Agriculture
; Forecasting
; Nutrients
; Phosphorus
; Runoff
; Storms
; Water quality
; Agricultural catchments
; Catchment response
; High frequency data
; High temporal resolution
; Phosphorus transfer
; Precipitation patterns
; Second-order models
; Simulation efficiency
; Catchments
; antecedent conditions
; catchment
; data set
; ecosystem health
; frequency analysis
; hydrological modeling
; nutrient dynamics
; peak discharge
; precipitation (climatology)
; prediction
; rainfall
; runoff
; surface water
; water quality
; United Kingdom
英文摘要:
Excess nutrients in surface waters, such as phosphorus (P) from agriculture, result in poor water quality, with adverse effects on ecological health and costs for remediation. However, understanding and prediction of P transfers in catchments have been limited by inadequate data and over-parameterised models with high uncertainty. We show that, with high temporal resolution data, we are able to identify simple dynamic models that capture the P load dynamics in three contrasting agricultural catchments in the UK. For a flashy catchment, a linear, second-order (two pathways) model for discharge gave high simulation efficiencies for short-term storm sequences and was useful in highlighting uncertainties in out-of-bank flows. A model with non-linear rainfall input was appropriate for predicting seasonal or annual cumulative P loads where antecedent conditions affected the catchment response. For second-order models, the time constant for the fast pathway varied between 2 and 15ĝ€h for all three catchments and for both discharge and P, confirming that high temporal resolution data are necessary to capture the dynamic responses in small catchments (10-50ĝ€km2). The models led to a better understanding of the dominant nutrient transfer modes, which will be helpful in determining phosphorus transfers following changes in precipitation patterns in the future.
Lancaster Environment Centre, Lancaster University, Bailrigg, Lancaster, United Kingdom; Rothamsted Research North Wyke, Okehampton, Devon, United Kingdom; Global Sustainability Institute, Anglia Ruskin University, Cambridge, United Kingdom; Met Office Hadley Centre, Exeter, Devon, United Kingdom; School of Environmental Sciences, Norwich Research Park, University of East Anglia, Norwich, United Kingdom; James Hutton Institute, Aberdeen, United Kingdom; School of Engineering, Liverpool University, Liverpool, United Kingdom; School of Environment, Natural Resources and Geography, Bangor University, Bangor, Gwynedd, United Kingdom; School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester, United Kingdom; British Geological Survey, Keyworth, Nottingham, United Kingdom; School of Geographical Sciences, University of Bristol, Bristol, United Kingdom
Recommended Citation:
Ockenden M,C,, Tych W,et al. Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data[J]. Hydrology and Earth System Sciences,2017-01-01,21(12)