Contributions of terrestrial and GRACE data to the study of the secular geoid changes in North America

This paper tests and discusses different statistical methods for modelling secular rates of change of the geoid in North America. In particular, we use the method of principal component/empirical orthogonal functions (PC/EOF) analysis to model the geoid rates from Gravity Recovery and Climate Experiment (GRACE) satellite data. As demonstrated, the PC/EOF analysis is useful for studying the contributions from different signals (mainly residual hydrology signals and leakage effects) to the GRACE-derived geoid rates. The PC/EOF analysis leads to smaller geoid rates compared to the conventional least-squares fitting of a trend and annual and semi-annual cycles to the time series of the spherical harmonic coefficients. This is because we filter out particular spatiotemporal modes of the regional geoid changes.We apply the method of least-squares collocation with parameters to combine terrestrial data (GPS vertical velocities from the Canadian Base Network and terrestrial gravity rates from the Canadian Gravity Standardization Net) with the GRACE-derived vertical motion to obtain again the geoid rates. The combined model has a peak geoid rate of 1.4 mm/year in the southeastern area of Hudson Bay contrary to the GRACE-derived geoid rates that show a large peak of 1.6–1.7 mm/year west of Hudson Bay. We demonstrate that the terrestrial data, which have a longer time span than the GRACE data, are important for constraining the GRACE-derived secular signal in the areas that are well sampled by the data.     

Accelerated continental-scale snowmelt and ecohydrological impacts in the four largest Siberian river basins in response to spring warming

River runoff from the four largest Siberian river basins (the Ob, Yenisei, Lena, and Kolyma) considerably contributes to freshwater flux into the Arctic Ocean from the Eurasian continent. However, the effects of variation in snow cover fraction on the ecohydrological variations in these basins are not well understood. In this study, we analysed the spatiotemporal variability of the maximum snow cover fraction (SCF_max) in the four Siberian river basins. We compared the SCF_max from 2000 to 2016 with data in terms of monthly temperature and precipitation, night-time surface temperatures, the terrestrial water storage anomaly (TWSA), the normalised difference vegetation index (NDVI), and river runoff. Our results exhibit a decreasing trend in the April SCF_max values since 2000, largely in response to warming air temperatures in April. We identified snowmelt water as the dominant control on the observed increase in the runoff contribution in May across all four Siberian river basins. In addition, we detected that the interannual river runoff was predominantly controlled by interannual variations in the TWSA. The NDVI in June was strongly controlled by the timing of the snowmelt along with the surface air temperature and TWSA in June. The rate of increase in the freshwater flux from the four Siberian rivers decreased from 2000 to 2016, exhibiting large interannual variations corresponding to interannual variations in the TWSA. However, we identified a clear increase trend in the freshwater flux of ~4 km³/year when analysing the long-term 39-year historical record (1978–2016). Our results suggest that continued global warming will accelerate the transition towards the earlier timing of snowmelt and spring freshwater flux into the Arctic Ocean. Our findings also highlight the effects of earlier snowmelt on ecohydrological changes in the Northern Hemisphere.     

Evaluation and bias correction of SNODAS snow water equivalent (SWE) for streamflow simulation in eastern Canadian basins

ABSTRACT

In snow-dominated basins, collection of snow data while capturing its spatio-temporal variability is difficult; therefore, integrating assimilation products could be a viable alternative for improving streamflow simulation. This study evaluates the accuracy of daily snow water equivalent (SWE) provided by the SNOw Data Assimilation System (SNODAS) of the National Weather Service at a 1-km² resolution for two basins in eastern Canada, where SWE is a critical variable intensifying spring runoff. A geostatistical interpolation method was used to distribute snow observations. SNODAS SWE products were bias-corrected by matching their cumulative distribution function to that of the interpolated snow. The corrected SWE was then used in hydrological modelling for streamflow simulation. The results indicate that the bias-correction method significantly improved the accuracy of the SNODAS products. Moreover, the corrected SWE improved the simulation performance of the peak values. Although the uncertainty of SNODAS estimates is high for eastern Canadian basins, they are still of great value for regions with few snow stations.     

2005—2017年两次强ENSO事件对中国区域陆地水储量变化影响的卫星重力观测

近年来极端气候事件的频发对全球和区域性水循环产生了重大影响,特别是2005—2017年间两次强ENSO(El Nino-Southern Oscillation)事件使得全球陆地水储量出现了较大的年际波动.GRACE(Gravity Recovery and Climate Experiment)重力卫星随着数据质量的提高、后处理方法的完善和超过十年的连续观测,捕捉陆地水储量异常的能力明显提高,这为研究2005—2017年间两次强ENSO事件对中国区域陆地水储量变化的影响提供了观测基础.本文综合利用GRACE卫星重力数据、GLDAS水文模型和实测降水资料分析了中国区域陆地水储量年际变化和与ENSO的关系.研究发现:长江流域中、下游地区和东南诸河流域与ENSO存在较高的相关性,与ENSO的相关系数最大值分别为0.55、0.78、0.70,较ENSO分别滞后约7个月、5个月和5个月.其中长江流域下游地区与ENSO的相关性最强,2010/11 La Nina和2015/16 El Nino两次强ENSO事件使得陆地水储量分别发生了约-24.1亿吨和27.9亿吨的波动.在2010/11 La Nina期间,长江流域下游地区和东南诸河流域陆地水储量异常约在2011年4—5月达到谷值,而长江流域中游地区晚1~2月达到谷值.在2015/16 El Nino期间,长江流域中、下游地区和东南诸河流域陆地水储量从2015年9月到2016年7月持续出现正异常信号.其中,2015年秋冬季(2015年9月至2016年1月)陆地水储量异常明显是受此次El Nino同期影响的结果;2016年春季(4—5月)陆地水异常是受到此次厄尔尼诺峰值的滞后影响所致;2016年7月的陆地水储量异常则与西北太平洋存在的异常反气旋环流有关.     

Snow water equivalent estimation in the Mallero basin using snow gauge data and MODIS images and fieldwork validation

Snow accumulation and ablation rule the temporal dynamics of water availability in mountain areas and cold regions. In these environments, the evaluation of the snow water amount is a key issue. The spatial distribution of snow water equivalent (SWE) over a mountain basin at the end of the snow accumulation season is estimated using a minimal statistical model (SWE‐SEM). This uses systematic observations such as ground measurements collected at snow gauges and snow‐covered area (SCA) data retrieved by remote sensors, here MODIS. Firstly, SWE‐SEM calculates local SWE estimates at snow gauges, then the spatial distribution of SWE over a certain area using an interpolation method; linear regressions of the first two order moments of SWE with altitude. The interpolation has been made by both confining and unconfining the spatial domain by SCA. SWE‐SEM is applied to the Mallero basin (northern Italy) for calculating the snow water equivalent at the end of the winter season for 6 years (2001–2007). For 2007, SWE‐SEM estimates are validated through fieldwork measurements collected during an ‘ad hoc’ campaign on March 31, 2007. Snow‐surveyed measurements are used to check SCA, snow density and SWE estimates. Copyright © 2009 John Wiley & Sons, Ltd.     

Satellite based estimates of terrestrial water storage variations in Turkey

In recent years, the Gravity Recovery and Climate Experiment (GRACE) has provided a new tool to study terrestrial water storage variations (TWS) at medium and large spatial scales, providing quantitative measures of TWS change. Linear trends in TWS variations in Turkey were estimated using GRACE observations for the period March 2003 to March 2009. GRACE showed a significant decrease in TWS in the southern part of the central Anatolian region up to a rate of 4 cm/year. The Global Land Data Assimilation System (GLDAS) model also captured this TWS decrease event but with underestimated trend values. The GLDAS model represents only a part of the total TWS variations, the sum of soil moisture (2 m column depth) and snow water equivalent, ignoring groundwater variations. Therefore, GLDAS model derived TWS variations were subtracted from GRACE derived TWS variations to estimate groundwater storage variations. Results revealed that decreasing trends of TWS observed by GRACE in the southern part of central Anatolia were largely explained by the decreasing trends of groundwater variations which were confirmed by the limited available well groundwater level data in the region.     

利用广义三角帽方法评估GRACE反演中国大陆地区水储量变化的不确定性

在无真实观测值的情况下,本文利用广义三角帽方法评估了五种GRACE时变重力场模型（CSR、GFZ、GRGS、HUST发布的球谐系数解和JPL发布的Mascon解）反演中国大陆地区2003-2013年水储量变化的不确定性.研究结果表明,CSR、GFZ、JPL、HUST和GRGS反演月水储量变化不确定性的区域平均RMS分别为14.4 mm、26.3 mm、25.3 mm、26.6 mm和56.1 mm,其中GRGS的结果未恢复泄漏信号;在季和年尺度上,模型的不确定性均小于月尺度;扣除周期和趋势信号后,各模型反演结果更为一致.除长江流域外,CSR在13个流域的不确定性均小于其他模型,GRGS反演各流域水储量变化的不确定性通常较大,且可能高估了温带大陆性气候地区水储量的波动;CSR和JPL的不确定性受流域周边水文特征、气候类型、流域面积和形状的影响相对较小,不确定性变化范围分别为2.3~17.1 mm和5.6~22.5 mm,GFZ和HUST受影响较大,不确定性变化范围分别为5.5~35.1 mm和4.0~40.6 mm.本文的研究结果为GRACE产品不确定性评估提供了新的途径,为GRACE时变重力场模型的选取提供参考.     

Subgrid variability of snow water equivalent at operational snow stations in the western USA

The spatial distribution of snow water equivalent (SWE) is a key variable in many regional‐scale land surface models. Currently, the assimilation of point‐scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid‐scale SWE. To improve the understanding of the relationship between point‐scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1‐, 4‐ and 16‐km² grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger‐scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd.     

Evaluation of snow water equivalent datasets over the Saint‐Maurice river basin region of southern Québec

A 10‐km gridded snow water equivalent (SWE) dataset is developed over the Saint‐Maurice River basin region in southern Québec from kriging of observed snow survey data for evaluation of SWE products. The gridded SWE dataset covers 1980–2014 and is based on manual gravimetric snow surveys carried out on February 1, March 1, March 15, April 1, and April 15 of each snow season, which captures the annual maximum SWE (SWEM) with a mean interpolation error of ±19%. The dataset is used to evaluate SWEM from a range of sources including satellite retrievals, reanalyses, Canadian regional climate models, and the Canadian Meteorological Centre operational snow depth analysis. We also evaluate a number of solid precipitation datasets to determine their contribution to systematic errors in estimated SWEM. None of the evaluated datasets is able to provide estimates of SWEM that are within operational requirements of ±15% error, and insufficient solid precipitation is determined to be one of the main reasons. The Climate System Forecast Reanalysis is the only dataset where snowfall is sufficiently large to generate SWEM values comparable to observations. Inconsistencies in precipitation are also found to have a strong impact on year‐to‐year variability in SWEM dataset performance and spread. Version 3.6.1 of the Canadian Land Surface Scheme land surface scheme driven with ERA‐Interim output downscaled by Version 5.0.1 of the Canadian Regional Climate Model was the best physically based model at explaining the observed spatial and temporal variability in SWEM (root‐mean‐square error [RMSE] = 33%) and has potential for lower error with adjusted precipitation. Operational snow products relying on the real‐time snow depth observing network performed poorly due to a lack of real‐time data and the strong local scale variability of point snow depth observations. The results underscore the need for more effort to be invested in improving solid precipitation estimates for use in snow hydrology applications.     

基于多源数据分析维多利亚湖流域水储量变化

本文利用CSR发布的GRACE RL06时变重力场模型,结合两种水文模式、卫星测高、降雨和蒸散等多源数据,从多个角度综合系统地分析维多利亚湖流域2003-01-2017-06的陆地水储量变化.比较了正向建模方法和单一尺度因子对泄漏误差的改正效果,经对比采用正向建模方法在此流域效果更好.基于多源数据得出以下三点与此前研究...     

Analysis and partitioning of terrestrial water storage in the Yellow River source region

In order to increase the capability to understand and quantify the spatial differences in terrestrial water storage (TWS), and to reflect the unique energy balance processes and soil freeze–thaw mechanisms in the Qinghai-Tibet Plateau (QTP), this study improved the energy balance processes of the water and energy transfer processes model, including its surface radiation calculations and snowmelt module. By integrating these improvements, a water and energy transfer processes model in Qinghai-Tibet Plateau (WEP-QTP) for the Yellow River source region (YRSR) is developed. Using the improved WEP-QTP model to perform simulations, we assessed the daily changes in snow cover, soil moisture (SM), permafrost (PM), and groundwater storage (GWS) in the YRSR. Our analysis revealed an increase in TWS of 0.24 mm/yr from 1961 to 2020. Snow water equivalent (SWE), SM, PM, and GWS have proportional contributions of 8.33%, 216.67%, −154.17%, and 29.17% to the increased TWS, respectively. SM is the primary component of TWS. Temperature (T), precipitation (P), evapotranspiration (E), and solar radiation (Rs) influence the spatiotemporal variations in TWS, as well as those of its components. The increase in P is the primary cause for the rise in TWS, SWE, and SM, while the increase in T predominantly contributes to the decrease in PM. Furthermore, permafrost degradation and climate-induced warming and humidification lead to increased infiltration, resulting in elevated GWS.     

应用GPS数据和Slepian基函数反演川云渝地区陆地水储量变化

局部Slepian函数是将局部区域内的地球物理信号转化为空间谱的一种方法,其可以保证在球面上局部范围内获得最优谱平滑解,非常适用于局部范围地球物理信号的研究.本文利用中国陆态网西南地区72 个测站的连续GPS观测资料分析川云渝地区陆地水负荷形变特征,并基于Slepian函数方法解算 60 阶的空间谱基函数,结合弹性质量...     

关于利用重力卫星对青藏高原水储量年际变化和季节性变化进行监测并用微波数据产品进行验证的研究

本文利用GRACE重力卫星和被动微波传感器TMI,AMSR-E的数据产品对青藏高原的水储量的月平均变化进行了研究.首先介绍了对青藏高原进行水储量变化研究的意义,指出了目前研究手段的不足.然后利用GRACE重力卫星的数据计算了青藏高原的月平均水储量变化,并对计算的结果用微波数据进行解释.结果表明：利用重力数据计算的青藏高原的月平均水储量的时间分布,可以很好的用微波数据产品进行定性的解释.最后还对计算的结果进行了简单的误差分析.     

Integration of GRACE mass variations into a global hydrological model

Time-variable gravity data of the GRACE (Gravity Recovery And Climate Experiment) satellite mission provide global information on temporal variations of continental water storage. In this study, we incorporate GRACE data for the first time directly into the tuning process of a global hydrological model to improve simulations of the continental water cycle. For the WaterGAP Global Hydrology Model (WGHM), we adopt a multi-objective calibration framework to constrain model predictions by both measured river discharge and water storage variations from GRACE and illustrate it on the example of three large river basins: Amazon, Mississippi and Congo. The approach leads to improved simulation results with regard to both objectives. In case of monthly total water storage variations we obtained a RMSE reduction of about 25 mm for the Amazon, 6 mm for the Mississippi and 1 mm for the Congo river basin. The results highlight the valuable nature of GRACE data when merged into large-scale hydrological modeling. Furthermore, they reveal the utility of the multi-objective calibration framework for the integration of remote sensing data into hydrological models.     

Comparison of five snow water equivalent estimation methods across categories

Snow water equivalent (SWE) is an important indicator used in hydrology, water resources, and climate change impact. There are various methods of estimating SWE (falling in 3 categories: indirect sensors, empirical models, and process‐based models), but few studies that provide comparison across these different categories to help users make decisions on monitoring site design or method selection. Five SWE estimation methods were compared against manual snow course data collected over 2 years (2015–2016) from the Dorset Environmental Science Centre, including the gamma‐radiation‐based CS725 sensor, 3 empirical estimation models (Sexstone snow density model, McCreight & Small snow density model, and a meteorology‐based model), and the University of British Columbia Watershed Model snow energy‐balance model. Snow depth, density, and SWE were measured at the Dorset Environmental Science Centre weather station in south‐central Ontario, on a daily basis over 6 winters from 2011 to 2016. The 2 snow density‐based models, requiring daily snow depth as input, gave the best performance (R² of .92 and .92 for McCreight & Small and Sexstone models, respectively). The CS725 sensor that receives radiation coming from soil penetrating the snowpack provided the same performance (R² = .92), proving that the sensor is an applicable method, although it is expensive. The meteorology‐based empirical model, requiring daily climate data including temperature, precipitation and solar radiation, gave the poorest performance (R² = .77). The energy‐balance‐based University of British Columbia Watershed Model snow module, only requiring climate data, worked better than the empirical meteorology‐based model (R² = .9) but performed worse than the density models or CS725 sensor. Given differences in application objectives, site conditions, and budget, this comparison across SWE estimation methods may help users choose a suitable method. For ongoing and new monitoring sites, installation of a CS725 sensor coupled with intermittent manual snow course measurements (e.g., weekly) is recommended for further SWE method estimation testing and development of a snow density model.     

Wavelet and Gaussian Approaches for Estimation of Groundwater Variations Using GRACE Data

In this study, a scheme is presented to estimate groundwater storage variations in Iran. The variations are estimated using 11 years of Gravity Recovery and Climate Experiments (GRACE) observations from period of 2003 to April 2014 in combination with the outputs of Global Land Data Assimilation Systems (GLDAS) model including soil moisture, snow water equivalent, and total canopy water storage. To do so, the sums of GLDAS outputs are subtracted from terrestrial water storage variations determined by GRACE observations. Because of stripping errors in the GRACE data, two methodologies based on wavelet analysis and Gaussian filtering are applied to refine the GRACE data. It is shown that the wavelet approach could better localize the desired signal and increase the signal‐to‐noise ratio and thus results in more accurate estimation of groundwater storage variations. To validate the results of our procedure in estimation of ground water storage variations, they are compared with the measurements of pisometric wells data near the Urmia Lake which shows favorable agreements with our results.     

Evaluating the potential of a blended passive microwave‐interactive multi‐sensor product for improved mapping of snow cover and estimations of snow water equivalent

The retrieval of Snow Water Equivalent (SWE) from remote sensing satellites continues to be a very challenging problem. In this paper, we evaluate the accuracy of a new SWE product derived from the blending of a passive microwave SWE product based on the Advanced Microwave Sounding Unit (AMSU) with a multi‐sensor snow cover extent product based on the Interactive Multi‐sensor Snow and Ice Mapping System (IMS). The microwave measurements have the ability to penetrate the snow pack, and thus, the retrieval of SWE is best accomplished using the AMSU. On the other hand, the IMS maps snow cover more reliably due to the use of multiple satellite and ground observations. The evolution of global snow cover from the blended, the AMSU and the IMS products was examined during the 2006 snow season. Despite the overall good inter‐product agreement, it was shown that the retrievals of snow cover extent in the blended product are improved when using IMS, with implications for improved microwave retrievals of SWE. In a separate investigation, the skill of the microwave SWE product was also examined for its ability to correctly estimate SWE globally and regionally. Qualitative evaluation of global SWE retrievals suggested dependence on land surface temperature: the lower the temperature, the higher the SWE retrieved. This temperature bias was attributed in part to temperature effects on those snow properties that impact microwave response. Therefore, algorithm modifications are needed with more dynamical adjustments to account for changing snow cover. Quantitative evaluation over Slovakia in central Europe, for a limited period in 2006, showed reasonably good performance for SWE less than 100 mm. Sensitivity to deeper snow decreased significantly. Copyright © 2007 John Wiley & Sons, Ltd.     

Snow distribution from SSM/I and its relationships to the hydroclimatology of the Mackenzie River Basin,Canada

The spatial and temporal distribution of snow cover extent (SCE) and snow water equivalent (SWE) play vital roles in the hydrology of northern watersheds. We apply remotely sensed Special Sensor Microwave Imager (SSM/I) data from 1988 to 2007 to explore the relationships between snow distribution and the hydroclimatology of the Mackenzie River Basin (MRB) of Canada and its major sub-basins. The Environment Canada (EC) algorithm is adopted to retrieve the SWE from SSM/I data. Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow cover extent products (MOD10A2) are used to estimate the different thresholds of retrieved SWE from SSM/I to classify the land cover as snow or no snow for various sub-basins in the MRB. The sub-basins have varying topography and hence different thresholds that range from 10 mm to 30 mm SWE. The accuracy of snow cover mapping based on the combination of several thresholds for the different sub-basins reaches ≈ 90%. The northern basins are found to have stronger linear relationships between the date on which snow cover fraction (SCF) reaches 50% or when SWE reaches 50% and mean air temperatures, than the southern basins. Correlation coefficients between SCF, SWE, and hydroclimatological variables show the new SCF products from SSM/I perform better than SWE from SSM/I to analyze the relationships with the regional hydroclimatology. Statistical models relating SCF and SWE to runoff indicate that the SCF and SWE from EC algorithms are able to predict the discharge in the early snow ablation seasons in northern watersheds.     

A comparison between two statistical and a physically-based model in snow water equivalent mapping

Snow water equivalent (SWE) estimates at the end of the winter season have been compared for the 2002–2006 period in a 200 km² mountainous area in Switzerland, using three different models. The first model, ALPINE3D, is a physically based process-oriented model, which solves the snowpack energy and mass balance equations. The other two models, SWE-SEM and HS-SWE, are statistical algorithms interpolating snow data on a grid. While SWE-SEM interpolates local estimates of SWE, HS-SWE converts interpolated snow depth maps into maps of SWE using a regionally-calibrated conversion model. We discuss similarities and differences among the models’ results, both in terms of total volume, and spatial distribution of SWE. The comparison shows a general good agreement of the results of the three models, with a mean difference in the total volumes between the two statistical models of ∼8%, and between the physical model and the statistical ones of ∼−3% to −10%.     

Estimating the distribution of snow water equivalent using remotely sensed snow cover data and a spatially distributed snowmelt model: A multi-resolution,multi-sensor comparison

Time series of fractional snow covered area (SCA) estimates from Landsat Enhanced Thematic Mapper (ETM+), Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Very High Resolution Radiometer (AVHRR) data were combined with a spatially distributed snowmelt model to reconstruct snow water equivalent (SWE) in the Rio Grande headwaters (3419 km²). In this reconstruction approach, modeled snowmelt over each pixel is integrated during the period of satellite-observed snow cover to estimate SWE. Due to underestimates in snow cover detection, maximum basin-wide mean SWE using MODIS and AVHRR were, respectively, 45% and 68% lower than SWE estimates obtained using ETM+ data. The mean absolute error (MAE) of SWE estimated at 100-m resolution using ETM+ data was 23% relative to observed SWE from intensive field campaigns. Model performance deteriorated when MODIS (MAE = 50%) and AVHRR (MAE = 89%) SCA data were used. Relative to differences in the SCA products, model output was less sensitive to spatial resolution (MAE = 39% and 73% for ETM+ and MODIS simulations run at 1 km resolution, respectively), indicating that SWE reconstructions at the scale of MODIS acquisitions may be tractable provided the SCA product is improved. When considering tradeoffs between spatial and temporal resolution of different sensors, our results indicate that higher spatial resolution products such as ETM+ remain more accurate despite the lower frequency of acquisition. This motivates continued efforts to improve MODIS snow cover products.