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Dennis P. Lettenmaier
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Dennis P. Lettenmaier
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Dennis P. Lettenmaier
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Antti Arola and Dennis P. Lettenmaier

Abstract

The results of simulation experiments are reported in which two 1° lat × 1° long regions were discretized into pixels of size roughly 180 m × 120 m and modeled using a hydrologically based, spatially distributed water and energy balance model. Fluxes aggregated from the distributed model (ADM), and computed using a macroscale equivalent model (MSE), which treats the entire region as a point, were compared for 2 years for two regions in Montana: one in the mountainous, semihumid western part of the state, and another in the drier, less mountainous east. The forcings for MSE were the spatial averages of precipitation, downward shortwave and longwave radiation, air temperature, wind, and vapor pressure over the respective regions spatially averaged from the distributed model.

In the western region, major differences in predicted snow water equivalent between ADM and MSE were observed during the spring snowmelt period, primarily due to snow at high elevations, which is not represented by MSE. These differences persisted for smaller 0.2° × 0.2° subregions; however, an alternate probability-based partitioning of the region into 10 elevation bands greatly reduced the differences. In the eastern region, where snow accumulations are episodic, differences in snow water equivalent were due primarily to the failure of MSE to represent topographic variations in solar radiation. Differences in latent and sensible heat fluxes between ADM and MSE were greatest when MSE predicted no snow cover and ADM predicted partial area snow coverage. When both models predicted at least partial snow cover, or both models predicted no snow cover, the diurnal patterns in latent and sensible heat fluxes were similar, although MSE tended to predict larger diurnal extremes. This is attributable to the representation of partial area coverage of snow in winter and spring, and partial areas coverage of convective rainfall in summer and fall, in ADM.

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Chunmei Zhu and Dennis P. Lettenmaier

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Studying the role of land surface conditions in the Mexican portion of the North American monsoon system (NAMS) region has been a challenge due to the paucity of long-term observations. A long-term gridded observation-based climate dataset suitable for forcing land surface models, as well as model-derived land surface states and fluxes for a domain consisting of all of Mexico, is described. The datasets span the period of January 1925–October 2004 at 1/8° spatial resolution at a subdaily (3 h) time step. The simulated runoff matches the observations plausibly over most of the 14 small river basins spanning all of Mexico, which suggests that long-term mean evapotranspiration is realistically reproduced. On this basis, and given the physically based model parameterizations of soil moisture and energy fluxes, the other surface fluxes and state variables such as soil moisture should be represented reasonably. In addition, a comparison of the surface fluxes from this study is performed with North American Regional Reanalysis (NARR) data on a seasonal mean basis. The results indicate that downward shortwave radiation is generally smaller than in the NARR data, especially in summer. Net radiation, on the other hand, is somewhat larger in the Variable Infiltration Capacity (VIC) hydrological model than in the NARR data for much of the year over much of the domain. The differences in radiative and turbulent fluxes are attributed to (i) the parameterization used in the VIC forcings for solar and downward longwave radiation, which links them to the daily temperature and temperature range, and (ii) differences in the land surface parameterizations used in VIC and the NCEP–Oregon State University–U.S. Air Force–NWS/Hydrologic Research Lab (Noah) land scheme used in NARR.

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Fengge Su and Dennis P. Lettenmaier

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The Variable Infiltration Capacity (VIC) land surface hydrology model forced by gridded observed precipitation and temperature for the period 1979–99 is used to simulate the land surface water balance of the La Plata basin (LPB). The modeled water balance is evaluated with streamflow observations from the major tributaries of the LPB. The spatiotemporal variability of the water balance terms of the LPB are then evaluated using offline VIC model simulations, the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40), and inferences obtained from a combination of these two. The seasonality and interannual variability of the water balance terms vary across the basin. Over the Uruguay River basin and the entire LPB, precipitation (P) exceeds evapotranspiration (E) and the basins act as a moisture sink. However, the Paraguay River basin acts as a net source of moisture in dry seasons (strong negative PE). The annual means and monthly time series of ERA-40 P are in good agreement with gauge observations over the entire LPB and its subbasins, except for the Uruguay basin. The E estimates from VIC and inferred from the ERA-40 atmospheric moisture budget are consistent in both seasonal and interannual variations over the entire LPB, but large discrepancies exist between the two E estimates over the subbasins. The long-term mean of atmospheric moisture convergence PE agrees well with observed runoff R for the upper Paraná River basin, whereas the imbalance is large (28%) for the Uruguay basin—possibly because of its small size. Major problems appear over the Paraguay basin with negative long-term mean of atmospheric moisture convergence PE, which is not physically realistic. The computed precipitation recycling in the LPB (for L = 500 km) exhibits strong seasonal and spatial variations with ratios of 0%–3% during the cold season and 5%–7% during the warm season.

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Ana P. Barros and Dennis P. Lettenmaier

Abstract

Precipitation in remote mountainous areas dominates the water balance of many water-short areas of the globe, such as western North America. The inaccessibility of such environments prevents adequate measurement of the spatial distribution of precipitation and, hence, direct estimation of the water balance from observations of precipitation and runoff. Resolution constraints in atmospheric models can likewise result in large biases in prediction of the water balance for grid cells that include highly diverse topography. Modeling of the advection of moisture over topographic barriers at a spatial scale sufficient to resolve the dominant topographic features offers one method of better predicting the spatial distribution of precipitation in mountainous areas. A model is described herein that simulates Lagrangian transport of moist static energy and total water through a 3D finite-element grid, where precipitation is the only scavenging agent of both variables. The model is aimed primarily at the reproduction of the properties of high-elevation precipitation for long periods of time, but it operates at a time scale (during storm periods) of 10 min to 1 h and, therefore, is also able to reproduce the distribution of storm precipitation with an accuracy that may make it appropriate for the forecasting of extreme events. The model was tested by application to the Olympic Mountains, Washington, for a period of eight years (1967–74). Areal average precipitation, estimated through use of seasonal and annual runoff, was reproduced with errors in the 10%–15% range. Similar accuracy was achieved using point estimates of monthly precipitation from snow courses and low-elevation precipitation gauges.

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Edwin P. Maurer and Dennis P. Lettenmaier

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Understanding the links between remote conditions, such as tropical sea surface temperatures, and regional climate has the potential to improve streamflow predictions, with associated economic benefits for reservoir operation. Better definition of land surface moisture states (soil moisture and snow water storage) at the beginning of the forecast period provides an additional source of streamflow predictability. The value of long-lead predictive skill added by climate forecast information and land surface moisture states in the Missouri River basin is examined. Forecasted flows were generated that represent predictability achievable through knowledge of climate, snow, and soil moisture states. For the current main-stem reservoirs (90 × 109 m3 storage volume) only a 1.8% improvement in hydropower benefits could be achieved with perfect forecasts for lead times up to one year. This low value of prediction skill is due to the system's large storage capacity relative to annual inflow. To evaluate the effects of hydrologic predictability on a smaller system, a hypothetical system was specified with a reduced storage volume of 36 × 109 m3. This smaller system showed a 7.1% difference in annual hydropower benefits for perfect forecasts, representing $25.7 million. Using realistic streamflow predictability, $6.8 million of the $25.7 million are realizable. The climate indices provide the greatest portion of the $6.8 million, and initial soil moisture information provides the largest increment above climate knowledge. The results demonstrate that use of climate forecast information along with better definition of the basin moisture states can improve runoff predictions with modest economic value that, in general, will increase as the size of the reservoir system decreases.

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Jennifer C. Adam and Dennis P. Lettenmaier

Abstract

River runoff to the Arctic Ocean has increased over the last century, primarily during the winter and spring and primarily from the major Eurasian rivers. Some recent studies have suggested that the additional runoff is due to increased northward transport of atmospheric moisture (and associated increased precipitation), but other studies show inconsistencies in long-term runoff and precipitation trends, perhaps partly due to biases in the observational datasets. Through trend analysis of precipitation, temperature, and streamflow data, the authors investigate the extent to which Eurasian Arctic river discharge changes are attributable to precipitation and temperature changes as well as to reservoir construction and operation between the years of 1936 and 2000. Two new datasets are applied: a gridded precipitation product, in which the low-frequency variability is constrained to match that of long-term bias-corrected precipitation station data, and a reconstructed streamflow product, in which the effects of reservoirs have been minimized using a physically based reservoir model. It is found that reservoir operations have primarily affected streamflow seasonality, increasing winter discharge and decreasing summer discharge. To understand the influences of climate on streamflow changes, the authors hypothesize three cases that would cause precipitation trends to be inconsistent with streamflow trends: first, for the coldest basins in northeastern Siberia, streamflow should be sensitive to warming primarily as a result of the melting of excess ground ice, and for these basins positive streamflow trends may exceed precipitation trends in magnitude; second, evapotranspiration (ET) in the warmer regions of western Siberia and European Russia is sensitive to warming and increased precipitation, therefore observed precipitation trends may exceed streamflow trends; and third, streamflow from the central Siberian basins should respond to both effects. It is found that, in general, these hypotheses hold true. In the coldest basins, streamflow trends diverged from precipitation trends starting in the 1950s to 1960s, and this divergence accelerated thereafter. In the warmest basins, precipitation trends consistently exceeded streamflow trends, suggesting that increased precipitation contributed to increases in both ET and streamflow. In the central basins, permafrost degradation and ET effects appear to be contributing to long-term streamflow trends in varying degrees for each basin. The results herein suggest that the extent and state of the permafrost underlying a basin is a complicating factor in understanding long-term changes in Eurasian Arctic river discharge.

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Kingtse C. Mo and Dennis P. Lettenmaier

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The authors analyzed the skill of monthly and seasonal soil moisture (SM) and runoff (RO) forecasts over the United States performed by driving the Variable Infiltration Capacity (VIC) hydrologic model with forcings derived from the National Multi-Model Ensemble hindcasts (NMME_VIC). The grand ensemble mean NMME_VIC forecasts were compared to ensemble streamflow prediction (ESP) forecasts derived from the VIC model forced by resampling of historical observations during the forecast period (ESP_VIC), using the same initial conditions as NMME_VIC. The forecast period is from 1982 to 2010, with the forecast initialized on 1 January, 1 April, 5 July, and 3 October. Overall, forecast skill is seasonally and regionally dependent. The authors found that 1) the skill of the grand ensemble mean NMME_VIC forecasts is comparable with that of the individual model that has the highest skill; 2) for all forecast initiation dates, the initial conditions play a dominant role in forecast skill at 1-month lead, and at longer lead times, forcings derived from NMME forecasts start to contribute to forecast skill; and 3) the initial conditions dominate contributions to skill for a dry climate regime that covers the western interior states for all seasons and the north-central part of the country for January. In this regime, the forecast skill for both methods is high even at 3-month lead. This regime has low mean precipitation and precipitation variations, and the influence of precipitation on SM and RO is weak. In contrast, a wet regime covers the region from the Gulf states to the Tennessee and Ohio Valleys for forecasts initialized in January and April, the Southwest monsoon region, the Southeast, and the East Coast in summer. In these dynamically active regions, where rainfall depends on the path of the moisture transport and atmospheric forcing, forecast skill is low. For this regime, the climate forecasts contribute to skill. Skillful precipitation forecasts after lead 1 have the potential to improve SM and RO forecast skill, but it was found that this mostly was not the case for the NMME models.

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