Simulating low flows over a heterogeneous landscape in southeastern Poland

Abstract

This paper presents a scheme describing low flow formation processes in areas with different environmental conditions, including the impact of the selection and explanatory power of predictors for a probabilistic model based on the Logit model. The research was carried out using 29 daily streamflow gauges located in the Lublin region of southeastern Poland for the hydrological period 1976-2018. Analysis resulted in two distinct low flow schemes. In the lowland rivers, low flows occur during the warm season and are related to evaporation exceeding precipitation. In the upland rivers, hydrogeological factors related to water levels in the local Cretaceous aquifers determine the occurrence of low flows. This differentiation affects the quality of the predictive models. For lowland rivers, models based on the climatic water balance with a monthly shift have a better fit, while these models used for upland rivers are characterized by an approximately 10% decrease in accuracy. For upland rivers, the combined C t H t models without shifts produce the best model fit. The generalized precision of the Logit models is around 80%-90%.

Development of an Objective Low Flow Identification Method Using Breakpoint Analysis

“…At the same time, however, the multitude of definition criteria, parameterizations, and assumptions introduces numerous combinations, and it is up to the researcher to choose a specific method appropriate to the research application. These choices have direct consequences on the impact and applicability of the results, as using a common criterion ensures the comparability of results while adjusting the criterion to the problem under study (e.g., temporal, spatial, or environmental dependence) makes the results potentially more locally representative but not directly comparable [32]. Although statistical criteria are currently the most popular, especially the Q 10 flow (corresponding to the 10th percentile of the flow), also referred to as Q 90 if the cumulative distribution function is used [33] or the 7Q 10 criterion (as the 10th percentile of the 7-day average flows) [34], the development of modern methods of numerical modeling introduces another issue: discretization and uniqueness of data.…”