Quantifying local rainfall dynamics and uncertain boundary conditions into a nested regional-local flood modeling system

[Abstract:] Inflow discharge and outflow stage estimates for hydraulic flood models are generally derived from river gauge data. Uncertainties in the measured inflow data and the neglect of rainfall-runoff contributions to the modeled domain downstream of the gauging locations can have a significant impact on these estimated “whole reach” inflows and consequently on flood predictions. In this study, a method to incorporate rating curve uncertainty and local rainfall-runoff dynamics into the predictions of a reach-scale flood model is proposed. The methodology is applied to the July 2007 floods of the River Severn in UK. Discharge uncertainty bounds are generated applying a nonparametric local weighted regression approach to stage-discharge measurements for two gauging stations. Measured rainfall downstream from these locations is used as input to a series of subcatchment regional hydrological model to quantify additional local inflows along the main channel. A regional simplified-physics hydraulic model is then applied to combine these contributions and generate an ensemble of discharge and water elevation time series at the boundaries of a local-scale high complexity hydraulic model. Finally, the effect of these rainfall dynamics and uncertain boundary conditions are evaluated on the local-scale model. Accurate prediction of the flood peak was obtained with the proposed method, which was only possible by resolving the additional complexity of the extreme rainfall contributions over the modeled area. The findings highlight the importance of estimating boundary condition uncertainty and local rainfall contributions for accurate prediction of river flows and inundation at regional scales.María Bermúdez gratefully acknowledges financial support from the Spanish Regional Government of Galicia (postdoctoral grant reference ED481B 2014/156-0). Gemma Coxon was supported by NERC MaRIUS: Managing the Risks, Impacts and Uncertainties of droughts and water Scarcity, grant NE/L010399/1. Jim Freer and Paul Bates by NERC Susceptibility of catchments to INTense RAinfall and flooding, grant NE/K00882X/1. A free version of the model LISFLOOD-FP is available for download at www.bristol.ac.uk/geography/research/hydrology/models/lisflood/. A free version of the model Iber is available for download at www.iberaula.es. The river cross-section data, the LiDAR digital elevation model and the gauging station rainfall, stage, flow and rating curve data of the presented case study are freely available from the Environment Agency ([email protected])Xunta de Galicia; ED481B 2014/156-0Inglaterra. University of Bristol; NE/L010399/1Inglaterra. University of Bristol; NE/K00882X/

Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging

Bayesian Model Averaging (BMA) is a popular approach to combine hydrologic forecasts from individual models, and characterize the uncertainty induced by model structure. In the original form of BMA, the conditional probability density function (PDF) of each model is assumed to be a particular probability distribution (e.g. Gaussian, gamma, etc.). If the predictions of any hydrologic model do not follow certain distribution, a data transformation procedure is required prior to model averaging. Moreover, it is strongly recommended to apply BMA on unbiased forecasts, whereas it is sometimes difficult to effectively remove bias from the predictions of complex hydrologic models. To overcome these limitations, we develop an approach to integrate a group of multivariate functions, the so-called copula functions, into BMA. Here, we introduce a copula-embedded BMA (Cop-BMA) method that relaxes any assumption on the shape of conditional PDFs. Copula functions have a flexible structure and do not restrict the shape of posterior distributions. Furthermore, copulas are effective tools in removing bias from hydrologic forecasts. To compare the performance of BMA with Cop-BMA, they are applied to hydrologic forecasts from different rainfall-runoff and land-surface models. We consider the streamflow observation and simulations for ten river basins provided by the Model Parameter Estimation Experiment (MOPEX) project. Results demonstrate that the predictive distributions are more accurate and reliable, less biased, and more confident with small uncertainty after Cop-BMA application. It is also shown that the post-processed forecasts have better correlation with observation after Cop-BMA application

Impact of temporal data resolution on parameter inference and model identification in conceptual hydrological modeling: insights from an experimental catchment

This study presents quantitative and qualitative insights into the time scale dependencies of hydrological parameters, predictions and their uncertainties, and examines the impact of the time resolution of the calibration data on the identifiable system complexity. Data from an experimental basin (Weierbach, Luxembourg) is used to analyze four conceptual models of varying complexity, over time scales of 30 min to 3 days, using several combinations of numerical implementations and inference equations. Large spurious time scale trends arise in the parameter estimates when unreliable time-stepping approximations are employed and/or when the heteroscedasticity of the model residual errors is ignored. Conversely, the use of robust numerics and more adequate (albeit still clearly imperfect) likelihood functions markedly stabilizes and, in many cases, reduces the time scale dependencies and improves the identifiability of increasingly complex model structures. Parameters describing slow flow remained essentially constant over the range of subhourly to daily scales considered here, while parameters describing quick flow converged toward increasingly precise and stable estimates as the data resolution approached the characteristic time scale of these faster processes. These results are consistent with theoretical expectations based on numerical error analysis and dataaveraging considerations. Additional diagnostics confirmed the improved ability of the more complex models to reproduce distinct signatures in the observed data. More broadly, this study provides insights into the information content of hydrological data and, by advocating careful attention to robust numericostatistical analysis and stringent processoriented diagnostics, furthers the utilization of dense-resolution data and experimental insights to advance hypothesis-based hydrological modeling at the catchment scale.Dmitri Kavetski, Fabrizio Fenicia and Martyn P. Clar

Invasive Flora

Flowers are a beautiful, ever changing part of nature. Historically, flowers have been symbols for beauty, love, and fertility. However, I choose to paint poisonous flowers to represent the fatal beauty of the things we are drawn to even when they are not good for us. Within my work, poisonous flowers symbolize negative thoughts and emotions I have about self-harm and being trapped. Little moments in my life bring about anxious feelings. To both represent and to combat these feelings, I create images of domestic space relatable to the viewer, but fraught with tense energy from proliferating flowers and intense impasto textures. The vacant rooms in my paintings seem normal at first glance, but upon further reflection, the viewer finds that the flowers are positioned in unnatural locations and are growing intrusively throughout the environment. I create a visual language throughout my paintings that allows me to translate color, texture, and overall energy of an everyday moment into something that makes the viewer concerned. Heavy impasto pulls the viewers’ attention through the space, while the flatter areas are places of rest or of avoidance. I implore the viewer to question the narrative setting, the meaning of the flowers, and their personal role in the scene. My work reflects on the ways doubt, avoidance, and guilt can grow profusely and pervasively in our minds. I want to explore the intensity of the intrusive thoughts that people experience in their daily life, even if others can’t see them. As in gardening, it can be hard to get rid of stubborn, invasive flora, even for the best gardeners. When harmful thoughts and feelings come back, season after season, what matters is how we respond to them. Do we let them take over everything in their path, or do we learn to live with their presence, giving them the attention they need, but slowly reclaiming the breathable air around us as our own?https://digitalcommons.murraystate.edu/art498/1040/thumbnail.jp