No abstract

Evapotranspiration (ET) is the most significant water balance component and is also a very complex component to evaluate in spatio-temporal scales. Remotely-sensed data greatly increases the accuracy of basin wide ET estimation but only in periods with available satellite images. This paper describes an attempt to estimate daily ET regardless of the availability of the satellite images. The method is based on application of the interpolated evaporative fraction (Λ) from "historical" satellite images to periods with no satellite data available. Basin wide daily ET is obtained by combining interpolated Λ and standard PET methods on meteorological stations. The reliability of such approach was evaluated by comparing the obtained daily ET to the SEBAL ET estimates through the analysis of residuals (∆), standard deviations of residuals (σ) and the Nash-Sutcliffe coefficient (NSE) over the basin. The SEBAL ET estimates were validated with the data from two lysimeters. The discrepancy of obtained ET versus the SEBAL ET estimates (∆ = 0.13 mm day −1 , σ = 0.64 mm day −1 , NSE = 0.07) indicated that the proposed concept has relatively high accuracy, which is notably higher than the Penman-Monteith interpolated ET estimates (∆ = 1.94 mm day −1 , σ = 1.03 mm day −1 , NSE = −4.71). It was shown that a total of five images can provide a reliable estimate of interpolated Λ and thus represent specific characteristics of a basin. As the presented concept requires minimum remote sensing data and ground based inputs, it could be applied to estimate basin wide daily ET in data scarce regions and in periods with no satellite images available.

Since the beginning of the 21st Century, Europe has been affected by destructive floods. European Union Member States have an obligation to develop flood hazard and flood risk maps as support to the Flood Risk Management Plan (FRMP). The main objective of this study is to propose a methodological framework for hazard and risk assessment of pluvial flash floods in Croatia at the catchment level, which can be integrated into the FRMP. Therefore, a methodology based on the source–pathway–consequence approach for flood risk assessment is presented, which complies with the EU Floods Directive. This integrated and comprehensive methodology is based on high-resolution open data available for EU Member States. Three scenarios are defined for a low, medium, and high probability, defined by design storms of different durations. The proposed methodology consists of flood hazard analysis, vulnerability assessment, and risk analysis. Pluvial flash flood hazards are analyzed using a 2D hydrologic–hydraulic model. The flood vulnerability assessment consists of a GIS analysis to identify receptors potentially at risk of flooding and an assessment of susceptibility to potential flood damage using depth–damage curves. Flood risk is assessed both qualitatively in terms of risk levels and quantitatively in terms of direct damages expressed in monetary terms. The developed methodology was applied and tested in a case study in the Gospić catchment in Croatia, which surrounds a small rural town frequently affected by pluvial flash floods.

U radu je provedena ocjena pogodnosti područja za izgradnju solarnih parkovaprimjenom višekriterijske analize, i to metode analitičkog hijerarhijskog procesa(AHP) u GIS okruženju. U prvoj fazi analize pogodnosti izuzeta su područjanepogodna za izgradnju solarnih parkova, dok su u drugoj fazi klasificirane razinepogodnosti preostalih područja primjenom AHP metode.

Oborine su izrazito varijabilna komponenta bilance voda, koja ovisi o nizu faktora, kao što su geografski položaj, udaljenost od mora i nadmorska visina, a ključne su u razumijevanju hidroloških procesa nekog područja. Mjerenja oborina provode se na diskretnim lokacijama meteoroloških postaja (osim u slučaju radarskih opažanja oborina) te su saznanja o njihovoj varijabilnosti u prostoru rezultat primjene različitih metoda interpolacije izmjerenih vrijednosti u točki na analiziranom prostoru. U GIS okruženju, oborina može biti prikazana u formi diskretnog ili kontinuiranog polja pa će o tome ovisiti i odabir metode interpolacije. U radu su, na primjeru srednjih godišnjih količina oborina za razdoblje 1961. – 1990. na području Istre, prikazane i uspoređene tri najčešće primjenjivane metode prostorne interpolacije: Thiessenovi poligoni, TIN (Triangular Irregular Network) te VLR (metoda višestruke linearne regresije). Prve dvije metode ne uzimaju u obzir faktore koji utječu na količinu palih oborina, već su procijenjene vrijednosti u funkciji udaljenosti promatrane točke od oborinskih postaja. Za razliku od njih, metoda višestruke linearne regresije omogućuje određivanje vrijednosti oborina u prostoru u ovisnosti o drugim čimbenicima; u ovome slučaju to su geografski položaj, udaljenost od mora te nadmorska visina.

<p>Flood hazard prediction is a critical component of flood risk assessment, flood risk management plans, and implementation of flood mitigation measures. In the EU, there is currently a growing interest in floods caused by extreme heavy rainfall, commonly known as pluvial floods. Due to the rapid development of computational and remote sensing technology, as well as the public availability of high-resolution spatial data, pluvial floods are now simulated using integrated hydrological-hydraulic approaches consisting of time-dependent 2D numerical models and so-called rain-on-grid approaches with spatially variable infiltration. In this paper, we will present the recent progress and methodological framework for pluvial flood hazard assessment in the city of Poreč in the northern coastal part of Croatia, focusing on the interpretation and modification of spatial input data, precipitation data processing, and numerical modelling of pluvial flooding. We show what spatial data were collected and improved, what spatial data were generated, how the precipitation data were processed for this purpose, and discuss some modelling aspects specific to pluvial flooding in urban areas. Finally, we present the results of the pluvial flood hazard assessment for the city of Poreč and its catchment area and provide some recommendations for further research.</p>

<p>This study presents a forecasting model for pluvial flooding in the city of Zadar, Croatia, where a huge mesoscale convective system recently caused massive pluvial flooding and widespread property damage. Flood forecasting approaches based on hydrologic-hydraulic models require a large set of accurate data to provide reliable simulations. They also require many simulations, which can be computationally expensive and time consuming. Therefore, we are investigating the possibility of using a data-driven approach based on local news reports of pluvial flooding combined with a local high-resolution rain gauge. To this end, we considered two different computational approaches. The first - a conventional one - is based on rainfall threshold curves that define the critical rainfall depth for different time periods above which flooding is likely to occur. The second approach is based on machine learning and a classification problem - predicting whether accumulated rainfall depths over different time periods will lead to pluvial flooding. For the second approach, we considered 10 different methods that belong to five categories of machine learning typically used for classification problems. They are logistic regression, support vector machine, discriminant analysis, decision trees, and nearest neighbours. After a careful analysis, we defined rainfall threshold curves for Zadar that can be used for an early warning system and flood forecasting. We show that some machine learning models can provide slightly more accurate predictions than the threshold curve, with quadratic discriminant analysis being the most successful method for this purpose. Overall, this study shows that flood forecasting based on news reports in the city of Zadar can be a reliable approach. The analysis conducted in this study has laid the foundation for the implementation of an early warning system and pluvial flood forecasting in the Croatian coastal area.</p>

U radu su, na primjeru poplave koja je u svibnju 2014. godine zadesila istočnu Hrvatsku, uspoređene tri metode kartiranja i procjene opsega poplavljenog područja: metoda analize refleksije s površine u blisko infracrvenom (IC) dijelu spektra (jednokanalna metoda) te metode vegetacijskog indeksa NDVI (Normalized Difference Vegetation Index) i vodenog indeksa NDWI (Normalized Difference Water Index). Metode kao ulazne podatke koriste snimke snimljene pasivnim senzorom ugrađenim na satelitsku platformu Landsat 8. Analizirane su četiri snimke; snimljene su prije (jedna snimka), tijekom (jedna snimka) i nakon poplave (dvije snimke). Procjena temeljena na jednokanalnoj metodi rezultirala je površinom manjom od površina procijenjenih primjenom višekanalnim metodama. Rezultati se mogu objasniti kompleksnošću spektralnog potpisa plitkih poplavnih voda s visokim udjelom suspendiranog nanosa koji će utjecati na refleksiju takvih površina u blisko IC dijelu spektra i klasificirati ih kao nevodene površine. S druge strane, kombiniranjem različitih spektralnih kanala u višekanalnim metodama kompenzira se utjecaj suspendiranog nanosa na refleksiju takvih voda te je klasifikacija na vodene i nevodene površine preciznija.