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ABSTRACT:Primary Productivity is the ultimate source of energy for all organisms in an ecosystem. It is associated with the food production and the global carbon cycle. Sensors on remote platforms (satellites) are capable of estimating the Chlorophyll-a concentration in surface waters by measurement of spectral changes of the upwelling light. From these data, which connected with other remotely sensed data, it is possible to use algorithms to estimate the primary production. In this paper, an initial attempt is made to estimate the Primary Productivity along the east coast of India. Vertically Generalized Productivity Model (VGPM) which is a depth (euphotic depth) integrated model is used for the estimation. The common input variables or geophysical parameters used for the model are chlorophyll-a concentration (chl-a), vertically diffuse attenuation coefficient (Kd-490), Photosynthetically Available Radiation (PAR), and Sea Surface Temperature (SST). The chlorophyll-a and Kd-490 parameters were estimated using Oceansat-2 OCM data whereas PAR and SST were taken from MODIS-aqua data. Oceansat-2 Ocean Colour Monitor (OCM) data for the year 2013 is used in the analysis to compute the primary productivity using the weekly (8-day) data products of all the parameters as mentioned above. These products were inter compared with the MODIS Weekly (8-day) Primary Productivity products which were estimated at a global scale using the modified Vertically Generalized Productivity Model (VGPM) with which uses the exponential function of Sea surface temperature (SST).

In this study, an event based rainfall runoff model has been integrated with Single objective Genetic Algorithm (SGA) and Multi-objective Genetic Algorithm (MGA) for optimization of calibration parameters (i.e. saturated hydraulic conductivity (K s ), average capillary suction at the wetting front (S av ), initial water content (θ i ) and saturated water content (θ s )). The integrated model has been applied for Harsul watershed located in India, and Walnut Gulch experimental watershed located in Arizona, USA. Nash-Sutcliffe Efficiency (NSE) and correlation coefficient (r) between observed and simulated runoff have been used to test the performance of runoff models. The SGA and MGA integrated runoff model performance is also compared with the performance of the Hydrologic Engineering Center-Hydrologic Modeling System (HEC_HMS) model. Range of NSE values for study watersheds with integrated MGA, integrated SGA, HEC_HMS and for the event based rainfall runoff models are [−0.61 to 0.79], [−0.5 to 0.74], [−3.37 to 0.82] and [−5.78 to 0.53] respectively. Range of correlation coefficient values for study watersheds with integrated MGA, integrated SGA, HEC_HMS and for the event based rainfall runoff models are [0.18 to 0.95], [−0.55 to 0.90], [−0.18 to 0.97] and [−0.12 to 0.86] respectively. From the results, it is evident that the integrated model is giving the best calibrated parameters as compared to manual calibration methods. Genetic Algorithm (GA) integrated runoff models can be used to simulate the flow parameters of data sparse watersheds.

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