Kriging methods as a tool to estimate spring flood peak discharge in ungauged watersheds in Lithuania

Abstract

Responsible and efficient management of water resources relies on accurate hydrological data, but changing economic situation results inthe fact that natural researches are becoming more and more expensive. Decrease in network of water gauging stations leave many small watersheds ungagged and with no ability to assess and understand local hydrology. Ordinary Kriging might be used in order to assess the values between measuring points and to connect the hydrologic data with the other types of regional spatial information. Kriging algorithms use various mathematical functions for the spatial modeling of the variability of z values between known points. The parameters of these functions are then optimized for the best fit of the experimental semivariogram. The interpolated surface is then constructed using statistical conditions of unbiasedness and minimum variance. The objective of this study was to investigate the suitability and accuracy of ordinary kriging to predicted spring flood peak discharge 1% probability in ungagged watersheds. The study used data of 74 water gauging stations (WGS) on 55 rivers almost totally covering the area of Lithuania. It is concluded that ordinary kriging was useful for prediction of spring flood peak discharge data in ungauged watersheds in Lithuania. Applying the Ordinary Kriging selected all parameters with probability p=0.95.The model describedabout74 % of all investigated values