On Creating Global Gridded Terrestrial Water Budget Estimates from Satellite Remote Sensing

The increasing availability and reliability of satellite remote sensing products [e.g., precipitation (P), evapotranspiration (ET), and the total water storage change (TWSC)] make it feasible to estimate the global terrestrial water budget at fine spatial resolution. In this study, we start from a reference water budget dataset that combines all available data sources, including satellite remote sensing, land surface model (LSM) and reanalysis, and investigate the roles of different non-satellite remote sensing products in closing the terrestrial water budget through a sensitivity analysis by removing/replacing one or more categories of products during the budget estimation. We also study the differences made by various satellite products for the same budget variable. We find that the gradual removal of non-satellite data sources will generally worsen the closure errors in the budget estimates, and remote sensing retrievals of P, ET, and TWSC together with runoff (R) from LSM give the worst closure errors. The gauge-corrected satellite precipitation helps to improve the budget closure (4.2–9 % non-closure errors of annual mean precipitation) against using the non-gauge-corrected precipitation (7.6–10.4 % non-closure errors). At last, a data assimilation technique, the constrained Kalman filter, is applied to enforce the water balance, and it is found that the satellite remote sensing products, though with worst closure, yield comparable budget estimates in the constrained system to the reference data. Overall, this study provides a first comparison between the water budget closure using the satellite remote sensing products and a full combination of remote sensing, LSM, and reanalysis products on a quasi-global basis. This study showcases the capability and potential of the satellite remote sensing in closing the terrestrial water budget at fine spatial resolution if properly constrained.     

Effect of watershed subdivision on simulation of water balance components

The present effect of watershed subdivision on simulated water balance components using the thoroughly tested Soil and Water Assessment Tool (SWAT) model has been evaluated for the Nagwan watershed in eastern India. Observed meteorological and hydrological data (daily rainfall, temperature, relative humidity and runoff) for the years 1995 to 1998 were collected and used. The watershed and sub‐watershed boundaries, slope and soil texture maps were generated using a geographical information system. A supervised classification method was used for land‐use/cover classification from satellite imagery of 1996. In order to study the effect of watershed subdivision, the watershed was spatially defined into three decomposition schemes, namely a single watershed, and 12 and 22 sub‐watersheds. The simulation using the SWAT model was done for a period of 4 years (1995 to 1998). Results of the study showed a perfect water balance for the Nagwan watershed under all of the decomposition schemes. Results also revealed that the number and size of sub‐watersheds do not appreciably affect surface runoff. Except for runoff, there was a marked variation in the individual components of the water balance under the three decomposition schemes. Though the runoff component of the water balance showed negligible variation among the three cases, variations were noticed in the other components: evapotranspiration (5 to 48%), percolation (2 to 26%) and soil water content (0·30 to 22%). Thus, based on this study, it is concluded that watershed subdivision has a significant effect on the water balance components. Copyright © 2005 John Wiley & Sons, Ltd.     

Estimation of potential evapotranspiration in the mountainous Panama Canal watershed

Spatially distributed hydrometeorological and plant information within the mountainous tropical Panama Canal watershed is used to estimate parameters of the Penman–Monteith evapotranspiration formulation. Hydrometeorological data from a few surface climate stations located at low elevations in the watershed are complemented by (a) typical wet‐ and dry‐season fields of temperature, wind, water vapour and pressure produced by a mesoscale atmospheric model with a 3 × 3 km² spatial and hourly temporal resolution, and (b) leaf area index fields estimated over the watershed during a few years using satellite data with two different spatial and temporal resolutions. The mesoscale model estimates of spatially distributed surface hydrometeorological variables provide the basis for the extrapolation of the surface climate station data to produce input for the Penman–Monteith equation. The satellite information and existing digital spatial databases of land use and land cover form the basis for the estimation of Penman–Monteith spatially distributed parameter values. Spatially distributed 3 × 3 km² potential evapotranspiration estimates are obtained for the 3300 km² Panama Canal watershed. Estimates for Gatun Lake within the watershed are found to reproduce well the monthly and annual lake evaporation obtained from submerged pans. Sensitivity analysis results of potential evapotranspiration estimates with respect to cloud cover, dew formation, leaf area index distribution and mesoscale model estimates of surface climate are presented and discussed. The main conclusion is that even the limited spatially distributed hydrometeorological and plant information used in this study contributes significantly toward explaining the substantial spatial variability of potential evapotranspiration in the watershed. These results also allow the determination of key locations within the watershed where additional surface stations may be profitably placed. Copyright © 2006 John Wiley & Sons, Ltd.     

On the usefulness of remote sensing input data for spatially distributed hydrological modelling: case of the Tarim River basin in China

The ecological situation of the Tarim River basin in China seriously declined since the early 1950s, mainly due to a strong increase in water abstraction for irrigation purposes. To restore the ecological system and support sustainable development of the Tarim River basin region in China, more hydrological studies are demanded to properly understand the processes of the watershed and efficiently manage the water resources. Such studies are, however, complicated due to the limited data availability, especially in the mountainous headwater regions of the Tarim River basin. This study investigated the usefulness of remote sensing (RS) data to overcome that lack of data in the spatially distributed hydrological modelling of the basin. Complementary to the conventional station‐based (SB) data, the RS products that are directly used in this study include precipitation, evapotranspiration and leaf area index. They are derived from raw image data of the Chinese Fengyun meteorological satellite and from the Moderate Resolution Imaging Spectroradiometer (MODIS). The MODIS land surface temperature was used to calculate the atmospheric temperature lapse rate to describe the temperature dependency on topographical variations. Moreover, MODIS‐based snow cover images were used to obtain model initial conditions and as validation reference for the snow model component. Comparison of model results based on RS input versus conventional SB input exhibited similar results in terms of high and low river runoff extremes, cumulative runoff volumes in both runoff and snow melting seasons and spatial and temporal variability of snow cover. During summer time, when the snow cover shrinks in the permanent glacier region, it was found that the model resolution influences the model results dramatically, hence, showing the importance of detailed (RS based) spatially distributed input data. Copyright © 2011 John Wiley & Sons, Ltd.     

Effects of land cover change on streamflow in the interior Columbia River Basin (USA and Canada)

An analysis of the hydrological effects of vegetation changes in the Columbia River basin over the last century was performed using two land cover scenarios. The first was a reconstruction of historical land cover vegetation, c. 1900, as estimated by the federal Interior Columbia Basin Ecosystem Management Project (ICBEMP). The second was current land cover as estimated from remote sensing data for 1990. Simulations were performed using the variable infiltration capacity (VIC) hydrological model, applied at one‐quarter degree spatial resolution (approximately 500 km² grid cell area) using hydrometeorological data for a 10 year period starting in 1979, and the 1900 and current vegetation scenarios. The model represents surface hydrological fluxes and state variables, including snow accumulation and ablation, evapotranspiration, soil moisture and runoff production. Simulated daily hydrographs of naturalized streamflow (reservoir effects removed) were aggregated to monthly totals and compared for nine selected sub‐basins. The results show that, hydrologically, the most important vegetation‐related change has been a general tendency towards decreased vegetation maturity in the forested areas of the basin. This general trend represents a balance between the effects of logging and fire suppression. In those areas where forest maturity has been reduced as a result of logging, wintertime maximum snow accumulations, and hence snow available for runoff during the spring melt season, have tended to increase, and evapotranspiration has decreased. The reverse has occurred in areas where fire suppression has tended to increase vegetation maturity, although the logging effect appears to dominate for most of the sub‐basins evaluated. Predicted streamflow changes were largest in the Mica and Corralin sub‐basins in the northern and eastern headwaters region; in the Priest Rapids sub‐basin, which drains the east slopes of the Cascade Mountains; and in the Ice Harbor sub‐basin, which receives flows primarily from the Salmon and Clearwater Rivers of Idaho and western Montana. For these sub‐basins, annual average increases in runoff ranged from 4·2 to 10·7% and decreases in evapotranspiration ranged from 3·1 to 12·1%. In comparison with previous studies of individual, smaller sized watersheds, the modelling approach used in this study provides predictions of hydrological fluxes that are spatially continuous throughout the interior Columbia River basin. It thus provides a broad‐scale framework for assessing the vulnerability of watersheds to altered streamflow regimes attributable to changes in land cover that occur over large geographical areas and long time‐frames. Copyright © 2000 John Wiley & Sons, Ltd.     

Assessing the long-term impact of climatic variability and human activities on the water resources of a meso-scale Mediterranean catchment

Abstract

This article addresses the critical need for a better quantitative understanding of how water resources from the Hérault River catchment in France have been influenced by climate variability and the increasing pressure of human activity over the last 50 years. A method is proposed for assessing the relative impacts of climate and growing water demand on the decrease in discharge observed at various gauging stations in the periods 1961–1980 and 1981–2010. An annual water balance at the basin scale was calculated first, taking into account precipitation, actual evapotranspiration, water withdrawals and water discharge. Next, the evolution of the seasonal variability in hydroclimatic conditions and water withdrawals was studied. The catchment was then divided into zones according to the main geographical characteristics to investigate the heterogeneity of the climatic and human dynamics. This delimitation took into account the distribution of climate, topography, lithology, land cover and water uses, as well as the availability of discharge series. At the area scale, annual water balances were calculated to understand the internal changes that occurred in the catchment between both past periods. The decrease in runoff can be explained by the decrease in winter precipitation in the upstream areas and by the increase during summer in both water withdrawals and evapotranspiration in the downstream areas, mainly due to the increase in temperature. Thus, water stress increased in summer by 35%. This work is the first step of a larger research project to assess possible future changes in the capacity to satisfy water demand in the Hérault River catchment, using a model that combines hydrological processes and water demand.

Editor Z.W. Kundzewicz     

Land cover mapping using time series HJ-1/CCD data

It is very difficult to have remote sensing data with both high spatial resolution and high temporal frequency; thus, two categories of land-use mapping methodology have been developed separately for coarser resolution and finer resolution data. The first category uses time series of data to retrieve the variation of land surface for classification, which are usually used for coarser resolution data with high temporal frequency. The second category uses fine spatial resolution data to classify different land surface. With the launch of Chinese satellite constellation HJ-1in 2008, four 30 m spatial resolution CCDs with about 360 km coverage for each one onboard two satellites made a revisit period of two days, which brought a new type of data with both high spatial resolution and high temporal frequency. Therefore, by taking the spatiotemporal advantage of HJ-1/CCD data we propose a new method for finer resolution land cover mapping using the time series HJ-1/CCD data, which can greatly improve the land cover mapping accuracy. In our two study areas, the very high resolution remote sensing data within Google Earth are used to validate the land cover mapping results, which shows a very high mapping accuracy of 95.76% and 83.78% and a high Kappa coefficient of 0.9423 and 0.8165 in the Dahuofang area of Liaoning Province and the Heiquan area of Gansu Province respectively.     

Evaluation of multi-sensor satellite data for monitoring different drought impacts

Drought is a natural disaster that significantly affects human life; therefore, precise monitoring and prediction is necessary to minimize drought damage. Conventional drought monitoring is based predominantly on ground observation stations; however, satellite imagery can be used to overcome the disadvantages of existing monitoring methods and has the advantage of monitoring wide areas. In this research, we assess the applicability of drought monitoring based on satellite imagery, focusing on historic droughts in 2001 and 2014, which caused major agricultural and hydrological issues in South Korea. To assess the applicability and accuracy of the drought index, drought impact areas in the study years were investigated, and spatiotemporal comparative analyses between the calculated drought index and previously affected areas were conducted. For drought monitoring based on satellite imagery, we used hydro-meteorological factors such as precipitation, land surface temperature, vegetation, and evapotranspiration, and applied remote sensing data from various sensors. We verified the effectiveness of using precipitation data for meteorological drought monitoring, vegetation and land surface temperature data for agricultural drought monitoring, and evapotranspiration data for hydrological drought monitoring. Moreover, we confirmed that the Standard Precipitation Index (SPI) can be indirectly applied to agricultural or hydrological drought monitoring by determining the temporal correlation between SPI, calculated for various time scales, and satellite-based drought indices.     

Remote sensing in hydrology

Remote sensing provides a means of observing hydrological state variables over large areas. The ones which we will consider in this paper are land surface temperature from thermal infrared data, surface soil moisture from passive microwave data, snow cover using both visible and microwave data, water quality using visible and near-infrared data and estimating landscape surface roughness using lidar. Methods for estimating the hydrometeorlogical fluxes, evapotranspiration and snowmelt runoff, using these state variables are also described.     

The impact of different spatial land cover data sets on the outputs of a hydrological model - a modelling exercise in the Ucker catchment, North-East Germany

The spatial distribution of forests, meadows, arable land, water bodies and settlements in a catchment influences the spatial and temporal dynamics of evapotranspiration, surface runoff, soil moisture and ground water recharge. Four digital data sets from different sources were available for land cover distribution to be applied in a regional case study in the Ucker catchment with an area of about 2415 km². The first data set was obtained from the German digital topographic data set “Atkis” and the second one from the federal German biotope mapping procedure “Biotoptypenkartierung”. In addition, Corine land cover data and a land cover obtained from a supervised, multitemporal classification of three Landsat-TM5-scenes from the year 2000 were used in our study. These data sets differ in spatial resolution and in information content and this leads to different areal proportions of the main land cover classes forests, meadows, arable land, water bodies and settlements. This has to be considered as an uncertainty in the land cover data. In our case study, we analyzed how and to which extent this uncertainty influences the outputs of a hydrological catchment model such as evapotranspiration and discharge. For the time period 1996-2001, meteorological time series were obtained from four meteorological stations and five additional precipitation stations. Measured daily discharge rates were available from two gauges located in the catchment. In the different land cover data sets, the proportions of arable land ranged from 52.7% to 61.7% of the catchment area and for forests from 19.5% to 24.6%. These different proportions showed only minor impacts with small differences below ±10 mm y⁻¹ on the simulated annual rates of evapotranspiration and ground water recharge. In contrast, the simulated surface runoff rates showed a strong correlation to the amount of the settlement areas in the catchment. The highest proportion of settlements with 4.9% of the catchment area in comparison to the lowest proportion of 2.9% leads to an increase in the simulated surface runoff of 70%.     

Soil erosion risk assessment with CORINE model: case study in the Danjiangkou Reservoir region, China

Soil erosion is one of the major threats to the conservation of soil and water resources in the Danjiangkou Reservoir region (DRR), China. In order to describe the areas with high soil erosion risk (SER) and to develop adequate erosion prevention measures, SER in the DRR was assessed by integrating the CORINE model with GIS and RS. The main factors of soil erosion including erosivitiy, soil erodibility, topography and vegetation cover were determined from daily meteorological data, field survey soil profile data and soil sample analysis, digital elevation model (DEM), and land use and land cover (LULC), respectively. Landsat 5 TM imagery was used to generate a LULC classification. The results indicate that 59.1%, 31.2%, and 2.3% of the study area were under low, moderate, and high actual erosion risks, respectively. The results also indicate the study area is in low to moderate erosion risk level on the whole. The areas with moderate to high erosion risk continuously distributed in the southwest of the study area, and sporadically distributed in the north of the study area. Low erosion risk areas mainly located in the east. Up till now, most of the semi-quantitative models have not been applied extensively. The semi-quantitative CORINE model was mostly applied in the European and Mediterranean countries, while spatial comparison of actual SER map and field investigation in this study indicates that the CORINE model can be applicable in the monsoon region of China.     

Fine scale mapping of fractional tree canopy cover to support river basin management

Management of water, regionally, nationally and globally will continue to be a priority and complex undertaking. In riverine systems, biotic components like flora and fauna play critical roles in filtering water so it is available for human use and consumption. Preservation of ecosystems and associated ecosystem functions is therefore vital. In highly regulated large river basins, natural ecosystems are often supported through provision of environmental flows. Flow delivery, however, should be underpinned by rigorous monitoring to identify and prioritise biotic water requirements. Currently, large-scale monitoring solutions are scaled from remote sensing data via measurement of field evapotranspiration for woody tree vegetation species. However, as there is generally a mismatch between field data collection area and remote sensing pixel size, new methods are required to proportion tree evapotranspiration based on tree fractional canopy area per pixel. We present a novel method to derive tree fractional canopy cover (FTCC) at 20 m resolution in semi-arid and arid floodplain areas. The method employs LiDAR as a canopy area field measurement proxy (10 m resolution). We used Sentinel-1 and Sentinel-2 (radar and multispectral imagery) in a Random Forest analysis, undertaken to develop a predictive FTCC model trained using LiDAR for two regions in the Murray–Darling Basin. A predictor model combining the results of both regions was able to explain between 71%–85% of FTCC variation when compared to LiDAR FTCC when output in 10% increments. Development of this method underpins the advancement of woody vegetation monitoring to inform environmental flow management in the Murray–Darling Basin. The method and fine scale outputs will also be of value to other catchment management concerns such as altered catchment water yields related to bushfires and as such has application to water management worldwide.     

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.     

II. Snow hydrology processes and remote sensing

In the last 20 years remote sensing research has led to significant progress in monitoring and measuring certain snow hydrology processes. Snow distribution in a drainage basin can be adequately assessed by visible sensors. Although there are still some interpretation problems, the NOAA-AVHRR sensor can provide frequent views of the areal snow cover in a basin, and snow cover maps are produced operationally by the National Weather Service on about 3000 drainage basins in North America. Measurement of snow accumulation or snow water equivalent with microwave remote sensing has great potential because of the capabilities for depth penetration, all-weather observation and night-time viewing. Several critical areas of research remain, namely, the acquisition of snow grain size information for input to microwave models and improvement in passive microwave resolution from space. Methods that combine both airborne gamma ray and visible satellite remote sensing of the snowpack with field measurements also hold promise for determining areal snow water equivalent. Some remote sensing techniques can also be used to detect different stages of snow metamorphism. Various aspects of snowpack ripening can be detected using microwave and thermal infra-red capabilities. The capabilities for measurement of snow albedo and surface temperature have direct application in both snow metamorphism and snowpack energy balance studies. The potentially most profitable research area here is the study of the bidirectional reflectance distribution function to improve snow albedo measurements. Most of the remote sensing capabilities in snow hydrology have been developed for improving snowmelt-run-off forecasting. Most applications have used the input of snow cover extent to deterministic models, both of the degree day and energy balance types. Snowmelt-run-off forecasts using satellite derived snow cover depletion curves and the models have been successfully made. As the extraction of additional snow cover characteristics becomes possible, remote sensing will have an even greater impact on snow hydrology. Important remote sensing capabilities will become available in the next 20 years through space platform observing systems that will improve our capability to observe the snowpack on an operational basis.     

High-resolution remote sensing mapping of global land water

Land water, one of the important components of land cover, is the indispensable and important basic information for climate change studies, ecological environment assessment, macro-control analysis, etc. This article describes the overall study on land water in the program of global land cover remote sensing mapping. Through collection and processing of Landsat TM/ETM+, China’s HJ-1 satellite image, etc., the program achieves an effective overlay of global multi-spectral image of 30 m resolution for two base years, namely, 2000 and 2010, with the image rectification accuracy meeting the requirements of 1:200000 mapping and the error in registration of images for the two periods being controlled within 1 pixel. The indexes were designed and selected reasonably based on spectral features and geometric shapes of water on the scale of 30 m resolution, the water information was extracted in an elaborate way by combining a simple and easy operation through pixel-based classification method with a comprehensive utilization of various rules and knowledge through the object-oriented classification method, and finally the classification results were further optimized and improved by the human-computer interaction, thus realizing high-resolution remote sensing mapping of global water. The completed global land water data results, including Global Land 30-water 2000 and Global Land 30-water 2010, are the classification results featuring the highest resolution on a global scale, and the overall accuracy of self-assessment is 96%. These data are the important basic data for developing relevant studies, such as analyzing spatial distribution pattern of global land water, revealing regional difference, studying space-time fluctuation law, and diagnosing health of ecological environment.     

Estimation of land surface evapotranspiration over complex terrain based on multi‐spectral remote sensing data

Land surface evapotranspiration (ET) plays an important role in energy and water balances. ET can significantly affect the runoff yield of a basin and the available water resources in mountainous areas. The existing models to estimate ET are typically applicable to plains, and excessive data are required to calculate the surface fluxes accurately. This study established a simple and practical model capable of depicting the surface fluxes, while using relatively less parameters. Considering the complex terrain, solar radiation was corrected by importing a series of topographic factors. The water deficit index, a measure of land surface wetness, was calculated by applying the f_c (vegetation fractional cover)‐T_rad (land surface temperature) framework in the two‐source trapezoid model for evapotranspiration model to mountainous areas after corrections of temperature based on altitude variations. The model was successfully applied to the Kaidu River Basin, a basin with few gauges located in the east Tien Shan Mountains of China. Based on the time scale extensions, ET was analyzed at different time scales from 2000 to 2013. The results demonstrated that the corrected solar radiation and water deficit index were reasonably distributed in space and that this model is applicable to ungauged catchments, such as the Kaidu River Basin.     

Evaluating the spatial distribution of water balance in a small watershed,Pennsylvania

A conceptual water‐balance model was modified from a point application to be distributed for evaluating the spatial distribution of watershed water balance based on daily precipitation, temperature and other hydrological parameters. The model was calibrated by comparing simulated daily variation in soil moisture with field observed data and results of another model that simulates the vertical soil moisture flow by numerically solving Richards' equation. The impacts of soil and land use on the hydrological components of the water balance, such as evapotranspiration, soil moisture deficit, runoff and subsurface drainage, were evaluated with the calibrated model in this study. Given the same meteorological conditions and land use, the soil moisture deficit, evapotranspiration and surface runoff increase, and subsurface drainage decreases, as the available water capacity of soil increases. Among various land uses, alfalfa produced high soil moisture deficit and evapotranspiration and lower surface runoff and subsurface drainage, whereas soybeans produced an opposite trend. The simulated distribution of various hydrological components shows the combined effect of soil and land use. Simulated hydrological components compare well with observed data. The study demonstrated that the distributed water balance approach is efficient and has advantages over the use of single average value of hydrological variables and the application at a single point in the traditional practice. Copyright © 2000 John Wiley & Sons, Ltd.     

Analysis of long‐term water balance in the source area of the Yellow River basin

To analyse the long‐term water balance of the Yellow River basin, a new hydrological model was developed and applied to the source area of the basin. The analysis involved 41 years (1960–2000) of daily observation data from 16 meteorological stations. The model is composed of the following three sub‐models: a heat balance model, a runoff formation model and a river‐routing network model. To understand the heat and water balances more precisely, the original model was modified as follows. First, the land surface was classified into five types (bare, grassland, forest, irrigation area and water surface) using a high‐resolution land‐use map. Potential evaporation was then calculated using land‐surface temperatures estimated by the heat balance model. The maximum evapotranspiration of each land surface was calculated from potential evaporation using functions of the leaf area index (LAI). Finally, actual evapotranspiration was estimated by regulating the maximum evapotranspiration using functions of soil moisture content. The river discharge estimated by the model agreed well with the observed data in most years. However, relatively large errors, which may have been caused by the overestimation of surface flow, appeared in some summer periods. The rapid decrease of river discharge in recent years in the source area of the Yellow River basin depended primarily on the decrease in precipitation. Furthermore, the results suggested that the long‐term water balance in the source area of the Yellow River basin is influenced by land‐use changes. Copyright © 2007 John Wiley & Sons, Ltd.     

Remote sensing-based artificial surface cover classification in Asia and spatial pattern analysis

Artificial surfaces, characterized with intensive land-use changes and complex landscape structures, are important indicators of human impacts on terrestrial ecosystems. Without high-resolution land-cover data at continental scale, it is hard to evaluate the impacts of urbanization on regional climate, ecosystem processes and global environment. This study constructed a hierarchical classification system for artificial surfaces, promoted a remote sensing method to retrieve subpixel components of artificial surfaces from 30-m resolution satellite imageries(Globe Land30) and developed a series of data products of high-precision urban built-up areas including impervious surface and vegetation cover in Asia in 2010. Our assessment, based on multisource data and expert knowledge, showed that the overall accuracy of classification was 90.79%. The mean relative error for the impervious surface components of cities was 0.87. The local error of the extracted information was closely related to the heterogeneity of urban buildings and vegetation in different climate zones. According to our results, the urban built-up area was 18.18×104 km2, accounting for 0.59% of the total land surface areas in Asia; urban impervious surfaces were 11.65×104 km2, accounting for 64.09% of the total urban built-up area in Asia. Vegetation and bare soils accounted for 34.56% of the urban built-up areas. There were three gradients: a concentrated distribution, a scattered distribution and an indeterminate distribution from east to west in terms of spatial pattern of urban impervious surfaces. China, India and Japan ranked as the top three countries with the largest impervious surface areas, which respectively accounted for 32.77%, 16.10% and 11.93% of the urban impervious surface area of Asia. We found the proportions of impervious surface and vegetation cover within urban built-up areas were closely related to the economic development degree of the country and regional climate environment. Built-up areas in developed countries had relatively low impervious surface and high public green vegetation cover, with 50–60% urban impervious surfaces in Japan, South Korea and Singapore. In comparison, the proportion of urban impervious surfaces in developing countries is approaching or exceeding 80% in Asia. In general, the composition and spatial patterns of built-up areas reflected population aggregation and economic development level as well as their impacts on the health of the environment in the sub-watershed.     

Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter

Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system.