Contents lists available at ScienceDirect Physics and Chemistry of the Earth journal homepage: www.elsevier.com/locate/pce Assessment of empirical and regression methods for infilling missing streamflow data in Little Ruaha catchment Tanzania Stanislaus Kamwaga, Deogratias M.M. Mulungu * , Patrick Valimba University of Dar es Salaam, College of Engineering and Technology, Department of Water Resource Engineering, P.O. Box 35131, Dar es Salaam, Tanzania ARTICLE INFO Keywords: Infilling data Little Ruaha river catchment Great Ruaha river sub-basin Rufiji river basin Streamflow ABSTRACT Water resources and engineering projects are of major importance due to great demand of water and power supplies, irrigation needs, drought mitigation and flood control. In order to plan and design these projects, complete and reliable hydrological datasets are required. Missing data can severely compromise data quality and utility. The Rufiji Water Basin Office has developed and kept a database of daily streamflow records from 1950s to-date for at least 87 gauging stations. While the majority of the records are complete, data checks revealed significant gaps. Some stations are experiencing large gaps of up to 19 consecutive years. There is no dedicated study that looked at assessment of suitable methods for infilling such gaps. This study therefore made a contribution by appraising empirical and regression rainfall runoff based methods in the Little Ruaha River catchment, a sub-catchment of the Great Ruaha River sub-basin both within the Rufiji River Basin. The methods employed included simple linear regression (with untransformed and log- transformed data), multiple linear regression (with untransformed and log-transformed data), rainfall-runoff relationship using double mass curve technique, flow duration matching and drainage-area ratio. In addition, rainfall runoff modeling using HBV-Light was done for further comparison. With exception to the rainfall-runoff relationship and HBV-Light model, all other methods relied upon data transfer from donor stations (upstream & downstream station(s)) for infilling a downstream/upstream station. Data quality and consistency checks were performed, and performances of infilling methods were evaluated based on three performance criteria namely Nash-Sutcliffeefficiency coefficient (NSE), Coefficient of determination (R 2 ) and standard error of estimate (SE) during calibration and validation periods. Four gauging stations (2 each upstream and downstream) were se- parately used to infill artificially created gaps to the target station. Overall, the calibration and validation daily results at 1KA21A indicated that the flow duration matching technique and multiple linear regression methods performed better than other methods with NSE (71%; 93%) and NSE (55%; 75%) respectively. These results have a potential for wide application in other basins of Tanzania for hydrological analysis and water resource management, where missing data is very common. 1. Introduction Water is a resource, which appears in different forms such as in surface streams, in groundwater or in lakes and reservoirs. The devel- opment of these resources for a variety of purposes (including water supply, irrigation hydropower, flood protection and the like) is the basis for socio-economic development of a society. As such, hydrological data are the fundamental basis for the design of all types of water resources development projects. Adequate and long time series of hydrologic data are essential in the planning of development schemes, the design of hydraulic structures and the optimum development and management of water resources (Aregahegn, 2003). To plan and design these projects, complete datasets are necessary and required on many variables such as rainfall, streamflow, evapora- tion, temperature, etc. Also, complete datasets are required for re- construction of past climate, hydrologic history of a site and modelling and management of water resources system (Teegavarapu, 2012). (Machiwal and Jha, 2012). Unfortunately, records of hydrological data in developing countries, Tanzania in particular, are usually short and often have breaks (e.g. Mbungu et al., 2012; Natkhin et al., 2015; Ndomba, 2014), to the extent that the gaps prolong for many months. Moreover, Vaze et al. (2012) indicated that water level observations for flow estimations are only available for limited number of gauging sta- tions and for limited time span. Mwale et al. (2012) pointed out that the existence of data gaps might be attributed to a number of factors such as interruption of measurements because of instrument failure, effect of https://doi.org/10.1016/j.pce.2018.05.008 Received 30 May 2016; Received in revised form 26 March 2018; Accepted 11 May 2018 * Corresponding author. E-mail addresses: dmulungu@udsm.ac.tz, deorgm@yahoo.co.uk (D.M.M. Mulungu). Physics and Chemistry of the Earth xxx (xxxx) xxx–xxx 1474-7065/ © 2018 Elsevier Ltd. All rights reserved. Please cite this article as: Kamwaga, S., Physics and Chemistry of the Earth (2018), https://doi.org/10.1016/j.pce.2018.05.008