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.

The spatiotemporal variations in the water balance caused by environmental changes lead to unsteady-state conditions with long- and short-term implications on the social and economic conditions. The aim of this study is to extend the current knowledge of the Budyko approach in unsteady-state conditions and the importance of model structural uncertainties. We used three Budyko-based models (Turc-Pike, Zhang, Fu) with and without the concept of effective precipitation scenarios. The effective precipitation and model parameters were determined together during the calibration processes. The effective precipitation-based approach improved the model performance in predicting the annual water balance in most of the River Vistula catchment. We also discuss the importance of considering structural uncertainties in water balance modelling. Climate change is the main driver for the changes in the water balance of the River Vistula. The estimation of water balance dynamics, considering environmental changes, is essential to understand the changes in hydrological processes.

Water balance modelling is often applied in studies of climate and human impacts on water resources. Annual water balance is usually derived based on precipitation, discharge and temperature observations under an assumption of negligible changes in annual water storage in a catchment. However, that assumption might be violated during very dry or very wet years. In this study we apply groundwater level measurements to improve water balance modelling in nine sub-catchments of the River Vistula basin starting from the river sources downstream. Annual and inter-annual water balance is studied using a Budyko framework to assess actual evapotranspiration and total water supply. We apply the concept of effective precipitation to account for possible losses due to water interception by vegetation. Generalised Likelihood Uncertainty Estimation GLUE is used to account for parameter and structural model uncertainty, together with the application of eight Budyko-type equations. Seasonal water balance models show large errors for winter seasons while summer and annual water balance models follow the Budyko framework. The dryness index is much smaller in winter than in summer for all sub-catchments. The spatial variability of water balance modelling errors indicate an increasing uncertainty of model predictions with an increase in catchment size. The results show that the added information on storage changes in the catchments provided by groundwater level observations largely improves model accuracy. The results also indicate the need to model groundwater level variability depending on external factors such as precipitation and evapotranspiration and human interventions. The modelling tools developed will be used to assess future water balance in the River Vistula basin under different water management scenarios and climate variability.

The ongoing climate change disrupts the water cycle and alters the hydrometeorological patterns at global and regional scales. The alarming intensification of droughts in Europe in recent decades may become the future new norm.This study aims to assess the future spatio-temporal drought variability in the River Vistula basin, the largest river in Poland. This basin’s vulnerability to drought is also investigated. Two drought indices, namely the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are evaluated using the observed climatic data and meteorological projections for the 21st century. These projections of climatic variables are obtained from the EURO-CORDEX initiative based on fourteen climate models for the period 1971–2100 for the RCP4.5 and RCP8.5 emission scenarios. Drought vulnerability analysis is carried out by combining exposure, adaptive capacity and sensitivity metrics, using the vulnerability scoping diagram (VSD) method.Preliminary results show that projected changes in precipitation and air temperature result in significant variations in temporal drought patterns in the River Vistula basin. The divergent results were obtained for the two analysed drought indices. The SPEI projections indicate drier conditions in the basin to be expected in the far-future period, whereas the SPI indicates wetter conditions. The analysis shows that areas in the central and southern parts of the basin are more vulnerable to drought.AcknowledgementsThis work was supported by the project HUMDROUGHT, carried out in the Institute of Geophysics Polish Academy of Sciences and funded by National Science Centre (contract 2018/30/Q/ST10/00654).

One of the main driving forces - human factors - directly affects runoff processes by influencing the amount of water available and the development of hydrological droughts.The study investigated the role of human factors in the development of hydrological droughts in the Kamienna catchment and attempts to identify the main human factors that caused these changes through the analysis of remote sensing data. The hydro-climatic variables are divided into a baseline period and a disturbed period, using the method to detect changes in the runoff processes. We applied the rainfall-runoff model - SWAT - calibrated during the baseline period and used it to reconstruct the naturalized runoff during the disturbed period. The baseflow filtering algorithm was applied to separate baseflow from direct runoff. Drought indices: Streamflow Drought Index (SDI) and the Standardized Baseflow Index (SBFI) were used as indicators for hydrological drought analysis using observed and naturalized hydrological variables. The observation-based runoff indicators showed more successive hydrological droughts of longer duration and intensity in the catchment than the naturalization-based runoff indicators, especially in the 1980s, 1990s and 2000s. From 2010, shortly after the Wiory reservoir became operational, naturalized runoff matched observed runoff, indicating increased groundwater recharge and baseflow and⁄or systematic release of excess water from the reservoir during the dry season. This indicates the positive role of reservoirs on downstream runoff by mitigating hydrological drought events. Analyses of changes in land cover dynamics supported these changes caused by human factors in the watershed. This study contributes to our understanding of how human factors influence hydrological drought in the catchment, which is important for drought adaptation. Keywords: Rain-runoff model, hydrological drought, drought indices, land cover dynamics, human factors.