Rajith Mukundan

A Saturation Excess Erosion Model

Abstract. Scaling-up sediment transport has been problematic because most sediment loss models (e.g., the Universal Soil Loss Equation) are developed using data from small plots where runoff is generated by infiltration excess. However, in most watersheds, runoff is produced by saturation excess processes. In this article, we improve an earlier saturation excess erosion model that was only tested on a limited basis, in which runoff and erosion originated from periodically saturated and severely degraded areas, and apply it to five watersheds over a wider geographical area. The erosion model is based on a semi-distributed hydrology model that calculates saturation excess runoff, interflow, and baseflow. In the development of the erosion model, a linear relationship between sediment concentration and velocity in surface runoff is assumed. Baseflow and interflow are sediment free. Initially during the rainy season in Ethiopia, when the fields are being plowed, the sediment concentration in the river is limited by the ability of the surface runoff to move sediment. Later in the season, the sediment concentration becomes limited by the availability of sediment. To show the general applicability of the Saturation Excess Erosion Model (SEEModel), the model was tested for watersheds located 10,000 km apart, in the U.S. and in Ethiopia. In the Ethiopian highlands, we simulated the 1.1 km 2 Anjeni watershed, the 4.8 km 2 Andit Tid watershed, the 4.0 km 2 Enkulal watershed, and the 174,000 km 2 Blue Nile basin. In the Catskill Mountains in New York State, the sediment concentrations were simulated in the 493 km 2 upper Esopus Creek watershed. Discharge and sediment concentration averaged over 1 to 10 days were well simulated over the range of scales with comparable parameter sets. The Nash-Sutcliffe efficiency (NSE) values for the validation runs for the stream discharge were between 0.77 and 0.92. Sediment concentrations had NSE values ranging from 0.56 to 0.86 using only four calibrated sediment parameters together with the subsurface and surface runoff discharges calculated by the hydrology model. The model results suggest that correctly predicting both surface runoff and subsurface flow is an important step in simulating sediment concentrations.

Watershed sediment source identification: tools, approaches, and case studies

Many of us who have watched a stream turn turbid during a rainstorm have often wondered—where is all that sediment coming from? Geomorphologists, engineers, and environmental scientists have thought about this question for decades and have placed understanding sediment sources under the general framework of sediment budgets. This framework which examines not only sediment sources but the storage, transport, and delivery of sediment has been used to understand surficial processes, as well as address management concerns as they relate to stream turbidity and surface-water quality. Traditional tools used in sediment budgets have included field measurements of erosion, storage and transport rates, photogrammetric analysis, and modeling. The relationship between soil erosion and sediment yield at the watershed outlet has been long identified as a major research need (Walling 1983) and is still a poorly understood or misunderstood component of fluvial sediment transport (Kinnell 2004). In recent decades, sediment fingerprinting approaches using the geochemical and physical properties of sediment to determine sediment sources have become increasingly popular. This special issue on Watershed Sediment Source Identification, for the Journal of Soils and Sediments was produced as a result of a special session convened at the Association of American Geographers (AAG) 2012 annual meeting in New York City, NY, USA. The session was entitled “Watershed Sediment Source Identification” and brought together leading experts in the field of sediment sourcing. As a result of the AAG session, the Journal of Soils and Sediments offered to have the session and a few other papers on the same topic published as a special issue in the journal. This special issue reflects contributions from 29 researchers discussing sediment sourcing results from studies in Africa, Asia, Europe, and North America where settings range from urban to agricultural to forested. Sediment source assessment is not only important to our understanding of sediment dynamics in fluvial systems but is increasingly becoming an important management tool. The papers in this special issue reflect a range of studies that address both topics. Studies emphasize traditional field approaches to determine sediment yields and sources (Davis and Sims 2013; Zhu 2013) as well as the use of the sediment fingerprinting approach to determine significant sediment sources (Dutton et al. 2013; Gellis and Noe 2013; Huisman et al. 2013; Koiter et al. 2013; Mckinley et al. 2013; Voli et al. 2013). Several papers highlight site-specific methods and approaches to streamline the sediment fingerprinting approach into a practical management tool. Other papers relate findings from sediment fingerprinting to hydrologic conditions, and caution users on over-interpreting the sediment fingerprinting results. Walling (2013), in the opening paper, provides a history on the sediment fingerprinting approach and illustrates how publications on the subject have increased exponentially since the 1970s. A review of these publications indicate that many studies have used sediment fingerprinting to determine sediment sources as a means to improve our understanding of erosion and sediment delivery processes. Other papers use sediment fingerprinting as a management tool. Walling (2013) lists key advances in the sediment fingerprinting approach over the last 30 years that include the use of multiple or composite fingerprints, statistical tests and models, size and organic correction factors, increased source and target assessments, and improved estimates of uncertainty. Given these advances in the sediment A. C. Gellis (*) U.S. Geological Survey, 5522 Research Park Drive, Baltimore, MD 21228, USA e-mail: agellis@usgs.gov

Quantifying the Contribution of On-Site Wastewater Treatment Systems to Stream Discharge Using the SWAT Model.

In the southeastern United States, on-site wastewater treatment systems (OWTSs) are widely used for domestic wastewater treatment. The degree to which OWTSs represent consumptive water use has been questioned in Georgia. The goal of this study was to estimate the effect of OWTSs on streamflow in a gauged watershed in Gwinnett County, Georgia using the Soil and Water Assessment Tool (SWAT) watershed-scale model, which includes a new OWTS algorithm. Streamflow was modeled with and without the presence of OWTSs. The model was calibrated using data from 1 Jan. 2003 to 31 Dec. 2006 and validated from 1 Jan. 2007 to 31 Dec. 2010 using the auto-calibration tool SWAT-CUP 4. The daily and monthly streamflow Nash-Sutcliffe coefficients were 0.49 and 0.71, respectively, for the calibration period and 0.37 and 0.68, respectively, for the validation period, indicating a satisfactory fit. Analysis of water balance output variables between simulations showed a 3.1% increase in total water yield at the watershed scale and a 5.9% increase at the subbasin scale for a high-density OWTS area. The percent change in water yield between simulations was the greatest in dry years, implying that the influence of OWTSs on the water yield is greatest under drought conditions. Mean OWTS water use was approximately 5.7% consumptive, contrary to common assumptions by water planning agencies in Georgia. Results from this study may be used by OWTS users and by watershed planners to understand the influence of OWTSs on water quantity within watersheds in this region.

Predicting trihalomethanes in the new york city water supply.

Chlorine, a commonly used disinfectant in most water supply systems, can combine with organic carbon to form disinfectant byproducts, including carcinogenic trihalomethanes. We used water quality data from 24 monitoring sites within the New York City water supply distribution system, measured between January 2009 and April 2012, to develop an empirical model for predicting total trihalomethane (TTHM) levels. Terms in the model included the following water quality parameters: total organic carbon, pH, water age (reaction time), and water temperature. Reasonable estimates of TTHM levels were achieved with overall of about 0.75, and predicted values on average were within 6 μg L of measured values. A sensitivity analysis indicated that total organic carbon and water age are the most important factors for TTHM formation, followed by water temperature; pH was the least important factor within the boundary conditions of observed water quality. Although never out of compliance in 2011, the TTHM levels in the water supply increased after tropical storms Irene and Lee, with 45% of the samples exceeding the 80 μg L maximum contaminant level in October and November. This increase was explained by changes in water quality parameters, particularly by the increase in total organic carbon concentration during this period. This study demonstrates the use of an empirical model to understand TTHM formative factors and their relative importance in a drinking water supply. This has implications for simulating management scenarios and real-time estimation of TTHMs in water supply systems under changing environmental conditions.