Herbert Ssegane

Curve Number Approaches to Estimate Drainage from a Yard Waste Windrow Composting Pad

Abstract. Estimation of runoff from windrow compost pads is a challenge due to the different hydrologic properties of the compost and pad, and moisture storage in the compost, both of which change with time. The surface of a compost pad is usually crushed rock on top of a compacted layer of clay. The curve number method is widely used for estimating runoff from rainfall, but because the porous layer of gravel promotes greater infiltration and subsurface drainage, this study investigated the effectiveness of this standard approach. Four curve number based methods are assessed for their utility in estimating drainage from a 7284-m 2 windrow compost pad in Athens, Georgia, using 16 storm events. The methods estimate drainage using (1) a tabulated curve number, (2) a quasi-dynamic curve number based on the magnitude of the rainfall, antecedent rainfall, and areal coverage of the compost piles, (3) an asymptotic curve number, and (4) an average event-based curve number. Using the tabulated curve number, event runoff (r 2 = 0.92) was consistently underestimated. A quasi-dynamic curve number improved the runoff estimation (r 2 = 0.98). The asymptotic (r 2 = 0.90) and event-based averaged (r 2 = 0.92) curve number methods performed comparable to the tabulated curve number method. Although curve numbers for maturing compost decreased from approximately 95 to 75 over time, this study recommends use of a conservative curve number = 95 for containment of design storms, while curve numbers of 70 to 75 may be appropriate for estimating average annual runoff from mature compost and the area necessary for land application of the pad runoff.

Calibration of paired watersheds: Utility of moving sums in presence of externalities

Historically, paired watershed studies have been used to quantify the hydrological effects of land use and management practices by concurrently monitoring two similar watersheds during calibration (pre-treatment) and post-treatment periods. This study characterizes seasonal water table and flow response to rainfall during the calibration period and tests a change detection technique of moving sums of recursive residuals (MOSUM) to select calibration periods for each control-treatment watershed pair when the regression coefficients for daily water table elevation (WTE) were most stable to minimize regression model uncertainty. The control and treatment watersheds were one watershed of 3−4 year-old intensely managed loblolly pine (Pinus taeda L.) with natural understory, one watershed of 3−4 year-old loblolly pine intercropped with switchgrass (Panicum virgatum), one watershed of 14−15 year-old thinned loblolly pine with natural understory (control), and one watershed of switchgrass only. The study period spanned from 2009 to 2012. Silvicultural operational practices during this period acted as external factors, potentially shifting hydrologic calibration relationships between control and treatment watersheds. MOSUM results indicated significant changes in regression parameters due to silvicultural operations and were used to identify stable relationships for WTE. None of the calibration relationships developed using this method were significantly different from the classical calibration relationship based on published historical data. We attribute that to the similarity of historical and 2010−2012 leaf area index (LAI) on control and treatment watersheds as moderated by the emergent vegetation. While the MOSUM approach does not eliminate the need for true calibration data or replace the classic paired watershed approach, our results show that it may be an effective alternative approach when true data is unavailable, as it minimizes the impacts of external disturbances other than the treatment of interest.

Runoff Generation from Shallow Water Table Southeastern Forests: Unusual Behavior of Paired Watersheds Following a Major Disturbance

Shallow water table soils are common in the forested watersheds of the southeastern Atlantic and Gulf coasts of the United States. The process of runoff generation is still poorly understood on these low-gradient watersheds, where soil saturation is common across the watershed. Storm runoff varies widely from none to over 70 percent of rainfall, and is believed to be related to antecedent soil water, and depression storage. Understanding runoff generation is critical in these watersheds because of the dual threat of booming coastal development and climate change.

Riparian Sediment Delivery Ratio: Stiff Diagrams and Artificial Neural Networks

Various methods are used to estimate sediment transport through riparian buffers and grass filters, with the sediment delivery ratio having been the most widely applied. The U.S. Forest Service developed a sediment delivery ratio using the stiff diagram and a logistic curve to integrate some of the factors influencing sediment delivery heuristically. This study independently tested the Forest Service sediment delivery ratio contrasted with artificial neural networks to represent the multiple nonlinearities between important factors and sediment delivery. The Forest Service sediment delivery ratio was not adequate when compared to published sediment yields from 30 small experimental buffers from three countries, including four forested buffers. However, artificial neural networks gave estimates of the delivery ratio that were highly correlated to the observations. The 30 buffer observations produced such good estimates of the sediment delivery ratio with both seven and five buffer parameters that this study suggests that as few as 30 sediment yield observations can be the basis for applying neural networks to interpolate the complex, multiple nonlinearities of hydrology and sediment transport on riparian buffers.

Geospatial Modeling of Site Suitability for Pond-Based Tilapia and Clarias Farming in Uganda

Seven criteria (water requirement, water temperature, soil texture, terrain slope, potential farm gate sales, availability of farm inputs, and access to local and regional markets) were analyzed to determine site suitability for tilapia and clarias farming in Uganda. Crisp and fuzzy approaches of criterion classification were implemented using GIS, and the results were compared. There was a statistically significant difference between maps generated by crisp and fuzzy approaches. For both the crisp and the fuzzy approaches, over 98% of the land was classified as moderately suitable or suitable. Overall, the crisp method classified 16,322 hectares (0.09%) as very suitable compared to zero hectares (0%) by the fuzzy method. Simultaneously, the crisp method gave 297,344 hectares (1.96%) as unsuitable compared to 168,592 hectares (0.96%) by the fuzzy method. Of the 138 surveyed fishponds that were operational, the crisp method classified 71% as suitable and 29% as moderately suitable, while the fuzzy method classified 71.7% as suitable and 28.3% as moderately suitable.

An Instantaneous Unit Hydrograph for Estimating Runoff from Windrow Composting Pads

A MatLab-Simulink® compartmental dynamic model was developed for a windrow composting pad. The physically based MatLab-Simulink approach offered the benefit of effectively evaluating the effects of different pad configurations on runoff rates and amounts. Between December 23, 2010 and June 11, 2011, we recorded measurements of rainfall and containment pond storage and pond pumping on 10-minute time intervals. Infiltration rate and percolation through the crusher-run media were measured using double ring infiltrometers. We compared the containment pond stage predictions from the MatLab-Simulink compartmental models to the observed pond stage and found R2values greater than 0.9, Nash-Sutcliffe statistics greater than 0.9 and Root-mean-square-errors (RMSE) less than 1% of the full pond volume on 16 storms occurring from Dec 23, 2010 to June 21, 2011). A continuous simulation over the calibration period (December 23, 2010 to January 30, 2011) revealed good agreement of MatLab-Simulink and observed pond volume. The MatLab-Simulink compartment model appeared to capture the physical runoff process occurring on the pad, except for the impact of the compost material itself. With the MatLab Control Systems Toolbox®, one could linearize the MatLab-Simulink model at several levels of compartmental storage. A transfer function for each linearized model made possible the determination of the impulse response at each linearized condition. The instantaneous unit hydrograph is the impulse response at the appropriate linearization. The analysis yielded a typical unit hydrograph reminiscent of the tanks-in-series analyses. From these analyses, we proposed an instantaneous unit hydrograph for similarly constructed windrow compost pads, which is also usable in other situations.

Analyzing Runoff from a Windrow Composting Pad

Estimation of runoff from windrow compost pads is a challenge due to the different hydrologic properties of the compost pad and moisture storage in the compost, both of which change with time. The surface of a compost pad is usually crushed rock on top of a compacted layer of clay and may contain a thin covering of washed compost over the gravel along with the mounds of compost extending above the surface. The curve number method is popular for evaluating runoff from rainfall, but because the porous aggregate promotes greater infiltration and drainage, a new approach was considered. Four curve number based methods are assessed for accuracy in estimating runoff on a windrow compost pad using 16 storm events occurring on a 7284 m2 composting facility in Athens, Georgia. The methods include (1) a tabulated effective curve number, (2) a dynamic curve number based on the magnitude of the rainfall, antecedent rainfall, and areal coverage of the compost piles, (3) an asymptotic curve number, and (4) an event-based optimized curve number. Using the NRCS effective curve number consistently under estimated event runoff (r2 = 0.92). Using a dynamic curve number improved the runoff estimation (r2 = 0.98). The asymptotic curve number method performed comparable to the effective CN method (r2 = 0.90) while the event based optimization process only slightly improved over the curve numbers selected from the standard NRCS tables (r2 = 0.92).

Riparian Sediment Delivery ratio: Stiff Diagrams and Artificial Neural Networks

A stiff diagram with a logistic function was tested to empirically integrate the effects of seven riparian buffer parameters in calculating the sediment delivery ratio as initially applied by the U.S. Forest Service in 1980 silvicultural nonpoint source pollution guidance. The use of artificial neural networks to heuristically capture the stochastic nature of sediment transport and deposition was also tested and compared using sediment yield measured on 30 small experimental buffers located in the U.S., Canada, and Germany. No other comparison existed before this study of the application of the stiff diagram to calculate sediment delivery ratios with ratios calculated from actual measurements. An average of the sediment delivery ratios from the measurements on the 30 buffers is better than estimates obtained from the stiff diagram used to composite the effect of the seven characteristics—runoff, soil texture, ground cover, slope shape, delivery distance, roughness, and slope gradient. Although only three experimental buffers were forested, these tests establish that the stiff diagram poorly estimates sediment delivery, leaving the original U.S. Forest Service hypothesis unproven. The estimated delivery ratio was over an order of magnitude too small for all three forested buffers compared to sediment delivery ratios calculated from field observations. Specification of the dimensionless stiff diagram area for an artificial neural network established that the logistic function selected by the Forest Service to relate stiff diagram area to delivery ratio does not adequately represent the complex nonlinearity of sediment delivery. Specifying the same seven buffer parameters provided adequate estimates because neural networks inherently account for nonlinearities using more than just the logistic or the other functions calibrated and tested. The overall single best trained network structure with an optimum of 10 hidden neurons with just five buffer characteristics achieved even better performance with a dimensionless mean square error of 0.001, a coefficient of determination of 0.98 and a Nash-Sutcliffe efficiency of 0.98. Although artificial neural networks tend to require large numbers of data, this investigation showed that with appropriately independent variables that explain larger portions of the variability, the minimum recommended number of data can be used to estimate sediment delivery ratios. Thus, artificial neural networks should be trained to estimate sediment delivery ratios instead of using the stiff diagram, which is limited to one function to describe nonlinearities. The paper has been accepted to appear in Transactions of ASABE.

Technical Note: Estimation of Micro-Watershed Topographic Parameters Using Earth Observatory Tools

The study set out to analyze the feasibility of using Earth observatory tools to derive elevations to characterize topographic parameters of slope gradient and area useful in predicting erosion and for natural resources engineering education and instruction. Earth observatory tools are geographic information systems that enable remote exploration of the Earth in three dimensions. They are Web-based mapping platforms that overlay satellite imagery, aerial photography, topographic data, and other GIS data over 3D Earth models. The systems include Google™ Earth, Microsoft Virtual Earth, and NASA World Wind. Google™ Earth is the most common and therefore this analysis explored its feasibility as a screening tool for extracting micro-watershed topographic parameters of slope gradient and area. Data from nine U.S micro-watersheds was used for predicting slope gradient and micro-watershed area. Elevations, areas, and slope gradients derived from Google™ Earth data were not significantly different from reported ground measurements. In addition to being powerful 3D observatory tools, earth observatory tools like Google™ Earth can serve as useful instruction and screening tools for remotely extracting micro-watershed topographic parameters in many locations, making them valuable resources for enhancing spatial thinking and teaching natural resources engineering.