Application of a Hillslope-Scale Soil Moisture Data Assimilation System to Military Trafficability Assessment

Soil moisture is an important environmental variable that impacts military operations and weapons systems. Accurate and timely forecasts of soil moisture at appropriate spatial scales, therefore, are important for mission planning. We present an application of a soil moisture data assimilation system to military trafficability assessment. The data assimilation system combines hillslope-scale (e.g., 10s to 100s of m) estimates of soil moisture from a hydrologic model with synthetic L-band microwave radar observations broadly consistent with the planned NASA Soil Moisture Active–Passive (SMAP) mission. Soil moisture outputs from the data assimilation system are input to a simple index-based model for vehicle trafficability. Since the data assimilation system uses the ensemble Kalman Filter, the risks of impaired trafficability due to uncertainties in the observations and model inputs can be quantified. Assimilating the remote sensing observations leads to significantly different predictions of trafficability conditions and associated risk of impaired trafficability, compared to an approach that propagates forward uncertainties in model inputs without assimilation. Specifically, assimilating the observations is associated with an increase in the risk of “slow go” conditions in approximately two-thirds of the watershed, and an increase in the risk of “no go” conditions in approximately 40% of the watershed. Despite the simplicity of the trafficability assessment tool, results suggest that ensemble-based data assimilation can potentially improve trafficability assessment by constraining predictions to observations and facilitating quantitative assessment of the risk of impaired trafficability

Representation of uncertainty in market models for operational planning and forecasting in renewable power systems: a review

As the power system is becoming more weather-dependent and integrated to meet decarbonization targets, the level and severity of uncertainty increase and inevitably introduce higher risk of demand rationing or economic loss. This paper reviews the representation of uncertainty in power market models for operational planning and forecasting. A synthesis of previous reviews is used to find the prevalence of stochastic tools in power and energy system applications, and it concludes that most approaches are deterministic. A selection of power market tools handling uncertainty is reviewed in terms of the uncertain parameters they capture, and the methods used to describe them. These all use probabilistic methods and typically cover weather-related uncertainty, including demand. Random outages are also covered by several short-term power market models, while uncertainty in fuel and CO2 emission prices were generally not found to be included, nor other types of uncertainty. A gap in power market models representing multiple dimensions of uncertainty, solvable on a realistic, large-scale system in a reasonable time, is identified. The paper concludes with a discussion on topics to address when representing uncertainty, where the main challenges are that uncertainty can be difficult to describe and quantify, and including uncertainty adds additional complexity and computational burden to the problem.publishedVersio

Impact of irreversibility and uncertainty on the timing of infrastructure projects

This paper argues that because of the irreversibility and uncertainty associated with Build - Operate - Transfer (BOT) infrastructure projects, their financial evaluation should also routinely include the determination of the value of the option to defer the construction start-up. This ensures that project viability is comprehensively assessed before any revenue or loan guarantees are considered by project sponsors to support the project. This paper shows that the framework can be used even in the context of the intuitive binomial lattice model. This requires estimating volatility directly from the evolution of the net operating income while accounting for the correlation between the revenue and costs functions. This approach ensures that the uncertainties usually associated with toll revenues, in particular, are thoroughly investigated and their impact on project viability is thoroughly assessed. This paper illustrates the usefulness of the framework with data from an actual (BOT) toll road project. The results show that by postponing the project for a couple of years the project turns out to be viable, whereas it was not without the deferral. The evaluation approach proposed therefore provides a better framework for determining when and the extent of government financial support, if any, that may be needed to support a BOT project on the basis of project economics. The analysis may also be applicable to private sector investment projects, which are characterized by irreversibility and a high rate of uncertainty