The impacts of climate change on global hydrology and water resources Simon Gosling and Nigel Arnell, Walker Institute for Climate System Research, University of Reading Dan Bretherton and Keith Haines, Reading e-Science Centre, University of Reading Summary of current research Explores how uncertainties associated with climate change propagate through to estimated changes in the global hydrological cycle and water resources stresses with climate change. The application of both pattern-scaling and High Throughput Computing (HTC) is central to the research. Changes in the global hydrological cycle Different modelling institutes use different plausible representations of the climate system within their global climate models (GCMs), giving a range of climate projections for a single emissions scenario. A method of accounting for this “climate model structural uncertainty” in climate change impacts assessment is to use this range of projections from the ensemble of plausible GCMs, to produce an ensemble of impacts projections. First the patterns of climate change from each of 21 GCMs are identified associated with globally averaged warmings of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0 and 6.0°C (relative to ), 189 patterns in all. These patterns are pre-generated from the GCM output by the ClimGen model developed at UEA. This represents a large reduction of input data for the future impacts modelling, although the approach fairly assumes that the pattern of climate change simulated by GCMs is relatively constant (for a given GCM) under a range of rates and amounts of global warming. Nevertheless, the use of GCM output directly in the future would avoid this assumption. These patterns are then applied to Mac-PDM.09, a global hydrological model (GHM). In order to run the ensemble of GHM with different patterns of climate change a Grid computing solution is used. Figure 1 shows some of the results. Effects on water resources A water-resources model, which assumes that watersheds with <1000m3/capita/year are water-stressed, is used to assess global water resources stresses, for different assumptions of future population change and global warming. Figure 2 shows some of the results. Figure 2. Percentage of the global population that experiences increases in water stress for different degrees of global warming. Campus Grid enables many model runs to take place simultaneously. This is an example of High Throughput Computing. Reduced time taken for 189 runs from 32 days (single computer) to 9 hours 2 main challenges in running GCMs + MAC-PDM models on the Grid: 1 Required to make minimal changes to models for Grid execution. 2 Large amount of input and output. 160GB storage required for 189 runs. This would increase greatly if GCM forcing were used directly for the GHM simulations Total Grid storage only 600GB, shared by all users; 160GB not always available. Solution chosen was SSH File System (SSHFS - as shown in Figure 3. Scientist’s own file system was mounted on Grid server via SSH. Data transferred on demand to/from compute nodes via Condor’s remote I/O mechanism. Model remained unmodified, accessing data via file system interface. It is easy to mount remote data with SSHFS, using a single Linux command. Running models on the Reading Campus Grid The Grid is a Condor Pool ( containing library & lab computers. SSHFS limitations and alternatives Maximum simultaneous model runs was 60 for our models, implemented using a Condor Group Quota. This allowed us to submit all jobs, but only 60 were allowed to run simultaneously. Limited by Grid and data server load. Need SSH access to data server, not always possible for other institutes’ data. Software requires sys.admin. to install. We are now experimenting with Parrot following earlier work by CamGrid at the University of Cambridge. Parrot is another way to mount remote data. It talks to HTTP, FTP, GridFTP and other remote I/O services, so SSH access to data is not required. Figure 3. Using SSHFS to run models on Grid with I/O to remote file system... Campus Grid Large file system Grid storage, not needed Grid server Scientist’s data server in Reading Remote FS mounted using SSHFS Data transfer via SSH Data transfer via Condor Further work We would like to run the hydrological model with climate data forcing stored in repositories at various different institutes. Running climate simulations locally would not then be necessary, but the amount of data transfer involved would be much larger. We would like the running models to access the forcing repositories directly, to avoid storing copies of all the forcing data sets locally. Data transfer would then be over much larger distances, with slower network connections in some cases. Current e-research effort is focussed on these challenges, and we also plan to apply the techniques to other models and to other grids. Figure 1. (A) Ensemble-mean change from present in average annual runoff. (B) Number of GCMs showing an increase in average annual runoff. Both are for a global- average temperature rise of 2ºC.