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Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the Model Conditional Processor and the Ensemble Model Output Statistics method. Using 2-years of reforecasts with daily timesteps this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations both in terms of the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead-times (up to 15 days) but is largest at short lead-times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time-series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end-users to make more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

In this study the impacts of Soil Moisture and Ocean Salinity (SMOS) soil moisture data assimilation upon the streamflow prediction of the operational Global Flood Awareness System (GloFAS) were investigated. Two GloFAS experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both sets of experiment results were verified against streamflow observations in the United States and Australia. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where GloFAS skill may be affected by the assimilation of SMOS soil moisture data. In addition, a global assessment was made of the impact upon the 5th and 95th GloFAS flow percentiles to see how SMOS data assimilation affected low and high flows respectively. Results against in-situ observations found that GloFAS skill score was only affected by a small amount. At a global scale, the results showed a large impact on high flows in areas such as the Hudson Bay, central United States, the Sahel and Australia. There was no clear spatial trend to these differences as opposing signs occurred within close proximity to each other. Investigating the differences between the simulations at individual gauging stations showed that they often only occurred during a single flood event; for the remainder of the simulation period the experiments were almost identical. This suggests that SMOS data assimilation may affect the generation of surface runoff during high flow events, but may have less impact on baseflow generation during the remainder of the hydrograph. To further understand this, future work could assess the impact of SMOS data assimilation upon specific hydrological components such as surface and subsurface runoff.

Dobro praćenje oborine od velike je važnosti u mnogim granama ljudskoga djelovanja koje se koriste meteorološkim informacijama. Oborina je varijabilna u vremenu i prostoru te je zato poželjno primjenjivati alate koji osiguravaju dobru prostornu i vremensku razlučivost te pojave. U tu se svrhu koristi meteorološki radar radarskoga centra Bilogora. No, radarom se oborina ne mjeri izravno, već se procjenjuje tako da se reflektirani elektromagnetski signal pomoću Z-R relacije pretvara u oborinu. U ovome radu analizirano je razdoblje od 1. prosinca 2015. do 31. kolovoza 2016. usporedbom zemaljskih mjerenja satne akumulirane oborine s glavnih meteoroloških postaja i radarske procjene oborine pripadajućih piksela. Utvrdilo se da radar precjenjuje oborinu. Takvu sustavnu pogrešku radarske procjene moguće je umanjiti klimatološkim prilagođavanjem pristranosti. Također, nacrtana je karta sumarne procjene oborine radarom za razdoblje od 1. prosinca 2015. do 31. kolovoza 2016. za središnju Hrvatsku interpolacijskom metodom inverzne udaljenosti., Good monitoring of precipitation is of great importance in many fields of human activity that use meteorological information. Precipitation is highly variable in space and time and therefore it is advisable to use tools that provide good spatial and temporal resolution of the phenomenon. The meteorological radar from the Radar Center of Bilogora is used for this purpose. However, radar precipitation is not measured directly but is estimated in a way that reflected electromagnetic signal turns into precipitation using the Z-R relation. This paper analyses the period from 1. 12. 2015. to 31. 8. 2016., comparing the ground measurements of hourly accumulated precipitation from the main meteorological stations and radar associated pixels precipitation estimation. It was found that radar overestimates precipitation. It is possible to reduce such systematic radar estimation errors with a climatological bias adjustment.

The Prandtl model couples, probably in the most succinct way, basic boundary-layer dynamics andthermodynamics for pure anabaticand katabaticflowsoverinclined surfaces by assuming a one-dimensional steady-state balance between buoyancy and turbulent friction. Although the classic Prandtl model is linear, having an a priori assigned vertically constanteddydiffusivityandheatconductivity,K,inthisanalyticworkwepartlyrelaxboth of these restrictions. The first restriction is loosened by using a weakly nonlinear approach where a small parameter, e, controls feeding of the flow-induced potential temperature gradient back to the environmental potential temperature gradient, because the former, belowthekatabaticjet,canbe20‐50timesstrongerthanthelatter,backgroundorfree-flow gradient. An appropriate range of values for e, controlling the weak nonlinearity for pure katabatic flow, is provided. In this way, the near-surface potential temperature gradient becomes stronger and the corresponding katabatic jet somewhat weaker (at a slightly lower height) than that in the classic Prandtl solution. The second restriction is partly relaxed by usingaprescribed,graduallyvaryingK withdistancefromtheunderlyingsurface,allwithin the usual validity of the zero-order Wentzel‐Kramers‐Brillouin approximation to solve the coupled differential equations. The new model is compared with the glacier wind data from the Pasterze experiment (PASTEX-94), Austria. Further discussion includes gradient Richardson number consideration and an application to simple anabatic flows. The model may be applied for estimation and interpretation of the wind affecting glacier mass balance and air pollution.

U radu je analizirana ustrajnost i evolucija trendova srednjih i maksimalnih godišnjih protoka s hidroloških stanica na Kupi (Kupari, Kamanje), pritoku Čabranka (Zamost 2), Dobri (Stative donje, Trošmarija), Mrežnici (Mrzlo polje) i godišnjih oborina s tri meteorološke stanice (Parg, Ogulin i Karlovac). Rad je usredotočen na promatranje sedam veličina: srednji godišnji protok, srednji sezonski protok (srednji zimski i ljetni protok), trenutačni maksimalni godišnji protok, godišnja i sezonska količina oborine. Analizirani vremenski niz je od 1951. do 2013. godine, a fiksno razdoblje uzeto je od 1984. do 2013. godine. Vremenski niz svake razmatrane veličine je normiran na standardiziranu anomaliju toka, te je trend testiran Mann-Kendallovim Z testom, nakon čega je niz izglađen LOESS algoritmom. Analiza je izvedena za cijeli vremenski niz (primjerice, 1951.–2013., 1952.–2013. i tako dalje do 1984.–2013.). Time veličina uzorka varira od 63 do 30 godina. Standardizirana anomalija toka se tako lako može usporediti između različitih hidroloških stanica. Na svim analiziranim stanicama za sve analizirane veličine, nakon sredine 1980-ih godina kada počinje fiksno analizirano razdoblje, standardizirana anomalija toka ima niže vrijednosti od dugogodišnje srednje vrijednosti. Detaljnija analiza srednjih ljetnih i zimskih protoka pokazuje drukčije odstupanje od dugogodišnjeg prosjeka. Evolucija trenda razmatranih veličina je pokazana Mann-Kendallovim Z testom, prikazavši vrijednosti Z za početnu godinu svakog analiziranog vremenskog niza (od 1951. do 1984.)., Persistence and evolution of trends of mean and high river flows from six hydrological stations on watercourses of the Kupa River (Kupari, Kamanje), tributary Čabranka (Zamost 2), Dobra River (Stative donje, Trošmarija), Mrežnica River (Mrzlo polje) and annual precipitation on three meteorological stations (Parg, Ogulin and Karlovac) were analysed. The paper focuses on seven indicators: annual mean flow, seasonal mean flows (winter and summer mean flows), instantaneous annual maximum flow, annual and seasonal precipitation. Analysed time series range from 1951 to 2013, and the fixed period ranges from 1984 to 2013. Time series of each indicator was scaled to standardized flow anomaly and was analysed using the Mann-Kendall Z test for monotonic trend, after which it is smoothed using LOESS algorithm. The analysis was conducted for each indicator for full record, e.g. 1951–2013, then 1952–2013 and so on until 1984–2013. Thus, the sample size varies from 63 to 30 years. The smoothed standardized flow anomaly is easily comparable among different hydrologic stations. The standardized flow anomaly on all analysed stations for all analysed indicators shows lower mean value than long-time average after mid-1980’s, when fixed period starts. Further analysis of summer and winter seasonal mean flows revealed different deviation from long-term annual flow average. Trend evolution of certain indicators was proven using Mann-Kendall Z test, by plotting Z values for each iteration of start year (1951 to 1984).

Persistence and evolution of trends of mean and high river flows from six hydrological stations on watercourses of the Kupa River (Kupari, Kamanje), tributary Čabranka (Zamost 2), Dobra River (Stative donje, Trošmarija), Mrežnica River (Mrzlo polje) and annual precipitation on three meteorological stations (Parg, Ogulin and Karlovac) were analysed. The paper focuses on seven indicators: annual mean flow, seasonal mean flows (winter and summer mean flows), instantaneous annual maximum flow, annual and seasonal precipitation. Analysed time series range from 1951 to 2013, and the fixed period ranges from 1984 to 2013. Time series of each indicator was scaled to standardized flow anomaly and was analysed using the Mann-Kendall Z test for monotonic trend, after which it is smoothed using LOESS algorithm. The analysis was conducted for each indicator for full record, e.g. 1951- 2013, then 1952-2013 and so on until 1984-2013. Thus, the sample size varies from 63 to 30 years. The smoothed standardized flow anomaly is easily comparable among different hydrologic stations. The standardized flow anomaly on all analysed stations for all analysed indicators shows lower mean value than long-time average after mid- 1980’s, when fixed period starts. Further analysis of summer and winter seasonal mean flows revealed different deviation from long- term annual flow average. Trend evolution of certain indicators was proven using Mann- Kendall Z test, by plotting Z values for each iteration of start year (1951 to 1984).