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- Author or Editor: Dennis P. Lettenmaier x
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Abstract
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.
Abstract
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.
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.
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.
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.
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.
Abstract
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.
Abstract
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.
Streamflow forecasting is critical to water resources management in the western United States. Yet, despite the passage of almost 50 years since the development of the first computerized hydrologic simulation models and over 30 years since the development of hydrologic ensemble forecast methods, the prevalent method used for forecasting seasonal streamflow in the western United States remains the regression of spring and summer streamflow volume on spring snowpack and/or the previous winter's accumulated precipitation. A recent retrospective analysis have shown that the skill of the regression-based forecasts have not improved in the last 40 years, despite large investments in science and technology related to the monitoring and assessment of the land surface and in climate forecasting. We describe an experimental streamflow forecast system for the western United States that applies a modern macroscale land surface model (akin to those now used in numerical weather prediction and climate models) to capture hydrologic states (soil moisture and snow) at the time of forecast, incorporates data assimilation methods to improve estimates of initial state, and uses a range of climate prediction ensembles to produce ensemble forecasts of streamflow and associated hydrologic states for lead times of up to one year. The forecast system is intended to be a real-time test bed for evaluating new seasonal streamflow forecast methods. Experience with the forecast system is illustrated using results from the 2004/05 forecast season, in which an evolving drought in the Pacific Northwest diverged strikingly from extreme snow accumulations to the south. We also discuss how the forecast system relates to ongoing changes in seasonal streamflow forecast methods in the two U.S. operational agencies that have major responsibility for seasonal streamflow forecasts in the western United States.
Streamflow forecasting is critical to water resources management in the western United States. Yet, despite the passage of almost 50 years since the development of the first computerized hydrologic simulation models and over 30 years since the development of hydrologic ensemble forecast methods, the prevalent method used for forecasting seasonal streamflow in the western United States remains the regression of spring and summer streamflow volume on spring snowpack and/or the previous winter's accumulated precipitation. A recent retrospective analysis have shown that the skill of the regression-based forecasts have not improved in the last 40 years, despite large investments in science and technology related to the monitoring and assessment of the land surface and in climate forecasting. We describe an experimental streamflow forecast system for the western United States that applies a modern macroscale land surface model (akin to those now used in numerical weather prediction and climate models) to capture hydrologic states (soil moisture and snow) at the time of forecast, incorporates data assimilation methods to improve estimates of initial state, and uses a range of climate prediction ensembles to produce ensemble forecasts of streamflow and associated hydrologic states for lead times of up to one year. The forecast system is intended to be a real-time test bed for evaluating new seasonal streamflow forecast methods. Experience with the forecast system is illustrated using results from the 2004/05 forecast season, in which an evolving drought in the Pacific Northwest diverged strikingly from extreme snow accumulations to the south. We also discuss how the forecast system relates to ongoing changes in seasonal streamflow forecast methods in the two U.S. operational agencies that have major responsibility for seasonal streamflow forecasts in the western United States.
Abstract
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 P − E). 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 P − E 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 P − E, 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.
Abstract
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 P − E). 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 P − E 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 P − E, 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.
Abstract
Under certain conditions, passive microwave satellite observations can be used to estimate snow water equivalent (SWE) across large areas, either through direct retrieval or data assimilation. However, the layered character of snowpacks increases the complexities of estimation algorithms. A multilayer model of snowpack stratigraphy that can serve as the forward model of a snow data assimilation system is described and evaluated. The model’s ability to replicate large-scale snowpack layer features is evaluated using observations from the Cold Land Processes Experiment (Colorado, 2002 and 2003) and a 2002 Nome–Barrow snowpit transect [Snow Science Traverse—Alaska Region (SnowSTAR2002)]. The multilayer model linked with a radiative transfer scheme improved the estimation of brightness temperatures both in terms of absolute values and frequency/polarization differences (error reductions ranging from 47% to 72%) relative to a one-layer model with similar, but depth-averaged, physics at the Colorado sites. The two models were also employed along the SnowSTAR2002 transect of snowpit measurements. The general unavailability of meteorological forcings along the transect made the use of coarse-scale reanalysis data necessary to simulate snow properties and microwave radiances. Errors in the precipitation forcings led to overestimation of SWE, and the simulated brightness temperatures from the two models showed large differences, due mostly to the inability of the single-layer model to simulate the observed larger grain sizes. These differences had implications for the estimation of snow depth; assimilation of Special Sensor Microwave Imager (SSM/I) observations into the multilayer model resulted in improved snow depth estimates (RMSEs of 18.1 cm versus 34.1 cm without assimilation), while the single-layer assimilation slightly decreased the estimation skill (RMSEs of 34.1 versus 33.6 cm).
Abstract
Under certain conditions, passive microwave satellite observations can be used to estimate snow water equivalent (SWE) across large areas, either through direct retrieval or data assimilation. However, the layered character of snowpacks increases the complexities of estimation algorithms. A multilayer model of snowpack stratigraphy that can serve as the forward model of a snow data assimilation system is described and evaluated. The model’s ability to replicate large-scale snowpack layer features is evaluated using observations from the Cold Land Processes Experiment (Colorado, 2002 and 2003) and a 2002 Nome–Barrow snowpit transect [Snow Science Traverse—Alaska Region (SnowSTAR2002)]. The multilayer model linked with a radiative transfer scheme improved the estimation of brightness temperatures both in terms of absolute values and frequency/polarization differences (error reductions ranging from 47% to 72%) relative to a one-layer model with similar, but depth-averaged, physics at the Colorado sites. The two models were also employed along the SnowSTAR2002 transect of snowpit measurements. The general unavailability of meteorological forcings along the transect made the use of coarse-scale reanalysis data necessary to simulate snow properties and microwave radiances. Errors in the precipitation forcings led to overestimation of SWE, and the simulated brightness temperatures from the two models showed large differences, due mostly to the inability of the single-layer model to simulate the observed larger grain sizes. These differences had implications for the estimation of snow depth; assimilation of Special Sensor Microwave Imager (SSM/I) observations into the multilayer model resulted in improved snow depth estimates (RMSEs of 18.1 cm versus 34.1 cm without assimilation), while the single-layer assimilation slightly decreased the estimation skill (RMSEs of 34.1 versus 33.6 cm).
