Global Sensitivity Analysis of a Coupled Hydro-Economic Model and Groundwater Restriction Assessment

The modeling of coupled food-water systems to represent the effect of water supply variability as well as shocks that may emerge from changes in policies, economic drivers, and productivity requires an understanding of dominant uncertainties. These uncertainties cascade into forecasts of impacts of water management policies, such as groundwater pumping restrictions. This paper assesses how parametric, crop price, crop yields, surface water price, and electricity price uncertainties shape hydro-economic model estimates for agricultural production through a diagnostic global sensitivity analysis (GSA).The diagnostic GSA explores how the uncertainties in combination with a candidate groundwater pumping restriction influence three metrics of concern: total economic revenue, total land use and groundwater depth change. The hydro-economic model integrates a Groundwater Response Function (GRF) by integrating an Artificial Neural Network (ANN) into a calibrated Positive Mathematical Programming (PMP) production model for the Wheeler Ridge-Maricopa Water Storage District located in Kern County, California. Our results show that in addition to groundwater pumping restriction, performance metrics of the system are highly sensitive to prices and yields particularly of profitable crops. These sensitivities become salient during dry years when there is a higher reliance on groundwater.

Modeling and Validation of Hydro Cascade Operation Considering Price Uncertainty

Most studies involving the use of hydropower in an electric power system tend to consider the point of view of the system operator even though under liberalized markets in Europe, the operation of hydro units is set by the owner to maximize their profits. Such studies also often neglect uncertainties related to hydropower operation and instead assume perfect knowledge of the system conditions over the simulation horizon. This paper presents a methodology to overcome the aforementioned limitations. We optimize the operational choices of a hydropower cascade owner with multiple linked hydro assets and the ability to participate in several energy and reserve markets while also accounting for the impact of market price uncertainties on the owner’s operating decisions. The versatile optimization model created includes a detailed representation of any selected hydro cascade’s topology, constraints to reflect the machinery characteristics, and a rolling horizon approach to account for the price uncertainties in the daily operating schedule. The model is first validated using historical data for a hydro cascade in Switzerland and a perfect-knowledge approach. Next, price uncertainty is added to improve the historical simulation results and find a trade-off between accuracy and computational time.

The Dynamic Spillover Effects of Macroeconomic and Financial Uncertainty on Commodity Markets Uncertainties

The present paper has two main objectives: first, to accurately estimate commodity price uncertainty; and second to analyze the uncertainty connectedness among commodity markets and the macroeconomic uncertainty, using the time-varying vector-autoregressive (TVP-VAR) model. We use eight main commodity markets, namely energy, fats and oils, beverages, grains, other foods, raw materials, industrial meals, and precious metals. The sample covers the period from January 1960 to June 2020. The estimated commodity price uncertainties are proven to be leading indicators of uncertainty rather than volatility in commodity markets. In addition, the time-varying connectedness analysis indicates that the macroeconomic uncertainty has persistent spillover effects on the commodity uncertainty, especially during the recent COVID-19 pandemic period. It has also found that the energy uncertainty shocks are the main drivers of connectedness among commodity markets, and that fats and oils uncertainty is the influence driver of uncertainty spillovers among agriculture commodities. The achieved results are of important significance to policymakers, firms, and investors to build accurate forecasts of commodity price uncertainties.