Abstract
Water scientists and managers currently face the question of whether trends in climate variables that affect water supplies and hazards can be anticipated. We investigate to what extent climate model simulations may provide accurate forecasts of future hydrologic nonstationarity in the form of changes in precipitation amount. We compare gridded station observations (GPCC Full Data Product, 1901–2010) and climate model outputs (CMIP5 Historical and RCP8.5 simulations, 1901–2100) in real and synthetic-data hindcast experiments. The hindcast experiments show that imputing precipitation trends based on the climate model mean reduced the root mean square error of precipitation trend estimates for 1961–2010 by 9% compared to making the assumption (implied by hydrologic stationarity) of no trend in precipitation. Given the accelerating pace of climate change, the benefits of incorporating climate model assessments of precipitation trends in water resource planning are projected to increase for future decades. The distribution of climate models’ simulated precipitation trends shows substantial spatially coherent biases, suggesting that there may be room for further improvement in how climate models are parametrized and used for precipitation estimation. Linear extrapolation of observed trends in long precipitation records may also be useful, particularly for lead times shorter than about 25 years. Overall, our findings suggest that simulations by current global climate models, combined with the continued maintenance of in situ hydrologic observations, can provide useful information on future changes in the hydrologic cycle.
Highlights
In the present nonstationary regime associated with climate change, predicting trends in hydrologic variables such as precipitation would be of great value for water resources planning, with applications ranging from municipal decision making to energy modeling to ecological management to disaster preparedness [1]
Our results show that global climate models, as represented by Coupled Model Intercomparison Project Phase 5 (CMIP5) submissions, have some skill in representing precipitation trends over recent decades, even when evaluated at a spatial scale close to the original model grid scale (2.5◦)
The ability of modeled climate trends to directly inform projections of streamflow, reservoir storage, soil moisture, and other land-surface hydrologic variables remains an important practical question. Other climate variables such as precipitation intensity are important for water resource planning and natural hazard preparedness, and the ability of global climate models (GCMs) to contribute to estimates of future trends in these quantities could be explored using the methods used here for mean precipitation
Summary
In the present nonstationary regime associated with climate change, predicting trends in hydrologic variables such as precipitation would be of great value for water resources planning, with applications ranging from municipal decision making to energy modeling to ecological management to disaster preparedness [1]. Given continuing uncertainties as to the ability of GCMs to model precipitation change, some hydrologists and water managers have recommended maintaining the stationarity assumption for water resources planning, while building in robustness and resiliency whenever possible as precautionary measures, pending more definitive cues from observations and improvements in scientific understanding [6,7,8]. We employ two ways to empirically assess the quality of GCM simulations of trends in precipitation: (1) synthetic-data experiments and (2) hindcasts with observational precipitation data. Synthetic-data experiments evaluate the ability of GCMs to predict precipitation trends simulated, for example by another GCM, under given forcing. Synthetic data have the advantage of completeness and ability to be fully characterized (being the output of a numerical model), and synthetic-data experiments can be carried out for any desired boundary conditions and climate forcing. Combining the two approaches allows the more ambitious comparisons that can be carried out with synthetic data to be anchored by findings on how well synthetic data compare in predictability to actual observations
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