Nutrient Transport Modelling in the Daugava River Basin - DiVA Portal

More documents

Recommendations

Info

4.2 IMPACT OF THE CURVE NUMBER ON NUTRIENT SOURCEAPPORTIONMENTThe model was run with different choices of curve numbers according to Approach Aand B. The curve numbers are presented in table 3.Table 3. The two sets of curve numbers usedLand UseCurve numberApproach A Approach BArtificial surfaces 100 100Cultivated land 80 70Herbaceous land 80 70Lichens and wetlands 80 70Coniferous forest 80 60Mixed forest 80 60Deciduous forest 80 60Water 100 100Figure 9 illustrates the impact of the curve number on the distribution of total nitrogenloads from different land uses. The total nitrogen load includes surface runoff anderosion from each land use. Water surfaces and cultivated land are the maincontributors. With approach A cultivated land makes up most of the nutrient loads(71 %) and water surfaces make up 23 %. With approach B the relative impact of watersurfaces is enlarged and makes up 54 % of the total load while the relative impact ofcultivated land is lessened (46 %). The impact of forested land is smaller for approach Bthan for approach A since the curve number is the same for forested land and cultivatedland in approach A, but smaller for forested land than for cultivated land in approach B.The phosphorus source apportionment is not displayed since the model fails to predictphosphorus loads.Approach AApproach BCultivated landHerbaceous landLichens and w etlandsConiferous forestMixed forestDeciduous forestWaterCultivated landHerbaceous landLichens and w etlandsConiferous forestMixed forestDeciduous forestWaterFigure 9. The relative contribution to nitrogen loads from each land usewith different methods for choosing the curve number.4.3 SENSITIVITY ANALYSISThe result of the sensitivity analysis is presented in figure 10. The nitrogenconcentration in groundwater box 1, C1 g N, is the one of the analysed parameters whosevalue has the greatest impact on the nutrient load. A change of this value by 50 % willchange the nitrogen load by 27 %. The variable that has the least impact on the nitrogenload is the recession coefficient for box 2, r2. A change of the value of this coefficientby 50 % will change the nitrogen load by less than 1 %. It should be noted that thechange in nutrient load will be directly proportional to the change in the nitrogen18
0 1 2 3 4 5 6concentration in box 1 and 2 due to the linearity of the model equations that determinenutrient loads from the groundwater.Change in monthly nitrogen load, %1401301201101009080700.50.7511.251.560r1 r2 gr C1gN C2gNFigure 10. Result of the sensitivity analysis. On the xaxis are theanalysed parameters. The yaxis shows the average change in monthly nitrogen loadover 10 years when the parameter values have been multiplied by the factors 0.5, 0.75,1, 1.25 and 1.5, respectively.5. DISCUSSIONThe aim of the model is to predict longterm nutrient loads from a watershed. Themodel predicts yearly nitrogen loads from the Daugava basin quite well. The meanabsolute percentage error of estimations of yearly nitrogen loads during the years 19931999 is 18 %, and the corresponding R 2 value is 0.78. Considering the simplicity of themodel and the defectiveness of input data this can be considered a quite good result.During the 1980’s (validation period) the model underestimates nitrogen loads byapproximately 30 % even though streamflow is quite well predicted during this period.A reason for this may be that the surface runoff and groundwater nitrogenconcentrations were higher during the 1980’s than during the 1990’s. This is probablesince the use of fertilisers, especially in the Latvian part of the basin, droppeddramatically in the beginning of the 1990’s due to the collapse of the Soviet Union.There has been a negative trend in nitrogen loads from the Daugava during the years19871998 (Stålnacke et al., 2003). Since the nutrient concentration in the GWLF modelis constant, the model can never predict trends in nutrient concentration due to changesin the groundwater or surface runoff concentrations. In that sense the model is static andnot suitable for longterm nutrient load predictions.The GWLF model, as implemented, fails to predict phosphorus loads. The modelledphosphorus loads are about 90 % less than reported loads. One reason could be thatphosphorus concentrations in surface runoff and groundwater used were too low.However, in order to reach the observed levels, the input concentrations in surfacerunoff and groundwater needed to be increased almost by a magnitude, which does notseem reasonable. A possible cause to this discrepancy could instead be anunderestimation of phosphorus loads from erosion. The erosion factor, the rainfallerosivity and the sediment delivery ratio are dependent on parameters such asagricultural management practices and crop coverage. Since no such data were available19
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 20 and 21: There is therefore no guarantee tha
Page 22 and 23: a) Streamflow, calibration periodb)
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

Nutrient Transport Modelling in the Daugava River Basin - DiVA Portal

Create successful ePaper yourself

Delete template?

Save as template?