HESS Opinions "A random walk on water"

Abstract

Abstract. According to the traditional notion of randomness and uncertainty, natural phenomena are separated into two mutually exclusive components, random (or stochastic) and deterministic. Within this dichotomous logic, the deterministic part supposedly represents cause-effect relationships and, thus, is physics and science (the "good"), whereas randomness has little relationship with science and no relationship with understanding (the "evil"). Here I argue that such views should be reconsidered by admitting that uncertainty is an intrinsic property of nature, that causality implies dependence of natural processes in time, thus suggesting predictability, but even the tiniest uncertainty (e.g. in initial conditions) may result in unpredictability after a certain time horizon. On these premises it is possible to shape a consistent stochastic representation of natural processes, in which predictability (suggested by deterministic laws) and unpredictability (randomness) coexist and are not separable or additive components. Deciding which of the two dominates is simply a matter of specifying the time horizon and scale of the prediction. Long horizons of prediction are inevitably associated with high uncertainty, whose quantification relies on the long-term stochastic properties of the processes.

Spatial cross‐correlation patterns of European low, mean and high flows

“…However, studies addressing persistence in annual runoff series have several common features that distinguish them from those analyzing low-frequency variance of monthly time series. For example, it is common only to consider annual time series [e. g Tallaksen et al, 1997;Vogel et al, 1998;Koutsoyiannis, 2002Koutsoyiannis, , 2003Koutsoyiannis, , 2010, and the results are thus not directly comparable to results obtained from analyses of monthly time series. Only a few "Hurst" studies consider daily or monthly time series [e.g.…”

“…Observed temperature and precipitation series were not available for the catchments, so instead the WATCH forcing data Weedon et al [WFD;2010 were used. The WFD provide bias corrected variables, based on the ERA-40 reanalysis , on a 0.5 degree grid.…”

Do probabilistic forecasts lead to better decisions?

“…The way we phrased our question is fairly simplistic and can be challenged on multiple accounts. For example, critical questions are: what does one mean by "probabilistic", how are these probabilities generated, how trustworthy are they, are they a synonym for uncertainties (see discussion in Koutsoyiannis, 2010 and …”

Freely available daily hydrometeorological data from Czechia: further insights

“…Although some estimators were developed exactly in hydrology as well (see e.g. [25,26]) and they are generally considered very good [27], in our study, the technique making use of the socalled maximal overlap discrete wavelet transform (MODWT; [17,28,29]) was utilized because it should be insensitive to the presence of deterministic trends [12], which is beneficial in the cases when different components of time series may interact and mimic each other (deterministic and stochastic trends inclusive). The method builds on the estimation of wavelet variance at the so-called dyadic scales (i.e.…”