1,1,2,1,1- 1BOKU University, Institute of Statistics, Department of Natural Sciences and Sustainable Resources, Wien, Austria
- 2Wageningen University and Research, Hydrology and Environmental Hydraulics Group, Wageningen, Netherlands
- 3BOKU University, Institute of Hydrobiology and Water Management, Wien, Austria
Abstract
Extreme low flow is a defining aspect of river regimes, posing significant risks to water management through reduced water availability and deteriorating water quality. Reliable estimates of design low flows for given non-exceedance probabilities are therefore essential. Traditional low-flow frequency analysis assumes independent and identically distributed (i.i.d.) data, an assumption increasingly violated under climate change and by distinct summer-winter generation processes. In snow-influenced climates, annual low flows can arise from events in both seasons with potential seasonal dependence, that challenges conventional models. This study extends traditional low-flow frequency analysis to non-stationary conditions by jointly accounting for temporal trends, process heterogeneity, and seasonal dependence. Building on the mixed distribution and mixed copula frameworks of Laaha (2023a, 2023b), the approach is extended to non-stationary conditions using the three-parameter Weibull distribution, allowing the seasonal low-flow distributions to change over time. The resulting models are evaluated across the European Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) dataset. Results indicate that neglecting non-stationarity when present can misrepresent low-flow severity, particularly for longer return periods. By preserving the conceptual consistency of the previous stationary modelling framework, the proposed non-stationary framework improves the statistical description of extreme low-flow events and provides an enhanced basis for low-flow frequency analysis, offering new insights into past and current low-flow behaviour under climate change.
Keywords: Non-stationary frequency analysis, low flow, drought, climate change, seasonality
Reference
Laaha, G. (2023a). A mixed distribution approach for low-flow frequency analysis – Part 1: Concept, performance, and effect of seasonality. Hydrol. Earth Syst. Sci., 27(3), 689-701. https://doi.org/10.5194/hess-27-689-2023
Laaha, G. (2023b). A mixed distribution approach for low-flow frequency analysis – Part 2: Comparative assessment of a mixed probability vs. copula-based dependence framework. Hydrol. Earth Syst. Sci., 27(10), 2019-2034. https://doi.org/10.5194/hess-27-2019-2023
How to cite: Fitriana, F. S., Fischer, S., Weigelhofer, G., Laimighofer, J., and Laaha, G.: Non-stationary low-flow frequency analysis with mixed Weibull components and Copula-based dependence framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1750, https://doi.org/10.5194/egusphere-egu26-1750, 2026.
