Sediment supply to rivers, subsequent fluvial transport, and the resulting sediment connectivity on network scales are often sparsely monitored and subject to major uncertainty. In a new paper on JGR we approach that uncertainty by adopting a stochastic method for modeling network sediment connectivity, which we present for the Se Kong, Se San, and Sre Pok (3S) tributaries of the Mekong. We quantify how unknown properties of sand sources translate into uncertainty regarding network connectivity by running the CASCADE (CAtchment Sediment Connectivity And DElivery) modeling framework in a Monte Carlo approach for 7,500 random realizations. Only a small ensemble of realizations reproduces downstream observations of sand transport. This ensemble presents an inverse stochastic approximation of the magnitude and variability of transport capacity, sediment flux, and grain size distribution of the sediment transported in the network (i.e., upscaling point observations to the entire network). The approximated magnitude of sand delivered from each tributary to the Mekong is controlled by reaches of low transport capacity (“bottlenecks”). These bottlenecks limit the ability to predict transport in the upper parts of the catchment through inverse stochastic approximation, a limitation that could be addressed by targeted monitoring upstream of identified bottlenecks. More HERE
The DAFNE project team (Andrea Castelletti and Marco Micotti) is meeting the stakeholders of the Omo-Turkana system in Nairobi (Kenya) first and then Addis Abeba (Ethiopia) to identify existing and projected issues and conflicts in the Omo river and Turkana lake basins. Two intensive days of interaction, idea exchanges and workshops with Kenyan and Ethiopian stakeholders separately will be followed up by two days of Negotiation Simulation Lab in Addis Abeba, where we will work in multiple plenary sessions to identify potential development pathways and evaluation indicators for modelling and managing the water-energy-food nexus in the Omo-Turkana system.
How stable is the ranking of alternatives determined by different robustness metrics? A new paper on Earth’s Future
Robustness is being used increasingly for decision analysis in relation to deep uncertainty
and many metrics have been proposed for its quantification. Recent studies have shown that the application of different robustness metrics can result in different rankings of decision alternatives, but there has been little discussion of what potential causes for this might be. To shed some light on this issue, we present a unifying framework for the calculation of robustness metrics, which assists with understanding how robustness metrics work, when they should be used, and why they sometimes disagree. The framework categorizes the suitability of metrics to a decision-maker based on (1) the decision-context (i.e., the suitability of using absolute performance or regret), (2) the decision-maker’s preferred level of risk aversion, and (3) the decision-maker’s preference toward maximizing performance, minimizing variance, or some higher-order moment. More HERE
Understanding the tradeoff between the information of high-resolution water use data and the costs of smart meters to collect data with sub-minute resolution is crucial to inform smart meter networks. To explore this tradeoff, we first present STREaM, a STochastic Residential water End-use Model that generates synthetic water end-use time series with 10-s and progressively coarser sampling resolutions. Second, we apply a comparative framework to STREaM output and assess the impact of data sampling resolution on end-use disaggregation, post meter leak detection, peak demand estimation, data storage, and meter availability. Our findings show that increased sampling resolution allows more accurate end-use disaggregation, prompt water leakage detection, and accurate and timely estimates of peak demand. Simultaneously, data storage requirements and limited product availability mean most large-scale, commercial smart metering deployments sense data with hourly, daily, or coarser sampling frequencies. Overall, this work provides insights for further research and commercial deployment of smart water meters.
A. Cominola, M. Giuliani, A. Castelletti, D.E. Rosenberg, A.M. Abdallah, Implications of data sampling resolution on water use simulation, end-use disaggregation, and demand management, Environmental Modelling & Software, Volume 102, April 2018, Pages 199-212