Abstract. Regional frequency analyses based on index flood procedures have been used within the hydrologic community since 1960. It appears that when the index flood method was first suggested, the index flood was taken to be the at-site population mean, which, in turn, in the last two or three decades, has been estimated by the at-site sample mean. The objectives of this paper are to investigate the consequences of replacing a population characteristic with its sample counterpart and to propose an analytically correct regional model dubbed as the population index flood (PIF) method. In this method the homogeneity of the region is embedded in the structure of the parameter space of the underlying distribution model. Simulation experiments are conducted to test the proposed PIF method based on the generalized extreme value distribution with parameters estimated using the method of maximum likelihood (MLE) and the method of probabilityweighted moments (PWM). Furthermore, in the simulation experiments the PIF method is compared with the Hosking and Wallis [1997] regional estimation scheme (HW scheme). Comparing among all index flood methods investigated herein, the PIF method with parameters estimated using MLE provides the best overall results for the 0.95 and the 0.99 quantiles in terms of both bias and root-mean-square error for moderate to sufficiently large sample sizes, but for the 0.995 quantile the HW scheme seems to perform best for the investigated sample sizes.

The stochastic analysis, modeling, and simulation of climatic and hydrologic processes such as precipitation, streamflow, and sea surface temperature have usually been based on assumed stationarity or randomness of the process under consideration. However, empirical evidence of many hydroclimatic data shows temporal variability involving trends, oscillatory behavior, and sudden shifts. While many studies have been made for detecting and testing the statistical significance of these special characteristics, the probabilistic framework for modeling the temporal dynamics of such processes appears to be lacking. In this paper a family of stochastic models that can be used to capture the dynamics of abrupt shifts in hydroclimatic time series is proposed. The applicability of such ''shifting mean models'' are illustrated by using time series data of annual Pacific decadal oscillation (PDO) indices and annual streamflows of the Niger River.

No abstract

Abstract. During the melt season, absorbed solar energy, modulated at the surface predominantly by albedo, is one of the main governing factors controlling surface-melt variability for glaciers in Iceland. Using MODIS satellite-derived daily surface albedo, a gap-filled temporally continuous albedo product is derived for the melt season (May to August (MJJA)) for the period 2000–2019. The albedo data are thoroughly validated against available in situ observations from 20 glacier automatic weather stations for the period 2000–2018. The results show that spatio-temporal patterns for the melt season have generally high annual and inter-annual variability for Icelandic glaciers, ranging from high fresh-snow albedo of about 85 %–90 % in spring to 5 %–10 % in the impurity-rich bare-ice area during the peak melt season. The analysis shows that the volcanic eruptions in 2010 and 2011 had significant impact on albedo and also had a residual effect in the following years. Furthermore, airborne dust, from unstable sandy surfaces close to the glaciers, is shown to enhance radiative forcing and decrease albedo. A significant positive albedo trend is observed for northern Vatnajökull while other glaciers have non-significant trends for the study period. The results indicate that the high variability in albedo for Icelandic glaciers is driven by climatology, i.e. snow metamorphosis, tephra fallout during volcanic eruptions and their residual effects in the post-eruption years, and dust loading from widespread unstable sandy surfaces outside the glaciers. This illustrates the challenges in albedo parameterization for glacier surface-melt modelling for Icelandic glaciers as albedo development is driven by various complex phenomena, which may not be correctly captured in conventional energy-balance models.

The performance of different models and procedures for forecasting aggregated May-July streamflow for the Churchill Falls basin on the Québec-Labrador peninsula is compared. The models compared have different lead times and include an autoregressive model using only past streamflow data, an autoregressive with exogenous input model utilizing both past streamflow and precipitation, and a linear regression model using the principal components of exogenous measures of atmospheric circulation inferred from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis project. The forecast skills of the different approaches are compared using a variety of measures of performance. The results indicate that relatively accurate forecasts using only measures of atmospheric circulation can be issued as early as in December of the prior year. A multimodel combination approach is found to be more effective than the use of a single forecast model. In addition, it is concluded that forecasting models utilizing atmospheric circulation data are useful, especially for basins where hydroclimatic observations are scarce and for basins where flows and other hydroclimatic variables are not strongly autocorrelated ͑do not depend on their past͒.