Multi-Source Uncertainty Analysis in Simulating Floodplain Inundation under Climate Change

Maier, Nadine; Chamorro, Alejandro; Kraft, Philipp; Houska, Tobias

doi:10.3390/w10060809

Cited by 4 publications

(4 citation statements)

References 67 publications

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“…The use of hydrological models for assessing changes in floods, especially for future projections, adds another dimension of uncertainty on top of uncertainty in the driving climate projections, including emissions scenarios, and in the driving climate models (both RCMs and GCMs) (Arnell and Gosling, 2016;Hundecha et al, 2016;Krysanova et al, 2017). The differences in hydrological models (Roudier et al, 2016;Thober et al, 2018), as well as postprocessing of climate model output for the hydrological models (Muerth et al, 2013;Maier et al, 2018), add to uncertainty for flood projections.…”

Section: Model Evaluationmentioning

confidence: 99%

Weather and Climate Extreme Events in a Changing Climate

Douville¹,

Raghavan²,

Renwick³

et al. 2023

Climate Change 2021 – The Physical Science Basis

302

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Section: Model Evaluationmentioning

confidence: 99%

Weather and Climate Extreme Events in a Changing Climate

Douville¹,

Raghavan²,

Renwick³

et al. 2023

Climate Change 2021 – The Physical Science Basis

302

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“…For example, it is possible to run the model with different land use change scenarios (Maier, Breuer, Chamorro, Kraft, & Houska, 2018). Our habitat model is capable of simulating changes in vegetation cover (selection of species and changes in land management), morphological characteristics (floodplain reconstruction, construction of embankments, and river regulation), and climate (precipitation amounts and seasonal patterns, and temperature affecting evapotranspiration).…”

Section: Conclusion and Further Applicationsmentioning

confidence: 99%

Modelling of rare flood meadow species distribution by a combined habitat surface water–groundwater model

et al. 2019

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Floodplains are highly complex and dynamic systems in terms of their hydrology.Thus, they comprise a wide habitat heterogeneity and therefore harbour highly specialized species. For future projections of habitat and species diversity, processbased models simulating ecohydrological conditions and resulting habitat and species distributions are needed. We present a new modelling framework that includes a physically based, surface water-groundwater model coupled with a habitat model.Using the model framework, we simulate the occurrence of 23 flood meadow plant species in a Rhine River floodplain. To benchmark the data, results are compared with a conventional approach with simple spatial hydrological information. Our results show that models with predictors obtained from the surface water-groundwater model are significantly more accurate for rare and endangered species, as well as for typical flood meadow species. A total of 15 hydrological predictors were defined, which look relevant and promising for a good prediction, but at the same time reflect very different hydrological conditions. The standard deviation of the groundwater level, wet soil conditions, and inundation belong to the most relevant predictors for an accurate prediction. Therefore, we recommend including more specific hydrological information in habitat models of species in complex floodplain ecosystems. Such spatial explicit habitat models can also open up further possibilities, such as the projection of global change impact studies or nature conservation planning.KEYWORDS ensembles of small models, flood meadow, habitat modelling, hydrological model, rare species, Rhine River, riparian ecosystems, surface water-groundwater model *These authors contributed equally to the work.

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“…The study area as shown in Figure 2 is located in Northeast China. The catchment is about 2085 km 2 . The Biliu River reservoir plays a significant role in supplying water to nearby big cities.…”

Section: Study Areamentioning

confidence: 99%

“…Uncertainty analysis is important when simulating runoff because it is difficult for hydrological models to reflect the hydrological process perfectly. Outputs of the Global Climate Models (GCMs) are the most effective data to represent future climate conditions and are widely used to study future hydrological responses to the impact of climate change [1][2][3]. Generally, hydrological models are driven by future climate data from GCMs, and then the projected future runoff is compared with historically measured runoff to analyze the runoff's response to climate change.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Impacts of Climate Change and Downscaling Methods on Future Runoff Projections in the Biliu River Basin

Zhu

Zhang

et al. 2019

Water

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This paper assesses the uncertainties in the projected future runoff resulting from climate change and downscaling methods in the Biliu River basin (Liaoning province, Northeast China). One widely used hydrological model SWAT, 11 Global Climate Models (GCMs), two statistical downscaling methods, four dynamical downscaling datasets, and two Representative Concentration Pathways (RCP4.5 and RCP8.5) are applied to construct 22 scenarios to project runoff. Hydrology variables in historical and future periods are compared to investigate their variations, and the uncertainties associated with climate change and downscaling methods are also analyzed. The results show that future temperatures will increase under all scenarios and will increase more under RCP8.5 than RCP4.5, while future precipitation will increase under 16 scenarios. Future runoff tends to decrease under 13 out of the 22 scenarios. We also found that the mean runoff changes ranging from −38.38% to 33.98%. Future monthly runoff increases in May, June, September, and October and decreases in all the other months. Different downscaling methods have little impact on the lower envelope of runoff, and they mainly impact the upper envelope of the runoff. The impact of climate change can be regarded as the main source of the runoff uncertainty during the flood period (from May to September), while the impact of downscaling methods can be regarded as the main source during the non-flood season (from October to April). This study separated the uncertainty impact of different factors, and the results could provide very important information for water resource management.

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Multi-Source Uncertainty Analysis in Simulating Floodplain Inundation under Climate Change

Cited by 4 publications

References 67 publications

Weather and Climate Extreme Events in a Changing Climate

Weather and Climate Extreme Events in a Changing Climate

Modelling of rare flood meadow species distribution by a combined habitat surface water–groundwater model

Uncertainty Impacts of Climate Change and Downscaling Methods on Future Runoff Projections in the Biliu River Basin

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