Approaches to analyse and model changes in impacts:reply to discussions of “How to improve attribution of changes in drought and flood impacts”^*

We thank the authors, Brunella Bonaccorso and Karsten Arnbjerg-Nielsen for their constructive contributions to the discussion about the attribution of changes in drought and flood impacts. We appreciate that they support our opinion, but in particular their additional new ideas on how to better understand changes in impacts. It is great that they challenge us to think a step further on how to foster the collection of long time series of data and how to use these to model and project changes. Here, we elaborate on the possibility to collect time series of data on hazard, exposure, vulnerability and impacts and how these could be used to improve e.g. socio-hydrological models for the development of future risk scenarios.</p

Participatory development of storymaps to visualize the spatiotemporal dynamics and impacts of extreme flood events for disaster preparedness

Floods are one of the costliest natural hazards in Switzerland and worldwide. Therefore, society is confronted with questions about protecting people and assets from flood risks. Key instruments are protective measures, land use regulations, spatial planning, and the interventions by civil protection units if flood magnitudes exceed the protection standards. Both prevention and preparedness require risk awareness from professionals, politicians, and the public. Risk awareness is generally high after an event and low after a period without major events. However, the rarity of extreme flood events limits learning from flood events. The training of intervention forces who should manage flood events with magnitudes beyond hitherto observed flood events requires a comprehensive description and visualization of the flood processes and their impacts. To address this, together with stakeholders and civil protection and intervention planning experts, we co-developed a new way to visualize the spatiotemporal dynamics of extreme flood events and thereby communicate their impacts using dynamical flood storymaps. We selected physically plausible precipitation scenarios from reforecasts to develop storylines of extreme river flood events and their socioeconomic impacts in Switzerland. The co-development process revealed which information is relevant to potential users and how it must be presented. It is shown that storylines of extreme events presented as storymaps are a valuable tool to communicate scientific results in a way that allows practitioners to gain relevant information for their work. Therefore, we built an interactive online tool (www.flooddynamics.ch), enabling the user to analyze the spatiotemporal unfolding of flood events in Switzerland from the start of precipitation to the recession of the flood. The visualization includes maps of inundated areas at hourly timesteps and the related impacts in terms of affected persons, buildings, roads, and infrastructure. Such a temporally explicit (dynamic) representation of extreme events in storymaps, in contrast to static hazard maps, which are commonly used today, is favorable for emergency intervention planning and training and thus for awareness creation and better disaster preparedness

Ensemble flood forecasting: current status and future opportunities

Ensemble flood forecasting has gained significant momentum over the past decade due to the growth of ensemble numerical weather and climate prediction, expansion in high performance computing, growing interest in shifting from deterministic to risk-based decision-making that accounts for forecast uncertainty, and the efforts of communities such as the international Hydrologic Ensemble Prediction Experiment (HEPEX), which focuses on advancing relevant ensemble forecasting capabilities and fostering its adoption. With this shift, comes the need to understand the current state of ensemble flood forecasting, in order to provide insights into current capabilities and areas for improvement, thus identifying future research opportunities to allow for better allocation of research resources. In this paper, we provide an overview of current research activities in ensemble flood forecasting and discuss knowledge gaps and future research opportunities, based on a review of 70 papers focussing on various aspects of ensemble flood forecasting around the globe. Future research directions include opportunities to improve technical aspects of ensemble flood forecasting, such as data assimilation techniques and methods to account for more sources of uncertainty, and developing ensemble forecasts for more variables, for example flood inundation, by applying techniques such as machine learning. Further to this, we conclude that there is a need to not only improve technical aspects of flood forecasting, but also to bridge the gap between scientific research and hydro-meteorological model development, and real-world flood management using probabilistic ensemble forecasts, especially through effective communication

Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations

High computational cost is often the most limiting factor when running high-resolution hydrodynamic models to simulate spatial-temporal flood inundation behavior. To address this issue, a recent study introduced the hybrid Low-fidelity, Spatial analysis, and Gaussian Process learning (LSG) model. The LSG model simulates the dynamic behavior of flood inundation extent by upskilling simulations from a low-resolution hydrodynamic model through Empirical Orthogonal Function (EOF) analysis and Sparse Gaussian Process learning. However, information on flood extent alone is often not sufficient to provide accurate flood risk assessments. In addition, the LSG model has only been tested on hydrodynamic models with structured grids, while modern hydrodynamic models tend to use unstructured grids. This study therefore further develops the LSG model to simulate water depth as well as flood extent and demonstrates its efficacy as a surrogate for a high-resolution hydrodynamic model with an unstructured grid. The further developed LSG model is evaluated on the flat and complex Chowilla floodplain of the Murray River in Australia and accurately predicts both depth and extent of the flood inundation, while being 12 times more computationally efficient than a high-resolution hydrodynamic model. In addition, it has been found that weighting before the EOF analysis can compensate for the varying grid cell sizes in an unstructured grid and the inundation extent should be predicted from an extent-based LSG model rather than deriving it from water depth predictions.Niels Fraehr, Quan J. Wang, Wenyan Wu, and Rory Natha

A method to reconstruct flood scenarios using field interviews and hydrodynamic modelling: application to the 2017 Suleja and Tafa, Nigeria flood

Abstract The scarcity of model input and calibration data has limited efforts in reconstructing scenarios of past floods in many regions globally. Recently, the number of studies that use distributed post-flood observation data collected throughout flood-affected communities (e.g. face-to-face interviews) are increasing. However, a systematic method that applies such data for hydrodynamic modelling of past floods in locations without hydrological is lacking. In this study, we developed a method for reconstructing plausible scenarios of past flood events in data-scarce regions by applying flood observation data collected through field interviews to a hydrodynamic model (CAESAR-Lisflood). We tested the method using 300 spatially distributed flood depths and duration data collected using questionnaires on five river reaches after the 2017 flood event in Suleja and Tafa region, Nigeria. A stepwise process that aims to minimize the error between modelled and observed flood depth and duration at the locations of interviewed households was implemented. Results from the reconstructed flood depth scenario produced an error of ± 0.61 m for all observed and modelled locations and lie in the range of error produced by studies using comparable hydrodynamic models. The study demonstrates the potential of utilizing interview data for hydrodynamic modelling applications in data-scarce regions to support regional flood risk assessment. Furthermore, the method can provide flow depths and durations at houses without observations, which is useful input data for physical vulnerability assessment to complement disaster risk reduction efforts

Addressing Variability in Hydrologic Systems Using Efficient Uncertainty Quantification

The scale and complexity of environmental and earth systems introduce an array of uncertainties that need to be systematically addressed. In numerical modeling, the ever-increasing complexity of representation of these systems confounds our ability to resolve relevant uncertainties. Specifically, the numerical simulation of the governing processes involve many inputs and parameters that have been traditionally treated as deterministic. Considering them as uncertain with traditional approaches introduces a large computational burden, stemming from the requirement of a prohibitive number of model simulations. Furthermore, within hydrology, most catchments are sparsely monitored, and there are limited, disparate types of data available to confirm the model's behavior. Here I present a blueprint of a general, computationally efficient approach to uncertainty quantification for complex hydrologic models, taking advantage of recent methodological developments. The framework is used in two basic science problems in hydrology. First, it is applied to the problem of combining heterogeneous data sources representing different physical processes to infer physical parameters for the complex hydrologic model tRIBS-VEGGIE. The inference provides a probabilistic interpretation of bulk soil characteristics and related hydraulic properties for an experimental watershed in central Amazonia. These parameters are then used to propagate uncertainty in hydrologic response to an array of quantities of interest through tRIBS-VEGGIE and determine their sensitivity to uncertain model inputs. Second, the framework is used to explore landscape controls mediated by subsurface hydrologic dynamics on the distribution of vegetative traits in a mature Amazon rainforest. This study features a large parameter set as uncertain across three different soil types and three layers of vegetation, explicitly incorporating interactions between subsurface moisture and vegetation biophysical function. Vegetative performance is examined using a hypothesized cost-benefit approach between vegetation carbon uptake and hydraulic effort required to maintain long-term production. The research enables model-driven inference using a disparate set of observed hydrologic variables including stream discharge, water table depth, evapotranspiration, soil moisture, and gross primary production from the Asu experimental catchment near Manaus, Brazil. Computationally inexpensive model surrogates are constructed and shown to mimic solution of the complex hydrologic model tRIBS-VEGGIE with a high skill. The two applications demonstrate the flexibility of the framework for hydrologic inference in watershed with sparse, irregular observations of varying accuracy. Significant computational savings imply that problems of greater computational complexity and dimension can be addressed. Furthermore, the framework simultaneously yields probabilistic representation of model behavior, robust parameter inference, and sensitivity analysis without the need for greater investment in computational resources.PHDEnvironmental EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147605/1/dwellem_1.pd

The effects of upstream Natural Flood Management on urban surface drainage performance

The evaluation of rural Natural Flood Management (NFM) has traditionally focused on the ability of interventions to mitigate downstream fluvial flooding by attenuating catchment response. This research expands this focus – investigating whether these same interventions could also mitigate surface flood risk in downstream urban areas. By moderating water levels in receiving watercourses, upstream NFM could promote free discharge from urban drainage outfalls and thereby improve local surface drainage performance. A novel modelling methodology has been developed to characterise the response of three separate catchments – the Bin Brook in Cambridgeshire, the Asker in Dorset and a sub-catchment of the upper Calder in Yorkshire. The upstream, rural response is simulated by coupling Dynamic TOPMODEL (a semi-distributed hydrological model) and HEC-RAS (which solves the shallow flow equations). This offers a freely-available, spatially-informed approach for the evaluation of a range of upstream NFM interventions (located both within and beyond the riparian zone) at a catchment-scale. This modelled rural response then provides the input for a downstream, integrated urban drainage model (Infoworks ICM). This is then be used to examine how any consequent changes in outfall inundation by the urban watercourse alter local drainage performance. Each case study is examined separately before a comparative study of all three is undertaken to identify broader trends. These trends suggest that during more frequent events (e.g. 1 in 10 year), upstream NFM interventions create the greatest reductions for the time low-lying outfalls are submerged by local watercourses. As storm severity increases (heightening risk of drainage surcharge or failure) these reductions diminish. Despite this, the slight delay in rural response continues to allow more water to escape surface systems before outfall inundation occurs, increasing the effective capacity of networks and reducing surface flood volumes. While any improvements to outfall discharge would not, in themselves, justify NFM implementation, these interventions have the potential to contribute to a downstream water level management strategy in certain locations and therefore will be of interest to urban flood risk practitioners.Engineering and Physical Sciences Research Counci

Decadal development of CREST hydrological model family: review, improvements, applications, and outlook

A hydrological model is an indispensable tool in Earth system science and engineering operations to understand, predict, and manage water resources on Earth. The Coupled Routing and Excess Storage (CREST) model, released in 2011, is one such to simulate distributed hydrologic states and fluxes at variable scales. Over the last decade, CREST model has been actively under development and applied by different sectors to tackle water-related problems worldwide. This dissertation is dedicated to expanding the capacity of CREST model from three main fronts: (1) hydrologic data, (2) model development, and (3) applications. To start, the decadal development and applications of CREST model family were reviewed to lay the foundation for my contribution (Chapter 1). First, uncertainties in hydrologic input data were evaluated comprehensively for three state-of-the-science precipitation datasets derived from in-situ instruments, ground weather radar, and satellites during extreme events (Chapter 2); then a 120-year CONUS-wide flood database was compiled into a unified format as a validation source for models and hydroclimatic research (Chapter 3). From the model development front, a Hydrologic&Hydraulic (H&H) framework was developed to empower flood inundation mapping capacity for CREST (Chapter 4); furthermore, the re-infiltration, an important yet often ignored hydrologic process during the flooding period, was incorporated to improve the more realistic rainfall-runoff modeling representation (Chapter 5). To further improve the model efficiency, a vector-based CREST model was developed that can achieve 10x speedup for a continental-scale simulation, as well as improved model accuracy (Chapter 6). Finally on the model application, the high-resolution CREST model was applied in quantifying future US floods in a warmer climate: flood flashiness is becoming 7.9% higher for the continent (Chapter 7); and extreme rainfall and floods are becoming more frequent, widespread, and less seasonal (Chapter 8). The final Chapter 9 summarizes the contributions to the CREST model family development, outlooks, and general remarks for advancing our understanding of hydrologic science and engineering

Catchment-scale spatial targeting of flood management measures to reduce flood hazard: An end-to-end modelling approach applied to the East Rapti catchment, Nepal

Globally, practical approaches to managing flood hazards are moving away from mitigation solely at the point of the impact, and towards an integrated catchment-scale approach which considers flood source areas, flow pathways of flood waters and impacted communities. The current method for managing the fluvial flood risk in Nepal, however, generally involves localised structural interventions in affected areas using a static and reactive approach. This method does not create long term resilience to the hazards. There is therefore the need to rely less on these large-scale structural measures and focus instead on sustainable and non-structural measures for flood mitigation that allow the catchments and communities within them to be more resilient. The three-stage, end-to-end approach developed in this thesis provides a process to help shift towards an integrated catchment management for flood hazard reduction in Nepal. The approach centres on identifying flood water source areas within the catchment and spatially targeting flood management measures in these locations. Consideration is also given to the potential impact of future, flow magnitude increasing, land cover change such as deforestation and the abandonment of terraced agriculture that is evident in many Nepali catchments. Stage 1 adopts SCIMAP-Flood, a catchment-scale decision support framework that identifies critical source areas for flood waters. The framework uses maps flood water generating areas based on spatial rainfall patterns and land cover, the incorporation of travel times across a catchment, and modelling of hydrological connectivity. Outputs are used to create catchment-scale flood management scenarios which target flood source areas; tested flood management measures include targeted afforestation, check dams in key sub-catchments and abandoned terrace restoration. In Stage 2 the flood management scenarios are assessed using CRUM3, a physically-based, spatially distributed, catchment-scale hydrological model. The impact of the flood management measures can be evaluated throughout the catchment using the modelled change in discharge. Stage 3 uses LISFLOOD-FP, a 2D flood inundation model, to establish the change flood inundation patterns at key flood impacted communities within the catchment from the created flood management scenarios. Stage 2 and Stage 3 utilise a coupled hydrological-hydraulic modelling approach with the results from the CRUM3 model entering the LISFLOOD-FP model as inflow hydrographs. The approach is applied to the East Rapti catchment, a 3,084 km2 sub-catchment of the Nayarani River in southern central Nepal. The catchment contains three river flow gauges (Lothar Khola [catchment area - 169 km2], Manahari Khola [427 km2] and Rapti River [471 km2]) placed within the main sub-catchments and eight rainfall gauges. Additional data used to drive the approach was attained from global datasets and acquired during fieldwork. This thesis has researched the potential effectiveness of the implementation of flood management interventions at the catchment-scale and evidences an alternative approach to flood management that is applicable in both Nepal and the wider Himalayan Region. Based on the integrated modelling approach, the results predict that the high flow magnitudes in the East Rapti catchment can be reduced through a catchment-scale approach. However, even with a combined approach of large scale spatially targeted afforestation and check dam implementation (Q99.9 decrease of <=5.3%), the use of solely catchment-scale flood management approaches to combat flood hazard might not be effective at reducing the flood impact to at-risk communities. A significant outcome from the catchment-scale modelling work was that there is a far greater potential for land use change to increase, rather than reduce through mitigation, flow magnitudes in the East Rapti catchment. The model results suggest that any land within the East Rapti catchment that is altered from existing forest will contribute to increasing the flow magnitude (Q99.9 increase of up to 48.2%)

Understanding Flood Regime Changes in Europe: a state-of-the-art assessment

There is growing concern that flooding is becoming more frequent and severe in Europe. A better understanding of flood regime changes and their drivers is therefore needed. The paper reviews the current knowledge on flood regime changes in European rivers that has traditionally been obtained through two alternative research approaches. The first approach is the data-based detection of changes in observed flood events. Current methods are reviewed together with their challenges and opportunities. For example, observation biases, the merging of different data sources and accounting for nonlinear drivers and responses. The second approach consists of modelled scenarios of future floods. Challenges and opportunities associated with flood change scenarios are discussed such as fully accounting for uncertainties in the modelling cascade and feedbacks. To make progress in flood change research, we suggest that a synthesis of these two approaches is needed. This can be achieved by focusing on long duration records and flood-rich and flood-poor periods rather than on short duration flood trends only, by formally attributing causes of observed flood changes, by validating scenarios against observed flood regime dynamics, and by developing low-dimensional models of flood changes and feedbacks. The paper finishes with a call for a joint European flood change research network