julieshortridge
commented
2 years ago Also here is the text from our proposal about the time series - I think we have some flexibility in terms of the methods we use to generate the time series so I think the proposed path forward is consistent with what's in the proposal:
"The results of Tasks 2 and 3 will be evaluated to determine a final set of time series for incorporation into the VAHydro data system. Depending on the results, this may include a single time series assumed to the best estimate of unreported withdrawals, or multiple time series that can represent a reasonable range of possible values."
laljeet
@laljeet @jdkleiner @rburghol
Notes from working session (3/15)
One important thing that has become apparent in our analysis so far of this project is that the USDA Census and Irrigation Survey data should be considered a partial record of irrigation that overlaps with VWUDS reports (which is also a partial record). In other words, both datasets are going to have some farms not reporting to them. This is important because our initial conceptualization of the USDA data in the proposal was that it would fully encompass the VDEQ data (in other words, a superset of VWUDS reports). This difference only became apparent after doing a detailed comparison of county-level USDA and VWUDS irrigation estimates. While this will not impact the final products developed, it has required some additional analysis and refinements of our coefficient estimation methods to account for this change.
Method 2 and 3 both aim to do the same thing: estimate the total volume of water (reported + unreported) based on multiplying USDA irrigated acres data with some estimation of irrigated depth. The main difference is how that irrigation depth is calculated. Method 2 simply applies the state average depth everywhere, whereas Method 3 calculates a county-specific depth based on the water demand of crops grown under irrigation in that county and rainfall received.
The main advantage of Method 3 is that it can account for differences in crop water demands (e.g., a county growing "thirsty" crops will use more water) and rainfall (a county that receives more rain will irrigate less). The main disadvantage is that crop specific irrigated acres data is not available for all counties. It varies from year-to-year, but generally only 30%-50% of counties with USDA irrigated acres have that data broken down into specific crops. Even in counties where this data is available, it doesn't cover all irrigated acres - ie, if a county has 3000 acres of irrigation, only 2000 of those acres are associated with specific crops, and the remaining 1000 don't specify what's being irrigated. Method 3 requires making a lot of assumptions about what crops are grown, what their water demand (ET) is, and whether growers are irrigating precisely to those demands.
A proposed path forward would be to use the crop water demand estimates (from literature), along with county-level rainfall data, to refine the estimates of county-level irrigation depth used in Method 2. The advantage of this is that it would account for year-to-year variability in a consistent way across the whole state. We could use multiple crop water demand estimates (e.g., a high, low, and median value) to account for uncertainty in what crops specifically are being grown.
Task 4 from the proposal (meteorological analysis) is an application of the unreported withdrawal estimates. The goal of Task 4 is to quantify the relationship between total (reported + unreported) withdrawals and weather characteristics. One part of this task would be a multivariate regression. We discussed my concern that in that regression, rainfall would be one of our predictor variables, but is also part of the calculation used to determine our response variable (total withdrawals). As I've thought about this more, I think this is OK, but we need to be clear about the goals of the multivariate regression. It is not to determine the statistical significance of predictor variables - in this case, we fully expect rainfall to be a significant predictor because it was used in the calculation of total withdrawals. Rather, we are using that regression as a way of simplifying the complex relationship between rainfall and total withdrawals into a simple form (i.e., for every 1 inch reduction in growing season rainfall, withdrawals increase X%). We can then use this simple form to generate different "scenarios" of withdrawal under different meteorological conditions. So I think we can still proceed with analyses as described in the proposal, we just need to be clear about what they are and are not telling us.
Steps to Wrap-Up Project
Objective 1 (Tasks 1 & 2)
Objective 2 (Task 3)
Objective 3 (Task 4)