Correcting satellite precipitation data and assimilating satellite-derived soil moisture data to generate ensemble hydrological forecasts within the HBV rainfall-runoff model

An implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) as a procedure, dynamically coupled with the conceptual rainfall-runoff Hydrologiska Byråns Vattenbalansavdelning (HBV) model, was assessed for the hydrological modeling of seasonal hydrographs. The enhanced HBV model generated ensemble hydrographs and an average stream-flow simulation. The proposed approach was developed to examine the possibility of using data (e.g., precipitation and soil moisture) from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Support to Operational Hydrology and Water Management (H-SAF), and to explore its usefulness in improving model updating and forecasting. Data from the Sola mountain catchment in southern Poland between 1 January 2008 and 31 July 2014 were used to calibrate the HBV model, while data from 1 August 2014 to 30 April 2015 were used for validation. A bias correction algorithm for a distribution-derived transformation method was developed by exploring generalized exponential (GE) theoretical distributions, along with gamma (GA) and Weibull (WE) distributions for the different data used in this study. When using the ensemble Kalman filter, the stochastically-generated ensemble of the model states generally induced bias in the estimation of non-linear hydrologic processes, thus influencing the accuracy of the Kalman analysis. In order to reduce the bias produced by the assimilation procedure, a post-processing bias correction (BC) procedure was coupled with the ensemble Kalman filter (EnKF), resulting in an ensemble Kalman filter with bias correction (EnKF-BC). The EnKF-BC, dynamically coupled with the HBV model for the assimilation of the satellite soil moisture observations, improved the accuracy of the simulated hydrographs significantly in the summer season, whereas, a positive effect from bias corrected (BC) satellite precipitation, as forcing data, was observed in the winter. Ensemble forecasts generated from the assimilation procedure are shown to be less uncertain. In future studies, the EnKF-BC algorithm proposed in the current study could be applied to a diverse array of practical forecasting problems (e.g., an operational assimilation of snowpack and snow water equivalent in forecasting models

Applying the Theory of Reliability to the Assessment of Hazard, Risk and Safety in a Hydrologic System: A Case Study in the Upper Sola River Catchment, Poland

River basin safety issues and hazards arising from extreme hydrological and meteorological events pose significant risks to human life and can entail economic and financial losses. In this study, the practical aspects of reliability theory linked to reliability engineering, and the associated mathematical tools used to describe technical systems, were applied to explore the structural reliability of a quasi-natural system—a portion of the Upper Sola River catchment in Poland. As part of this study, methods such as the Fault Tree Method (FTM), Event Tree Method (ETM), Risk Matrix and Ranking Method for assessing hazard, risk and losses connected with the occurrence of such events are suggested to improve flood risk management and enhance the capacity to safeguard against such events by improving current flood protection protocols in accordance with EC Flood Directives

Snow-melt flood frequency analysis by means of copula based 2D probability distributions for the Narew River in Poland

Study region: Narew River in Northeastern Poland. Study focus: Three methods for frequency analysis of snowmelt floods were compared. Two dimensional (2D) normal distribution and copula-based 2D probability distributions were applied to statistically describe floods with two parameters (flood peak Qmax,f and flood volume Vf). Two copula functions from different classes – the elliptical Gaussian copula and Archimedean 1-parameter Gumbel–Hougaard copula – were evaluated based on measurements. New hydrological insights for the region: The results indicated that the 2D normal probability distribution model gives a better probabilistic description of snowmelt floods characterized by the 2-dimensional random variable (Qmax,f, Vf) compared to the elliptical Gaussian copula and Archimedean 1-parameter Gumbel–Hougaard copula models, in particular from the view point of probability of exceedance as well as complexity and time of computation. Nevertheless, the copula approach offers a new perspective in estimating the 2D probability distribution for multidimensional random variables. Results showed that the 2D model for snowmelt floods built using the Gumbel–Hougaard copula is much better than the model built using the Gaussian copula