scale parameter, but also the probability of threshold exceedance. Thus a first objective of this paper is to derive some expressions to parameterize a simple threshold-invariant three-parameter distribution function which assures a perfect overlapping with the GPD fitted on the exceedances over any threshold larger than the optimum one. Since the proposed distribution does not depend on the local thresholds adopted for fitting the GPD, it is expected to reflect the on-site climatic signature and thus appears particularly suitable for hydrological applications and regional analyses. A second objective is to develop and test the Multiple Threshold Method (MTM) to infer the parameters of interest by using exceedances over a wide range of thresholds applying again the concept of parameters threshold-invariance. We show the ability of the MTM in fitting historical daily rainfall time series recorded with different resolutions and with a significative percentage of heavily quantized data. Finally, we prove the supremacy of the MTM fit against the standard single threshold fit, often adopted for partial duration series, by evaluating and comparing the performances on Monte Carlo samples drawn by GPDs with different shape and scale parameters and different discretizations.

Assessing impact of global change on flood regimes

PurposeGlobal change results both from climatic and land practice evolution in response to anthropogenic needs for development and human safety. The purpose of this paper is to describe a method to assess the respective effect of both sources of change on the flood regimes.Design/methodology/approachThe research takes place in the Western periurban part of Lyon (France), which is characterized by a rapidly expanding, scattered urban development since the 1970s. An increase in frequency of large floods is reported. At the same time, a long daily rainfall time series exhibits sensitive changes in rainfall durations and intensities. Independent analysis of global change components is performed using observed rainfall and land‐use data from two disconnected decades. A marked difference in natural climatic regime variability between decades is used as a surrogate to study effect of climate change.FindingsAnthropogenic activity at the observed rate of land use change, in particular urban change, mainly influences the frequent flood distribution. The observed large flood increase results both of longer rainfall events and more heavy daily rainfall. From simulations, a 43 percent urban cover as planned from 2025 projection would have a very sensitive effect also on larger floods, giving the hand to a more anthropogenic‐based flood control.Originality/valueDespite an expected increase in rainfall and flow variability regimes as a result of climate change and a projected growing of the world urban population, there is a lack of methodology to address combination of both processes on the flood regimes. A method is proposed to judge on the respective importance and interplay of these processes.