Abstract
Abstract. The question of the environmental risks of social and economic infrastructure has recently become apparent due to an increase in the number of extreme weather events. Extreme runoff events include floods and droughts. In water engineering, extreme runoff is described in terms of probability and uses methods of frequency analysis to evaluate an exceedance probability curve (EPC) for runoff. It is assumed that historical observations of runoff are representative of the future; however, trends in the observed time series show doubt in this assumption. The paper describes a probabilistic hydrological MARCSHYDRO (the MARkov Chain System) model that can be applied to predict future runoff extremes. The MARCSHYDRO model simulates statistical estimators of multi-year runoff in order to perform future projections in a probabilistic form. Projected statistics of the meteorological variables available in climate scenarios force the model. This study introduces the new model's core version and provides a user guide together with an example of the model set-up in a single case study. In this case study, the model simulates the projected EPCs of annual runoff under three climate scenarios. The scope of applicability and limitations of the model's core version 0.2 are discussed.
Highlights
Streamflow runoff serves as a water resource for humans, food production and energy generation, while the risks of water-sensitive economics are usually connected to runoff extremes
In water engineering runoff extremes are evaluated from the tails of exceedance probability curves (EPCs) that are used in risk assessment for water infrastructure and decision-making in cost–loss situations (Mylne, 2002; Murphy, 1977, 1976)
The MARCSHYDRO model allows the simulation of the noncentral moments of runoff that can be used for the construction of probability distribution; in other words, it provides a probabilistic form of prediction
Summary
Streamflow runoff serves as a water resource for humans, food production and energy generation, while the risks of water-sensitive economics are usually connected to runoff extremes. In the frequency-analysis approach, historical yearly time series of runoff are used to evaluate statistical estimators, that is, the mean value, the coefficient of variation (CV) and the coefficient of skewness (CS) (van Gelder, 2006) These estimators are applied to calculate runoff values with their exceedance probability (Guidelines SP 33-101-2003, 2004; Guidelines, 1984; Bulletin 17–B, 1982) needed to support the designing of roads, dams, bridges or water-withdrawal stations. The statistically significant trends are founded on historical time series; the water engineers and managers are motivated to revise the basic stationarity assumption that lies behind infrastructures’ risk assessment since past observations are not representative of the future (Madsen et al, 2013; Kovalenko, 2009; Milly at al., 2008). The main features of the model and the limitations of the AFA method are formulated in the Discussions section in order to better place the MARCSHYDRO model among other hydrological models
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