Use of very high resolution climate model data for hydrological modelling: baseline performance and future flood changes

Abstract

193-208. 10.1007/s10584-015-1455-6 Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. AbstractIncreasingly, data from Regional Climate Models (RCMs) are used to drive hydrological models, to investigate the potential water-related impacts of climate change, particularly for flood and droughts. Generally, some form of further downscaling of RCM data has been required, but recently the first decadallength runs of very high resolution RCMs (with convection-permitting scales) have been performed. Here, a set of such runs for southern Britain has been used to drive a gridded hydrological model. Results using a 1.5km RCM nested in a 12km RCM driven by European-reanalysis boundary conditions show that the 1.5km RCM generally performs worse than the 12km RCM for simulating river flows in 32 example catchments. The clear spatial patterns of bias are consistent with bias patterns shown in the RCM precipitation data. Results using 1.5km and 12km RCM runs for the current climate and a potential future climate (driven by GCM boundary conditions) show clear differences in projected changes in flood peaks. The 1.5km RCM tends towards larger increases than the 12km RCM, particularly in spring and winter. If robust, this could have important consequences for adaptation planning under climate change, but further research is required, particularly given the greater biases in the baseline flow simulations driven by 1.5km RCM data, and the use of only a single short future climate projection.

SPREAD: a high-resolution daily gridded precipitation dataset for Spain – an extreme events frequency and intensity overview

“…High-resolution spatiotemporal precipitation datasets are useful tools for land management, and their availability over a complete region can be useful to many other fields due to the relevance of precipitation in many disciplines as hydrology (for instance in water resources management at catchment scale) (e.g. Werner and Cannon, 2016;Kay et al, 2015;Lorenz et al, 2014;Maurer et al, 2002), environmental risks such as droughts (e.g. Touchan et al, 2011;Vicente-Serrano et al, 2010a;Bordi et al, 2009;Andreadis et al, 2005), floods (e.g.…”

Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data

“…Metrics such as the Palmer drought severity index (PDSI; Palmer, 1965) use potential evapotranspiration (PET) as an input to represent AED, while many hydrological models such as Climate and Land use Scenario Simulation in Catchments (CLASSIC; Crooks and Naden, 2007) or Gridto-Grid (G2G; Bell et al, 2009), which also require an input representing AED, use a distinct form of the PET which includes the intercepted water from rainfall (this is described later in the text) which we hereby name PETI. While hydrological models can make use of high-resolution topographic information and precipitation datasets, they are often driven with PET calculated at a coarser resolution (Bell et al, 2011(Bell et al, , 2012Kay et al, 2015). Therefore, we have also created a 1 km × 1 km resolution dataset, the Climate Hydrology and Ecology research Support System Potential Evapotranspiration dataset for Great Britain (1961Britain ( -2012) (CHESS-PE; Robinson et al, 2015a), consisting of estimates of PET and PETI, which can be used to run high-resolution hydrological models.…”

Investigating Alternative Climate Data Sources for Hydrological Simulations in the Upstream of the Amu Darya River

“…One of the most common ways of determining quality is to assess the accuracy of the data source and test its performance in a hydrologic model, or uncertainty assessments of the potential impacts of weather inputs for model prediction using latent variables [11], simultaneous data assimilation and parameter estimation [12] and using probabilistic techniques such as Bayesian Model Averaging (BMA) or the Integrated Bayesian Uncertainty Estimator (IBUNE) [13,14]. Most studies have focused on evaluating the performance of grid-based precipitation data in simulating hydrologic processes [15][16][17][18][19][20][21][22][23][24][25], while others have focused on evaluating the performances of different parameters in one data set in simulating hydrologic processes [26][27][28][29]. Some studies have evaluated the respective performances of different variables associated with multisource grid-based data in hydrologic modeling [30,31].…”