The application is a diagnosis method for the response of large-scale river net
primary productivity to diurnal
temperature difference, and the steps are as follows: reconstructing the
data set, constructing the
time series input index and constant input index; screening the net
primary productivity data of high-quality rivers, and calculating the
monthly average value as the output index; using the
random forest model to construct the prediction model of large-scale river net
primary productivity, and using the network iterative optimization method to obtain the optimal
model parameters; driving the trained
random forest model to obtain the prediction data of river net primary productivity under different diurnal temperature differences and corresponding daily average
water temperature; calculating the difference between the maximum value of net primary productivity and the current prediction data to determine the optimal
temperature difference; comparing the current diurnal
temperature difference with the optimal temperature difference to calculate the increment of fluctuation amplitude and the respiratory carbon emission. The application determines the response function of river net primary productivity to diurnal temperature difference change through the control experiment, and reveals the influence mechanism of diurnal asymmetric warming on net primary productivity.