首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 31 毫秒
1.
There are many areas of uncertainty when solving the inverse problems of snow water equivalent (SWE) reconstruction. These include (i) the ability to infer the Final Date of the Seasonal Snow (FDSS) cover, particularly from remote sensing; (ii) errors in model forcing data (such as air temperature or radiation fluxes); and (iii) weaknesses in the snow model used for the reconstruction, associated with both the fidelity of the equations used to simulate snow processes (structural uncertainty) and the parameter values selected for use in the model equations. We investigate the trade-offs among these sources of uncertainty using 10,000 station-years worth of data from the western US SNOTEL network. Model structural and parameter uncertainty are eliminated by using a perfect model scenario i.e. comparing results to modelled control runs. The model was calibrated for each station-year to ensure that the model simulations reflect reality. Results indicate that for a temperature index model, a ±5 days error in FDSS gives a median −25%/+32% error in maximum SWE. A 1 °C air temperature bias produces a SWE error larger than a 5 days error in the FDSS for 50% of the 10,000 cases. Similarly, a 5 days error in FDSS could be accounted for by a net radiation error of 13 W m−2 or less during the melt period, in 50% of cases. Mean absolute errors of 1 °C or more are typically reported in the literature for air temperature interpolations at high elevations. Observed solar radiation during the melt season can differ by 30 W m−2 over relatively short distances, while estimates from reanalysis (NARR, ERA-Interim, MERRA, CFSRR) and GOES satellites typically span more than 40 W m−2. Using data from both MODIS sensors (Terra & Aqua) at all snow covered points in the western US, a consecutive 5 days gap in imagery at time of FDSS is likely to occur only 5–10% of the time. This work shows that errors in model forcing data are at least as important, if not more, than image availability when reconstructing SWE.  相似文献   

2.
This study analyzes spatial variability of snow depth and density from measurements made in February and April of 2010 and 2011 in three 1–2 km2 areas within a valley of the central Spanish Pyrenees. Snow density was correlated with snow depth and different terrain characteristics. Regression models were used to predict the spatial variability of snow density, and to assess how the error in computed densities might influence estimates of snow water equivalent (SWE).The variability in snow depth was much greater than that of snow density. The average snow density was much greater in April than in February. The correlations between snow depth and density were generally statistically significant but typically not very high, and their magnitudes and signs were highly variable among sites and surveys. The correlation with other topographic variables showed the same variability in magnitude and sign, and consequently the resulting regression models were very inconsistent, and in general explained little of the variance. Antecedent climatic and snow conditions prior to each survey help highlight the main causes of the contrasting relation shown between snow depth, density and terrain. As a consequence of the moderate spatial variability of snow density relative to snow depth, the absolute error in the SWE estimated from computed densities using the regression models was generally less than 15%. The error was similar to that obtained by relating snow density measurements directly to adjacent snow depths.  相似文献   

3.
In non-forested mountain regions, wind plays a dominant role in determining snow accumulation and melt patterns. A new, computationally efficient algorithm for distributing the complex and heterogeneous effects of wind on snow distributions was developed. The distribution algorithm uses terrain structure, vegetation, and wind data to adjust commonly available precipitation data to simulate wind-affected accumulations. This research describes model development and application in three research catchments in the Reynolds Creek Experimental Watershed in southwest Idaho, USA. All three catchments feature highly variable snow distributions driven by wind. The algorithm was used to derive model forcings for Isnobal, a mass and energy balance distributed snow model. Development and initial testing took place in the Reynolds Mountain East catchment (0.36 km2) where R2 values for the wind-affected snow distributions ranged from 0.50 to 0.67 for four observation periods spanning two years. At the Upper Sheep Creek catchment (0.26 km2) R2 values for the wind-affected model were 0.66 and 0.70. These R2 values matched or exceeded previously published cross-validation results from regression-based statistical analyses of snow distributions in similar environments. In both catchments the wind-affected model accurately located large drift zones, snow-scoured slopes, and produced melt patterns consistent with observed streamflow. Models that did not account for wind effects produced relatively homogenous SWE distributions, R2 values approaching 0.0, and melt patterns inconsistent with observed streamflow. The Dobson Creek (14.0 km2) application incorporated elevation effects into the distribution routine and was conducted over a two-dimensional grid of 6.67 × 105 pixels. Comparisons with satellite-derived snow-covered-area again demonstrated that the model did an excellent job locating regions with wind-affected snow accumulations. This final application demonstrated that the computational efficiency and modest data requirements of this approach are ideally suited for large-scale operational applications.  相似文献   

4.
Snowpack water equivalent (SWE) is a key variable for water resource management in snow-dominated catchments. While it is not feasible to quantify SWE at the catchment scale using either field surveys or remotely sensed data, technologies such as airborne LiDAR (light detection and ranging) support the mapping of snow depth at scales relevant to operational water management. To convert snow depth to water equivalent, models have been developed to predict SWE or snowpack density based on snow depth and additional predictor variables. This study builds upon previous models that relate snowpack density to snow depth by including additional predictor variables to account for (1) long-term climatologies that describe the prevailing conditions influencing regional snowpack properties, and (2) the effect of intra- and inter-year variability in meteorological conditions on densification through a cumulative degree-day index derived from North American Regional Reanalysis products. A non-linear model was fit to 114 506 snow survey measurements spanning 41 years from 1166 snow courses across western North America. Under spatial cross-validation, the predicted densities had a root-mean-square error of 47.1 kg m−3, a mean bias of −0.039 kg m−3, and a Nash-Sutcliffe Efficiency of 0.70. The model developed in this study had similar overall performance compared to a similar regression-based model reported in the literature, but had reduced seasonal biases. When applied to predict SWE from simulated depths with random errors consistent with those obtained from LiDAR or Structure-from-Motion, 50% of the SWE estimates for April and May fell within −45 to 49 mm of the observed SWE, representing prediction errors of −15% to 20%.  相似文献   

5.
Reliable estimation of the volume and timing of snowmelt runoff is vital for water supply and flood forecasting in snow‐dominated regions. Snowmelt is often simulated using temperature‐index (TI) models due to their applicability in data‐sparse environments. Previous research has shown that a modified‐TI model, which uses a radiation‐derived proxy temperature instead of air temperature as its surrogate for available energy, can produce more accurate snow‐covered area (SCA) maps than a traditional TI model. However, it is unclear whether the improved SCA maps are associated with improved snow water equivalent (SWE) estimation across the watershed or improved snowmelt‐derived streamflow simulation. This paper evaluates whether a modified‐TI model produces better streamflow estimates than a TI model when they are used within a fully distributed hydrologic model. It further evaluates the performance of the two models when they are calibrated using either point SWE measurements or SCA maps. The Senator Beck Basin in Colorado is used as the study site because its surface is largely bedrock, which reduces the role of infiltration and emphasizes the role of the SWE pattern on streamflow generation. Streamflow is simulated using both models for 6 years. The modified‐TI model produces more accurate streamflow estimates (including flow volume and peak flow rate) than the TI model, likely because the modified‐TI model better reproduces the SWE pattern across the watershed. Both models also produce better performance when calibrated with SCA maps instead of point SWE data, likely because the SCA maps better constrain the space‐time pattern of SWE.  相似文献   

6.
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.  相似文献   

7.
In this paper, a new snow density estimation methodology is proposed for full-polarimetric Synthetic Aperture Radar (SAR) data. The generalized four component polarimetric decomposition with unitary transformation (G4U) based generalized volume parameter is utilized to invert snowpack dielectric constant using the Fresnel transmission coefficients. The snow density is then estimated using an empirical relationship. Six Radarsat-2 fine resolution full-polarimetric C-band datasets were acquired over Himachal Pradesh, India. The near-real time in-situ measurements were collected with the satellite pass to validate the proposed method. The mean absolute error (MAE) of the proposed method is 0.027 g cm−3 and the root mean square error (RMSE) is 0.032 g cm−3. The snow density variation within a season were also analyzed using multi-temporal Radarsat-2 data.  相似文献   

8.
Accurate snow accumulation and melt simulations are crucial for understanding and predicting hydrological dynamics in mountainous settings. As snow models require temporally varying meteorological inputs, time resolution of these inputs is likely to play an important role on the model accuracy. Because meteorological data at a fine temporal resolution (~1 hr) are generally not available in many snow‐dominated settings, it is important to evaluate the role of meteorological inputs temporal resolution on the performance of process‐based snow models. The objective of this work is to assess the loss in model accuracy with temporal resolution of meteorological inputs, for a range of climatic conditions and topographic elevations. To this end, a process‐based snow model was run using 1‐, 3‐, and 6‐hourly inputs for wet, average, and dry years over Boise River Basin (6,963 km2), which spans rain dominated (≤1,400 m), rain–snow transition (>1,400 and ≤1,900 m), snow dominated below tree line (>1,900 and ≤2,400 m), and above tree line (>2,400 m) elevations. The results show that sensitivity of the model accuracy to the inputs time step generally decreases with increasing elevation from rain dominated to snow dominated above tree line. Using longer than hourly inputs causes substantial underestimation of snow cover area (SCA) and snow water equivalent (SWE) in rain‐dominated and rain–snow transition elevations, due to the precipitation phase mischaracterization. In snow‐dominated elevations, the melt rate is underestimated due to errors in estimation of net snow cover energy input. In addition, the errors in SCA and SWE estimates generally decrease toward years with low snow mass, that is, dry years. The results indicate significant increases in errors in estimates of SCA and SWE as the temporal resolution of meteorological inputs becomes coarser than an hour. However, use of 3‐hourly inputs can provide accurate estimates at snow‐dominated elevations. The study underscores the need to record meteorological variables at an hourly time step for accurate process‐based snow modelling.  相似文献   

9.
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.  相似文献   

10.
The 1.0 Ma Kidnappers supereruption (~ 1200 km3 DRE) from Mangakino volcanic centre, Taupo Volcanic Zone, New Zealand, produced a large phreatomagmatic fall deposit followed by an exceptionally widespread ignimbrite. Detailed sampling and analysis of glass shards and mineral phases have been undertaken through a proximal 4.0 m section of the fall deposit, representing the first two-thirds of erupted extra-caldera material. Major and trace element chemistries of glass shards define three distinct populations (types A, B and C), which systematically change in proportion through the fall deposit and are inferred to represent three magma types. Type B glass and biotite first appear at the same level (~ 0.95 m above base) in the fall deposit suggesting later tapping of a biotite-bearing magma. Plagioclase and Fe–Ti oxide compositions show bimodal distributions, which are linked to types A and B glass compositions. Temperature and pressure (T–P) estimates from hornblende and Fe–Ti oxide equilibria from each magma type are similar and therefore the three magma bodies were adjacent, not vertically stacked, in the crust. Most hornblende model T–P estimates range from 770 to 840 °C and 90 to 170 MPa corresponding to storage depths of ~ 4.0–6.5 km. Hornblende model T–P estimates coupled with in situ trace element fingerprinting imply that the magma bodies were individually well mixed, and not stratified. Compositional gaps between the three glass compositional types imply that no mixing between these magmas occurred. We interpret these data, coupled with the systematic changes in shard compositional proportions through the fall deposit, to reflect that three independent melt-dominant bodies of magma contributed large (A, ~ 270 km3), medium (B, ~ 90 km3) and small (C, ~ 40 km3) volumes (as reflected in the fall deposits) and were systematically tapped during the eruption. We propose that the systematic evacuation of the three independent magma bodies implies that there was tectonic triggering and linkage of eruptions. Our results show that supereruptions can be generated by near simultaneous multiple eruptions from independent magma chambers rather than the evacuation of a large single unitary magma chamber.  相似文献   

11.
Streamflow simulation is often challenging in mountainous watersheds because of incomplete hydrological models, irregular topography, immeasurable snowpack or glacier, and low data resolution. In this study, a semi-distributed conceptual hydrological model (SWAT-Soil Water Assessment Tool) coupled with a glacier melting algorithm was applied to investigate the sensitivity of streamflow to climatic and glacial changes in the upstream Heihe River Basin. The glacier mass balance was calculated at daily time-step using a distributed temperature-index melting and accumulation algorithm embedded in the SWAT model. Specifically, the model was calibrated and validated using daily streamflow data measured at Yingluoxia Hydrological Station and decadal ice volume changes derived from survey maps and remote sensing images between 1960 and 2010. This study highlights the effects of glacier melting on streamflow and their future changes in the mountainous watersheds. We simulate the contribution of glacier melting to streamflow change under different scenarios of climate changes in terms of temperature and precipitation dynamics. The rising temperature positively contributed to streamflow due to the increase of snowmelt and glacier melting. The rising precipitation directly contributes to streamflow and it contributed more to streamflow than the rising temperature. The results show that glacial meltwater has contributed about 3.25 billion m3 to streamflow during 1960–2010. However, the depth of runoff within the watershed increased by about 2.3 mm due to the release of water from glacial storage to supply the intensified evapotranspiration and infiltration. The simulation results indicate that the glacier made about 8.9% contribution to streamflow in 2010. The research approach used in this study is feasible to estimate the glacial contribution to streamflow in other similar mountainous watersheds elsewhere.  相似文献   

12.
Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as snow water equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions, but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment and the Helsinki University of Technology microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 and 37 GHz vertically polarised microwaves are consistent with advanced microwave scanning radiometer-earth observing system and special sensor microwave imager retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10-cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method, then it is equivalent to ±13 mm SWE (7 % of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model.  相似文献   

13.
This study examines the recent evolution of the Greenland ice sheet and its six major drainage basins. Based on laser altimetry data acquired by the Ice, Cloud and Land Elevation Satellite (ICESat), covering the period September–November 2003 to February–March 2008, ice surface height changes and their temporal variations were inferred. Our refined repeat track analysis is solely based on ICESat data and is independent of external elevation models, since it accounts for both ice height changes and the local topography. From the high resolution ice height change pattern we infer an overall mean surface height trend of −0.12 ± 0.006 m yr−1. Furthermore, the largest changes could be identified at coastal margins of the ice sheet, exhibiting rates of more than −2 m yr−1. The total ice volume change of the entire ice sheet amounts to −205.4 ± 10.6 km3 yr−1. In addition, we assessed mass changes from 78 monthly Gravity Recovery and Climate Experiment (GRACE) solutions. The Release-04 gravity field solutions of GeoForschungsZentrum Potsdam cover the period between August 2002 and June 2009. We applied an adjusted regional integration approach in order to minimize the leakage effects. Attention was paid to an optimized filtering which reduces error effects from different sources. The overall error assessment accounts for GRACE errors as well as for errors due to imperfect model reductions. In particular, errors caused by uncertainties in the glacial isostatic adjustment models could be identified as the largest source of errors. Finally, we determined both seasonal and long-term mass change rates. The latter amounts to an overall ice mass change of −191.2 ± 20.9 Gt yr−1 corresponding to 0.53 ± 0.06 mm yr−1 equivalent eustatic sea level rise. From the combination of the volume and mass change estimates we determined a mean density of the lost mass to be 930 ± 11 kg m−3. This value supports our applied density assumption 900 ± 30 kg m−3 which was used to perform the volume–mass-conversion of our ICESat results. Hence, mass change estimates from two independent observation techniques were inferred and are generally in good agreement.  相似文献   

14.
《Continental Shelf Research》1999,19(15-16):1905-1932
The M2 tidal component of the flow in the Dover Straits is reconstructed using a natural combination of two independent data sources: HF Ocean Surface Current Radar (HF OSCR) system and coastal tidal measurements. The method used is the variational data assimilation technique into a quasi-linearized finite element tidal model. The model uses triangular elements with horizontal resolution varying from 800 to 1200 m. It is controlled by the boundary conditions at open boundaries, which are adjusted to fit the available data in an optimal way. A “weak” formulation of the dynamical constraints is used. The interpolation scheme allows small (0.01%) deviations from the exact dynamics specified by the model. The optimal state of M2 parameters (sea surface elevation and depth-averaged velocities) is used to map both the M2 tidal flux through the strait and the M2 energy flux. The respective values obtained are the tidal flux amplitude 1.18±0.09×106 m3 s−1, the net residual transport (Stoke's drift) 40±3×103 m3 s−1, and the net energy flux 1.19±0.09×1010 W. These figures within the statistically estimated error band are in the close agreement with those obtained by Prandle et al., 1993. A rigorous error analysis is performed using an explicit inversion of the Hessian matrix, associated with the assimilation scheme. As a result, error charts for M2 velocities and sea surface elevation are obtained. It is shown that OSCR data combined with coastal level measurements and constrained by dynamics is able to provide quite accurate velocity estimates whose errors vary within the range of 0.05–0.45 m s−1 depending upon the location. Error maps also enable us to determine areas requiring better coverage by data, thus forming a basis of optimization approach to the design of the HFR measurements. The use of variational assimilation technique in providing integrated interpolation patterns from various sources of data demonstrates its capabilities in relation to future oceanographic monitoring systems of shelf circulation.  相似文献   

15.
Statistical wind models were developed based on the existing observational wind data for near-space altitudes between 60 000 and 100 000 ft (18–30 km) above ground level (AGL) at two locations, Akon, OH, USA, and White Sands, NM, USA. These two sites are envisioned as playing a crucial role in the first flights of high-altitude airships. The analysis shown in this paper has not been previously applied to this region of the stratosphere for such an application. Standard statistics were compiled for these data such as mean, median, maximum wind speed, and standard deviation, and the data were modeled with Weibull distributions. These statistics indicated, on a yearly average, there is a lull or a “knee” in the wind between 65 000 and 72 000 ft AGL (20–22 km). From the standard statistics, trends at both locations indicated substantial seasonal variation in the mean wind speed at these heights. The yearly and monthly statistical modeling indicated that Weibull distributions were a reasonable model for the data. Forecasts and hindcasts were done by using a Weibull model based on 2004 data and comparing the model with the 2003 and 2005 data. The 2004 distribution was also a reasonable model for these years. Lastly, the Weibull distribution and cumulative function were used to predict the 50%, 95%, and 99% winds, which are directly related to the expected power requirements of a near-space station-keeping airship. These values indicated that using only the standard deviation of the mean may underestimate the operational conditions.  相似文献   

16.
We performed measurements using an SO2 imaging camera of the SO2 gas mass emitted during five discrete explosive events on Stromboli volcano on 3 October 2006. The SO2 gas mass released during discrete explosions was 15–40 kg per explosion, producing 3–8% of the total daily SO2 gas emission, demonstrating that in terms of gas flux Strombolian explosions are a second-order phenomenon compared with quiescent degassing. Using the typical gas composition measured with OP-FTIR allows us to determine the total gas mass released during an explosion as 360–960 kg with a volume of 1500–4100 m3 at 1 bar. At the probable source pressure of gas slug formation of 75 MPa this gas amount would occupy a volume equivalent to a sphere with a radius of 0.8–1 m, comparable with estimates of Stromboli's conduit geometry.  相似文献   

17.
Surface soil moisture is a critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purposes are sensors that are installed at depths of approximately 5 cm. There are however, sensor technologies and network designs that do not provide an estimate at this depth. If soil moisture estimates at deeper depths could be extrapolated to the near surface, in situ networks providing estimates at other depths would see their values enhanced. Soil moisture sensors from the U.S. Climate Reference Network (USCRN) were used to generate models of 5 cm soil moisture, with 10 cm soil moisture measurements and antecedent precipitation as inputs, via machine learning techniques. Validation was conducted with the available, in situ, 5 cm resources. It was shown that a 5 cm estimate, which was extrapolated from a 10 cm sensor and antecedent local precipitation, produced a root-mean-squared-error (RMSE) of 0.0215 m3/m3. Next, these machine-learning-generated 5 cm estimates were also compared to AMSR-E estimates at these locations. These results were then compared with the performance of the actual in situ readings against the AMSR-E data. The machine learning estimates at 5 cm produced an RMSE of approximately 0.03 m3/m3 when an optimized gain and offset were applied. This is necessary considering the performance of AMSR-E in locations characterized by high vegetation water contents, which are present across North Carolina. Lastly, the application of this extrapolation technique is applied to the ECONet in North Carolina, which provides a 10 cm depth measurement as its shallowest soil moisture estimate. A raw RMSE of 0.028 m3/m3 was achieved, and with a linear gain and offset applied at each ECONet site, an RMSE of 0.013 m3/m3 was possible.  相似文献   

18.
Characterization of snow is critical for understanding Earth’s water and energy cycles. Maps of snow from MODIS have seen growing use in investigations of climate, hydrology, and glaciology, but the lack of rigorous validation of different snow mapping methods compromises these studies. We examine three widely used MODIS snow products: the “binary” (i.e., snow yes/no) global snow maps that were among the initial MODIS standard products; a more recent standard MODIS fractional snow product; and another fractional snow product, MODSCAG, based on spectral mixture analysis. We compare them to maps of snow obtained from Landsat ETM+ data, whose 30 m spatial resolution provides nearly 300 samples within a 500 m MODIS nadir pixel. The assessment uses 172 images spanning a range of snow and vegetation conditions, including the Colorado Rocky Mountains, the Upper Rio Grande, California’s Sierra Nevada, and the Nepal Himalaya. MOD10A1 binary and fractional fail to retrieve snow in the transitional periods during accumulation and melt while MODSCAG consistently maintains its retrieval ability during these periods. Averaged over all regions, the RMSE for MOD10A1 fractional is 0.23, whereas the MODSCAG RMSE is 0.10. MODSCAG performs the most consistently through accumulation, mid-winter and melt, with median differences ranging from −0.16 to 0.04 while differences for MOD10A1 fractional range from −0.34 to 0.35. MODSCAG maintains its performance over all land cover classes and throughout a larger range of land surface properties. Characterizing snow cover by spectral mixing is more accurate than empirical methods based on the normalized difference snow index, both for identifying where snow is and is not and for estimating the fractional snow cover within a sensor’s instantaneous field-of-view. Determining the fractional value is particularly important during spring and summer melt in mountainous terrain, where large variations in snow, vegetation and soil occur over small distances and when snow can melt rapidly.  相似文献   

19.
It is understood that sample size could be an issue in earthquake statistical studies, causing the best estimate being too deterministic or less representative derived from limited statistics from observation. Like many Bayesian analyses and estimates, this study shows another novel application of the Bayesian approach to earthquake engineering, using prior data to help compensate the limited observation for the target problem to estimate the magnitude of the recurring Meishan earthquake in central Taiwan. With the Bayesian algorithms developed, the Bayesian analysis suggests that the next major event induced by the Meishan fault in central Taiwan should be in Mw 6.44±0.33, based on one magnitude observation of Mw 6.4 from the last event, along with the prior data including fault length of 14 km, rupture width of 15 km, rupture area of 216 km2, average displacement of 0.7 m, slip rate of 6 mm/yr, and five earthquake empirical models.  相似文献   

20.
《Journal of Hydrology》2006,316(1-4):213-232
The Magdalena River, a major fluvial system draining most of the Colombian Andes, has the highest sediment yield of any medium-sized or large river in South America. We examined sediment yield and its response to control variables in the Magdalena drainage basin based on a multi-year dataset of sediment loads from 32 tributary catchments. Various morphometric, hydrologic, and climatic variables were estimated in order to understand and predict the variation in sediment yield. Sediment yield varies from 128 to 2200 t km−2 yr−1 for catchments ranging from 320 to 59,600 km2. The mean sediment yield for 32 sub-basins within the Magdalena basin is ∼690 t km−2 yr−1. Mean annual runoff is the dominant control and explains 51% of the observed variance in sediment yield. A multiple regression model, including two control variables, runoff and maximum water discharge, explains 58% of the variance. This model is efficient (ME=0.89) and is a valuable tool for predicting total sediment yield from tributary catchments in the Magdalena basin. Multiple correlations for those basins corresponding to the upper Magdalena, middle basin, Eastern Cordillera, and catchment areas greater than 2000 km2, explain 75, 77, 89, and 78% of the variance in sediment yield, respectively. Although more variance is explained when dataset are grouped into categories, the models are less efficient (ME<0.72). Within the spatially distributed models, six catchment variables predict sediment yield, including runoff, precipitation, precipitation peakedness, mean elevation, mean water discharge, and relief. These estimators are related to the relative importance of climate and weathering, hillslope erosion, and fluvial transport processes. Time series analysis indicates that significant increases in sediment load have occurred over 68% of the catchment area, while 31% have experienced a decreasing trend in sediment load and thus yield. Land use analysis and increasing sediment load trends indicate that erosion within the catchment has increased over the last 10–20 years.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号