Land Surface Datasets Research Articles

Addressing uncertainty in reflectivity-rainfall relations in mountain watersheds during summer convection

The use of precipitation estimates from weather radar reflectivity has become widespread in hydrologic predictions. However, uncertainty remains in the use of the nonlinear reflectivity–rainfall (Z-R) relation, in particular for mountainous regions where ground validation stations are often lacking, land surface data sets are inaccurate and the spatial variability in many features is high. In this study, we assess the propagation of rainfall errors introduced by different Z-R relations on distributed hydrologic model performance for four mountain basins in the Colorado Front Range. To do so, we compare spatially integrated and distributed rainfall and runoff metrics at seasonal and event time scales during the warm season when convective storms dominate. Results reveal that the basin simulations are quite sensitive to the uncertainties introduced by the Z-R relation in terms of streamflow, runoff mechanisms and the water balance components. The propagation of rainfall errors into basin responses follows power law relationships that link streamflow uncertainty to the precipitation errors and streamflow magnitude. Overall, different Z-R relations preserve the spatial distribution of rainfall relative to a reference case, but not the precipitation magnitude, thus leading to large changes in streamflow amounts and runoff spatial patterns at seasonal and event scales. Furthermore, streamflow errors from the Z-R relation follow a typical pattern that varies with catchment scale where higher uncertainties exist for intermediate-sized basins. The relatively high error values introduced by two operational Z-R relations (WSR-57 and NEXRAD) in terms of the streamflow response indicate that site-specific Z-R relations are desirable in the complex terrain region, particularly in light of other uncertainties in the modelling process, such as model parameter values and initial conditions. Copyright © 2012 John Wiley & Sons, Ltd.

Configuration and Evaluation of the WRF Model for the Study of Hawaiian Regional Climate

Abstract The Weather Research and Forecasting (WRF) model V3.3 has been configured for the Hawaiian Islands as a regional climate model for the region (HRCM). This paper documents the model configuration and presents a preliminary evaluation based on a continuous 1-yr simulation forced by observed boundary conditions with 3-km horizontal grid spacing in the inner nested domain. The simulated vertical structure of the temperature and humidity are compared with twice-daily radiosonde observations at two stations. Generally the trade wind inversion (TWI) height and occurrence days are well represented. The simulation over the islands is compared with observations from nine surface climatological stations and a dense network of precipitation stations. The model simulation has generally small biases in the simulated surface temperature, relative humidity, and wind speed. The model realistically simulated the magnitude and geographical distribution of the mean rainfall over the Hawaiian Islands. In addition, the model simulation reproduced reasonably well the individual heavy rainfall events as seen from the time series of pentad mean rainfall averaged over island scales. Also the model reproduced the geographical variation of the mean diurnal rainfall cycle even though the observed diurnal cycle displays quite different features over different islands. Comparison with results obtained using the land surface dataset from the official release of the WRF model confirmed that the newly implemented land surface dataset generally improved the simulation of surface variables. These results demonstrate that the WRF can be a useful tool for dynamical downscaling of regional climate over the Hawaiian Islands.

Verification and Intercomparison of Multimodel Simulated Land Surface Hydrological Datasets over the United States

Abstract Several land surface datasets, such as the observed Illinois soil moisture dataset; three retrospective offline run datasets from the Noah land surface model (LSM), Variable Infiltration Capacity (VIC) LSM, and Climate Prediction Center leaky bucket soil model; and three reanalysis datasets (North American Regional Reanalysis, NCEP/Department of Energy Global Reanalysis, and 40-yr ECMWF Re-Analysis), are used to study the spatial and temporal variability of soil moisture and its response to the major components of land surface hydrologic cycles: precipitation, evaporation, and runoff. Detailed analysis was performed on the evolution of the soil moisture vertical profile. Over Illinois, model simulations are compared to observations, but for the United States as a whole some impressions can be gained by comparing the multiple soil moisture–precipitation–evaporation–runoff datasets to one another. The magnitudes and partitioning of major land surface water balance components on seasonal–interannual time scales have been explored. It appears that evaporation has the most prominent annual cycle but its interannual variability is relatively small. For other water balance components, such as precipitation, runoff, and surface water storage change, the amplitudes of their annual cycles and interannual variations are comparable. This study indicates that all models have a certain capability to reproduce observed soil moisture variability on seasonal–interannual time scales, but offline runs are decidedly better than reanalyses (in terms of validation against observations) and more highly correlated to one another (in terms of intercomparison) in general. However, noticeable differences are also observed, such as the degree of simulated drought severity and the locations affected—this is due to the uncertainty in model physics, input forcing, and mode of running (interactive or offline), which continue to be major issues for land surface modeling.

Observational evidence that agricultural intensification and land use change may be reducing the Indian summer monsoon rainfall

Using gridded daily rainfall observations, and monthly satellite land surface data sets, the connection between land use change and monsoonal rainfall climatology is analyzed. A combination of statistical analysis involving genetic algorithm (GA), empirical orthogonal function (EOF), and causal discovery algorithms (CDA) are used. Study objectives are (1) to identify regional trends in the observed precipitation data over the Indian summer monsoon region, (2) to investigate the relation between land use change/agriculture intensification and changes in rainfall, and (3) to explore whether land use change and agricultural intensification have caused change in the rainfall climatology. The satellite‐based vegetation data set shows significant agricultural intensification over northern India. For the period just before start of the summer monsoon season (April and May), the normalized differential vegetation index (NDVI) shows an increase only over Peninsular India. The EOF‐ and GA‐based analysis identified the strongest climatic signal for monsoon rainfall with an increasing trend over the east central regions of India and a decreasing trend in monsoon seasonal precipitation over north/northwest India. The areas of decreasing rainfall coincided with regions of agricultural intensive land use and are analyzed further. The correlation and the causal data analysis suggest that premonsoon (March–April) vegetation affects July month precipitation over peninsular India. In particular, a negative relationship exists between them. The results are more robust over peninsular and northern India indicating that an increase in NDVI has possibly weakened the early monsoon rainfall in this region. The results of this study suggest that land use change associated with agricultural intensification could be reducing the summer monsoon rainfall over certain regions of India.

Enhancement of land surface information and its impact on atmospheric modeling in the Heihe River Basin, northwest China

With increasing computational resources, atmospheric/environmental models continue to run at finer‐grid spacing that can resolve land surface characteristics, such as topography, land use/land cover, and soil texture. This paper assesses the improvement in land surface information data sets and its impact on atmospheric modeling. The study focuses on the Heihe River Basin (HRB) in northwestern China. Fine‐scale, remotely sensed, and in situ land surface data in HRB are derived and compared with the global data sets used in most mesoscale models. The incorporation of these fine‐scale land surface data, compared to those currently used in MM5, yields substantially improved HRB land surface data sets. HRB local and regional data sets and the global land data set are used in a nonhydrostatic mesoscale model (MM5) to investigate the influences of land surface uncertainty on meteorological modeling in the lower atmosphere. Main results suggest the following: (1) enhanced land data sets have a stronger impact on atmospheric water vapor fields in the lower boundary layer than other meteorological fields. Soil texture data greatly impacts the local precipitation simulation and landuse data improves the air temperature simulation in the lower atmosphere; (2) generally, the average land surface temperature biases are reduced using the enhanced land surface information, but the low bias in zones with higher elevation and high bias in zones with lower elevation still persist; (3) the wet bias over rugged terrain and dry bias in the simulated water vapor in the flat plains are both reduced. Area mean bias of simulated accumulated monthly precipitation is greatly reduced using the enhanced soil data. Convective available potential energy was larger in the HRB mountain regions using the default land data, while it was decreased using the enhanced ones; (4) analysis of the correlation coefficient between simulation bias and the geographic features shows that there are some patterns in the simulation bias distribution. Generally, larger bias still exists in the foothills of the mountains.

Flood and landslide applications of near real-time satellite rainfall products

Floods and associated landslides account for the largest number of natural disasters and affect more people than any other type of natural disaster. With the availability of satellite rainfall analyses at fine time and space resolution, it has also become possible to mitigate such hazards on a near-global basis. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 h and 0.25° × 0.25°. In combination with global land surface datasets it is now possible to expand regional hazard modeling components into a global identification/monitoring system for flood/landslide disaster preparedness and mitigation.

Area-Averaged Surface Fluxes in a Semiarid Region with Partly Irrigated Land: Lessons Learned from EFEDA

Abstract The European Field Experiment in a Desertification-Threatened Area (EFEDA) provides a comprehensive land surface dataset for a semiarid Mediterranean environment with natural vegetation and cultivated dry and irrigated land. This paper discusses the methods and practical aspects of deriving area-averaged fluxes for a range of areas from the whole EFEDA region to several numerical weather prediction model grid cells (on 10–100-km scales). A time series of grid-scale surface fluxes for the entire observational period of 1 month was obtained from weighted surface averages, using a crop phenology–based land use classification together with a homogenized set of surface observations representative of the four major vegetation classes. The flux-aggregated surface observations were compared with two other approaches to obtain grid-scale fluxes (airborne flux observations and radiosondes in conjunction with a simple mixed-layer model). The area-aggregated fluxes (in particular of latent heat) depend strongly on the location of the area boundaries whenever a significant fraction of irrigated land is present. This result confirms clearly the importance of adequately accounting for tiles of irrigated land in surface schemes and corresponding physiographic databases of large-scale models. A simple way to accommodate for minimum information on the canopy water status is proposed in terms of the distinction of at least two seasonal classes of irrigated crops—one of spring and one of summer growing cycles. The main lesson from this aggregation exercise concerns the role of irrigation. First, this study quantifies the uncertainties in the space–time pattern and its effects on aggregated surface fluxes for the first time on the grounds of observational data. Second, it demonstrates practical ways to accomplish the parameterization of irrigation in flux aggregation schemes, by identifying the key data along with their possible sources and by defining a practical implementation procedure.

Impact of new land boundary conditions from Moderate Resolution Imaging Spectroradiometer (MODIS) data on the climatology of land surface variables

This paper uses the Community Land Model (CLM2) to investigate the improvements of a new land surface data set, created from multiple high‐quality collection 4 Moderate Resolution Imaging Spectroradiometer data of leaf area index (LAI), plant functional type, and vegetation continuous fields, for modeled land surface variables. The previous land surface data in CLM2 underestimate LAI and overestimate the percent cover of grass/crop over most of the global area. For snow‐covered regions with abundant solar energy the increased LAI and percent cover of tree/shrub in the new data set decreases the percent cover of surface snow and increases net radiation and thus increases ground and surface (2‐m) air temperature, which reduces most of the model cold bias. For snow‐free regions the increased LAI and changes in the percent cover from grass/crop to tree or shrub decrease ground and surface air temperature by converting most of the increased net radiation to latent heat flux, which decreases the model warm bias. Furthermore, the new data set greatly decreases ground evaporation and increases canopy evapotranspiration over tropical forests, especially during the wet season, owing to the higher LAI and more trees in the new data set. It makes the simulated ground evaporation and canopy evapotranspiration closer to reality and also reduces the warm biases over tropical regions.

Land boundary conditions from MODIS data and consequences for the albedo of a climate model

This paper compares seasonal and spatial variations of MODIS albedos with those from the Community Land Model (CLM2). MODIS surface albedo data from September 2000 to August 2002 were used to investigate the model biases. To assess how inaccuracies in the land surface data used in CLM2 contribute to the model albedo biases, we created a new land surface dataset using the highest quality reprocessed MODIS products of leaf area index (LAI), plant functional type (PFT), and fraction of bare soil. A sensitivity experiment using this new data set quantifies the role of each variable and its contribution to the albedo biases. Our results indicate that most of the positive albedo biases result from an underestimation of LAI or an overestimation of the grass/crop fraction. Such biases can be largely reduced when the new data set is used. These results provide information on improving albedo in the model. This new land surface data set is available for use in CLM2.

Modeling Mackenzie Basin Surface Water Balance during CAGES with the Canadian Regional Climate Model

Abstract The Canadian Regional Climate Model has been used to estimate surface water balance over the Mackenzie River basin during the water year 1998–99 in support of the Canadian Global Energy and Water Cycle Experiment (GEWEX) Enhanced Study (CAGES). The model makes use of a developmental third-generation physics parameterization package from the Canadian Centre for Climate Modelling and Analysis GCM, as well as a high-resolution land surface dataset. The surface water balance is simulated reasonably well, though Mackenzie basin annual mean daily maximum and minimum temperatures were both colder than observed by 1.7°C. The cold bias contributed to a longer snow-covered season and larger peak snow water equivalent than was observed, though snow accumulated realistically compared with two independently observed estimates after 1 November. Mackenzie basin annual precipitation was simulated as 496 mm, about 9% larger than observed, and P − E was 225 mm. Net soil moisture change during this water year was f...

A Global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data

Abstract Global, monthly, 1° by 1° biophysical land surface datasets for 1982–90 were derived from data collected by the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA-7, -9, and -11 satellites. The AVHRR data are adjusted for sensor degradation, volcanic aerosol effects, cloud contamination, short-term atmospheric effects (e.g., water vapor and aerosol effects ⩽2 months), solar zenith angle variations, and missing data. Interannual variation in the data is more realistic as a result. The following biophysical parameters are estimated: fraction of photosynthetically active radiation absorbed by vegetation, vegetation cover fraction, leaf area index, and fraction of green leaves. Biophysical retrieval algorithms are tested and updated with data from intensive remote sensing experiments. The multiyear vegetation datasets are consistent spatially and temporally and are useful for studying spatial, seasonal, and interannual variability in the biosphere related to the hydrological cycle, th...

Identifying and Correcting Urban Bias in Regional Time Series: Surface Temperature in China's Northern Plains

A detailed study of urban bias in surface temperatures of China's northern plains is described. Temperatures of climatological surface stations were examined using a statistical rank-score procedure that allows screening of the data without knowledge of the station history information. Time series found to exhibit large potential discontinuities (i.e., those introduced as a result of nonclimatic factors such as observation schedule changes, instrument replacements, and station moves) were excluded from further analysis. In addition to the usual total population statistics, census area classifications and population densities were used to distinguish between 21 urban and 8 rural stations. Location-related biases associated with latitude and longitude positions were first removed from all station data, however, using ordinary least-squares regression techniques. Finally, a systematic sampling strategy was employed to estimate magnitudes and trends of urban bias in annual and seasonal mean temperatures. Results of the study indicate that temperatures for stations located in or near the most highly and densely populated urban centers exhibit the largest biases. For most of these urban stations, magnitudes and trends of the bias are greater during spring or summer than during autumn or winter. Standard errors of the estimated urban biases are large, however. Therefore, only the regionally averaged temperatures were adjusted to remove magnitudes and trends of urban bias. Trends in the original and adjusted temperatures of this study and in gridded temperatures taken from the widely used dataset of Jones et al. were also compared. It is suggested that despite past efforts to remove the effects of the urban beat islands from this and other large-scale, land-surface datasets, large urban warming biases may still remain.

Recording Accuracies in Global Land Surface Datasets.

According to the growing recognition of the importance of global environmental research, many global datasets are being produced. The common shortcoming of the existing global datasets is the lack of accuracy data and other auxiliary data. This paper describes accuracies in three types of information, i. e. space, time, and attribute, of the global land surface data, and shows what auxiliary data including accuracies need to be recorded. Moreover, some examples are given to show how accuracy data are used in data processing.