Appendix B
Selected Papers Presented at Workshop on Predictability and Limits-to-Prediction In Hydrologic Systems



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems Appendix B Selected Papers Presented at Workshop on Predictability and Limits-to-Prediction In Hydrologic Systems

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems This page in the original is blank.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems PREDICTABILITY OF REGIONAL HYDROLOGIC SYSTEMS ASSOCIATED WITH TERRESTRIAL COUPLING Randy Koster NASA Goddard Space Flight Center Seasonal Prediction: Recent Results From NSIPP Seasonal prediction of meteorological conditions cannot rely on the initialization and modeling of the atmosphere alone, since the timescales over which atmospheric anomalies dissipate are much too short. Seasonal forecasting must instead rely on the modeling of slower components of the earth system—namely, the oceans and the land surface. Although the ocean has the longer memory of the two, various studies (e.g., Kumar and Hoerling, 1995; Trenberth et al., 1998; Shukla, 1998; Koster et al., 2000) suggest that ocean conditions have only a limited impact on predictability over midlatitude continents. Thus, the memory associated with land surface soil moisture may turn out to be the chief source of midlatitude forecast skill. The accurate initialization and modeling of soil moisture can contribute to a seasonal forecast only if two conditions are met: (1) the soil moisture has adequate “memory” (i.e., an anomaly lasts well into the forecast period) and (2) the atmosphere responds in a predictable way to the soil moisture anomaly. Various studies in the literature have addressed soil moisture memory and atmospheric response, both in the real world and in the modeling environment (Delworth and Manabe, 1988; Vinnikov et al., 1996; Huang et al., 1996; Liu and Avissar, 1999). In this paper, in place of a comprehensive literature review, we illustrate some key issues with recent research performed under the National Aeronautics and

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems Space Administration (NASA) Seasonal-to-Interannual Prediction Project (NSIPP). Soil Moisture Memory We recently manipulated the water balance equation at the soil surface into a relationship between the autocorrelation of soil moisture and the statistics of the atmospheric forcing, the variance of soil moisture at the beginning of the time period in question, and the structure of the land surface scheme used (Koster and Suarez, in prep.). The equation, despite its various approximations, successfully reproduces, to first order, the spatial distribution of soil moisture autocorrelation produced by the NSIPP modeling system. Figure 1, for example, shows that although many fine-scale details are missed, the equation captures the large-scale structure of the simulated 30-day-lagged autocorrelation for July. The equation works far better than the more traditional “water holding capacity divided by atmospheric demand” approach. Further manipulation of the equation reveals four distinct physical controls on soil moisture memory: (1) temporal memory in the precipitation and radiation forcing fields, as perhaps induced by land–atmosphere feedback, (2) nonstationarity in the statistics of the forcing, as induced by seasonality, (3) reduction in anomaly size through the functional dependence of runoff on soil moisture, and (4) reduction in anomaly size through the functional dependence of evaporation on soil moisture. The relative importance of each control can be established through analysis of climate model data; thus, the equation can be used to characterize and explain geographical variations in simulated soil moisture memory. For example, the main physical control on memory loss in the eastern United States is seasonality of precipitation, and its impact is not large. Memory is reduced much more to the West because of the evaporation effect, which is influenced in part by low water holding capacities there. Autocorrelations in the far West increase again because of precipitation seasonality (acting in the opposite direction) and precipitation persistence.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems Atmospheric Response to Soil Moisture Anomalies A recent study of the atmosphere's responsiveness to soil moisture anomalies focused on two ensembles of simulations with the NSIPP modeling system (Koster et al., 2000). Ensemble 1 consisted of 16 45-year simulations with interannually varying sea surface temperatures (SSTs) and interactive land surface processes. Ensemble 2 was similar except that land–atmosphere feedback was effectively deactivated; interannually varying land surface evaporation efficiencies (derived from a single member of Ensemble 1) were prescribed in each simulation of Ensemble 2. Figure 2 shows the main result. The precipitation statistics from each ensemble were transformed into an index that describes the robustness of precipitation response to the specified boundary conditions. If, at a given point, all members of an ensemble produce basically the same time series of precipitation, then this index has a value close to 1, and we say that precipitation at that point is tied strongly to the surface boundary conditions—precipitation is predictable if the surface boundary conditions are themselves predictable (at least for the general circulation model (GCM) climate). If, on the other hand, the different ensemble members produce very different time series of precipitation, then the index is close to zero, and the potential for predictability is low. In this case, chaotic atmospheric dynamics overwhelm any control on precipitation imposed by the boundary conditions. The left plot shows this “robustness” index over North America, as computed from boreal summer data (JJA) from Ensemble 1. Notice that foreknowledge of SSTs contributes to the predictability of precipitation only in the tropical areas. The right plot shows this index as computed from Ensemble 2. Foreknowledge of land surface moisture conditions leads to enhanced predictability over a significant part of midlatitude North America. The land's contribution to precipitation predictability can be isolated by subtracting the values in the left plot from those in the right plot. Over North America, and in fact across the globe, the land contributions are highest in the transition zones between humid and dry areas.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems The low contribution in dry areas appears to reflect the low flux of evaporated moisture into the lower atmosphere. Contributions are low in humid areas partly because evaporation there is controlled more by atmospheric demand than by variations in soil moisture. Unfortunately, Figures 1 and 2 cannot easily be compared, since the underlying simulations were performed at different resolutions—Figure 1 is based on runs with the 2X2.5 GCM, whereas Figure 2 is based on runs with the 4X5 version, with a correspondingly different climatology. Nevertheless, the figures suggest that land contributions can be high where they need to be—namely in regions with significant soil moisture memory. Indeed, soil moisture memory is fostered by land–atmosphere feedback that promotes precipitation persistence. Two more results, though preliminary, are included here. The first comes from an idealized experiment in which all surface boundary conditions, including temperatures, are assumed to be perfectly known into the future. The NSIPP atmospheric GCM was first run for a specific July, using climatological SSTs. At each time step in the simulation, the values of all land surface model prognostic variables were written out to a special file. Then, an ensemble of 16 Julys using the same SSTs was run. At each time step of each member simulation, the updated values of all land surface prognostic variables were discarded and replaced by values read in from the special file. Thus, although the members of the ensemble differed because of their different atmospheric initial conditions, each was forced to maintain the same time series of (geographically varying) land surface prognostic variables. By quantifying the variations of atmospheric variables (precipitation, air temperature, etc.) seen between the ensemble members, using techniques similar to those used to generate Figure 2, we generate the estimates of land–atmosphere feedback strength shown in Figure 3a. Note that this experiment is basically a simple, computationally cheap version of that which produced Figure 2. The idea is to promote an intercomparison of coupling strength among different models. Shown in Figures 3b and 3c are corresponding results for two other GCMs (Andrea Hahmann and Paul Dirmeyer,

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems personal communication., 2000). The distinction between the GCMs is obvious; the NSIPP general circulation model (GCM) clearly shows a higher land–atmosphere feedback strength than either the CCM or COLA GCMs. How do we know which level of feedback strength is most realistic? An additional experiment addresses this, though the results are currently inconclusive. The “control” in this experiment is an ensemble of Atmospheric Model Intercomparison Project (AMIP)-type simulations in which prescribed, realistic SSTs are used to force the GCM over the time period 1996–1999. The corresponding “experiment” ensemble is identical to the control ensemble except for one thing—at every time step in a member simulation, the precipitation generated by the GCM over the United States is replaced by observed precipitation (from a special hourly dataset generated by Wayne Higgins at National Centers for Environment Prediction (NCEP)) just before it hits the ground. Only the land surface feels this more realistic precipitation; the GCM's water vapor fields and the latent heating of the atmosphere are not replaced. The land surface presumably develops more realistic soil moisture states in response to the more realistic precipitation forcing. Three global precipitation datasets are then compared: (1) the observed precipitation; (2) the precipitation from the AMIP-style runs (i.e., precipitation guided only by SST variability), and (3) the (constantly replaced) precipitation generated by the GCM in the experiment ensemble (i.e., precipitation guided by both SST variability and the presumably more realistic soil moistures). If the precipitation generated in the experiment ensemble is significantly closer to the observations than that generated in the AMIP ensemble, then we will have demonstrated a positive impact of more realistic soil moisture on precipitation in the GCM, and we will have also shown that land–atmosphere feedback is operating in the real world. Some improvement is indeed seen in our preliminary runs—Figure 4 shows significant reductions in precipitation error over the United States, especially in summer.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems FIGURE 4 Seasonal Prediction: Science Questions and Research Needs The results above serve as background for three science questions related to seasonal prediction. Over what timescales can soil moisture be predicted in the real world? How well can these timescales be simulated in a modeling system? The analysis leading to Figure 1 shows that several factors can influence soil moisture memory. How relevant is each factor in the real world, and do GCMs simulate their relative importance correctly? Can we specify regions and seasons for which useful soil moisture memory is essentially unattainable?

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems that is as large or larger than claimed for the radiative effect of doubled CO2. Landscape change has been accelerating and our largest alterations have occurred in recent years (Leemans 1999; O'Brien 2000). This conclusion requires that we must include landscape as an initial value within models of the climate. The success of seasonal weather forecasts has primarily been a result of the treatment of the sea surface temperatures as a static lower boundary condition (Landsea and Knaff 2000). These seasonal "nowcasts" work because the dominant atmospheric-ocean feedbacks occur over a relatively long time (i.e., longer than a season). Similarly, antecedent soil moisture provides a long enough memory for useful seasonal nowcasts (such an inertia within the climate system can help explain the persistent of the Texas drought and heat this summer, since the vegetation in the region is not transpiring due to poor soil moisture). The lack of skilled multi-year SST predictions apparently results because the forecasts on this time period are no longer nowcasts, but true initial value problems with the inherent limitation on the ability to forecast the future. For these reasons, the concept of weather prediction as an "initial value problem" while "climate is a boundary problem" is being replaced with the new paradigm that weather occurs over a short enough time period so that many of the feedbacks within the climate system do not occur. Both weather and climate are, in fact, initial value problems. "Weather" should be viewed as one subset of the "climate" system.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems REFERENCES FOR PRESENTED PAPERS Anagnostou, E. N., and W. F. Krajewski. 1998. Calibration of the NEXRAD precipitation processing subsystem. Weather and Forecasting 13:396-406. Anagnostou, E. N., and W. F. Krajewski. 1999. Real-time radar rainfall estimation. Part 1: algorithm formulation. Journal of Atmospheric and Oceanic Technology 16:189-197. Andrieu H., and J. D. Creutin. 1995. Identification of vertical profiles of reflectivities for hydrological applications using an inverse method. Part I: formulation. J. Appl. Meteor. 34:225-239. Arkin, P. A., and P. Xie. 1994. The global precipitation climatology project: First algorithm intercomparison project. Bull. Am. Meteorol. Soc. 75:401-419. Chen, F., R. A. Pielke, Sr., and K. Mitchell. 2000. Development and application of land-surface models for mesoscale atmospheric models: Problems and promises. AMS Monograph. Chu, P. 1999. Two kinds of predictability in the Lorenz system. J. Atmos. Sciences 56:1427-1432. Ciach, J. G., and W. F. Krajewski. 1999a. On the estimation of radar rainfall error variance. Advances in Water Resources 22:585-595.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems Ciach, G. J., and W. F. Krajewski. 1999b. Conceptualization of radar-raingage comparisons under observational uncertainties. Journal of Applied Meteorology 38:519-1525. Ciach, G. J., M. L. Morrissey, and W. F. Krajewski. 2000. Conditional bias in radar rainfall estimation. Journal of Applied Meteorology. Crook, N. A. 1996. Sensitivity of moist convection forced by boundary layer processes to low-level thermodynamic fields. Mon. Wea. Rev. 124:1767-1785. Delworth, T., and S. Manabe. 1988. The influence of potential evaporation on the variabilities of simulated soil wetness and climate. J. Climate 1:523-547. Eastman, J. L., M. B. Coughenour, and R. A. Pielke. 2000. The effects of CO2 and landscape change using a coupled plant and meteorological model. Global Change Biology. Epstein, E. S. 1969. Stochastic dynamic prediction. Tellus 21:739-759. Errico, R., and D. Stensrud. 2000. Estimation of Error Statistics of Precipitation Produced by Convective Parameterization Schemes for Application to the Variational Assimilation of Precipitation Observations. Draft Manuscript. Fankhauser, J. C. 1998. Estimates of thunderstorm precipitation efficiency from field measurements in CCOPE. Monthly Weath. Rev. 116:663-684. Ferrier, B. S., J. Simpson, and W-K Tao. 1996. Factors responsible for precipitation efficiencies in midlatitude and tropical squall simulations. Monthly Weath. Rev. 124:2100-2125. French, M. N., and W. F. Krajewski. 1994. A model for real-time quantitative rainfall forecasting using remote-sensing, 1, formulation. Water Resources Research 30:1075-1083. Gent, P. R. 2000. Will the North Atlantic Ocean Thermohaline Circulation Weaken During the Next Century? Georgakakos, K. P. 2000. Covariance propagation and updating in the context of real-time radar data assimilation by quantitative precipitation forecast models. Journal of Hydrology.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems Grecu, M., and W. F. Krajewski. 2000a. Rainfall forecasting using variational assimilation of radar data in numerical cloud models. Advances in Water Resources. Grecu, M., and W. F. Krajewski. 2000b. A Comprehensive investigation of statistical procedures for radar-based shortterm, quantitative precipitation forecasting, Journal of Hydrology. Grecu, M., and W. F. Krajewski. 2000c. An efficient methodology for detection of anomalous propagation echoes in radar reflectivity data using neural networks. Journal of Oceanic and Atmospheric Technology 17:121-129. Habib, E., W. F. Krajewski, V. Ne_por, and A. Kruger. 1999. Numerical simulation studies of raingage data correction due to wind effect. Journal of Geophysical Research-Atmospheres Research 104:(19)723-734. Harris, D., and E. Foufoula-Georgiou. 2000. Subgrid variability and stochastic downscaling of modeled precipitation and its effects on radiative transfer computations. J. Geophys. Res. Harris, D., E. Foufoula-Georgiou, K. Droegemeir and J. Levit. 2000. Multiscale statistical properties of a high resolution precipitation forecast. J. Hydrometeorology. Held, I. M. 1993. Large-scale dynamics and global warming. BAMS 74:228-241. Huang, J., and H. M. van den Dool. 1993. Monthly precipitation-temperature relation and temperature prediction over the U.S. J. Climate 6:1111-1132. Huang, J., H. M. van den Dool, and K. G. Georgakakos. 1996. Analysis of model-calculated soil moisture over the U.S. (1931-1993) and applications to long range temperature forecasts. J Climate. 9:1350-1362. Huffman, G. J., R. F. Adler, P. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, A. McNab, B. Rudolf, and U. Schneider. 1997. The global precipitation climatology product (GPCP) combined precipitation data set. Bull. Am. Meteorol. Soc. 78:5-20.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems Joss, J., and R. Lee. 1995. The application of radar-gauge comparisons to operational profile corrections. J. Appl. Meteor. 34:2612-2630. Kitchen M., R. Brown, A. G. Davis. 1994. Real time correction of weather radar for effects of bright band, range and orographic growth in widespread precipitation. Q.J.R. Meteorol. Soc. 120:1231-1254. Koster, R. D., and P. C. D. Milly. 1997. The interplay between transpiration and runoff formulations in land surface schemes used with atmospheric models. J . Climate. 10:1578-1591. Koster, R. D., M. J. Suarez, and M. Heiser. 2000. Variance and predictability of precipitation at seasonal-to-interannual timescales. J. Hydrometeor. 26-46. Krajewski, W. F., G. J. Ciach, J. R. McCollum, and C. Bacotiu. 2000. Initial validation of the global precipitation climatology project over the United States. Journal of Applied Meteorology 39:1071-1086. Kumar, A., and M. P. Hoerling. 1995. Prospects and limitations of seasonal atmospheric GCM predictions. Bull. Amer. Met. Soc. 335-345. Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson. 1998. The tropical rainfall measuring mission (TRMM) sensor package. Journal of Atmospheric and Oceanic Technology 15:809–817. Landsea, C. W., and J. A. Knaff. 2000. How much skill was there in forecasting the very strong 1997-98 El Nino? Bull. Amer. Meteor. Soc. 81:2107-2119. Lee, T.-H., and K. P. Georgakakos. 1996. Operational rainfall prediction on meso-gamma scales for hydrologic applications. Water Resources Research 32:987-1003. Leemans, R. 1999. Land-use change and the terrestrial carbon cycle. IGBP Global Change Newsletter 37:24-26. Legates, D. R., and T. L. DeLiberty. 1993. Precipitation measurement biases in the United States. Water Resources Bulletin 29:855-861. Leith, C. E. 1974. Theoretical skill of Monte-Carlo forecasts. Mon. Wea. Rev. 102:409-418.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems Liu, Y., and R. Avissar. 1999. A study of persistence in the land-atmosphere system using a general circulation model and observations. J. Clim. 2139-2153. Lohmann, D., and co-authors. 1998. The project for the intercomparison of land-surface parameterization schemes (PILPS) phase 2c Red Arkansas river basin experiment: 3. spatial and temporal analysis of water fluxes. Glob. and Planet. Change 19:161-180. Lorenz, E. N. 1975. Climate predictability. The physical basis of climate modeling. WMO GARP Publication Series 16:132-136. Lorenz, E. 1969. The predictability of a flow, which possesses many scales of motion. Tellus 3:289-307. Lovelock, J. 1995. The Ages of Gaia. Oxford University Press, Oxford, UK. Lu, L., R. A. Pielke, G. E. Liston, W. J. Parton, D. Ojima, and M. Hartman. 2000. Imple-mentation of a two-way interactive atmospheric and ecological model and its application to the central United States. J. Climate. Ne_por, V., and B. Sevruk. 1999. Estimation of wind-induced error of rainfall gauge measurements using a numerical simulation. Journal of Atmospheric and Oceanic Technology 16:450-464. Nykanen D, E. Foufoula-Georgiou, and W. Lapenta. 2000. Impact of small-scale precipitation variability on larger-scale spatial organization of land-atmosphere fluxes. J. of Hydro-meteorology. Nykanen D., and E. Foufoula-Georgiou. 2000. Soil moisture variability and its effect on scale-dependency of nonlinear parameterizations on coupled land-atmosphere models. Advances in Water Resources. O'Brien, K. L. 2000. Upscaling tropical deforestation: Implications for climate change. Climatic Change 44:311-329. Petty, G., and W. F. Krajewski. 1996. Satellite estimation of precipitation. Hydrological Sciences Journal 41:433-451. Petty, G. W. 1995. The status of satellite-based rainfall estimation over land. Remote Sens. Environ. 51:125-137.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems Pielke, R. A., and X. Zeng. 1994. Long-term variability of climate. J. Atmos. Sci. 51:155-159. Pielke, R. A. 1998. Climate prediction as an initial value problem. Bull. Amer. Meteor. Soc. 79:2743-2746. Pielke, R. A. 2000. Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev. Geophys. Pielke, R. A., G. E. Liston, J. L. Eastman, L. Lu, and M. Coughenour. 1999. Seasonal weather prediction as an initial value problem. J. Geophys. Res. 104:19463-19479. Pielke, R., Sr., K. Reckhow, and F. Swanson. 2000. New Interdisciplinary Initiative Combines Water, Earth, and Biota, Trans. AGU (EOS). PIP-1. 1994. The first WetNet precipitation intercomparison project. Remote Sensing Reviews 11:373. Pitman, A., R. Pielke Sr., R. Avissar, M. Claussen, J. Gash, and H. Dolman, The role of the land surface in weather and climate: Does the land surface matter. IGBP Newsletter, 39, 4-11, 1999. Reiners, W. A. 1988. Complementary models for ecosystems. American Naturalist 127:59-73. Ross, R. J., and W. P. Elliot. 1996. Tropospheric water vapor climatology and trends over North America: 1973-1993. J. Clim. 9:3561-3574. Schlosser, C. A., and P. C. D. Milly. 2000. The potential impact of soil moisture initialization on soil moisture predictability and associated climate predictability. Proceedings of the GEWEX/BAHC International Workshop on Soil Moisture Monitoring, Analysis, and Prediction for Hydrometeorological and Hydroclimatological Applications. Pg. 31. Seo, D. J., J. P. Breidenbach, R. Fulton, D. Miller and T. O’Banon. 2000. Real time adjustment of range dependent biases in WSR-88D rainfall estimates due to nonuniform vertical profile of reflectivity. Journal of Hydrometeorology. Shimizu, K., D. A. Short, and B. Kedem. 1993. Single- and double-threshold methods for estimating the variance of area rain rate. J. Meteor. Soc. Japan 71:673-683.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems Short, D. A., K. Shimizu, and B. Kedem. 1993a. Optimal thresholds for the estimation of area rain rate moments by the threshold method. J. Appl. Meteor. 32:182-192. Short, D. A., D. B. Wolff, D. Rosenfeld, and D. Atlas. 1993b. A study of the threshold method utilizing rain gauge data. J. Appl. Meteor. 32:1379-1387. Shukla, J. 1998. Predictability in the midst of chaos: A scientific basis for climate forecasting. Science 728-731. Simpson, J., C. Kummerow, W.-K. Tao, and R. F. Adler. 1996. On the tropical rainfall measuring mission (TRMM). Meteor. Atmos. Phys. 60:19-36. Steiner, M., J. A. Smith, S. J. Burges, C.V. Alonso, and R. W. Darden. 1999. Effect of bias adjustment and rain gauge data quality control on radar rainfall estimation. Water Resour. Res. 35(8):2487-2503. Sun, J.-Z., and Crook, N. A. 1997. Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. 1. Model development and simulated data experiments. Journal of the Atmospheric Sciences 54:1642-1661. Trenberth, K. E. 1998. Atmospheric moisture residence times and cycling: Implications for rainfall rates with climate change. Climatic Change 39:667-694. Tustison, B., D. Harris, and E. Foufoula-Georgiou. 2000. Scale issues in verification of precipitation forecasts. J. Geophys. Res. Vieux, B. E., V. F. Bralts, L. J. Segerlind, and R. B. Wallace. 1990. Finite element watershed modeling: One-dimensional elements. J. of Water Resources Management Planning 116:803-819. Vieux, B. E. 1991. Geographic information systems and nonpoint source water quality modeling. J. of Hydrological Processes 5:110-123. Vieux, B. E. 1993. DEM aggregation and smoothing effects on surface runoff modeling. J. of Computing in Civil Engineering 7:310-338.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems Vieux, B. E., and N. Gaur. 1994. Finite element modeling of storm water runoff using GRASS GIS. Microcomputers in Civil Engineering 9:263-270. Vieux, B. E., F. LeDimet, and D. Armand. 1998. Optimal control and adjoint methods applied to distributed hydrologic model calibration. Proceedings of Int. Assoc. for Computational Mechanics, IV World Congress on Computational Mechanics, 29 June-2 July, Buenos Aires, Argentina. Pg. II1050. Vieux, B. E. 2000. Distributed Hydrologic Modeling Using GIS. Kluwer Academic Publishers, Spuilboulevard 50, Dordrecht, The Netherlands. Water Science and Technology Series. Vinnikov, K. Y., A. Robock, N. A. Speranskaya, and A. Schlosser. 1996. Scales of temporal and spatial variability of midlatitude soil moisture. J. Geophys. Res. 7163-7174. Webster, P. J. 1994. The role of hydrological processes in ocean-atmosphere interactions. Reviews of Geophysics 32:427-436. Wilson, J. W., N. A. Crook, C. K. Mueller, J. Sun, and M. Dixon. 1998. Nowcasting thunderstorms: A status report. Bulletin of the American Meteorological Society 79:2079-2099. Yang, D., B. E. Goodisson, and J. R. Metcalfe. 1998. Accuracy of NWS 8” standard nonrecording precipitation gauge: Results and application of WMO intercomparison. Journal of Atmospheric and Oceanic Technology 15:54-67. Young, B., A. A. Bradley, W. F. Krajewski, and A. Kruger. 2000. An evaluation study of NEXRAD multisensor precipitation estimates for operational hydrologic forecasting. Journal of Hydrometeorology 1:241-254. Zawadzki, I., J. Morneau, and R. Laprise. 1999. Predictability of precipitation patterns--An operational approach. Journal of Applied Meteorology 33:1562-1571. Zeng, X., R. A. Pielke, and R. Eykholt. 1990. Chaos in Daisy-world. Tellus 42B:309-318. Zepeda-Arce, J., E. Foufoula-Georgiou, and K. Droegemeier. 2000. Space-time rainfall organization and its role in vali

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems dating quantitative precipitation forecasts, J. Geophys. Res. 105(10):129-146. Zhai, P., and R. E. Eskridge. 1997. Atmospheric water vapor over China. J. Clim. 10:2643-2652.

OCR for page 41
Report of a Workshop on Predictability & Limits-to-Prediction in Hydrologic Systems This page in the original is blank.