Developing machine learning-based snow depletion curves and analysing their sensitivity over complex mountainous areas

A snow depletion curve (SDC), the relationship between snow mass (e.g., snow depth [SD]) and fractional snow cover area (SCF), is essential to parameterize the effect of snowpack within a physically based snow model. Existing SDCs are constructed using traditional statistic methods may not be applicable in complex mountainous areas. In this study, we developed an information fusion framework to define the relationship between SCF and SD as well as 12 auxiliary factors by using a traditional statistical method and four prevailing machine learning (ML) algorithms, which have comprehensively considered the variable conditions that cause spatiotemporal heterogeneity of snow cover. We also performed a single-dimensional sensitivity analysis to investigate the physical rationality of the newly developed SDCs. The Northern Xinjiang, Northwest China, is selected as the study area, and the data from 46 meteorological stations covering five snow seasons from 2010 to 2015 are used. The results illustrated that ML techniques can be used to establish high-accuracy and robust SDCs for complex mountainous areas. Compared with SDCs constructed by traditional statistical, the performance of the four ML-based SDCs is significantly improved, the RMSE values can be reduced by 50%, R² above 0.75, and an average relative variance close to 0. ML-based SDCs predicted SCF values showed a range of sensitivities to different input variables (e.g., Land surface temperature, aspect, longwave radiation and land cover type), in addition to SD, that were physically representative of effects that snow cover is sensitive to. Moreover, the complexity of SDCs can be reduced by removing insensitive input variables.     

Deriving snow‐cover depletion curves for different spatial scales from remote sensing and snow telemetry data

During the melting of a snowpack, snow water equivalent (SWE) can be correlated to snow‐covered area (SCA) once snow‐free areas appear, which is when SCA begins to decrease below 100%. This amount of SWE is called the threshold SWE. Daily SWE data from snow telemetry stations were related to SCA derived from moderate‐resolution imaging spectroradiometer images to produce snow‐cover depletion curves. The snow depletion curves were created for an 80 000 km² domain across southern Wyoming and northern Colorado encompassing 54 snow telemetry stations. Eight yearly snow depletion curves were compared, and it is shown that the slope of each is a function of the amount of snow received. Snow‐cover depletion curves were also derived for all the individual stations, for which the threshold SWE could be estimated from peak SWE and the topography around each station. A station's peak SWE was much more important than the main topographic variables that included location, elevation, slope, and modelled clear sky solar radiation. The threshold SWE mostly illustrated inter‐annual consistency. Copyright © 2015 John Wiley & Sons, Ltd.     

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km²) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.     

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

The spatial variability of snow water equivalent (SWE) can exert a strong influence on the timing and magnitude of snowmelt delivery to a watershed. Therefore, the representation of sub-grid or sub-watershed snow variability in hydrologic models is important for accurately simulating snowmelt dynamics and runoff response. The U.S. Geological Survey National Hydrologic Model infrastructure with the precipitation-runoff modelling system (NHM-PRMS) represents the sub-grid variability of SWE with snow depletion curves (SDCs), which relate snow-covered area to watershed-mean SWE during the snowmelt period. The main objective of this research was to evaluate the sensitivity of simulated runoff to SDC representation within the NHM-PRMS across the continental United States (CONUS). SDCs for the model experiment were derived assuming a range of SWE coefficient of variation values and a lognormal probability distribution function. The NHM-PRMS was simulated at a daily time step for each SDC over a 14-year period. Results highlight that increasing the sub-grid snow variability (by changing the SDC) resulted in a consistently slower snowmelt rate and longer snowmelt duration when averaged across the hydrologic response unit scale. Simulated runoff was also found to be sensitive to SDC representation, as decreases in simulated snowmelt rate by 1 mm day⁻¹ resulted in decreases in runoff ratio by 1.8% on average in snow-dominated regions of the CONUS. Simulated decreases in runoff associated with slower snowmelt rates were approximately inversely proportional to increases in simulated evapotranspiration. High snow persistence and peak SWE:annual precipitation combined with a water-limited dryness index was associated with the greatest runoff sensitivity to changing snowmelt. Results from this study highlight the importance of carefully parameterizing SDCs for hydrologic modelling. Furthermore, improving model representation of snowmelt input variability and its relation to runoff generation processes is shown to be an important consideration for future modelling applications.     

Inter-annual variability in snow cover depletion patterns and atmospheric circulation indices in the Upper Irtysh basin,Central Asia

The Irtysh River is the main water resource of Eastern Kazakhstan and its upper basin is severely affected by spring floods each year, primarily as a result of snowmelt. Knowledge of the large-scale processes that influence the timing of these snow-induced floods is currently lacking, but critical for the management of water resources in the area. In this study, we evaluated the variability in winter–spring snow cover in five major sub-basins of the Upper Irtysh basin between 2000 and 2017 as a possible explanatory factor of spring flood events, assessing the time of peak snow cover depletion rate and snow cover disappearance from the moderate-resolution imaging spectroradiometer (MODIS) MOD10A2 data set. We found that on average, peak snow cover retreat occurs between 22 March and 14 April depending on the basin, with large interannual variations but no clear trend over the MODIS period, while our comparative analysis of longer-term snow cover extent from the National Oceanic and Atmospheric Administration Climate Data Record data set suggests a shift to earlier snow cover disappearance since the 1970s. In contrast, the annual peak snow cover depletion rate displays a weak increasing trend over the study period and exceeded 5,900 km²/day in 2017. The timing of snow disappearance in spring shows significant correlations of up to 0.82 for the largest basin with winter indices of the Arctic Oscillation (AO) over the region. The primary driver is the impact of the large-scale pressure anomalies upon the mean spring (MAM) air temperatures and resultant timing of snow cover disappearance, particularly at elevations 500–2,000 m above sea level. This suggests a lagged effect of this atmospheric circulation pattern in spring snow cover retreat. The winter AO index could therefore be incorporated into long-term runoff forecasts for the Irtysh. Our approach is easily transferable to other similar catchments and could support flood management strategies in Kazakhstan and other countries.     

Snow model sensitivity analysis to understand spatial and temporal snow dynamics in a high‐elevation catchment

In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km²). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.     

Towards a comprehensive approach to parameter estimation in land surface parameterization schemes

In climate models, the land–atmosphere interactions are described numerically by land surface parameterization (LSP) schemes. The continuing improvement in realism in these schemes comes at the expense of the need to specify a large number of parameters that are either directly measured or estimated. Also, an emerging problem is whether the relationships used in LSPs are universal and globally applicable. One plausible approach to evaluate this is to first minimize uncertainty in model parameters by calibration. In this paper, we conduct a comprehensive analysis of some model diagnostics using a slightly modified version of the Simple Biosphere 3 model for a variety of biomes located mainly in the Amazon. First, the degree of influence of each individual parameter in simulating surface fluxes is identified. Next, we estimate parameters using a multi‐operator genetic algorithm applied in a multi‐objective context and evaluate simulations of energy and carbon fluxes against observations. Compared with the default parameter sets, these parameter estimates improve the partitioning of energy fluxes in forest and cropland sites and provide better simulations of daytime increases in assimilation of net carbon during the dry season at forest sites. Finally, a detailed assessment of the parameter estimation problem was performed by accounting for the decomposition of the mean squared error to the total model uncertainty. Analysis of the total prediction uncertainty reveals that the parameter adjustments significantly improve reproduction of the mean and variability of the flux time series at all sites and generally remove seasonality of the errors but do not improve dynamical properties. Our results demonstrate that error decomposition provides a meaningful and intuitive way to understand differences in model performance. To make further advancements in the knowledge of these models, we encourage the LSP community to adopt similar approaches in the future. Copyright © 2012 John Wiley & Sons, Ltd.     

MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+

Daily swath MODIS Terra Collection 6 fractional snow cover (MOD10_L2) estimates were validated with two‐day Landsat TM/ETM + snow‐covered area estimates across central Idaho and southwestern Montana, USA. Snow cover maps during spring snowmelt for 2000, 2001, 2002, 2003, 2005, 2007, and 2009 were compared between MODIS Terra and Landsat TM/ETM + using least‐squared regression. Strong spatial and temporal map agreement was found between MODIS Terra fractional snow cover and Landsat TM/ETM + snow‐covered area, although map disagreement was observed for two validation dates. High‐altitude cirrus cloud contamination during low snow conditions as well as late season transient snowfall resulted in map disagreement. MODIS Terra's spatial resolution limits retrieval of thin‐patchy snow cover, especially during partially cloudy conditions. Landsat's image acquisition frequency can introduce difficulty when discriminating between transient and resident mountain snow cover. Furthermore, transient snowfall later in the snowmelt season, which is a stochastic accumulation event that does not usually persist beyond the daily timescale, will skew decadal snow‐covered area variability if bi‐monthly climate data record development is the objective. As a quality control step, ground‐based daily snow telemetry snow‐water‐equivalent measurements can be used to verify transient snowfall events. Users of daily MODIS Terra fractional snow products should be aware that local solar illumination and sensor viewing geometry might influence fractional snow cover estimation in mountainous terrain. Cross‐sensor interoperability has been confirmed between MODIS Terra and Landsat TM/ETM + when mapping snow from the visible/infrared spectrum. This relationship is strong and supports operational multi‐sensor snow cover mapping, specifically climate data record development to expand cryosphere, climate, and hydrological science applications. Copyright © 2013 John Wiley & Sons, Ltd.     

A new approach of dynamic monitoring of 5‐day snow cover extent and snow depth based on MODIS and AMSR‐E data from Northern Xinjiang region

Taking the Northern Xinjiang region as an example, we develop a snow depth model by using the Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E) horizontal and vertical polarization brightness temperature difference data of 18 and 36 GHz bands and in situ snow depth measurements from 20 climatic stations during the snow seasons November–March) of 2002–2005. This article proposes a method to produce new 5‐day snow cover and snow depth images, using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products and AMSR‐E snow water equivalent and daily brightness temperature products. The results indicate that (1) the brightness temperature difference (Tb_18h–Tb_36h) provides the most accurate and precise prediction of snow depth; (2) the snow, land and overall classification accuracies of the new images are separately 89.2%, 77.7% and 87.2% and are much better than those of AMSR‐E or MODIS products (in all weather conditions) alone; (3) the snow classification accuracy increases as snow depth increases; and (4) snow accuracies for different land cover types vary as 88%, 92.3%, 79.7% and 80.1% for cropland, grassland, shrub, and urban and built‐up, respectively. We conclude that the new 5‐day snow cover–snow depth images can provide both accurate cloud‐free snow cover extent and the snow depth dynamics, which would lay a scientific basis for water management and prevention of snow‐related disasters in this dry and cold pastoral area. After validations of the algorithms over other regions with different snow and climate conditions, this method would also be used for monitoring snow cover and snow depth elsewhere in the world. Copyright © 2011 John Wiley & Sons, Ltd.     

Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin

Abstract

Remote sensing is considered the most effective tool for estimating evapotranspiration (ET) over large spatial scales. Global terrestrial ET estimates over vegetated land surfaces are now operationally produced at 1-km spatial resolution using data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the MOD16 algorithm. To evaluate the accuracy of this product, ground-based measurements of energy fluxes obtained from eddy covariance sites installed in tropical biomes and from a hydrological model (MGB-IPH) were used to validate MOD16 products at local and regional scales. We examined the accuracy of the MOD16 algorithm at two sites in the Rio Grande basin, Brazil, one characterized by a sugar-cane plantation (USE), the other covered by natural savannah vegetation (PDG) for the year 2001. Inter-comparison between 8-day average MOD16 ET estimates and flux tower measurements yielded correlations of 0.78 to 0.81, with root mean square errors (RMSE) of 0.78 and 0.46 mm d^-1, at PDG and USE, respectively. At the PDG site, the annual ET estimate derived by the MOD16 algorithm was 19% higher than the measured amount. For the average annual ET at the basin-wide scale (over an area of 145 000 km²), MOD16 estimates were 21% lower than those from the hydrological model MGB-IPH. Misclassification of land use and land cover was identified as the largest contributor to the error from the MOD16 algorithm. These estimates improve significantly when results are integrated into monthly or annual time intervals, suggesting that the algorithm has a potential for spatial and temporal monitoring of the ET process, continuously and systematically, through the use of remote sensing data.

Editor D. Koutsoyiannis; Associate editor T. Wagener

Citation Ruhoff, A.L., Paz, A.R., Aragao, L.E.O.C., Mu, Q., Malhi, Y., Collischonn, W., Rocha, H.R., and Running, S.W., 2013. Assessment of the MODIS global evapotranspiration algorithm using eddy covariance measurements and hydrological modelling in the Rio Grande basin. Hydrological Sciences Journal, 58 (8), 1658–1676.     

一种改进的全局优化算法及其在面波频散曲线反演中的应用

建议一种SA和GA相结合的策略,较好地解决了GA收敛早熟及SA搜索效率较低的问题,提高了全局优化计算效率;在应用其依据面波频散曲线反演工程场地剪切波速时,利用简化剥层法提供较小的模型空间,取得了较好的反演效果.     

Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling

Synthetic aperture radar (SAR) sensors are often used to characterize the surface of bare soils in agricultural environments. They enable the soil moisture and roughness to be estimated with constraints linked to the configurations of the sensors (polarization, incidence angle and radar wavelength). These key soil characteristics are necessary for different applications, such as hydrology and risk prediction. This article reviews the potential of currently operational SAR sensors and those planned for the near future to characterize soil surface as a function of users' needs. It details what it is possible to achieve in terms of mapping soil moisture and roughness by specifying optimal radar configurations and the precision associated with the estimation of soil surface characteristics. The summary carried out for the present article shows that mapping soil moisture is optimal with SAR sensors at low incidence angles (<35 ). This configuration, which enables an estimated moisture accuracy greater than 6% is possible several times a month taking into account all the current and future sensors. Concerning soil roughness, it is best mapped using three classes (smooth, moderately rough, and rough). Such mapping requires high‐incidence data, which is possible with certain current sensors (RADARSAT‐1 and ASAR both in band C). When L‐band sensors (ALOS) become available, this mapping accuracy should improve because the sensitivity of the radar signal to Soil Surface Characteristics (SSC) increases with wavelength. Finally, the polarimetric mode of certain imminent sensors (ALOS, RADARSAT‐2, TerraSAR‐X, etc.), and the possibility of acquiring data at very high spatial resolution (metre scale), offer great potential in terms of improving the quality of SSC mapping. Copyright © 2007 John Wiley & Sons, Ltd.     

A land surface model incorporated with soil freeze/thaw and its application in GAME/Tibet

Land surface process is of great importance in global climate change, moisture and heat exchange in the interface of the earth and atmosphere, human impacts on the environment and eco- system, etc. Soil freeze/thaw plays an important role in cold land surface processes. In this work the diurnal freeze/thaw effects on energy partition in the context of GAME/Tibet are studied. A sophisti- cated land surface model is developed, the particular aspect of which is its physical consideration of soil freeze/thaw and vapor flux. The simultaneous water and heat transfer soil sub-model not only reflects the water flow from unfrozen zone to frozen fringe in freezing/thawing soil, but also demon- strates the change of moisture and temperature field induced by vapor flux from high temperature zone to low temperature zone, which makes the model applicable for various circumstances. The modified Picard numerical method is employed to help with the water balance and convergence of the numerical scheme. Finally, the model is applied to analyze the diurnal energy and water cycle char- acteristics over the Tibetan Plateau using the Game/Tibet datasets observed in May and July of 1998. Heat and energy transfer simulation shows that: (i) There exists a negative feedback mechanism between soil freeze/thaw and soil temperature/ground heat flux; (ii) during freezing period all three heat fluxes do not vary apparently, in spite of the fact that the negative soil temperature is higher than that not considering soil freeze; (iii) during thawing period, ground heat flux increases, and sensible heat flux decreases, but latent heat flux does not change much; and (iv) during freezing period, soil temperature decreases, though ground heat flux increases.     

Land use and land cover classification using phenological variability from MODIS vegetation in the Upper Pangani River Basin,Eastern Africa

In arid and semi-arid areas, evaporation fluxes are the largest component of the hydrological cycle, with runoff coefficient rarely exceeding 10%. These fluxes are a function of land use and land management and as such an essential component for integrated water resources management. Spatially distributed land use and land cover (LULC) maps distinguishing not only natural land cover but also management practices such as irrigation are therefore essential for comprehensive water management analysis in a river basin. Through remote sensing, LULC can be classified using its unique phenological variability observed over time. For this purpose, sixteen LULC types have been classified in the Upper Pangani River Basin (the headwaters of the Pangani River Basin in Tanzania) using MODIS vegetation satellite data. Ninety-four images based on 8 day temporal and 250 m spatial resolutions were analyzed for the hydrological years 2009 and 2010. Unsupervised and supervised clustering techniques were utilized to identify various LULC types with aid of ground information on crop calendar and the land features of the river basin. Ground truthing data were obtained during two rainfall seasons to assess the classification accuracy. The results showed an overall classification accuracy of 85%, with the producer’s accuracy of 83% and user’s accuracy of 86% for confidence level of 98% in the analysis. The overall Kappa coefficient of 0.85 also showed good agreement between the LULC and the ground data. The land suitability classification based on FAO-SYS framework for the various LULC types were also consistent with the derived classification results. The existing local database on total smallholder irrigation development and sugarcane cultivation (large scale irrigation) showed a 74% and 95% variation respectively to the LULC classification and showed fairly good geographical distribution. The LULC information provides an essential boundary condition for establishing the water use and management of green and blue water resources in the water stress Pangani River Basin.     

一种基于地表能量平衡的遥感干旱监测新方法及其在甘肃河东地区干旱监测中的应用初探

目前遥感干旱监测方法的精度普遍不高,探求新的遥感干旱监测方法有助于干旱监测预警技术的提升与发展.波文比是感热通量与潜热通量之比,能综合反映地表水热特征,可尝试将其引入到遥感干旱监测领域加以利用.应用甘肃河东地区的EOS-MODIS卫星资料和同步地面气象资料,基于地表能量平衡原理构建了波文比干旱监测模型,对比分析了波文比（β）指数、温度植被指数（TVX）与土壤水分的相关性,并以典型晴空影像（2014年10月5日）为例初步建立了β的干旱分级标准,对研究区进行了旱情评估.结果表明：β与土壤相对湿度呈现出高度负相关,相比于当下广泛应用的TVX,β与0～20 cm平均土壤相对湿度具有更好的相关性,监测精度得到了显著提高.用β干旱分级标准评估的研究区干湿状况与前期降水空间分布吻合得相当好,评估表明2014年10月5日研究区基本为适宜（无旱）,与2014年9月的降水距平百分率特征一致.基于地表能量平衡的波文比（β）指数在干旱监测中效果突出,具有很好的应用前景.     

基于MODIS数据的2000-2010年乌梁素海"黄苔"时空变化

利用2000-2010年每年5-9月MODIS数据根据比值算法提取乌梁素海湖区黄苔的面积和空间分布信息并进行统计分析,探求乌梁素海黄苔产生的时空分布规律及特征,从而为黄苔的预防和治理提供支持.结果表明:(1)黄苔面积变化的年际和月际特征方面,2000、2001、2005、2006、2008、2010年黄苔面积超过了多年平均值(24 km2).5—7月份黄苔面积较小,保持在20 km2左右;8月黄苔面积迅速增长(约28 km2),9月黄苔面积最大,达到40 km2左右.(2)黄苔发生频率方面,2001年黄苔的规模和频率最高,发生频率达到0.58;2005、2006、2010年次之,发生频率在0.25附近波动(多年年均黄苔暴发频率为0.19);其他年份黄苔的发生频率处于低于0.10的水平.黄苔发生规模较大、次数较多的月份集中在8、9月,发生频率分别达到0.27、0.52,超过多年月均黄苔暴发频率0.19;其他月份黄苔的发生频率处于低于0.10的水平.(3)黄苔出现的空间分布方面,西大滩为东大滩的北部至中部,以及乌梁素海南部明水区排干口附近的西部沿岸是黄苔出现频率较高的区域.(4)2个月前的日均温度、降雨和营养盐浓度及当月风速与黄苔的产生具有极显著相关性;营养盐含量(TN、TP)的空间分布与黄苔的空间分布表现出较好的相关性.乌梁素海黄苔面积的年际变化受人类活动特别是生态补水的影响明显.     

The assessment of surface water resources for the semi‐arid Yongding River Basin from 1956 to 2000 and the impact of land use change

The assessment of surface water resources (SWRs) in the semi‐arid Yongding River Basin is vital as the basin has been in a continuous state of serious water shortage over the last 20 years. In this study, the first version of the geomorphology‐based hydrological model (GBHM) has been applied to the basin over a long period of time (1956–2000) as part of an SWR assessment. This was done by simulating the natural hydrological processes in the basin. The model was first evaluated at 18 stream gauges during the period from 1990 to 1992 to evaluate both the daily streamflows and the annual SWRs using the land use data for 1990. The model was further validated in 2000 with the annual SWRs at seven major stream gauges. Second, the verified model was used in a 45‐year simulation to estimate the annual SWRs for the basin from 1956 to 2000 using the 1990 land use data. An empirical correlation between the annual precipitation and the annual SWRs was developed for the basin. Spatial distribution of the long‐term mean runoff coefficients for all 177 sub‐basins was also achieved. Third, an additional 10‐year (1991–2000) simulation was performed with the 2000 land use data to investigate the impact of land use changes from 1990 to 2000 on the long‐term annual SWRs. The results suggest that the 10‐year land use changes have led to a decrease of 8·3 × 10⁷ m³ (7·9% of total) for the 10‐year mean annual SWRs in the simulation. To our knowledge, this work is the first attempt to assess the long‐term SWRs and the impact of land use change in the semi‐arid Yongding River Basin using a semi‐distributed hillslope hydrological model. Copyright © 2010 John Wiley & Sons, Ltd.     

Smooth constraint inversion technique in genetic algorithms and its application to surface wave study in the Tibetan Plateau

IntroductionThe dispersion phenomena, which can be observed when surface wave travels through the Earth interior, has been extensively applied in investigating the velocity structure of the Earth interior. Usually, the dispersion curve is a nonlinear function of the thickness, S and P wave velocities and density of each layer. Because surface wave inversion is a multiple-minima problem, the result strongly depends on the initial model in traditional linear inversion. Genetic algorithm (GA) …     

Time‐varying,nonlinear suspended sediment rating curves to characterize trends in water quality: An application to the Upper Hudson and Mohawk Rivers,New York

This work proposes two modelling frameworks for diagnosing temporal variations in nonlinear rating curves that describe suspended sediment–discharge relationships. A variant of the weighted regression on time, discharge, and season model is proposed and is compared against dynamic nonlinear modelling, a newly developed nonlinear time series filter based on sequential Monte Carlo sampling. Both approaches estimate a time series of rating curve parameters, with uncertainty, that can be used to diagnose variability in the sediment–discharge relationship over time. We evaluate the models with a variety of synthetic scenarios to highlight their ability to estimate signals of known rating curve change. Results reveal important bias‐variance trade‐offs unique to each approach, and in general, suggest that dynamic nonlinear modelling is better suited for rapid rating curve changes, whereas the weighted regression on time, discharge, and season variant more precisely estimates slow change. The techniques are then applied in two case studies in the Upper Hudson and Mohawk Rivers in New York. We conclude with a discussion of the implications of dynamic rating curves for the management of water quality in riverine and estuary systems.     

利用背景噪声数据提取地震台站间 面波的可靠性分析 以中国大陆中东部地区的宽频带台站为例

利用中国大陆中东部地区以国家台网为主的100个分布均匀的宽频带地震台记录到的21个月的连续波形数据, 经过单台数据处理和互相关叠加计算后, 由时频分析法提取了研究区各台站对间瑞雷波的格林函数. 为了检验经验格林函数的可靠性和稳定性, 对沿部分路径的经验格林函数和频散曲线进行了质量评估. 检测结果表明, 自21个月叠加的台站对间背景噪声中提取的经验格林函数与实际的地震面波一致, 提取的格林函数可靠. 此外, 统计了使用从3—21个月不同长度数据叠加后, 经验格林函数信噪比大于10的频散曲线数目. 结果表明, 至少要使用12个月的数据才能提取到信噪比足够大, 数目足够多, 可用于反演面波速度结构的经验格林函数; 12个月的叠加时长, 可以保证30 s以下周期的频散曲线在时间上稳定.