The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114,, doi: /2008jd010969, 2009 The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting Haibin Li, 1 Lifeng Luo, 1 Eric F. Wood, 1 and John Schaake 2 Received 11 August 2008; revised 2 December 2008; accepted 9 January 2009; published 27 February [1] A series of hydrologic forecasts with lead times up to 6 months are conducted to investigate the relative contributions of atmospheric forcing and hydrologic initial conditions (IC) to the overall errors in hydrologic forecasting during cold and warm seasons. These experiments are known as the ensemble streamflow prediction (ESP) and the reverse-esp (R-ESP). Analysis of these hindcasts suggests that IC uncertainties outweigh forcing uncertainties thus dominating forecast errors in a short lead time up to about 1 month; at longer lead times, forcing uncertainties become a more important contributor. Further investigation shows that forecast errors at short lead times due to uncertain ICs are mainly determined by the prescribed IC variability, while the evolution of forecast errors due to imperfect atmospheric forcings mainly corresponds to the interannual variability of precipitation. With respect to difference in forecasts initialized in winter and summer times, ICs tend to have longer impacts on warm season forecasts than on cold season ones, due mainly to drier initial moisture state in the summer time. As far as the basin size is concerned, we find that the larger the basin, the stronger the impacts from ICs at short lead times. Small basins are more sensitive to forcing fields. Regardless of basin size, forcing uncertainties dominate relative forecast errors for long lead times. In order to see whether statistically downscaled forcing fields from dynamic climate models are more skillful than traditional ESP, we conducted additional ESP-type experiments using the statistically downscaled climate forecast system (CFS) fields to drive the hydrological model. In comparison to traditional ESP, the IC errors show a larger impact on the forecasts when forced by the CFS fields, which suggests that the latter contains more skill than the traditional ESP approach. Citation: Li, H., L. Luo, E. F. Wood, and J. Schaake (2009), The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting, J. Geophys. Res., 114,, doi: /2008jd Introduction [2] The chaotic nature of the climate system prevents accurate weather prediction beyond about two weeks. With slightly different initial values, the simulated system ends up in quite different states. This initial-value issue, known as the butterfly phenomena first described by Lorenz, plays a crucial role in weather forecasting (i.e., the first type predictability [Lorenz, 1963]). Initial conditions (ICs) are also important for seasonal prediction in that the predictability at this timescale is associated with the starting conditions of the climate system s lower boundaries [Palmer and Anderson, 1994]. On the other hand, the slowly changing property of the lower boundary conditions, particularly the tropical sea surface temperature (SST), provides the rationale for forecasting on seasonal and longer timescales (the second type predictability [Lorenz, 1993]). The prerequisite is, of course, that we can predict the evolution of 1 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA. 2 NOAA NWS, Annapolis, Maryland, USA. Copyright 2009 by the American Geophysical Union /09/2008JD SSTs well. As such, how predictable the system is depends on how well we know the initial and boundary conditions. In other words, forecast skills are limited by our knowledge of IC and boundary conditions, and the inherent uncertainties are actually the primary sources resulting in forecast errors. [3] For hydrologic forecasting, the ICs refer to the land surface state, often the moisture terms namely soil moisture and snow cover. Numeric modeling studies suggest that a realistic initial soil moisture state can help enhance seasonal atmospheric prediction [Fennessy and Shukla, 1999]. This can be attributed to long memory of soil moisture resulting from the relatively large soil storage capacity. As anomalous dry or wet conditions may take weeks to months to dissipate [Mahanama and Koster, 2003], so does the impact from ICs on the subsequent forecasts. Koster et al. [2000] further pointed out that foreknowledge of land surface moisture states in a long-range forecasting system would be especially beneficial in transition zones between dry and humid climates due to relatively strong land and atmosphere interaction over those regions, which enhances the impact of ICs. Snow cover plays a similar or even bigger role than soil moisture over climate zones where snow represents the major water resource. 1of10

2 [4] For streamflow prediction, it s been well known for many decades that surface soil moisture conditions control the partitioning of precipitation into infiltration and surface runoff. Additionally, surface moisture conditions influence the soil moisture profile, the depth to the water table and subsurface drainage that contributes to streamflow during nonrain periods [Kirkby, 1978]. The initial soil moisture conditions have been shown to influence the magnitude of floods [e.g., Sivapalan et al., 1990]. In engineering hydrology, empirical indices are used operationally to predict runoff volumes from design rainfall events [Dingman, 2002]. While the connections between surface moisture IC and streamflow processes are well established, especially for very short-term predictions using observed precipitation, the effect of IC and its uncertainty are not well quantified for longer-term seasonal streamflow predictions that is the focus of this paper. Maurer et al. [2004] showed that knowledge of the land surface state during drought (i.e., dry conditions) improved streamflow predictability at lead times up to about 4 months. Unfortunately, the exact ICs can never be known because of lack of accurate, complete description of the land surface state, despite the apparent importance. This suggests there is always unavoidable uncertainty associated with the IC that will propagate into forecasts. Notably, recent acrossinstitution initiatives, especially the North American Land Data Assimilation System (NLDAS) [Mitchell et al., 2004], are able to provide near real-time land surface observations that can be used to give good estimates of ICs. For snowdominated western US, SWE assimilation is demonstrated to help further improve the ICs in winter and spring [Wood and Lettenmaier, 2006]. [5] Meteorological forcing (precipitation and temperature in particular), another major source of uncertainty contributing to forecast error, can be analogously thought as the lower boundary conditions in hydrologic forecasting. Precipitation is notoriously hard to model. On scales of 1000 km the predictability of rainfall is still a challenge [Palmer and Anderson, 1994], let alone on the finer scale of km for hydrologic application to which coarse resolution forcing fields have to be downscaled. It is also on such spatial scales that the fundamental role of precipitation in hydrological forecasting has long been recognized. [6] Uncertainty is, de facto, a fundamental characteristic of prediction which varies by climate characteristics, geographic location, and length of forecast [National Research Council, Committee on Estimating and Communicating Uncertainty in Weather and Climate Forecasts, 2006]. Even though experienced forecasters may have an approximate perspective of the underlying uncertainty, to date there are very few case studies that have quantified this uncertainty. Wood and Lettenmaier [2008] used a combination of the traditional ensemble streamflow prediction (ESP) and reverse-esp (R-ESP) (see section 2 for technique details) to understand the relative role of IC versus forcing uncertainty in determining hydrologic forecasting errors for several basins in the western U.S., where the snowpack from the cold season serves as the primary source of water from spring snowmelt. Their preliminary results suggest that ICs play a big role during the transitional period from cold to warm seasons. Understanding the balance between ICs and forcing uncertainty in nonsnow-dominated regions (or snow-free climates) provides the motivation behind our Figure 1. The simulation domain and river network for the Ohio River Basin (dark gray) and Southeast U.S. (light gray). Circles represent gage stations falling into different drainage area categories: 10 2 km 2, 10 3 km 2, >10 5 km 2, respectively. The circle with a cross sign enclosed represents the outlet for the Ohio River Basin at Metropolis, IL. study. Thus the paper offers a broader picture by providing results from more diverse climate regimes that will help hydrologic forecasters improve current forecasting frameworks and achieve better forecast skill. In this paper we carry out our experiments over the Ohio River Basin and the Southeast U.S. (SE) as shown in Figure 1. The experiments address the following specific research questions. [7] 1. How do the relative forecast errors evolve with lead time? [8] 2. Do the relative forecast errors initialized in warm season differ from that in cold season? [9] 3. Are the contributions from IC and forcing uncertainties to the overall forecast errors basin-size dependent? [10] The first two questions address the time dependence of forecast errors. Question 3 investigates the spatial patterns. In recent years, there is a rapid growth in developing dynamic model-based ensemble forecasts. This offers the hydrological community a different tool to the traditional ESP method. In order to use ensemble outputs from dynamic models as forcing inputs to hydrologic forecasts, bias correction and spatial downscaling have to be performed because of the aforementioned reasons, which can be achieved by statistical methods [e.g., Luo et al., 2007]. An interesting question that naturally arises is whether statistically downscaled forcing from dynamic climate models offers advantages over the traditional ESP approach. This leads to the forth question we are going to address in this paper. [11] 4. Do forecasts made with downscaled CFS forcing contain more useful information than traditional ESP, which is based on resampling the historical meteorological record? 2of10

3 Figure 2. Schematic illustration of hydrologic forecasts by ESP and R-ESP methods in a hindcast framework. For a given hindcast year, ESP uses the perfect ICs, derived by using corresponding perfect retrospective forcing fields for spinup period. Then an ensemble forcing, generated from randomly resampling of historical observations, is used to generate ensemble forecasts with its spread representing forcing uncertainty. In a reverse sense, R-ESP uses an ensemble of ICs generated from random resampling of retrospective observations for spinup, and then uses corresponding perfect retrospective forcing fields during forecast period with spread representing IC uncertainty. [12] The paper is structured as follows: section 2 details the methodology and experimental design; the results and analysis are presented in section 3, followed by a brief summary and discussion in section Methodology and Experimental Design [13] To investigate the relative contribution of hydrologic ICs and forcing uncertainties to the overall forecast errors in seasonal hydrologic forecasting, we adopt a framework introduced by Wood and Lettenmaier [2008] in which they contrast ESP [Twedt et al., 1977; Day, 1985] for forcing uncertainty representation with an approach they called reverse-esp (R-ESP) for IC errors assessment ESP [14] The ESP method is intended to capture the forecast uncertainty resulting from inherent forcing uncertainty. This approach has in theory the advantage of focusing on forcing uncertainties only. It is achieved by running a candidate hydrological model with observed meteorological fields for a long enough spinup period to the time of forecast to get true or the current initial land surface states. Then, ensemble hydrological forecasts are produced by running the same hydrological model into the future using ensemble forcing traces that are randomly sampled from the observed historical records (Figure 2) R-ESP [15] The R-ESP approach reverses the ESP structure as the name implies; it starts with an ensemble of ICs but forced by a single perfect meteorological forcing set during the forecast period (Figure 2). Rather than generating ensemble of ICs by perturbation, R-ESP derives the ensemble by forcing the hydrological model with randomly resampled meteorological fields during the spinup period up to the point of forecast, and then forces the model with the observed meteorology over the forecast period. Whereas ESP derives its skill from the ICs and the ensemble spread comes from boundary forcing uncertainty, R-ESP skill comes from boundary forcings and the ensemble spread from IC uncertainty [Wood and Lettenmaier, 2008] CFS-Based Forecast [16] The climate forecast system (CFS)-based [Saha et al., 2006] experiment is essentially the same as the traditional ESP experiment except the forcing ensembles are generated differently. Instead of using observational records, we use statistically downscaled CFS forcing fields (precipitation and temperature). Developed using Bayesian probability theory, the downscaling method calculates the statistical relationship between climate hindcasts and past observations, and applies the derived relationship to the raw forecasts to infer the posterior distribution for future observations (see the work of Luo et al. [2007] for details). We are interested in determining whether CFS (hereafter we use the term CFS to represent the ESP-type hindcast forced with downscaled CFS forcing) provides more skillful hydrological forecasts than resampling historical meteorology from accurate ICs, i.e., traditional ESP. [17] A series of 6-month ESP, CFS, and R-ESP forecasts are generated for the overlapping period between the observed meteorology [Maurer et al., 2002] and archived CFS forecasts [Saha et al., 2006]. For details about the observed meteorology and downscaled CFS forecasts, readers are referred to a recent paper by Luo and Wood [2008]. The forecasts are initialized from ICs generated at the end of January and July that will represent cold and warm season forecasts respectively over the study domains. Thus there are two forecast ensembles for each of the three experiments per year for the period with each forecast ensemble having 19 members drawn from the same historical period (Table 1). The model simulation is implemented with the macrohydrological model, namely, the Variable Infiltration Capacity (VIC) model [Liang et al., 1994] run at a daily time step. [18] An underling assumption in the framework is that the ICs for ESP and forcings used in R-ESP forecast period are error free. This helps simplify our underlying research questions by not worrying about other sources of errors. 3of10

4 Table 1. Summary of the Experimental Design Forecast Type ICs Forecast Length (6 months) Ensemble Members Hindcast Period Data Sets Used ESP End of Jan Feb July Observations [Maurer et al., 2002] R-ESP Downscaled CFS fields [Luo et al., 2007] CFS End of Jul Aug Jan Experiments that incorporate model errors will be considered in future work. As snow only covers the near lake northern edge of Ohio River Basin in winter, we will focus primarily on soil moisture and streamflow. Meanwhile, the analysis presented in section 3 is based on monthly mean of regional average of the forecast hydrologic variable (e.g., soil moisture, snow water equivalent) unless otherwise specifically described (e.g., streamflow). With respect to quantitative measurement, forecast error is quantified by the root mean square error (RMSE). The relative forecast error or simply relative error (RE) frequently used in this paper is defined as the ratio between forecast errors due to forcing uncertainties (ESP or CFS) and forecast errors resulting from IC uncertainties (R-ESP). [19] Similar to the definitions used by Wood and Lettenmaier [2008], let h im (t) represent the hydrologic forecast values of the variables to be analyzed from IC i and forcing m at lead time t. ICs are the hydrologic states for the same day of year from a period of I years. The forcings are the daily temperature and precipitation time sequence, beginning on the day of year of the ICs, from a period of M years. Here, both I and M are equal to 19. For a lead time t, the mathematical formulas of RMSE for ESP and R-ESP as well as RE can be written as vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi " # u1 X I 1 X M RMSE ESP ðþ¼ t t ðh im ðþ h t ii ðþ t Þ 2 t ¼ 1;...; 6 I M i¼1 m¼1 ð1þ impacts change with lead times, looking at the RMSE patterns for individual terms will help us have a clearer understanding of the evolution of RE. Forecast errors with lead times for ESP, CFS, and R-ESP experiments were plotted in Figure 3. Only soil moisture is shown since the patterns for streamflow are essentially identical. For Ohio River Basin, we routed simulated runoff with a linear routing model [Lohmann et al., 1996, 1998] to the outlet of the basin (the circle with an enclosed cross in Figure 1), which corresponds to the USGS gage station at Metropolis, IL. As there is no single river outlet representative of the entire SE, we simply used the runoff averages from all model grid points inside the basin to represent the basin average streamflow conditions. The RMSE for both ESP and CFS increases with lead times, but the RMSE for R-ESP decreases almost exponentially with the lead time. At 1-month lead time, RMSEs for R-ESP are larger than that in ESP or CFS regardless of the experiment (January or July). Roughly speaking, forecast errors for the first month from R-ESP correspond well to the variations of IC: the stronger the variability, the larger the forecast errors. Furthermore, forecast errors at 1-month lead time for forecasts initialized at the end of the January for SE stands out because the standard deviation of January ICs is at least 1.5 times larger than vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi " # u 1 X M 1 X I RMSE R ESP ðþ¼ t t ðh im ðþ h t mm ðþ t Þ 2 M I t ¼ 1;...; 6 m¼1 i¼1 ð2þ REðÞ¼ t RMSE ESPðÞ t RMSE R ESP ðþ t t ¼ 1;...; 6 ð3þ [20] If we put the forecast values in a matrix with the columns as the R-ESP (i.e., different ICs) and rows as the ESP (i.e., different forcing traces), then the diagonals happen to be the control or truth. From equation (3), we can conclude that the forecast is more sensitive to forcing quality if RE is larger than 1. Otherwise, the forecast is more prone to be affected by errors in the ICs. 3. Results and Analysis 3.1. How Do the Relative Forecast Errors Evolve With Lead Time? [21] Because the evolution of the RE, based on its definition, is determined by how forcing uncertainty and IC Figure 3. RMSE for soil moisture forecasts for ESP (solid), R-ESP (dashed line with cross), and CFS (dash-dotted). (top) Ohio; (bottom) SE. 4of10

5 Figure 4. (top) Seasonal cycles of monthly total precipitation and (bottom) coefficient of variations of monthly total precipitation. others. At 6-month lead time, R-ESP forecast errors drop to near zero because IC impacts by then become very small (or even negligible). On the other hand, the shape of RMSE for ESP or CFS roughly corresponds to the interannual variability of precipitation, defined as coefficient of variation (see Figure 4): the higher the precipitation variability, the lower the precipitation predictability. However, the first month forecasts always have the smallest errors, probably because of the combination of relatively strong IC impacts and the use of true initial moisture estimation in ESP-type experiment. [22] Figure 5 shows the calculated relative errors (REs) (log scale) with lead times for soil moisture, streamflow and snow water equivalent (SWE) respectively. Overall, IC matters more at short lead times but becomes less important as lead time increases. In contrast, forcing uncertainties play a much larger role in determining REs in comparison to ICs for lead times above 2 months. For soil moisture and streamflow, the dominance of ICs only lasts about 1 month and then succumbs to forcing uncertainties, which then dominate the REs. In agreement with the analysis for the Western U.S. by Wood and Lettenmaier [2008], the ICs have the longest impact on SWE, which is about 2 months in the cold season for Ohio. [23] Because of the way the experiments were set up, the forecasts can be displayed in a 2-D image (Figures 6 and 7). Taking soil moisture as an example, along the rows display the forecasts from different forcing traces and the columns are the results from the ensemble of ICs. The resulting patterns conveniently disclose the nature of the different impact from boundary forcings and ICs: if the forecast errors are primarily determined by forcing errors, then the patterns will be characterized with horizontal stripes (e.g., forecasts with lead time from 3 to 6 months). On the contrary, if the ICs are the controlling uncertainty variable, then a vertical pattern will dominate. If both are competing with each other, the image will be a hatched cross pattern, which is the typical pattern for 1-month lead time forecasts and less obvious for 2-month lead time. [24] This analysis framework is an effective, simple procedure for investigating the impact from forcing versus IC errors, and their evolution in time. The forecasts are also sorted according to monthly total precipitation (columns) and soil moisture state (rows) to see whether additional structure will stand out, specifically, whether high precipitation or wet IC corresponds to wet soil and vice versa. From Figures 6 and 7, it is apparent that no such relationship exists. This is because, for a given month, both the total rainfall and the rainfall characteristics (intensity, frequency, and duration) play a role. A 150-mm monthly total precipitation due to two or three thunderstorms may produce a quite different hydrometeorological condition from the same amount precipitation falling in more frequent but much less intense events. This result is not surprising from the perspective of infiltration theory going back almost 100 years. For example in the infiltration model of Green and Ampt [1911], the fraction of the total precipitation that infiltrates, given a specific IC, depends on the precipitation intensity, and infiltration from two identical storms depends on the ICs. For the results shown in Figures 6 and 7, let s take the July soil moisture forecasts from ESP as an example (row 2, column 3 in Figure 6), the monthly total precipitation is very similar for years 7 10 (about 110 mm, after sorted from low to high as shown in Figure 6), but the total soil moisture in year 8 is about 50 mm higher than the surrounding years (the dark gray in contrast with light gray). It happens that precipitation falls exclusively in light form (0 10 mm/day category) in that particular year which favorably recharges soil. Thus the way the given total precipitation for a period 5of10

6 Figure 5. The evolution of RE, defined as the ratio between forecast errors due to forcing uncertainty to that due to IC uncertainty with lead time. (stars) ESP/R-ESP, (circles) CFS/R-ESP (see text for explanation). Solid and dashed lines correspond to forecasts starting with ICs at the end of January and the end of July, respectively. (left) Ohio River Basin; (right) SE. of time is partitioned, depending on rainfall characteristics, can be very different [Sheffield et al., 2004] Do the Relative Forecast Errors for Forecasts Initialized in Warm Season Differ From That in Cold Season? [25] It is interesting to notice the disparity between forecast errors in warm and cold seasons. Warm season REs are less than the counterparts in the cold season, especially for the Ohio basin domain. This is mainly due to relatively dry ICs in the warm season and favorable meteorological conditions; August October happens to be the driest months (see Figure 4). As a result, IC impacts tend to persist longer. The persisting IC influence in the warm season prevents the corresponding REs from increasing rapidly even when the forecast errors due to forcing uncertainty (ESP and CFS) are comparable to those in the cold season. This can be verified by looking at Figure 6 in which the hatched cross pattern can still be discernible for Julyinitialized forecasts for Ohio River Basin at the 3-month lead time. [26] The strongest IC impacts are for 1-month lead time forecasts initialized at the end of January for SE because of extraordinary variability in the specified IC ensemble in R-ESP. However, influence from ICs for wet moisture states drops rapidly in comparison to the much slower decay in the warm season when land surface is drier. The IC effects are in general negligible at 6-month lead time in cold season but still exert impacts on the forecast errors for forecasts made in the warm season. Of course, by then the IC impact on the forecast errors is much smaller than the impact resulting from forcing uncertainty. [27] Our results are consistent with previous findings related to model spinup behavior [Cosgrove et al., 2003; Li, 2007], which can be explained by the basic nature of the spinup process. For the wet initialization, this process progresses steadily as the soil column loses excess water through evaporation, drainage and baseflow runoff. In the case of a dry initialization, this spinup process proceeds as the soil column moistens, and thus can only improve during precipitation events [Cosgrove et al., 2003]. In the case of infrequent precipitation events, spinup from a dry 6of10

7 Figure 6. Soil moisture forecasts (units: mm) by ESP (column) and R-ESP (row) for the Ohio River Basin. (top) Forecasts initialized at the end of January. (bottom) Forecasts initialized at the end of July. The ESP (precipitation forcing) and R-ESP (initial conditions) were sorted from dry to wet, as suggested by the text arrows. initialization can be expected to take longer than the time required to reach equilibrium from a wet initialization. From the perspective of the water balance, during summer time and early autumn, water is the limiting factor in the system for both the Ohio and SE; soil moisture is at its lowest level and recharge to the soil is also low because of stronger evaporation than in winter time. The signal from a nearly depleted soil column will take a relatively long time to dissipate, which explains why the dry season ICs are more effective in reducing RE. On the contrary, winter time soil moisture level is the highest of all seasons, and recharge rate is also the highest primarily because of low evaporation demand a typical energy-limited environment. In this case, a soil column close to saturation presented with continued recharge cannot retain any signal from that recharge, which may be a factor in wet season ICs being less capable of reducing RE Are the Contributions From IC and Forcing Errors Uncertainties to the Overall Forecast Errors Basin-Size Dependent? [28] Another important question is whether REs are sensitive to basin size. Because of the longer time lags in large basins and the sensitivity to spatial variability in the forcing fields in small basins [Li et al., 2008], one may well suppose that prediction errors for large basins are impacted more by IC errors while small basins are more affected by errors in the forcing fields. Runoff routed various distances through a large basin to the river outlet integrates information over a larger area and time period. We did analysis for gauging stations in the Ohio River Basin that has wide range of basin sizes from a few hundred to over ten thousand square miles. The basins are grouped into three categories: 10 2 km 2, 10 3 km 2, and larger than 10 5 km 2 (see Figure 1 for gauging locations). The results are presented in Figure 8. The first observation is the smaller REs in July regardless of basin size, as compared to the January REs. This corresponds well to our early results. Not surprisingly, large basins consistently show a strong IC influence at the 1-month lead time, and the result is more significant in summer. For lead times beyond two months, the forcing uncertainty impact begins to stand out. Meanwhile, small basins are more sensitive to forcing errors than large ones, and strikingly different for forecasts started in January. 7of10

8 Figure 7. Same as Figure 6 but for SE. Figure 8. RE versus basin size for streamflow forecasts. The color gradients correspond to basin size: the larger the basin, the darker the color. See Figure 1 for gauge locations. 8of10

9 3.4. Do Forecasts Made With Downscaled CFS Forcing Contain More Useful Information Than Traditional ESP? [29] The evolution of the forecast errors with lead times for both ESP and CFS are very similar as shown in Figure 5, and the errors essentially follow the structure of interannual variability of precipitation (see Figure 4). The stronger the interannual variability, the larger the errors. For any forecast system, the predictability is almost inversely proportional to natural variability of the variables of interest, which means that the higher variability within the system, the harder its prediction. It is interesting to notice that forecast errors actually drop at 5 6-month lead times for Ohio River Basins. The exact reason is still unresolved but definitely deserves further attention. At short lead times, the forecast errors between CFS and ESP are comparable. Then differences gradually unfold with CFS always lying below the ESP errors curve, indicating superior forecast skills for the former. This also explains smaller REs for CFS compared to ESP seen in Figure 3. The small, sometimes indistinguishable differences, in the first month might be due to IC impacts that overshadow the forcing influences. [30] Similar findings were also reported by Luo and Wood [2008]. They concluded that CFS performs better than ESP for 1 3-month lead time streamflow forecasts, though the differences become less noticeable for longer lead time. They further attributed the better performance from CFS to the Bayesian approach they used to downscale the original CFS precipitation and temperature fields that produces more skillful posterior distribution than the traditional ESP procedure that generates forcing from a random resampling of the historic record. 4. Summary and Discussion [31] In this paper, we investigated the relative importance of IC and forcing uncertainties to seasonal hydrologic forecasting. In the order of the scientific questions raised in section one, the results can be summarized as [32] 1. REs are primarily controlled by ICs at short lead time (1 month) with forcing uncertainties becoming more important and dominating the REs for longer lead times. [33] 2. The evolution of REs for warm season forecasts behaves differently from that in cold season. This evolution depends on the spread of the ICs and the natural variability in the forcing, particularly precipitation. [34] 3. The contribution from imperfect forcing and IC uncertainties on streamflow errors depends on basin size, with large basins being more sensitive to ICs and small basins more sensitive to forcing errors that are correlated with forcing variability as discussed earlier. [35] 4. Statistically downscaled CFS forcing is more skillful than traditional ESP for the domains studied in this paper, resulting in forecasts having lower overall errors. [36] Notably, ensembles generated by ESP or R-ESP may have an unrealistic large spread since both methods use a randomization strategy that resamples from the historical record with every year having an equal chance of providing an ensemble member. Alternative approaches might improve the ensemble set; e.g., by confining the members to those historical years that exhibit similar climate signals to the forecast year [e.g., Hamlet and Lettenmaier, 1999]. Nonetheless, our findings based on essentially snow-free domains over the eastern U.S. and the results of Wood and Lettenmaier [2008] for the snow-dominated western regions highlight the complex nature of influence from ICs and boundary forcing errors on seasonal hydrologic forecasting. [37] On a practical level, knowing the role of ICs and boundary conditions (meteorological forcing for hydrologic forecasts) will be crucial in the design of any forecasting system. This, in the most extreme bifurcation, reduces to either a first-type or second-type prediction problem. If predictability on the interested timescale turns out to be a prediction problem of the first type (IC uncertainty), then there is a requirement for investing in routine (operational) observations in order to provide accurate ICs for the forecast system. This suggests that integrating satellitebased soil moisture observations from the current NASA Soil Moisture Active Passive (SMAP) mission within a hydrologic forecasting system would be beneficial for lowvegetated (<5 kg/m 2 ) basins, research in this direction is still in early stage though. Conversely, for situations where the IC error impact remains constant, this results in the forecast responding to changing boundary conditions. Then understanding the forecast errors will depend on understanding the model response to changes in forcing [Collins and Allen, 2002]. Taking hydrologic drought prediction as an example, when the boundary conditions (e.g., precipitation) are predicted to be very likely below normal in the coming months, then the IC estimation should have more focus as the forecasts up to several months can be affected by the initial estimation of land surface states. [38] For forecast system development, improving forecast skill and getting the appropriate IC estimates are the most important and challenging tasks. To improve forecast skill, experiments must resort to hindcast-type experiments to evaluate model performance so that to take advantage of available past observations. With the now-available continuous observational record for ocean, atmosphere and land, more comprehensive re-evaluation of the forecast systems becomes possible. Also, the recent availability of real-time observations also provides an opportunity for the development of nowcast systems. Currently the authors employ a state-of-the-art land surface model (VIC), forced with observed meteorological forcing variables derived from the NLDAS project [Mitchell et al., 2004] in near real-time fashion. Such a system creates in our experience perhaps the best IC analysis field that can be directly used to initialize the forecast system [e.g., Luo and Wood, 2007; Li et al., 2008]. With advanced assimilation system incorporated into nowcast and/or forecast system, it is expected that even better ICs can be derived. [39] Acknowledgment. The research conducted in Princeton University is supported by NOAA Climate Program Office grant NA17RJ2612 A Hydrologic Ensemble Seasonal Forecast System over the Eastern U.S. under the Climate Prediction Program for the Americas (CPPA) program. References Collins, M., and M. R. 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