In this section we discuss the impact of river runoff on SST, SSS, MLD and stability defined in our study region (0º–26ºN, 50º–105ºE). The influence of river runoff on the ocean subsurface parameters is also analyzed. The seasonal averages are computed as December-January-February (DJF), March-April-May (MAM), July-August (JA), and September-October (SO) months. In order to discuss the model validation against the observations, we compared the seasonal mean model results of SST and SSS with that of ocean reanalysis system 4 (ORAS4) (
Balmaseda et al., 2013). The surface current is validated with Ocean Surface Current Analysis Real-time (OSCAR) data. The OSCAR (
Bonjean and Lagerloef, 2002) currents are taken on global 1/3 degree grid with a 5 d resolution. The correlation between CTRL SST and ORAS4 SST is found to be 0.85 and root mean square error (RMSE) of the CTRL run and ORAS4 to be 0.8°C. The standard deviation (SD) of ORAS4 SST is 1.2°C. The RMSE is much smaller than the SD which implies quality of our model output is good. The SSS correlation coefficient between the CTRL run and the ORAS4 is 0.91. The RMSE of the CTRL run w.r.t ORAS4 SSS is 0.35 which is much smaller than the standard deviation 0.85 of the ORAS4 SSS, which further suggests that the quality of our model output is reasonably good. The detailed validation of the model output against different observations has been documented in
Gera et al. (2013) and it is found that model simulations agree well with the observations in the Indian Ocean.
Figure 1a shows the bathymetry (shaded) and the major adjoining rivers (black shaded boxes) in the study region.
Figure 1b shows the monthly river runoff and freshwater flux (P-E) in the BoB and AS. BoB receives more river runoff than AS through-out the year. BoB receives maximum (19 mm/d) river runoff in mid of July and August (
Jana et al., 2015) while minimum in January (1.9 mm/d). Similarly, AS also receives the maximum (2.2 mm/d) and minimum (0.1 mm/d) river runoff in the same months but the magnitudes are much less compared to that in BoB. From
Fig. 1b we observe that low salinities in the BoB region is mainly due to river runoff. Freshwater flux (P-E) also plays an important role in maintaining low salinity in the BoB. However the contribution of river runoff is more than that of P-E in the the BoB.
Figure 2 shows the latitude-depth plot of upper ocean temperature section at 90ºE from model experiments and ORAS4 and the difference from the observation. From this figure, we observe that temperature decreases northward during DJF and MAM season in both CTRL and ORAS4 while in JA and SO season same variability is observed from 10ºN to 15ºN. We also observe from this figure that in DJF season ORAS4 shows 27°C temperature at 80 m while in CTRL run it occurs at a shallower depth of 60 m.
Figure 3 shows latitude depth comparison of ORAS4 (1st row), CTRL (2nd row), NROF (3rd row) and their difference (CTRL-ORAS4; 4th row) salinity at 90ºE. From
Fig. 3, we find that CTRL run captures the salinity variability very well in all seasons because of the model’s high vertical resolution. The CTRL run has marginally overestimated the salinity (0.5) from 0º–10ºN from surface to subsurface in DJF season with respect to ORAS4. In DJF and SO season CTRL run shows marginally negative bias (0.5) from 16º–22ºN. However, in MAM and JA seasons very small differences (0.3) in salinity are observed. Therefore, the model gives a good salinity state representation in the vertical in BoB. Hence, it is understood that our model configuration is reasonably good to study the impact of river runoff in BoB.