Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1016/j.jhydrol.2017.07.021
Volltext Akzeptiertes Manuskript
Titel (primär) Predicting nitrate discharge dynamics in mesoscale catchments using the lumped StreamGEM model and Bayesian parameter inference
Autor Woodward, S.J.R.; Wöhling, T.; Rode, M.; Stenger, R.
Journal / Serie Journal of Hydrology
Erscheinungsjahr 2017
Department ASAM
Band/Volume 552
Seite von 684
Seite bis 703
Sprache englisch
Keywords Stream flow hydrograph; water quality; direct runoff; groundwater discharge; DREAM; uncertainty
UFZ Querschnittsthemen RU2;
Abstract The common practice of infrequent (e.g., monthly) stream water quality sampling for state of the environment monitoring may, when combined with high resolution stream flow data, provide sufficient information to accurately characterise the dominant nutrient transfer pathways and predict annual catchment yields. In the proposed approach, we use the spatially lumped catchment model StreamGEM to predict daily stream flow and nitrate concentration (mg L-1 NO3-N) in four contrasting mesoscale headwater catchments based on four years of daily rainfall, potential evapotranspiration, and stream flow measurements, and monthly or daily nitrate concentrations. Posterior model parameter distributions were estimated using the Markov Chain Monte Carlo sampling code DREAMZS and a log-likelihood function assuming heteroscedastic, t-distributed residuals. Despite high uncertainty in some model parameters, the flow and nitrate calibration data was well reproduced across all catchments (Nash-Sutcliffe efficiency against Log transformed data, NSL, in the range 0.62–0.83 for daily flow and 0.17–0.88 for nitrate concentration). The slight increase in the size of the residuals for a separate validation period was considered acceptable (NSL in the range 0.60–0.89 for daily flow and 0.10–0.74 for nitrate concentration, excluding one data set with limited validation data). Proportions of flow and nitrate discharge attributed to near-surface, fast seasonal groundwater and slow deeper groundwater were consistent with expectations based on catchment geology. The results for the Weida Stream in Thuringia, Germany, using monthly as opposed to daily nitrate data were, for all intents and purposes, identical, suggesting that four years of monthly nitrate sampling provides sufficient information for calibration of the StreamGEM model and prediction of catchment dynamics. This study highlights the remarkable effectiveness of process based, spatially lumped modelling with commonly available monthly stream sample data, to elucidate high resolution catchment function, when appropriate calibration methods are used that correctly handle the inherent uncertainties.
dauerhafte UFZ-Verlinkung
Woodward, S.J.R., Wöhling, T., Rode, M., Stenger, R. (2017):
Predicting nitrate discharge dynamics in mesoscale catchments using the lumped StreamGEM model and Bayesian parameter inference
J. Hydrol. 552 , 684 - 703