IIHR - Hydroscience & Engineering The University of Iowa Iowa City, Iowa

Size: px

Start display at page:

Download "IIHR - Hydroscience & Engineering The University of Iowa Iowa City, Iowa"

Stanley Richard
5 years ago
Views:

1 The Effects of Agriculture Management Practices on Hydrologic Forcing at the Watershed Scale Under Various Hydrologic Conditions by Thanos Papanicolaou IIHR - Hydroscience & Engineering The University of Iowa Iowa City, Iowa

2 Processes of upland erosion and sediment conveyance Watershed erosion processes starts with the interrill erosion where soil particle detaches by rain drop impact and gets delivered into the rills by sheet flow.

3 Hypothesis Although precipitation is the driving mechanism of water erosion, land use and associated management practices may be the major control of long-term erosion in small agricultural watersheds such as the headwaters of Clear Creek, IA (South Amana)

4 Study Site

5 Clear Creek is an ideal place to test our hypothesis ~90% of the land use within the SASW is as follows: FTC-NTB (31.1%) NTB-STC (25.1%) NTC-FTB (22.8%) CRP, hay and pasture (7.9%)

6 Study Rational The few existing watershed scale studies do not provide a systematic representation of the heterogeneity of different physical and biogeochemical constituents in a watershed. We argue that continuous simulations via distributed, physically based NPS modeling complemented with long-term observations (e.g., weather, land management practices) can provide an improved and scientifically based understanding of the role that land use and associated management practices play on long-term net erosion at significant scales. A hindrance to performing continuous distributed simulations is the lack of long-term available data, especially at large scales. As a result, very few models are calibrated and verified (e.g. Yu et al., 2000). Also, there is a misconception in some of the literature that physically based models do not require calibration as it is pointed in the ingenious work by Vieux (2004).

7 Objective The overarching objective of this research is to provide an improved understanding of the longterm role of land use and associated management practices on net erosion in intense agricultural watersheds under different hydrologic conditions by utilizing an established, physically based, distributed parameter model.

8 NPS model selection criteria Water Erosion Prediction Project (WEPP version ) was employed to simulate runoff and erosion processes in the study site.this is the latest version of WEPP and has extensive improvements to the winter hydrology and frost/thaw calculations; The hydrology extension of ESRI s ArcGIS version 9.2 is used to map the drainage network structure component for flow and sediment routing (Wu et al., 2002; Dun et al., 2006; Dun et al., 2007). The Windows interface slope editor of the latest WEPP model version ( ) supports up to 50 segments per hillslope, thus allowing a more accurate representation of the landscape geometry in terms of hillslope gradient and curvature.

Water Erosion Prediction Project model structure Weather The effective hydraulic conductivity is a key hydraulic soil parameter that determines the amount of estimated runoff (Nearing et al.

9 Water Erosion Prediction Project model structure Weather The effective hydraulic conductivity is a key hydraulic soil parameter that determines the amount of estimated runoff (Nearing et al., 1990; Schoeneberger and Wysocki, 2005). Key soil parameters affecting the rate of net erosion during an event are the critical erosional strength and rill/interrill erodibilities (Tiscareno-Lopez et al., 1993; Pieri et al., 2007). Pedotransfer functions (PTFs) have been developed, and built in WEPP for these key soil parameters.

10 Water Erosion Prediction Project model structure Detachment, transport and deposition of sediment in rills are calculated by employing a steady state solution to the 1-D sediment continuity equation (Foster et al., 1995): where G is sediment load (kg.s 1.m 1 ), x represents distance downslope (m), D f is rill erosion rate (kg.s 1.m 2 ), and D i is interrill sediment delivery to the rill (kg.s 1.m 2 ). Similarly, sediment conveyance within channels are also calculated by a steady-state solution to the 1-D sediment continuity equation (Ascough et al., 1995).

11 Water Erosion Prediction Project model structure Baseline hydraulic conductivity, K b (mm/hr) Baseline interrill erodibility, K ib (kg.s.m -4 ) Baseline rill erodibility, K rb (s.m -1 ) and the baseline critical erosional strength, τ cb (Pa)

12 Weather WEPP inputs

13 Water Erosion Prediction Project model structure Effective hydraulic conductivity, K e (mm/hr) Adjusted interrill erodibility, K iadj, (kg.s.m -4 ) Adjusted rill erodibility, K radj (s.m -1 ) Adjusted critical erosional strength, τ cadj (Pa)

14 Weather WEPP inputs

15 Weather For the continuous WEPP simulations two sources of precipitation (P) data were employed: 1) CLIGEN; 2) NCEP Stage IV data through IOWA Mesonet. Daily Monthly Yearly Mesonet CLIGEN Mesonet CLIGEN Mesonet CLIGEN Mean amount of (P) (mm) Standard deviation of (P) (mm) Maximum amount (mm) Mean # of raindays/year NA NA NA NA CLIGEN overpredicted daily amounts but underpredicted the average number of raindays per year. Similar to Zhang and Garbrecht (2003), these two balanced each other in the long-term and resulted in accurate annual precipitation estimates for continuous WEPP simulations.

The hydrology extension of ESRI s ArcGIS version 9.

16 Topography and drainage network structure 30 m resolution USGS National Elevation Dataset was employed. The hydrology extension of ESRI s ArcGIS version 9.2 was utilized to map the flow directions. The drainage network structure of the WEPP model was developed for the SASW which comprises of 135 hillslopes and 87 channels.

17 Soils ISPAID and SSURGO Databases were utilized. Tama, Downs (formed from Peorian loess); and Colo (formed from alluvium) soil types comprised approximately 80% of the total acreage. ~225 topsoil surface samples were analyzed for important soil biogeochemical properties. Soil cores were collected for soil profile characterization.

18 Soils c b d a

19 Keff measurements

20 The automated Amoozemeter & DRI measurements

21 The automated Amoozemeter & DRI measurements

22 Rill, interrill erodibility & τ c measurements

23 Rill, interrill erodibility & τ c measurements

24 Land use and management practices ~90% of the land use within the SASW is as follows: FTC-NTB (31.1%) NTB-STC (25.1%) NTC-FTB (22.8%) CRP, hay and pasture (7.9%)

25 Outlet Measurements

26 WEPP calibration Calibration of WEPP was performed by adjusting the key parameters within physical ranges

27 WEPP model calibration A 100 years CLIGEN generated weather data was employed for the calibration simulation to: 1) account for average weather conditions; 2) reach steady conditions with the WEPP model results. First, the hydrologic component was calibrated through the K b Next, the upland erosion component was calibrated via τ c, K i and K r Finally, the sediment component was calibrated by adjusting Manning s n and channel erodibility.

28 Physical ranges for the key parameters Parameter Range Reference Colo, Tama and Downs soil types Interrill erodibility 1E5-1E7 kg.s.m -4 Elliot et al. 1989, Papanicolaou et al Rill erodibility s/m Elliot et al. 1989, Gilley et al Critical erosional strength Pa Elliot et al. 1989, Gilley et al. 1993, Julian &Torres 2006 Effective hydraulic conductivity mm/hr Bouwer 1969, Chow et al. 1988, Risse et al., 1995 Tillage - Ripple coulters PRB on rill/interrill areas for non-fragile crops % Al-Kaisi and Hanna 2002 PRB on rill/interrill areas for fragile crops % Al-Kaisi and Hanna 2002 Random roughness value after tillage cm Alberts et al. 1995, Gilley and Finkner 1991 Surface area disturbed % USDA-NRCS-Williamsburg Office (Personal contact) Clear Creek Manning's n for bare soil in channel Phillips and Tadayon 2006 Total Manning's n allowing for vegetation Phillips and Tadayon 2006 Channel erodibility factor s/m Elliot et al PRB for percent residue buried

29 WEPP model calibration In order to smoothen out the short-term fluctuations and highlight the long-term trends, a 5 year moving-average was applied to both water discharge and sediment flux time series.

30 WEPP model calibration As the final step for calibration assessment, a relationship of sediment delivery ratio to the drainage area was developed by delineating all the nested subcatchments within SASW to the smallest scale possible (i.e., hillslope scale).

31 WEPP model calibration

32 WEPP validation

33 WEPP model validation Validation on a daily basis is not common due to immense data requirement. Additionally, time shifts in the precipitation and flow data can make the task even more difficult to accurately accomplish (Benaman et al., 2005). Validation of the WEPP model was performed based on monthly and yearly time series for the period without making any further adjustments to the governing parameters. The NCEP Stage IV data was used for the WEPP model weather data input.

34 WEPP model validation Monthly predicted loads agreed well with the measured quantities. However, the WEPP model had a tendency to underpredict large monthly loads.

35 WEPP model validation Goodness of fit statistics Monthly Yearly Water Dis. Sed. Dis. Water Dis. Sed. Dis. R Mean [P/O] E f Yearly predictions were slightly better as expected.

36 WEPP model predictions

37 WEPP model predictions

38 Conclusions 1) Results of this study showed that for all crop rotations, a strong correspondence existed between net erosion fluxes and high magnitude precipitation events during the period of mid-april and late July, as expected. The magnitude of this correspondence, however, was strongly affected by the crop rotation characteristics, such as canopy/residue cover provided by the crop, and the type and associated timing of tillage.

39 Conclusions 2) Tillage type (i.e., primary and secondary tillages) affected the roughness of the soil surface and resulted in increases of the rill/interrill erodibilities up to 35% and 300%, respectively. Particularly, the NTC-FTB crop rotation, being the most intense land use in terms of tillage operations, caused the highest average annual erosion rate within the SASW yielding quadrupled erosion rates comparatively to NTB-STC.

40 Conclusions 3) Timing of operations affected the life-time of residue cover and as a result the degree of protection that residue cover offers against the water action on soil surface. In the case of NTC-FTB crop rotation, dense corn residue stayed on the ground for only 40 days, whereas for the other two rotations corn residue provided a protective layer for nearly 7 months, lessening thus the degree of net erosion. The cumulative effects of tillage type and timing in conjunction to canopy/residue cover led to the overall conclusion that land management practices can significantly amplify or deamplify the impact of precipitation on long-term net erosion in small agricultural watersheds.

41 WEPP model predictions Heterogeneous Model Homogeneous Model Sediment Yield = 5004 tons/yr Sediment Yield = 5794 tons/yr

42 Future directions 1) Better estimation of Random Roughness variability Random roughness decay with time after tillage is predicted from a modified relationship of Potter (1990): where RR t is the random roughness at time t (m), C br is the adjustment factor for buried residue, R c is the cumulative rainfall since tillage (m), and b is a coefficient.

43 2) The transectionally averaged sheetflow sediment concentration where,, are respectively, the averaged sheetflow depth, sediment concentration, and raindrop erosion rate at the scale of a hillslope transect; is transect-scale mean parameter vector for interrill areas ( ) ( ) ( ) = S i j r r j i x j i x S c h r r r K r r r K x t c h 2 3/ 0 2 0, cov ( ) ( ) ( ) S i j r r j i y j i y ir rd S c h r r r K r r r K D 2 3/ 0 2, cov = = ρ ρ c s 0 h s c rd D r Future directions

44 Future directions K = C S + x y z z K = C S + 2 [ 1 ( S S ) ] 1/ 4 1/ 2 0 x / 0 y / 0x 2 [ 1 ( S S ) ] 1/ 4 1/ 2 0 y / 0x / 0 y where C z is Chezy s roughness coefficient; S ox and S oy respectively are the bed slopes in x and y directions ρ ( λ ) ir = Nro / 1 ra λua where N ro is rill occurrence density; λ ra is rill surface proportion and λ ua is unsaturated surface proportion of a hillslope transect length.

45 Future directions 3) Linking WEPP with daily CENTURY for SOM Soil temperature plant production Plant carbon Deposition and fixation Plant nutrients Soil moisture TEMPERATUR E PRECIPITATIO N Decomposition rate SOIL TEXTURE Lignin content Soil carbo n Litterfall/ death Soil nutrients available (mineralize d) nutrients Hydrological model Microbial respiration Leaching removal MANAGEMENT (burning, grazing, harvesting, tillage)

46 Future directions WEPP erosion inputs to CENTURY Period 1: Initialization Prairie 81 year Simulation Val ue Units Average Annual Precipitation inches Average Annual Runoff 4.02 inches Average Annual Soil Loss ton/a Average Annual Sediment Yield ton/a Period 2: Corn, Corn, Oats-Meadow (3 yr. crop rotation) 45 year Simulation Val ue Units Average Annual Precipitation inches Average Annual Runoff 6.08 inches Average Annual Soil Loss ton/a Average Annual Sediment Yield ton/a Period 3: Corn, Corn and Soybean (3 yr. crop rotation) 15 year Simulation Val ue Units Average Annual Precipitation inches Average Annual Runoff 5.72 inches Average Annual Soil Loss ton/a Average Annual Sediment Yield ton/a Period 4and 5a: Corn-Soybean (2 yr. crop rotation) 110 year Simulation Val ue Units Average Annual Precipitation inches Average Annual Runoff 5.48 inches Average Annual Soil Loss ton/a Average Annual Sediment Yield ton/a Period 5b: Continuous Corn Period 93 year Simulation Val ue Units Average Annual Precipitation inches Average Annual Runoff 4.76 inches Average Annual Soil Loss ton/a Average Annual Sediment Yield ton/a

47 End of presentation!!!

Conservation Planning evaluate land management alternatives to reduce soil erosion to acceptable levels. Resource Inventories estimate current and

Conservation Planning evaluate land management alternatives to reduce soil erosion to acceptable levels. Resource Inventories estimate current and projected erosion levels and their impact on natural resource