The conventional flood frequency analysis typically assumes the annual maximum flood series (AMFS) result from a homogeneous flood population. However, actually AMFS are frequently generated by distinct flood generation mechanisms (FGMs), which are controlled by the interaction between different meteorological triggers (e.g., thunderstorms, typhoon, snowmelt) and properties of underlying surface (e.g., antecedent soil moisture and land-cover types). To consider the possibility of two FGMs in flood frequency analysis, researchers often use the two-component mixture distributions (TCMD) without explicitly linking each component distribution to a particular FGM. To improve the mixture distribution modeling in seasonally snow covered regions, an index called flood timescale (FT), defined as the ratio of the flood volume to peak value and chosen to reflect the relevent FGM, is employed to classify each flood into one of two types, i.e., the snowmelt-induced long-duration floods and the rainfall-induced short-duration floods, thus identifying the weighting coefficient of each component distribution beforehand. In applying the FT-based TCMD to model the AMFS of 34 watersheds in Norway, ten types of mixture distributions are considered. The design floods and associated confidence intervals are calculated using parametric bootstrap method. The results indicate that the FT-based TCMD model reduces the uncertainty in the estimation of design floods for high return periods by up to 40% with respect to the traditional TCMD. The improved predictive ability of the FT-based TCMD model is attributed to its explicit recognition of distinct 3 generation mechanisms of floods, thereby being able to identify the weighting coefficient and FGM of each component distribution without optimization.
In traditional flood frequency analysis, a minimum of 30 observations is required to guarantee the accuracy of design results with an allowable uncertainty; however, there has not been a recommendation for the requirement on the length of data in NFFA (nonstationary flood frequency analysis). Therefore, this study has been carried out with three aims: (i) to evaluate the predictive capabilities of nonstationary (NS) and stationary (ST) models with varying flood record lengths; (ii) to examine the impacts of flood record lengths on the NS and ST design floods and associated uncertainties; and (iii) to recommend the probable requirements of flood record length in NFFA. To achieve these objectives, 20 stations with record length longer than 100 years in Norway were selected and investigated by using both GEV (generalized extreme value)-ST and GEV-NS models with linearly varying location parameter (denoted by GEV-NS0). The results indicate that the fitting quality and predictive capabilities of GEV-NS0 outperform those of GEV-ST models when record length is approximately larger than 60 years for most stations, and the stability of the GEV-ST and GEV-NS0 is improved as record lengths increase. Therefore, a minimum of 60 years of flood observations is recommended for NFFA for the selected basins in Norway.
<p>In the traditional flood frequency analysis, researchers typically assume the flood events result from a homogeneous flood population. However, actually flood events are likely to be generated by distinct flood generation mechanisms (FGMs), such as snowmelt-induced floods and rainfall-induced floods. To address this problem in flood frequency analysis, currently, the most popular practice for mixture modeling of flood events is to use two-component mixture distributions (TCMD) without a priori classification of distict FGMs, which could result in component distributions without physical reality or lead to a larger standard error of the estimated quantiles. To improve the mixture distribution modeling in Norway, we firstly classify the flood series of 34 watersheds into snowmelt-induced long-duration floods and rainfall-induced short-duration floods based on an index named flood timescale (FT), defined as the ratio of the flood volume to peak value. A total of ten types of mixture distributions are considered in the application of FT-based TCMD to model the flood events in Norway. The results indicate that the FT-based TCMD model can reduce the uncertainty in the estimation of design floods. The improved predictive ability of the FT-based TCMD model is largely due to its explicit recognition of distinct FGMs, enabling the determination of the weighting coefficient without optimization.</p>
Detection and attribution of precipitation variability are fundamentally challenging, especially in the presence of complex nonlinear relationships between precipitation variability and large‐scale teleconnections. The aim of this study is twofold. First, we identify abrupt changes and the trend and periodicity characteristics of long‐term (1950–2019) annual precipitation series over the Norway mainland by using the Multiple Comparison Procedures, the Mann‐Kendall test, and the Wavelet Analysis. Second, we interpret the variability characteristics found over five regions of Norway by exploring their relationships with teleconnections through the Maximal Information Coefficient. The results indicate that significant abrupt changes appeared in the mean (variance) value of annual precipitation series at 117 (49) out of 159 rainfall stations of five regions in Norway at a significance level of 0.05. The occurrences of change points varied from 1979 to 1984 in five regions of Norway. The mean and variance of the annual precipitation series increased by 32% and 16% at most, respectively, compared with those values before the change points. The first periodicity (spanning four to five decades) was the dominant periodic component and could be used to best characterize the Norwegian precipitation variability. Because the subtropical Azores High (subpolar Icelandic Low) moves toward (away from) Europe, the relationship between the annual precipitation series and the Scandinavian pattern (Atlantic Multidecadal Oscillation) series tended to be of an upward (downward) hook structure. The association of precipitation variability and teleconnections found in this study can pave the way for new possibilities with regard to detection and attribution of precipitation variability.
<p>We present a new runoff map for Norway for the reference period 1991-2020. A new framework combing precipitation runoff-modelling with geostatistical interpolation was used. The precipitation-runoff model WASMOD was used to simulate runoff on a 1x1 km grid covering all Norway and nearby catchments in Sweden and Finland. The parameters of WASMOD were conditioned on land-use and climate classes and calibrated globally using data from 215 streamflow stations. Figure 1a) shows the runoff estimates from WASMOD that are biased when compared to observations (Figure 2a) .</p><p>To correct the biases in the gridded simulations we used runoff observations from 732 locations of which 198 had data covering the whole period. A geostatistical approach was used to estimate the 30 year mean annual runoff from short records (1-29 years) for 482 catchments. For 45 catchments with substantial glacier coverage or regulation capacity, a manual extension of mean annual runoff was performed. Another 7 stations with between 25 and 30 years of data were included.</p><p>The biases from WASMOD were corrected by simple linear regression using the raw runoff map as a covariate and the runoff observations as the dependent variable. The regression coefficients were modelled as spatial fields using a geostatistical Bayesian approach where SPDE and INLA ensured fast Bayesian inference. The mean annual runoff after the correction is shown in Figure 1b), and in Figure 1c) the difference between the two previous maps is shown. Figure 2b shows a scatter plot of corrected and observed runoff. The scatter is now much smaller.</p><p><img src="data:image/png;base64, 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