Macroscale Hydrologic Model Research Articles

The Operational Eta Model Precipitation and Surface Hydrologic Cycle of the Columbia and Colorado Basins

Abstract The surface hydrology of the United States’ western basins is investigated using the National Centers for Environmental Prediction operational Eta Model forecasts. During recent years the model has been subject to changes and upgrades that positively affected its performance. These effects on the surface hydrologic cycle are discussed by analyzing the period June 1995–May 2003. Prior to the model assessment, three gauge-based precipitation analyses that are potential sources of model validation are appraised. A fairly large disparity between the gridded precipitation analyses is found in the long-term area averages over the Columbia basin (∼23% difference) and over the Colorado basin (∼12% difference). These discrepancies are due to the type of analysis scheme employed and whether an orographic correction was applied. The basin-averaged Eta Model precipitation forecasts correlate well with the observations at monthly time scales and, after 1999, show a small bias. Over the Columbia basin, the model precipitation bias is typically positive. This bias is significantly smaller with respect to orographically corrected precipitation analyses, indicating that the model’s large-scale precipitation processes respond reasonably well to orographic effects, though manifesting a higher bias during the cool season. Over the Colorado basin, the model precipitation bias is typically negative, and notably more so with respect to 1) the orographically corrected precipitation analyses and 2) the warm season, indicating shortfalls in the convection scheme over arid high mountains. The mean fields of the hydrological variables in the Eta Model are in qualitative agreement with those from the Variable Infiltration Capacity (VIC) macroscale hydrologic model at regional-to-large scales. As expected, the largest differences are found near mountains and the western coastline. While the mean fields of precipitation, evaporation, runoff, and normalized soil moisture are in general agreement, important differences arise in their mean annual cycle over the two basins: snowmelt in the Eta Model precedes that of VIC by 2 months, and this phase shift is also reflected in the other variables. In the last 3–4 yr of the study period, notable improvements are evident in the quality of the model’s precipitation forecast and in the reduction of the residual term of the surface water balance, suggesting that at least similar (or better) quality will be found in studies based on NCEP’s recently completed Eta Model–based North American regional reanalysis.

Implications of climate change for freshwater inflows to the Arctic Ocean

Observational evidence suggests that river inflows to the Arctic Ocean have increased over the last 30 years. Continued increases have the potential to alter the freshwater balance in the Arctic and North Atlantic Oceans and hence the thermohaline circulation. Simulations with a macroscale hydrological model and climate change scenarios derived from six climate models and two emissions scenarios suggest increases of up to 31% in river inflows to the Arctic by the 2080s under high emissions and up to 24% under lower emissions, although there are large differences between climate models. Uncertainty analysis suggests low sensitivity to model form and parameterization but higher sensitivity to the input data used to drive the model. The addition of up to 0.048 sverdrup (Sv, 106 m3 s−1) is a large proportion of the 0.06–0.15 Sv of additional freshwater that may trigger thermohaline collapse. Changes in the spatial distribution of inflows to the Arctic Ocean may influence circulation patterns within the ocean.

A new multiscale flow network generation scheme for land surface models

This paper presents a new multiscale approach for generating flow networks for land surface models that are applied at different spatial scales. This new approach has the following advantages: (1) it allows runoff in a land surface model grid to exit by multiple directions simultaneously, rather than be limited to one of the eight directions as in many other methods; and (2) it introduces a scaling factor, the tortuosity coefficient, to determine hydrologic parameters for more accurate flow routing across different spatial scales. The new flow network generation scheme has been applied, in conjunction with a kinematic wave routing method, to the Blue River basin in Oklahoma at different spatial resolutions. Comparison of the routed streamflows shows clear advantages of the new approach over the widely used eight directions (D8) method, especially at coarser resolutions. This method is particularly suitable for macroscale hydrologic models and climate models where the accuracy of river routing can be severely limited by the coarse spatial resolution.

Parameterization of Blowing-Snow Sublimation in a Macroscale Hydrology Model

Abstract An algorithm that parameterizes the topographically induced subgrid variability in wind speed, snow transport, and blowing-snow sublimation was designed for use within macroscale hydrology models and other large-scale land surface schemes (LSSs). The algorithm is intended to provide consistent estimates of the relative influence of sublimation from blowing snow for continental-scale river basins, while balancing the land surface water and energy budgets. In addition to the standard LSS inputs, the model requires specification of the standard deviation of terrain slope, the mean fetch, and the lag-1 autocorrelation of terrain gradients. Sublimation fluxes are solved for each vegetation class, for each model grid cell. Model results are compared to observed snow water equivalent (SWE) and simulated estimates of sublimation from blowing snow for two small tundra watersheds: Imnavait Creek, Alaska, and Trail Valley Creek, Northwest Territories, Canada, produced by two different small-scale distribute...

Variability and potential sources of predictability of North American runoff

Understanding the space‐time variability of runoff has important implications for climate because of the linkage of runoff and evapotranspiration and is a practical concern as well for the prediction of drought and floods. In contrast to many studies investigating the space‐time variability of precipitation and temperature, there has been relatively little work evaluating climate teleconnections of runoff, in part because of the absence of data sets that lend themselves to commonly used techniques in climate analysis like principal components analysis. We examine the space‐time variability of runoff over North America using a 50‐year retrospective spatially distributed data set of runoff and other land surface water cycle variables predicted using a calibrated macroscale hydrology model, thus avoiding some shortcomings of past studies based more directly on streamflow observations. We determine contributions to runoff variability of climatic teleconnections, soil moisture, and snow for lead times up to a year. High and low values of these sources of predictability are evaluated separately. We identify patterns of runoff variability that are not revealed by direct analysis of observations, especially in areas of sparse stream gauge coverage. The presence of nonlinear relationships between large‐scale climate changes and runoff pattern variability, as positive and negative values of the large‐scale climate indices rarely show opposite teleconnections with a runoff pattern. Dry soil moisture anomalies have a stronger influence on runoff variability than wet soil. Snow, and more so soil moisture, in many locations enhance the predictability due to climatic teleconnections.

Climate change and global water resources: SRES emissions and socio-economic scenarios

In 1995, nearly 1400 million people lived in water-stressed watersheds (runoff less than 1000 m 3/capita/year), mostly in south west Asia, the Middle East and around the Mediterranean. This paper describes an assessment of the relative effect of climate change and population growth on future global and regional water resources stresses, using SRES socio-economic scenarios and climate projections made using six climate models driven by SRES emissions scenarios. River runoff was simulated at a spatial resolution of 0.5×0.5° under current and future climates using a macro-scale hydrological model, and aggregated to the watershed scale to estimate current and future water resource availability for 1300 watersheds and small islands under the SRES population projections. The A2 storyline has the largest population, followed by B2, then A1 and B1 (which have the same population). In the absence of climate change, the future population in water-stressed watersheds depends on population scenario and by 2025 ranges from 2.9 to 3.3 billion people (36–40% of the world's population). By 2055 5.6 billion people would live in water-stressed watersheds under the A2 population future, and “only” 3.4 billion under A1/B1. Climate change increases water resources stresses in some parts of the world where runoff decreases, including around the Mediterranean, in parts of Europe, central and southern America, and southern Africa. In other water-stressed parts of the world—particularly in southern and eastern Asia—climate change increases runoff, but this may not be very beneficial in practice because the increases tend to come during the wet season and the extra water may not be available during the dry season. The broad geographic pattern of change is consistent between the six climate models, although there are differences of magnitude and direction of change in southern Asia. By the 2020s there is little clear difference in the magnitude of impact between population or emissions scenarios, but a large difference between different climate models: between 374 and 1661 million people are projected to experience an increase in water stress. By the 2050s there is still little difference between the emissions scenarios, but the different population assumptions have a clear effect. Under the A2 population between 1092 and 2761 million people have an increase in stress; under the B2 population the range is 670–1538 million, respectively. The range in estimates is due to the slightly different patterns of change projected by the different climate models. Sensitivity analysis showed that a 10% variation in the population totals under a storyline could lead to variations in the numbers of people with an increase or decrease in stress of between 15% and 20%. The impact of these changes on actual water stresses will depend on how water resources are managed in the future.

THE ISSUES IN THE IMPACT STUDY OF CLIMATE CHANGE ON THE TERRESTRIAL HYDROLOGICAL CYCLE

The terrestrial branch of water cycle is mainly influenced by the climate and human activities, the interaction between these elements and hydrological process as well. With population growth and socio-economic development, the impact of human activities such as land use change, water use consumption and ecosystem environment change will be increasing. Recently, in some river basins located in North China, the impact of human activities such as groundwater overexploitation, irrigation loss and evaporation from reservoirs is exceeding the impact of climate variability and climate warming on terrestrial hydrological cycle. The changing terrestrial hydrological cycle leads to decreasing runoff in short and decadal time scale. The water and heat exchanges between land and atmosphere created by either nature or anthropogenic sources are important not only to atmosphere, but also bringing impact to the outcomes of soil moisture, evaporation and runoff directly. Having reviewed the above mentioned, the paper discussed the weakness of ongoing method in the study of climate change impact on hydrological cycle. Being one way from outputs of GCM as the climatic scenarios input to the hydrological model, the change in terrestrial hydrological cycle caused by climate warming and human activities can hardly be involved in the feedback to atmosphere. Considering the large contribution of land surface evaporation in precipitation over land, it thus leads to inaccurate rainfall prediction and incorrect describing the change of land surface hydrological cycle as well. Nowadays, both climatologists and hydrologists from geophysical science group have gained successfully in the study on land surface model in GCM and macroscale hydrological models respectively, which are two aspects of compensation each to other in describing the land surface hydrological cycle and will provide the possibility of coupled hydro-climate model in the future study of climate change impact. The human activities make additional difficulties to land surface parameterization. How to improve the description of terrestrial hydrological cycle in the changing environment from climate warming and intensive human activities is still the challenge for coupled hydrology climate models.

Effect of precipitation sampling error on simulated hydrological fluxes and states: Anticipating the Global Precipitation Measurement satellites

Precipitation is the single most important determinant of the fluxes and states of the land surface hydrological system and the most important atmospheric input to hydrological models. Satellite‐based precipitation estimates, such as those anticipated from the Global Precipitation Measurement (GPM) satellites, hold great promise for application in hydrologic simulation and prediction, especially in parts of the world where surface observation networks are sparse. However, the usefulness of these precipitation products for hydrological applications will depend on their error characteristics. Of particular interest in satellite‐derived precipitation estimates is the sampling error, that is, the error in accumulated precipitation due to periodic sampling of the precipitation rate. To assess the effect of this error on simulated hydrological fluxes and states, synthetic error fields were imposed on an observation‐based 1/2° latitude/longitude gridded precipitation data set. In turn, the generated precipitation fields were used as input to a macroscale hydrology model (MHM). Our results show that (1) streamflow errors were large for small drainage areas but decreased rapidly for drainage areas larger than about 50,000 km2. Much of the streamflow error is associated with fast (near‐surface) runoff response. (2) Streamflow estimates were biased upward due to sampling errors, with the bias increasing with sampling interval and with drainage area. Evapotranspiration was biased downward in a compensating amount. (3) Spatial correlation of precipitation errors reduced the rate at which errors decreased with drainage area for all variables investigated, but the differences between the correlated and uncorrelated error cases were smaller for streamflow and evapotranspiration than for precipitation.

Mitigating the Effects of Climate Change on the Water Resources of the Columbia River Basin

The potential effects of climate change on the hydrology and water resources of the Columbia River Basin (CRB) were evaluated using simulations from the U.S. Department of Energy and National Center for Atmospheric Research Parallel Climate Model (DOE/NCAR PCM). This study focuses on three climate projections for the 21st century based on a `business as usual' (BAU) global emissions scenario, evaluated with respect to a control climate scenario based on static 1995 emissions. Time-varying monthly PCM temperature and precipitation changes were statistically downscaled and temporally disaggregated to produce daily forcings that drove a macro-scale hydrologic simulation model of the Columbia River basin at 1/4-degree spatial resolution. For comparison with the direct statistical downscaling approach, a dynamical downscaling approach using a regional climate model (RCM) was also used to derive hydrologic model forcings for 20-year subsets from the PCM control climate (1995–2015) scenario and from the three BAU climate(2040–2060) projections. The statistically downscaled PCM scenario results were assessed for three analysis periods (denoted Periods 1–3: 2010–2039,2040–2069, 2070–2098) in which changes in annual average temperature were +0.5,+1.3 and +2.1 °C, respectively, while critical winter season precipitation changes were –3, +5 and +1 percent. For RCM, the predicted temperature change for the 2040–2060 period was +1.2 °C and the average winter precipitation change was –3 percent, relative to the RCM controlclimate. Due to the modest changes in winter precipitation, temperature changes dominated the simulated hydrologic effects by reducing winter snow accumulation, thus shifting summer streamflow to the winter. The hydrologic changes caused increased competition for reservoir storage between firm hydropower and instream flow targets developed pursuant to the Endangered Species Act listing of Columbia River salmonids. We examined several alternative reservoir operating policies designed to mitigate reservoir system performance losses. In general, the combination of earlier reservoir refill with greater storage allocations for instream flow targets mitigated some of the negative impacts to flow, but only with significant losses in firm hydropower production (ranging from –9 percent in Period1 to –35 percent for RCM). Simulated hydropower revenue changes were lessthan 5 percent for all scenarios, however, primarily due to small changes inannual runoff.

Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel ClimateModel (PCM), and the implications of the comparison for a future(2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregation (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly(at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at 1/2-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.

The Effects of Climate Change on the Hydrology and Water Resources of the Colorado River Basin

The potential effects of climate change on the hydrology and water resources of the Colorado River basin are assessed by comparing simulated hydrologic and water resources scenarios derived from downscaled climate simulations of the U.S. Department of Energy/National Center for Atmospheric Research Parallel Climate Model (PCM) to scenarios driven by observed historical (1950–1999) climate. PCM climate scenarios include an ensemble of three 105-year future climate simulations based on projected `business-as-usual'(BAU) greenhouse gas emissions and a control climate simulation based on static 1995 greenhouse gas concentrations. Downscaled transient temperature and precipitation sequences were extracted from PCM simulations, and were used to drive the Variable Infiltration Capacity (VIC) macroscale hydrology model to produce corresponding streamflow sequences. Results for the BAU scenarios were summarized into Periods 1, 2, and 3 (2010–2039,2040–2069, 2070–2098). Average annual temperature changes for the Colorado Riverbasin were 0.5 °C warmer for control climate, and 1.0, 1.7, and 2.4 °C warmer for Periods 1–3, respectively, relative to the historicalclimate. Basin-average annual precipitation for the control climate was slightly(1%) less than for observed historical climate, and 3, 6, and 3%less for future Periods 1–3, respectively. Annual runoff in the controlrun was about 10% lower than for simulated historical conditions, and 14, 18, and 17% less for Periods 1–3, respectively. Analysis of watermanagement operations using a water management model driven by simulated streamflows showed that streamflows associated with control and future BAU climates would significantly degrade the performance of the water resourcessystem relative to historical conditions, with average total basin storage reduced by 7% for the control climate and 36, 32 and 40% for Periods 1–3, respectively. Releases from Glen Canyon Dam to the LowerBasin (mandated by the Colorado River Compact) were met in 80% of years for the control climate simulation (versus 92% in the historical climate simulation), and only in 59–75% of years for the future climate runs. Annual hydropower output was also significantly reduced for the control and future climate simulations. The high sensitivity of reservoir system performance for future climate is a reflection of the fragile equilibrium that now exists in operation of the system, with system demands only slightly less than long-term mean annual inflow.

Potential Implications of PCM Climate Change Scenarios for Sacramento–San Joaquin River Basin Hydrology and Water Resources

The potential effects of climate change on the hydrology and water resources of the Sacramento–San Joaquin River Basin were evaluated using ensemble climate simulations generated by the U.S. Department of Energy and National Center for Atmospheric Research Parallel Climate Model (DOE/NCAR PCM). Five PCM scenarios were employed. The first three were ensemble runs from 1995–2099 with a `business as usual' global emissions scenario, eachwith different atmospheric initializations. The fourth was a `control climate'scenario with greenhouse gas emissions set at 1995 levels and run through 2099. The fifth was a historical climate simulation forced with evolving greenhouse gas concentrations from 1870–2000, from which a 50-yearportion is taken for use in bias-correction of the other runs. From these global simulations, transient monthly temperature and precipitation sequences were statistically downscaled to produce continuous daily hydrologic model forcings, which drove a macro-scale hydrology model of theSacramento–San Joaquin River Basins at a 1/8-degree spatial resolution, and produceddaily streamflow sequences for each climate scenario. Each streamflow scenario was used in a water resources system model that simulated current and predicted future performance of the system. The progressive warming of the PCM scenarios (approximately 1.2 °C at midcentury, and 2.2 °C by the 2090s), coupled with reductions in winter and spring precipitation (from 10 to 25%), markedly reduced late spring snowpack (by as much as half on average by the end of the century). Progressive reductions in winter, spring, and summer streamflow were less severe in the northern part of the study domain than in the south, where a seasonality shift was apparent. Results from the water resources system model indicate that achieving and maintaining status quo (control scenario climate) system performance in the future would be nearly impossible, given the altered climate scenario hydrologies. The most comprehensive of the mitigation alternatives examined satisfied only 87–96% of environmental targets in the Sacramento system, and less than 80% in the San Joaquin system. It is evident that demand modification and system infrastructure improvements will be required to account for the volumetric and temporal shifts in flows predicted to occur with future climates in the Sacramento–San JoaquinRiver basins.

Simulation of spatial variability in snow and frozen soil

The spatial distribution of frozen soil and snow cover at the start of the spring melt season plays an important role in the generation of spring runoff and in the exchange of energy between the land surface and the atmosphere. Field observations were made at the University of Minnesota's Rosemount Agricultural Experiment Station to identify statistical distributions that can be used to describe the spatial variability of frozen soil and snow in macroscale hydrology models. These probability distributions are used to develop algorithms that simulate the subgrid spatial variability of snow and soil ice content for application within the framework of the variable infiltration capacity macroscale hydrologic model. Point simulations show that the new snow algorithm increases the melt rate for thin snowpacks, and the new soil frost algorithm allows more drainage through the soil during the winter. Simulations of the Minnesota River show that the new snow algorithm makes little difference to regional streamflow but does play an important role in the regional energy balance, especially during the spring snowmelt season. The new soil frost algorithm has a larger impact on spring streamflow and plays a minor role in the surface energy balance during the spring soil thaw season.

Eta model estimated land surface processes and the hydrologic cycle of the Mississippi basin

This article discusses the water and surface energy budgets over the Mississippi River basin and subbasins using NCEP's Eta model forecasts. It also discusses the relation between surface states (soil moisture) and other variables that affect the surface energy balance, potentially interacting with precipitation processes. The Eta model is NCEP's operational mesoscale model and therefore has been subject to changes and upgrades over the period of this evaluation. While the atmospheric water cycle is analyzed here using a 7‐year long (June 1995 to May 2002) data set, the research focusing on surface processes is based on a 4‐year period (June 1998 to May 2002) after substantial upgrades to the land surface component were performed. On the 7‐year average the Eta model 12–36 h forecast precipitation averaged over the Mississippi basin differs from the observed precipitation by 2%, while the estimate of evaporation computed as a residual of the water balance equation differs by 5% from the evaporation estimate resulting from a data set of land surface fluxes prepared with the macroscale hydrologic variable infiltration model (VIC). However, the difference between the model parameterized evaporation and VIC's is about 17% due to excessive bare soil evaporation in the Eta model. Notably, the long‐term average of moisture flux convergence also estimated from the 12–36 h forecasts is 0.54 mm day−1 over the Mississippi basin, while streamflow observations at Vicksburg average 0.50 mm day−1. This agreement within 10% is a strict test of the quality of the hydrologic cycle estimates; therefore these are promising results for estimates of the water cycle from regional analysis or future regional reanalysis. Subbasins of the Mississippi have diverse land surface‐atmosphere interactions at monthly timescales. In the western half of the Mississippi basin, feedbacks can be described as follows: increased soil moisture is associated with a slight increase of net radiation at the surface; latent heat also increases with soil moisture while sensible heat decreases, resulting in an almost linear increase of the evaporative fraction. Increased soil moisture is also associated with a lower lifting condensation level and an increase of observed precipitation (though not statistically significant). The overall results support the concept of a positive feedback in which increased soil moisture affects surface fluxes in such a manner that increased precipitation results. However, toward the east (e.g., the Ohio basin), there are no well‐defined land surface‐atmosphere interactions, suggesting that other effects, like the advection of moisture, may be more relevant for precipitation processes.

Effects of IPCC SRES* emissions scenarios on river runoff: a global perspective

Abstract. This paper describes an assessment of the implications of future climate change for river runoff across the entire world, using six climate models which have been driven by the SRES emissions scenarios. Streamflow is simulated at a spatial resolution of 0.5°x0.5&#176 using a macro-scale hydrological model, and summed to produce total runoff for almost 1200 catchments. The effects of climate change have been compared with the effects of natural multi-decadal climatic variability, as determined from a long unforced climate simulation using HadCM3. By the 2020s, change in runoff due to climate change in approximately a third of the catchments is less than that due to natural variability but, by the 2080s, this falls to between 10 and 30%. The climate models produce broadly similar changes in runoff, with increases in high latitudes, east Africa and south and east Asia, and decreases in southern and eastern Europe, western Russia, north Africa and the Middle East, central and southern Africa, much of North America, most of South America, and south and east Asia. The pattern of change in runoff is largely determined by simulated change in precipitation, offset by a general increase in evaporation. There is little difference in the pattern of change between different emissions scenarios (for a given model), and only by the 2080s is there evidence that the magnitudes of change in runoff vary, with emissions scenario A1FI producing the greatest change and B1 the smallest. The inter-annual variability in runoff increases in most catchments due to climate change — even though the inter-annual variability in precipitation is not changed — and the frequency of flow below the current 10-year return period minimum annual runoff increases by a factor of three in Europe and southern Africa and of two across North America. Across most of the world climate change does not alter the timing of flows through the year but, in the marginal zone between cool and mild climates, higher temperatures mean that peak streamflow moves from spring to winter as less winter precipitation falls as snow. The spatial pattern of changes in the 10-year return period maximum monthly runoff follows changes in annual runoff. Keywords: SRES emissions scenarios, climate change impacts on runoff, multi-decadal variability, macro-scale hydrological model, drought frequency, flood frequency

GCIP water and energy budget synthesis (WEBS)

As part of the World Climate Research Program's (WCRPs) Global Energy and Water‐Cycle Experiment (GEWEX) Continental‐scale International Project (GCIP), a preliminary water and energy budget synthesis (WEBS) was developed for the period 1996–1999 from the “best available” observations and models. Besides this summary paper, a companion CD‐ROM with more extensive discussion, figures, tables, and raw data is available to the interested researcher from the GEWEX project office, the GAPP project office, or the first author. An updated online version of the CD‐ROM is also available at http://ecpc.ucsd.edu/gcip/webs.htm/. Observations cannot adequately characterize or “close” budgets since too many fundamental processes are missing. Models that properly represent the many complicated atmospheric and near‐surface interactions are also required. This preliminary synthesis therefore included a representative global general circulation model, regional climate model, and a macroscale hydrologic model as well as a global reanalysis and a regional analysis. By the qualitative agreement among the models and available observations, it did appear that we now qualitatively understand water and energy budgets of the Mississippi River Basin. However, there is still much quantitative uncertainty. In that regard, there did appear to be a clear advantage to using a regional analysis over a global analysis or a regional simulation over a global simulation to describe the Mississippi River Basin water and energy budgets. There also appeared to be some advantage to using a macroscale hydrologic model for at least the surface water budgets.

Assessing snowmelt dynamics with NASA scatterometer (NSCAT) data and a hydrologic process model

The presence of snow strongly impacts the exchange of moisture and energy between the land surface and atmosphere. In the interior of the northern hemisphere continents, snowmelt on frozen soils can cause or exacerbate major floods. Microwave remote sensing from satellite platforms has the potential to monitor the freeze–thaw status of soil and overlying snow packs over large areas. We evaluate the backscatter response of the NSCAT scatterometer to changing snow surface conditions, especially freeze and thaw status, using a macroscale hydrology model and the NSCAT backscatter data for the upper Mississippi River basin of the north central U.S. and the Boreal Ecosystem Atmosphere Study (BOREAS) region in central Canada. We compared the snowmelt conditions simulated by the Variable Infiltration Capacity (VIC) macroscale hydrology model driven with surface meteorological observations with NSCAT measurements for 1996–1997 snow season. A mid-winter thaw event (in February) and late season melt (April–May) are evaluated for both regions. Comparison of backscatter images with daily and hourly-modeled snow surface wetness and temperature showed that the model agreed with the backscatter for snow surface wetness on some days but not on others. Factors such as NSCAT overpass times, vegetation on the ground and their freeze–thaw state, and liquid moisture content appear to contribute to these discrepancies.

Variable infiltration capacity cold land process model updates

The Variable Infiltration Capacity (VIC) macroscale hydrologic model is distinguished from other Soil–Vegetation–Atmosphere Transfer schemes (SVATS) by its focus on runoff processes. These are represented via the variable infiltration curve, a parameterization of the effects of subgrid variability in soil moisture holding capacity, from which the model takes its name, and a representation of nonlinear baseflow. Recent upgrades to the model have improved its representation of cold land processes, and the effects of surface storage in lakes and wetlands. Specific improvements described in this paper include the following: (1) explicit representation of the canopy energy balance separate from the land surface when snow is intercepted in the canopy; (2) parameterization of the effects of spatial variability in soil freeze–thaw state and snow distribution on moisture and energy fluxes; and (3) effects of advection on snowmelt under conditions of partial snow cover. The effects of these model updates are demonstrated using data from the PILPS Phase 2(e) validation catchments within the Torne-Kalix River basin, Sweden.

Long‐range experimental hydrologic forecasting for the eastern United States

We explore a strategy for long‐range hydrologic forecasting that uses ensemble climate model forecasts as input to a macroscale hydrologic model to produce runoff and streamflow forecasts at spatial and temporal scales appropriate for water management. Monthly ensemble climate model forecasts produced by the National Centers for Environmental Prediction/Climate Prediction Center global spectral model (GSM) are bias corrected, downscaled to 1/8° horizontal resolution, and disaggregated to a daily time step for input to the Variable Infiltration Capacity hydrologic model. Bias correction is effected by evaluating the GSM ensemble forecast variables as percentiles relative to the GSM model climatology and then extracting the percentiles' associated variable values instead from the observed climatology. The monthly meteorological forecasts are then interpolated to the finer hydrologic model scale, at which a daily signal that preserves the forecast anomaly is imposed through resampling of the historic record. With the resulting monthly runoff and streamflow forecasts for the East Coast and Ohio River basin, we evaluate the bias correction and resampling approaches during the southeastern United States drought from May to August 2000 and also for the El Niño conditions of December 1997 to February 1998. For the summer 2000 study period, persistence in anomalous initial hydrologic states predominates in determining the hydrologic forecasts. In contrast, the El Niño‐condition hydrologic forecasts derive direction both from the climate model forecast signal and the antecedent land surface state. From a qualitative standpoint the hydrologic forecasting strategy appears successful in translating climate forecast signals to hydrologic variables of interest for water management.

Spatial Heterogeneity of the Soil Moisture Content and Its Impact on Surface Flux Densities and Near-Surface Meteorology

Using a subgrid distribution for the soil moisture content derived from a macroscale hydrologic model, it is investigated how lateral subgrid variations in the soil moisture content impact both the daily and the seasonal cycle of the spatially averaged surface flux densities and near-surface meteorology. In agreement with earlier studies it is found that in wet conditions the use of one uniform volumetric soil moisture content, referred to as the bulk approach, gives larger estimates of the latent heat flux density than a quasi-distributed approach where the lateral variation in the volumetric soil moisture content is taken into account. In dry conditions the bulk approach gives lower estimates of the latent heat flux density than the quasi-distributed approach. In this study, the differences between the flux density estimates obtained by both approaches appear even when the developing convective boundary layer is allowed to feed back on the surface. It is also shown that differences in the estimated surface flux densities lead to differences between the predicted atmospheric specific humidity and the predicted near-surface temperature. The differences due to the subgrid variations in the soil moisture content appear to impact the seasonal hydrologic balance. Especially for dry climates, the quasi-distributed approach predicts a more gradual decrease of the evapotranspiration during the dry season, resulting in a larger cumulative evapotranspiration over the dry season. Thus, taking account of the spatial heterogeneity of the soil moisture content is a prerequisite for a proper representation of the seasonal hydrological cycle within largescale atmospheric models.