Spatial and temporal dust source variability in northern China identified using advanced remote sensing analysis

The aim of this research is to provide a detailed characterization of spatial patterns and temporal trends in the regional and local dust source areas within the desert of the Alashan Prefecture (Inner Mongolia, China). This problem was approached through multi‐scale remote sensing analysis of vegetation changes. The primary requirements for this regional analysis are high spatial and spectral resolution data, accurate spectral calibration and good temporal resolution with a suitable temporal baseline. Landsat analysis and field validation along with the low spatial resolution classifications from MODIS and AVHRR are combined to provide a reliable characterization of the different potential dust‐producing sources. The representation of intra‐annual and inter‐annual Normalized Difference Vegetation Index (NDVI) trend to assess land cover discrimination for mapping potential dust source using MODIS and AVHRR at larger scale is enhanced by Landsat Spectral Mixing Analysis (SMA). The combined methodology is to determine the extent to which Landsat can distinguish important soils types in order to better understand how soil reflectance behaves at seasonal and inter‐annual timescales. As a final result mapping soil surface properties using SMA is representative of responses of different land and soil cover previously identified by NDVI trend. The results could be used in dust emission models even if they are not reflecting aggregate formation, soil stability or particle coatings showing to be critical for accurately represent dust source over different regional and local emitting areas. Copyright © 2012 John Wiley & Sons, Ltd.     

A review of low‐cost space‐borne data for flood modelling: topography,flood extent and water level

During the last two decades, remote sensing data have led to tremendous progress in advancing flood inundation modelling. In particular, low‐cost space‐borne data can be invaluable for large‐scale flood studies in data‐scarce areas. Various satellite products yield valuable information such as land surface elevation, flood extent and water level, which could potentially contribute to various flood studies. An increasing number of research studies have been dedicated to exploring those low‐cost data towards building, calibration and evaluation, and remote‐sensed information assimilation into hydraulic models. This paper aims at reviewing these recent scientific efforts on the integration of low‐cost space‐borne remote sensing data with flood modelling. Potentials and limitations of those data in flood modelling are discussed. This paper also introduces the future satellite missions and anticipates their likely impacts in flood modelling. Copyright © 2015 John Wiley & Sons, Ltd.     

How does DEM resolution affect microtopographic characteristics,hydrologic connectivity,and modelling of hydrologic processes?

The resolution of a digital elevation model (DEM) is a crucial factor in watershed hydrologic and environmental modelling. DEM resolution can cause significant variability in the representation of surface topography, which further affects quantification of hydrologic connectivity and simulation of hydrologic processes. The objective of this study is to examine the effects of DEM resolution on (1) surface microtopographic characteristics, (2) hydrologic connectivity, and (3) the spatial and temporal variations of hydrologic processes. A puddle‐to‐puddle modelling system was utilized for surface delineation and modelling of the puddle‐to‐puddle overland flow dynamics, surface runoff, infiltration, and unsaturated flow for nine DEM resolution scenarios of a field plot surface. Comparisons of the nine modelling scenarios demonstrated that coarser DEM resolutions tended to eliminate topographic features, reduce surface depression storage, and strengthen hydrologic connectivity and surface runoff. We found that reduction in maximum depression storage and maximum ponding area was as high as 97.56% and 76.36%, respectively, as the DEM grid size increased from 2 to 80 cm. The paired t‐test and fractal analysis demonstrated the existence of a threshold DEM resolution (10 cm for the field plot), within which the DEM‐based hydrologic modelling was effective and acceptable. The effects of DEM resolution were further evaluated for a larger surface in the Prairie Pothole Region subjected to observed rainfall events. It was found that simulations based on coarser resolution DEMs (>10 m) tended to overestimate ponded areas and underestimate runoff discharge peaks. The simulated peak discharge from the Prairie Pothole Region surface reduced by approximately 50% as the DEM resolution changed from 2 to 90 m. Fractal analysis results elucidated scale dependency of hydrologic and topographic processes. In particular, scale analysis highlighted a unique constant–threshold–power relationship between DEM scale and topographic and hydrologic parameters/variables. Not only does this finding allow one to identify threshold DEM but also further develop functional relationships for scaling to achieve valid topographic characterization as well as effective and efficient hydrologic modelling. Copyright © 2016 John Wiley & Sons, Ltd.     

Exploring the effect of data assimilation by WRF‐3DVar for numerical rainfall prediction with different types of storm events

The mesoscale Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community in providing high‐resolution rainfall forecasts at the catchment scale. Although the performance of the model has been verified in capturing the physical processes of severe storm events, the modelling accuracy is negatively affected by significant errors in the initial conditions used to drive the model. Several meteorological investigations have shown that the assimilation of real‐time observations, especially the radar data can help improve the accuracy of the rainfall predictions given by mesoscale NWP models. The aim of this study is to investigate the effect of data assimilation for hydrological applications at the catchment scale. Radar reflectivity together with surface and upper‐air meteorological observations is assimilated into the Weather Research and Forecasting (WRF) model using the three‐dimensional variational data‐assimilation technique. Improvement of the rainfall accumulation and its temporal variation after data assimilation is examined for four storm events in the Brue catchment (135.2 km²) located in southwest England. The storm events are selected with different rainfall distributions in space and time. It is found that the rainfall improvement is most obvious for the events with one‐dimensional evenness in either space or time. The effect of data assimilation is even more significant in the innermost domain which has the finest spatial resolution. However, for the events with two‐dimensional unevenness of rainfall, i.e. the rainfall is concentrated in a small area and in a short time period, the effect of data assimilation is not ideal. WRF fails in capturing the whole process of the highly convective storm with densely concentrated rainfall in a small area and a short time period. A shortened assimilation time interval together with more efficient utilisation of the weather radar data might help improve the effectiveness of data assimilation in such cases. Copyright © 2012 John Wiley & Sons, Ltd.     

Comparison of dust emissions,transport, and deposition between the Taklimakan Desert and Gobi Desert from 2007 to 2011

The Taklimakan Desert(TD) and Gobi Desert(GD) are two of the most important dust sources in East Asia, and have important impact on energy budgets, ecosystems and water cycles at regional and even global scales. To investigate the contribution of the TD and the GD to dust concentrations in East Asia as a whole, dust emissions, transport, and deposition over the TD and the GD in different seasons from 2007 to 2011 were systematically compared, based on the Weather Research and Forecasting model coupled with Chemistry(WRF-Chem). Dust emissions, uplift, and long-range transport related to these two dust source regions were markedly different due to differences in topography, elevation, thermal conditions, and atmospheric circulation. Specifically,the topography of the GD is relatively flat, and at a high elevation, and the area is under the influence of two jet streams at high altitudes, resulting in high wind speeds in the upper atmosphere. Deep convective mixing enables the descending branch of jet streams to continuously transport momentum downward to the mid-troposphere, leading to enhanced wind speeds in the lower troposphere over the GD which favors the vertical uplift of the GD dust particles. Therefore, the GD dust was very likely to be transported under the effect of strong westerly jets, and thus played the most important role in contributing to dust concentrations in East Asia. Approximately 35% and 31% of dust emitted from the GD transported to remote areas in East Asia in spring and summer, respectively. The TD has the highest dust emission capabilities in East Asia, with emissions of about 70.54 Tg yr.1 in spring, accounting for 42% of the total dust emissions in East Asia. However, the TD is located in the Tarim Basin and surrounded by mountains on three sides. Furthermore, the dominant surface wind direction is eastward and the average wind speed at high altitudes is relatively small over the TD. As a result, the TD dust particles are not easily transported outside the Tarim Basin, such that most of the dust particles are re-deposited after uplift, at a total deposition rate of about 40 g m.2. It is only when the TD dust particles are uplifted above 4 km, and entrained in westerlies that they begin to undergo a long-range transport. Therefore,the contribution of the TD dust to East Asian dust concentrations was relatively small. Only 25% and 23% of the TD dust was transported to remote areas over East Asia in spring and summer, respectively.     

Drivers of Australian dust: a case study of frontal winds and dust dynamics in the lower lake Eyre basin

The roles of pre‐frontal, frontal and post‐frontal winds as the primary wind systems for dust entrainment and transport in Australia are well established. While the relevance of each system has been observed across different wind erosion events in central Australia, the entrainment of dust by all three winds during the passage of an individual front has not been demonstrated until now. Synoptic information, satellite aerosol and imagery, meteorological and dust concentration data are presented for a single case study erosion event in the lower Lake Eyre Basin. This event demonstrates variable dust transport in three different directions from one of the southern Hemisphere's most significant source regions, and the changing nature of the active dust pathways during the passage of a frontal system. While only a single dust event is considered, the findings show the complexity of mineral aerosol emission and transport patterns even within an individual dust outbreak. For the lower Lake Eyre Basin, this appreciation of pathway behaviour is significant for better understanding the role of aeolian inputs from the dominant Australian source to surrounding marine systems. In a wider context, the findings exhibit the detailed insights into major dust source dynamics that can be obtained from high resolution spatial and particularly temporal data, as used in combination. This work highlights the importance of adequately resolved data for the accurate determination of dust entrainment and transport patterns of major dust sources. Copyright © 2015 John Wiley & Sons, Ltd.     

Sediment yield modelling for small agricultural catchments: land‐cover parameterization based on remote sensing data analysis

Vegetation and soil properties and their associated changes through time and space affect the various stages of soil erosion. The island of Ishigaki in Okinawa Prefecture, Japan is of particular concern because of the propensity of the red‐soil‐dominated watersheds in the area to contribute substantial sediment discharge to adjacent coastal areas. This paper discusses the application of remote sensing techniques in the retrieval of vegetation and soil parameters necessary for the distributed soil‐loss modelling in small agricultural catchments and analyses the variation in erosional patterns and sediment distribution during rainfall events using numerical solutions of overland flow simulations and sediment continuity equations. To account for the spatial as well as temporal variability of selected parameters of the soil‐loss equations, a method is proposed to account for the variability of associated vegetation cover based on their spectral characteristics as captured by remotely sensed data. To allow for complete spatial integration, modelling the movement of sediment is accomplished under a loose‐coupled GIS computational framework. This study lends a theoretical support and empirical evidence to the role of vegetation as a potential agent for soil erosion control. Copyright © 2003 John Wiley & Sons, Ltd.     

Evaluation of remote sensing‐based evapotranspiration estimates using a water transfer numerical simulation under different vegetation conditions in an arid area

Daily actual evapotranspiration (AET) and seasonal AET values are of great practical importance in the management of regional water resources and hydrological modelling. Remotely sensed AET models and Landsat satellite images have been used widely in producing AET estimates at the field scale. However, the lack of validation at a high spatial frequency under different soil water conditions and vegetation coverages limits their operational applications. To assess the accuracies of remote sensing‐based AET in an oasis‐desert region, a total of 59 local‐scale daily AET time series, simulated using HYDRUS‐1D calibrated with soil moisture profiles, were used as ground truth values. Of 59 sampling sites, 31 sites were located in the oasis subarea and 28 sites were located in the desert subarea. Additionally, the locally validated mapping evapotranspiration at high resolution with internalized calibration surface energy balance model was employed to estimate instantaneous AET values in the area containing all 59 of the sampling sites using seven Landsat subimages acquired from June 5 to August 24 in 2011. Daily AET was obtained using extrapolation and interpolation methods with the instantaneous AET maps. Compared against HYDRUS‐1D, the remote sensing‐based method produced reasonably similar daily AET values for the oasis sites, while no correlation was observed for daily AET estimated using these two methods for the desert sites. Nevertheless, a reasonable monthly AET could be estimated. The correlation analysis between HYDRUS‐1D‐simulated and remote sensing‐estimated monthly AET values showed relative root‐mean‐square error values of 15.1%, 12.1%, and 12.3% for June, July, and August, respectively. The root mean square error of the summer AET was 10.0%. Overall, remotely sensed models can provide reasonable monthly and seasonal AET estimates based on periodic snapshots from Landsat images in this arid oasis‐desert region.     

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.     

Regional variability in dust‐on‐snow processes and impacts in the Upper Colorado River Basin

Dust deposition onto mountain snow cover in the Upper Colorado River Basin frequently occurs in the spring when wind speeds and dust emission peaks on the nearby Colorado Plateau. Dust loading has increased since the intensive settlement in the western USA in the mid 1880s. The effects of dust‐on‐snow have been well studied at Senator Beck Basin Study Area (SBBSA) in the San Juan Mountains, CO, the first high‐altitude area of contact for predominantly southwesterly winds transporting dust from the southern Colorado Plateau. To capture variability in dust transport from the broader Colorado Plateau and dust deposition across a larger area of the Colorado River water sources, an additional study plot was established in 2009 on Grand Mesa, 150 km to the north of SBBSA in west central, CO. Here, we compare the 4‐year (2010–2013) dust source, deposition, and radiative forcing records at Grand Mesa Study Plot (GMSP) and Swamp Angel Study Plot (SASP), SBBSA's subalpine study plot. The study plots have similar site elevations/environments and differ mainly in the amount of dust deposited and ensuing impacts. At SASP, end of year dust concentrations ranged from 0.83 mg g^?1 to 4.80 mg g^?1, and daily mean spring dust radiative forcing ranged from 50–65 W m^?2, advancing melt by 24–49 days. At GMSP, which received 1.0 mg g^?1 less dust per season on average, spring radiative forcings of 32–50 W m^?2 advanced melt by 15–30 days. Remote sensing imagery showed that observed dust events were frequently associated with dust emission from the southern Colorado Plateau. Dust from these sources generally passed south of GMSP, and back trajectory footprints modelled for observed dust events were commonly more westerly and northerly for GMSP relative to SASP. These factors suggest that although the southern Colorado Plateau contains important dust sources, dust contributions from other dust sources contribute to dust loading in this region, and likely account for the majority of dust loading at GMSP. Copyright © 2015 John Wiley & Sons, Ltd.     

Hyperscale terrain modelling of braided rivers: fusing mobile terrestrial laser scanning and optical bathymetric mapping

Quantifying the morphology of braided rivers is a key task for understanding braided river behaviour. In the last decade, developments in geomatics technologies and associated data processing methods have transformed the production of precise, reach‐scale topographic datasets. Nevertheless, generating accurate Digital Elevation Models (DEMs) remains a demanding task, particularly in fluvial systems. This paper identifies a threefold set of challenges associated with surveying these dynamic landforms: complex relief, inundated shallow channels and high rates of sediment transport, and terms these challenges the ‘morphological’, ‘wetted channel’ and ‘mobility’ problems, respectively. In an attempt to confront these issues directly, this paper presents a novel survey methodology that combines mobile terrestrial laser scanning and non‐metric aerial photography with data reduction and surface modelling techniques to render DEMs from the resulting very high resolution datasets. The approach is used to generate and model a precise, dense topographic dataset for a 2.5 km reach of the braided Rees River, New Zealand. Data were acquired rapidly between high flow events and incorporate over 5 x 10⁹ raw survey observations with point densities of 1600 pts m^‐2 on exposed bar and channel surfaces. A detailed error analysis of the resulting sub‐metre resolution is described to quantify DEM quality across the entire surface model. This reveals unparalleled low vertical errors for such a large and complex surface model; between 0.03 and 0.12 m in exposed and inundated areas of the model, respectively. Copyright © 2013 John Wiley & Sons, Ltd.     

Geomorphological control on boulder transport and coastal erosion before,during and after an extreme extra‐tropical cyclone

Extreme wave events in coastal zones are principal drivers of geomorphic change. Evidence of boulder entrainment and erosional impact during storms is increasing. However, there is currently poor time coupling between pre‐ and post‐storm measurements of coastal boulder deposits. Importantly there are no data reporting shore platform erosion, boulder entrainment and/or boulder transport during storm events – rock coast dynamics during storm events are currently unexplored. Here, we use high‐resolution (daily) field data to measure and characterize coastal boulder transport before, during and after the extreme Northeast Atlantic extra‐tropical cyclone Johanna in March 2008. Forty‐eight limestone fine‐medium boulders (n = 46) and coarse cobbles (n = 2) were tracked daily over a 0.1 km² intertidal area during this multi‐day storm. Boulders were repeatedly entrained, transported and deposited, and in some cases broken down (n = 1) or quarried (n = 3), during the most intense days of the storm. Eighty‐one percent (n = 39) of boulders were located at both the start and end of the storm. Of these, 92% were entrained where entrainment patterns were closely aligned to wave parameters. These data firmly demonstrate rock coasts are dynamic and vulnerable under storm conditions. No statistically significant relationship was found between boulder size (mass) and net transport distance. Graphical analyses suggest that boulder size limits the maximum longshore transport distance but that for the majority of boulders lying under this threshold, other factors influence transport distance. Paired analysis of 20 similar sized and shaped boulders in different morphogenic zones demonstrates that geomorphological control affects entrainment and transport distance – where net transport distances were up to 39 times less where geomorphological control was greatest. These results have important implications for understanding and for accurately measuring and modelling boulder entrainment and transport. Coastal managers require these data for assessing erosion risk. © 2016 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.     

Understanding subgrid variability of snow depth at 1‐km scale using Lidar measurements

It is well known that snow plays an important role in land surface energy balance; however, modelling the subgrid variability of snow is still a challenge in large‐scale hydrological and land surface models. High‐resolution snow depth data and statistical methods can reveal some characteristics of the subgrid variability of snow depth, which can be useful in developing models for representing such subgrid variability. In this study, snow depth was measured by airborne Lidar at 0.5‐m resolution over two mountainous areas in south‐western Wyoming, Snowy Range and Laramie Range. To characterize subgrid snow depth spatial distribution, measured snow depth data of these two areas were meshed into 284 grids of 1‐km × 1‐km. Also, nine representative grids of 1‐km × 1‐km were selected for detailed analyses on the geostatistical structure and probability density function of snow depth. It was verified that land cover is one of the important factors controlling spatial variability of snow depth at the 1‐km scale. Probability density functions of snow depth tend to be Gaussian distributions in the forest areas. However, they are eventually skewed as non‐Gaussian distribution, largely due to the no‐snow areas effect, mainly caused by snow redistribution and snow melt. Our findings show the characteristics of subgrid variability of snow depth and clarify the potential factors that need to be considered in modelling subgrid variability of snow depth.     

Exploiting Maximum Entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (north‐eastern Sicily,Italy)

This study aims at evaluating the performance of the Maximum Entropy method in assessing landslide susceptibility, exploiting topographic and multispectral remote sensing predictors. We selected the catchment of the Giampilieri stream, which is located in the north‐eastern sector of Sicily (southern Italy), as test site. On 1 October 2009, a storm rainfall triggered in this area hundreds of debris flow/avalanche phenomena causing extensive economical damage and loss of life. Within this area a presence‐only‐based statistical method was applied to obtain susceptibility models capable of distinguishing future activation sites of debris flow and debris slide, which where the main source of failure mechanisms for flow or avalanche type propagation. The set of predictors used in this experiment comprised primary and secondary topographic attributes, derived by processing a high resolution digital elevation model, CORINE land cover data and a set of vegetation and mineral indices obtained by processing multispectral ASTER images. All the selected data sources are dated before the disaster. A spatially random partition technique was adopted for validation, generating 50 replicates for each of the two considered movement typologies in order to assess accuracy, precision and reliability of the models. The debris slide and debris flow susceptibility models produced high performances with the first type being the best fit. The evaluation of the probability estimates around the mean value for each mapped pixel shows an inverted relation, with the most robust models corresponding to the debris flows. With respect to the role of each predictor within the modelling phase, debris flows appeared to be primarily controlled by topographic attributes whilst the debris slides were better explained by remotely sensed derived indices, particularly by the occurrence of previous wildfires across the slope. The overall excellent performances of the two models suggest promising perspectives for the application of presence‐only methods and remote sensing derived predictors. Copyright © 2016 John Wiley & Sons, Ltd.     

Use of the Connectivity of Runoff Model (CRUM) to investigate the influence of storm characteristics on runoff generation and connectivity in semi‐arid areas

Much attention has been given to the surface controls on the generation and transmission of runoff in semi‐arid areas. However, the surface controls form only one part of the system; hence, it is important to consider the effect that the characteristics of the storm event have on the generation of runoff and the transmission of flow across the slope. The impact of storm characteristics has been investigated using the Connectivity of Runoff Model (CRUM). This is a distributed, dynamic hydrology model that considers the hydrological processes relevant to semi‐arid environments at the temporal scale of a single storm event. The key storm characteristics that have been investigated are the storm duration, rainfall intensity, rainfall variability and temporal structure. This has been achieved through the use of a series of defined storm hydrographs and stochastic rainfall. Results show that the temporal fragmentation of high‐intensity rainfall is important for determining the travel distances of overland flow and, hence, the amount of runoff that leaves the slope as discharge. If the high‐intensity rainfall is fragmented, then the runoff infiltrates a short distance downslope. Longer periods of high‐intensity rainfall allow the runoff to travel further and, hence, become discharge. Therefore, storms with similar amounts of high‐intensity rainfall can produce very different amounts of discharge depending on the storm characteristics. The response of the hydrological system to changes in the rainfall characteristics can be explained using a four‐stage model of the runoff generation process. These stages are: (1) all water infiltrating, (2) the surface depression store filling or emptying without runoff occurring, (3) the generation and transmission of runoff and (4) the transmission of runoff without new runoff being generated. The storm event will move the system between the four stages and the nature of the rainfall required to move between the stages is determined by the surface characteristics. This research shows the importance of the variable‐intensity rainfall when modelling semi‐arid runoff generation. The amount of discharge may be greater or less than the amount that would have been produced if constant rainfall intensity is used in the model. Copyright © 2006 John Wiley & Sons, Ltd.     

Using reanalysis‐driven regional climate model outputs for hydrology modelling

Bias correction methods are usually applied to climate model outputs before using these outputs for hydrological climate change impact studies. However, the use of a bias correction procedure is debatable, due to the lack of physical basis and the bias nonstationarity of climate model outputs between future and historical periods. The direct use of climate model outputs for impact studies has therefore been recommended in a few studies. This study investigates the possibility of using reanalysis‐driven regional climate model (RCM) outputs directly for hydrological modelling by comparing the performance of bias‐corrected and nonbias‐corrected climate simulations in hydrological simulations over 246 watersheds in the Province of Québec, Canada. When using RCM outputs directly, the hydrological model is specifically calibrated using RCM simulations. Two evaluation metrics (Nash–Sutcliffe efficiency [NSE] and transformed root mean square error [TRMSE]) and three hydrological indicators (mean, high, and low flows) are used as criteria for this comparison. Two reanalysis‐driven RCMs with resolutions of 45 km and 15 km are used to investigate the scale effect of climate model simulations and bias correction approaches on hydrology modelling. The results show that nonbias‐corrected simulations perform better than bias‐corrected simulations for the reproduction of the observed streamflows when using NSE and TRMSE as criteria. The nonbias‐corrected simulations are also better than or comparable with the bias‐corrected simulations in terms of reproducing the three hydrological indicators. These results imply that the raw RCM outputs driven by reanalysis can be used directly for hydrological modelling with a specific calibration of hydrological models using these datasets when gauged observations are scarce or unavailable. The nonbias‐corrected simulations (at a minimum) should be provided to end users, along with the bias‐corrected ones, especially for studying the uncertainty of hydrological climate change impacts. This is especially true when using an RCM with a high resolution, since the scale effect is observed when the RCM resolution increases from a 45‐km to a 15‐km scale.     

Perspectives in using a remotely sensed dryness index in distributed hydrological models at the river‐basin scale

In a previous study a spatially distributed hydrological model, based on the MIKE SHE code, was constructed and validated for the 375 000 km² Senegal River basin in West Africa. The model was constructed using spatial data on topography, soil types and vegetation characteristics together with time‐series of precipitation from 112 stations in the basin. The model was calibrated and validated based on river discharge data from nine stations in the basin for 11 years. Calibration and validation results suggested that the spatial resolution of the input data in parts of the area was not sufficient for a satisfactory evaluation of the modelling performance. The study further examined the spatial patterns in the model input and output, and it was found that particularly the spatial resolution of the precipitation input had a major impact on the model response. In an attempt to improve the model performance, this study examines a remotely sensed dryness index for its relationship to simulated soil moisture and evaporation for six days in the wet season 1990. The index is derived from observations of surface temperature and vegetation index as measured by the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor. The correlation results between the index and the simulation results are of mixed quality. A sensitivity analysis, conducted on both estimates, reveals significant uncertainties in both. The study suggests that the remotely sensed dryness index with its current use of NOAA AVHRR data does not offer information that leads to a better calibration or validation of the simulation model in a spatial sense. The method potentially may become more suitable with the use of the upcoming high‐resolution temporal Meteosat Second Generation data. Copyright © 2002 John Wiley & Sons, Ltd.     

Investigation of three-dimensional circulation and hydrography over the Pearl River Estuary of China using a nested-grid coastal circulation model

The Pearl River Estuary (PRE) in South China's Guangdong Province is a subtropical estuary with highly irregular topography and dynamically complicated circulations. A nested-grid coastal circulation modelling system is used in this study to examine dynamic responses of the PRE to tides, meteorological forcing and buoyancy forcing. The nested-grid modelling system is based on the Princeton Ocean Model and consists of three downscaling subcomponents: including an outer-most model with a coarse horizontal resolution of ~7 km for simulating tidally forced and wind-driven surface elevations and depth-mean currents over the China Seas from Bohai Sea to the northern South China Sea and an innermost model with a fine resolution of ~1.2 km for simulating the 3D coastal circulation and hydrography over the PRE and adjacent coastal waters. Model results during the winter northeast monsoon surge in January and super typhoon Koryn in June of 1993 are used to demonstrate that the 3D coastal circulation and hydrographic distributions in the PRE are affected by tides, winds and buoyancy forcing associated with river discharge from the Pearl River with significant seasonal and synoptic variabilities.