Can Hydrologic Complexity Simplify Field Level Modeling?

Transcription

1 VirginiaTech Can Hydrologic Complexity Simplify Field Level Modeling? Zachary M. Easton, Virginia Tech,

2 How is management evaluated? Models.3 1% % Forest % Total P (mg/l) If land use is your only variable, management options are extremely limited. % % Agriculture 1%

3 As a result, we have sometimes dogmatically* developed nonpoint source pollution control practices based on specific land uses and ignored the interaction between land management and physical, landscape scale processes. *dog. ma, n. 2. a specific tenet of doctrine authoritatively put forth: the dogma of the Assumption. The Random House Dictionary X

4 The HRU concept Soils Landuse Many models define HRUs as the coincidence of soil type and landuse Hydrological/chemical properties are defined at the HRU HRUs So runoff/p loss is the same here (lowland pasture) As here (upland pasture) Easton et al., 28. J. Hydrol.

5 Models Need to: Consider spatially distributed properties other than land use and soils

6 Models Need to: Consider the spatial distribution of hydrological patterns Account for locations of features (e.g., potential pollutant sources) relative to hydrologically active zones Ditch Stream Pollutant Sources

7 Models Need to: Consider the spatial distribution of hydrological and chemical patterns Account for locations of features (e.g., potential pollutant sources) relative to hydrologically active zones Minimize (or eliminate) model calibration

8 Scientific Background Hydrology Hillside Shallow bedrock, fragipan, or other restrictive layer Hillside Drainage Stream Flood Plain (valley bottoms) Baseflow (groundwater)

9 Scientific Background Common Perception of Runoff CN method based on this theory (Horton 1933, 194) Rain Infiltration Runoff Infiltration Excess a.k.a. Hortonian Flow

10 Is Infiltration Excess Runoff Common? % of Area Generating Hortonian Flow September 5 4 August 3 2 October 1 April November March Return Period (yr) 78%

11 Scientific Background Saturation Excess Runoff Rain Subsurface water rises Some areas saturate to the surface

12 Scientific Background Saturation Excess Runoff Rain Upland interflow may exfiltrate Rain on saturated areas becomes overland flow Dunne and Black Water Resour. Res.

13 Scientific Background Variable Source Areas Most Watershed Models were not intended to capture this complexity Soil and Water Assessment Tool (SWAT) General Watershed Loading Function (GWLF) Agricultural Nonpoint Source Pollution Model (AGNPS) Hydrologic Simulation Program Fortran (HSPF)

14 Topographic Index Wetness Index Classes Scientific Background a ln tan =f(s) =f(s) =f(s) 1 =f(s) =f(s) =f(s) =f(s) =f(s) Topographic Index 33.1 f(s) High: Low: 3.52 =f(s) Wetness Index Classes = S i Local Storage S = Watershed Storage Easton et al. 28. J. Hydrol Easton et al Hydrol. Proc

15 TI Soils Landuse VSA concept defines HRUs as the coincidence of topographic index and landuse HRUs So runoff/p loss is now not the same here (lowland pasture) As here (upland pasture) Better Assumption?

16 VirginiaTech Variable Source Area Hydrology USDA WE38, PA

17 Topography to explain soil depth and fractions Soil Depth (cm) Sand or Clay (%) TI Class (Dry to Wet) TI Class (Dry to Wet) Using measured pedon data from ARS long term watershed Collick et al Hydrol Proc

18 Soil genesis as explained by Topography! ln a tan Z 1 = 13 cm Topographic Index 33.1 High: Wetness Index Classes 1 Low: Z 6 =1 cm Z 2 = 8 cm Z i = local soil depth Easton et al. 28 J. Hydrol Collick et al Hydrol Proc Fuka et al Hydrol Proc

19 Collick et al., 215. Hydrol Proc

20 Hortonian Dominated Systems Riesel, TX

21 Soil Depth Depth to C Hor.(mm) (cm) Bulk Bulk Density Density(g/cm^3) cm -3 ) Soil (a) Depth R^2=.28 y=5.8 x TI Class TI Class (d) Bulk Density R^2=.17 y=.1 x TI Class TI Class Meas. Ksat Ksat(mm/hr) Porosity (%) Porosity(%) Ksat (b) R^2=.19 y=.88 x TI Class TI Class Porosity (e) R^2=.5 y=.1 x TI Class TI Class Field capacity (33kpa %) Field Capacity at 33kpa(%) AWC (cm 3 cm -3 ) AWC(volume) Field Capacity (c) R^2=.79 y= 2.17 x R^2=.71 y=.1 x +.13 TI Class AWC(f) Fuka et al. 215 Hydrol Proc TI Class TI Class TI Class

22 Soil moisture as explained by Topography! ln a tan AWC 1 =.6 Topographic Index 33.1 High: Wetness Index Classes Low: 3.52 AWC 6 =.9 AWC 2 =.12 Easton et al. 28 J. Hydrol Collick et al Hydrol Proc Fuka et al Hydrol Proc

23 How does SSURGO perform? 2 Soil Depth (a) 6 Ksa(b) t Soil Based Soil Depth (mm) Est. ZMX R^2=.28 y=.33 x Soil Based Est. Ksat(mm/hr) (mm/hr) R^2=.59 y=2.7 x Depth to C Hor.(mm) Pit Soil Depth (mm) Meas. Ksat(mm/hr) Pit Ksat (mm/hr) TI adjusted FAO Soils ( ) vs base SSURGO Soils ( ) Fuka et al. 215 Hydrol Proc

24 Y=125.ha 4 W1=71.2ha 4 Y2=53.4ha 4 4 W6=17.1ha Y1=8.5ha Jan 2 Feb 1 Y6=8.5ha 4 4 Jan 2 Feb 1 Y8=8.4ha 4 4 Jan 2 Feb 1 W1=8.ha 4 4 Jan 2 Feb 1 Y13=4.6ha 4 4 Jan 2 Feb 1 W13=4.6ha Jan 2 Feb 1 W12=4.ha 4 4 Jan 2 Feb 1 Y14=2.3ha 4 4 Jan 2 Feb 1 SW17=1.2ha 4 4 Jan 2 Feb 1 SW12=1.2ha 4 4 Jan 2 Feb 1 PS=.4ha Distr. SSURGO Meas.

25 1. Commercial fertilizer Corn- Hay Soluble P loss, mg L Observed Std P routines New P routines. 2/26/4 2/26/6 2/26/8 2/26/1 2/26/12 Commercial fertilizer and poultry litter Corn-Hay Soluble P loss, mg L Observed Std P routines New P routines. 2/26/4 3/3/7 3/3/8 1/5/9 3/25/1 12/23/1 Collick et al. 215 JEQ

26 No manure application P loss, mg L -1 Poultry litter/manure application in January (2 tons per ac) P loss, mg L /1/21 2/2/21 4/11/ Observed Std P routines New P routines Observed Std P routines New P routines Precipitation, mm. 1/1/21 2/2/21 4/11/21 Collick et al. 215 JEQ

27 Discussion TI initialization exhibited strong correlations with field level measurements Resulted in improved predictions of field level contaminant transport, particularly for P SSURGO initialization captured soil properties and thus the hydrologic response poorly Sometimes under predicting and sometimes over predicting, in part due to the overestimated soil depth and AWC These results indicate that adjusting model parameters based on topography can result in more accurate P estimation at the field scale

28 Closing Thought "Many... modelers seem to follow... the example of Pigmalion, the sculptor... who carved a statue so beautiful that he fell deeply in love with his own creation... It is feared... hydrologists fall in love with the models they create. In hydrology... the proliferation of models has not been matched by the development of criteria for [evaluating] their effectiveness..." (Dooge 1986, Water Resour. Res. 22 page S49)