In the past few years, demands on flash flood forecasting have grown. The Flash Flood Indicator (FFI) is a system used at the Czech Hydrometeorological Institute for the evaluation of the risk of possible occurrence of flash floods over the whole Czech Republic. The FFI calculation is based on the current soil saturation, the physical-geographical characteristics of every considered area, and radar-based quantitative precipitation estimates (QPEs) and forecasts (QPFs). For higher reliability of the flash flood risk assessment, calculations of QPEs and QPFs are crucial, particularly when very high intensities of rainfall are reached or expected. QPEs and QPFs entering the FFI computations are the products of the Czech Weather Radar Network. The QPF is based on the COTREC extrapolation method. The radar-rain gauge-combining method MERGE2 is used to improve radar-only QPEs and QPFs. It generates a combined radar-rain gauge QPE based on the kriging with an external drift algorithm, and, also, an adjustment coefficient applicable to radar-only QPEs and QPFs. The adjustment coefficient is applied in situations when corresponding rain gauge measurements are not yet available. A new adjustment coefficient scheme was developed and tested to improve the performance of adjusted radar QPEs and QPFs in the FFI.

Flash floods are one of the most risky natural phenomena. It is very difficult to predict exactly where they will occur and they usually come very quickly. When there is a threat of flash floods, early warning is exactly what decides on the protection of the lives and property of inhabitants. For these reasons, a Flash Flood Indicator (FFI) has being developed in the Czech Hydrometeorological Institute (CHMI) for a long time, aiming to draw attention to the possibility of a flash flood or local flooding. The main purpose of this contribution is not to describe details of FFI algorithms, but to evaluate the functioning of the FFI on the basis of more than ten years of operation experience. On a basis of specific cases of significant rainfall episodes, especially from recent years, the potential benefits of FFI are shown, not only for the operational forecasting service of the CHMI, but also for wider public. At the end of the contribution, the possibilities of further development of FFI are outlined.

In September 2024, a flood occurred in the Czech Republic, ranking among the most significant hydrological events of the past several decades. The flood was caused by extreme precipitation totals, which in the most affected area of the Jeseníky region exceeded 500 mm over five days, with more than 300 mm falling on the windward side of the Jeseníky Mountains within 24 hours on September 14. The return period of peak flows exceeded 50 years at 60 gauging stations. In the Jeseníky region, the flood can be described as the largest recorded one in the entire period of observations, with an estimated return period even over 500 years. Before the flood, streamflows in the Czech Republic were often at hydrological-drought levels, which had a favourable effect on the development of the flood. Discharges began to increase substantially only after 60 to 100 mm of rainfall totals occurrence, demonstrating the significant retention capacity of the soil and landscape during dry periods. Since some regions experienced a second appearance of an extreme flood in less than 30 years (since July 1997), it will be necessary to verify the N-year discharges and very likely proceed with their revision.

Hydrological methods based on the analysis of data from a large sample of catchments with different characteristics (large-sample hydrology; comparative hydrology) allow a comprehensive analysis of the hydrological regime and thus a description of hydrological variability and change in the components of the water balance. These methods provide insight into hydrological processes that are shaped by environmental and climatic factors and allow more general conclusions to be drawn. However, in addition to climate and runoff data, catchment attributes, such as geology, soils, topography and vegetation, are essential for effective hydrological behaviour analysis. For these reasons, the global hydrological community has recently developed a number of freely available large-scale datasets known as CAMELS (Catchment Attributes and MEteorology for Large-sample Studies), which provide catchment attributes, as well as hydrological and meteorological time series, in a comparable structure at national scales. The aim of this contribution is to present the current state of preparation the CAMELS database for Czechia (CAMELS-CZ) as a reference data platform for analysis and modelling, using large-sample of catchments.