laljeet
commented
2 years ago Problem: (Meeting June 22)
The above method uses the total ag census average at the county level, which gives us demand under different scenarios. But we are more interested in unreported demand under different scenarios.
We don't have unreported acreage for large farms. *We have unreported in a million gallons.**
A possible solution is subtracting. Demand under each scenario - DEQ reported (latest year)??
Possible Limitations: Basing this on 2017 Ag census data.
Task 4: Meteorological analysis The final task of the project will be to use the time series generated in Task 3 to evaluate how unreported and total irrigation withdrawals will vary under different weather conditions. One of the key ways that irrigation withdrawals can stress water supply is related to their timing. Because irrigation needs are highest in hot, dry weather, irrigation withdrawals are likely to be greatest at times when surface water supplies will be lowest. Because the USDA census data is only collected every five years, it is unable to capture year-to-year variations in irrigation withdrawals needed to characterize this climatic sensitivity. For instance, total summer rainfall in 2017 in Augusta and Caroline counties was average (approximately 660mm), but higher than average in Accomack County (Figure 1). Because of this, irrigation withdrawals in Accomack county in 2017 may be somewhat lower than the long-term average. Further, this data will not reflect withdrawals during dry years (such as 2010) when irrigation usage might be highest and have the greatest impact on water supply.
Figure 1: Total summer (June – August) rainfall in three Virginia counties with high levels of irrigation. To address these limitations, this step of the project will evaluate the climatic sensitivity of total irrigation withdrawals to better characterize water use during dry periods. For each county included in the analysis, the PRISM climate data will be used to identify the driest year on record for that county and estimate and map “dry year” total withdrawals across study counties. These results will be compared to estimated total withdrawals in an “average” year to provide a sense of the climatic sensitivity of irrigation water use. However, irrigation withdrawals are not only sensitive to total growing season rainfall; for instance, high temperatures and extended dry periods will also increase crop water needs but may not be reflected in total growing season rainfall (Paoletti and Shortridge, 2020). To account for this, total growing-season withdrawals will be regressed against multiple weather characteristics obtained from the PRISM climate data, including total rainfall, average temperature, and dry-period length. These regressions will be used to estimate withdrawals under more extreme weather scenarios than were experienced between 2002 and 2017. Collectively, these results will be used to create a set of total withdrawal scenarios that account for unreported withdrawal under different drought conditions (e.g. “average year,” “moderate drought,” and “drought of record”). These scenarios can then be available for drought simulation modeling to better characterize how unreported agricultural withdrawals may impact low-flow metrics important for water supply planning. This work will be completed by a Virginia Tech graduate student, and reviewed by Dr. Shortridge, Dr. Scott, Mr. Green, and Mr. Burgholzer.
Steps:
County level:
Using Precipitation Data (Effective precipitation):
Obtain the latest acreage from Ag census data (2017) Demand under each scenario = Ag census Acerage * Scenario-based Irrigation demand (point 1 or 2)