Abstract

A Land Surface Microwave Emission Model (LSMEM) is used to derive soil moisture estimates over Iowa during the Soil Moisture Experiment 2002 (SMEX02) field campaign, using brightness temperature data from the Advanced Microwave Sounding Radiometer (AMSR)-E satellite. Spatial distributions of the near-surface soil moisture are produced using the LSMEM, with data from the North American Land Data Assimilation System (NLDAS), vegetation and land surface parameters estimated through recent Moderate Imaging Spectroradiometer (MODIS) land surface products, and standard soil datasets. To assess the value of soil moisture estimates from the 10.7-GHz X-band sensor on the AMSR-E instrument, retrievals are evaluated against ground-based sampling and soil moisture estimates from the airborne Polarimetric Scanning Radiometer (PSR) operating at C band. The PSR offers high-resolution detail of the soil moisture distribution, which can be used to analyze heterogeneity within the scale of the AMSR-E pixel. Preliminary analysis indicates that retrievals from the AMSR-E instrument at 10.7 GHz using the LSMEM are surprisingly robust, with accuracies within 3% vol/vol compared with in situ samples. Results from these AMSR-E comparisons also indicate potential in determining soil moisture patterns over regional scales, even in the presence of vegetation. Assessment of soil moisture determined through local-scale sampling within the larger-scale AMSR-E footprint reveals a consistent level of agreement over a range of meteorological and surface conditions, offering promise for improved land surface hydrometeorological characterization.

Abstract

To relate general circulation model (GCM) hydrologic output to readily available river hydrographic data, a runoff routing scheme that routes gridded runoffs through regional- or continental-scale river drainage basins is developed. By following the basin overland flow paths, the routing model generates river discharge hydrographs that can be compared to observed river discharges, thus allowing an analysis of the GCM representation of monthly, seasonal and annual water balances over large regions. The runoff routing model consists of two linear reservoirs a surface reservoir and a groundwater reservoir, which store and transport water. The water transport mechanisms operating within these two reservoirs are differentiated by their time scares, the groundwater reservoir transports water much more slowly than the surface reservoir. The groundwater reservoir feeds the corresponding surface store and the surface stores are connected via the river network.

The routing model is implemented over the GEWEX (Global Energy and Water Cycle Experiment) Continental-Scale International Project Mississippi River basin on a rectangular grid of 2° × 2.5°. Two land surface hydrology parameterizations provide the gridded runoff data required to run the runoff routing scheme: the variable infiltration capacity model, and the soil moisture component of the simple biosphere model. These parameterizations are driven with 4° × 51° gridded climatological potential evapotranspiration and 1979 First GARP (Global Atmospheric Research Program) Global Experiment precipitation. These investigations have quantified the importance of physically realistic soil moisture holding capacities evaporation parameters and runoff mechanisms in land surface hydrology formulations.

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 predicted energy fluxes and rain amount in comparison with predictions from the original LM. The initialization of the LM just using soil moisture fields based on TOPLATS does not result in an improvement of the local weather forecast: although the simulation of the sensible and latent heat fluxes is improved, the representation of the boundary layer structure is not captured well. In the original LM, the surface processes are not modeled in sufficient detail, which resulted in significant overprediction of precipitation for one case study. The main reason for the improved performance of the two-way coupled modeling system on the basis of TOPLATS probably is the more accurate representation of vegetation and soil hydrologic processes. This results in more realistically simulated soil moisture fields and better simulation of the dynamic range of the surface temperature when compared with the other model configurations.

Abstract

The Surface Energy Balance System (SEBS) model was developed to estimate land surface fluxes using remotely sensed data and available meteorology. In this study, a dual assessment of SEBS is performed using two independent, high-quality datasets that are collected during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The purpose of this comparison is twofold. First, using high-quality local-scale data, model-predicted surface fluxes can be evaluated against in situ observations to determine the accuracy limit at the field scale using SEBS. To accomplish this, SEBS is forced with meteorological data derived from towers distributed throughout the Walnut Creek catchment. Flux measurements from 10 eddy covariance systems positioned on these towers are used to evaluate SEBS over both corn and soybean surfaces. These data allow for an assessment of modeled fluxes during a period of rapid vegetation growth and varied hydrometeorology. Results indicate that SEBS can predict evapotranspiration with accuracies approaching 10%–15% of that of the in situ measurements, effectively capturing the temporal development of surface flux patterns for both corn and soybean, even when the evaporative fraction ranges between 0.50 and 0.90. Second, utilizing high-resolution remote sensing data and operational meteorology, a catchment-scale examination of model performance is undertaken. To extend the field-based assessment of SEBS, information derived from the Landsat Enhanced Thematic Mapper (ETM) and data from the North American Land Data Assimilation System (NLDAS) were combined to determine regional surface energy fluxes for a clear day during the field experiment. Results from this analysis indicate that prediction accuracy was strongly related to crop type, with corn predictions showing improved estimates compared to those of soybean. Although root-mean-square errors were affected by the limited number of samples and one poorly performing soybean site, differences between the mean values of observations and SEBS Landsat-based predictions at the tower sites were approximately 5%. Overall, results from this analysis indicate much potential toward routine prediction of surface heat fluxes using remote sensing data and operational meteorology.

Abstract

The sensitivity of sensible and latent heat fluxes and surface temperatures to the parameterization of the soil thermal conductivity is demonstrated using a soil vegetation atmosphere transfer scheme (SVATS) applied to intensive field campaigns (IFCs) 3 and 4 of the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE). In particular, the commonly used function for soil thermal conductivity presented by M. C. McCumber and R. A. Pielke results in overestimation during wet periods and underestimation during dry periods, as confirmed with thermal conductivity data collected at the FIFE site. The ground heat flux errors affect all components of the energy balance, but are partitioned primarily into the sensible heat flux and surface temperatures in the daytime. At nighttime, errors in the net radiation also become significant in relative terms, although all fluxes are small. In addition, this method erroneously enhances the spatial variability of fluxes associated with soil moisture variability. The authors propose the incorporation of an improved method for predicting thermal conductivity in both frozen and unfrozen soils. This method requires the specification of two additional parameters, and sensitivity studies and tables of recommended parameter values to facilitate the incorporation of this method into SVATS are presented.

Abstract

Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.

Abstract

We present a comprehensive global evaluation of monthly precipitation and temperature forecasts from 16 seasonal forecasting models within the NMME Phase-1 system, using Multi-Source Weighted-Ensemble Precipitation version 2 (MSWEP-V2; precipitation) and Climate Research Unit TS4.01 (CRU-TS4.01; temperature) data as reference. We first assessed the forecast skill for lead times of 1–8 months using Kling–Gupta efficiency (KGE), an objective performance metric combining correlation, bias, and variability. Next, we carried out an empirical orthogonal function (EOF) analysis to compare the spatiotemporal variability structures of the forecasts. We found that, in most cases, precipitation skill was highest during the first lead time (i.e., forecast in the month of initialization) and rapidly dropped thereafter, while temperature skill was much higher overall and better retained at higher lead times, which is indicative of stronger temporal persistence. Based on a comprehensive assessment over 21 regions and four seasons, we found that the skill showed strong regional and seasonal dependencies. Some tropical regions, such as the Amazon and Southeast Asia, showed high skill even at longer lead times for both precipitation and temperature. Rainy seasons were generally associated with high precipitation skill, while during winter, temperature skill was low. Overall, precipitation forecast skill was highest for the NASA, NCEP, CMC, and GFDL models, and for temperature, the NASA, CFSv2, COLA, and CMC models performed the best. The spatiotemporal variability structures were better captured for precipitation than temperature. The simple forecast averaging did not produce noticeably better results, emphasizing the need for more advanced weight-based averaging schemes.

Abstract

Using observation-driven simulations of global terrestrial hydrology and a cluster algorithm that searches for spatially connected regions of soil moisture, the authors identified 296 large-scale drought events (greater than 500 000 km² and longer than 3 months) globally for 1950–2000. The drought events were subjected to a severity–area–duration (SAD) analysis to identify and characterize the most severe events for each continent and globally at various durations and spatial extents. An analysis of the variation of large-scale drought with SSTs revealed connections at interannual and possibly decadal time scales. Three metrics of large-scale drought (global average soil moisture, contiguous area in drought, and number of drought events shorter than 2 years) are shown to covary with ENSO SST anomalies. At longer time scales, the number of 12-month and longer duration droughts follows the smoothed variation in northern Pacific and Atlantic SSTs. Globally, the mid-1950s showed the highest drought activity and the mid-1970s to mid-1980s the lowest activity. This physically based and probabilistic approach confirms well-known droughts, such as the 1980s in the Sahel region of Africa, but also reveals many severe droughts (e.g., at high latitudes and early in the time period) that have received relatively little attention in the scientific and popular literature.

Abstract

Land surface heterogeneity and its effects on surface processes have been a concern to hydrologists and climate scientists for the past several decades. The contrast between the fine spatial scales at which heterogeneity is significant (1 km and finer) and the coarser scales at which most climate simulations with land surface models are generated (hundreds of kilometers) remains a challenge, especially when incorporating land surface and subsurface lateral fluxes of mass. In this study, long-term observational land surface forcings and derived solar radiation were used to force high-resolution land surface model simulations over the Arkansas–Red River basin in the Southern Great Plains region of the United States. The most unique aspect of these simulations is the fine space (1 km²) and time (hourly) resolutions within the model relative to the total simulation period (51 yr) and domain size (575 000 km²). Runoff simulations were validated at the subbasin scale (600–10 000 km²) and were found to be in good agreement with observed discharge from several unregulated subbasins within the system. A hydroclimatological approach was used to assess simulated annual evapotranspiration for all subbasins. Simulated evapotranspiration values at the subbasin scale agree well with predictions from a simple one-parameter empirical model developed in this study according to Budyko’s concept of “geographical zonality.” The empirical model was further extended to predict runoff and evapotranspiration sensitivity to precipitation variability, and good agreement with computed statistics was also found. Both the empirical model and simulation results demonstrate that precipitation variability was amplified in the simulated runoff. The finescale at which the study is performed allows analysis of various aspects of the hydrologic cycle in the system including general trends in precipitation, runoff, and evapotranspiration, their spatial distribution, and the relationship between precipitation anomalies and runoff and soil water storage anomalies at the subbasin scale.

ABSTRACT

In this study, the equidistant cumulative distribution function (EDCDF) quantile-based mapping method was used to develop bias-corrected and downscaled monthly precipitation and temperature for China at 0.5° × 0.5° spatial resolution for the period 1961–2099 for eight CMIP5 GCM simulations. The downscaled dataset was constructed by combining observations from 756 meteorological stations across China with the monthly GCM outputs for the historical (1961–2005) and future (2006–99) periods for the lower (RCP2.6), medium (RCP4.5), and high (RCP8.5) representative concentration pathway emission scenarios. The jackknife method was used to cross validate the performance of the EDCDF method and was compared with the traditional quantile-based matching method (CDF method). This indicated that the performance of the two methods was generally comparable over the historic period, but the EDCDF was more efficient at reducing biases than the CDF method across China. The two methods had similar mean absolute error (MAE) for temperature in January and July. The EDCDF method had a slight advantage over the CDF method for precipitation, reducing the MAE by about 0.83% and 1.2% at a significance level of 95% in January and July, respectively. For future projections, both methods exhibited similar spatial patterns for longer periods (2061–90) under the RCP8.5 scenario. However, the EDCDF was more sensitive to a reduction in variability.