Hydrology. Raymond Venneker UNESCO-IHE Institute for Water Education, Delft, the Netherlands

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1 Hydrology Formatted: Line spacing: single Raymond Venneker UNESCO-IHE Institute for Water Education, Delft, the Netherlands Taikan Oki Institute of Industrial Science, The University of Tokyo, Japan Stefan Uhlenbrook UNESCO-IHE Institute for Water Education, Delft, the Netherlands Formatted: Line spacing: single 1. Introduction The fields of hydrology and climatology both involve study of the water cycle and the space and time variability of water fluxes on the earth and in the atmosphere. It can, therefore, be stated at the outset that climatological information and services are of major importance for operational hydrology and water resources assessment. The economic and social benefits of climatological information and services have been reviewed by Nicholls (1996) in a wider sense. A further review by Kunkel et al. (1999) describes the economic and societal impacts directly caused by extreme weather situations in the United States during the last century. The need for past or present records of weather data in applied hydrology emphasizes the importance of climatological information to hydrologists. The value of such information, therefore, often lies in the economic and social benefit that can be gained from the hydrological assessments or studies for which the data are used. One way to quantify the economic and societal impacts of extreme natural and/or humaninduced phenomena is to assess potential risk. Risk in earth sciences is often defined as the product of vulnerability, hazard and the economic value of the area that is investigated. For groundwater related risk, the economic value of the aquifer system needs to be assessed separately (Ducci, 1999). For surface and subsurface water resources related risk, the hazard clearly derives from extreme weather events, such as heavy rain storms in the case of flooding or prolonged dry spells in the case of droughts. The vulnerability, at least for the United States, appears to have significantly increased during the second half of the past century, probably as a result of societal and demographic changes that increasingly expose property and lives to potential damage from extreme weather events (Kunkel et al., 1999). The availability and input of climatological information influences the quality of hydrological assessment outcomes (Nicholls, 1996), and the economic value of hydrological forecasts in particular depends on the accuracy of the output and the period of time that is covered by the study (Malkoc et al., 2002). Areas in hydrology that will benefit from availability of climatological data include water resources assessment, assessment of water resources vulnerability, and flood and drought assessment and respectively Deleted: are Deleted: assessment Deleted: study in Deleted: impact from Deleted: are of influence to Deleted: outcome Deleted: ). The Deleted: vulnerability of

2 forecasting. Besides the technological progress in obtaining hydrological and hydrometeorological information from ground observation networks and satellite remote sensing (Valeo et al., 2006), these areas can also benefit from climate and weather forecast and reanalysis products, which are becoming increasingly available through the Internet. Moreover, Margulis et al. (2006) note a transition from a data poor to a data rich environment, which increases the scope and scale of research questions and practical problems that can be addressed by water cycle researchers. In this contribution, the initial focus is on climate and weather data requirements for water resources assessment and river basin hydrological modelling. Subsequently, the potential of weather and climate forecasting data in particular are described. Some concluding remarks are presented in the final section. 2. Water Resources Assessment At a global scale, climate change is expected to accelerate water cycles and, therefore, have the long-term effect of increasing available renewable fresh water resources; however, changes in seasonal patterns and more frequent occurrence of extreme events, such as floods and droughts, may have an adverse effect on the availability of water resources. Despite the increased knowledge of global water resources that has been acquired in hydrology over the forty years since the International Hydrological Decade, an increased amount of weather and climate forecasting data is still needed to assess the inter-annual and seasonal effects of climate change on the available global water resources (Oki, 2006). Notably, weather extremes may have a delayed impact on water resources and the effects may have seasonal differences, depending on antecedent hydrological conditions, snowpack accumulation. For groundwater contaminant risk evaluation, the value of groundwater resources can be incorporated in the form of socio-economic value maps (Ducci, 1999). Although the benefits of climatological information may not be immediately obvious, scenarios for risk assessment may have to be adapted in the light of expected long-term climate change and the accompanying seasonal effects as mentioned above. Decisions on reservoir operations and water resources allocation would ideally have to be anticipated weeks to months ahead. For example, China is currently undertaking several large engineering projects to transfer water from the Yangtze River (Changjiang) Basin to areas in northern China experiencing regular water shortages. The design capacity of these projects is on the order of billion m 3 /y. Details of the diversion projects and the associated economic characteristics are described by Shao et al. (2003). These projects, together with the long-term effects of climate change, are expected to increase the temporal variability of water flows and water quality in the entire Yangtze River Basin (Chen et al., 2002). The management of such diversions is complex, involving considerations of water availability and demand, water quality, ecology, economy, legislation and water pricing policy. Such considerations can likely benefit from medium to long-range hydrological flow and water resources forecasts based on actual and reliably forecasted meteorological information. Deleted: also become Deleted: a Deleted:. However Deleted: increasing Deleted: during Deleted: increasing Deleted: will be Deleted: and Kanae Deleted: It is noted that Deleted: that Deleted: differ with season Deleted: etc Deleted: operation Deleted: basin Deleted: in Deleted: basin Deleted: It Deleted: reliable

3 3. River Basin Modelling and Forecasting Hydrological modelling and forecasting of large river basins has attracted increasing attention from the hydrological community in the past twenty years. Examples include studies conducted with the Variable Infiltration Capacity (VIC) land surface hydrology model (Liang et al., 1994), the LISFLOOD model developed for European river basins (De Roo et al., 2003), the UP modelling system (Ewen et al., 1999), application of the TOPKAPI model in the Huaihe river basin in China (Liu et al., 2005), and distributed hydrological modelling systems being developed for the Yellow River in China (Maskey and Venneker, 2006). These models generally focus on medium to large-scale river basins ranging from to km 2 or larger and are consistent with the requirement for river basin authorities to consider effects of their measures or water allocation policies throughout entire basins. In many cases, these authorities also need to take account of the trans-boundary character of their basins. This is notably the case in Europe as a result of European Union regulatory frameworks, but the generality applies to other continents as well. Large-scale hydrological models require large amounts of spatial data. Time-invariant data are compiled from existing data bases (De Roo et al., 2003), or from a growing number of global data resources publicly available on the Internet (Liu et al., 2005; Maskey, 2006). An example of such a data collection is the International Satellite Land Surface Climatology Project, Initiative II (ISLSCP 2, see Surface atmospheric data for large scale-hydrological models, notably precipitation, but also evaporation, temperature, radiation, humidity, wind speed is, however, not always available in near real-time or with sufficient spatial coverage. A long-term hydrologicalbased data set for at three hour time intervals and largely derived from ground observations for the conterminous United States is described by Maurer et al. (2002). Alternative data sources are satellite observations and weather and climate forecasts. For example, Maskey and Venneker (2006) apply precipitation and evaporation fields derived from meteorological satellites in a macro-scale river basin for which only a limited number of ground stations are available. A concise overview of satellite-based precipitation measurement is presented in Valeo et al. (2006). 4. Climate Simulation and Weather Forecast Data for Hydrology Short and medium range weather predictions can be particularly useful for operational flood forecasting, and the potential for coupling meteorological and hydrological models has been investigated in several recent studies. Results of a probabilistic approach based on ensemble predictions from numerical weather prediction models used in the meteorological community in combination with large-scale river basin hydrological models are described by Gouweleeuw et al. (2005) for known historic events in the Meuse and Odra rivers in Europe. Furthermore, Bartholmes and Todini (2005) note interesting results for a large part of the Po River (Italy) in a case study using deterministic and probabilistic ensemble forecasting model modes in combination with a hydrological model. Deleted: of Deleted:. This is y Deleted: and Venneker etc. Deleted: ly Deleted: 3- Deleted:. The Deleted: of Deleted: a Deleted: model Deleted: river Deleted: However, some

4 Some problems, however, still need to be tackled. General circulation models (GCM) use computational grid scales that are typically one or two orders of magnitude larger than those of river basin hydrological models, which operate at scales of 1-10 km. To overcome this mismatch, Hay et al. (2006) investigated a nested downscaling approach to resolutions of 20, 5 and 1.7 km in a regional meteorological model (MM5) with initial and lateral boundary conditions from a coarse resolution GCM, providing input to a distributed hydrological model (the U.S. Geological Survey Precipitation Runoff Modeling System). The simulation results of the one-way coupled system did not, however, improve those of the station-based hydrological models alone. This deficiency was attributed to biases in the MM5 near-surface simulations of rainfall and temperature (Hay et al., 2006). Moreover, in some cases precipitation forecasts are notably not always reliable and need to be improved in order to provide deterministic flood forecasts for an extended time horizon (Bartholmes and Todini, 2005; Gouweleeuw et al., 2005). There is also a need to further investigate the performance for non-flood situations, particularly in preventing false-alarm flood predictions (Gouweleeuw et al., 2005). Reanalysis data may be of use to hydrological studies requiring long records of past data at relatively short time intervals. Examples include studies on changes of land use or restoration, model performance validation, or spin-up models set to an internal state for simulation runs. The basic idea of reanalysis is to carry out simulations with assimilation of past data using a non-changing forecasting and analysis system (Kalnay et al., 1996). Reanalyses are carried out on global scales and provide a large number of surface data variables of interest to hydrology; however, these variables are often simulation products and the spatial resolution is quite coarse at approximately 2 to 2.5 degrees (approximately 210 to 280 km at the equator). Results can be accessed through the Internet. The National Centers for Environmental Prediction provide the results of the NCEP/NCAR Reanalysis Project covering the period 1948 to present (Kalnay et al., 1948) and of the NCEP-DOE AMIP II Reanalysis project (Kanamitsu et al., 2002) covering the period 1979 to present (see The European Centre for Medium-Range Weather Forecasts carried out the ERA-15 ( ) and the ERA-40 reanalysis (Uppala et al., 2005) for (see Long-term climate forecasts may prove useful in assessment of renewable fresh water resources. Although the effects of climate change on hydrological cycles are yet uncertain, changing spatial and temporal precipitation patterns need to be considered in light of growing populations and of increasing demands for irrigation and industrial waters, particularly in developing countries. Current water resources assessments often lack seasonal details, whereas current climate scenario predictions suggest an increasing variability in seasonal and annual extreme weather patterns (Oki and Kanae, 2006). One way to reduce uncertainty in assessment of future hydrological cycles is the use of multimodel ensemble evaluations (Nohara et al., 2006), which can be driven by multiple climate forecasts. 5. Concluding Remarks In order to assess and predict the short and long-term hydrological effects of humaninduced changes on the earth system and the hydrological risk associated with changes in Formatted: Line spacing: single Deleted: were, however, Deleted: improving Deleted: model Deleted: it is noted that Deleted: view of the requirement to prevent Deleted: change Deleted: to Deleted: to a Deleted: simulation Deleted:. Deleted:. However Deleted: effect Deleted: demand Deleted: in

5 population concentrations, changing living patterns and economic development, hydrologists will need increasing amounts of weather and climate information. Forecasts of water resources and runoff are to a large extent dependent on the availability of weather and climate forecasts. Improvement in the predictive skills of atmospheric models will likely improve the capabilities of hydrological models, too. The costs of routine acquisition of hydrometeorological data as well as weather, climate and hydrological forecasts are probably only a small fraction of the potential economic and societal benefit or loss reduction that can be gained from this information; however, technological advances in data provision from ground observation, remote sensing and prediction systems requires further investigation of how to best utilize and combine these data sources in solving practical hydrological problems and understanding the water cycle in general. References Bartholmes, J., and Todini, E., Coupling meteorological and hydrological models for flood forecasting, Hydrol. Earth Sys. Sci. 9: , Deleted: prediction skill Deleted: that and Deleted:. However Deleted: also Deleted: to Deleted: investigate Deleted: of Chen, X., Zhang, D., and Zhang, E., The south to north water diversions in China: review and comments, J. Environ. Plann. Management, 45: , De Roo, A., Schmuck, G., Perdigao, V., and Thielen, J., The influence of historic land use changes and future planned land use scenarios on floods in the Oder catchment, Phys. Chem. Earth, 28: , Ducci, D., GIS techniques for mapping groundwater contamination risk, Natural Hazards, 20: , Ewen, J., Sloan, W.T., Kilsby, C.G., and O Connell, E.P., UP modelling system for largescale hydrology: deriving large-scale physically-based parameters for the Arkansas-Red River basin, Hydrol. Earth Sys. Sci., 3: , Gouweleeuw, B.T., Thielen, J., Franchello, G., De Roo, A.P.J., and Buizza, R., Flood forecasting using medium-range probabilistic weather prediction, Hydrol. Earth. Sys. Sci., 9: , Hay, L.E., Clark, M.P., Pagowski, M., Leavesley, G.H., and Gutowski Jr., W.J., One-way coupling of an atmospheric and a hydrologic model in Colorado, J. Hydrometeor., 7: , Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D., The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77: , 1996.

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