Santa Fe River Basin Sam B. Upchurch, Ph.D., P.G. Jian Chen, P.G. - PowerPoint PPT Presentation

Springsheds of the Santa Fe River Basin Sam B. Upchurch, Ph.D., P.G. Jian Chen, P.G. Crystal R. Cain SDII Global Corporation Tampa, Florida May 9, 2008

Springshed • Area contributing water to the discharge of a spring. • Includes – Groundwater basin and – Surface water basin

Sample Map – Ichetucknee Springs • Based on high resolution data • 1-foot contour interval • Note that basin appears to pass under the Northern Highlands Upchurch and Champion (2005)

Project Expanded to Two Phases • Phase I – Literature review – Springshed delineation using existing upper Floridan potentiometric surface data from 2000 – Capture zones modeled using USGS and SRWMD groundwater flow models – Reported on in June 2007 – Comments by Alachua County and FDEP

Project Expanded to Two Phases • Phase II – Alachua County developed • High-resolution monitoring network • “Newberry Plain” of the Western Valley – Sites located and surveyed – Water levels measured in September 2007

Project Expanded to Two Phases – Phase II Continued • Geostatistical analysis to evaluate adequacy of monitoring network • Delineation of springsheds using high- resolution monitoring network and 1 foot contours • Revision of report

Area Overview µ SUWANNEE 0 1 2 4 COLUMBIA Miles UNION ICHETUCKNEE HEAD SPRING CEDAR HEAD SPRING MISSION SPRING VENT DEVIL'S EYE SPRING VENT GRASSY HOLE COFFEE SPRINGS SUNBEAM SPRINGS WORTHINGTON SPRINGS JAMISON SPRINGS BETTY SPRINGS SANTA FE SPRINGS WILSON SPRINGS OASIS SPRINGS BRADFORD SANTA FE RISE LAFAYETTE SIPHON CREEK RISE TREEHOUSE COLUMBIA SPRINGS JULY SPRING GINNIE SPRINGS HORNSBY SPRING DEVIL'S EAR DARBY SPRINGS DEVIL'S EYE BLUE SPRING POE SPRINGS GILCHRIST ALACHUA LEGEND STUDY AREA County Boundaries Hydrography Springs

Spring Clusters µ BAKER ! ( ( ! ( ! ( ! SUWANNEE UNION COLUMBIA ( ! ( ! ( ! ! ( ! ( ( ! ( ! Ichetucknee Cluster ( ! ( ! ( ! ! ( ( ! ( ! ! ( ! ( Worthington Spring Betty Spring Cluster Sunbeam Cluster ( ! ( ! ! ( ! ( ! ( ! ( Santa Fe Springs Cluster ! ( ( ! ! ( ( ! ! ( ! ( ( ! ( ! LAFAYETTE ! ( ( ! BRADFORD ( ! ! ( ( ! ( ! ! ( ! ( ! ( Siphon Creek Cluster ( ! ! ( Santa Fe Rise ( ! ! ( ( ! ( ! ! ( ! ( ! ( ! ( ! ( ! ( ( ! ( ! ! ( ! ( Wilson Spring Cluster ( ! ! ( ! ( ! ( ! ( ( ! (! ! ( ! ! ( ( ! ( ! ( ! ( ( ! ! ( ( ! ! ( ( ! ( ! ( ! ( ( ! ! ! ( ( ! ! ( ! ( Ginnie Springs Cluster Hornsby-Columbia Cluster ! ( ! ( Poe-July Cluster ( ! ALACHUA GILCHRIST SPRING CLUSTERS DIXIE County Boundaries ( ! Hydrography ! ( ( ! 0 2.5 5 10 ! ( Springs ! ( ! ( Miles ! (

Physiographic Provinces µ NASSAU MADISON DUVAL Duval Upland BAKER SUWANNEE COLUMBIA UNION Trail Ridge Northern Highlands LAFAYETTE BRADFORD CLAY Gulf Coastal Lowlands High Springs Gap TAYLOR GILCHRIST Bell Ridge ALACHUA DIXIE Brooksville Ridge PUTNAM LEGEND Western Valley 0 2.5 5 10 Kenwood Gap County Boundaries Alachua Lake Cross Valley Miles LEVY Hydrography Central Valley Coastal Swamps

Aquifer Confinement µ BAKER SUWANNEE UNION COLUMBIA LAFAYETTE BRADFORD GILCHRIST ALACHUA DIXIE County Boundaries Hydrography Springs HYDROGEOLOGIC CONDITONS CONFINED SEMICONFINED 0 2 4 8 LEVY UNCONFINED Miles

2000 Potentiometric Surface µ BAKER ! ! 2 0 ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 70 SUWANNEE 50 60 ! ! ! UNION ! COLUMBIA ! ! 40 ! 40 ! ! ! ! ! ! LAFAYETTE ! ! ! ! ! ! ! ! BRADFORD ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 3 ! 0 ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !! 50 60 ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 30 GILCHRIST 4 7 0 5 0 0 ! 40 ALACHUA ! ! 0 1 DIXIE ! 70 ! ! 60 50 LEGEND ! ! ! County Boundaries ! Hydrography ! ! ! Potentiometric Surface 0 2 4 8 LEVY Springs Miles !

Why Index Period of 2000? • The District’s groundwater flow model was calibrated to conditions in 2000 (Schneider and others, 2008), • 2000 was a dry year, so the potentiometric surface should have maximum relief and enhance ability to identify springsheds, and • Potentiometric data were relatively abundant.

Springshed Delineation from Potentiometric Surface Data • Well density is low in many critical areas • Available data contoured at 1-foot interval • Contour map reconciled with known geology and USGS 2000 potentiometric surface

Results B = Betty Spring cluster (north) A = Ichetucknee cluster µ BAKER D = Sunbeam and Wilson Spring clusters SUWANNEE UNION H = Santa Fe Rise COLUMBIA A G = Hornsby-Columbia B LAFAYETTE Spring cluster BRADFORD C D H E = Ginnie Springs G E cluster C = Betty Spring cluster (south) GILCHRIST F = Poe-July Springs F ALACHUA County Boundaries cluster DIXIE Hydrography Springs Springshed Boundaries HYDROGEOLOGIC CONDITONS CONFINED SEMICONFINED 0 2 4 8 LEVY UNCONFINED Miles

Phase I Delineation Evaluation • Individual springsheds could not be identified because of – Low monitoring well density – High hydraulic conductivities and/or conduit flow results in relatively flat potentiometric surfaces near springs • Springsheds could be identified for spring clusters

Phase II, High-Resolution Springshed Delineation • Mix of domestic wells, monitoring 19 20 wells and 17 18 15 14 16 13 piezometers 12 47 185 10 48 11 9 49 8 45 54 53 192 196 7 191 187 • Includes 190 52 41 50 46 184 163 194 203 170 96 174 156 186 60 76 167 177 122 90 189 124 30 51 38 168 94 59 123 169 157158 34 55 188 66 40 80 36 159 160 193 6 195 69 93 57 58 44 78 97 119 35 39 154 95 162 85 161 202 62 43 77 23 89 98 82 125 133 42 68 197 5 126 37 205 100 99 92 132 63 28 127 84 – Alachua County 183 4 118 129 70 134 155 204 74 130 71 72 198 199 87 26 172 61 64 56 29 200 75 121 148 86 83 128 135 139 173 180 120 79 131 117 81 201 179 151 178 67 143 176 181 3 21 2 33 91 22 137 24 153 175 182 1 136 25 150 network, 32 138 88 108 65 31 73 141 27 140 171 116 166 107 109 152 114 142 149 145 111 144 106 147 146 102 103 – SRWMD WARN 104 data, 101 110 112 165 164 113 115 105 – Danone/Coca-Cola wells

Regional Potentiometric Surface • Distal wells provide boundary conditions • Reproduces potentials in Newberry Plain well

Local Potentiometric Surface • Focus on Newberry Plain area • Note cone- of- depression near Gainesville

Geostatistical Analysis • Used to evaluate monitoring network in terms of – Spacing of sampling points – Need for additional sampling points – Level of uncertainty associated with contour maps (I.e., potentiometric surface maps) – Identification of anomalous data points

Geostatistical Analysis • Two steps – Structural analysis • “Rules” of contouring • Detection of local variability • Uncertainty related to distance between sampling points – Kriging • Map showing property distribution • Map showing uncertainty distribution • Map that identifies “outliers”

Variogram • Reflects model Sill developed to characterize variability between sample pairs as a Range function of Nugget sample point spacing Nugget = 15 ft. 2 Range =350,000 ft. Sill =800 ft. 2

Nugget Effect • Caused by 25 local 20 Water Level (feet, NGVD) variability – Short term 15 – Transient 10 – Caused, in 5 part, by sampling 0 over a Jan-76 Dec-80 Dec-85 Dec-90 Jan-96 Dec-00 Dec-05 month Date period Gilchrist County Well

Kriged Potentiometric Surface Kriged Potentiometric Surface 350000 Elevation (feet NGVD) 70 55 300000 40 25 10 250000 Legend Springs Wells Contours - 45 - 200000 2500000 2600000 2700000 Easting (Feet)

Kriged Standard Deviation • Shows the Kriged Standard Deviation 350000 (KSD; feet) distribution 10 of uncertainty 300000 6 Northing (Feet) • Units of feet based 2 250000 on K SD Legend Springs Wells (kriged - 45 - Contours standard 200000 deviations) 2500000 2600000 2700000 Easting (Feet)

Residuals 350000 Residuals • Observed (feet) 20 water level 16 12 – kriged 8 4 300000 0 water level Northing (Feet) -4 -8 -12 • Helps -16 -20 identify 250000 Legend Springs outliers Wells - 45 - Contours 200000 2500000 2600000 2700000 Easting (Feet)

Network Evaluation • Network is good within the Newberry Plain and vicinity • There is no need for additional wells in the Newberry Plain area • There is an uncertainty (the nugget) when contouring between wells of up to ±10 feet because of local variability

High-Resolution Springshed Delineation

Springshed Evaluation • High-resolution monitoring allows for resolution of springsheds with much more confidence than with the typical regional potentiometric surface map • Confidence in springsheds of major spring clusters ranges from moderate to high • There is still a problem with where the water in the southern part of the Newberry Plain discharges