Ongoing PhD Dissertation Work at Colorado State University:
My ongoing PhD Dissertation efforts involve Robust Decision Making (RDM) for Climate Policy. In short, RDM is based on the fact that current regional climate models can not accurately predict climate variables, particularly temperature and precipitation, to a precise enough degree to be applied to hydrologic models that will in turn, result in accurate runoff forecasts with high levels of prediction confidence. Hydrologic models produce forecasts which lead to designing engineering projects (flood control, hydropower generation, irrigation, etc.) to an optimal level. Because of the large uncertainties in the driving climate parameters, the hydrologic forecasts provided by standard hydrologic models have huge inherent uncertainties and it’s pointless to try and determine optimum engineering designs for future climate scenarios. RDM, consequently, is based on the principle that due to these huge uncertainties in the driving climate parameters, the pursuit of optimum designs is not either justified or wise and it is better to develop engineering designs that perform reasonably well for a wide range of plausible climate scenarios.
A key component of RDM is developing exploratory models that evaluate the impacts of these ranges of plausible climate scenarios on some physical system as well as impacts engineering interventions have on the system. The exploratory model is not utilized as a means of prediction, but as a means to analyze how a certain physical system reacts to a wide range of uncertain inputs. Typically, an RDM analysis using exploratory models results in a large database of possible scenarios and an analysis of how engineering inputs to the system affect the outcomes. For water resources applications, RDM can be used to evaluate a wide range of climate scenarios to determine how water resources infrastructure, such as flood control dams, hydropower dams, or irrigations dams, or even water management policies will be affected throughout a wide range of feasible climate scenarios that all have a high degree of uncertainty.
Proposal:
Develop a Robust Decision Making Exploratory Model (EM) for reservoir design and operations to evaluate the design and performance of the reservoir for a wide range of CMIP6 climate scenarios. The developed EM will be a fully integrated model developed to specifically incorporate the basic principles of RDM. The integrated model will be comprised of previously developed routines, namely:
Rainfall-Runoff Model -
Complete one-dimensional finite-difference physically based rainfall runoff reservoir stream network model for Master’s thesis. Model determines: surface-water runoff using precipitation and infiltration rates, performs overland flow routing using explicit solution of finite difference of kinematic wave equations for overland flow, sets up sub-basin connections, and combines flows at upstream, and routes flows through any given network of streams and reservoirs, including contaminant transport through streams.
Reservoir Operations-
Incorporate reservoir operations originally developed for analysis of over 500 potential small irrigation dam sites and $150M Dahla Dam Restoration in Afghanistan as the basis for USG strategic investment strategies for Government of Afghanistan.
Irrigation Support: Developed program AFDAM integrates FAO crop water demands, stochastic stream flow generation (Stochastic Analysis Model (SAM) – Colorado State University), reservoir storage accounting to determine the amount of irrigated cropland a given reservoir could support in a sustainable manner over the entire hydrologic regime.
Hydropower Generation: This AFDAM routine computes hydropower generation associated with reservoir water accounting model to meet downstream demands, where the generation is a function of monthly timestep of reservoir storage, head, and release.
Flood Damage Analysis: Windows-based GUI/FORTRAN program to numerically integrate discharge-frequency curve, stage-discharge curve, and stage-damage curve for a critical damage area to determine the average annual damage associated with the damage area for with and without dams in order to access economic viability of future upstream dams and reservoirs.
Model Architecture:
The proposed model will be based on the AFDAM model which used thousands of potential series of monthly time-series stochastic flow realizations as input into reservoirs in order to analyze reservoir operations under a wide range of inflows and inflow sequences. Instead of using stochastic generations of flow as model inputs, the EM will use precipitation from multiple CMIP6 scenarios as inputs into the model. From these climate precipitations, the model will use kinematic wave theory to transform precipitation to overland flow runoff, then route the flows using finite-difference approximations of the St. Venant’s Equations, all while combining and routing flows as the stream network dictates. Once the flows reach reservoirs, the model will operate the reservoirs for authorized purposes of hydropower generation, irrigation support, flood control, or combinations of uses. From these runs for various CMP6 climate scenarios, the Exploratory Model will live up to its name and explore how the various climate scenarios affect both the physical processes involved with the rainfall-runoff relations and the anthropogenically affected reservoir operations and associated results in hydropower generation, flood control, and irrigation support. Will develop some metrics to evaluate the performance of the reservoir system versus the various climate scenarios, understanding that because of the huge uncertainties in driving climate parameters, there cannot be an optimal reservoir operation management strategy, but looking for management strategies that perform reasonably well over a wide-range of plausible climate scenarios.
