International Satellite Land Surface Climatology Project Research Articles

How can the First ISLSCP Field Experiment contribute to present-day efforts to evaluate water stress in JULESv5.0?

Abstract. The First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), Kansas, US, 1987–1989, made important contributions to the understanding of energy and CO2 exchanges between the land surface and the atmosphere, which heavily influenced the development of numerical land-surface modelling. Now, 30 years on, we demonstrate how the wealth of data collected during FIFE and its subsequent in-depth analysis in the literature continue to be a valuable resource for the current generation of land-surface models. To illustrate, we use the FIFE dataset to evaluate the representation of water stress on tallgrass prairie vegetation in the Joint UK Land Environment Simulator (JULES) and highlight areas for future development. We show that, while JULES is able to simulate a decrease in net carbon assimilation and evapotranspiration during a dry spell, the shape of the diurnal cycle is not well captured. Evaluating the model parameters and results against this dataset provides a case study on the assumptions in calibrating “unstressed” vegetation parameters and thresholds for water stress. In particular, the responses to low water availability and high temperatures are calibrated separately. We also illustrate the effect of inherent uncertainties in key observables, such as leaf area index, soil moisture and soil properties. Given these valuable lessons, simulations for this site will be a key addition to a compilation of simulations covering a wide range of vegetation types and climate regimes, which will be used to improve the way that water stress is represented within JULES.

Analysis of BRDF and Albedo Retrieved by Kernel-Driven Models Using Field Measurements

The algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface (AMBRALS) includes a series of kernel-driven bidirectional reflectance distribution function (BRDF) models. Among these models, the RossThick-LiSparse-Reciprocal (RTLSR) model has been selected as the operational MODIS BRDF/Albedo algorithm. With the newly developed LiTransit kernel and Ross kernel with the hotspot effect by Breon, there is a need to further evaluate models' potentials in retrieving land surface properties for user community. In this paper, 16 kernel-driven models in reciprocal form have been analyzed using 70 measurements mainly from ground campaigns of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) and the Boreal Ecosystem Atmosphere Study (BOREAS). Results show that all models under investigation generally fit the ground measurements with reasonable accuracy—although the “sparse” geometric-optical (GO) kernels appear to present better behaviors compared with the “dense” GO kernels—in combining with volumetric shapes. Estimated albedos from these models show high correlations, while the black-sky albedos (BSAs) present higher correlations in small solar zenith angles than in large ones, indicating that the difference in these kernels may arise from large solar geometry. A further investigation shows that the hotspot effects for these models display a significant discrepancy. Although the additional hotspot factor for Ross kernels significantly improves the hotspot effects, LiTransit kernel can well characterize the hotspot effects in combination with Ross kernels without the hotspot factor. Such an assessment may be helpful in understanding the models' potentials in retrieving vegetation structural characteristics.

Estimating soil thermal properties from sequences of land surface temperature using hybrid Genetic Algorithm–Finite Difference method

Most models used in land surface hydrology, vadose zone hydrology, and hydro-climatology require an accurate representation of soil thermal properties (soil thermal conductivity and volumetric heat capacity). Various empirical relations have been suggested to estimate soil thermal properties. However, they require many input parameters such as soil texture, mineralogical composition, porosity and water content, which are not always available from laboratory experiments and field measurements. In this paper, to overcome the above challenge, a hybrid numerical method, Genetic Algorithm–Finite Difference (GA–FD), is proposed to estimate soil thermal properties using land surface temperature (LST) as the only input. The genetic algorithm (GA) optimization method coupled with the finite difference (FD) modeling technique is a viable hybrid approach for estimating soil thermal properties. The finite difference method is employed to solve the heat diffusion equation and simulate LST, while a robust optimization technique (GA) is used to retrieve soil thermal properties by minimizing the difference between observed and simulated LST. Furthermore, a generalization of the hybrid model is developed for inhomogeneous soil, in which soil thermal properties are not constant throughout the soil slab. The proposed model is applied to the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE). The results show that the proposed hybrid numerical method is able to estimate soil thermal properties accurately, and therefore effectively eliminate the need for the unavailable soil parameters which are required by empirical methods for determining the soil thermal conductivity and volumetric heat capacity. Remarkably, the temporal variation of the retrieved soil thermal conductivity is consistent with the volumetric water content, even though no water content information is used in the model.

The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements

[1] This first paper of the two‐part series describes the objectives of the community efforts in improving the Noah land surface model (LSM), documents, through mathematical formulations, the augmented conceptual realism in biophysical and hydrological processes, and introduces a framework for multiple options to parameterize selected processes (Noah‐MP). The Noah‐MP’s performance is evaluated at various local sites using high temporal frequency data sets, and results show the advantages of using multiple optional schemes to interpret the differences in modeling simulations. The second paper focuses on ensemble evaluations with long‐term regional (basin) and global scale data sets. The enhanced conceptual realism includes (1) the vegetation canopy energy balance, (2) the layered snowpack, (3) frozen soil and infiltration, (4) soil moisture‐groundwater interaction and related runoff production, and (5) vegetation phenology. Sample local‐scale validations are conducted over the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site, the W3 catchment of Sleepers River, Vermont, and a French snow observation site. Noah‐MP shows apparent improvements in reproducing surface fluxes, skin temperature over dry periods, snow water equivalent (SWE), snow depth, and runoff over Noah LSM version 3.0. Noah‐MP improves the SWE simulations due to more accurate simulations of the diurnal variations of the snow skin temperature, which is critical for computing available energy for melting. Noah‐MP also improves the simulation of runoff peaks and timing by introducing a more permeable frozen soil and more accurate simulation of snowmelt. We also demonstrate that Noah‐MP is an effective research tool by which modeling results for a given process can be interpreted through multiple optional parameterization schemes in the same model framework.

Effects of Vegetation Cover on Hydrological Processes in a Large Region: Huaihe River Basin, China

AbstractVegetation plays an important role in water and energy cycles on land surfaces. Nevertheless, the physical effects of vegetation are not explicitly considered in many hydrologic-modeling works. In this study, a coupled land-surface-hydrologic model was used to investigate the vegetation effects on hydrologic processes over the years 1980–1987 in the Huaihe River basin, China. The vegetation coverage of the basin was assessed by the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II historic and potential land cover data set. Farmland was declared the dominant vegetation type of the basin in 1970 by the historic land cover scenario, and the potential vegetation cover is mixed forest. Firstly, the coupled model was calibrated by observed streamflow at Bengbu station. The correlation coefficient and Nash-Sutcliffe coefficient of efficiency of multiannual daily series were 0.987 and 0.968, respectively, which indicate that the capability of the coupled model system is acce...

Impact of uncertainties in meteorological forcing data and land surface parameters on global estimates of terrestrial water balance components

AbstractThe quality of near‐surface meteorology and land surface parameters strongly influences the simulation of terrestrial water balance components by land surface models. In this article, the sensitivity of global estimates of terrestrial water balance components to uncertainties in meteorological forcing data and land surface parameters is investigated using the SWAP land surface model and different global datasets. The latter were prepared within the framework of the International Satellite Land‐Surface Climatology Project (ISLSCP) Initiative II and the Second Global Soil Wetness Project (GSWP‐2). Sixteen variants of estimations of mean annual water balance components on the global scale were obtained. The discrepancies among the variants were found to be large: global annual run‐off varied by 1·9 times; its surface and sub‐surface components by 2·2 and 1·8 times, respectively; run‐off ratio by 1·5 times and the other hydrological quantities by 1·2–1·3 times. Uncertainties in precipitation datasets translate to uncertainties in run‐off and evapotranspiration depending on the ratio of real evapotranspiration to its potential value E/E0. In the areas with high E/E0, differences in P across precipitation datasets mainly translated to differences in R. In the areas with the lowest values of E/E0, run‐off is nearly insensitive to changes in P, while uncertainty in P mostly translates to uncertainty in E. Uncertainties in E and R due to different radiation datasets are the same in absolute units. Impact of uncertainties in soil parameters on estimations of water balance components can be comparable with the impact of uncertainties in forcing data. Copyright © 2010 John Wiley & Sons, Ltd.

A calibrated advection‐aridity evaporation model requiring no humidity data

A modified advection‐aridity model was tested with 24 h averaged data from the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment‐87 and the Cooperative Atmospheric‐Surface Exchange Study (CASES) ‐97 experiments. Two modifications were made. First, the need for humidity measurements in the “drying power” term of the equation was circumvented by assuming the daily average specific humidity for a 24 h day is equal to the specific humidity at the minimum temperature during the day. When compared to measured latent heat fluxes, this modification resulted in no deterioration in advection‐aridity estimates compared to versions using measured humidity. A second modification followed previous researchers by formulating the drying power in the Penman equation using Monin‐Obukhov similarity (MOS) theory. However, this version calibrated kB−1 (≡ln(zo/zov) by setting the Priestley‐Taylor evapotranspiration rate equal (on average) to the Penman evapotranspiration on several moist days and solving for the value of kB−1 as the only unknown. The calibration involved no latent heat flux measurements. The results suggest that the advection‐aridity model performs modestly better in the calibrated MOS version than with Penman's original wind function. Further investigation is recommended because MOS theory accounts for varying momentum roughness lengths, and the calibration was not done under ideal (well‐watered or nearly saturated) conditions that deteriorated the results somewhat with the CASES data set.

An Improved Method for Estimating Global Evapotranspiration Based on Satellite Determination of Surface Net Radiation, Vegetation Index, Temperature, and Soil Moisture

Abstract A simple and accurate method to estimate regional or global latent heat of evapotranspiration (ET) from remote sensing data is essential. The authors proposed a method in an earlier study that utilized satellite-determined surface net radiation (Rn), a vegetation index, and daytime-averaged/daily maximum air temperature (Ta) or land surface temperature (Ts) data. However, the influence of soil moisture (SM) on ET was not considered and is addressed in this paper by incorporating the diurnal Ts range (DTsR). ET, measured by the energy balance Bowen ratio method at eight enhanced facility sites on the southern Great Plains in the United States and by the eddy covariance method at four AmeriFlux sites during 2001–06, is used to validate the improved method. Site land cover varies from grassland, native prairie, and cropland to deciduous forest and evergreen forest. The correlation coefficient between the measured and predicted 16-day daytime-averaged ET using a combination of Rn, enhanced vegetation index (EVI), daily maximum Ts, and DTsR is about 0.92 for all the sites, the bias is −1.9 W m−2, and the root-mean-square error (RMSE) is 28.6 W m−2. The sensitivity of the revised method to input data error is small. Implemented here is the revised method to estimate global ET using diurnal Ta range (DTaR) instead of DTsR because DTsR data are not available yet, although DTaR-estimated ET is less accurate than DTsR-estimated ET. Global monthly ET is calculated from 1986 to 1995 at a spatial resolution of 1° × 1° from the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary monthly dataset and is compared with the 15 land surface model simulations of the Global Soil Wetness Project-2. The results of the comparison of 118 months of global ET show that the bias is 4.5 W m−2, the RMSE is 19.8 W m−2, and the correlation coefficient is 0.82. Incorporating DTaR distinctively improves the accuracy of the estimate of global ET.

Simulação na Bacia Amazônica com Dados Limitados: Rio Madeira

Hydrological simulation of the rain-discharge transformation using a hydrological model is applied to water resources management in a number of activities such as estimate, forecast and evaluation of the hydrological processes. The large basins (> 10,000 km2 ) have many effects such as climate, geology and human activities. The Amazon is a region where there are major human impacts due to the expansion of economic development. The Hydrological Model for Large Basins (MGB-IPH) was applied in the Amazon basin in order to evaluate its potential in this basin. Different hydrological data sources were tested. Daily precipitation data were used from the hydrometeorological network of the National Water Agency (ANA) and from NCEP/NCAR re-analysis corrected by the Center for Ocean Land Atmosphere (COLA). Climatological data from the International Satellite Land Surface Climatology Project (ISLSCP) and from NCEP/NCAR reanalysis were used to calculate evapotranspiration. The model was applied in the Madeira river basin, one of the most important sub-basins of the Amazon River. The results showed that daily COLA precipitation series have values very close to ANA precipitation series for the 1979-1990 period. In the portion of the basin located outside Brazil, COLA precipitation was compared with monthly data bases. At some points in the basin we had to correct the precipitation using these data bases. The model simulations showed good quality despite the limited hydrological information in the Amazon region

Large-scale characteristics of the distribution of blowing-snow sublimation

AbstractTo consider the large-scale characteristics of blowing-snow sublimation and its importance in the hydrological cycle in the cryosphere, we investigated the sublimation of blowing snow particles on a global scale using the global datasets of the European Centre for Medium-RangeWeather Forecasts (ECMWF) re-analysis data and the International Satellite Land Surface Climatology Project (ISLSCP) Initiative I data for 1987. The sublimation fluxes of blowing snow particles were estimated globally with 2.5˚ resolution at 6 hour intervals. We found that the sublimation of blowing snow particles occurs more widely in the Northern Hemisphere than in the Southern Hemisphere, does not increase monotonously with latitude, and becomes more active in the polar coast regions and highlands, although the annual mean sublimation fluxes of the Northern and Southern Hemispheres are almost equal. In addition, we confirmed the characteristic seasonal changes in the area of sublimation in the Northern Hemisphere. Although we need to incorporate continuous parameters from systematic ground-based studies of the structure of blowing snow in specific fields to reduce uncertainty regarding the characteristics of blowing snow, our results point to a need to review the current understanding of the hydrological cycle.

Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites

Numerous models of evapotranspiration have been published that range in data-driven complexity, but global estimates require a model that does not depend on intensive field measurements. The Priestley–Taylor model is relatively simple, and has proven to be remarkably accurate and theoretically robust for estimates of potential evapotranspiration. Building on recent advances in ecophysiological theory that allow detection of multiple stresses on plant function using biophysical remote sensing metrics, we developed a bio-meteorological approach for translating Priestley–Taylor estimates of potential evapotranspiration into rates of actual evapotranspiration. Five model inputs are required: net radiation ( R n), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), maximum air temperature ( T max), and water vapor pressure (ea). Our model requires no calibration, tuning or spin-ups. The model is tested and validated against eddy covariance measurements (FLUXNET) from a wide range of climates and plant functional types—grassland, crop, and deciduous broadleaf, evergreen broadleaf, and evergreen needleleaf forests. The model-to-measurement r 2 was 0.90 (RMS = 16 mm/month or 28%) for all 16 FLUXNET sites across 2 years (most recent data release). Global estimates of evapotranspiration at a temporal resolution of monthly and a spatial resolution of 1° during the years 1986–1993 were determined using globally consistent datasets from the International Satellite Land-Surface Climatology Project, Initiative II (ISLSCP-II) and the Advanced Very High Resolution Spectroradiometer (AVHRR). Our model resulted in improved prediction of evapotranspiration across water-limited sites, and showed spatial and temporal differences in evapotranspiration globally, regionally and latitudinally.

ISLSCP Initiative II global data sets: Surface boundary conditions and atmospheric forcings for land‐atmosphere studies

We report herein the publication and evaluation of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II global interdisciplinary data record. The record consists of 52 data sets, with a common series in the 10‐year period 1985 to 1996. Selected data series extend well beyond this period. All series are coregistered to a common grid and gap‐filled for continuity using uniform procedures. We describe briefly the individual data sets within the collection; provide user guidance; and contrast, compare and evaluate those data sets containing similar parameters (land cover, NDVI, albedo, precipitation and near‐surface meteorology). We also describe the process used to develop the Initiative II collection which involved a broad international scientific community focused on addressing a well‐defined set of carbon, water and energy cycle questions within the context of a specific set of analysis tools. The communities that drove the definition of the Initiative II collection were investigators within the international scientific communities of the Global Energy and Water cycle Experiment, GEWEX, program (http://www.gewex.org/); the International Geosphere/Biosphere Program IGBP (http://www.igbp.kva.se); and the U.S. Global Change Research Program, USGCRP (http://www.usgcrp.gov/). Finally, we report usage statistics based on access and download of files from the ISLSCP Initiative II collection available at http://www.daac.ornl.gov.

Evaluation of the Second Global Soil Wetness Project soil moisture simulations: 1. Intermodel comparison

Driven with the meteorological data sets based on the reanalyses and gridded observational data archived by the International Satellite Land‐Surface Climatology Project (ISLSCP) Initiative II, eleven different land surface models generated global soil moisture data sets for the 10‐year period (1986–1995) for the Second Global Soil Wetness Project (GSWP‐2). We evaluate these model simulations against in situ observations over grasslands and agricultural regions in the former Soviet Union, United States (Illinois), China, and Mongolia from the Global Soil Moisture Data Bank in terms of their ability to estimate the actual column plant‐available soil moisture in the top 1‐m soil layer, to simulate the phasing of the annual cycle, and to represent observed interannual variability. Results from these 11 land surface models show that they reproduce reasonably well the observed interannual variability and phasing of the annual cycle. Statistical analysis also shows that the median root mean square of errors among these models ranges from 4 to 8 cm of soil moisture. Similar to what has been found in soil moisture simulations for GSWP‐1, the absolute values of soil moisture are poorly simulated by most models. However, the models do a good job of reproducing the soil moisture anomalies. This suggests that the global soil wetness data set produced by GSWP‐2 can be used for analyzing climate variability and initializing GCMs by using transform strategies. This also has relevance to subseasonal to seasonal forecasts since soil moisture anomalies may potentially have impact on precipitation.

Evaluation of the Second Global Soil Wetness Project soil moisture simulations: 2. Sensitivity to external meteorological forcing

The quality of meteorological forcing data has a strong impact on the simulation of the land surface component of the hydrological cycle. In this paper, the sensitivity of soil moisture simulations to combinations of different external meteorological forcing and vegetation parameters supplied by the International Satellite Land‐Surface Climatology Project (ISLSCP) Initiative II (II2) as part of the Second Global Soil Wetness Project (GSWP‐2) is evaluated by using the SSiB land surface model. The simulated plant‐available soil moisture in the top 1 m of soil is compared against in situ observations over grasslands and agricultural regions in the former Soviet Union, United States (Illinois), China, and Mongolia from the Global Soil Moisture Data Bank. It is found that the skill of the simulations is very sensitive to the source of meteorological forcing and that hybridization of reanalysis products with observational data substantially improves the soil moisture simulations compared to reanalysis data alone. Sensitivity is highest among products of precipitation, radiation, and vegetation class. The range of changes in the skill of soil moisture simulations with the same land surface model in 13 different sensitivity studies is as large as that resulting from 11 different land surface models driven with the same meteorological forcing from the baseline integrations of GSWP‐2. Assuming differences among versions of meteorological forcing fields are indicative of uncertainties in our knowledge of these drivers of the land surface climate, the impact of that uncertainty on land surface hydrology is as large as that from the variations among land surface models.

Evaluation of ISLSCP Initiative II satellite‐based land cover data sets and assessment of progress in land cover data for global modeling

As an important component of the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II data collection, eight state‐of‐the‐art land cover/use data sets have been compiled and made consistent with the ISLSCP Initiative II land/water mask in support of global modeling efforts. These data sets contain new and improved global data sets at coarse resolutions (1/4, 1/2 and 1°) describing historical, recent and present land cover conditions and are a testament to the tremendous progress made in this area over the past decade. In addition to the historical data, data describing the subcell heterogeneity in land cover are also provided, both in terms of subcell proportions of land cover classes and vegetation continuous fields such as % tree, grass and bare cover. Here we present the various ISLSCPII land cover data sets and compare the principal satellite‐derived data sets and the effect of their respective aggregation methods. We find that despite some notable disagreements among similar classes, the satellite‐based data sets agree remarkably well over large portions of the Earth's surface (over 50% for all resolutions). We also find that the methods of aggregation, whether done by a strictly dominant type, or using more information on subcell tree cover, can have an important impact on the final output and need to be considered by the user. Finally, by integrating the vegetation continuous fields data into our analyses we are able to show that the principal differences in terms of discrete land cover classes are in fact transition zones between similar classes.

Complementary relationship between daily evaporation in the environment and pan evaporation

Daily actual evaporation observed locally is compared to daily pan evaporation to clarify and test the validity of the complementary relationship at this timescale. For this purpose, use was made of actual evaporation measurements at two sites, namely, in the Konza Prairie, Kansas, during the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment, and in the Little Washita River Basin, Oklahoma, as part of the Atmospheric Radiation Measurement Program. The corresponding pan evaporation data were obtained from nearby class A pan stations. The results of the analyses confirm the complementary relationship at this shorter timescale and lead to a procedure to estimate actual landscape evaporation by means of net radiation and pan evaporation data; they also provide support for the notion that even under conditions of decreasing incoming radiation, a negative trend in pan evaporation may still indicate a positive trend in landscape evapotranspiration.

A Comparison of the Triangle Retrieval and Variational Data Assimilation Methods for Surface Turbulent Flux Estimation

Abstract Future operational frameworks for estimating surface turbulent fluxes over the necessary spatial and temporal scales will undoubtedly require the use of remote sensing products. Techniques used to estimate surface fluxes from radiometric surface temperature generally fall into two categories: retrieval-based and data assimilation approaches. Up to this point, there has been little comparison between retrieval- and assimilation-based techniques. In this note, the triangle retrieval method is compared to a variational data assimilation approach for estimating surface turbulent fluxes from radiometric surface temperature observations. Results from a set of synthetic experiments and an application using real data from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site indicate that the assimilation approach performs slightly better than the triangle method because of the robustness of the estimation to measurement errors and parsimony of the system model, which leads to fewer sources of structural model errors. Future comparison work using retrieval and data assimilation algorithms will provide more insight into the optimal approach for diagnosis of land surface fluxes using remote sensing observations.

Ground Heat Flux Determination according to Land Skin Temperature Observations from In Situ Stations and Satellites

Abstract Due to rapid progress in the development of remote sensing techniques, skin temperature can now be observed with global coverage from satellites. This study derives an equation for utilizing skin temperature measurements to determine ground heat flux. This equation is verified at the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) campaign site and four Fluxnet sites. A preliminary monthly global dataset of ground heat flux with 1° resolution, covering the years 1984–95, is derived based on skin temperature observations obtained via satellite. It shows that the seasonal variation of ground heat flux over land can be determined at 1.5 months ahead by observing the increasing rate of skin temperature, and that the variation increases from ±2 W m−2 at the equator to ±20 W m−2 at the poles. Over land, the resulting ground heat flux can be compared with that of the ERA-15 reanalysis, showing rms differences of about 4 W m−2.

Variability in global land surface energy budgets during 1987–1988 simulated by an off-line land surface model

The International Satellite Land-Surface Climatology Project (ISLSCP) Initiative-I 1-degree 1987–1988 data were used to drive a land surface model (LSM) to simulate global surface energy budgets. Simulated surface heat fluxes show remarkable spatial variability and seem to capture well their annual and interannual variability. A shift of maximum evaporation across the equator is more closely related to the seasonal shifting of precipitation pattern than to surface radiation changes. The NCEP/NCAR reanalysis did not reflect this shift, presumably due to its dominant rainfall maximum in the Southern Hemisphere. To assess the “reliability” of these fields, both Global Soil Wetness Project (GSWP) and reanalysis were verified against observations, at two sites. Monthly mean ISLSCP forcing conditions agree fairly well with observations, but its precipitation is usually lower during spring and summer. Low summer GSWP evaporation may be due to low precipitation and incorrect specification of vegetation and soil conditions. The reanalysis had larger seasonal variability than GSWP and observations, and overestimated summer heat fluxes because of its large rainfall and surface radiation. Despite uncertainty in ISLSCP data, an LSM with a modest treatment of vegetation was able to capture reasonably well the seasonal variations in surface heat fluxes at global scales. With some caution, these types of simulations can be used as “pseudo-observations” to evaluate climate-model simulations and to investigate global energy budgets. For the next phase of ISLSCP data development, higher resolution data, which can reflect local heterogeneity of vegetation and soil characteristics, include more rain gauge data are highly desirable to improve model simulations.

HSPF Simulation of Runoff and Sediment Loads in the Upper Changjiang River Basin, China

To evaluate the performance of a computer model simulating runoff and sediment load in the upper region of the Changjiang (Yangtze River) basin over a relatively short time interval, including examining the applicability of the input precipitation data generated from global circulation models and satellite data, we used a spatially distributed model, HSPF with the International Satellite Land Surface Climatology Project (ISLSCP) precipitation data for 1987 and 1988 as input data. The Nash–Sutcliffe coefficient (R2) for 5-day average streamflow was 0.94 in the calibration period and 0.95 in the verification period for the whole upper region. Moreover, the model simulated the 5-day average streamflow well in each main tributary, as shown by R2 values of 0.46–0.96, except that it underestimated the peak flow rates during the flood season over 2 years by up to 71% in Tuojiang and 61% in Jialingjiang. The model simulated the 5-day concentrations of suspended solids (SS) fairly well in the headwaters and upper regions of the Jinshajiang, Yalongjiang, and Minjiang watersheds, as shown by R2 values of 0.31–0.65. In the other regions, however, the model underestimated the SS load by up to 72%, and rarely simulated the fluctuation of SS concentration in each river channel during the flood season. These errors led to the underestimation of sediment runoff volume from the whole upper region during the flood season, as shown by the ratio of the simulated sediment load to the observed data at Yichang: 0.69 in the calibration period and 0.68 in the verification period. The ISLSCP precipitation tended to be more frequent and less intense than the measured precipitation. This was probably the main reason why the HSPF did not perform well in all regions at all times.