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Ecosystem characteristics documented to influence the 'preventing' type of flood regulation

Ecosystem characteristics documented to influence the 'preventing' type of flood regulation

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Riverine floods cause increasingly severe damages to human settlements and infrastructure. Ecosystems have a natural capacity to decrease both severity and frequency of floods. Natural flood regulation processes along freshwaters can be attributed to two different mechanisms: flood prevention that takes place in the whole catchment and flood mitiga...

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Context 1
... to Czúcz et al. (2021), we do not regard the extent of ecosystem type or the management type as EC indicators, but as 'ancillary' data. However, as much of the literature reviewed focuses on these features, and, at the same time, they might be very well of relevance for management decisions, we do note them in the review tables (Tables 3 and 4). ...
Context 2
... though this can be conceptualized as a different benefit or ES, the underlying mechanism (water retention) is the same. Tables 3 and 4 provide an overview on the characteristics of the main ecosystem types that were found to influence the two sub-types of flood regulation respectively. According to the scientific literature sampled in the underlying systematic review, the most relevant characteristic influencing the flood prevention ES is vegetation cover (or biomass; structural ecosystem characteristics), whereas flood mitigation ES is most influenced by the opportunities for stream water to spread out (availability of 'retention space', water storage capacity). ...
Context 3
... the flood preventing function, a feature that is not already reflected in Tables 3 and 4 is topography, or rather its influence on flow accumulation. The expert group agreed on using the topographic wetness index as a proxy in order to represent local runoff processes. ...

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... pressure variables) acceptable. Integration of the EC indicators into the modelled flow of ES can be ensured by selecting EC variables that feed into the ES models (La Notte et al., 2022bVári et al., 2022a). Neither quantifying, nor integrating EC variables into ES models was often performed in the past (Schröter et al., 2016). ...
... Currently, 85 % of the national MAES surveyed reported integration between some EC variables and the ES models. Finding and especially quantifying these links is challenging (Rendon et al., 2019;Vári et al., 2022a), while it is broadly acknowledged that only healthy ecosystems can provide ES at the appropriate levels (EC, 2020). ...
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... pressure variables) acceptable. Integration of the EC indicators into the modelled flow of ES can be ensured by selecting EC variables that feed into the ES models (La Notte et al., 2022bVári et al., 2022a). Neither quantifying, nor integrating EC variables into ES models was often performed in the past (Schröter et al., 2016). ...
... Currently, 85 % of the national MAES surveyed reported integration between some EC variables and the ES models. Finding and especially quantifying these links is challenging (Rendon et al., 2019;Vári et al., 2022a), while it is broadly acknowledged that only healthy ecosystems can provide ES at the appropriate levels (EC, 2020). ...
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Backed by the Biodiversity Strategy to 2020 and 2030, numerous 'Mapping and Assessment of Ecosystem Ser-vices' (MAES) projects have been completed in recent years in the member states of the European Union, with substantial results and insights accumulated. The experience from the different approaches is a valuable source of information for developing assessment processes further, especially with regard to their uptake into policy and more recently, into ecosystem accounting. Systematic approaches towards best practices and lessons learned from national MAES projects are yet lacking. This study presents the results of a survey conducted with participants of national MAES projects overviewing 13 European MAES processes. Focus hereby is put on the types of methods used, the assessed ecosystem services, and the perceived challenges and advancements. All MAES projects assessed ecosystem services at several levels of the ecosystem service cascade (69% at least three levels), using a diverse set of data sources and methods (with 4.7 types of methods on average). More accessible data was used more frequently (e.g., statistical and literature data being the most popular). Challenges regarding policy uptake, synthesizing results, and data gaps or reliability were perceived as the most severe. Insufficient evaluation of uncertainty was seen as a major critical point, and emphasized as crucial for uptake and implementation.
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Ecosystem services (ES) for water flow regulation provided by urban green spaces (UGS) are an effective mechanism for keeping the ecological balance of the city. The volumes of ES depend on the effectiveness of the UGS. If the assessment of ES is based on the efficiency of the functioning based on general natural laws, universal methods can be achieved, which can be applied to any territory. It is also important to use open remote sensing data, which will allow this assessment to be used not only by scientists but also by urban planners. The purpose of the paper is to assess the effectiveness of UGS in regulating water flow based on remote sensing data and the use of open source GIS tools on the example of Kyiv. The effectiveness of each green space (or the whole blue-green infrastructure (BGI) in providing water regulation is the level of its impact on the overall water balance within the basin. Effectiveness should be assessed at two territorial levels: city-wide and local. At the city-wide level, the whole city's BGI will be assessed, and at the local level – individual UGS. The results of modeling at the city-wide level show that the BGI in sub-basins on the city's outskirts is above average in its effectiveness in regulating water flow. At the local level, it was determined that the soil and vegetation cover in most of the UGS of Kyiv determines similar efficiency levels. Converting the obtained efficiency values into volumes of ES showed that 10 % of UGSs provide ES in maximum volumes, 70 % – in volumes above average. At the city-wide level, the BGI of Kyiv provides ES in average and below the average volumes, which is observed in 19 and 2 sub-basins correspondingly. Such an assessment of the effectiveness of green spaces is a good way to demonstrate their role and value in regulating water flow, as well as to identify ways to design BGI effectively and prioritise management decisions to achieve the greatest flood control effect.
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... Також при проведенні оцінки зважатимемо на: -тип рослинності. Рослинність впливає на водорегулювання за рахунок процесів: евапотранспірації, транспірації, перехоплення тощо [30]. Як йдеться у [26], у разі використання рослинного покриву як додаткової змінної, у моделюванні впливу на водорегулювання кореляційні зв'язки посилюються з 0,62 до 0,74. ...
... -Тип ґрунту, адже інфільтрація тісно пов'язана з такими ґрунтовими характеристиками, як: пористість, водопроникність, вологоємність, вміст органічної речовини тощо [20,30]. Наприклад, болотні ґрунти мають неоднорідну структуру, що призводить до утворення великого порового простору. ...
Article
In the course of its functioning, the city’s blue-green infrastructure (BGI), among other ecosystem services (ES), provides water flow regulation services. These are not only economic benefits associated with minimizing flood mitigation losses, but also environmental benefits, which include the normalization of the water cycle. To justify decisions on preventing or reducing the effects of floods in cities, it is very important to quantify the ES of water flow regulation. However, to present time, there is no universal structure and clear mechanism for conducting such an assessment in difficult urban conditions. The purpose of this study is to develop a methodology for geoinformation assessment of water flow regulation ES provided by urban green spaces (UGS) based on remote sensing data using open-source GIS tools. In accordance with the goal, the study developed a methodology based on determining the effectiveness of the relevant function of both individual UGS and the entire city’s BGI. In accordance with the indicators that characterize the properties of green spaces that mainly affect water regulation within the basin, a set of estimated indicators for the assessment was substantiated. These are the characteristics that determine the volume of water runoff, which were generalised and combined into a single calculation parameter (Fall), the coefficient of water regulating capacity due to the quality characteristics of vegetation cover (ω_veg); the coefficient of water regulating capacity due to soil properties (K_soil); the coefficient of water regulating capacity due to the type of underlying surface (ω). These parameters make it possible to implement an assessment methodology that consists in the step-by-step determination of: water flow indicators within small river basins (sub-basins); the efficiency of performing the water flow control function of each individual green area (E_water control (g_a)), and the efficiency of water flow control within the river basin by all available BGI within its boundaries (E_water control). Effectiveness indicators, based on the Harrington’s desirability function, allow to determine the volume of ES water control provision (ES_water_control(BGI) / ES_water_control(g_a)). The assessment based on this methodology can be a useful tool in urban planning decision-making. It allows identifying green areas that require priority actions to improve their capacity to provide flood control and adaptation of urban areas to climate change.