Purpose The sediment supply to rivers, lakes, and reservoirs has a great influence on hydro-morphological processes. For instance, long-term predictions of bathymetric change for modeling climate change scenarios require an objective calculation procedure of sediment load as a function of catchment characteristics and hydro-climatic parameters. Thus, the overarching objective of this study is to develop viable and objective sediment load assessment methods in data-sparse regions. Methods This study uses the Revised Universal Soil Loss Equation (RUSLE) and the SEdiment Delivery Distributed (SEDD) model to predict soil erosion and sediment transport in data-sparse catchments. The novel algorithmic methods build on free datasets, such as satellite and reanalysis data. Novelty stems from the usage of freely available datasets and the introduction of a seasonal snow memory into the RUSLE. In particular, the methods account for non-erosive snowfall, its accumulation over months as a function of temperature, and erosive snowmelt months after the snow fell. Results Model accuracy parameters in the form of Pearson’s r and Nash–Sutcliffe efficiency indicate that data interpolation with climate reanalysis and satellite imagery enables viable sediment load predictions in data-sparse regions. The accuracy of the model chain further improves when snow memory is added to the RUSLE. Non-erosivity of snowfall makes the most significant increase in model accuracy. Conclusion The novel snow memory methods represent a major improvement for estimating suspended sediment loads with the empirical RUSLE. Thus, the influence of snow processes on soil erosion and sediment load should be considered in any analysis of mountainous catchments.
Bayesian model selection (BMS) and Bayesian model justifiability analysis (BMJ) provide a statistically rigorous framework to compare competing conceptual models through the use of Bayesian model evidence (BME). However, BME-based analysis has two main limitations: (1) it's powerless when comparing models with different data set sizes and/or types of data and(2) doesn't allow to judge a model's performance based on its posterior predictive capabilities. Thus, traditional BME-based approaches ignore useful data or models due to issue (1) or disregards Bayesian updating because of issue (2). To address these limitations, we advocate to include additional information-theoretic scores into BMS and BMJ analysis: expected log-predictive density (ELPD), relative entropy (RE) and information entropy (IE). Exploring the connection between Bayesian inference and information theory, we explicitly link BME and ELPD together with RE and IE to indicate the information flow in BMS and BMJ analysis. We show how to compute and interpret these scores alongside BME, and apply it in a model selection and similarity analysis framework. We test the methodology on a controlled 2D groundwater setup considering five competing conceptual models accompanied with different data sets. The results show how the information-theoretic scores complement BME by providing a more complete picture concerning the Bayesian updating process. Additionally, we present how both RE and IE can be used to objectively compare models that feature different data sets. Overall, the introduced Bayesian information-theoretic framework helps to avoid any potential loss of information and leads to an informed decision for model selection and similarity.
Environmental modeling allows researchers to reproduce physical systems under different conditions, be they current or future, for design, management or decision making purposes. Due to the high complexity involved in environmental modeling, simplifications and assumptions are necessary to consider the different processes that interact with each other (Wainwright & Mulligan, 2013). Consequently, different sources of uncertainty arise in environmental modeling, including parameter, model input, measurement uncertainty and conceptual uncertainty (Gong et al., 2013;Refsgaard et al., 2007). The latter, also referred to as structural uncertainty, pertains to the choice of model itself, and has gained renewed interest in the past decades as an important source of predictive uncertainty (
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