Final Report for George County Lake - Climate Variability Analysis: Pascagoula River Minimum Flow Supply, Lake Option Analysis

Transcription

1 Final Report for George County - Climate Variability Analysis: Pascagoula River Minimum Flow Supply, Option Analysis Jonathan W. Pote, Charles L. Wax, and Mary Love Tagert Submitted to Pickering Firm, Inc. in fulfillment of MSU Project No (Child of ) January 8, 214

2 Table of Contents Table of Contents...2 Introduction...3 Data Inputs and Methodology...4 Model Results...11 Impacts of Climate Change on Model Results...16 Preliminary Results for Determining Volume...23 References...32 Appendix... A-1 2

3 George County - Climate Variability Analysis Pascagoula River Minimum Flow Supply Option Analysis I. Introduction J.W. Pote, C.L. Wax, and M.L. Tagert Water supply lakes are relatively rare in Mississippi, despite the plentiful rainfall that would make them an attractive source of water. However, there is an increasing interest in using surface water to meet a variety of needs, including irrigation of agricultural land, industrial use, and for supply of drinking water. When water is withdrawn from rivers and streams, caution is taken to maintain minimum stream flow levels. Therefore, a lake designed for water supply for the sole purpose of maintaining minimum flow in the Pascagoula River is an unusual, but sensible plan. The issue to be addressed was the degree to which a lake, constructed on one of the tributaries of the Pascagoula River, might be able to prevent the river from dropping below the 7Q1 (see Figure 1). This question should address both the present climate and what would occur in the event of climate change. The proposed process was to simulate the entire last fifty years of operation, using genuine records, and then repeat the simulation using data modified to reflect climate change. Figure 1. Pascagoula River Basin 3

4 II. Data Inputs and Methodology Approach The authors have a long-established approach to testing designs that are dependent on weather and climate (Pote et al., 1988; Wax and Pote, 199; Pote and Wax, 1993). This approach is dependent on very long term weather records. The typical approach would have been to use these records to simulate both the river flow and the lake itself. However, this case was unique in that there were excellent records for both weather and river stage for a full fifty-year period. Rather than simulating the relationship, we had genuine simultaneous records. River Stage There were long-term records near the mouth of the Pascagoula River for two stage recorders Merrill and Graham Ferry. The Merrill record was serially complete for the full fifty years, while the Graham s Ferry record had a long segment missing from 1995 to 24. On the other hand, Graham s Ferry was nearer the mouth of the Pascagoula River, an area considered much more susceptible to low river flow issues. It would certainly be possible to predict what the missing values would be for Graham s Ferry by using Merrill data, but two serious issues for such a derived record would be the loss of true data and the knowledge that additional tributaries between the two gages could change any predicted relationship between the records. In both cases, the flow rate records were included with the stage recorder data. Merrill Table 1 (see appendix, page A-1) shows the flow rate record for the Pascagoula River at the Merrill stage recorder over a fifty year period, and this tabular data can also be seen in Figure 2 below Daily River Discharge in ac-ft/day, Merrill 4

5 Figure 2. Daily discharge at the Merrill stage recorder from Graham s Ferry The Merrill and Graham s Ferry records are similar, indicating that synthesizing the Graham s Ferry record from the Merrill record to complete the missing years of data from should be possible. This was attempted using several methods. 1. Linear Relationship The simplest approach is typically to find the relationship between two records using the equation y = mx + b Where x = the known value y = the synthesized value m = the slope of the line of best match b = the constant that adjusts the line to best match (also called intersection) A linear relationship was used to synthesize the missing Graham s Ferry data from the Merrill data. Examination of the resultant prediction revealed a systematic error in this approach. The information of greatest interest is low flow. A linear equation, by adding a constant to any value, will mask low flow incidents. Basically, we have inserted a level below which the water cannot fall. 2. Non-linear relationships Other approaches were attempted, including quadratic relationships, higher powers and other alternatives. Each of these was deemed to be most accurate during normal and high flow and least accurate during low flow. 3. Seasonal linear relationship The most effective approach was to use a linear relationship, but to evaluate the relationship for winter, spring, summer, and fall separately. This did, indeed, allow the flow rate to fall below the minimum normal flow during drier periods. Table 2 (see appendix page A-61) and Figure 3 shows the flow rate at Graham s Ferry for the full fifty year period, made serially complete using this system. The synthesized portions of the record are used for years

6 Daily River Discharge, ac-ft/day: Graham's Ferry Seasonal Figure 3. Daily river discharge at Graham s Ferry with synthesized data from Final Selection In the end, Merrill was chosen for the analysis. The importance of a true, non-synthesized record was considered to be of great importance. In addition, it became clear that because the record is so good, other agencies use this record for evaluation of the Pascagoula River as well. From this point forward, all river flow analysis was based on records form the Merrill stage recorder. Minimum Flow The preferred method, as chosen by the Mississippi Department of Environmental Quality, for finding the minimum flow for a stream is the 7Q1. This is defined by EPA as the lowest 7-day average flow that occurs (on average) once every 1 years. With the present available record, this could be directly derived over several time series. The number was very near the number derived by MDEQ; consequently, the MDEQ 7Q1 value (2,23 ac-ft/day) was used for the low flow standard. Climate Rainfall Rainfall data is available throughout the state, although completeness of the record does vary some. For this study, data from Merrill was used. The record there had the requisite fifty years of record with only a few missing data points (less than.5%). Missing data were filled by using the record of the station in closest proximity. Table 3 (see appendix, page A-122) and Figure 4 show this record. 6

7 Daily Precipitation, Merrill Figure 4. Daily precipitation at Merrill station from Pan evaporation Pan evaporation records are much less common than rainfall records and very few are serially complete. Wax and Pote have produced serially complete evaporation records for several locations throughout the state. The process by which they checked and cleaned these records is well documented (Cooke et al., 28). Two locations for which they have developed serially complete records are Starkville, MS and Fairhope, AL. Fairhope is in closer proximity to the location of interest, but it is right on the coast. The adjacent Gulf of Mexico can have a significant influence on weather, even as compared to just a few miles inland. Table 4 (see appendix, page A-182) and Figure 5 show the evaporation record for Fairhope, AL. 7

8 Daily Evaporation, Fairhope Figure 5. Daily evaporation record for Fairhope, AL from Starkville, MS has one of the most complete evaporation records in the state, and while it is significantly farther from the Pascagoula River, it does not have the immediate coastal influence, which might make it a better model. Table 5 (see appendix, page A-242) and Figure 6 show these data..6 Daily Evaporation, MSU Figure 6. Daily evaporation record for Starkville, MS from

9 Figure 7 shows an overlay of these two data sets. Comparing the two records, it is obvious that they are extremely similar, with Fairhope having a slightly lower evaporation record. Clearly, if the Fairhope location were to have a slight bias, the lower expected evaporation might paint too optimistic a picture of how much water would be maintained by the lake. In the end, the Starkville location was selected as being more complete, probably more representative of the site, and more conservative in its expectations. In an earlier publication (1986), Pote and Wax demonstrated that evaporation data can be safely used to represent a relatively wide area Daily Evaporation, MSU vs Fairhope Fairhope MSU Figure 7. Overlay of daily evaporation data for Fairhope, AL and Starkville, MS from P-.8E From this point on, data from the Starkville weather station was used as the source for all evaporation data. Using procedures described in Pote et al. (1988), each day s precipitation was reduced by that day s evaporation and adjusted to change pan values to values for an open water surface (Boyd, 1985). As a result, the daily water budget was simulated, producing the data necessary to simulate the impact of weather and climate on a water surface. Note that the rainfall is from the Merrill location, while evaporation is based on the Starkville record. The results of this analysis are shown in Table 6 (see appendix, page A-32) and Figure 8. 9

10 Daily P-.8E Figure 8. Daily water budget from using Merrill rainfall data and Starkville, MS evaporation data. Model The initial estimates for the lake are based on generalized design parameters chosen to be realistic, but not specific to a single location. The lake area was set at 5,2 acres, while the watershed draining into the lake is 17,55 acres. For the purposes of this analysis, neither the area of the lake nor the area of the watershed change, regardless of the lake s change in volume. The lake size was estimated based on the best guess of potential size. The watershed size was selected based on the ratio of watershed to lake area in similar designs. Inputs to the lake The inputs to the lake included rainfall directly into the lake, based on precipitation records and rainfall runoff into the lake from the watershed. This number is always a percentage of the rainfall on the watershed, removing a portion for what soaks into the soil. The runoff to precipitation rate was recommended by the project s hydrogeologist. This method is takes into account the soil type specific to that region that allows significant interflow water that infiltrates quickly but re-emerges downhill, due to a restrictive layer. The rainfall record is for Merrill. Losses from the lake The model allows several losses from the lake. Base flow from the lake, designed to maintain a portion of the flow as required by the stream, can be set at any level. Evaporation from the lake itself is derived from the pan evaporation record, which is from the Starkville data. Infiltration into the soil under the lake can be set, and this rate was provided by the project hydrogeologist. Also, overflow is lost from the lake when total volume of inflow exceeds the lake s maximum volume. 1

11 Rules of operation for the lake The model was first tested to show the lake itself with no use made of the lake to supplement low flow in the river. From that point on, the lake was managed such that when the river falls below the 7Q1, sufficient volume is released to raise it back to that level. The lake did not stop supplementing low flow below 7Q1 until the lake had lost all of its twenty feet of depth. III. Model Results Model Results As a first test, the lake model was run for a fifty year period, making no attempt to use it as source water for the river. Table 7 (see appendix, page A-362) and Figure 9 show the daily volume of water over the fifty year period. Even though droughts definitely impact the volume, it stays amazingly consistent for the entire period, never approaching a total loss of the entire volume of water. This indicates that the climate should support a large reservoir in these conditions. 12 Volume, No Use Figure 9. Projected lake volume from , with no use. The next run of the model simulated use of the lake to maintain the 7Q1, but the five years of record having the lowest flow levels were selected out to determine if the lake size was anywhere near the necessary size to serve this purpose. The results of the test for the five worst years of low flow are shown in Table 8 (see appendix, page A-422) and Figure 1. Model results indicated that the worst year with the lowest flow levels, 2, would exhaust the volume of the lake. 11

12 Volume with Use, Five Critical Years Figure 1. volume with contribution to river flow for five critical low-flow years. At this point, we have carried out our primary mission modeling use of the lake to supplement low flow in the river for the full fifty-year period. Each time the river declined below the 7Q1, the lake released enough water to bring it back to that level. Again, the rainfall data were from Merrill, the river stage and flow rate data were from Merrill, and the evaporation data were from Starkville, Mississippi. In theory, this should indicate how the whole system would have operated had it been in existence for the last fifty years. Stated another way, it is an excellent indicator of how it will perform for the next fifty years, should the climate remain the same. Table 9 (see appendix, page A-43) and Figure 11 show how the lake would behave under these conditions. Note that during this period, the lake is actually completely used up once, for a 79-day period. After that, a rainfall event feeds the system, and it never drops that low again. For reference, the lake level, should it not be used to supplement the river, can be seen in Figure 9. As it turns out, the initial test of running the worst five years was flawed, in that the worst case was two drought years in sequence, 1999 and 2, so that the lake begins the summer already low. This test showed that occurrence. The river level during that same period is shown in Figure 2. Due to the density and volume of the data, the river level with and without supplemental water from a lake is shown in five-year increments in a series of graphs (Figure 12). This analysis indicates that under the existing climate and conditions, the lake would sustain the river above the 7Q1 in all but 71 days out of the 5 year period. This is less than four tenths of one percent of the time. 12

13 Volume with Use Figure 11. volume supplementing river below 7Q1 from River River Flowrate Rate (ac-ft/day) (ac-ft/day) River River Flowrate Flowrate (ac-ft/day) (ac-ft/day)

14 River Flowrate (ac-ft/day) River Flowrate (ac-ft/day)

15 River River Flow Flow Rate Rate (ac-ft/day) (ac-ft/day) River River Flow Flow Rate Rate (ac-ft/day) (ac-ft/day)

16 Figure 12. River flow rate with and without supplemental water from a lake, in five year increments. IV. Impacts of Climate Change on Model Results Climate Change There was also interest in showing the impact of climate change on the system. Several alternatives for modeling climate change were considered. Selection of Years One possibility was to take the existing record, selecting only the hottest 25 years, and run the model. This option has an immediate problem in that climate change could be expected to produce values outside any of those in the past fifty years. This approach will never have values that have not been previously experienced. Shift of Location 16

17 Another option was to look at the weather record for a coastal location, farther to the west. Thompson, Texas has an excellent set of weather records and is located in a hotter and drier location. The biggest drawback for this option is that one of the great advantages of the Pascagoula site was the independent weather and river records. Here, those make such a shift impossible. There is no link to say how the Pascagoula would have behaved had it been in Thompson, Texas. Existing Climate Change Models One attractive solution was to find an existing, accepted prediction of climate change that might allow us to modify our data to reflect their predicted climate shift. A model contracted by USEPA and performed by TetraTech (21) was perfect for our purposes. One of their products was a detailed map of the United States showing the anticipated change in several weather factors, including evaporation and rainfall. Evaporation was expected to increase by 9.73% while rainfall was expected to decrease by 1.57% in this specific location. The climate change model was constructed by increasing evaporation of every daily value of the 5-year period by 9.73%. Likewise, a new precipitation data set was constructed by reducing every daily value over the 5- year period by 1.57%. In addition, it was assumed that river flow would be directly affected by rainfall. Therefore, the flowrate in the Pascagoula was also reduced by 1.57%. Table 1 (see appendix, page A-49) and Figure 13 show revised rainfall which could be contrasted with the original values. Table 11 (see appendix, page A-55) and Figure 14 show revised values for the daily water budget, or P-.8E precipitation values. Table 12 (see appendix, page A-61) and Figure 15 show the revised river discharge. Because all of these values were increased by small static percentages, and the data is so dense, the full comparisons are very difficult to detect Daily Precipitation, Merrill (Climate Change) Figure 13. Daily revised rainfall for the Merrill stage recorder, reduced by 1.57%. 17

18 Daily P-.8E (Climate Change) Figure 14. Daily water budget with 9.73% increased evaporation and 1.57% reduced precipitation. 25 Daily River Discharge, Graham's Ferry Seasonal (Climate Change) Figure 15. Revised daily river discharge, reduced by 1.57%. Climate Change Model Results The entire model was run again with weather data and river data revised to reflect climate change. The results for the lake performance under a potential climate change scenario are shown in Table 13 (see appendix, page A-67) and Figure 16. These results are shown with the original lake performance under actual historical climate conditions (without climate change) for comparison. It can be seen that with climate change, there is still only one time in the fifty year period when the lake runs dry, and it is the same period as the non-climate change model. With 18

19 climate change, the period is longer (87 days) and begins earlier, but the same rainfall event makes both models begin recovery on the same date. 12 Volume with Use, Climate Change Original Climate Change Figure 16. Use of lake volume to supplement the Pascagoula River, under both historical climate conditions and under potential climate change conditions. The impact of climate change on the river, with and without supplemental water from a lake, is shown in five-year increments in Figure 17. The river is lower, but the only noticeable impact is again the period when the lake could no longer maintain minimum flow

20

21 River Flowrate (ac-ft/day)

22 Figure 18. River flow rate, with and without supplemental water from lake, under climate change scenario in five-year increments, from

23 Latest Progress While this exercise has shown a great deal, the real test will be selecting an actual site, placing a lake design in it, and testing the model. This procedure has begun. The relationship between lake area/volume and watershed area will become variables in this stage of the model, as the lake fills and covers more of the watershed. The following describes our progress on this phase of the project. V. Preliminary Results for Determining Volume The lake basin volume was calculated using geospatial data and data processing methods outlined on the website by Erika Akin and Skye Cooley. First, digital elevation model data was downloaded in ArcGrid format from the National Elevation Dataset (NED) 1/3 arc-second dataset using the NED Download Tool at (Gesch 27; Gesch et al. 22). NED data can also be downloaded through the National Map Viewer found at The NED data was downloaded in geographic coordinates (latitude and longitude), North American Datum The DEM was projected to the Mississippi Transverse Mercator (MSTM) projection in ESRI s ArcGIS software package. The original NED DEM data for the study area can be seen in Figure

24 Figure 18. NED 1/3 arc-second (1m) DEM, with study area enclosed in box. Next, ArcGIS software was used to fill the DEM and delineate the approximate boundary area for the study area. Clipping the DEM more closely to the study area speeds processing times for subsequent GIS tasks. The ArcToolbox, Spatial Analyst Tools tab in ArcGIS was used for both tasks. Spatial Analyst Tools > Hydrology > Fill tool was used to fill the DEM, and Spatial Analyst Tools > Surface > Contour List was used to create contours of the area at 3.48 meters, or 1 feet, from the DEM data. It is important to note not only the horizontal units of the DEM but also the vertical units. The NED data has all elevation values in units of meters, which was verified during this procedure. Therefore, contours were generated at 3.48 meters elevation. Figure 19 shows the elevation contours which were generated from the filled DEM using geoprocessing techniques in Spatial Analyst Tools. After the contours were generated, the Editor toolbar in ArcGIS was used to modify the vertices of the contour line around the study area to create a polygon. The southern end of the polygon was placed where a potential dam might be located. Figure 2 shows the filled DEM which has been clipped to the approximate study area, using the polygon created from the contours as a general guideline for the proposed lake basin. In ArcGIS, the ArcToolbox, Data Management Tools tab > Raster > Raster Processing > Clip was used to clip the DEM to the study area. 24

25 Figure 19. Elevation contours at 3.48 meters (1 feet), with study area enclosed in box. 25

26 Figure 2. Filled DEM clipped to study area using polygon created from generated contours. 26

27 After the polygon was created from the contour lines and the filled DEM was clipped to the study area, the polygon was converted to points in ArcGis, ArcToolbox (Data Management > Features > Feature Vertices to Points). The results from this process can be seen in Figure 21, with the polygon on the left and the points shown on the right. The elevations of these points along the proposed shoreline were then extracted from the DEM and appended to the point attribute table created in the previous step, again by using Spatial Analyst Tools in ArcGIS (Spatial Analyst > Extraction > Extract Values to Points or Extract Multiple Values to Points). The attribute tables for the original point file and the point file with the appended point elevation values can be seen in a screenshot captured with both tables opened. The elevation value (in meters) is listed in the top appended table under the column heading RASTERVALU (Figure 22). Because the proposed lake boundary was developed from the 3.48 m (1 ft.) contours generated from the DEM, all of the extracted elevation values round to 3.48 m, but the actual values listed in the column RASTERVALU are carried out to six decimal places. Figure 21. Segment of polygon (left) and points created from the feature vertices of the polygon (right). 27

28 Figure 22. Attribute tables for points outlining the lake boundary (bottom) and appended table with extracted elevation values for each point (top). 28

29 For the next step, 3D Analyst Tools in ArcGIS was used to create a triangular irregular network (TIN) surface using the extracted points with elevation values as the defined input feature class, which was selected by clicking the drop-down arrow 3D Analyst Tools, then Data Management > TIN > Create TIN). Elevation values are located in the new field called RASTERVALU, which was selected by clicking on the drop-down arrow under the Column heading Height Field, and the SF Type was set to Masspoints before running the tool. The resulting TIN can be seen in Figure 23. Figure 23. TIN created from elevation points defining lake boundary. 29

30 Next, the TIN was converted to a raster (3D Analyst > Conversion > From TIN > TIN to Raster), and this raster was then clipped to the boundary of the lake polygon (Data Management > Raster > Raster Processing > Clip). When converting the TIN to a raster, the Output Data Type was set to Integer ( INT ), and the chosen interpolation method, or Method, was LINEAR. In addition, the Sampling Distance was defined as CELLSIZE. Also, when clipping the TIN raster to the lake boundary, the box by Use input features for clipping geometry was checked so that the raster was clipped based on the actual perimeter of the lake polygon. Figure 24 shows the raster that was created from the TIN along with the clipped TIN raster. Figure 24. Raster from TIN (left) and TIN raster clipped to lake polygon boundary (right). In the final steps of this procedure, Spatial Analyst Tools in ArcGIS, ArcToolbox was used to calculate the difference in elevation between the plane of the lake surface at 3.48 m (1ft.) and modern topography (DEM) using the tabs Map Algebra, followed by Raster Calculator. The resulting raster showing the difference in elevation is shown in Figure 25. The volume of the lake basin was calculated by first finding the total number of pixels between the two surfaces. The raster created through Raster Calculator to show the difference in elevation was rightclicked. Next, Properties was selected, followed by the Symbology tab. The Classified 3

31 option was selected, and the Classify button to the right was selected, opening another pop-up screen. In the Classification Statistics section in the upper right, the Sum value was located and noted as 64, m. The Sum value of 64,21.77 m was multiplied by the area of a single pixel, which was 2, m 2 (47.9 m x 47.9 m), resulting in a lake volume of 142,363,51 m 3 (or 142,363.5 km 3 or 5,28,291,47 ft 3 or 115, ac-ft). It is important to check the Properties > Source > Cell size of your DEM because cell size can vary with latitude and projection. This procedure can be run multiple times to check the volume of a potential lake built at different elevation contours. Figure 25. Raster showing the difference in elevation (m) between the TIN-developed raster of the lake s surface at 3.48 m and the original topography of the area (DEM). 31

32 References Boyd, C.E Pond Evaporation. Transactions of the American Fisheries Society. 114: Cooke, W.H., K. Grala and C.L. Wax, 28. A Method for Estimating Pan Evaporation for Inland and Coastal Regions of the Southeastern U.S. Southeastern Geographer, 48(2): Gesch, D.B., 27. The National Elevation Dataset, in Maune, D., ed., Digital Elevation Model Technologies and Applications: The DEM Users Manual, 2nd Edition: Bethesda, Maryland, American Society for Photogrammetry and Remote Sensing, p Gesch, D., Oimoen, M., Greenlee, S., Nelson, C., Steuck, M., and Tyler, D., 22. The National Elevation Dataset: Photogrammetric Engineering and Remote Sensing, v. 68, no. 1, p Pote, J.W. and C.L. Wax, Climatological Aspects of Irrigation Design Criteria in Mississippi, Technical Bulletin 138, Mississippi Agricultural and Forestry Experiment Station. September, Pote, J.W. and C.L. Wax, Modeling the Climatological Potential for Water Conservation in Aquaculture. Transactions of the American Society of Agricultural Engineers, Volume 36 (5), Pote, J.W., C.L. Wax and C.S. Tucker, Water in Catfish Production: Sources, Uses, and Conservation. Special Bulletin 88-3, Miss. Ag. and Forestry Experiment Station, MSU, Miss. State, MS. Tetra Tech Inc., 21. Evaluating sustainability of projected water demands under future climate change scenarios. Natural Resources Defense Council, 4 West 2 th Street, New York, NY. Wax, C.L. and J.W. Pote, 199. A Climatological Basis for Conservation of Groundwater in Aquacultural Production in the Southern Region. Technical Bulletin 169. Miss. Ag. and For. Experiment Station, MSU, Miss. State, MS. 32