Abstract
Abstract. The scientific initiative Prediction in Ungauged Basins (PUB) (2003–2012 by the IAHS) put considerable effort into improving the reliability of hydrological models to predict flow response in ungauged rivers. PUB's collective experience advanced hydrologic science and defined guidelines to make predictions in catchments without observed runoff data. At present, there is a raised interest in applying catchment models to large domains and large data samples in a multi-basin manner, to explore emerging spatial patterns or learn from comparative hydrology. However, such modelling involves additional sources of uncertainties caused by the inconsistency between input data sets, i.e. particularly regional and global databases. This may lead to inaccurate model parameterisation and erroneous process understanding. In order to bridge the gap between the best practices for flow predictions in single catchments and multi-basins at the large scale, we present a further developed and slightly modified version of the recommended best practices for PUB by Takeuchi et al. (2013). By using examples from a recent HYPE (Hydrological Predictions for the Environment) hydrological model set-up across 6000 subbasins for the Indian subcontinent, named India-HYPE v1.0, we explore the PUB recommendations, identify challenges and recommend ways to overcome them. We describe the work process related to (a) errors and inconsistencies in global databases, unknown human impacts, and poor data quality; (b) robust approaches to identify model parameters using a stepwise calibration approach, remote sensing data, expert knowledge, and catchment similarities; and (c) evaluation based on flow signatures and performance metrics, using both multiple criteria and multiple variables, and independent gauges for "blind tests". The results show that despite the strong physiographical gradient over the subcontinent, a single model can describe the spatial variability in dominant hydrological processes at the catchment scale. In addition, spatial model deficiencies are used to identify potential improvements of the model concept. Eventually, through simultaneous calibration using numerous gauges, the median Kling–Gupta efficiency for river flow increased from 0.14 to 0.64. We finally demonstrate the potential of multi-basin modelling for comparative hydrology using PUB, by grouping the 6000 subbasins based on similarities in flow signatures to gain insights into the spatial patterns of flow generating processes at the large scale.
Highlights
Numerical hydrological models have been used worldwide for operational needs and scientific research since the early 1970s (e.g. Hrachowitz et al, 2013; Pechlivanidis et al, 2011; Refsgaard et al, 2010; Singh, 1995)
Takeuchi et al (2013) recommend a six-step procedure for predicting runoff at locations where no observed runoff data are available (Fig. 1a). This best practice recommendation is intended for single catchments and requires modification when applied to multi-basins at the large scale (Fig. 1b)
For the Indian subcontinent, the following groups of HYPE parameters were calibrated stepwise: (i) general parameters, which significantly affect the water balance in the system, snowpack and distribution, and regional discharge; (ii) soil- and land-use-dependent parameters, which can influence the dynamics of the flow signal, groundwater levels, and transit time; (iii) regional parameters, which are applied as multipliers to some of the general soil and landuse parameters and may be seen as downscaling parameters as they compensate for the scaling effects and/or other types of uncertainty
Summary
Numerical hydrological models have been used worldwide for operational needs and scientific research since the early 1970s (e.g. Hrachowitz et al, 2013; Pechlivanidis et al, 2011; Refsgaard et al, 2010; Singh, 1995). We (a) identify specific challenges at the large scale (uncertain/erroneous basin delineation and routing, errors in global data sets, human impact; i.e. reservoirs/dams) and exemplify how to overcome them, (b) further develop and modify the PUB best practices to be applicable at the large scale, (c) illustrate the improvement on parameter identification by using remote sensing data and expert knowledge, (d) cluster catchments based on physiographic similarity and their hydrological functioning, (e) ensure model reliability using flow signatures and temporal variability of multiple modelled variables, (f) detect links between model performance and physiographical characteristics to understand model inadequacies along the gradient, and (g) discuss how process understanding can benefit from multi-basin modelling and what hydrological insights can be gained by analysing spatial patterns from largescale predictions in ungauged basins. We use examples from the recent HYPE (Hydrological Predictions for the Environment) model set-up of the Indian subcontinent, which experiences unique and strong hydroclimatic and physiographic characteristics and poses extraordinary scientific challenges to understand, quantify, and predict hydrological responses
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