Abstract
The analyses of water resources availability and impacts are based on the study over time of meteorological and hydrological data trends. In order to perform those analyses properly, long records of continuous and reliable data are needed, but they are seldom available. Lack of records as in gaps or discontinuities in data series and quality issues are two of the main problems more often found in databases used for climate studies and water resources management. Flow data series from gauging stations are not an exception. Over the last 20 years, forecasting models based on artificial neural networks (ANNs) have been increasingly applied in many fields of natural resources, including hydrology. This paper discusses results obtained on the application of cascade-correlation ANN models to predict daily water flow using Julian day and rainfall data provided by nearby weather stations in the Ebro river watershed (Northeast Spain). Five unaltered gauging stations showing a rainfall-dominated hydrological regime were selected for the study. Daily flow and weather data series covered 30 years to encompass the high variability of Mediterranean environments. Models were then applied to the in-filling of existing gaps under different conditions related to the characteristics of the gaps (6 scenarios). Results showed that when short periods before and after the gap are considered, this is a useful approach, although no general rule applied to all stations and gaps investigated. Models for low-water-flow periods provided better results (r = 0.76–0.8).
Highlights
Water resources management is based on the study over time of meteorological and hydrological data trends
Best models for each gauging station (GS), scenario, and gap are presented in Tables 4–8, if the models were judged robust and adequate according to the performance criteria (r and root mean square error (RMSE) in tables, absolute maximum error (AME) and mean absolute error (MAE) data not shown)
Cascade-correlation neural network models (CCANNs) were built for the estimation of daily water flow in five gauging stations with rainfall-dominated natural hydrological regime located in watersheds of the Ebro river
Summary
Water resources management is based on the study over time of meteorological and hydrological data trends. Lack of records (gaps) or discontinuities in data series and quality issues are two of the main problems more often found in databases used for climate studies and water resources management [1,2,3,4,5,6], especially in mountain regions with limited meteorological monitoring and abundant precipitation often associated to extreme events [7]. Flow data series from gauging stations are affected by these problems. Unusual flood events may cause breakdowns and failures in the gauging stations which usually result in gaps in the daily flow data series, for instance. Even very short gaps may compromise the calculation of statistics and data utility [8]
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