Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case

Abstract

Abstract. The scientific initiative Prediction in UngaugedBasins (PUB) (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(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.

Accelerating advances in continental domain hydrologic modeling

“…In Table 1 we present, from our own perspective, the extent to which these communities meet the modeling conditions for continental domain hydrologic models and highlight the contributions and weaknesses of each community in this context. [Reed et al, 2004;Smith et al, 2013], though recent applications extend catchment hydrologic models to large river basins [Arheimer et al, 2012;Weiskel et al, 2014] and even continental domains [Donnelly et al, 2015;Pechlivanidis and Arheimer, 2015] through the leveraging of continental and global domain forcings and geophysical data sets [Colombo et al, 2007;Atkinson et al, 2008;Viger, 2014;Viger and Bock, 2014;Newman et al, 2015] and providing a consistent approach to estimate spatially variable model parameter values [Kumar et al, 2013b;Samaniego et al, 2010]. These large-domain applications allow consistent spatial comparisons while still providing model results at the spatial scale needed for water management decisions.…”

Multi-Basin Modelling of Future Hydrological Fluxes in the Indian Subcontinent

“…timing and volume, are better represented than variability during the evaluation period. Further analysis and discussion on model performance and consistency can be found in [40]. …”

“…To overcome such a problem, we use global datasets to extract the information required for hydrological applications (see Table 1 in [40]). …”

“…The years 1969-1970 were used as a model warm-up period, the next five years for model calibration (1971)(1972)(1973)(1974)(1975)) and the final four years for temporal performance evaluation (1976)(1977)(1978)(1979). The model's predictability was tested using various performance measures (i.e., objective functions and flow signatures) and additional data sources (i.e., remote sensing potential evapotranspiration records); see details in [40]. Here, we assess the model's predictability based on the Nash-Sutcliffe Efficiency, NSE [49] and relative error, RE (defined as the difference between the mean modelled value and the mean observed value divided by the mean observed value).…”

A geostatistical data-assimilation technique for enhancing macro-scale rainfall-runoff simulations

“…surface water availability assessment, instream water quality studies, ecohydrological studies, etc. ); (2) they can be used to compute a variety of hydrological signatures everywhere along the stream network at the resolution of the model; (3) model outputs in some cases are open-access and freely distributed, their regional runs represent a wealth of information for addressing the problem of hydrological predictions in data scarce regions of the world (Pechlivanidis and Arheimer, 2015;Donnelly et al, 2016;Beck et al, 2016). Accurate regional hydrological 15 simulations undoubtedly foster and support the implementation of improved large-scale and trans-boundary policies for water resources system management and flood-risk mitigation or climate change adaptation (de Paiva et al, 2013;Sampson et al, 2015;Falter et al, 2016;Arheimer et al, 2017).…”