Analytical uncertainties and model output

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Maj Johansson
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1 Analytical uncertainties and model output Anders Grimvall 1, Claudia von Brömssen 2 and Göran Lindström 3 1) Swedish Institute for the Marine Environment 2) Swedish Univerity of Agricultural Sciences 3) Swedish Meteorological and Hydrological Institute

2 Monitoring and modelling riverine loads of substances Measured water quality and discharge Process-based catchment model (e.g. HYPE) High quality monitoring is a prerequisite for high quality modelling!

3 Monitoring and modelling river water quality Process-based catchment models (e.g. HYPE) Measured water quality and discharge Can process-based models help to reduce the uncertainty of measured data?

4 Principle premise Constant landuse and point emissions Observed temperature and rainfall Process-based catchment model (e.g. HYPE) Weatherdriven temporal variation The impact of human interventions in the catchment will emerge more clearly If weather-driven fluctuations are removed from observed data

5 Adjustment methods Simplistic method Substract calculated weather-driven fluctuations from observed values Slightly more advanced method Regress observed values on modelled values and compute residuals. Focus on the feasibility to predict the irregular components (anomalies) of time series

6 HYPE HYdrological Predictions for the Environment The soil is modelled as several layers which may have different thickness for each soil class

7 S-HYPE simulations S-HYPE is a HYPE setup that covers the whole of Sweden Sweden was divided into sub-basins The anthropogenic forcing was kept fixed to the conditions prevailing in 2005 The physical forcing consisted of time series of meteorological data from 1992 to We used monthly mean model outputs for water discharge and nitrogen and phosphorus concentrations at major river mouths

8 River Sampling site Observational data Sampling period Sea area Monthly sampling for water quality. Daily water discharge. River Sampling site Sampling period Sea area Norrström Stockholm Centralbron Baltic Proper Örekilsälven Munkedal Skagerrak Norrström Stockholm Norrström Baltic Proper Enningdalsälven N. Bullaren Skagerrak Nyköpingsån Spånga Baltic Proper Forsmarksån Johannisfors Gulf of Bothnia Motala ström Norrköping Baltic Proper Dalälven Älvkarleby Gulf of Bothnia Ljungbyån Ljungbyholm Baltic Proper Gavleån Gävle Gulf of Bothnia Alsterån Getebro Baltic Proper Ljusnan Ljusne Strömmar Gulf of Bothnia Emån Emsfors Baltic Proper Delångersån Iggesund Gulf of Bothnia Botorpström Brunnsö Baltic Proper Ljungan Skallböleforsen Gulf of Bothnia Gothemsån Hörsne Baltic Proper Indalsälven Bergeforsen Gulf of Bothnia Mörrumsån Mörrum Baltic Proper Ångermanälven Sollefteå Gulf of Bothnia Lyckebyån Lyckeby Baltic Proper Gide älv Gideåbacka Gulf of Bothnia Skivarpsån Skivarp Baltic Proper Lögde älv Lögdeå Gulf of Bothnia Kävlingeån Högsmölla Baltic Proper Öre älv Torrböle Gulf of Bothnia Helgeån Hammarsjön Baltic Proper Ume älv Stornorrfors Gulf of Bothnia Råån Helsingborg Öresund Rickleån Rickleån outflow Gulf of Bothnia Rönneå Klippan Kattegat Skellefte älv Kvistforsen Gulf of Bothnia Smedjeån V. Mellby Kattegat Pite älv Bölebyn Gulf of Bothnia Lagan Laholm Kattegat Alterälven Norrfjärden Gulf of Bothnia Nissan Halmstad Kattegat Lule älv Luleå Gulf of Bothnia Ätran Falkenberg Kattegat Kalix älv Karlsborg Gulf of Bothnia Viskan Åsbro Kattegat Töre älv Bölträsket inflow Gulf of Bothnia Göta älv Alelyckan Kattegat Torne älv Mattila Gulf of Bothnia Göta älv Trollhättan Kattegat Råne älv Niemisel Gulf of Bothnia Bäveån Uddevalla Skagerrak

9 S-HYPE performance: annual means

10 S-HYPE performance: seasonal components for total N

11 S-HYPE performance: seasonal components for total P

12 Prediction of anomalies, i.e the irregular components of a time series Which is the best predictor of observed anomalies? S-HYPE anomalies or water discharge anomalies?

13 S-HYPE performance: monthly anomalies for total N Measured anomalies are sometimes slightly better correlated to modelled anomalies than to water discharge anomalies

14 S-HYPE performance: annual anomalies for total N Measured anomalies are sometimes better correlated to modelled anomalies than to water discharge anomalies

15 S-HYPE performance: annual anomalies for total P With few exceptions, measured anomalies are poorly correlated to both modelled anomalies and water discharge anomalies

16 Weather-normalized annual anomalies of total N Red solid line: modelled anomalies Blue dashed line: observed anomalies

17 Mann-Kendall tests for monotone trends: unadjusted and weather-normalized total N data Annual measured anomaly (Total N: mg/l) Annual regression residual (Total N: mg/l) River/Sea area p-value (twosided) Significance code Slope (change/year) p-value (twosided) Significance code Slope (change/year) Mean total N (mg/l) Enningdalsälv Bäveån Örekilsälven Skagerrak Göta älv, Trollhättan Göta älv, Alelyckan Lagan Nissan Ätran Viskan Rönneå Smedjeån Kattegat Kävlingeån Råån Öresund

18 Mann-Kendall tests for monotone trends: unadjusted and weather-normalized total N data Annual measured anomaly (Total N: mg/l) Annual regression residual (Total N: mg/l) River/Sea area p-value (twosided) Significance code Slope (change/year) p-value (twosided) Significance code Slope (change/year) Mean total N (mg/l) Norrström, Centralbron Alsterån Norrström, Norrström Botorpström Mörrumsån Motala ström Emån Lyckebyån Nyköpingsån Helgeån Ljungbyån Gothemsån Skivarpsån Baltic Proper

19 Mann-Kendall tests for monotone trends: observed total-p anomalies and residuals after regressing on S-HYPE anomalies Annual measured anomaly (Total P: mg/l) Annual regression residual (Total P: mg/l) River/Sea area p-value (twosided) Significance code Slope (change/year) p-value (twosided) Significance code Slope (change/year) Mean total P (mg/l) Enningdalsälv Bäveån Örekilsälven Skagerrak Göta älv, Trollhättan Göta älv, Alelyckan Lagan Ätran Nissan Viskan Rönneå Smedjeån Kattegat Kävlingeån Råån Öresund Alsterån Emån Botorpström Mörrumsån Lyckebyån Ljungbyån Norrström, Centralbron Motala ström Helgeån Norrström, Norrström Nyköpingsån Gothemsån Skivarpsån Baltic Proper

20 Mann-Kendall tests for monotone trends: observed total-p anomalies and residuals after regressing on S-HYPE anomalies Annual measured anomaly (Total P: mg/l) Annual regression residual (Total P: mg/l) River/Sea area p-value (twosided) Significance code Slope (change/year) p-value (twosided) Significance code Slope (change/year) Mean total P (mg/l) Indalsälven Skellefte älv Lule älv Ångermanälven Ume älv Delångersån Ljungan Ljusnan Pite älv Råne älv Gide älv Dalälven Kalix älv Rickleån Öre älv Torne älv Forsmarksån Lögde älv Gavleån Alterälven Töre älv Gulf of Bothnia

21 Level shifts in particulate and soluble P: in low total P rivers

22 Statistical break-point analysis of particulate P anomalies Mean concentration of total phosphorus (mg/l) Level shifts in standardized concentration anomalies Magnitude Two-sided p-value Level shifts in standardized regression residuals Magnitude Two-sided p-value > Mixed linear model with quadratic mean function and a level shift between 2001 and 2002

23 Conclusions Joint analysis of model outputs and measured total N and total P has: Helped to attribute trends in total N to measures in agriculture Helped to reveal a break-point in particulate P Strengthened the need for better feedback from modelling yo monitoring Raised the question if flow adjustment of riverine loads can be substituted for procedures involving joint analysis of measured data and model outputss

24 Reference Grimvall, A., von Brömssen, C. and Lindström, G. (2014) Using process-based models to filter out natural variability in observed concentrations of nitrogen and phosphorus in river water. Environmental Monitoring and Assessment. 186:

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