Daily Estimates of Rainfall, Water Runoff, and Soil Erosion in Iowa

Daily estimates of rainfall, water runoff, and soil erosion in Iowa

R. Cruse, D. Flanagan, J. Frankenberger, B. Gelder, D. Herzmann, D. James, W. Krajewski, M. Kraszewski, J. Laflen, J. Opsomer, and D. Todey

ABSTRACT: The major water quality impairment in the Midwest United States is sediment eroded from agricultural lands. Yet, few understand where or when erosion occurs, or the dynamics of soil erosion, the relative impact of precipitation, topography, land management and severe events over time and space. The objectives of this project are to: 1) develop methodology for estimating near real time spatial and temporal soil erosion and water runoff losses; and 2) explore issues in applying the method to a large area by setting up and running a prototype system for the state of Iowa. To accomplish this, soil erosion and water runoff loss are estimated daily at the township level (~10 by ~10 km area) (~6.2 mi)2 and a map is posted on the Internet daily showing precipitation with runoff and soil erosion estimates for every Iowa township. We use Water Erosion Prediction Project (WEPP), a daily simulation model, to compute soil erosion and surface runoff. WEPP uses accumulated precipitation by 15-minute periods obtained with NEXRAD radar. Other needed weather data are obtained from an Iowa weather network. The National Resources Inventory provides soil, topography, cropping and management information required for running WEPP. Daily and annual spatial precipitation, runoff and erosion estimates illustrate a high level of spatial variability related to topography, precipitation characteristics, soils and management practices.

Keywords: NRI, radar, WEPP

Sediment from eroded cropland is one of Richard Cruse is a professor and Brian Gelder is a re- this country’s biggest water quality prob- search assistant in the Department of Agronomy at lems. Soil erosion affects everyone. Erosion Iowa State University in Ames, Iowa. Dennis Flana- reduces soil productivity (Craft et al., 1992; gan an agricultural engineer at the National Soil Ero- sion Research Laboratory for the USDA-ARS in West Pierce et al., 1984) and causes a range of Lafayette, Indiana. Jim Frankenberger is an IT spe- downstream impacts. Yearly erosion costs in cialist at the National Soil Erosion Research Laborato- the United States are in the billions of dollars, ry for the USDA-ARS in West Lafayette, Indiana. Daryl with annual off-site and on-site costs estimated Herzmann is a program assistant in the Department of at $17 billion and $27 billion, respectively Agronomy at Iowa State University in Ames, Iowa. (Pimentel et al., 1995). David James is a geographic information specialist at Soil erosion is dependent on rainfall char- the National Soil Tilth Laboratory for the USDA-ARS in Ames, Iowa. Witold Krajewski is a professor with the acteristics, soil type, topography, soil and crop Department of Civil and Environmental Engineering at management, and soil conservation practices the University of Iowa in Iowa City, Iowa. Michal (Hudson, 1995). Because these factors vary Kraszewski is a research assistant in the Department across the landscape, soil erosion losses are of Civil and Environmental Engineering at the Universi- spatially variable, often to a surprising degree. ty of Iowa in Iowa City, Iowa. John Laflen is currently Localized soil erosion losses can be extreme, retired from the USDA-ARS in Buffalo Center, Iowa. Jean Opsomer is a professor in the Department of Sta- as experienced in Northeast Iowa in 1999 tistics at Iowa State University in Ames, Iowa. Dennis (Ballew and Fischer, 1999). Localized heavy Todey is the extension state climatologist for the De- rainstorms are a relatively common occur- partment of Ag and Biosystems Engineering at South rence in many locations. Ideally, areas most Dakota State University in Brookings, South Dakota. prone to severe damage should be prioritized

Reprinted from the Journal of Soil and Water Conservation J|A 2006 VOLUME 61 NUMBER 4 191 Volume 61, Number 4 Copyright © 2006 Soil and Water Conservation Society for erosion control measures to minimize fur- Materials and Methods Soil, slope, and management database ther damage and productivity losses. Limited A system capable of making and presenting development. The NRI is the best source of research/observation suggests spatial rainfall pat- daily estimates of rainfall, soil erosion and publicly available soil, slope and cropping/ terns over fairly small areas (i.e., county-size or runoff requires development and identifica- management input data required for this smaller) may be both distinct and stable tion of various system components. These project. The NRI is a longitudinal survey of (Kuehnast et al., 1975; Causey, 1953). include erosion model selection; database all non-federal land in the United States and Topographic features or areas associated with (soil, topography, management and climate) has been conducted every five years since higher rainfall may also be more prone to development for state-wide application of 1982 by NRCS in cooperation with the intense rainstorms (Kuehnast et al.,1975). Most daily modeling at sub county scale; a hard- Center for Survey Statistics and Methodology importantly, those areas more prone to erosion ware system to perform the computations; at Iowa State University. The NRI collects losses and in need of greater soil conservation and a software system to prepare the input information on land cover and use, soil ero- measures should be precisely identified,allowing and to present the output information. sion, prime farmland soils, wetlands, habitat better targeting of finite resources for enhanced Erosion model selection. We selected the diversity, selected conservation practices, and soil conservation measures. Water Erosion Prediction Project (WEPP) related resource attributes. The 1997 NRI Most current erosion and runoff modeling model (Flanagan and Nearing, 1995) for contains information on 34,120 points in is typically limited in spatial and temporal estimating soil erosion and water runoff. Iowa, of which 17,848 are for agricultural extent due to the use of field measurement WEPP has been a good predictor of soil ero- land use and could be used for this project. for precipitation data and/or use of long- sion at time scales ranging from individual Details on the NRI survey design and objec- term average precipitation data to estimate events to annual averages (Laflen et al., 2004). tives can be found in Nusser and Goebel probable soil erosion rates. Long-term aver- WEPP is a process-based, continuous daily (1997). The NRI data used in this project ages are the basis for estimating rainfall simulation computer software program for for each of the 17,848 points were soil, slope, erosivity characteristics used in empirically estimating sheet and rill erosion by water. slope length, crop grown, the USLE cropping based soil erosion models such as the Runoff and soil loss resulting from individual and management (C) value and the USLE Universal Soil Loss Equation (Wischmeier storm events are calculated and accumulated, support practice (P) factor. Other informa- and Smith, 1965) and its derivatives. These and output by day, month, year or over the tion that we used to evaluate study proce- estimates may be suitable for planning entire simulation period are available. Both dures and to develop parameters include the management and conservation practices to temporal and spatial soil detachment and/or soil erodibility (K) value as used in the USLE meet government compliance requirements. deposition are predicted (Flanagan and and the NRI average annual predicted soil However, impacts of highly variable and Nearing, 1995). WEPP can be applied to erosion. This information is only publicly extreme rainfall events cannot be estimated virtually any type of landscape management, available at the county level, but a confiden- with long-term averages. These extreme including rural, urban, cropland, rangeland, tiality agreement was developed allowing the events cause the majority of damage in most construction sites, and roads. project access to township level (~10 km x locations and situations (Ghidey and Alberts, Basic input requirements for the WEPP ~10 km) (6 mi)2 data. 1996). Opportunities to target these areas for model include weather, topography, and soil The NRI database provides basic soil remedial or preferential treatment to mini- and crop management. In this project, properties such as soil texture, soil organic mize further degradation requires location topography, and soil and crop management matter content, and cation exchange capacity specific information. inputs are developed utilizing information through a link to the SOILS5 database The use of NEXt-Generation Weather from the U.S. Department of Agriculture (USDA, 2000). These basic properties allow RADar (NEXRAD) radar significantly Natural Resources Conservation Service determination of rill and interrill erodibility, expands the possible areas over which soil (USDA-NRCS) 1997 National Resources critical shear and baseline hydraulic conduc- erosion and water runoff models can be Inventory (NRI) (Nusser and Goebel, 1997). tivity using the equations presented in Alberts spatially and temporally applied as nearly the NEXRAD precipitation radar and the Iowa et al. (1995). We used an albedo of 0.23 and entire United States is covered with high- Environmental Mesonet are able to provide an initial saturation of 75 percent for all soil resolution radar data. The opportunity to the needed individual storm precipitation files. The WEPP slope gradient file uses the assemble technologies to estimate soil erosion distributions and the daily information (tem- slope and slope length given in the NRI for from field conditions and specific rainfall peratures, relative humidity, solar radiation each of the 17,848 points. A uniform slope events over large areas now seems possible. and wind data) required to drive WEPP’s was assumed, as is the case in the NRI USLE The objectives of this project are to: 1) plant growth and hydrology components. average annual computations. develop methodology for estimating near real With these data,WEPP is operated in a con- The 1997 NRI gives the crops or plants time spatial and temporal soil erosion and tinuous simulation mode on a daily basis, grown for the years 1994 to 1997. We water runoff losses; and 2) explore issues in resulting in daily runoff and soil erosion pre- assumed that the next four years would be the applying the method to a large area by setting dictions for all simulated points across the same as the last four, and these crops would up and running a prototype system for the state of Iowa. Subsequently, output maps of repeat every four years. There was a total of state of Iowa. rainfall, runoff and soil erosion across the state 71,382 crop or plant years over a four-year for each day are produced and posted on an period. Nearly 65 percent were in continu- Internet website. ous row crop and over 20 percent were in

192 JOURNAL OF SOIL AND WATER CONSERVATION J|A 2006 Table 1. Cover and management (C) factor values for various mulch levels and crops with different tillage options. Mulch level Soybeans Corn Corn silage Wheat, barley, and oats Sorghum No-till 0.02 0.02 0.03 .02 .02 Very high mulch 0.08 0.03 0.11 .03 .03 High mulch 0.19 0.08 0.14 .05 .06 Medium mulch 0.30 0.13 0.23 .12 .12 Low mulch 0.33 0.17 0.27 .18 .19 Fall moldboard plow 0.35 0.18 0.29 .23 .26

No till - No tillage except by no till planter, conventional planter used for other mulch levels. Very high mulch - spring field cultivate, plant (included fall chisel plow for corn). High mulch - Fall chisel plow, spring field cultivate, plant (included disk for corn, disk was substituted for field cultivate for sorghum). Medium mulch - Fall chisel plow, spring disk, field cultivate, plant (included an additional spring disking for corn). Low mulch - Fall chisel plow, two spring disk, field cultivate, plant (for corn, moldboard plow was substituted for chisel plow, and no spring disk). Fall moldboard plow - Fall moldboard plow, spring disk, spring field cultivate, plant (no spring disk for corn silage). When planting trees and grass, used fall chisel plow and spring field cultivate. Pasture was grazed at one cow/acre, Hay was cut three times/year. For vegetables, Fall moldboard plow, spring disk and field cultivate, row cultivate three times in summer. continuous grass, either as pasture, hay, were selected to give reasonable canopy and for each point based on the cover and man- Conservation Reserve Program (CRP) or set residue covers,based on personal observations. agement (C) value given in the NRI. The C aside. Nearly 41 percent of the crops or Both canopy and residue cover are quite values given in the NRI were used to select a plants was corn, 30 percent was soybeans, important in the erosion process, but trees combination of tillages that appeared to well about 28 percent was grass and about 1.5 per- and vegetables were present on only represent that cent was small grains. A very small fraction, 38 of the more than 17,000 NRI points. for the individual points in the NRI. The C 38 out of 71,382 crop or plant years Any poor estimates of plant growth for trees values given in the NRI are average annual (0.05 percent) was trees (which included and vegetables would have little impact on values, and are based on the combination of forests and orchards) and vegetable crops. erosion estimates. crop rotation and management. Plant parameters were determined based For corn and soybeans, all parameters For each plant, with the exception of upon expected average annual production. except for growing-degree days were used in grasses, trees and vegetables, USLE C values Ten Iowa soils representing major soils in Iowa all regions of the state. Growing-degree days were computed based on the WEPP estimate and covering a wide range of soil properties for both corn and soybeans were selected of annual soil loss for that plant grown con- were selected to compute average annual pro- based on maximum yield. Maximum yields tinuously with six levels of tillage that leave duction. A 100-year climate file was generat- were achieved when growing- degree days various mulch levels (Table 1) - mulch level as ed for WEPP using CLIGEN (Nicks et al., (rounded to the nearest 100) were 1,500 for used here is a qualitative term that distin- 1995) for the center of each of nine regions of the northern third of the state, 1,600 for the guishes the differences in burial of crop Iowa (the state is divided into thirds north- central third of the state, and 1,700 for the residue between the tillage options. The south and east-west, producing nine regions). southern third of the state. The values used USLE C value can be written as: We then operated WEPP for continuous pro- are reasonably near those reported by Kiniry duction of corn, soybeans, sorghum, grass (for (personal communication, 1991). Average C = A/(RKLSP) (1) hay, pasture and no cutting or grazing), oats, 100-year simulated state wide crop yields for wheat and barley for the 100-year period for the 10 soils and nine climates were 9,281 kg where, each soil for each region. Average crop yields corn ha-1 (148 bushels of corn ac-1) and 2,956 A = average annual soil loss, were determined for these 100-year model kg soybeans ha-1 (44 bushels soybeans ac-1). R = precipitation factor, runs, and the biomass energy ratio (potential We used common statewide planting, K = soil erodibility factor, growth rate per unit of photosynthetically harvesting, grazing and haying dates for each LS = slope length factor, and active radiation) was adjusted to give yields crop or plant. Statewide corn planting was P = support practice factor. comparable with state average yields from April 30, while soybeans were planted on May Estimates of A were based on 100-year long 1994 to 2003 (USDA, 2004). We used a sin- 5. Wheat, barley and oats were spring planted WEPP runs for each of the nine regions for gle set of parameters for each plant for all small grains in Iowa, and were all planted on the 10 soils with a “Unit Slope” [22.1 m (72.6 regions and soils, except for corn and soy- April 5. Hay was cut three times, grazing on ft)] long at nine percent slope gives beans. Parameter values used for other than pasture was at the rate of one LS = 1), and for up-and-down hill tillage the biomass energy ratio were those suggested cow 0.4 ha-1 (1 cow ac-1) from May 1 to (P = 1). USLE K values were determined for by Flanagan and Livingston (1995). October 15. each of the 10 soils using the nomograph Plant parameters for trees and vegetables We selected a tillage management system (based on soil texture, soil structure and

J|A 2006 VOLUME 61 NUMBER 4 193 permeability) developed by Wischmeier et al. each point based on the USLE. The revised National Center for Environmental (1971), and R values were based upon the USLE, RUSLE (Renard et al., 1997) has an Prediction Stage IV Multisensor Radar Revised Universal Soil Loss Equation (Renard improved slope-length factor as compared to Product. This product is originally distrib- et al., 1997). The C values shown in Table 1 the USLE that is used in the NRI. We com- uted as data of national coverage with one- are the average values for the entire state. puted this factor based upon the NRI slope hour time resolution on the Hydrologic All grass, pasture, hay and trees had no and length (which was also used in WEPP),and Rainfall Analysis Project grid and with one- tillage, except at planting (planted only if they multiplied by the other factors in the NRI— hour delay. The National Center for were in a rotation that included a different the R, K, C and P factors—to compute an Environmental Prediction product is derived plant). All vegetables had enough tillage to average annual soil erosion for comparison of from a radar reflectivity vs. rainfall rate bury virtually all residue (moldboard plow, WEPP and NRI predictions (Figure 2). (Z-R) relationship with quality control disk, field cultivate and row cultivate three Climate database development. We used correction algorithms applied (Fulton et al., times between planting and harvest). The two separate sources for WEPP weather input. 1998). This product was then merged with C values shown in Table 1 for grass, pasture, The Iowa Environmental Mesonet, a network rain gauge measurements using mean field hay, trees and vegetables were based also on of meteorological observation stations through- bias correction by the Kalman filter algorithm 100-year WEPP estimated soil loss for the 10 out the state, supplied the non-precipitation (e.g., Seo et al., 2000) and organized into a typical soils and the nine regions of the state. weather data input for WEPP. Spatial resolu- mosaic of multi radar data producing one We chose a particular mulch level for each tion of this data is limited to the nine climate nationwide map. The merged radar-gauge point for each year of the four-year crop zones (described above) because this data is product is then further processed separately sequence. Mulch levels for each year of the already being distributed and because no evi- by each National Weather Service River rotation were chosen so that the average four- dence exists suggesting that greater spatial reso- Forecast Center, using regional data and often year C value was the nearest match to the C lution of this data has a significant impact on involving a significant degree of human inter- value given in the NRI, with the limitation simulation accuracy. action (Fulton et al., 1998). Iowa is under that when the same crop occurs more than The nationwide network of WSR-88D the North Central and Missouri Basin River once in a rotation, they all have the same radars (Crum et al., 1998), known as Forecast Centers. mulch level. Also, mulch levels could not NEXRAD, gathers precipitation data in real At this point two rainfall products of vary more than one mulch level up or down time, covering the state of Iowa through the same coverage and coordinate system within a rotation. When grass, trees or veg- stations in Davenport and Des Moines, Iowa; exist. One of them—National Centers for etables were in the rotation, there was no Minneapolis, Minnesota; Omaha, Nebraska; Environmental Prediction—consists of choice for those crops as to what mulch level La Crosse,Wisconsin; and Sioux Falls, South hourly accumulation information and is to use. Also, if the C value when corn was Dakota. They survey the atmosphere around based on the operational algorithms using in the NRI was such that corn silage gave a them every six to 10 minutes and are capable full resolution data, while the other better fit than corn for grain, we assumed of providing basic data (Level II) with resolu- (NEXRAD Level III) is based on degraded corn was grown for silage. tion of one degree in azimuth and 1 km (0.62 data but has the time resolution of 15 min- There are many farm tools typically used miles) in range. This Level II data would be utes. Integrating the Level III based product that were not included in the tillage ideal for erosion simulation as erosion events files to one-hour accumulation and compar- sequences for the various mulch levels. We are strongly impacted by short, intense rainfall ing with the National Center’s maps, we assumed that these six tillage sequences periods often lasting five minutes or less. observed significant local differences between encompassed the range of residues that However, computational demands, current those two rainfall estimates (see Nelson et al. remain on the land for erosion control, and data distribution procedures by National (2005) for detailed discussion of a similar that they well represent the normal ways that Weather Service, which operates the radars, NEXRAD based rainfall product). This these tillage tools are used. and budget constraints of our project dictate discrepancy came from the fact that the Comparison of NRI and WEPP average a simpler approach. We developed our pre- National Centers for Environmental annual soil erosion predictions. Average cipitation input to erosion models by an Prediction data product is multisensor, i.e., it annual soil erosion estimates for each of the innovative combination of two currently is built from radar data as well as from rain 17,848 NRI points was made using WEPP. available precipitation products: high quality gauge data and the other data set is created A series of 100-year WEPP runs was made National Center for Environmental using only the degraded radar information. using the WEPP input data derived from the Prediction one-hour accumulation maps and Also, radar data used for the National Center’s NRI, and using the CLIGEN generated National Weather Service 15-minute Level product is subject to several quality control climate data for the center point of each III (with only eight effective levels of reflec- procedures that cannot be applied to the county. The WEPP estimated average annual tivity degraded from 256 levels of Level II NEXRAD Level III data due to their soil loss for each NRI point was multiplied by data available every 6 minutes) NEXRAD degraded quantization. Although the the P factor value given for that point (about data to obtain 15-minute rainfall products in NEXRAD Level III rainfall product is of 82 percent of the points had a P value of 1, the Hydrologic Rainfall Analysis Project pro- inferior quality compared to the National indicating they have no support practice for jection grid (Reed and Maidment, 1999). Centers for Environmental Prediction prod- controlling soil erosion) to compute the aver- The spatial resolution of this grid is about uct, it provides good qualitative information age annual soil erosion. 4 × 4 km2 (2.5 × 2.5 mi)2. about time distribution of 15-minute rainfall The NRI gives predicted soil erosion for Our main radar rainfall input was the within the hour of the National Centers for

194 JOURNAL OF SOIL AND WATER CONSERVATION J|A 2006 Figure 1 Ground reference rain gauge network in the vicinity of Iowa City, Iowa. Environmental Prediction estimate. Thus, the one-hour National Center for Environmental Prediction accumulation is distributed into four 15-minute accumulations according to the 15-minute NEXRAD Level III rainfall estimates for each Hydrologic Rainfall Analysis Project grid cell. As a result, 15-minute rainfall products are obtained with the same one-hour accumulation as this National Centers for Environmental Prediction product. Computation of the product is conducted within two hours after midnight for the day that the data are valid. After all data have been transferred to Iowa State University by file transfer protocol (FTP) from national file servers at the National Center for Environmental Prediction, breakpoint precipitation data is calculated. If total daily accumulation is less than 5 mm, only one break point is created, reducing unnecessary processor usage. This break point precipitation and other real-time weather data are then added to the WEPP input database. Precipitation totals for an Hydrologic Rainfall Analysis Project cell Figure 2 greater than zero will trigger an insertion in the Long-term WEPP erosion estimates vs. National Resources Inventory (NRI) erosion estimates made using the RUSLE slope factor. WEPP database signaling that the Hydrologic Rainfall Analysis Project cell requires process- 250 ing by WEPP. The daily rainfall totals per Best Fit: WEPP = 0.95 NRI - .01, R2 = 0.84 Hydrologic Rainfall Analysis Project cell are 1:1 Line then downloaded to the web server. Linear (Best fit)

Since there are still numerous poorly ) 200 1 - understood issues regarding radar-rainfall r y 1

accuracy (see Krajewski and Smith, 2003 - a

for discussion), a limited validation of our h t product against rain gauge data was per- ( 150 formed. For comparisons two networks n o i

were used: 1) the Iowa City Airport Piconet s o

(e.g., Krajewski et al., 2003) and its expansion r e l

which includes 25 sites spaced on approxi- i 100 o

mately a 5 km (3.1 mi) grid with dual tipping s

bucket platforms (Figure 1); and 2) the Iowa P P

State University Ag-Climate network. The E first network has been developed and main- W 50 tained by the University of Iowa IIHR- Hydroscience and Engineering institute and is located approximately 80 km (49.6 mi) west from the Davenport WSR-88D 0 (KDVN) NEXRAD radar and over 200 km 0 50 100 150 200 250 (132 mi) from the Des Moines WSR-88D (KDSM). The ISU ag-climate stations are dis- NRI estimated erosion (t ha-1 yr-1) tributed throughout the state. To minimize the effect of the spatial resolution mismatch Statewide application at the township scale. radar data from the University of Iowa via FTP. between the radar-based product and the rain To successfully run a model of this complexity If the data has not been successfully transferred gauge observed value, the validation results at tens of thousands of data points, significant by 10:30 a.m., an email warning is generated were based at the monthly accumulation scale. software automation is needed. At 12:30 a.m. and the script terminates. local time, a script executes to download the Before the WEPP model can run, the

J|A 2006 VOLUME 61 NUMBER 4 195 Figure 3 Scatter plots of monthly radar and rain gauge accumulations for each gauge of the AMSR-E network from April 2003 to August 2004 (winter months excluded). WEPP used 99 different climate files, each representing a county. The range of manage- 150 ment systems and the method of selection of management systems while based on the 50 NRI, would not always produce the same ) management system used in the NRI, and m

m hence would contribute to the variability

( 150

n shown in Figure 2. o i Tiwari et al. (2000), using runoff and soil t 50 a l erosion data collected from natural runoff plots u

m at 20 different locations compared soil erosion

u 150

c predictions made using the USLE, RUSLE, c

a and WEPP. Only average annual soil erosion 50 r estimates were compared with measured data. a

d They concluded that WEPP performance was a r 150 comparable to the traditional empirical meth- y l

h ods, despite the bias toward the USLE and t

n 50 RUSLE because these empirical technologies o

M were developed using data from these same 20 150 locations. The data set included two Iowa locations, and five locations from contiguous 50 states. It also included a wide range of crops, slopes, and soils. The earliest data collected 50 150 50 150 50 150 50 150 50 150 was in 1931, the latest in 1971. The shortest Monthly gauge accumulation (mm) data collection period was two years, the longest was 23 years. Precipitation product. In Figure 3, we non-precipitation data must be linked to the erosion and runoff events for the previous show the comparison results for the Iowa precipitation data by assigning them to the day. When an event is found, the script City network on a gauge-by-gauge basis. correct Hydrologic Rainfall Analysis Project makes an entry in the WEPP database. After The subplots are organized according to the cells. After completion, a script executes all the output is processed and entered into actual geographic layout of the five by five checking the WEPP database for Hydrologic the database, total rainfall, runoff and soil ero- network. The agreement between the two Rainfall Analysis Project cells that need to be sion is averaged at the township level and this sources of rainfall information is rather good. processed (precipitation occurred). Runs is uploaded to the web server for display There is no apparent bias (average bias is 1.01) continue until the WEPP database contains (http://wepp.mesonet.agron.iastate.edu/). in the radar product and the correlation no more cells needing to be run. Because of coefficient between rain gauges and radar the differing resolutions of input data, i.e., the Results and Discussion estimates is around 0.9 with the exception of 4 km (2.5 mi) × 4 km Hydrologic Rainfall Comparison of NRI and WEPP average a couple of outliers which we traced to rain Analysis Project cells and the ~10 km (6 mi) annual soil erosion predictions. Figure 2 gauge data problems. Root mean square × ~10 km townships containing management shows a comparison of average annual soil difference is about 12 percent of the month- data, input data do not align perfectly. To erosion as given in the NRI with WEPP ly amount. The same kind of comparison overcome this problem each Hydrologic predicted average annual soil erosion. The conducted for the Iowa State University Rainfall Analysis Project cell is assigned to average annual soil erosion predicted by the Agriculture Network shows much higher the township that contains its centroid. An NRI (with RUSLE slope length factor) over scatter about the line of agreement (Figure 4) additional challenge comes from the inability all points was 9.5 t ha-1 (4.2 t ac-1) [ranging with the correlation coefficient around to match individual NRI points with from 0 to 230 t ha-1 (103 t ac-1)], while WEPP 0.7 and the root mean square difference Hydrologic Rainfall Analysis Project climate predicted average annual soil erosion was 9.0 about 25 percent. Also, a slight bias of about information, since the exact geographical t ha-1 (4.0 t ac-1) [ranging from 0 to 213 t 10 percent appears (radar overestimates with locations of the NRI points within a town- ha-1 (95 t ac-1)]. As shown in Figure 2, there respect to gauges). We speculate reasons for ship are unknown. The solution we used was was considerable variability between the esti- this: 1) data quality is likely to be better at the to run all possible Hydrologic Rainfall mates. There are a number of factors that IIHR network as there are two identical Analysis Project cells in a township against all could cause these differences. WEPP uses a gauges at each site for measurement verifica- NRI locations in a township, which implies much different prediction approach, and tion; and 2) the agricultural stations are under that the reported runoff and erosion results hence uses far different characteristics of the different radars and at different distances from are averaged output by township. soil and climate for estimating soil erosion. the radars while the IIHR network is entirely After a given script executes, another script Also, while the NRI uses only two values of under the same radar and at a favorable dis- will execute and search the WEPP output for the R factor for the entire state of Iowa, tance. For example, the Muscatine site is

196 JOURNAL OF SOIL AND WATER CONSERVATION J|A 2006 Figure 4 Scatter plots of monthly radar and rain gauge accumulations for each gauge of the Iowa State close [60 km (37.2 mi)] to the same University Ag-Climate network from April 2002 to November 2004 (winter months excluded). (Davenport, Iowa) radar as the IIHR network Ames Calmar Castana Cedar Rpaids but the scatter there is high while the Crawfordsville site performs well at a similar 150 distance [90 km (55.9 mi)] from the same )

radar. Clearly, while radar-based rainfall m

m 50 products provide wide area coverage, a mon- ( itoring strategy needs to be in place with n o i high-quality rain gauge data to quantify radar t Chariton Crawforshireville Nashua Sutherland a l

product performance. u 150 Data processing efficiency. May 4, 2003 is m u selected for discussion because this date had c c rainfall throughout the state and required runs a

r 50 for nearly every combination in the domain. a d

On a given day rainfall normally covers only a a r Lewis Rhodes Kanawha Muscatine y

portion of the state resulting in proportionate- l h ly less computer time to complete the calcula- t 150 n tions. Twelve hours of computer runtime o were required for this date. This time is pri- M marily input/output limited. The results are 50 available easily within 24 hours of the day on which rain occurred. 50 150 50 150 50 150 50 150 Precipitation, erosion, and runoff product. Estimated 24-hour rainfall, water runoff and Monthly gauge accumulation (mm) soil erosion for May 4, 2003 are illustrated in Figures 5, 6, and 7. Not surprisingly, not only topography as Figure 7 illustrates. and water runoff are based on near real time estimated 24-hour rainfall amounts were An impact of an additional major factor, rainfall estimates coupled with historical soil quite spatially variable ranging from less than cropping and management, is not illustrated and crop management records simply extended 1.3 to more than 7.6 cm (0.5 to more than in Figures 6 and 7. to estimate current conditions. Farmer prac- 3 in). Patterns of the heavier rainfall amounts Limitations and opportunities. Erosion tices change periodically, sometime annually. run from southwest to northeast, typical of storm track movement for this area. Figure 5 Estimated average runoff ranged from 0 to Estimated rainfall for the 24-hour period of May 4, 2003. 5.1 cm (2 in). Runoff amounts for this 24-hour period are spatially correlated to rainfall amounts, as can be seen comparing Figures 5 and 6. Soil erosion estimates are also spatially correlated with rainfall amounts and range from 0 to over 11.2 t ha-1 (5 t ac-1) for one day. Three areas of the state received between 6.4 to 12.8 cm (2.5 to 5 in) of rainfall in this 24-hour period—West Central, North Central, and Northeast Iowa. With similar rainfall amounts, estimated erosion losses in the loess hills of Western Iowa exceeded losses in either North Central or Northeast Iowa. The North Central and Northeast Iowa areas receiving the heavy rainfall have more gentle topography that that in West Central Iowa. Topography plays a key role in these erosion estimates, as should be expected. Likewise, soil erosion estimates in the loess hills of Western Iowa are spatially correlated with the rainfall occurring in the region, i.e., it is the combination of rainfall and topography that strongly influenced estimates for this day and

J|A 2006 VOLUME 61 NUMBER 4 197 Figure 6 Average runoff estimates for all townships in Iowa for May 4, 2003. ing rainfall with that specific location and its surface conditions. Currently, because of restrictions placed on public distribution of NRI locations, statistical procedures must be implemented to estimate losses based on all possible rainfall and NRI combinations found in a given township. This is an accept- able methodology for prototype development, but one with distinct limitations. For this project, because of NRI data limitations, erosion and runoff estimates are limited to simple slopes with no considera- tion given to delivery to water courses or water bodies. Adapting, or developing, technology capable of appropriately routing materials through small watersheds to water courses would permit use of this approach for determining storm and land management impacts on sediment delivery to water bodies. Further development that estimates the con- tribution of subsurface sources of runoff and the development of technology that estimates nutrient, pesticide, and/or pathogen movement with sediment and runoff water would permit remote estimates of water quality The approach of extending historical records Identification of current land management, impairment of specific water bodies on a to the current time, while suitable for devel- through remote sensing for example, would temporal scale. oping this prototype, introduces real-time be a valuable resource for this model. water runoff and soil erosion estimate errors Additionally, identifying practices used at a Summary and Conclusion independent of model performance. given location and time would allow match- In this project we successfully demonstrated the feasibility of linking diverse technologies Figure 7 to estimate daily rainfall, soil erosion and Average soil erosion estimates for each township in Iowa for May 4, 2003. water runoff at the township scale over a large region. Our prototype system provides the public with Internet accessible maps of daily soil erosion and water runoff in Iowa (http://wepp.mesonet.agron.iastate.edu/). While it is difficult to quantify the accuracy of our estimates because of the limitations in several of our data sources, our system sets the stage for rigorous long-term study of soil and sediment transport budgets over large river basins. Our estimates represent the amount of soil detached on a given day but are not linked to the transport processes of the runoff water and sediment to surface waters. This could clearly be a significant extension of the system that would undoubtedly contribute to a better understanding of the soil loss and sedimentation problem at the state and national level. Other significant extensions of our work should include increased resolution of the computations with the emphasis on including high-resolution topography and land management data, and increased resolution of rainfall estimates. Another important element

198 JOURNAL OF SOIL AND WATER CONSERVATION J|A 2006 of an improved system should be a carefully Ghidey, F. and E.E. Alberts. 1996. Comparison of measured U.S. Department of Agriculture (USDA). 2004.Agricultural designed monitoring network for independent and WEPP predicted runoff and soil loss for Midwest statistics, 2004. U.S. Department of Agriculture, claypan soil.Transactions of the ASAE 39(4):1395-1402. Agricultural Statistics Board, National Agricultural validation of the model-based estimates of soil Hudson, N. 1995. Soil conservation. Iowa State University Statistics Service. erosion and sediment transport. Press,Ames, Iowa. Wischmeier,W.H., C.B. Johnson, and B.V. Cross. 1971. Soil Our results indicate that soil erosion and Kuehnast, E., D. Baker, and J. Enz. 1975. Climate of erodibility nomograph for farmland and construction runoff in Iowa are spatially and temporally Minnesota IIX. Precipitation patterns in the sites. Journal Soil and Water Conservation 26:189-193. variable. Analysis of one year’s data shows Minneapolis-St. Paul metropolitan area and surrounding Wischmeier,W.H.and D.D.Smith. 1965. Predicting soil ero- counties. Minnesota Agriculture Experiment Station sion from croplands east of the Rocky Mountains. U.S. that some sections of the state experienced Technical Bulletin No. 301. St. Paul, Minnesota. Department of Agriculture (USDA) Agricultural much greater soil loss than did other areas, Krajewski, W.F. and J.A. Smith. 2003. Radar hydrology: Handbook No. 282. and that significant areas experienced soil Rainfall estimation. Advances in Water Resources erosion losses much greater than the T value 25:1387-1394. Krajewski,W.F.,G.J. Ciach, and E. Habib. 2003.An analysis of and many times greater than expected soil small-scale rainfall variability in different climatological renewal rates. This clearly calls for concern regimes. Hydrologic Sciences Journal 48:151-162. and justifies further efforts to establish an Laflen, J.M., D.C. Flanagan, and B.A. Engel. 2004. Soil erosion improved monitoring system and research on and sediment yield prediction accuracy using WEPP. process studies. Journal of American Water Resources Association 40(2):289-297. Nelson, B., W.F. Krajewski, J.A. Smith, E. Habib, and G. Acknowledgments Hoogenboom, 2005.Archival precipitation data set for the Anton Kruger, Radoslaw Goska and Pradeep Mississippi River Basin: Evaluation. Geophysical Research Mandapakavenkata are acknowledged for Letters, 32(18):L18403,10.1029/2005 GL023334. Nicks, A.D., L.J. Lane, and G.A. Gander. 1995. Chapter 2. providing support with computations and Weather generator. Pp. 2.1-2.22. In: U.S. Department of graphics. 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