Predictability of seasonal runoff in the Mississippi River basin

Recent advances in climate prediction and remote sensing offer the potential to improve long-lead streamflow forecasts and to provide better land surface state estimates at the time of forecast. We characterize predictability of runoff at seasonal timescales in the Mississippi River basin due to climatic persistence (represented by El Niño-Southern Oscillation and the Arctic Oscillation) and persistence related to the initial land surface state (soil moisture and snow). These climate and land surface state indicators, at varying lead times, are then used in a multiple linear regression to explain the variance of seasonal average runoff. Soil moisture dominates runoff predictability for lead times of 1 1/2 months, except in summer in the western part of the basin, where snow dominates. For the western part of the basin, the land surface state has a stronger predictive capability than climate indicators through leads of two seasons; climate indicators are more important in the east at lead times of one season or greater. Modest winter runoff predictability exists at a lead time of 3 seasons due to both climate and soil moisture, but this is in areas producing little runoff and is therefore of lessened importance. Local summer runoff predictability is limited to the western mountainous areas (generating high runoff) through a lead of 2 seasons. This could be useful to water managers in the western portion of the Mississippi River basin, because it suggests the potential to provide skillful forecast information earlier in the water year than currently used in operational forecasts

Evaluation of the Land Surface Water Budget in NCEP/NCAR and NCEP/DOE Reanalyses using an Off-line Hydrologic Model

The ability of the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis (NRA1) and the follow-up NCEP/Department of Energy (DOE) reanalysis (NRA2), to reproduce the hydrologic budgets over the Mississippi River basin is evaluated using a macroscale hydrology model. This diagnosis is aided by a relatively unconstrained global climate simulation using the NCEP global spectral model, and a more highly constrained regional climate simulation using the NCEP regional spectral model, both employing the same land surface parameterization (LSP) as the reanalyses. The hydrology model is the variable infiltration capacity (VIC) model, which is forced by gridded observed precipitation and temperature. It reproduces observed streamflow, and by closure is constrained to balance other terms in the surface water and energy budgets. The VIC-simulated surface fluxes therefore provide a benchmark for evaluating the predictions from the reanalyses and the climate models. The comparisons, conducted for the 10-year period 1988–1997, show the well-known overestimation of summer precipitation in the southeastern Mississippi River basin, a consistent overestimation of evapotranspiration, and an underprediction of snow in NRA1. These biases are generally lower in NRA2, though a large overprediction of snow water equivalent exists. NRA1 is subject to errors in the surface water budget due to nudging of modeled soil moisture to an assumed climatology. The nudging and precipitation bias alone do not explain the consistent overprediction of evapotranspiration throughout the basin. Another source of error is the gravitational drainage term in the NCEP LSP, which produces the majority of the model\u27s reported runoff. This may contribute to an overprediction of persistence of surface water anomalies in much of the basin. Residual evapotranspiration inferred from an atmospheric balance of NRA1, which is more directly related to observed atmospheric variables, matches the VIC prediction much more closely than the coupled models. However, the persistence of the residual evapotranspiration is much less than is predicted by the hydrological model or the climate models

Long range experimental hydrologic forecasting for the eastern U.S.

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\u27 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

Potential effects of long-lead hydrologic predictability on Missouri River main-stem reservoirs

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\u27s 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

A Long-Term Hydrologically-Based Data Set of Land Surface Fluxes and States for the Conterminous United States

A frequently encountered difficulty in assessing model-predicted land–atmosphere exchanges of moisture and energy is the absence of comprehensive observations to which model predictions can be compared at the spatial and temporal resolutions at which the models operate. Various methods have been used to evaluate the land surface schemes in coupled models, including comparisons of model-predicted evapotranspiration with values derived from atmospheric water balances, comparison of model-predicted energy and radiative fluxes with tower measurements during periods of intensive observations, comparison of model-predicted runoff with observed streamflow, and comparison of model predictions of soil moisture with spatial averages of point observations. While these approaches have provided useful model diagnostic information, the observation-based products used in the comparisons typically are inconsistent with the model variables with which they are compared—for example, observations are for points or areas much smaller than the model spatial resolution, comparisons are restricted to temporal averages, or the spatial scale is large compared to that resolved by the model. Furthermore, none of the datasets available at present allow an evaluation of the interaction of the water balance components over large regions for long periods. In this study, a model-derived dataset of land surface states and fluxes is presented for the conterminous United States and portions of Canada and Mexico. The dataset spans the period 1950–2000, and is at a 3-h time step with a spatial resolution of ⅛ degree. The data are distinct from reanalysis products in that precipitation is a gridded product derived directly from observations, and both the land surface water and energy budgets balance at every time step. The surface forcings include precipitation and air temperature (both gridded from observations), and derived downward solar and longwave radiation, vapor pressure deficit, and wind. Simulated runoff is shown to match observations quite well over large river basins. On this basis, and given the physically based model parameterizations, it is argued that other terms in the surface water balance (e.g., soil moisture and evapotranspiration) are well represented, at least for the purposes of diagnostic studies such as those in which atmospheric model reanalysis products have been widely used. These characteristics make this dataset useful for a variety of studies, especially where ground observations are lacking

A spatially distributed model for the dynamic prediction of sediment erosion and transport in mountainous forested watersheds

Erosion and sediment transport in a temperate forested watershed are predicted with a new sediment model that represents the main sources of sediment generation in forested environments (mass wasting, hillslope erosion, and road surface erosion) within the distributed hydrology-soil-vegetation model (DHSVM) environment. The model produces slope failures on the basis of a factor-of-safety analysis with the infinite slope model through use of stochastically generated soil and vegetation parameters. Failed material is routed downslope with a rule-based scheme that determines sediment delivery to streams. Sediment from hillslopes and road surfaces is also transported to the channel network. A simple channel routing scheme is implemented to predict basin sediment yield. We demonstrate through an initial application of this model to the Rainy Creek catchment, a tributary of the Wenatchee River, which drains the east slopes of the Cascade Mountains, that the model produces plausible sediment yield and ratios of landsliding and surface erosion when compared to published rates for similar catchments in the Pacific Northwest. A road removal scenario and a basin-wide fire scenario are both evaluated with the model

Contribution of Soil Moisture Information to Streamflow Prediction in the Snowmelt Season: A Continental-Scale Analysis

In areas dominated by winter snowcover, the prediction of streamflow during the snowmelt season may benefit from three pieces of information: (i) the accurate prediction of weather variability (precipitation, etc.) leading up to and during the snowmelt season, (ii) estimates of the amount of snow present during the winter season, and (iii) estimates of the amount of soil moisture underlying the snowpack during the winter season. The importance of accurate meteorological predictions and wintertime snow estimates is obvious. The contribution of soil moisture to streamflow prediction is more subtle yet potentially very important. If the soil is dry below the snowpack, a significant fraction of the snowmelt may be lost to streamflow and potential reservoir storage, since it may infiltrate the soil instead for later evaporation. Such evaporative losses are presumably smaller if the soil below the snowpack is wet. In this paper, we use a state-of-the-art land surface model to quantify the contribution of wintertime snow and soil moisture information -- both together and separately -- to skill in forecasting springtime streamflow. We find that soil moisture information indeed contributes significantly to streamflow prediction skill

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

Prediction of Hydrological Drought: What Can We Learn From Continental-Scale Offline Simulations?

Land surface model experiments are used to quantify, across the coterminous United States, the contributions (isolated and combined) of soil moisture and snowpack initialization to the skill of seasonal streamflow forecasts at multiple leads and for different start dates. Forecasted streamflows are compared to naturalized streamflow observations where available and to synthetic (model-generated) streamflow data elsewhere. We find that snow initialization has a major impact on skill in the mountainous western U.S. and in a portion of the northern Great Plains; a mid-winter (January 1) initialization of snow in these areas leads to significant skill in the spring melting season. Soil moisture initialization also contributes to skill, and although the maximum contributions are not as large as those seen for snow initialization, the soil moisture contributions extend across a much broader geographical area. Soil moisture initialization can contribute to skill at long leads (up to 5 or 6 months), particularly for forecasts issued during winter

Validation of Land Surface Models Using Satellite-Derived Surface Temperature

This research examines the feasibility of using remotely sensed surface temperature for validation and updating of land surface hydrologic models. Surface temperature simulated by the Variable Infiltration Capacity (VIC) hydrologie model is compared over the Arkansas-Red River basin with surface temperature retrievals from TOVS and GOES. The results show that modeled and satellite-derived surface temperatures agree well when aggregated in space or time. In particular, monthly mean temperatures agree on the pixel scale, and basin mean temperatures agree instantaneously. At the pixel scale, however, surface temperatures from both satellites were found to have higher spatial and temporal variabilities than the modeled temperatures, although the model and satellites display similar patterns of variability through space and time. The largest differences between modeled and remotely sensed surface temperature variability occur at times of maximum net radiation both diurnally and seasonally, i.e., afternoon and summer. Comparison of temporal and spatial patterns of VIC-predicted surface temperature variability with similar predictions by nine other models involved in the PILPS-2c experiment show that the VIC patterns are similar to those of the other models. Observed surface temperature and air temperature from FIFE are used to identify possible errors in satellite-retrieved surface temperatures. The FIFE comparisons show that satellite retrieved surface temperatures likely contain errors that increase variabilit