Priority questions in multidisciplinary drought research

Addressing timely and relevant questions across a multitude of spatio-temporal scales, state-of-the-art interdisciplinary drought research will likely increase in importance under projected climate change. Given the complexity of the various direct and indirect causes and consequences of a drier world, scientific tasks need to be coordinated efficiently. Drought-related research endeavors ranging from individual projects to global initiatives therefore require prioritization. Here, we present 60 priority questions for optimizing future drought research. This topical catalogue reflects the experience of 65 scholars from 21 countries and almost 20 fields of research in both natural sciences and the humanities. The set of drought-related questions primarily covers drought monitoring, impacts, forecasting, climatology, adaptation, as well as planning and policy. The questions highlight the increasingly important role of remote sensing techniques in drought monitoring, importance of drought forecasting and understanding the relationships between drought parameters and drought impacts, but also challenges of drought adaptation and preparedness policies

Zpřesnění odhadů dopadů klimatické změny na vodní zdroje s využitím scénářů založených na simulacích modelem ALADIN-Climate/CZ

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Stanovení hodnot minimálních zůstatkových průtoků v podmínkách ČR

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33. kongres IAHR

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The Role of Hydrological Signatures in Calibration of Conceptual Hydrological Model

Determining an optimal calibration strategy for hydrological models is essential for a robust and accurate water balance assessment, in particular, for catchments with limited observed data. In the present study, the hydrological model Bilan was used to simulate hydrological balance for 20 catchments throughout the Czech Republic during the period 1981–2016. Calibration strategies utilizing observed runoff and estimated soil moisture time series were compared with those using only long-term statistics (signatures) of runoff and soil moisture as well as a combination of signatures and time series. Calibration strategies were evaluated considering the goodness-of-fit, the bias in flow duration curve and runoff signatures and uncertainty of the Bilan model. Results indicate that the expert calibration and calibration with observed runoff time series are, in general, preferred. On the other hand, we show that, in many cases, the extension of the calibration criteria to also include runoff or soil moisture signatures is beneficial, particularly for decreasing the uncertainty in parameters of the hydrological model. Moreover, in many cases, fitting the model with hydrological signatures only provides a comparable fit to that of the calibration strategies employing runoff time series

Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic

Evaporation is an important factor in the overall hydrological balance. It is usually derived as the difference between runoff, precipitation and the change in water storage in a catchment. The magnitude of actual evaporation is determined by the quantity of available water and heavily influenced by climatic and meteorological factors. Currently, there are statistical methods such as linear regression, random forest regression or machine learning methods to calculate evaporation. However, in order to derive these relationships, it is necessary to have observations of evaporation from evaporation stations. In the present study, the statistical methods of linear regression and random forest regression were used to calculate evaporation, with part of the models being designed manually and the other part using stepwise regression. Observed data from 24 evaporation stations and ERA5-Land climate reanalysis data were used to create the regression models. The proposed regression formulas were tested on 33 water reservoirs. The results show that manual regression is a more appropriate method for calculating evaporation than stepwise regression, with the caveat that it is more time consuming. The difference between linear and random forest regression is the variance of the data; random forest regression is better able to fit the observed data. On the other hand, the interpretation of the result for linear regression is simpler. The study introduced that the use of reanalyzed data, ERA5-Land products using the random forest regression method is suitable for the calculation of evaporation from water reservoirs in the conditions of the Czech Republic

Varovný systém pro pražskou vodárenskou soustavu před znečištěním mikropolutanty

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Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic

Evaporation is an important factor in the overall hydrological balance. It is usually derived as the difference between runoff, precipitation and the change in water storage in a catchment. The magnitude of actual evaporation is determined by the quantity of available water and heavily influenced by climatic and meteorological factors. Currently, there are statistical methods such as linear regression, random forest regression or machine learning methods to calculate evaporation. However, in order to derive these relationships, it is necessary to have observations of evaporation from evaporation stations. In the present study, the statistical methods of linear regression and random forest regression were used to calculate evaporation, with part of the models being designed manually and the other part using stepwise regression. Observed data from 24 evaporation stations and ERA5-Land climate reanalysis data were used to create the regression models. The proposed regression formulas were tested on 33 water reservoirs. The results show that manual regression is a more appropriate method for calculating evaporation than stepwise regression, with the caveat that it is more time consuming. The difference between linear and random forest regression is the variance of the data; random forest regression is better able to fit the observed data. On the other hand, the interpretation of the result for linear regression is simpler. The study introduced that the use of reanalyzed data, ERA5-Land products using the random forest regression method is suitable for the calculation of evaporation from water reservoirs in the conditions of the Czech Republic

Regionalizace nedostatkových objemů v České republice

Cílem této studie je regionalizace České republiky z hlediska rizika výskytu sucha v jednotlivých povodích. Regionalizace České republiky byla provedena pro sadu 133 (mezi-)povodí pro období 1901-2015. Primárním indexem popisu- jícím sucho v rámci této studie jsou nedostatkové objemy vzhledem k prahu odpovídajícímu 20% kvantilu měsíčních průtoků. Na základě srážek, výparu, cel- kového a základního odtoku a hydrogeologických rajonů byla pomocí shlukové analýzy provedena regionalizace České republiky z hlediska chování v době sucha. Tato regionalizace byla následně expertně revidována. Nedostatkové objemy v simulaci modelu Bilan byly vyčísleny a byl vytvořen statistický model pro odhad N-letých nedostatkových objemů. Charakteristiky sucha v simulaci modelu Bilan pro povodí s dostupnými pozorovanými daty i výsledky statistic- kého modelu byly úspěšně validovány.121

Hydrologická bilance a disponibilní vodní zdroje v České republice v době hydrologického sucha

Článek se zabývá hodnocením hydrologické bilance na celém území České republiky, které bylo rozděleno do 133 mezipovodí za období 1981-2015 v měsíčním časovém kroku. Pro ověření, jak se suchá období chovala, byl použit model hydrologické bilance Bilan, pomocí kterého byly tyto epizody za posledních 35 let vyhodnoceny, a to jak z pohledu jednotlivých zásob vody (sníh, půda, podzemní vody), tak podle jednotlivých toků vody (srážky, evapotranspirace, infiltrace, odtok). Článek dále seznamuje s výsledky disponibilní vody za normálních podmínek a při pětiletém a desetiletém suchu ve dvou variantách. První se zabývá vyhodnocením zdrojové oblasti, ve druhé je vyhodnocení pomocí zjednodušeného modelu vodohospodářské bilance WATERES.61