Developing best practice for infilling daily river flow data

Abstract

Complete river flow time series are indispensable to the sustainable management of water resources and even very short gaps can severely compromise data utility. Suitably-flagged flow estimates, derived via judicious infilling, are thus highly beneficial. The UK National River Flow Archive provides stewardship of and access to daily river flow records from over 1500 gauging stations and, whilst the majority are sensibly complete, historical validation reveals a significant quantity of gaps. A full assessment of the suitability of existing techniques for infilling such gaps is lacking. This paper therefore presents an appraisal of various simple infilling techniques, including regression, scaling and equipercentile analysis, according to their ability to generate daily flow estimates for 25 representative UK gauging stations. All of the techniques rely upon data transfer from donor stations and results reveal that the equipercentile and multiple regression approaches perform best. Case studies offer further insight and an example of infilling is presented, along with areas of future study. The results demonstrate the potential for developing generic infilling methodologies to ensure a consistent and auditable approach towards infilling, which could find wider application both within the UK and internationally.

Quantification of Temporal Variations in Base Flow Index Using Sporadic River Data: Application to the Bua Catchment, Malawi

“…(1) infill the missing data or (2) ignore the missing data and analyze only the raw data. Although there are merits to infilling data [50,51], most studies agree with the recommendation by Ladson et al [52] that BFI should be determined from raw data only [20,23,53,54]. As such, this study did not infill data and analyzed the raw river flow data only.…”

Forecasting GRACE Data over the African Watersheds Using Artificial Neural Networks

“…Analysis of TWS GRACE time series and other relevant time series has been successfully used in many hydrological and climatological studies including management of water resources [5,7,8,[23][24][25][26][27][28][29], and analysis, prediction, and forecasting of climate change and variability in climatic (e.g., rainfall, evapotranspiration, and wind speed), and hydrologic (e.g., stream flow, flood, drought, infiltration, recharge, and ground/surface water quantity, quality, and consumption) parameters [30][31][32][33][34][35][36]. The successful implementation of these analyses heavily relies on the availability of comprehensive, continuous, and uninterrupted time series records [37,38].…”

Use of Hydrological Models in Global Stochastic Flood Modeling

“…The data infilling was performed using either linear interpolation or a simple equipercentile infilling technique: the linear interpolation was used for gaps shorter than 20 days, while the equipercentile approach was preferred for longer series of missing data. The equipercentile method is one of the simplest ways to fill daily streamflow missing data, while still having an acceptable performance (Harvey et al., 2010); it consists of selecting a suitable donor gauge and assuming the percentile value of the donor flow as equal to the percentile value of the missing flow. The selection of the most suitable donor gauge was based on finding the closest station on the network with data.…”