Abstract:The widely used, partly-deterministic Soil and Water Assessment Tool (SWAT) requires a large amount of spatial input data, such as a digital elevation model (DEM), land use, and soil maps. Modelers make an effort to apply the most specific data possible for the study area to reflect the heterogeneous characteristics of landscapes. Regional data, especially with fine resolution, is often preferred. However, such data is not always available and can be computationally demanding. Despite being coarser, global data are usually free and available to the public. Previous studies revealed the importance for single investigations of different input maps. However, it remains unknown whether higher-resolution data can lead to reliable results. This study investigates how global and regional input datasets affect parameter uncertainty when estimating river discharges. We analyze eight different setups for the SWAT model for a catchment in Luxembourg, combining different land-use, elevation, and soil input data. The Metropolis-Hasting Markov Chain Monte Carlo (MCMC) algorithm is used to infer posterior model parameter uncertainty. We conclude that our higher resolved DEM improves the general model performance in reproducing low flows by 10%. The less detailed soil-map improved the fit of low flows by 25%. In addition, more detailed land-use maps reduce the bias of the model discharge simulations by 50%. Also, despite presenting similar parameter uncertainty (P-factor ranging from 0.34 to 0.41 and R-factor from 0.41 to 0.45) for all setups, the results show a disparate parameter posterior distribution. This indicates that no assessment of all sources of uncertainty simultaneously is compensated by the fitted parameter values. We conclude that our result can give some guidance for future SWAT applications in the selection of the degree of detail for input data.

No abstract

No abstract

Natural flood management (NFM) is widely promoted for managing flood risks but the effectiveness of different types of NFM schemes at medium (100–1000 km2) and large scales (>1000 km2) remains widely unknown. This study demonstrates the importance of fully understanding the impact of model structure, calibration and uncertainty techniques on the results before the NFM assessment is undertaken. Land‐based NFM assessment is undertaken in two medium‐scale lowland catchments within the Thames River basin (UK) with a modelling approach that uses the Soil and Water Assessment Tool (SWAT) model within an uncertainty framework. The model performed poorly in groundwater‐dominated areas (P‐factor <0.5 and R‐factor >0.6). The model performed better in areas dominated by surface and interflow processes (P‐factor >0.5 and R‐factor <0.6) and here hypothetical experiments converting land to broadleaf woodland and cropland showed that the model offers good potential for the assessment of NFM effectiveness. However, the reduction of large flood flows greater than 4% in medium‐sized catchments would require afforestation of more than 75% of the area. Whilst hydrological models, and specifically SWAT, can be useful tools in assessing the effectiveness of NFM, these results demonstrate that they cannot be applied in all settings.

&lt;p&gt;Over the last decades, there has been much interest in natural flood management (NFM). However, there is still a lack of evidence on the effectiveness of NFM in general and particularly land and soil management based NFM in medium to large scale catchments. This study investigates the effects of soil and land management based NFM in the lowland Pang and Blackwater catchments in the UK. It uses the Soil and Water Assessment Tool (SWAT) to model the effects of broadscale land use and crop rotation scenarios on peak flows in selected catchments, while considering uncertainties. The broadscale land use scenarios consisted of the conversion of the catchments&amp;#8217; land to broadleaf woodland and cropland, respectively, except for water and urban areas. The results indicate that the NFM effects vary across the catchments and depend on landscapes characteristics. In addition, the blanket conversion to broadleaf woodland or cropland has a larger effect on small peak flows than on large floods such as those of January 9 and February 6, 2014. Afforestation leads to a reduction of 10 to 16% of the modelled 2014 winter flood events. In contrast, implementing crop rotation scenarios increases the peak flows, with the increase depending on the crops used and tillage practice. These findings suggest that via bespoke woodland planting and farming practices, combined with other measures that can reduce the amount of flow reaching the river channel or delay the timing of the peak flow (eg. leaky barriers), flood risks can be minimized. The results of this study provide information that can benefit future decision making on flood risk reduction in suitable catchments.&lt;/p&gt;