Estimation of the Dependence of Ice Phenomena Trends on Air and Water Temperature in River

Abstract

The identification of changes in the ice phenomena (IP) in rivers is a significant element of analyses of hydrological regime features, of the risk of occurrence of ice jam floods, and of the ecological effects of river icing (RI). The research here conducted aimed to estimate the temporal and spatial changes in the IP in a lowland river in the temperate climate (the Noteć River, Poland, Central Europe), depending on air temperature (TA) and water temperature (TW) during the multi-annual period of 1987–2013. Analyses were performed of IP change trends in three RI phases: freezing, when there appears stranded ice (SI), frazil ice (FI), or stranded ice with frazil ice (SI–FI); the phase of stable ice cover (IC) and floating ice (FoI); and the phase of stranded ice with floating ice (SI–FoI), frazil ice with floating ice (FI–FoI), and ice jams (IJs). Estimation of changes in IP in connection with TA and TW made use of the regression model for count data with a negative binomial distribution and of the zero-inflated negative binomial model. The analysis of the multi-annual change tendency of TA and TW utilized a non-parametric Mann–Kendall test for detecting monotonic trends with Yue–Pilon correction (MK–YP). Between two and seven types of IP were registered at individual water gauges, while differences were simultaneously demonstrated in their change trends over the researched period. The use of the Vuong test confirmed the greater effectiveness of estimates for the zero-inflated model than for the temporal trend model, thanks to which an increase in the probability of occurrence of the SI phenomenon in the immediate future was determined; this, together with FI, was found to be the most frequently occurring IP in rivers in the temperate climate. The models confirmed that TA is the best estimator for the evaluation of trends of the occurrence of IC. It was shown that the predictive strength of models increases when thermal conditions are taken into consideration, but it is not always statistically significant. In all probability, this points to the impact of local factors (changes in bed and valley morphology and anthropogenic pressure) that are active regardless of thermal conditions and modify the features of the thermal-ice regime of rivers at specific spatial locations. The results of research confirm the effectiveness of compilating a few models for the estimation of the dependence of IP trends on air and water temperature in a river.

Warming Climate-Induced Changes in Lithuanian River Ice Phenology

“…Many studies have highlighted the sensitivity of river ice to climate change, especially to rising air temperatures. Indeed, milder winter conditions have a decisive influence on the presence of river ice [10,14,17,50]. Our study found that the sum of the negative air temperatures of the cold season (from 1 November to 31 March) is a good predictor of the duration of river ice cover, and ice break-up and freeze-up dates (in this case, the air temperatures were summed up to this date).…”

“…Research shows that river ice is drastically declining globally and will continue to decline due to increasing surface air temperatures [6]. Since a vast number of scientific studies [7][8][9][10][11] indicate that alterations in river ice processes follow air temperature trends, the ice phenomenon can be used to indicate ongoing climate change.…”

Predicting Ice Phenomena in a River Using the Artificial Neural Network and Extreme Gradient Boosting

“…The results of modeling confirmed that the most important predictors in the analyzed case were the nature of the phenomenon on the day preceding the observation (most often class 2 or class 1), as well as water temperature, and then air temperature (Figure 8). Graf [12] examined the dependencies of the trends of ice phenomena in the Noteć River, in western Poland (a tributary of the Warta River), on air and water temperature using regression models for count data and the Zero-Inflated Negative Binomial Model; results showed that the temperature values are the best predictors. In some locations, however, the model predicting the number of ice phenomena-taking into account the relationship with temperature-turned out to be statistically insignificant.…”

“…Relations between river freezing and features of the hydrological regime, including flow, water state, and water temperature, are usually complex and non-linear, and are also spatially heterogeneous due to the variability of environmental conditions. In addition to the process that determines the number of occurrences of a given phenomenon, there is also a dichotomous process determining whether it has a chance of occurring in a given period [12]. This task is further complicated by the fact that ice phenomena occur in three phases: freezing of the river (first symptom of ice), permanent ice cover, and the disappearance phase when an ice floe is formed and related phenomena appear, such as ice jams, which often lead to winter floods.…”

ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images

“…Ice caps, ice plugs, or ice dams are often formed in high latitude rivers in winter, which could change the hydraulic, thermal, and geometric boundary conditions of water flow and form a unique ice phenomenon in winter [1]. Ice plugs or ice dams, i.e., drift ice in the river channel blocking the cross section of water flow, may cause water level rise, inundate farmland houses, damage the coastal hydraulic structures, cause shipping interruption, or cause hydraulic power loss [2,3]. Therefore, river ice monitoring is necessary in preparing for potential hazards.…”