Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

A recurrent problem in hydrology is the absenceof streamflow data to calibrate rainfall–runoff models.A commonly used approach in such circumstances conditionsmodel parameters on regionalized response signatures.While several different signatures are often available to beincluded in this process, an outstanding challenge is the selectionof signatures that provide useful and complementaryinformation. Different signatures do not necessarily provideindependent information and this has led to signatures beingomitted or included on a subjective basis. This paperpresents a method that accounts for the inter-signature errorcorrelation structure so that regional information is neitherneglected nor double-counted when multiple signatures areincluded. Using 84 catchments from the MOPEX database,observed signatures are regressed against physical and climaticcatchment attributes. The derived relationships are thenutilized to assess the joint probability distribution of the signatureregionalization errors that is subsequently used in aBayesian procedure to condition a rainfall–runoff model. Theresults show that the consideration of the inter-signature errorstructure may improve predictions when the error correlationsare strong. However, other uncertainties such as modelstructure and observational error may outweigh the importanceof these correlations. Further, these other uncertaintiescause some signatures to appear repeatedly to be misinformative

Flow Prediction in Ungauged Catchments Using Probabilistic Random Forests Regionalization and New Statistical Adequacy Tests

Flow prediction in ungauged catchments is a major unresolved challenge in scientific and engineering hydrology. This study attacks the prediction in ungauged catchment problem by exploiting advances in flow index selection and regionalization in Bayesian inference and by developing new statistical tests of model performance in ungauged catchments. First, an extensive set of available flow indices is reduced using principal component (PC) analysis to a compact orthogonal set of ?flow index PCs.? These flow index PCs are regionalized under minimal assumptions using random forests regression augmented with a residual error model and used to condition hydrological model parameters using a Bayesian scheme. Second, ?adequacy? tests are proposed to evaluate a priori the hydrological and regionalization model performance in the space of flow index PCs. The proposed regionalization approach is applied to 92 northern Spain catchments, with 16 catchments treated as ungauged. It is shown that (1) a small number of PCs capture approximately 87% of variability in the flow indices and (2) adequacy tests with respect to regionalized information are indicative of (but do not guarantee) the ability of a hydrological model to predict flow time series and are hence proposed as a prerequisite for flow prediction in ungauged catchments. The adequacy tests identify the regionalization of flow index PCs as adequate in 12 of 16 catchments but the hydrological model as adequate in only 1 of 16 catchments. Hence, a focus on improving hydrological model structure and input data (the effects of which are not disaggregated in this work) is recommended

Identification of Dominant Hydrological Mechanisms Using Bayesian Inference, Multiple Statistical Hypothesis Testing, and Flexible Models

ABSTARCT: In hydrological modeling, the identification of model mechanisms best suited for representing individual hydrological (physical) processes is of major scientific and operational interest. We present a statistical hypothesis-testing perspective on this model identification challenge and contribute a mechanism identification framework that combines: (i) Bayesian estimation of posterior probabilities of individual mechanisms from a given ensemble of model structures; (ii) a test statistic that defines a ?dominant? mechanism as a mechanism more probable than all its alternatives given observed data; and (iii) a flexible modeling framework to generate model structures using combinations of available mechanisms. The uncertainty in the test statistic is approximated using bootstrap sampling from the model ensemble. Synthetic experiments (with varying error magnitude and multiple replicates) and real data experiments are conducted using the hydrological modeling system FUSE (7 processes and 2?4 mechanisms per process yielding 624 feasible model structures) and data from the Leizarán catchment in northern Spain. The mechanism identification method is reliable: it identifies the correct mechanism as dominant in all synthetic trials where an identification is made. As data/model errors increase, statistical power (identifiability) decreases, manifesting as trials where no mechanism is identified as dominant. The real data case study results are broadly consistent with the synthetic analysis, with dominant mechanisms identified for 4 of 7 processes. Insights on which processes are most/least identifiable are also reported. The mechanism identification method is expected to contribute to broader community efforts on improving model identification and process representation in hydrology.The authors from IHCantabria acknowledge the financial support from the Government of Cantabria through the FÉNIX Program (ID 2020.03.03.322B.742.09)

Combining information from multiple flood projections in a hierarchical Bayesian framework

This study demonstrates, in the context of flood frequency analysis, the potential of a recently proposed hierarchical Bayesian approach to combine information from multiple models. The approach explicitly accommodates shared multi-model discrepancy as well as the probabilistic nature of the flood estimates, and treats the available models as a sample from a hypothetical complete (but unobserved) set of models. The methodology is applied to flood estimates from multiple hydrological projections (the Future Flows Hydrology dataset) for 135 catchments in the UK. The advantages of the approach are shown to be: 1) to ensure adequate ‘baseline' with which to compare future changes; 2) to reduce flood estimate uncertainty; 3) to maximise use of statistical information in circumstances where multiple weak predictions individually lack power, but collectively provide meaningful information; 4) to diminish the importance of model consistency when model biases are large; and 5) to explicitly consider the influence of the (model performance) stationarity assumption. Moreover, the analysis indicates that reducing shared model discrepancy is the key to further reduction of uncertainty in the flood frequency analysis. The findings are of value regarding how conclusions about changing exposure to flooding are drawn, and to flood frequency change attribution studies. This article is protected by copyright. All rights reserved

r_CurveNumber: Second release

<p>This is the second release. The first release was on figshare</p

SCS Curve Number method (R-package)

<p>This package implements an adaptation of the SCS Curve Number method according to Hawkins (1993).</p

Identifying Early Warning Signals from News Using Network Community Detection

The paper addresses the challenge of accelerating identification of changes in risk drivers in the insurance industry. Specifically, the work presents a method to identify significant news events ("signals") from batches of news data to inform Life & Health insurance decisions. Signals are defined as events that are relevant to a tracked risk driver, widely discussed in multiple news outlets, contain novel information and affect stakeholders. The method converts unstructured data (news articles) into a sequence of keywords by employing a linguistic knowledge graph-based model. Then, for each time window, the method forms a graph with extracted keywords as nodes and draws weighted edges based on keyword co-occurrences in articles. Lastly, events are derived in an unsupervised way as graph communities and scored for the requirements of a signal: relevance, novelty and virality. The methodology is illustrated for a Life & Health topic using news articles from Dow Jones DNA proprietary data set, and assessed against baselines on a publicly available news data set. The method is implemented as an analytics engine in Early Warning System deployed at Swiss Re for the last 1.5 years to extract relevant events from live news data. We present the system's architectural design in production and discuss its use and impact