Showing papers in "Journal of Hydrometeorology in 2002"
TL;DR: In this paper, the authors describe the initial work toward the production of monthly global (land and ocean) analyses of precipitation for an extended period from 1948 to the present, called the precipitation reconstruction (PREC).
Abstract: This paper describes the initial work toward the production of monthly global (land and ocean) analyses of precipitation for an extended period from 1948 to the present. Called the precipitation reconstruction (PREC), the global analyses are defined by interpolation of gauge observations over land (PREC/L) and by EOF reconstruction of historical observations over ocean (PREC/O). This paper documents the creation of the land component of the analyses (PREC/L) on a 2.5° latitude/longitude grid for 1948–2000. These analyses are derived from gauge observations from over 17 000 stations collected in the Global Historical Climatology Network (GHCN), version 2, and the Climate Anomaly Monitoring System (CAMS) datasets. To determine the most suitable objective analysis procedure for gridding, the analyses generated by four published objective analysis techniques [those of Cressman, Barnes, and Shepard, and the optimal interpolation (OI) method of Gandin] were compared. The evaluation demonstrated two cru...
1,095 citations
TL;DR: In this paper, the most accurate estimate is based on streamflow data from the world's largest 921 rivers, supplemented with estimates of discharge from unmonitored areas based on the ratios of runoff and drainage area between the un-monitored and monitored regions.
Abstract: Annual and monthly mean values of continental freshwater discharge into the oceans are estimated at 18 resolution using several methods. The most accurate estimate is based on streamflow data from the world’s largest 921 rivers, supplemented with estimates of discharge from unmonitored areas based on the ratios of runoff and drainage area between the unmonitored and monitored regions. Simulations using a river transport model (RTM) forced by a runoff field were used to derive the river mouth outflow from the farthest downstream gauge records. Separate estimates are also made using RTM simulations forced by three different runoff fields: 1) based on observed streamflow and a water balance model, and from estimates of precipitation P minus evaporation E computed as residuals from the atmospheric moisture budget using atmospheric reanalyses from 2) the National Centers for Environmental Prediction‐National Center for Atmospheric Research (NCEP‐NCAR) and 3) the European Centre for Medium-Range Weather Forecasts (ECMWF). Compared with previous estimates, improvements are made in extending observed discharge downstream to the river mouth, in accounting for the unmonitored streamflow, in discharging runoff at correct locations, and in providing an annual cycle of continental discharge. The use of river mouth outflow increases the global continental discharge by ;19% compared with unadjusted streamflow from the farthest downstream stations. The river-based estimate of global continental discharge presented here is 37 288 6 662 km3 yr21, which is ;7.6% of global P or 35% of terrestrial P. While this number is comparable to earlier estimates, its partitioning into individual oceans and its latitudinal distribution differ from earlier studies. The peak discharges into the Arctic, the Pacific, and global oceans occur in June, versus May for the Atlantic and August for the Indian Oceans. Snow accumulation and melt are shown to have large effects on the annual cycle of discharge into all ocean basins except for the Indian Ocean and the Mediterranean and Black Seas. The discharge and its latitudinal distribution implied by the observation-based runoff and the ECMWF reanalysis-based P‐E agree well with the river-based estimates, whereas the discharge implied by the NCEP‐NCAR reanalysis-based P‐E has a negative bias.
995 citations
TL;DR: In this article, the performance of the extended Kalman filter (EKF) and the EnKF were assessed for soil moisture estimation, and the average actual estimation error in volumetric moisture content of the soil profile was 2.2% for the EKF and 2.1% (or 2.0%) for the En-KF with 4 (or 10; or 500) ensemble members.
Abstract: The performance of the extended Kalman filter (EKF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation. In a twin experiment for the southeastern United States synthetic observations of near-surface soil moisture are assimilated once every 3 days, neglecting horizontal error correlations and treating catchments independently. Both filters provide satisfactory estimates of soil moisture. The average actual estimation error in volumetric moisture content of the soil profile is 2.2% for the EKF and 2.2% (or 2.1%; or 2.0%) for the EnKF with 4 (or 10; or 500) ensemble members. Expected error covariances of both filters generally differ from actual estimation errors. Nevertheless, nonlinearities in soil processes are treated adequately by both filters. In the application presented herein the EKF and the EnKF with four ensemble members are equally accurate at comparable computational cost. Because of its flexibility and its performance in this study, the EnKF is a promising ...
372 citations
TL;DR: A simple model of large-scale land (continental) water and energy balances is presented in this article, which is an extension of an earlier scheme with a record of successful application in climate modeling.
Abstract: A simple model of large-scale land (continental) water and energy balances is presented. The model is an extension of an earlier scheme with a record of successful application in climate modeling. The most important changes from the original model include 1) introduction of non-water-stressed stomatal control of transpiration, in order to correct a tendency toward excessive evaporation; 2) conversion from globally constant parameters (with the exception of vegetation-dependent snow-free surface albedo) to more complete vegetation and soil dependence of all parameters, in order to provide more realistic representation of geographic variations in water and energy balances and to enable model-based investigations of land-cover change; 3) introduction of soil sensible heat storage and transport, in order to move toward realistic diurnal-cycle modeling; 4) a groundwater (saturated-zone) storage reservoir, in order to provide more realistic temporal variability of runoff; and 5) a rudimentary runoff-ro...
349 citations
TL;DR: In this paper, terrain-based parameters are developed to characterize wind effects in exposed alpine regions, and a drift delineator parameter, D0, is used to delineate sites of intense redeposition on lee slopes.
Abstract: Wind is widely recognized as one of the dominant controls of snow accumulation and distribution in exposed alpine regions. Complex and highly variable wind fields in rugged terrain lead to similarly complex snow distribution fields with areas of no snow adjacent to areas of deep accumulation. Unfortunately, these complexities have limited inclusion of wind redistribution effects in spatial snow distribution models. In this study the difficulties associated with physically exhaustive wind field modeling are avoided and terrain-based parameters are developed to characterize wind effects. One parameter, , was based on maximum upwind slopes relative to seasonally averaged winds to characterize the wind scalar at each pixel location in an alpine basin. A second parameter, , measured upwind breaks in slope from a given location and was combined with an upwind application of to create a drift delineator parameter, D0, which was used to delineate sites of intense redeposition on lee slopes. Based on 504 ...
300 citations
TL;DR: In this paper, a procedure for real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements is described, which is a generalized local bias estimator that may be used under varying conditions of rain gauge network density and types of rainfall.
Abstract: A procedure for real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements is described. Developed to complement the existing gauge-based bias correction procedures used in the National Weather Service (NWS), the proposed procedure is a generalized local bias estimator that may be used under varying conditions of rain gauge network density and types of rainfall. To arrive at the procedure, the correction problem is formulated as a space–time estimation of radar and bin-averaged gauge rainfall from radar rainfall data and rain gauge measurements, respectively, at all hours up to and including the current hour. The estimation problem is then solved suboptimally via a variant of exponential smoothing. To evaluate the procedure, parameter estimation and true validation were performed using hourly radar-rainfall and rain gauge data from the Arkansas–Red Basin River Forecast Center (ABRFC) area. The results indicate that the proposed procedure is generally su...
281 citations
TL;DR: The main components of the hydrologic cycle of the La Plata basin in southeastern South America were investigated using a combination of observations, satellite products, and National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) global reanalyses.
Abstract: The main components of the hydrologic cycle of the La Plata basin in southeastern South America are investigated using a combination of observations, satellite products, and National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) global reanalyses. La Plata basin is second only to the Amazon basin in South America in river discharge and size and plays a critical role in the economies of the region. It is a primary factor in energy production, water resources, transportation, agriculture, and livestock. Of particular interest was the evaluation of the annual cycle of the hydrologic cycle components. The La Plata annual-mean river discharge is about 21 000 m3 s−1, and the amplitude of its mean annual cycle is small: it is slightly larger during late summer, but continues with large volumes even during winter. The reason for this is that different precipitation regimes over different locations contribute to the total river discharge. One regime is found t...
264 citations
TL;DR: In this paper, principal component analysis is used to identify the primary modes of 1 April snowpack variability in the western United States and the relationship between these modes of variability and indices of Pacific Ocean climate is established.
Abstract: Snowpack, as measured on 1 April, is the primary source of warm-season streamflow for most of the western United States and thus represents an important source of water supply. An understanding of climate factors that influence the variability of this water supply and thus its predictability is important for water resource management. In this study, principal component analysis is used to identify the primary modes of 1 April snowpack variability in the western United States. Two components account for 61% of the total snowpack variability in the western United States. Relations between these modes of variability and indices of Pacific Ocean climate
245 citations
TL;DR: In this article, the authors evaluated the profiles of wind speed, temperature, and humidity in the atmospheric surface layer or modeling the turbulent surface fluxes of sensible and latent heat over horizontally homogeneous surfaces of snow or ice.
Abstract: Evaluating the profiles of wind speed, temperature, and humidity in the atmospheric surface layer or modeling the turbulent surface fluxes of sensible and latent heat over horizontally homogeneous surfaces of snow or ice requires five pieces of information. These are the roughness lengths for wind speed (z0), temperature (zT), and humidity (zQ) and the stratification corrections for the wind speed and scalar profiles ψm and ψh, respectively. Because over snow and ice the atmospheric surface layer is often stably stratified, the discussion here focuses first on which of the many suggested ψm and ψh functions to use over snow and ice. On the basis of four profile metrics—the critical Richardson number, the Deacon numbers for wind speed and temperature, and the turbulent Prandtl number—the manuscript recommends the Holtslag and de Bruin ψm and ψh functions because these have the best properties in very stable stratification. Next, a reanalysis of five previously published datasets confirms the valid...
214 citations
TL;DR: In this article, a blowing-snow model (SnowTran-3D) was combined with field measurements of end-of-winter snow depth and density to simulate solid (winter) precipitation, snow transport, and sublimation distributions over a 20 000-km2 arctic Alaska domain.
Abstract: A blowing-snow model (SnowTran-3D) was combined with field measurements of end-of-winter snow depth and density to simulate solid (winter) precipitation, snow transport, and sublimation distributions over a 20 000-km2 arctic Alaska domain. The domain included rolling uplands and a flat coastal plain. Simulations were produced for the winters of 1994/95, 1995/96, and 1996/97. The model, which accounts for spatial and temporal variations in blowing-snow sublimation, as well as saltation and turbulent-suspended transport, was driven with interpolated fields of observed temperature, humidity, and wind speed and direction. Model outputs include local (a few hundreds of meters) to regional (several tens of kilometers) distributions of winter snow-water-equivalent depths and blowing-snow sublimation losses, from which the regional winter precipitation distributions are computed. At regional scales, the end-of-winter snow depth is largely equal to the difference between winter precipitation and moisture ...
205 citations
TL;DR: In this article, daily precipitation and maximum and minimum temperature time series from a regional climate model (RegCM2) configured using the continental United States as a domain and run on a 52-km (approximately) spatial resolution were used as input to a distributed hydrologic model for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado; east fork of the Carson River near Gardnerville, Nevada; and Cle Elum River near Roslyn, Washington).
Abstract: Daily precipitation and maximum and minimum temperature time series from a regional climate model (RegCM2) configured using the continental United States as a domain and run on a 52-km (approximately) spatial resolution were used as input to a distributed hydrologic model for one rainfall-dominated basin (Alapaha River at Statenville, Georgia) and three snowmelt-dominated basins (Animas River at Durango, Colorado; east fork of the Carson River near Gardnerville, Nevada; and Cle Elum River near Roslyn, Washington). For comparison purposes, spatially averaged daily datasets of precipitation and maximum and minimum temperature were developed from measured data for each basin. These datasets included precipitation and temperature data for all stations (hereafter, All-Sta) located within the area of the RegCM2 output used for each basin, but excluded station data used to calibrate the hydrologic model. Both the RegCM2 output and All-Sta data capture the gross aspects of the seasonal cycles of precipit...
TL;DR: In this paper, the authors combine the strengths of microwave-based and geostationary satellite data to adjust the GOES-based estimates, mainly for long-term precipitation estimates.
Abstract: Estimates of precipitation from satellite data can provide timely information about rainfall in regions for which data from rain gauge networks are sparse or unavailable entirely and for which radar data are unavailable or are compromised by range effects and beam blockage. Two basic kinds of satellite-based estimates are available. Infrared data from geostationary satellite platforms such as the Geostationary Operational Environmental Satellite (GOES) can be used to infer cloud-top conditions on a continuous basis, but the relationship between cloud-top conditions and the rate of rainfall below can vary significantly. Microwave radiances are related more directly to precipitation rates, but microwave instruments are limited to polar-orbiting platforms, resulting in intermittent availability of estimates. A number of authors have made efforts to combine the strengths of both by using the microwave-based estimates to adjust the GOES-based estimates, mainly for long-term precipitation estimates at ...
TL;DR: In this article, a series of hypotheses of broad importance to the hydrology and hydrometeorology behavior of extreme floods are examined, including space-time variability of rainfall, antecedent soil moisture, expansion of impervious area, and alterations of the drainage network for extreme floods in urbanizing drainage basins.
Abstract: The Charlotte, North Carolina, metropolitan area has experienced extensive urban and suburban growth since 1960. Five of the largest flood peaks in the 74-yr discharge record of Little Sugar Creek, which drains the central urban corridor of Charlotte, have occurred since August of 1995. A central objective of this study is to explain how these two observations are linked. To achieve this goal, a series of hypotheses of broad importance to the hydrology and hydrometeorology behavior of extreme floods will be examined. These hypotheses concern the roles of 1) space–time variability of rainfall, 2) antecedent soil moisture, 3) expansion of impervious area, and 4) alterations of the drainage network for extreme floods in urbanizing drainage basins. The methodology used to examine these hypotheses centers on diagnostic studies of flood response for the five major flood events that have occurred since August of 1995. Diagnostic studies exploit the diverse range of extreme precipitation forcing for the ...
TL;DR: The diurnal cycle in streamflow constitutes a significant part of the variability in many rivers in the western United States and can be used to understand some of the dominant processes affecting the water balance of a given river basin this paper.
Abstract: The diurnal cycle in streamflow constitutes a significant part of the variability in many rivers in the western United States and can be used to understand some of the dominant processes affecting the water balance of a given river basin. Rivers in which water is added diurnally, as in snowmelt, and rivers in which water is removed diurnally, as in evapotranspiration and infiltration, exhibit substantial differences in the timing, relative magnitude, and shape of their diurnal flow variations. Snowmelt-dominated rivers achieve their highest sustained flow and largest diurnal fluctuations during the spring melt season. These fluctuations are characterized by sharp rises and gradual declines in discharge each day. In large snowmelt-dominated basins, at the end of the melt season, the hour of maximum discharge shifts to later in the day as the snow line retreats to higher elevations. Many evapotranspiration/infiltration-dominated rivers in the western states achieve their highest sustained flows during the winter rainy season but exhibit their strongest diurnal cycles during summer months, when discharge is low, and the diurnal fluctuations compose a large percentage of the total flow. In contrast to snowmelt-dominated rivers, the maximum discharge in evapotranspiration/infiltration-dominated rivers occurs consistently in the morning throughout the summer. In these rivers, diurnal changes are characterized by a gradual rise and sharp decline each day.
TL;DR: In this paper, the authors investigated how precipitation signals are manifested in vertical soil moisture profiles in the context of timescales, using 16 years of field observational data of soil moisture measured at 11 levels of various depths down to 2.0 m.
Abstract: Soil hydrology is a widely recognized low-pass filter for the interaction between land and atmosphere. However, the lack of adequate long-term measured data on soil moisture profiles has precluded examination of how soil wetness responds to long-term precipitation variations. Such a response can be characterized by its amplitude damping, phase shifting, and increasing persistence with soil depth. These should be correlated with the climate spectra through the interactions between the land and the atmosphere. The major objective of this study is to investigate how precipitation signals are manifested in vertical soil moisture profiles in the context of timescales. Thus, the natural variability of soil moisture profiles is documented using 16 yr of field observational data of soil moisture measured at 11 levels of various depths down to 2.0 m at 17 locations over Illinois. Detailed statistic analyses are made of the temporal variations of soil moisture profiles and concurrently measured precipitati...
TL;DR: In this paper, a two-way coupling of the operational mesoscale weather prediction model known as Lokal Modell (LM) with the land surface hydrologic "TOPMODEL"-based Land Surface-Atmosphere Transfer Scheme (TOPLATS; Princeton University) has been carried out to investigate the influence of a “state-of-the-art” land surface Hydrologic model on the predicted local weather.
Abstract: A two-way coupling of the operational mesoscale weather prediction model known as Lokal Modell (LM; German Weather Service) with the land surface hydrologic “TOPMODEL”-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS; Princeton University) has been carried out to investigate the influence of a “state-of-the-art” land surface hydrologic model on the predicted local weather. Two case studies are presented that quantify the influence of the combined modeling system on the turbulent fluxes and boundary layer structure and on the formation of precipitation. The model results are compared with ground-based measurements of turbulent fluxes, boundary layer structure, and precipitation. Furthermore, whether the initialization of the original LM with more realistic soil moisture fields would be sufficient to improve the weather forecast is investigated. The results of the two case studies show that, when compared with measurements, the two-way coupled modeling system using TOPLATS improves the predic...
TL;DR: In this article, a case study for the Ardeche catchment in France using the parameters of two S-band weather radars operated by Meteo-France at Nimes and Bollene is presented.
Abstract: A simulation procedure has been developed for use in predetermining the expected quality of rain-rate estimates that a given weather radar system operating in a mountainous region may obtain over a given hydrologic catchment. This first application of what is referred to as the ''hydrologic visibility'' concept focuses on the quantification of the rain-rate error resulting from the effects of ground clutter, beam blockage, and the vertical profile of reflectivity (VPR). The assessment of the impact of the space-time structure of the radar error in terms of discharge at the catchment outlet is also investigated using a distributed hydrologic model. A case study is presented for the Ardeche catchment in France using the parameters of two S-band weather radars operated by Meteo-France at Nimes and Bollene. Radar rain-rate error generation and rainfall-runoff simulations are performed using VPR and areal rainfall time series representative of the Cevennes rain climatology. The major impact of ground clutter on both rainfall and runoff estimates is confirmed. The ''hydrologic compositing procedure,'' based on the selection of the elevation angle minimizing the rain-rate error at a given point, is shown to be preferable to the ''pseudo-CAPPI'' procedure based on radar-range considerations only. An almost perfect ground-clutter reduction (GCR) technique is simulated in order to assess the effects of beam blockage and VPR alone. These error sources lead to severe and slight rain underestimations for the Nimes and Bollene radars, respectively, over the Ardeche catchment. The results, indicating an amplification of the errors on the discharge parameters (peak discharge, runoff volume) compared to the areal rainfall error, are of particular interest. They emphasize the need for refined corrections for ground clutter, beam blockage, and VPR effects, in addition to the optimization of the radar location and scanning strategy, if hydrologic applications are foreseen.
TL;DR: In this paper, satellite-derived vegetation indices extracted over locations representative of midwestern U.S. cropland and forest for the period 1990-94 are analyzed to determine the sensitivity of the indices to neutron probe soil moisture measurements of the Illinois Climate Network (ICN).
Abstract: Satellite-derived vegetation indices extracted over locations representative of midwestern U.S. cropland and forest for the period 1990–94 are analyzed to determine the sensitivity of the indices to neutron probe soil moisture measurements of the Illinois Climate Network (ICN). The deseasoned (i.e., departures from multiyear mean annual cycle) soil moisture measurements are shown to be weakly correlated with the deseasoned full resolution (1 km × 1 km) normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) data over both land cover types. The association, measured by the Pearson-moment-correlation coefficient, is stronger over forest than over cropland during the growing season (April–September). The correlations improve successively when the NDVI and FVC pixel data are aggregated to 3 km × 3 km, 5 km × 5 km, and 7 km × 7 km areas. The improved correlations are partly explained by the reduction in satellite navigation errors as spatial aggregation occurs, as well as t...
TL;DR: In this paper, an operational flood forecasting system and assessment of forecast uncertainty are developed for a Mediterranean environment, using the Ensemble Prediction System as input for a semidistributed hydrologic model.
Abstract: Development of an operational flood forecasting system and assessment of forecast uncertainty are the principal topics of this paper. Flood forecasting procedures are developed for a Mediterranean environment. A procedure that uses the Ensemble Prediction System as input for a semidistributed hydrologic model is presented. A rainfall downscaling model is used to bridge the scale gap between numerical weather prediction model output and hydrologic modeling input. The results are illustrated for the November 1994 Piedmont flood.
TL;DR: In this article, the authors describe vertically integrated global and regional water and energy budgets from the National Centers for Environmental Prediction (NCEP) and U.S. Department of Energy (DOE) Reanalysis II.
Abstract: During the past several years, the Global Energy and Water Cycle Experiment (GEWEX) continental-scale experiments (CSEs) have started to develop regional hydroclimatological datasets and water and energy budget studies (WEBS). To provide some global background for these regional experiments, the authors describe vertically integrated global and regional water and energy budgets from the National Centers for Environmental Prediction (NCEP)–U.S. Department of Energy (DOE) Reanalysis II (NCEPRII). It is shown that maintaining the NCEPRII close to observations requires some nudging to the short-range model forecast, and this nudging is an important component of analysis budgets. Still, to first order one can discern important hydroclimatological mechanisms in the reanalysis. For example, during summer, atmospheric water vapor, precipitation, evaporation, and surface and atmospheric radiative heating all increase, while the dry static energy convergence decreases almost everywhere over the land region...
TL;DR: In this article, the impact of spatially aggregating soil moisture imagery up to the footprint scale (32 −64 km) of spaceborne microwave radiometers on regional-scale prediction of surface energy fluxes is examined.
Abstract: Using high-resolution (1 km) hydrologic modeling of the 575 000-km2 Red‐Arkansas River basin, the impact of spatially aggregating soil moisture imagery up to the footprint scale (32‐64 km) of spaceborne microwave radiometers on regional-scale prediction of surface energy fluxes is examined. While errors in surface energy fluxes associated with the aggregation of soil moisture are potentially large ( . 50 Wm 22), relatively simple representations of subfootprint-scale variability are capable of substantially reducing the impact of soil moisture aggregation on land surface model energy flux predictions. This suggests that even crude representations of subgrid soil moisture statistics obtained from statistical downscaling procedures can aid regional-scale surface energy flux prediction. One possible soil moisture downscaling procedure, based on an assumption of spatial scaling (i.e., a power-law relationship between statistical moments and scale), is demonstrated to improve TOPmodel-based Land‐Atmosphere Transfer Scheme (TOPLATS) prediction of grid-scale surface energy fluxes derived from coarse-resolution soil moisture imagery.
TL;DR: In this article, both single-station weather generators and spatial networks of coherently operating weather generators are considered, and a subset of parameters for individual station models (proportion of wet days, precipitation mean parameters on wet days and daily temperature means and standard deviations) are found to depend appreciably on the seasonal temperature and precipitation outcomes.
Abstract: Stochastic daily weather time series models (‘‘weather generators’’) are parameterized consistent with both local climate and probabilistic seasonal forecasts. Both single-station weather generators, and spatial networks of coherently operating weather generators, are considered. Only a subset of parameters for individual station models (proportion of wet days, precipitation mean parameters on wet days, and daily temperature means and standard deviations) are found to depend appreciably on the seasonal temperature and precipitation outcomes, so that extension of the single-station models to coherent multisite weather generators is straightforward. The result allows stochastic simulation of multiple daily weather series, conditional on seasonal forecasts. Example applications of spatially integrated extreme daily precipitation and snowpack water content are used to illustrate the method.
TL;DR: In this paper, a coupled land-atmosphere model with prescribed ocean surface temperatures is used to assess the extent to which initial soil moisture fields explain variance of future predictands (soil moisture, near-surface air temperature, and precipitation).
Abstract: Soil moisture predictability and the associated predictability of continental climate are explored as an initialvalue problem, using a coupled land‐atmosphere model with prescribed ocean surface temperatures. Ensemble simulations are designed to assess the extent to which initial soil moisture fields explain variance of future predictands (soil moisture, near-surface air temperature, and precipitation). For soil moisture, the decrease of explained variance with lead time can be characterized as a first-order decay process, and a predictability timescale is introduced as the lead time at which this decay reaches e 21. The predictability timescale ranges from about 2 weeks or less (in midlatitudes during summer, and in the Tropics and subtropics) to 2‐6 months (in mid- to high latitudes for simulations that start in late fall and early winter). The predictability timescale of the modeled soil moisture is directly related to the soil moisture’s autocorrelation timescale. The degree of translation of soil moisture predictability to predictability of any atmospheric variable can be characterized by the ratio of the fraction of explained variance of the atmospheric variable to the fraction of explained soil moisture variance. By this measure, regions with the highest associated predictability of 30-day-mean near-surface air temperature (ratio greater than 0.5) are, generally speaking, coincident with regions and seasons of the smallest soil moisture predictability timescales. High associated temperature predictability is found where strong variability of soil moisture stress on evapotranspiration and abundant net radiation at the continental surface coincide. The associated predictability of 30-day-mean precipitation, in contrast, is very low.
TL;DR: In this paper, the degree to which the interannual variability of vegetation phenology affects hydrological fluxes over land is investigated through a series of simulations with the Mosaic land surface model, run both offline and coupled to the NASA Seasonal-to-Interannual Prediction Project (NSIPP) atmospheric general circulation model (GCM).
Abstract: The degree to which the interannual variability of vegetation phenology affects hydrological fluxes over land is investigated through a series of simulations with the Mosaic land surface model, run both offline and coupled to the NASA Seasonal-to-Interannual Prediction Project (NSIPP) atmospheric general circulation model (GCM). Over a 9-yr period, from 1982 to 1990, interannual variations of global biophysical land surface parameters (i.e., vegetation density and greenness fraction) are derived from Normalized Difference Vegetation Index (NDVI) data collected by the Advanced Very High Resolution Radiometers (AVHRRs). First the sensitivity of evapotranspiration to interannual variations in vegetation properties is evaluated through offline simulations that ignore feedbacks between the land surface and the atmospheric models, and interannual precipitation variations. Evapotranspiration is shown to be highly sensitive to variations in vegetation over wet continental surfaces that are not densely ve...
TL;DR: In this paper, the influence of ENSO on the monthly flows measured at Posadas (27°23′S, 55°53′W) during the period of 1901-97 was studied.
Abstract: The Upper Parana River is the main tributary of the La Plata River basin, the second largest in South America, contributing with an annual mean flow of 12 000 m3 s−1 to more than one-half of the total water flowing in the La Plata River system. The Parana River has a relevant importance in the region for transportation and hydroelectricity generation. This paper studies the influence of ENSO on the monthly flows measured at Posadas (27°23′S, 55°53′W) during the period of 1901–97. The original data are converted into standardized monthly anomalies, and the annual cycle is removed. Two data subsets are generated: a first group includes the years of warm ENSO events, or El Nino, and the second group includes the years of cold ENSO events, or La Nina. The elements of the subsets are composites of 24 consecutive months starting in January of the year when the ENSO event begins and ending in December of the following year. The results show that the averaged flows observed during El Nino events are alwa...
TL;DR: In this paper, the authors measured air and snow-ground interface temperatures at 33 stations spanning the 180 km-long Kuparuk basin in arctic Alaska, and found that 87% of the variation in the average interface temperature could be predicted from air temperature.
Abstract: Air and snow–ground interface temperatures were measured during two winters at 33 stations spanning the 180-km-long Kuparuk basin in arctic Alaska. Interface temperatures averaged 7.5°C higher than air temperatures and varied in a manner that was more complex, and on a spatial scale more than 100 times smaller, than the air temperature. Within the basin, two distinct thermal regimes could be identified, with the division at the boundary between coastal and uplands provinces. When each station was classified into one of three snow exposure classes (exposed, intermediate, or sheltered), accounting for variations in snow depth and thermal properties, 87% of the variation in the average interface temperature could be predicted from air temperature. Individual station interface temperature records were fit using a beta curve that captured the slow decrease in autumn and the rapid rise in spring. Beta curves were specified by three parameters (α, β, and γ) that could be predicted if province and snow e...
TL;DR: The multicriteria methodology, which provides a means to estimate optimal ranges for land surface model parameter values via calibration, is evaluated in this paper, where simulations are performed calibrating six modes of CHASM, representing a range of land surface complexity, against observed net radiation and latent and sensible heat fluxes.
Abstract: The multicriteria methodology, which provides a means to estimate optimal ranges for land surface model parameter values via calibration, is evaluated. Following calibration, differences between schemes resulting from effective parameter values can be isolated from differences resulting from scheme structure or scheme parameterizations. The method is applied to the Project for the Intercomparison of Land Surface Parameterization Schemes (PILPS) phase-2a data from the Cabauw site in the Netherlands using the Chameleon Surface Model (CHASM) as the surrogate for a range of land surface schemes. Simulations are performed calibrating six modes of CHASM, representing a range of land surface complexity, against observed net radiation and latent and sensible heat fluxes. The six modes range from a simple bucket model to a complex mosaic-type structure with separate energy balances for each mosaic tile and explicit treatment of transpiration, canopy interception, and bare-ground evaporation. Results demon...
TL;DR: In this paper, the authors evaluate the performance of a land model with and without information on the global distribution of albedo, rooting depth, and stomatal resistance, and find that the stOMatal resistance is, by far, the parameter among these three whose spatial variations add the most predictive power to the model in stand-alone mode.
Abstract: Land water and energy balances vary around the globe because of variations in amount and temporal distribution of water and energy supplies and because of variations in land characteristics. The former control (water and energy supplies) explains much more variance in water and energy balances than the latter (land characteristics). A largely untested hypothesis underlying most global models of land water and energy balance is the assumption that parameter values based on estimated geographic distributions of soil and vegetation characteristics improve the performance of the models relative to the use of globally constant land parameters. This hypothesis is tested here through an evaluation of the improvement in performance of one land model associated with the introduction of geographic information on land characteristics. The capability of the model to reproduce annual runoff ratios of large river basins, with and without information on the global distribution of albedo, rooting depth, and stomatal resistance, is assessed. To allow a fair comparison, the model is calibrated in both cases by adjusting globally constant scale factors for snow-free albedo, non-water-stressed bulk stomatal resistance, and critical root density (which is used to determine effective root-zone depth). The test is made in standalone mode, that is, using prescribed radiative and atmospheric forcing. Model performance is evaluated by comparing modeled runoff ratios with observed runoff ratios for a set of basins where precipitation biases have been shown to be minimal. The withholding of information on global variations in these parameters leads to a significant degradation of the capability of the model to simulate the annual runoff ratio. An additional set of optimization experiments, in which the parameters are examined individually, reveals that the stomatal resistance is, by far, the parameter among these three whose spatial variations add the most predictive power to the model in stand-alone mode. Further single-parameter experiments with surface roughness length, available water capacity, thermal conductivity, and thermal diffusivity show very little sensitivity to estimated global variations in these parameters. Finally, it is found that even the constant-parameter model performance exceeds that of the Budyko and generalized Turc‐Pike water-balance equations, suggesting that the model benefits also from information on the geographic variability of the temporal structure of forcing.
TL;DR: In this article, the authors used the Regional Atmospheric Modeling System (ClimRAMS) to investigate the sensitivity of regional climate simulations to changes in vegetation distribution in the Great Plains and Rocky Mountain regions of the United States.
Abstract: In this study, a climate version of the Regional Atmospheric Modeling System (ClimRAMS) was used to investigate the sensitivity of regional climate simulations to changes in vegetation distribution in the Great Plains and Rocky Mountain regions of the United States. The evolution of vegetation phenology was assimilated into the ClimRAMS in the form of estimates of the leaf area index (LAI) derived from the normalized difference vegetation index (NDVI). Initially, two model integrations were made. In the first, the NDVI-derived vegetation distribution was used, while the second integration used the model's “default” description of vegetation. The simulated near-surface climate was drastically altered by the introduction of NDVI-derived LAI, especially in the growing season, with the run in which observed LAI was assimilated producing, in general, a wetter and colder near-surface climate than the default run. A third model experiment was then carried out in which the (comparatively more homogeneous...
TL;DR: In this paper, an alternative retrieval technique that utilizes multipolarization was proposed for soil moisture estimation using low-frequency (<5 GHz) microwave sensors, which were typically not optimal for this particular type of satellite data.
Abstract: Studies have shown the advantages of low-frequency (<5 GHz) microwave sensors for soil moisture estimation. Although higher frequencies have limited soil moisture retrieval capabilities, there is a vast quantity of systematic global high-frequency microwave data that have been collected for 15 yr by the Special Sensor Microwave Imager (SSM/I). SSM/I soil moisture studies have mostly utilized antecedent precipitation indices as validation, while only a few have employed limited ground observations, which were typically not optimal for this particular type of satellite data. In the Southern Great Plains (SGP) hydrology experiments conducted in 1997 and 1999, ground observations of soil moisture were made over an extended region for developing and validating large-scale mapping techniques. Previous studies have indicated the limitations of both the higher-frequency data and models for soil moisture retrieval. Given these limitations, an alternative retrieval technique that utilizes multipolarization...