Until this day, data in industrial ecology (IE) have been commonly seen as existing within the domain of particular methods or models, such as input–output, life cycle assessment, urban metabolism, or material flow analysis data. This artificial division of data into methods contradicts the common phenomena described by those data: the objects and processes in the industrial system, or socioeconomic metabolism (SEM). A consequence of this scattered organization of related data across methods is that IE researchers and consultants spend too much time searching for and reformatting data from diverse and incoherent sources, time that could be invested into quality control and analysis of model results instead. This article outlines a solution to two major barriers to data exchange within IE: (a) the lack of a generic structure for IE data and (b) the lack of a bespoke platform to exchange IE datasets. We present a general data model for SEM that can be used to structure all data that can be located in the industrial system, including process descriptions, product descriptions, stocks, flows, and coefficients of all kind. We describe a relational database built on the general data model and a user interface to it, both of which are open source and can be implemented by individual researchers, groups, institutions, or the entire community. In the latter case, one could speak of an IE data commons (IEDC), and we unveil an IEDC prototype containing a diverse set of datasets from the literature.
Abstract. Reliable streamflow forecasts with associated uncertainty estimates are
essential to manage and make better use of Australia's scarce surface water
resources. Here we present the development of an operational 7 d ensemble
streamflow forecasting service for Australia to meet the growing needs of
users, primarily water and river managers, for probabilistic forecasts to
support their decision making. We test the modelling methodology for 100
catchments to learn the characteristics of different rainfall forecasts from
Numerical Weather Prediction (NWP) models, the effect of statistical
processing on streamflow forecasts, the optimal ensemble size, and
parameters of a bootstrapping technique for calculating forecast skill. A
conceptual rainfall–runoff model, GR4H (hourly), and lag and route channel
routing model that are in-built in the Short-term Water Information
Forecasting Tools (SWIFT) hydrologic modelling package are used to simulate
streamflow from input rainfall and potential evaporation. The statistical
catchment hydrologic pre-processor (CHyPP) is used for calibrating rainfall
forecasts, and the error reduction and representation in stages (ERRIS)
model is used to reduce hydrological errors and quantify hydrological
uncertainty. Calibrating raw forecast rainfall with CHyPP is an efficient
method to significantly reduce bias and improve reliability for up to 7
lead days. We demonstrate that ERRIS significantly improves forecast skill
up to 7 lead days. Forecast skills are highest in temperate perennially
flowing rivers, while it is lowest in intermittently flowing rivers. A
sensitivity analysis for optimising the number of streamflow ensemble
members for the operational service shows that more than 200 members are
needed to represent the forecast uncertainty. We show that the bootstrapping
block size is sensitive to the forecast skill calculation. A bootstrapping
block size of 1 month is recommended to capture maximum possible
uncertainty. We present benchmark criteria for accepting forecast locations
for the public service. Based on the criteria, 209 forecast locations out of
a possible 283 are selected in different hydro-climatic regions across
Australia for the public service. The service, which has been operational
since 2019, provides daily updates of graphical and tabular products of
ensemble streamflow forecasts along with performance information, for up to
7 lead days.
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