Understanding of the Common Land Model performance for water and energy fluxes in a farmland during the growing season in Korea

The Common Land Model (CLM) is one of the most widely used land surface models (LSMs) due to the practicality of its simple parameterization scheme and its versatility in embracing a variety of field datasets. The improved assessment of land surface water and energy fluxes using CLM can be an alternative approach for understanding the complex land–atmosphere interactions in data‐limited regions. The understanding of water and energy cycles in a farmland is crucial because it is a dominant land feature in Korea and Asia. However, the applications of CLM to farmland in Korea are in paucity. The simulations of water and energy fluxes by CLM were conducted against those from the tower‐based measurements during the growing season of 2006 at the Haenam site (a farmland site) in Korea without optimization. According to the International Geosphere–Biosphere Programme (IGBP) land cover classification, a homogeneous cropland was selected initially for this study. Although the simulated soil moisture had a similar pattern to that of the observed, the former was relatively drier (at 0·1 m³ m^?3) than the latter. The simulated net radiation showed good agreement with the observed, with a root mean squared error (RMSE) of 41 W m^?2, whereas relatively large discrepancies between the simulation and observation were found in sensible (RMSE of 66 W m^?2) and latent (RMSE of 60 W m^?2) heat fluxes. On the basis of the sensitivity analysis, soil moisture was more receptive to land cover and soil texture parameterizations when compared to soil temperature and turbulent fluxes. Despite the uncertainty in the predictive capability of CLM employed without optimization, the initial performance of CLM suggests usefulness in a data‐limited heterogeneous farmland in Korea. Further studies are required to identify the controls on water and energy fluxes with an improved parameterization. Copyright © 2010 John Wiley & Sons, Ltd.     

Spatio‐temporal distribution of near‐surface and root zone soil moisture at the catchment scale

Soil moisture is highly variable both spatially and temporally. It is widely recognized that improving the knowledge and understanding of soil moisture and the processes underpinning its spatial and temporal distribution is critical. This paper addresses the relationship between near‐surface and root zone soil moisture, the way in which they vary spatially and temporally, and the effect of sampling design for determining catchment scale soil moisture dynamics. In this study, catchment scale near‐surface (0–50 mm) and root zone (0–300 mm) soil moisture were monitored over a four‐week period. Measurements of near‐surface soil moisture were recorded at various resolutions, and near‐surface and root zone soil moisture data were also monitored continuously within a network of recording sensors. Catchment average near‐surface soil moisture derived from detailed spatial measurements and continuous observations at fixed points were found to be significantly correlated (r² = 0·96; P = 0·0063; n = 4). Root zone soil moisture was also found to be highly correlated with catchment average near‐surface, continuously monitored (r² = 0·81; P < 0·0001; n = 26) and with detailed spatial measurements of near‐surface soil moisture (r² = 0·84). The weaker relationship observed between near‐surface and root zone soil moisture is considered to be caused by the different responses to rainfall and the different factors controlling soil moisture for the soil depths of 0–50 mm and 0–300 mm. Aspect is considered to be the main factor influencing the spatial and temporal distribution of near‐surface soil moisture, while topography and soil type are considered important for root zone soil moisture. The ability of a limited number of monitoring stations to provide accurate estimates of catchment scale average soil moisture for both near‐surface and root zone is thus demonstrated, as opposed to high resolution spatial measurements. Similarly, the use of near‐surface soil moisture measurements to obtain a reliable estimate of deeper soil moisture levels at the small catchment scale was demonstrated. Copyright © 2007 John Wiley & Sons, Ltd.     

A GIS‐based variable source area hydrology model

Effective control of nonpoint source pollution from contaminants transported by runoff requires information about the source areas of surface runoff. Variable source hydrology is widely recognized by hydrologists, yet few methods exist for identifying the saturated areas that generate most runoff in humid regions. The Soil Moisture Routing model is a daily water balance model that simulates the hydrology for watersheds with shallow sloping soils. The model combines elevation, soil, and land use data within the geographic information system GRASS, and predicts the spatial distribution of soil moisture, evapotranspiration, saturation‐excess overland flow (i.e., surface runoff), and interflow throughout a watershed. The model was applied to a 170 hectare watershed in the Catskills region of New York State and observed stream flow hydrographs and soil moisture measurements were compared to model predictions. Stream flow prediction during non‐winter periods generally agreed with measured flow resulting in an average r² of 0·73, a standard error of 0·01 m³/s, and an average Nash‐Sutcliffe efficiency R² of 0·62. Soil moisture predictions showed trends similar to observations with errors on the order of the standard error of measurements. The model results were most accurate for non‐winter conditions. The model is currently used for making management decisions for reducing non‐point source pollution from manure spread fields in the Catskill watersheds which supply New York City's drinking water. Copyright © 1999 John Wiley & Sons, Ltd.     

Rainfall‐induced consolidation and sealing effects on soil erodibility during concentrated runoff for loess‐derived topsoils

Flume experiments simulating concentrated runoff were carried out on remolded silt loam soil samples (0·36 × 0·09 × 0·09 m³) to measure the effect of rainfall‐induced soil consolidation and soil surface sealing on soil erosion by concentrated flow for loess‐derived soils and to establish a relationship between soil erodibility and soil bulk density. Soil consolidation and sealing were simulated by successive simulated rainfall events (0–600 mm of cumulative rainfall) alternated by periods of drying. Soil detachment measurements were repeated for four different soil moisture contents (0·04, 0·14, 0·20 and 0·31 g g^?1). Whereas no effect of soil consolidation and sealing is observed for critical flow shear stress (τ_cr), soil erodibility (Kc) decreases exponentially with increasing cumulative rainfall depth. The erosion‐reducing effect of soil consolidation and sealing decreases with a decreasing soil moisture content prior to erosion due to slaking effects occurring during rapid wetting of the dry topsoil. After about 100 mm of rainfall, Kc attains its minimum value for all moisture conditions, corresponding to a reduction of about 70% compared with the initial Kc value for the moist soil samples and only a 10% reduction for the driest soil samples. The relationship estimating relative Kc values from soil moisture content and cumulative rainfall depth predicts Kc values measured on a gradually consolidating cropland field in the Belgian Loess Belt reasonably well (MEF = 0·54). Kc is also shown to decrease linearly with increasing soil bulk density for all moisture treatments, suggesting that the compaction of thalwegs where concentrated flow erosion often occurs might be an alternative soil erosion control measure in addition to grassed waterways and double drilling. Copyright © 2007 John Wiley & Sons, Ltd.     

Improvement of TOPLATS‐based discharge predictions through assimilation of ERS‐based remotely sensed soil moisture values

In this paper, we investigate the possibility to improve discharge predictions from a lumped hydrological model through assimilation of remotely sensed soil moisture values. Therefore, an algorithm to estimate surface soil moisture values through active microwave remote sensing is developed, bypassing the need to collect in situ ground parameters. The algorithm to estimate soil moisture by use of radar data combines a physically based and an empirical back‐scatter model. This method estimates effective soil roughness parameters, and good estimates of surface soil moisture are provided for bare soils. These remotely sensed soil moisture values over bare soils are then assimilated into a hydrological model using the statistical correction method. The results suggest that it is possible to determine soil moisture values over bare soils from remote sensing observations without the need to collect ground truth data, and that there is potential to improve model‐based discharge predictions through assimilation of these remotely sensed soil moisture values. Copyright © 2002 John Wiley & Sons, Ltd.     

Soil water content in southern England derived from a cosmic‐ray soil moisture observing system – COSMOS‐UK

Cosmic‐ray soil moisture sensors have the advantage of a large measurement footprint (approximately 700 m in diameter) and are able to operate continuously to provide area‐averaged near‐surface (top 10–20 cm) volumetric soil moisture content at the field scale. This paper presents the application of this technique at four sites in southern England over almost 3 years. Results show the soil moisture response to contrasting climatic conditions during 2011–2014 and are the first such field‐scale measurements made in the UK. These four sites are prototype stations for a UK COsmic‐ray Soil Moisture Observing System, and particular consideration is given to sensor operating conditions in the UK. Comparison of these soil water content observations with the Joint UK Land Environment Simulator 10‐cm soil moisture layer shows that these data can be used to test and diagnose model performance and indicate the potential for assimilation of these data into hydro‐meteorological models. The application of these large‐area soil water content measurements to evaluate remotely sensed soil moisture products is also demonstrated. Numerous applications and the future development of a national COsmic‐ray Soil Moisture Observing System network are discussed. Copyright © 2016 John Wiley & Sons, Ltd.     

Uncertainty of snow water equivalent retrieved from AMSR‐E brightness temperature in northeast Asia

Accurate estimation of snow water equivalent (SWE) has been significantly recognized to improve management and analyses of water resource in specific regions. Although several studies have focused on developing SWE values based on remotely sensed brightness temperatures obtained by microwave sensor systems, it is known that there are still a number of uncertainties in SWE values retrieved from microwave radiometers. Therefore, further research for improving remotely sensed SWE values including global validation should be conducted in unexplored regions such as Northeast Asia. In this regard, we evaluated SWE through comparison of values produced by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR‐E) from December 2002 to February 2011 with in situ SWE values converted from snow‐depth observation data from four regions in the South Korea. The results from three areas showed similarities which indicated that the AMSR‐E SWE values were overestimated when compared with in situ SWE values, and their Mean Absolute Errors (MAE) by month were relatively small (1.1 to 6.5 mm). Contrariwise, the AMSR‐E SWE values of one area were significantly underestimated when compared with in situ SWE values and the MAE were much greater (4.9 to 35.2 mm). These results were closely related to AMSR‐E algorithm‐related error sources, which we analyzed with respect to topographic characteristics and snow properties. In particular, we found that snow density data used in the AMSR‐E SWE algorithm should be based on reliable in situ data as the current AMSR‐E SWE algorithm cannot reflect the spatio‐temporal variability of snow density values. Additionally, we derived better results considering saturation effect of AMSR‐E SWE. Despite the demise of AMSR‐E, this study's analysis is significant for providing a baseline for the new sensor and suggests parameters important for obtaining more reliable SWE. Copyright © 2013 John Wiley & Sons, Ltd.     

Long-term SMOS soil moisture products: A comprehensive evaluation across scales and methods in the Duero Basin (Spain)

The European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) Level 2 soil moisture and the new L3 product from the Barcelona Expert Center (BEC) were validated from January 2010 to June 2014 using two in situ networks in Spain. The first network is the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), which has been extensively used for validating remotely sensed observations of soil moisture. REMEDHUS can be considered a small-scale network that covers a 1300 km² region. The second network is a large-scale network that covers the main part of the Duero Basin (65,000 km²). At an existing meteorological network in the Castilla y Leon region (Inforiego), soil moisture probes were installed in 2012 to provide data until 2014. Comparisons of the temporal series using different strategies (total average, land use, and soil type) as well as using the collocated data at each location were performed. Additionally, spatial correlations on each date were computed for specific days. Finally, an improved version of the Triple Collocation (TC) method, i.e., the Extended Triple Collocation (ETC), was used to compare satellite and in situ soil moisture estimates with outputs of the Soil Water Balance Model Green-Ampt (SWBM-GA). The results of this work showed that SMOS estimates were consistent with in situ measurements in the time series comparisons, with Pearson correlation coefficients (R) and an Agreement Index (AI) higher than 0.8 for the total average and the land-use averages and higher than 0.85 for the soil-texture averages. The results obtained at the Inforiego network showed slightly better results than REMEDHUS, which may be related to the larger scale of the former network. Moreover, the best results were obtained when all networks were jointly considered. In contrast, the spatial matching produced worse results for all the cases studied.These results showed that the recent reprocessing of the L2 products (v5.51) improved the accuracy of soil moisture retrievals such that they are now suitable for developing new L3 products, such as the presented in this work. Additionally, the validation based on comparisons between dense/sparse networks and satellite retrievals at a coarse resolution showed that temporal patterns in the soil moisture are better reproduced than spatial patterns.     

Integration of remotely sensed C factor into SWAT for modelling sediment yield

The C factor, representing the impact of plant and ground cover on soil loss, is one of the important factors of the Modified Universal Soil Loss Equation (MUSLE) in the Soil and Water Assessment Tool (SWAT) to model sediment yield. The daily update of C factors in SWAT was originally determined by land use types and plant growth cycles. This does not reflect the spatial variation of C values that exists within a large land use area. We present a new approach to integrate remotely sensed C factors into SWAT for highlighting the effect of detailed vegetative cover data on soil erosion and sediment yield. First, the C factor was estimated using the abundance of ground components extracted from remote sensing images. Then, the gridding data of the C factor were aggregated to hydrological response units (HRUs), instead of to land use units of SWAT. In the end, the C factor values in HRUs were integrated into SWAT to predict sediment yield by modifying the ysed subroutine. This substitution work not only increases the spatial variation of the C factor in SWAT, but also makes it possible to utilize other sources of C databases rather than those from the United States. The demonstration in the Dage basin shows that the modified SWAT produces reasonable results in water flow simulation and sediment yield prediction using remotely sensed C values. The Nash–Sutcliffe efficiency coefficient (E_NS) and R² for surface runoff range from 0·69 to 0·77 and 0·73 to 0·87, respectively. The coefficients E_NS and R² for sediment yield were generally above 0·70 and 0·60, respectively. The soil erosion risk map based on sediment yield prediction at the HRU level illustrates instructive details on spatial distribution of soil loss. Copyright © 2011 John Wiley & Sons, Ltd.     

AMSR‐E algorithm for snowmelt onset detection in sub‐arctic heterogeneous terrain

The onset of snowmelt in the upper Yukon River basin, Canada, can be derived from brightness temperatures (T_b) obtained by the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) on NASA's Aqua satellite. This sensor, with a resolution of 14 × 8 km² for the 36·5 GHz frequency, and two to four observations per day, improves upon the twice‐daily coverage and 37 × 28 km² spatial resolution of the Special Sensor Microwave Imager (SSM/I). The onset of melt within a snowpack causes an increase in the average daily 36·5 GHz vertically polarized T_b as well as a shift to high diurnal amplitude variations (DAV) as the snow melts during the day and re‐freezes at night. The higher temporal and spatial resolution makes AMSR‐E more sensitive to sub‐daily T_b oscillations, resulting in DAV that often show a greater daily range compared to SSM/I. Therefore, thresholds of T_b > 246 K and DAV > ± 10 K developed for use with SSM/I have been adjusted for detecting the onset of snowmelt with AMSR‐E using ground‐based surface temperature and snowpack wetness relationships. Using newly developed thresholds of T_b > 252 K and DAV > ± 18 K, AMSR‐E derived snowmelt onset correlates well with SSM/I observations in the small subarctic Wheaton River basin through the 2004 and 2005 winter/spring transition. In addition, the onset of snowmelt derived from AMSR‐E data gridded at a higher resolution than the SSM/I data indicates that finer‐scale differences in elevation and land cover affect the onset of snowmelt and are detectable with the AMSR‐E sensor. On the basis of these observations, the enhanced resolution of AMSR‐E is more effective than SSM/I at delineating spatial and temporal snowmelt dynamics in the heterogeneous terrain of the upper Yukon River basin. Copyright © 2007 John Wiley & Sons, Ltd.     

A new soil freeze/thaw discriminant algorithm using AMSR‐E passive microwave imagery

The soil freeze–thaw controls the hydrological and carbon cycling and thus affects water and energy exchanges at land surface. This article reported a newly developed algorithm for distinguishing the freeze/thaw status of surface soil. The algorithm was based on information from Advanced Microwave Scanning Radiometer Enhanced (AMSR‐E) which records brightness temperature (Tb) in the afternoon and after midnight. The criteria and discriminant functions were obtained from both radiometer observations and model simulations. First of all, the microwave radiation from freeze–thaw soil was examined by carrying out experimental measurements at 18·7 and 36·5 GHz using a Truck‐mounted Multi‐frequency Microwave Radiometer (TMMR) in the Heihe River of China. The experimental results showed that the soil moisture is a key component that differentiates the microwave radiation behaviours during the freeze–thaw process, and the differences in soil temperature and emissivity between frozen and thawed soils were found to be the most important criteria. Secondly, a combined model was developed to consider the impacts of complex ground surface conditions on the discrimination. The model simulations quite followed the trend of in situ observations with an overall relation coefficient (R) of approximately 0·88. Finally, the ratio of Tb18·7H (horizontally polarized Tb at 18·7 GHz) to Tb36·5V was considered primarily as the quasi‐emissivity, which is more reasonable and explicit in measuring the microwave radiation changes in soil freezing and thawing than the spectral gradient. By combining Tb36·5V to indicate the soil temperature variety, a Fisher linear discrimination analysis was used to establish the discriminant functions. After being corrected by TMMR measurements, the new discriminant algorithm had an overall accuracy of 86% when validated by 4‐cm soil temperature. The multi‐year discriminant results also provided a good agreement with the classification map of frozen ground in China. Copyright © 2011 John Wiley & Sons, Ltd.     

Assessing and Improving Land Surface Model Outputs Over Africa Using GRACE,Field, and Remote Sensing Data

The Gravity Recovery and Climate Experiment (GRACE), along with other relevant field and remote sensing datasets, was used to assess the performance of two land surface models (LSMs: CLM4.5-SP and GLDAS-Noah) over the African continent and improve the outputs of the CLM4.5-SP model. Spatial and temporal analysis of monthly (January 2003–December 2010) Terrestrial Water Storage (TWS) estimates extracted from GRACE (TWS_GRACE), CLM4.5-SP (TWS_CLM4.5), and GLDAS-Noah (TWS_GLDAS) indicates the following: (1) compared to GRACE, LSMs overestimate TWS in winter months and underestimate them in summer months; (2) the amplitude of annual cycle (AAC) of TWS_GRACE is higher than that of TWS_LSM (AAC: TWS_GRACE > TWS_GLDAS > TWS_CLM4.5); (3) higher, and statistically significant correlations were observed between TWS_GRACE and TWS_GLDAS compared to those between TWS_GRACE and TWS_CLM4.5; (4) differences in forcing precipitation and temperature datasets for GLDAS-Noah and CLM4.5-SP models are unlikely to be the main cause for the observed discrepancies between TWS_GRACE and TWS_LSM; and (5) the CLM4.5-SP model overestimates evapotranspiration (ET) values in summer months and underestimates them in winter months compared to ET estimates extracted from field-based (FLUXNET-MTE) and satellite-based (MOD16 and GLEAM) ET measurements. A first-order correction was developed and applied to correct the CLM4.5-derived ET, soil moisture, groundwater, and TWS. The corrections improved the correspondence (i.e., higher correlation and comparable AAC) between TWS_CLM4.5 and TWS_GRACE over various climatic settings. Our findings suggest that similar straightforward correction approaches could potentially be developed and used to assess and improve the performance of a wide range of LSMs.     

Controls of surface soil moisture spatial patterns and their temporal stability in a semi‐arid steppe

Temporal stability of soil moisture spatial patterns has important implications for optimal soil and water management and effective field monitoring. The aim of this study was to investigate the temporal stability of soil moisture spatial patterns over four plots of 105 m × 135 m in grid size with different grazing intensities in a semi‐arid steppe in China. We also examined whether a time‐stable location can be identified from causative factors (i.e. soil, vegetation, and topography). At each plot, surface soil moisture (0–6 cm) was measured about biweekly from 2004 to 2006 using 100 points in each grid. Possible controls of soil moisture, including soil texture, organic carbon, bulk density, vegetation coverage, and topographic indices, were determined at the same grid points. The results showed that the spatial patterns of soil moisture were considerably stable over the 3‐y monitoring period. Soil moisture under wet conditions (averaged volumetric moisture contents > 20%) was more stable than that under dry ( ) or moist ( ) conditions. The best representative point for the whole field identified in each plot was accurate in representing the field mean moisture over time (R² ≥ 0·97; p < 0·0001). The degree of temporal persistence varied with grazing intensity, which was partly related to grazing‐induced differences in soil and vegetation properties. The correlation analysis showed that soil properties, and to a lesser extent vegetation and topographic properties, were important in controlling the temporal stability of soil moisture spatial patterns in this relatively flat grassland. Response surface regression analysis was used to quantitatively identify representative monitoring locations a priori from available soil‐plant parameters. This allows appropriate selection of monitoring locations and enhances efficiency in managing soil and water resources in semi‐arid environments. Copyright © 2010 John Wiley & Sons, Ltd.     

Error distribution modelling of satellite soil moisture measurements for hydrological applications

Satellite‐based soil moisture data accuracies are of important concerns by hydrologists because they could significantly influence hydrological modelling uncertainty. Without proper quantification of their uncertainties, it is difficult to optimize the hydrological modelling system and make robust decisions. Currently, the satellite soil moisture data uncertainty has been limited to summary statistics with the validations mainly from the in situ measurements. This study attempts to build the first error distribution model with additional higher‐order uncertainty modelling for satellite soil moisture observations. The methodology is demonstrated by a case study using the Soil Moisture and Ocean Salinity satellite soil moisture observations. The validation is based on soil moisture estimates from hydrological modelling, which is more relevant to the intended data use than the in situ measurements. Four probability distributions have been explored to find suitable error distribution curves using the statistical tests and bootstrapping resampling technique. General extreme value is identified as the most suitable one among all the curves. The error distribution model is still in its infant stage, which ignores spatial and temporal correlations, and nonstationarity. Further improvements should be carried out by the hydrological community by expanding the methodology to a wide range of satellite soil moisture data using different hydrological models. Copyright © 2016 John Wiley & Sons, Ltd.     

An intercomparison of remotely sensed soil moisture products at various spatial scales over the Iberian Peninsula

Soil moisture (SM) can be retrieved from active microwave (AM), passive microwave (PM) and thermal infrared (TIR) observations, each having unique spatial and temporal coverages. A limitation of TIR‐based retrievals is a dependence on cloud‐free conditions, whereas microwave retrievals are almost all weather proof. A downside of SM retrievals from PM is the coarse spatial resolution. Although SM retrievals at coarse spatial resolution proved to be valuable for global‐scale and continental‐scale studies, their value for regional‐scale studies remains limited. To increase the use of SM retrievals from PM observations, an existing method to enhance their spatial resolution was applied. We present an intercomparison study over the Iberian Peninsula for three SM products on two different spatial sampling grids. The remotely sensed SM products were also compared with in situ observations from the Remedhus network. Variations between ground data and satellite‐based SM are observed; all three remotely sensed SM products show good agreement to the ground observations. The comparison shows that these ground observations and satellite data are consistent, based on the correlation coefficient (R) and root mean square error (RMSE). The remotely sensed products were intercompared after sampling at 25 × 25 km² and after applying the smoothing filter‐based intensity modulation (SFIM) downscaling technique at 10 × 10 km² grids. After the application of the SFIM technique, the SM retrievals from PM observations show better agreement with the other remotely sensed SM products for approximately 40% of the study area. For another 40% of the study area, we found a similar agreement between these product combinations, whereas in extreme environments, both arid and densely vegetated regions, the agreement decreases after the application of the SFIM technique. Agreement between retrievals of absolute SM content from PM and TIR observations is generally high (R = 0.77 for semi‐arid areas). This study enhances our understanding of the remotely sensed SM products for improvements of SM retrieval and merging strategies. Copyright © 2013 John Wiley & Sons, Ltd.     

Comparison of AnnAGNPS and SWAT model simulation results in USDA‐CEAP agricultural watersheds in south‐central Kansas

This study was conducted under the USDA‐Conservation Effects Assessment Project (CEAP) in the Cheney Lake watershed in south‐central Kansas. The Cheney Lake watershed has been identified as ‘impaired waters’ under Section 303(d) of the Federal Clean Water Act for sediments and total phosphorus. The USDA‐CEAP seeks to quantify environmental benefits of conservation programmes on water quality by monitoring and modelling. Two of the most widely used USDA watershed‐scale models are Annualized AGricultural Non‐Point Source (AnnAGNPS) and Soil and Water Assessment Tool (SWAT). The objectives of this study were to compare hydrology, sediment, and total phosphorus simulation results from AnnAGNPS and SWAT in separate calibration and validation watersheds. Models were calibrated in Red Rock Creek watershed and validated in Goose Creek watershed, both sub‐watersheds of the Cheney Lake watershed. Forty‐five months (January 1997 to September 2000) of monthly measured flow and water quality data were used to evaluate the two models. Both models generally provided from fair to very good correlation and model efficiency for simulating surface runoff and sediment yield during calibration and validation (correlation coefficient; R², from 0·50 to 0·89, Nash Sutcliffe efficiency index, E, from 0·47 to 0·73, root mean square error, RMSE, from 0·25 to 0·45 m³ s^?1 for flow, from 158 to 312 Mg for sediment yield). Total phosphorus predictions from calibration and validation of SWAT indicated good correlation and model efficiency (R² from 0·60 to 0·70, E from 0·63 to 0·68) while total phosphorus predictions from validation of AnnAGNPS were from unsatisfactory to very good (R² from 0·60 to 0·77, E from ? 2·38 to 0·32). The root mean square error–observations standard deviation ratio (RSR) was estimated as excellent (from 0·08 to 0·25) for the all model simulated parameters during the calibration and validation study. The percentage bias (PBIAS) of the model simulated parameters varied from unsatisfactory to excellent (from 128 to 3). This study determined SWAT to be the most appropriate model for this watershed based on calibration and validation results. Copyright © 2008 John Wiley & Sons, Ltd.     

Upscaling of field-scale soil moisture measurements using distributed land surface modeling

Accurate coarse-scale soil moisture information is required for robust validation of current- and next-generation soil moisture products derived from spaceborne radiometers. Due to large amounts of land surface and rainfall heterogeneity, such information is difficult to obtain from existing ground-based networks of soil moisture sensors. Using ground-based field data collected during the Soil Moisture Experiment in 2002 (SMEX02), the potential for using distributed modeling predictions of the land surface as an upscaling tool for field-scale soil moisture observations is examined. Results demonstrate that distributed models are capable of accurately capturing a significant level of field-scale soil moisture heterogeneity observed during SMEX02. A simple soil moisture upscaling strategy based on the merger of ground-based observations with modeling predictions is developed and shown to be more robust during SMEX02 than upscaling approaches that utilize either field-scale ground observations or model predictions in isolation.     

Improvement of distributed snowmelt energy balance modeling with MODIS‐based NDSI‐derived fractional snow‐covered area data

Describing the spatial variability of heterogeneous snowpacks at a watershed or mountain‐front scale is important for improvements in large‐scale snowmelt modelling. Snowmelt depletion curves, which relate fractional decreases in snow‐covered area (SCA) against normalized decreases in snow water equivalent (SWE), are a common approach to scale‐up snowmelt models. Unfortunately, the kinds of ground‐based observations that are used to develop depletion curves are expensive to gather and impractical for large areas. We describe an approach incorporating remotely sensed fractional SCA (FSCA) data with coinciding daily snowmelt SWE outputs during ablation to quantify the shape of a depletion curve. We joined melt estimates from the Utah Energy Balance Snow Accumulation and Melt Model (UEB) with FSCA data calculated from a normalized difference snow index snow algorithm using NASA's moderate resolution imaging spectroradiometer (MODIS) visible (0·545–0·565 µm) and shortwave infrared (1·628–1·652 µm) reflectance data. We tested the approach at three 500 m² study sites, one in central Idaho and the other two on the North Slope in the Alaskan arctic. The UEB‐MODIS‐derived depletion curves were evaluated against depletion curves derived from ground‐based snow surveys. Comparisons showed strong agreement between the independent estimates. Copyright © 2010 John Wiley & Sons, Ltd.     

Discriminating residual and colluvial soils using topographic analysis in a small,forested catchment in Japan

Soil moisture plays an important role in hydrology. Understanding factors (such as topography, vegetation, and meteorological conditions) that influence spatio‐temporal variability in soil moisture, and how this influence is manifested, is important for understanding hydrological processes. A number of distributed (quasi‐)physical hydrological models have been developed to investigate this subject. Previous studies have shown that the spatial differences in the distribution of soil types (residual and colluvial soils) dominantly reflect spatio‐temporal fluctuations in soil moisture and runoff. We present a methodology for assessing the spatial distribution of residual and colluvial soils, which differ with respect to their physical characteristics, in a 0·88 km² forested catchment with complex topography and a complex land‐use history. Our method is based on penetration resistance profile data; in this data set, each data point represents soil physical characteristics within an area of about 25 m². If the spatial distribution of soils under similar meteorological, geological, historical land use, and other conditions could be characterized on the basis of similarity in topographic features, then the spatial distribution of soil could be predicted based on relationships between various topographic indices (e.g. topographic index and local slope). We tested whether our model correctly assessed the reference data. The model's results were 90·5% correct for residual soils and 87·3% correct for colluvial soils. Further studies will quantify the relationships between topographic features of land covered by residual and colluvial soils and changes in spatio‐temporal variations in the catchment (e.g. vegetation and land use) as a function of geology or meteorology. Copyright © 2007 John Wiley & Sons, Ltd.     

Active microwave remote sensing for soil moisture measurement: a field evaluation using ERS‐2

Active microwave remote sensing observations of backscattering, such as C‐band vertically polarized synthetic aperture radar (SAR) observations from the second European remote sensing (ERS‐2) satellite, have the potential to measure moisture content in a near‐surface layer of soil. However, SAR backscattering observations are highly dependent on topography, soil texture, surface roughness and soil moisture, meaning that soil moisture inversion from single frequency and polarization SAR observations is difficult. In this paper, the potential for measuring near‐surface soil moisture with the ERS‐2 satellite is explored by comparing model estimates of backscattering with ERS‐2 SAR observations. This comparison was made for two ERS‐2 overpasses coincident with near‐surface soil moisture measurements in a 6 ha catchment using 15‐cm time domain reflectometry probes on a 20 m grid. In addition, 1‐cm soil moisture data were obtained from a calibrated soil moisture model. Using state‐of‐the‐art theoretical, semi‐empirical and empirical backscattering models, it was found that using measured soil moisture and roughness data there were root mean square (RMS) errors from 3·5 to 8·5 dB and r² values from 0·00 to 0·25, depending on the backscattering model and degree of filtering. Using model soil moisture in place of measured soil moisture reduced RMS errors slightly (0·5 to 2 dB) but did not improve r² values. Likewise, using the first day of ERS‐2 backscattering and soil moisture data to solve for RMS surface roughness reduced RMS errors in backscattering for the second day to between 0·9 and 2·8 dB, but did not improve r² values. Moreover, RMS differences were as large as 3·7 dB and r² values as low as 0·53 between the various backscattering models, even when using the same data as input. These results suggest that more research is required to improve the agreement between backscattering models, and that ERS‐2 SAR data may be useful for estimating fields‐scale average soil moisture but not variations at the hillslope scale. Copyright © 2004 John Wiley & Sons, Ltd.