Riparian Ecosystem Management Model (Remm): I. Testing of the Hydrologic Component for a Coastal Plain Riparian System

Inamdar, Shreeram; Sheridan, J. M.; Williams, Randall; Bosch, David D.; Lowrance, Richard; Altier, Lee S.; Thomas, Daniel L.

doi:10.13031/2013.13332

Cited by 35 publications

(41 citation statements)

References 9 publications

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“…Bryant et al (2005) reported an interception loss of 17.7% in the same watershed. For riparian forests located on coastal plain watersheds in Georgia, Inamdar et al (1999a) reported average annual interception loss of 15%. This discrepancy in interception losses between this study and reported literatures is not expected.…”

Section: Resultsmentioning

confidence: 99%

Surface runoff contribution of nitrogen during storm events in a forested watershed

et al. 2007

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We examined total Kjeldahl nitrogen (TKN) loading to a small forested stream during storm events. We hypothesized that upper soil and litter layers in riparian area are primary source of higher TKN concentrations during storm. A storm water sampling program was carried out to gather requisite flow and water quality data to calibrate and validate water and nutrient components of the Riparian Ecosystem Management Model for TKN. Water quality and storm flow data collected from January 2000 to December 2003 were used to simulate the hydrology and nitrogen transport over a second-order watershed within the Fort Benning Military Installation, Georgia. Intensive sampling conducted from October 2002 to May 2003 provided the necessary data to characterize the rising limb, peak, and recession limb of six major storm events. Simulated runoff and storm TKN loads were compared with their corresponding observed or calculated values. Hydrology and nitrogen data collected from February 21, 2003 to December 31, 2003 were used for the model validation. The hydrology component of the model showed a Nash-Sutcliffe efficiency of 87% for the validation period. The average absolute difference between simulated and calculated TKN loads was 25%. Even though the monthly water budget indicated the dominance of subsurface flow, TKN contribution from direct runoff was significantly greater than that from subsurface flow. On an average, 73% of the observed total TKN load at the watershed outlet was contributed by surface runoff during storm events. The results suggested that the surface runoff during the storm events washed off the nitrogen from the forest floor and transported to the stream.

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Section: Resultsmentioning

confidence: 99%

Surface runoff contribution of nitrogen during storm events in a forested watershed

et al. 2007

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“…This study was designed to provide information on both concentrations and loads of N, P, and chloride in direct surface runoff moving through a managed riparian buffer system. This is a companion study to previously published studies from the same site on sediment and water transport (Sheridan et al, 1999); subsurface hydrology (Bosch et al, 1994, 1996); herbicide transport (Lowrance et al, 1997); subsurface nutrient and chloride transport (Hubbard and Lowrance, 1997; Lowrance et al, 2000b); soil ecology (Lowrance, 1992; Ettema et al, 1999a, 1999b); and model testing (Inamdar et al, 1999a, 1999b; Lowrance et al, 2000a).…”

mentioning

confidence: 87%

Surface Runoff Water Quality in a Managed Three Zone Riparian Buffer

Lowrance

Sheridan

2005

J of Env Quality

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Managed riparian forest buffers are an important conservation practice but there are little data on the water quality effects of buffer management. We measured surface runoff volumes and nutrient concentrations and loads in a riparian buffer system consisting of (moving down slope from the field) a grass strip, a managed forest, and an unmanaged forest. The managed forest consisted of sections of clear-cut, thinned, and mature forest. The mature forest had significantly lower flow-weighted concentrations of nitrate, ammonium, total Kjeldahl N (TKN), sediment TKN, total N (nitrate + TKN), dissolved molybdate reactive P (DMRP), total P, and chloride. The average buffer represented the conditions along a stream reach with a buffer system in different stages of growth. Compared with the field output, flow-weighted concentrations of nitrate, ammonium, DMRP, and total P decreased significantly within the buffer and flow-weighted concentrations of TKN, total N, and chloride increased significantly within the buffer. All loads decreased significantly from the field to the middle of the buffer, but most loads increased from the middle of the buffer to the sampling point nearest the stream because surface runoff volume increased near the stream. The largest percentage reduction of the incoming nutrient load (at least 65% for all nutrient forms) took place in the grass buffer zone because of the large decrease (68%) in flow. The average buffer reduced loadings for all nutrient species, from 27% for TKN to 63% for sediment P. The managed forest and grass buffer combined was an effective buffer system.

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“…In RibAV , the actual evapotranspiration ( E ) is determined by the soil saturation, the roots' connectivity with the water table (Maddock and Baird, ; Baird and Maddock, ; Maddock et al , ) and the soil moisture content (Inamdar et al ., ; Altier et al ., ; Dahm et al ., ). This is the reason why the actual evapotranspiration estimation in the RibAV model includes the evapotranspiration from the unsaturated upper part of the soil ( E u ) represented by the static storage and the evapotranspiration from the saturated zone of the soil ( E s ):

E (t) = E_{u} (t) + E_{s} (t)

…”

Section: The Ribav Modelmentioning

confidence: 99%

Riparian evapotranspiration modelling: model description and implementation for predicting vegetation spatial distribution in semi‐arid environments

et al. 2013

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Biotic and abiotic interactions between the riparian zone and the river determine relevant hydrological processes and exert control over riparian and bordering upland vegetation types. Vegetation growth and development are mainly controlled by water availability on semi-arid regions so the closeness to the river yields a moisture gradient which clearly determines the boundaries between exuberant riparian zone and semi-arid upland. A mathematical model named RibAV is presented. Its conceptualization is based on the main worldwide ecosystem modelling approaches and field expertise. The implementation of RibAV that is proposed in this paper allows the simulation of the vegetation functional types distribution in riparian zones. An evapotranspiration index (E idx ) obtained through RibAV is used as criterion for long-term plant absence/presence prediction. Two permanent river reaches of semi-arid Mediterranean basins, the Terde reach (Mijares River, Spain) and the Lorcha reach (Serpis River, Spain), have been selected as case studies for the evaluation of the model performance. Several criteria based on the confusion matrix were used to analyze the efficiency of RibAV on the prediction of plant distribution. The model outstanding performance to establish riparian vegetation types distribution and the limit between this zone and the bordering upland is demonstrated in this paper; the strength of the E idx to classify plant functional types in riparian semi-arid environments is additionally proved. This is a pre-copyedited, author-produced PDF version following peer review of the article: García-Arias A., Francés F., Morales-de la Cruz M., Real J., Vallés-Morán F., Martínez-Capel F., Garófano-Gómez V. 2014. Riparian evapotranspiration modelling: model description and implementation for predicting vegetation spatial distribution in semi-arid environments. Ecohydrology, 7: 659-677.

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Riparian Ecosystem Management Model (Remm): I. Testing of the Hydrologic Component for a Coastal Plain Riparian System

Cited by 35 publications

References 9 publications

Surface runoff contribution of nitrogen during storm events in a forested watershed

Surface runoff contribution of nitrogen during storm events in a forested watershed

Surface Runoff Water Quality in a Managed Three Zone Riparian Buffer

Riparian evapotranspiration modelling: model description and implementation for predicting vegetation spatial distribution in semi‐arid environments

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