Spatial Interpolation of Annual Runoff in Ungauged Basins Based on the Improved Information Diffusion Model Using a Genetic Algorithm

Abstract

Prediction in Ungauged Basins (PUB) is an important task for water resources planning and management and remains a fundamental challenge for the hydrological community. In recent years, geostatistical methods have proven valuable for estimating hydrological variables in ungauged catchments. However, four major problems restrict the development of geostatistical methods. We established a new information diffusion model based on genetic algorithm (GIDM) for spatial interpolating of runoff in the ungauged basins. Genetic algorithms (GA) are used to generate high-quality solutions to optimization and search problems. So, using GA, the parameter of optimal window width can be obtained. To test our new method, seven experiments for the annual runoff interpolation based on GIDM at 17 stations on the mainstream and tributaries of the Yellow River are carried out and compared with the inverse distance weighting (IDW) method, Cokriging (COK) method, and conventional IDMs using the same sparse observed data. The seven experiments all show that the GIDM method can solve four problems of the previous geostatistical methods to some extent and obtains best accuracy among four different models. The key problems of the PUB research are the lack of observation data and the difficulties in information extraction. So the GIDM is a new and useful tool to solve the Prediction in Ungauged Basins (PUB) problem and to improve the water management.

Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review

“…Their final results demonstrated similar performances with both approaches, showing that the geomorphology‐based inversion approach is as reliable as the spatial proximity approach. Different from the regionalization comparison studies, the studies on the spatial proximity approach combining with other regionalization methods like physical similarity (Razavi & Coulibaly, 2017), regression‐based (Castellarin et al, 2018; Steinschneider, Yang, & Brown, 2015) method, as well as new techniques such as data assimilation (Pugliese et al, 2018), machine learning (Hong, Zhang, Wang, Qian, & Hu, 2017), have also gradually appeared in the last decade. At the same time, new improvements based on traditional spatial proximity approaches such as three‐dimensional canonical Kriging (Castellarin, 2014) and the streamflow–streamflow (Q–Q) method (Andréassian, Lerat, Le Moine, & Perrin, 2012) showed good performance in some areas, which needs further evaluation.…”

Climate Change Impact on Hydropower Resources in Gauged and Ungauged Lithuanian River Catchments

“…Information transfer from gauged to ungauged Lithuanian river catchments was performed using isoline maps created by interpolating specific runoff derived from the hydrological modeling. Such spatial proximity approach is one of the earliest and most widely used regionalization methods [38][39][40]. This method enabled us to get the data of ungauged catchments necessary for the projection described in the paper.…”

Modelling bivariate extreme precipitation distribution for data‐scarce regions using Gumbel–Hougaard copula with maximum entropy estimation

“…For example, MEE still provided a good result with only 5 or 10 samples, whereas MLE was proved unreliable with only 5 or 10 samples. MEE requires only the lower and upper bounds of two hydrological variables. Many methods can be used to estimate the upper or lower bounds of hydrological variables, such as the two‐site joint probability approach for transfer of hydrological information between two stations (Wang, ), a new information diffusion model for spatial interpolation of hydrological variables (Hong et al, ), and other spatial interpolation methods.…”

“…Spatial interpolation methods, such as kriging and its related algorithms, may be used to estimate the upper bounds of hydrological variables based on the values of neighbouring stations. Hong, Zhang, Wang, et al () proposed a new information diffusion model based on the genetic algorithm to spatially interpolate the run‐off in ungauged basins, and the new model can obtain a high level of accuracy based on sparse observed data. Therefore, the upper or lower bounds of hydrological variables can be estimated in data‐scarce regions.…”