Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> Using the long-term observed data at 10 meteorological stations and 17 hydrological gauging stations in a forest region in Germany, the objectives in the present paper were to examine the space-time variability of precipitation and snow (snow depth and snow water equivalent), to evaluate the importance of rainfall for snow melt process, to explore the simultaneous occurrence of snowmelt and rain on snow, to investigate the characteristics of discharges including peak discharges, to calculate runoff from snowmelt and rainfall, and to analyze runoff depths and runoff coefficients. The dependence of these variables on station elevation was studied. The results of the studied forest region were compared with those of other stations in Germany. The major climatic features were a precipitation maximum in summer, and a discharge maximum in winter. For topographical elevations larger than about 400 m above sea level, rain-on-snow events play a much more important role in runoff formation than pure rainfall events. <s> BIB001 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> Edited by R. GRAYSON and G. BLOSCHL. Cambridge University Press, Cambridge, UK. Hardback, 404 p. $95.00. ISBN 0-521-63316-8. ::: ::: The struggle to develop better models of hydrologic processes has been a continuing area of intense research for the last several decades. One promising avenue of watershed <s> BIB002 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> [1] Vegetation affects the climate by modifying the energy, momentum, and hydrologic balance of the land surface. Soil-vegetation-atmosphere transfer (SVAT) schemes explicitly consider the role of vegetation in affecting water and energy balance by taking into account its physiological properties, in particular, leaf area index (LAI) and stomatal conductance. These two physiological properties are also the basis of evapotranspiration parameterizations in physically based hydrological models. However, most current SVAT schemes and hydrological models do not parameterize vegetation as a dynamic component. The seasonal evolution of LAI is prescribed, and monthly LAI values are kept constant year after year. The effect of CO2 on the structure and physiological properties of vegetation is also neglected, which is likely to be important in transient climate simulations with increasing CO2 concentration and for hydrological models that are used to study climate change impact. The net carbon uptake by vegetation, which is the difference between photosynthesis and respiration, is allocated to leaves, stems, and roots. Carbon allocation to leaves determines their biomass and LAI. The timing of bud burst, leaf senescence, and leaf abscission (i.e., the phenology) determines the length of the growing season. Together, photosynthesis, respiration, allocation, and phenology, which are all strongly dependent on environmental conditions, make vegetation a dynamic component. This paper (1) familiarizes the reader with the basic physical processes associated with the functioning of the terrestrial biosphere using simple nonbiogeochemical terminology, (2) summarizes the range of parameterizations used to model these processes in the current generation of process-based vegetation and plant growth models and discusses their suitability for inclusion in SVAT schemes and hydrological models, and (3) illustrates the manner in which the coupling of vegetation models and SVAT schemes/hydrological models may be accomplished. <s> BIB003 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> The simulation of hydrological consequences of climate change has received increasing attention from the hydrology and land-surface modelling communities. There have been many studies of climate-change effects on hydrology and water resources which usually consist of three steps: (1) use of general circulation models (GCMs) to provide future global climate scenarios under the effect of increasing greenhouse gases, (2) use of downscaling techniques (both nested regional climate models, RCMs, and statistical methods) for “downscaling” the GCM output to the scales compatible with hydrological models, and (3) use of hydrologic models to simulate the effects of climate change on hydrological regimes at various scales. Great progress has been achieved in all three steps during the past few years, however, large uncertainties still exist in every stage of such study. This paper first reviews the present achievements in this field and then discusses the challenges for future studies of the hydrological impacts of climate change. <s> BIB004 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> [1] In this paper, we analyze how our evaluation of the capacity of a rainfall-runoff model to represent low or high flows depends on the objective function used during the calibration process. We present a method to combine models to produce a more satisfactory streamflow simulation, on the basis of two different parameterizations of the same model. Where we previously had to choose between a more efficient simulation for either high flows or low flows (but inevitably less efficient in the other range), we show that a balanced simulation can be obtained by using a seasonal index to weigh the two simulations, providing good efficiency in both low and high flows. <s> BIB005 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> An ensemble of experiments is used with a global climate model to assess the contribution of plant 'physiological forcing' to future changes in continental runoff. It is found that the effect increases simulated global mean runoff by 6 per cent when the concentration of carbon dioxide is doubled relative to pre-industrial levels; an increase that is comparable to that simulated in response to climate change caused by radiative forcing. This finding suggests that the risk of flooding may be greater than previously assumed under future global warming scenarios. <s> BIB006 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> This review summarizes current understanding of the mechanisms that underlie the response of photosynthesis and stomatal conductance to elevated carbon dioxide concentration ([CO2]), and examines how downstream processes and environmental constraints modulate these two fundamental responses. The results from free-air CO2 enrichment (FACE) experiments were summarized via meta-analysis to quantify the mean responses of stomatal and photosynthetic parameters to elevated [CO2]. Elevation of [CO2] in FACE experiments reduced stomatal conductance by 22%, yet, this reduction was not associated with a similar change in stomatal density. Elevated [CO2] stimulated light-saturated photosynthesis (Asat )i n C3 plants grown in FACE by an average of 31%. However, the magnitude of the increase in Asat varied with functional group and environment. Functional groups with ribulose1,5-bisphosphate carboxylase/oxygenase (Rubisco)-limited photosynthesis at elevated [CO2] had greater potential for increases in Asat than those where photosynthesis became ribulose-1,5-bisphosphate (RubP)-limited at elevated [CO2]. Both nitrogen supply and sink capacity modulated the response of photosynthesis to elevated [CO2] through their impact on the acclimation of carboxylation capacity. Increased understanding of the molecular and biochemical mechanisms by which plants respond to elevated [CO2], and the feedback of environmental factors upon them, will improve our ability to predict ecosystem responses to rising [CO2] and increase our potential to adapt crops and managed ecosystems to future atmospheric [CO2]. <s> BIB007 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> Abstract. Implications of 21st century climate change on the hydrology and water resources of the Colorado River Basin were assessed using a multimodel ensemble approach in which downscaled and bias corrected output from 11 General Circulation Models (GCMs) was used to drive macroscale hydrology and water resources models. Downscaled climate scenarios (ensembles) were used as forcings to the Variable Infiltration Capacity (VIC) macroscale hydrology model, which in turn forced the Colorado River Reservoir Model (CRMM). Ensembles of downscaled precipitation and temperature, and derived streamflows and reservoir system performance were assessed through comparison with current climate simulations for the 1950–1999 historical period. For each of the 11 GCMs, two emissions scenarios (IPCC SRES A2 and B1, corresponding to relatively unconstrained growth in emissions, and elimination of global emissions increases by 2100) were represented. Results for the A2 and B1 climate scenarios were divided into three periods: 2010–2039, 2040–2069, and 2070–2099. The mean temperature change averaged over the 11 ensembles for the Colorado basin for the A2 emission scenario ranged from 1.2 to 4.4°C for periods 1–3, and for the B1 scenario from 1.3 to 2.7°C. Precipitation changes were modest, with ensemble mean changes ranging from −1 to −2% for the A2 scenario, and from +1 to −1% for the B1 scenario. An analysis of seasonal precipitation patterns showed that most GCMs had modest reductions in summer precipitation and increases in winter precipitation. Derived April 1 snow water equivalent declined for all ensemble members and time periods, with maximum (ensemble mean) reductions of 38% for the A2 scenario in period 3. Runoff changes were mostly the result of a dominance of increased evapotranspiration over the seasonal precipitation shifts, with ensemble mean runoff changes of −1, −6, and −11% for the A2 ensembles, and 0, −7, and −8% for the B1 ensembles. These hydrological changes were reflected in reservoir system performance. Average total basin reservoir storage and average hydropower production generally declined, however there was a large range across the ensembles. Releases from Glen Canyon Dam to the Lower Basin were reduced for all periods and both emissions scenarios in the ensemble mean. The fraction of years in which shortages occurred increased by approximately 20% by period 3 for both emissions scenarios. <s> BIB008 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> Received 6 July 2007 Accepted 9 July 2007 Natural and anthropogenic changes constantly impact the environment surrounding us. Available moisture and energy change due to variability and shifts in climate, and the separation of precipitation into different pathways on the land surface are altered due to wildfires, beetle infestations, urbanization, deforestation, invasive plant species, etc. Many of these changes can have a significant impact on the hydrological regime of the watershed in which they occur (e.g. DeWalle et al., 2000; Porporato et al., 2004; Milly et al., 2005; Xu et al., 2005; Poff et al., 2006; Oki and Kanae, 2006; Hayhoe et al., 2007; Weiskel et al., 2007). Such changes to water pathways, storage and subsequent release (the blue and green water idea of Falkenmark and Rockstroem, 2004) are predicted to have significant negative impacts on water security for large population groups as well as for ecosystems in many regions of the world (e.g. Conway and Toenniessen, 1999; Falkenmark, 2001; Johnson et al., 2001; Sachs, 2007). The growing imbalances among freshwater supply, its consumption, and human population will only increase the problem (Vorosmarty et al., 2000). A major task for hydrologic science lies in providing predictive models based on sound scientific theory to support water resource management decisions for different possible future environmental, population and institutional scenarios. But can we provide credible predictions of yet unobserved hydrologic responses of natural systems? Mathematical models of the terrestrial hydrological cycle are the vehicles that (potentially) enable us to make such predictions (Ewen and Parkin, 1996). These models consist of two elements important for this discussion: (1) model equations (or the model structure), which are the mathematical descriptions of the underlying physical processes; and (2) model parameters, which are the descriptors of the specific physical characteristics of a particular natural system. While most model structures are applicable to a range of systems (e.g. watersheds) with similar dominant processes, most model parameters are specific to a certain system at a certain location (and potentially even at a certain time-period). Assuming, for simplicity, that our knowledge is generally sufficient to select a reasonable model structure to represent a specific natural system (though there might be more than one reasonable choice), then the main task left to the hydrologist is to decide on appropriate model parameters to represent the system at hand. In the case of environmental change impacts, the task is to decide which parameters will change and by how much to reflect the new characteristics of the altered system. This decision requires an understanding of how watershed characteristics relate to model parameters: <s> BIB009 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> Increased attention directed at the permafrost region has been prompted by resource development and climate change. This review surveys advances in permafrost hydrology since 2000. Data shortage and data quality remain serious concerns. Yet, there has been much progress in understanding fundamental hydrologic processes operating in a wide range of environments, from steep mountainous catchments, to the Precambrian Shield with moderate relief, to the low-gradient terrain of plains, plateaus and wetlands. Much of the recent research has focused on surface water, although springs and groundwater contribution to streamflow have also been studied. A compendium of water-balance research from 39 high-latitude catchments reveals the strengths and limitations of the available results, most of which are restricted to only a few years of study at the small watershed scale. The response of streamflow to climate receives continued if not increasing attention, from the occurrence of extreme hydrologic events to the changing regimes of river flow at a regional scale. The effect of climate change and the role of permafrost on the changing discharge of large boreal rivers are major topics for further investigation. Extended field and modelling research on physical processes will improve knowledge of permafrost hydrology and enhance its relevance to societal needs. Copyright # 2008 John Wiley & Sons, Ltd. <s> BIB010 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> A priori determined model structures are common in catchment rainfall-runoff modeling. While this has resulted in many ready-to-use modeling tools, there are several shortcomings of a one-size-fits-all model structure. The uniqueness of catchments with respect to their hydrological behavior and the need to adapt model complexity to data availability challenge this status quo. We present a flexible approach to model development where the model structure is adapted progressively based on catchment characteristics and the data described by the experimentalist. We demonstrate this approach with the Maimai catchment in New Zealand, a location with a large availability of data, including stream discharge, groundwater levels, and stream isotope measurements. Different types of data are introduced progressively, and the architecture of the model is adjusted in a stepwise fashion to better describe the processes suggested by the new data sources. The revised models are developed in a way to strike a balance between model complexity and data availability, by keeping models as simple as possible, but complex enough to explain the dynamics of the data. Our work suggests that (1) discharge data provides information on the dynamics of storage (represented by the “free” water in the reservoirs) subject to pressure wave propagation generated by rainfall into the catchment, (2) groundwater data provides information on thresholds and on the contribution of different portions of the catchment to stream discharge, and (3) isotope data provides information on particle transport and mixing of the rainfall with the storage present in the catchment. Moreover, while groundwater data appear to be correlated with discharge data, and only a marginal improvement could be obtained adding this information to the model development process, isotope data appear to provide an orthogonal view on catchment behavior. This result contributes to understanding the value of data for modeling, which may serve as a guidance in the process of gauging ungauged catchments. <s> BIB011 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> [1] Many physical-based models of surface and groundwater hydrology are constructed without the possibility of multiple stable states for the same parameter set. For such a conceptualization, at the cessation of a transient hydrological disturbance of any magnitude the model will return to the same stable state and thus show an infinite resilience. To highlight and falsify this assumption, a numerical distributed ecohydrological model (coupled hillslope Boussinesq-vertically lumped vadose zone) is presented, in which qualitatively different steady state water table elevations exist for the same parameter set. The multiple steady states are shown to emerge from a positive feedback arising from a reduction in leaf area index (LAI) and thus transpiration, as a saline water table approaches the surface. Limit cycle continuation is also undertaken to quantify the state-space location of the threshold (repellor) between the steady states (attractors) and quantify the resilience. While the model is biophysically simple, it is sufficiently complex to challenge this potentially significant assumption within water resource planning. <s> BIB012 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> Pan evaporation is just that - it is the evaporation rate of water from a small dish located at the ground-surface. Pan evaporation is a measure of the evaporative demand over terrestrial surfaces. Declines in pan evaporation have now been reported in many regions of the world. The trends vary from one pan to the next, but when averaged over many pans, they are typically in the range of −1 to − 4m m a −2 (mm per annum per annum). In energetic terms, a trend of − 2m m a −2 is equivalent to −0.16 W m −2 a −1 and over 30 years this is a change of −4.8 W m −2 . For comparison, the top-of-atmosphere forcing due to doubled CO2 is estimated by the Intergovernmental Panel on Climate Change (IPCC) to be ~3.7 W m −2 . Hence, the magnitude of the pan evaporation trend is large. What is of even greater interest is the direction - a decline - given the well-established warming of the last 30-50 years. In this article, the first in a two part series, we describe the underlying principles in using and interpreting pan evaporation data and then summarise the reported observations from different countries. In the second article, we describe the interpretation of the trends in terms of changes in the terrestrial water balance. <s> BIB013 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> Abstract. Global freshwater resources are sensitive to changes in climate, land cover and population density and distribution. The Land-surface Processes and eXchanges Dynamic Global Vegetation Model is a recent development of the Lund-Potsdam-Jena model with improved representation of fire-vegetation interactions. It allows simultaneous consideration of the effects of changes in climate, CO2 concentration, natural vegetation and fire regime shifts on the continental hydrological cycle. Here the model is assessed for its ability to simulate large-scale spatial and temporal runoff patterns, in order to test its suitability for modelling future global water resources. Comparisons are made against observations of streamflow and a composite dataset of modelled and observed runoff (1986–1995) and are also evaluated against soil moisture data and the Palmer Drought Severity Index. The model captures the main features of the geographical distribution of global runoff, but tends to overestimate runoff in much of the Northern Hemisphere (where this can be somewhat accounted for by freshwater consumption and the unrealistic accumulation of the simulated winter snowpack in permafrost regions) and the southern tropics. Interannual variability is represented reasonably well at the large catchment scale, as are seasonal flow timings and monthly high and low flow events. Further improvements to the simulation of intra-annual runoff might be achieved via the addition of river flow routing. Overestimates of runoff in some basins could likely be corrected by the inclusion of transmission losses and direct-channel evaporation. <s> BIB014 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> Human activities exert global-scale impacts on our environment with significant implications for freshwater-driven services and hazards for humans and nature. Our approach to the science of hydrology needs to significantly change so that we can understand and predict these implications. Such an adjustment is a necessary prerequisite for the development of sustainable water resource management strategies and to achieve long-term water security for people and the environment. Hydrology requires a paradigm shift in which predictions of system behavior that are beyond the range of previously observed variability or that result from significant alterations of physical (structural) system characteristics become the new norm. To achieve this shift, hydrologists must become both synthesists, observing and analyzing the system as a holistic entity, and analysts, understanding the functioning of individual system components, while operating firmly within a well-designed hypothesis testing framework. Cross-disciplinary integration must become a primary characteristic of hydrologic research, catalyzing new research and nurturing new educational models. The test of our quantitative understanding across atmosphere, hydrosphere, lithosphere, biosphere, and anthroposphere will necessarily lie in new approaches to benchmark our ability to predict the regional hydrologic and connected implications of environmental change. To address these challenges and to serve as a catalyst to bring about the necessary changes to hydrologic science, we call for a long-term initiative to address the regional implications of environmental change. <s> BIB015 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> [1] The recent drought in the southern Murray-Darling Basin has seen a larger reduction in annual runoff in many places compared to historical droughts with similar annual rainfall reductions. Several reasons have been suggested for this, including proportionally less autumn and winter rainfall, fewer high rainfall years, and increased temperatures. Using the SIMHYD daily rainfall-runoff model and scenarios of rainfall and potential evapotranspiration, we investigate the causes of the observed runoff reduction over 1997–2008 in the Campaspe river basin, an area representative of the southern Murray-Darling Basin. This method accounts for 83% of the runoff reduction, with the reduction in annual rainfall accounting for 52%. The remainder is not explained by any single hydroclimatic feature but is mostly accounted for by the combination of changes in rainfall variability outside monthly and annual time scales (15%), changed seasonality of rainfall (11%), and increased potential evapotranspiration (5%). <s> BIB016 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> The impact of future climate on runoff generation and the implications of these changes for management of water resources in a river basin are investigated by running these changes through catchment and river system models. Two conceptual daily rainfall-runoff models are used to simulate runoff across the Macquarie-Castlereagh region for historical (1895–2006) and future (∼2030) climate based on outputs from 15 of the 23 IPCC AR4 GCMs for the A1B global warming scenario. The estimates of future runoff are used as inputs to the river system model. The mean annual historical rainfall averaged across the Macquarie-Castlereagh region is 544 mm and the simulated runoff is 34 and 30 mm for SIMHYD and Sacramento rainfall-runoff models, respectively. The mean annual future rainfall and runoff across the region are projected to decrease. The modelling results show a median estimate of a 5% reduction for SIMHYD (50% confidence interval − 11 to + 7%) and a 7% reduction for Sacramento (50% confidence interval − 15 to + 8%) in mean annual runoff under a ∼2030 climate for the region. The results from the river system modelling indicate that under the ∼2030 climate scenario, the median of general security and supplementary diversions are projected to decrease by 4% (50% confidence interval − 10 to + 5%) and 2% (50% confidence interval − 5 to + 3%) respectively for the SIMHYD inflows and 8% (50% confidence interval − 17 to + 6%) and 5% (50% confidence interval − 11 to + 3%) for the Sacramento inflows. The future annual and seasonal storage volumes for the Burrendong Dam and inflows at all major locations across the region are projected to be lower than the historical records. Copyright © 2011 John Wiley & Sons, Ltd. <s> BIB017 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> Summary Better simulation and earlier prediction of river low flows are needed for improved water management. Here, a top–down structural analysis to improve a hydrological model in a low-flow simulation perspective is presented. Starting from a simple but efficient rainfall–runoff model (GR5J), we analyse the sensitivity of low-flow simulations to progressive modifications of the model’s structure. These modifications correspond to the introduction of more complex routing schemes and/or the addition of simple representations of groundwater–surface water exchanges. In these tests, we wished to improve low-flow simulation while avoiding performance losses in high-flow conditions, i.e. keeping a general model. In a typical downward modelling perspective, over 60 versions of the model were tested on a large set of French catchments corresponding to various low-flow conditions, and performance was evaluated using criteria emphasising errors in low-flow conditions. The results indicate that several best performing structures yielded quite similar levels of efficiency. The addition of a new flow component to the routing part of the model yielded the most significant improvement. In spite of the close performance of several model structures, we conclude by proposing a modified model version of GR5J with a single additional parameter. <s> BIB018 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> [1] Climate impact analyses are usually based on driving hydrological models by future climate scenarios, assuming that the model parameters calibrated to past runoff are representative of the future. In this paper we calibrate the parameters of a conceptual rainfall-runoff model to six consecutive 5 year periods between 1976 and 2006 for 273 catchments in Austria and analyze the temporal change of the calibrated parameters. The calibrated parameters representing snow and soil moisture processes show significant trends. For example, the parameter controlling runoff generation doubled, on average, in the 3 decades. Comparisons of different subregions, comparisons with independent data sets, and analyses of the spatial variability of the model parameters indicate that these trends represent hydrological changes rather than calibration artifacts. The trends can be related to changes in the climatic conditions of the catchments such as higher evapotranspiration and drier catchment conditions in the more recent years. The simulations suggest that the impact on simulated runoff of assuming time invariant parameters can be very significant. For example, if using the parameters calibrated to 1976 – 1981 for simulating runoff for the period 2001 – 2006, the biases of median flows are, on average, 15% and the biases of high flows are about 35%. The errors increase as the time lag between the simulation and calibration periods increases. The implications for hydrologic prediction in general and climate impact analyses in particular are discussed. <s> BIB019 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> List of contributors Foreword Thomas Dunne Preface Gunter Bloschl, Murugesu Sivapalan, Thorsten Wagener, Alberto Viglione and Hubert Savenije 1. Introduction Gunter Bloschl, Murugesu Sivapalan, Thorsten Wagener, Alberto Viglione and Hubert Savenije 2. A synthesis framework for runoff predictions in ungauged basins Thorsten Wagener, Gunter Bloschl, David Goodrich, Hoshin V. Gupta, Murugesu Sivapalan, Yasuto Tachikawa, Peter Troch and Markus Weiler 3. A data acquisition framework for predictions of runoff in ungauged basins Brian McGlynn, Gunter Bloschl, Marco Borga, Helge Bormann, Ruud Hurkmans, Jurgen Komma, Lakshman Nandagiri, Remko Uijlenhoet and Thorsten Wagener 4. Process realism: flow paths and storage Dorthe Tetzlaff, Ghazi Al-Rawas, Gunter Bloschl, Sean K. Carey, Ying Fan, Markus Hrachowitz, Robert Kirnbauer, Graham Jewitt, Hjalmar Laudon, Kevin J. McGuire, Takahiro Sayama, Chris Soulsby, Erwin Zehe and Thorsten Wagener 5. Prediction of annual runoff in ungauged basins Thomas McMahon, Gregor Laaha, Juraj Parajka, Murray C. Peel, Hubert Savenije, Murugesu Sivapalan, Jan Szolgay, Sally Thompson, Alberto Viglione, Ross Woods and Dawen Yang 6. Prediction of seasonal runoff in ungauged basins R. Weingartner, Gunter Bloschl, David Hannah, Danny Marks, Juraj Parajka, Charles Pearson, Magdalena Rogger, Jose Luis. Salinas, Eric Sauquet, Sri Srikanthan, Sally Thompson and Alberto Viglione 7. Prediction of flow duration curves in ungauged basins Attilio Castellarin, Gianluca Botter, Denis A. Hughes, Suxia Liu, Taha B. M. J. Ouarda, Juraj Parajka, David Post, Murugesu Sivapalan, Christopher Spence, Alberto Viglione and Richard Vogel 8. Prediction of low flows in ungauged basins Gregor Laaha, Siegfried Demuth, Hege Hisdal, Charles N. Kroll, Henny A. J. van Lanen, Thomas Nester, Magdalena Rogger, Eric Sauquet, Lena M. Tallaksen, Ross Woods and Andy Young 9. Prediction of floods in ungauged basins Dan Rosbjerg, Gunter Bloschl, Donald H. Burn, Attilio Castellarin, Barry Croke, Guliano Di Baldassarre, Vito Iacobellis, Thomas Kjeldsen, George Kuczera, Ralf Merz, Alberto Montanari, David Morris, Taha B. M. J. Ouarda, Liliang Ren, Magdalena Rogger, Jose Luis Salinas, Elena Toth and Alberto Viglione 10. Predictions of runoff hydrographs in ungauged basins Juraj Parajka, Vazken Andreassian, Stacey Archfield, Andras Bardossy, Francis Chiew, Qingyun Duan, Alexander Gelfan, Kamila Hlavcova, Ralf Merz, Neil McIntyre, Ludovic Oudin, Charles Perrin, Magdalena Rogger, Jose Luis Salinas, Hubert Savenije, Jon Olav Skoien, Thorsten Wagener, Erwin Zehe and Yongqiang Zhang 11. Case studies Hubert Savenije, Murugesu Sivapalan, Trent Biggs, Shaofeng Jia, Leonid M. Korytny, E.A.Ilyichyova, Boris Gartsman, John W. Pomeroy, Kevin Shook, Xing Fang, Tom Brown, Denis A. Hughes, Stacey Archfield, Jos Samuel, Paulin Coulibaly, Robert A. Metcalfe, Attilio Castellarin, Ralf Merz, Gunter Humer, Ataur Rahman, Khaled Haddad, Erwin Weinmann, George Kuczera, Theresa Blume, Armand Crabit, Francois Colin, Roger Moussa, Hessel Winsemius, Hubert Savenije, Jens Liebe, Nick van de Giesen, M. Todd Walter, Tammo S. Steenhuis, Jeffrey R. Kennedy, David Goodrich, Carl L. Unkrich, Dominic Mazvimavi, Neil R. Viney, Kuniyoshi Takeuchi, H. A. P. Hapuarachchi, Anthony S. Kiem, Hiroshi Ishidaira, Tianqi Ao, Jun Magome, Maichun C. Zhou, Mikhail Georgievski, Guoqiang Wang, Chihiro Yoshimura, Berit Arheimer, Goran Lindstrom and Shijun Lin 12. Synthesis across processes, places and scales Hoshin V. Gupta, Gunter Bloschl, Jeffrey McDonnell, Hubert Savenije, Murugesu Sivapalan, Alberto Viglione and Thorsten Wagener 13. Recommendations Kuniyoshi Takeuchi, Gunter. Bloschl, Hubert Savenije, John Schaake, Murugesu Sivapalan, Alberto Viglione, Thorsten Wagener and Gordon Young Appendix: summary of studies used in the comparative assessments References Index. <s> BIB020 </s> Hydrologic nonstationarity and extrapolating models to predict the future: overview of session and proceeding <s> Extrapolating hydrological models to predict the future <s> Abstract The new Scientific Decade 2013–2022 of IAHS, entitled “Panta Rhei—Everything Flows”, is dedicated to research activities on change in hydrology and society. The purpose of Panta Rhei is to reach an improved interpretation of the processes governing the water cycle by focusing on their changing dynamics in connection with rapidly changing human systems. The practical aim is to improve our capability to make predictions of water resources dynamics to support sustainable societal development in a changing environment. The concept implies a focus on hydrological systems as a changing interface between environment and society, whose dynamics are essential to determine water security, human safety and development, and to set priorities for environmental management. The Scientific Decade 2013–2022 will devise innovative theoretical blueprints for the representation of processes including change and will focus on advanced monitoring and data analysis techniques. Interdisciplinarity will be sought by increased efforts to connect with the socio-economic sciences and geosciences in general. This paper presents a summary of the Science Plan of Panta Rhei, its targets, research questions and expected outcomes. Editor Z.W. Kundzewicz Citation Montanari, A., Young, G., Savenije, H.H.G., Hughes, D., Wagener, T., Ren, L.L., Koutsoyiannis, D., Cudennec, C., Toth, E., Grimaldi, S., Blöschl, G., Sivapalan, M., Beven, K., Gupta, H., Hipsey, M., Schaefli, B., Arheimer, B., Boegh, E., Schymanski, S.J., Di Baldassarre, G., Yu, B., Hubert, P., Huang, Y., Schumann, A., Post, D., Srinivasan, V., Harman, C., Thompson, S., Rogger, M., Viglione, A., McMillan, H., Characklis, G., Pang, Z., and Belyaev, V., 2013. “Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022. Hydrological Sciences Journal. 58 (6) 1256–1275. <s> BIB021
With anthropogenic climate change, we know we will at the very least be extrapolating hydrological models to predict a future under changed rainfall distribution and characteristics, warmer conditions and higher CO 2 . Changes in rainfall characteristics may trigger a change to a hydrologic regime not seen in the past BIB002 BIB012 ; the surface-groundwater connection example earlier). Higher temperatures will influence evapotranspiration and energy and water balance and interactions at different scales BIB013  BIB016 , and in high altitudes and latitudes change the timing of snowmelt BIB010 and the importance of rain-on-snow rainfall events BIB001 . Higher CO 2 will reduce canopy conductance and increase leaf water use efficiency (CO 2 fertilisation) which could be offset by increased leaf area and forest biomass  BIB006 BIB007  . However, understanding these potential influences and the complex ecohydrology and atmospheric interactions and feedbacks under higher temperature and CO 2 is very difficult and is a significant area of current science and global research programs. In addition, any understanding, speculation or modelling of the physical processes can only be validated against past data, which will then be extrapolated to predict a future that will be significantly different from the past. "Stationarity is dead". However, it is not apparent what if any alternative methods should be used as a replacement for the different types of hydrological applications. For example, existing approaches may be sufficient for operational water management and short-term planning, but key aspects of "nonstationarity" must be taken into account for certain hydrologic design and long-term planning. Predicting the future is difficult if not impossible, and hydrologic planning will always consider probabilistic or multiple plausible realisations and adopt adaptive risk management with systems planned for particular levels of security or reliability. Hydrologists have used a variety of approaches to predict a future under nonstationarity. Hydrologic responses to changed climate inputs are generally modelled using hydrological models informed by climate projections from the large or entire range of global and regional climate models BIB004 BIB008   BIB017 . Improved understanding of vegetation behaviour and hydrological responses to warmer climate and enhanced CO 2 are increasingly incorporated to the more complex hydrological models BIB003 BIB014 . Improved conceptualisations are being introduced to hydrological models, particularly where they are used in studies predicting into the future under prolonged extreme conditions. Examples include attempts at parameterising semidistributed hydrological models or adapting existing models to simulate processes important under extreme conditions like long dry spells (farm dam interception  and surface-groundwater connectivity BIB018 ) and learning from catchments experiencing different or changing conditions BIB009 BIB011  . Many studies use existing models, but with smart approaches to parameterise and cal-20 F. H. S. Chiew and J. Vaze: Overview of session and proceeding ibrate the model, for example (i) with time varying parameters dependent on storage levels  BIB019 ; (ii) multi-criteria optimisation that also considers low flow simulations  BIB005 Efstratiadis and Koutsoyiannis, 2010) ; and (iii) predicting the future with parameters from model calibration against a similar climate period as the future climate projections. Hydrological modelling under changing conditions is a problem familiar in hydrology. This is highlighted by the two decadal initiatives of the International Association of Hydrological Sciences (IAHS), the 2003-2012 Decade on "Prediction in Ungauged Basin" (PUB) focussed on extrapolating model parameterisation in space  BIB020 and the "2013-2022 Decade on Panta Rhei -Change in Hydrology and Society" now focussing on prediction in a changing world (extrapolation in time) BIB021 . There have been several useful technical overviews and commentaries on hydrological prediction under change and these include  , BIB015 and Peel and Bloschl (2011) . The Colorado State University (2010) workshop on hydrologic nonstationarity and sessions in key international forums (e.g. AGU Fall Meeting 2012, IAHS Assembly 2013) also provides useful discussions on this issue and practical approaches to account for nonstationarity when extrapolating models to predict the future for design, operation and planning of water resources and related systems.