iEMSs AgES

International Environmental Modelling and Software Society (iEMSs) 7th Intl. Congress on Env. Modelling and Software, San Diego, CA, USA, Daniel P. Ames, Nigel W.T. Quinn and Andrea E. Rizzoli (Eds.) http://www.iemss.org/society/index.php/iemss-2014-proceedings

AgroEcoSystem-Watershed (AgES-W) Model Delineation and Scaling a

b

c

Timothy R. Green , Robert H. Erskine , James C. Ascough II , Bruce Vandenberg e f g h Björn Pfennig , Holm Kipka , Olaf David , Michael L. Coleman

d

U.S. Department of Agriculture – Agricultural Research Service (ARS), Fort Collins, Colorado, USA a b [email protected] [email protected] c d [email protected] [email protected] Friedrich-Schiller University, Jena, Germany e

[email protected]

Colorado State University, Department of Civil and Environmental Engineering, Fort Collins, Colorado, USA f g h [email protected] [email protected] [email protected]

Abstract: Water movement and storage within an agricultural watershed can be simulated at different spatial resolutions of land areas or hydrological response units (HRUs). Interactions between HRUs in space and time vary with the HRU sizes, such that natural scaling relationships are confounded with the simulation scales. Scaling concepts can be tested using the AgroEcoSystemWatershed (AgES-W) model with different resolutions of HRUs. Here, we delineated a 56-ha agricultural watershed in northern Colorado, USA farmed primarily under a wheat-fallow rotation. Factors used to distinguish HRUs included topography (surface flow paths), land use (crop strips and native grass), and mapped soil units (three types). The delineation algorithm was able to produce HRUs that follow the land use and soil boundaries, but adjustment of some of the inputs remains a trial-and-error process to avoid excessive aggregation and undesired spatial features. AgES-W model parameters were first calibrated using single-HRU simulations to fit measured daily soil moisture at different landscape positions and depths where soil moisture is being measured. Simulations (not shown here) are being conducted using these fixed parameter sets applied across the watershed. Questions to be addressed include: How do spatial soil moisture and runoff at the outlet vary with different HRU delineations? Is there an optimal or threshold spatial resolution of HRUs with respect to model fit to data? The results will help guide HRU delineation and hydrological simulations in this and other semi-arid agricultural watersheds. Keywords: Agriculture; Soil Hydrology; Scaling; Watershed Delineation.

1. BACKGROUND AND FIELD SITE Agricultural watersheds comprise large areas of the continental USA and other parts of the world. In the western part of the Great Plains, the climate is semi-arid, and commonly water is a limiting factor for agricultural production, vegetative cover and soil protection and health. Consequently, soil water and associated crop growth and grain yield vary with landscape position within each field (Green and Erskine, 2004; Green et al., 2007). Part of the spatial variation may be related to natural soil development and human management effects on soil hydraulic properties (Green et al., 2009; Strudley et al., 2008). The timing of plant development (phenology) can also vary spatially, but with different relationships to topography and amounts of the overall variance explained by terrain from year to year (McMaster et al., 2012). Soil water regimes also vary temporally and spatially as previously shown using detailed measurements across a field with undulating topography (Green and Erskine, 2011). These factors and dynamic scaling behaviors require improved understanding and space-time simulation of hydrological processes and their scaling behaviors (Meng et al., 2006) along

Green, T.R. et al. AgroEcoSystem-Watershed (AgES-W) Model Delineation and Scaling

with other processes (Kozak et al., 2005; Ruan et al., 2001) and their interactions. Effects of HRU delineation and resolution on model responses have been studied previously, but generally at large scales (Bongartz, 2003; Han et al., 2012; Khan et al., 2013; Zhang et al., 2014) with little transference to small agricultural watersheds (i.e., hillslope to field scales). Several of the factors mentioned above have been observed and quantified on a dryland (rain-fed) wheat field in Colorado (Fig. 1) and elsewhere in eastern Colorado (Ahuja et al., 2002; Green et al., 2003; Green and Erskine, 2004; Green et al., 2007). The field has been managed since 2001 in strips of approximately 120 m width in alternating rotations of winter wheat and fallow. Winter wheat is planted in September-October and harvested in July, followed by a fallow period of about 14 months. The soils (two loams and a sandy loam; see Fig. 1b and Green and Erskine, 2011) are particularly susceptible to wind and water erosion during fallow periods, and the management strips provide partial protection in the direction of prevailing winds and watershed flow (W-NW to E-SE). Even so, topographic gradients (up to 13%) (Erskine et al., 2006) along the primary axis of a strip, combined with tillage in the downslope direction can cause soil rills to develop and rapid water runoff during summer convective precipitation events. These occur once or twice per year, on average, despite high infiltration capacities in most locations (Green et al., 2009) and well-drained soils that rarely saturate the soil profile (Green and Erskine, 2011).

(b)

(a) (c)

Figure 1. Maps of the study location: (a) The Scott field is a rain-fed field within the Drake Farm near Severance, Colorado, USA. (b) A watershed (red line) is delineated based on a runoff flume at the east edge of the field. The figure shows elevation (color), soil units (patterns), and instrument locations (green symbols). (c) Topographic contours and stream lines (ephemeral) underlie the Scott field boundary on an upland location. Capturing spatial and temporal patterns in soil moisture affected by run-on of overland flow (Meng et al., 2008) and variably saturated subsurface lateral flow (Green and Freyberg, 1995) is a major challenge. Our ultimate goal is to compare different space-time patterns of soil moisture and watershed runoff events using a quasi-spatially distributed watershed model with different HRU delineations. A precursor to addressing that goal is to explore factors controlling HRU delineation using a semi-automated tool for watershed delineation (WDAML), which is the focus of this paper.


2. COMPUTATIONAL METHODS AND TOOLS We are using the quasi-spatially distributed AgroEcoSystem-Watershed (AgES-W) model (Ascough et al., 2012; Kunz, 2013) to capture some of the space-time process interactions between subwatershed areas or hydrological response units (HRUs) at different scales. This paper focuses on setup of the watershed model and delineation of HRUs based on topography, land use and soils. 2.1

Watershed and Hydrological Response Unit (HRU) Delineation

WDAML (Watershed Delineation in ARC Macro Language) is an AML tool based on multiple flow direction topology (Pfennig, 2009) and input layers for soils and land use. In this case, land use is uniform over the field if averaged over time, but crops are planted in alternating strips (Fig. 1b) using a wheat-fallow rotation every two years. Thus the timing of crop growth affects soil water in space and time, along with patterns of runoff, making delineation of HRUs by crop strips important. Soil units (Fig. 1b) tend to dissect the crop strips, which often affect the spatial interactions between HRUs in the direction of topographic slope. WDAML now includes a GUI with user inputs of options for each step: 1) cluster spatial data based on the DEM resolution and watershed outlet; 2) generate clustered HRUs with a specified number of relief classes and layers of land use, soil and/or hydrogeology; 3) cluster (merge) small polygons based on similar neighbors and weighted terrain attributes; 4) check for circular references; 5) calculate topology; and 6) create HRU shape files and AgES-W parameter files: hrus, reach, and routing (*.csv files). WDAML has various options and parameter values associated with the different steps outlined above. The effects of these parameter values combined with geospatial data layers produce variable results in terms of HRU delineations. Some are intuitive, but others are not predictable, which leads to trialand-error inputs. As currently implemented, each change of options and parameter values must be input in each new project. Thus all steps of WDAML were run multiple (45) times from beginning to end in order to explore sensitivities of the spatial outputs. 2.2

AgroEcoSystem-Watershed (AgES-W) Model

In each HRU, AgES-W hydrological simulations based on response functions store and move water into the surface layer, vertically between soil layers, and horizontally to/from adjacent HRUs. AgES-W may be viewed as more parsimonious and numerically efficient than a full 3-D physically based model (Mirus et al., 2011; VanderKwaak and Loague, 2001), while still mimicking responses in space and time. A companion paper (Ascough et al., 2014) reviews AgES-W and illustrates its applications on two large watersheds. 2.3

Source Data and Automated Tools for Model Inputs

AgES-W and other models require various spatial and temporal inputs, which define the model structure (spatial extents and topology), parameters (e.g., soil and plant properties) and weather sequences (daily meteorology in this case). Consequently, AgES-W contains the input files (as applied here) described in Table 1. All model inputs will apply to the future use of AgES-W on this watershed, but here the focus is on the spatial structure of the model, which is related to topography, land use (crop rotation) and soils. Topography is specified by the 5 m DEM, which provides highresolution topology for flow routing. Other mapped layers include land use (grassland and crop strips in this case) and soil map units (Order 2 NRCS survey map units in Fig. 1b). 3. RESULTS The results to date comprise delineation of a 56 ha watershed and AgES-W HRUs at different scales based on land surface topography (5 m DEM) and associated flow routing, land use patterns including crop strips and mapped soil units (see Fig. 1). The impacts of these features and options in WDAML are the focus of the results reported below. Anticipated results to be reported at the conference will include simulated soil water across the watershed focused upon HRUs where soil water is measured


Table 1. AgroEcoSystem-Watershed Model inputs. Category

File (*.csv)

Description

Example parameters

Spatial structure

topo hrus

Topology HRU attributes

reach routing

till crop

channel reach between multiple HRUs and to reaches land use, crop management, tillage and fertilizer apps rotation and management surface properties Fertilizer types and properties tillage operations default crop types

from, to, weight location, elevation, area, slope, landuse, soil length, width, slope, roughness weight (fraction of upslope area), from, to Operation dates for planting, harvest, tillage and fertilizers

soils_hor_swc

Soil properties by horizon

hgeo

groundwater properties

precip

precipitation depth

implement, tillage intensity, depth max LAI, rad. use efficiency, optimal temperature, … depth, field capacity, porosity, sat. hydraulic conductivity sat. hydraulic conductivity, response function storages rainfall

rhum or ahum tmax, tmin solrad wind

atm. humidity atm. temperatures solar radiation wind speed

relative or absolute humidity daily max/min temps incoming solar radiation station location, wind speed

Management

management

croprotation landuse fert

Parameters

Climate/weather variables

Rotation and Crop IDs albedo N and P values

Table 2. Watershed delineation tool (WDAML) inputs and output statistics. Step

Parameter

1. Cluster spatial data

Area (ha) for concentrated surface runoff Strahler Stream Order for water divide Number of relief classes

4. Generate clustered HRUs

5. Clustered surface pattern

6. Circular reference check 7. Calculate Topology 8. Create HRU shape & AgES-W input files Output Statistics:

Mass balance index Topo. Wetness index Solar radiation index Include Soil layer? Generalization method (neighbor) Number of neighbors Minimum HRU Size (ha) Minimum HRU Size (ha) none Stream reach width at the outlet (m)

Number of HRUs Minimum HRU size (ha) Median HRU size (ha) Mean HRU size (ha) Maximum HRU size (ha)

Set (a)

Values Set (b)

Set (c)

5

7

7

1

1

1

3

4

4

0.5 2.0 5 no

0.4 2.0 5 yes

8 1 5 yes

Similar

Similar

Largest

8 0.5

4 0.25

8 0.1

2.0

0.5

7.0

-

-

-

0.3

0.4

0.4

18 0.0050 2.20 3.15 9.54

46 0.0025 1.05 1.23 4.97

41 0.0450 1.08 1.38 5.02


and the effects of HRU delineations (sizes and spatial distributions) on simulated surface runoff events. Table 2 includes the main inputs that were adjusted to produce the three output maps shown in Fig. 2. Inputs that did not change were the 5-DEM and land use map spatial layers, homogeneous geology layer, flume (outlet) location, and the area pattern aggregation method (only the number of neighbors varied). Fig. 2a shows a result without delineation by soil units. The dominant features are due to land use (native grassland and wheat production), where crop management comprises alternating wheat-fallow strips. The alternating temporal cycle of management necessitated delineation of HRUs first by crop strips to represent the timing of management operations, including tillage, correctly in space. This is particularly true with a model like AgES-W that simulates space-time process interactions explicitly in terms of hydrological routing of water across the landscape. A secondary spatial feature in Fig. 2a is determined by convergence of flow accumulation from two sub-catchments to the first-order ephemeral channel (Strahler Stream Order in Table 2) in a simple dendritic pattern. Fractions of flow from each HRU are routed to adjacent downslope HRUs and/or the (ephemeral) stream channel. In general, however, the stream feature does not subdivide HRUs, so runoff is not generated separately from north and south banks of the stream under the current delineation. Figures 2b and 2c show the additional effects of soil units on HRU delineation. In this case, soil units tend to be elongated in the direction perpendicular to the management strips, so HRUs based on land use are further divided by soils. These features tend to partition hillslopes in the dominant slope direction, creating more potential for spatial interactions of runoff between HRUs. In our case, this is also important for disaggregating HRUs where soil moisture probes are located to allow comparisons of simulated and observed soil water by landscape position. Again runoff may flow from one to many HRUs (divergent) or many to one HRU (convergent) and to the channel. These relationships are generated by WDAML but not shown here. The example delineation in Fig. 2b is shown to illustrate a common feature resulting from the delineation algorithm. Narrow areas were merged (clustered in steps 2 and 3) with larger downslope areas, even though these features crossed land uses and soils. As mentioned above, that is not advantageous because management controls much of the soil water use. Comparing the delineations in Fig. 2b and 2c with parameter values for Sets (b) and (c) in Table 2 we see first that the differences are not due to the clustering of spatial data in Step 1, including the area for concentrated surface runoff, nor due to the number of relief classes in Step 2 (these were explored in other parameter sets). Although some parameter values differ in Step 3, these did not control the results shown in Fig. 2. Thus, the weights (0-10) in Step 2 (i.e., mass balance index and topographic wetness index) determined the clustering (Fig. 2b) and lack thereof (Fig. 2c). In particular, the weight of the topographic wetness index needed to be reduced compared with the mass balance index to avoid over-emphasis of those terrain attributes that dissect other spatial classes. The solar radiation index could influence partitioning of slopes by aspect if desired, but a value of 5 did not affect either case. Overall, the final delineation (Fig. 2c) preserved both land use and soil boundaries. The output statistics in Table 2 show that the number of HRUs varied primarily due to adding soil units, which increased the spatial complexity and will affect the model run time significantly. Despite setting the “minimum HRU size” to 0.5 ha in Step 3 for Set (a), the smallest delineated HRU was 2 0.0050 ha or 50 m (two 5x5 m DEM cells). This may not be visible in Fig. 2a, but land use separated those two cells along the southern tip of the watershed, which were not aggregated in the following steps. Likewise Set (b) delineated single DEM grid cells into individual HRUs, but these did not persist in Set (c) using the “largest neighbor” generalization method in Step 3. Other differences and similarities in the output statistics in Table 2 followed our expectations. The HRUs in the upper watershed were delineated differently among the three examples in Fig. 2. Again, land use was the major factor in Fig. 2a, which kept the perennial grass in a single HRU. In Fig. 2c, soil units were preserved in HRU boundaries, but not completely in Fig. 2b. These differences are due to the clustering of surface patterns (Step 3).


(a) 120 m

Perennial Grass Hydrological Response Units (HRUs) Alternating Wheat-Fallow Strips

(b) Soil moisture probes

Runoff flume

(c)

Soil Units

Figure 2. Output maps of the delineated HRUs (irregular polygons) based upon: (a) terrain and land use (crop strips and grass), and (b,c) terrain, land use and soil map units. Color schemes differ between maps. Crop strips are shown beyond the watershed boundary, ephemeral drainage lines (green) are only incised after a strong runoff event, but then smoothed by tillage. Soil units (see Fig. 1b) tend to segment the hillslopes in (b,c). Differences between (b) and (c) are due to weightings of terrain indices, particularly the topographic wetness index.


As noted in Section 2.1, the final step of WDAML created the spatial shape files used to create Fig. 2 and the AgES-W input files for routing of simulated flows within the watershed. Soil hydraulic properties are set to values based on vertical flow simulations and calibrations at each moisture probe. Likewise, management practices vary across HRUs, but the “climate” files (daily weather variables) are assumed to be spatially uniform over the 56 ha watershed, primarily due to a lack of data to estimate spatial patterns. In larger watersheds, AgES-W interpolates rainfall at each HRU from multiple stations. 6.

CONCLUSIONS

WDAML is a useful tool for watershed delineation into different HRUs based on various spatial inputs and parameter values for the delineation algorithm. It remains an iterative process to produce HRUs that meet expectations of HRU size distribution and match physiographic spatial features. Specification of weights for the topographic indices (Step 2) and clustering of surface patterns (Step 3) require further investigation. The interface also needs further work to allow batch execution of different parameter sets without manual user actions for each step. Work in progress includes applications of AgES-W using the delineations shown in Fig. 2a and 2c. These simulations will help us answer the question, “How do spatial soil moisture and runoff at the outlet vary with different HRU delineations?” Identification of an optimal or threshold spatial resolution of HRUs with respect to model fit to data is another potential outcome of this work, but cannot be addressed without further levels of watershed delineation and a broader spectrum of AgES-W simulations. 7.

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