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Flood inundation models are central components of any flood risk analysis system because they transform the bulk discharge outputs from flood‐frequency analyses or rainfall‐runoff models into distributed predictions of flood hazard in terms of water depth, inundation extent, and flow velocity. The accuracy of flood hazard maps depends on the availability of distributed observations of inundation outlines. Unfortunately, the acquisition of aerial photographs or satellite images is costly and in addition, their temporal resolution is strongly limited by weather conditions. Remote sensing based on unmanned aerial vehicles (UAV) is becoming increasingly popular due its flexibility and quickly decreasing costs. In particular, UAV can provide precise up to date georeferenced information about the location of a river shorelines, channel geometry, and vegetation. This information is particularly useful for the calibration and validation of distributed flood routing models. The application of cheap, well georeferenced UAV images of river shorelines is an unprecedented source of distributed observations. The aim of this article is to present a procedure for the updating of boundary conditions of the hydrodynamic model, based on UAV‐born data. The approach proposed is also a very effective means of on‐line updating of flood risk maps and their verification. [ABSTRACT FROM AUTHOR]

This paper aims to quantify the uncertainty in the projections of future hydrological extremes in the BialaTarnowska River basin, south Poland. We follow a multi-model approach based on several climate projections obtained from the EUROCORDEX initiative, raw and downscaled realizations of catchment precipitation and temperature, and flow simulations derived using the hydrological HBV model. The projections cover the 21st century. Three sources of uncertainty were considered: one related to the hydrological model parameters uncertainty, the second related to climate projection ensemble spread and the third related to the distribution fit. The uncertainty of projected extreme indices related to hydrological model parameters was conditioned on flow observations from the reference period using the Generalised Likelihood Uncertainty Estimation approach, with separate weighting for high and low flow extremes. Flood quantiles were estimated using Generalize Extreme Value (GEV) distribution at different return periods and were based on two different lengths of the flow time series. The sensitivity analysis based on ANOVA shows that the uncertainty introduced by the HBV model parameters can be larger than the climate model variability and distribution fit uncertainty for the low-flow extremes whilst for the high-flow extremes higher uncertainty is observed from climate models than from hydrological parameter and distribution fit uncertainties. This implies that ignoring one of the three uncertainty sources may cause great risk to future hydrological extreme adaptations and water resource planning and management. [ABSTRACT FROM AUTHOR]

The paper presents a methodology to give insight in the performance of ensemble streamflow forecasting systems. We developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times from 1 day to 10 days for low, medium and high streamflow and related runoff generating processes. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts serve as input to a deterministic lumped hydrological (HBV) model. Due to inconsistent bias, the best streamflow forecasts were obtained without pre- and post-processing of the meteorological and streamflow forecasts. Best forecast skill, relative to alternative forecasts based on historical measurements of precipitation and temperature, is shown for high streamflow and for snow accumulation low streamflow events. Forecasts of medium streamflow events and low streamflow events generated by precipitation deficit show less skill. To improve the performance of the forecasting system for high streamflow events, in particular the meteorological forecasts require improvement. For low streamflow forecasts, the hydrological model should be improved. The study recommends improving the reliability of the ensemble streamflow forecasts by including the uncertainties in hydrological model parameters and initial conditions, and by improving the dispersion of the meteorological input forecasts. [ABSTRACT FROM AUTHOR]

The author discusses drought which is considered as costliest natural hazard that is related to weather and it lead to deficiency among soil moisture as a hydrological drought. Topics discussed include effect of drought on Europe which occurred in 2015 due to lack of rainfall and high evapotranspiration, similarity shown by vegetation to the pattern of standardized precipitation evapotranspiration index (SPEI) and decrease in crop yields due to drought in soil water.

Possible future climate change effects on dryness conditions in Poland are estimated for six climate projections using the standardized precipitation index (SPI). The time series of precipitation represent six different climate model runs under the selected emission scenario for the period 1971-2099. Monthly precipitation values were used to estimate the SPI for multiple timescales (1, 3, 6, 12, and 24 months) for a spatial resolution of 25 km for the whole country. Trends in the SPI were analysed using the Mann- Kendall test with Sen's slope estimator for each grid cell for each climate model projection and aggregation scale, and results obtained for uncorrected precipitation and bias corrected precipitation were compared. Bias correction was achieved using a distribution-based quantile mapping (QM) method in which the climate model precipitation series were adjusted relative to gridded precipitation data for Poland. The results show that the spatial pattern of the trend depends on the climate model, the timescale considered and on the bias correction. The effect of change on the projected trend due to bias correction is small compared to the variability among climate models. We also summarize the mechanisms underlying the influence of bias correction on trends in precipitation and the SPI using a simple example of a linear bias correction procedure. In both cases, the bias correction by QM does not change the direction of changes but can change the slope of trend, and the influence of bias correction on SPI is much reduced. We also have noticed that the results for the same global climate model, driving different regional climate model, are characterized by a similar pattern of changes, although this behaviour is not seen at all timescales and seasons. [ABSTRACT FROM AUTHOR]

This paper presents the background, objectives, and preliminary outcomes from the first year of activities of the Polish-Norwegian project CHIHE (Climate Change Impact on Hydrological Extremes). The project aims to estimate the influence of climate changes on extreme river flows (low and high) and to evaluate the impact on the frequency of occurrence of hydrological extremes. Eight "twinned" catchments in Poland and Norway serve as case studies. We present the procedures of the catchment selection applied in Norway and Poland and a database consisting of near-natural ten Polish and eight Norwegian catchments constructed for the purpose of climate impact assessment. Climate projections for selected catchments are described and compared with observations of temperature and precipitation available for the reference period. Future changes based on those projections are analysed and assessed for two periods, the near future (2021-2050) and the far-future (2071-2100). The results indicate increases in precipitation and temperature in the periods and regions studied both in Poland and Norway. [ABSTRACT FROM AUTHOR]

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This paper describes data assimilation (DA) and adaptive forecasting techniques for flood forecasting and their application to forecasting water levels at various locations along a 120 km reach of the river Severn, United Kingdom. The methodology exploits the top-down, data-based mechanistic (DBM) approach to the modeling of environmental processes, concentrating on the identification and estimation of those 'dominant modes' of dynamic behavior that are most important for flood prediction. In particular, hydrological processes active in the catchment are modeled using the state-dependent parameter (SDP) method of estimating a nonlinear, effective rainfall transformation together with a linear stochastic transfer function (STF) method for characterizing both the effective rainfall-river level behavior and the river level routing processes. The complete model consists of these lumped parameter, linear and nonlinear stochastic, dynamic elements connected in a quasi-distributed manner that represents the physical structure of the catchment. The adaptive forecasting system then utilizes a state-space form of the complete catchment model, including allowance for heteroscedasticity in the errors, as the basis for data assimilation and forecasting using a Kalman filter forecasting engine. Here the predicted model states (water levels) and adaptive parameters are updated recursively in response to input data received in real time from sensors in the catchment. Direct water level forecasting is considered, rather than flow, because this removes the need to transform the level measurement through the rating curve and tends to decrease the forecasting errors. [ABSTRACT FROM AUTHOR]

Inter-annual variability of hydro-meteorological variables indirectly influence soil moisture conditions in winter and early spring seasons. The interactions between temperature changes and drought conditions are studied by an application of statistical analyses of minimum temperature (Tmin), consecutive days with temperature exceeding the 0 °C threshold value, the number of melting pulses in the winter season and Standardized Evaporation Precipitation Index (SPEI). Additionally, shifts in the onset of days with spring temperature and snow cover occurrence are analysed. A Mann–Kendall test is applied for the trend analysis. Studies have shown significant changes in thermal characteristics in the winter season over the past 70 years, which affect the moisture conditions in the Vistula River Basin. As a result of those changes, the Vistula Basin is more prone to droughts. [ABSTRACT FROM AUTHOR]

The continuous simulation approach to assessing the impact of climate change on future flood hazards consists of a chain of consecutive actions, starting from the choice of the global climate model (GCM) driven by an assumed CO2 emission scenario, through the downscaling of climatic forcing to a catchment scale, an estimation of flow using a hydrological model, and subsequent derivation of flood hazard maps with the help of a flow routing model. The procedure has been applied to the Biala Tarnowska catchment, Southern Poland. Future climate projections of rainfall and temperature are used as inputs to the precipitation-runoff model simulating flow in part of the catchment upstream of a modeled river reach. An application of a lumped-parameter emulator instead of a distributed flow routing model, MIKE11, substantially lowers the required computation times. A comparison of maximum inundation maps derived using both the flow routing model, MIKE11, and its lump-parameter emulator shows very small differences, which supports the feasibility of the approach. The relationship derived between maximum annual inundation areas and the upstream flow of the study can be used to assess the floodplain extent response to future climate changes. The analysis shows the large influence of the one-grid-storm error in climate projections on the return period of annual maximum inundation areas and their uncertainty bounds. [ABSTRACT FROM AUTHOR]