Nutrient Transport Modelling in the Daugava River Basin - DiVA Portal

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There is therefore no guarantee that the calibrated values are the optimal values. Whenthe calibration was complete the validation was done on data from the 1980’s.When the hydrological part of the model was calibrated, the next step was to addnutrient concentrations in groundwater and surface runoff, erosion of solid nutrients andnutrient loads from point sources. The concentration in the groundwater boxes 1 and 2were used as calibration parameters. Focus was placed on achieving the best fit forloads, but concentrations were also studied. The calibration was done in the same wayas for the hydrological part; by studying the quotient between predicted and observedaverage values, the average percentage error and how well top flows/concentrationswere predicted.As for streamflow, validation was done on data from the 1980’s.3.5 IMPACT OF THE CURVE NUMBER ON NUTRIENT SOURCEAPPORTIONMENTThe curve number (see Appendix B) determines the fraction of precipitation that will goto surface runoff. The curve number varies between 0 and 100. Curve number 0 meansthat all precipitation will infiltrate and curve number 100 means that all precipitationwill go to surface runoff. There are different views on how to choose the curve numberfor each land use. In the original GWLF manual curve numbers are suggested fordifferent land use types. However, in communication with Stefan Löfgren (2005) it wasdecided that in this study the same curve number should be used for all vegetationcoveredsurfaces. Since the curve number determines surface runoff and since surfacerunoff is the main contributor to nutrient loads from a certain land use, the curvenumber will have a large impact on the nutrient source apportionment from differentland uses. In order to illustrate this, the model was run both with the calibrated curvenumbers that were used in the rest of the study (Approach A) and with the curvenumbers suggested by the original GWLF manual (Approach B). The nutrientcontributions from each land use were studied for the two cases.3.5 SENSITIVITY ANALYSISA sensitivity analysis was conducted in order to determine which model parametershave the greatest influence on the output. The parameters chosen for the sensitivityanalysis were recession coefficients 1 and 2 (r1 and r2), the coefficient for groundwaterflow between box 1 and 2 (gr) and the nutrient concentration in groundwater box 1 and2 (C1 g N and C2 g N). The output that was studied was the average monthly load ofnutrients. The calibrated values of the chosen parameters were multiplied by a fixedfactor one at a time and the impact on the average load of nutrients was studied. Thefactors chosen were 0.5, 0.75, 1.25 and 1.5.4. RESULTS4.1 CALIBRATION AND VALIDATIONThe result of the modelling can be seen in figure 6, where observed and modelledmonthly values for streamflow and nutrient loads are presented for the calibrationperiod (19931999) and the validation period (19801989). For the calibration periodthe model predicts streamflow and nitrogen rather well although top flows aresometimes underestimated. Streamflow is also fairly well predicted for the validation14
period. Nitrogen loads are underestimated for this period, especially during peak flows.When it comes to phosphorus the model greatly underestimates monthly loads for boththe calibration period and the validation period.a) Streamflow, calibration periodb) Streamflow, validation periodReportedModelledcm/monthton/month121086420Apr9314000120001000080006000400020000Apr94Apr95Apr96Apr97Apr98c) Nitrogen load, calibration periodApr99cm/monthton/month12108642025000200001500010000Apr80Apr8150000Apr82Apr83Apr84Apr85Apr86Apr87d) Nitrogen load, validation periodApr88Apr89Apr93Apr94Apr95Apr96Apr97Apr98Apr99Apr80Apr81Apr82Apr83Apr84Apr85Apr86Apr87Apr88Apr89e) Phosphorus load, calibration periodf) Phosphorus load, validation periodton/month10008006004002000ton/month6005004003002001000Apr93Apr94Apr95Apr96Apr97Apr98Apr99Apr80Apr81Apr82Apr83Apr84Apr85Apr86Apr87Apr88Apr89Figure 6. Reported and modelled values of monthly means of streamflow,nitrogen loads and phosphorus loads from the Daugava River for the calibration period(19932000) and the validation period (19801990).The model output is monthly means of loads and streamflow, but since the purpose ofthe model is to estimate longterm loads it is of interest to see how well the modelpredicts yearly means. In figure 7 the reported and modelled yearly means ofstreamflow and nutrient loads are presented. The model seems to estimate yearlystreamflow quite well, both for the calibration period and the validation period. Thereare, however, tendencies towards a higher variance for modelled yearly flows than forobserved yearly flows. Yearly nitrogen loads are well estimated by the model for thecalibration period, but for the validation period nitrogen loads are underestimated.15
Page 1 and 2: UPTEC W05 009Examensarbete 20 pFebr
Page 3 and 4: Nyckelord: avrinning, näringsämne
Page 6 and 7: APPENDIX C. DETERMINATION OF POTENT
Page 8 and 9: simulation system CatchmentSim. The
Page 10 and 11: Figure 1. The hydrological part of
Page 12 and 13: TemperaturePrecipitation20.00120deg
Page 14 and 15: 3.2 MODEL DESCRIPTION3.2.1 An overv
Page 16 and 17: Groundwater discharge to the river
Page 18 and 19: Groundwater nutrient loadsThe groun
Page 22 and 23: a) Streamflow, calibration periodb)
Page 24 and 25: 4.2 IMPACT OF THE CURVE NUMBER ON N
Page 26 and 27: the chosen value is a very rough ap
Page 28 and 29: all cultivated land will have winte
Page 30 and 31: HELCOM, 2004a. 30 years of protecti
Page 32 and 33: Appendix A. Input data and paramete
Page 34 and 35: Description of parameter/input Name
Page 36 and 37: Description of parameter/input Name
Page 38 and 39: moisture.Figure B1. Selection of cu
Page 40 and 41: wherea t = rainfall erosivity coeff
Page 42 and 43: Appendix F. Regression lines of rep

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