基于星载被动微波遥感的青藏高原湖冰物候监测方法

湖冰物候影响着区域及全球气候,是全球变化的敏感因子,青藏高原湖泊众多,冻融现场监测数据缺乏,而微波具有对冰水相变敏感、时间分辨率高、历史存档数据长等特点,这对于长时间序列湖冰物候研究具有重要意义.然而,被动微波遥感空间分辨率低、湖泊亮温的精准定位难.论文通过获取AMSR-E/Aqua和AMSR-2/Gcom-W1的亮温数据,构建了基于轨道亮温数据的阈值判别法,通过对青藏高原不同区域和不同大小的青海湖、色林错、哈拉湖以及阿其克库勒湖进行测试研究:与青海湖现场观测对比,湖泊完全冻结日期与开始融化日期最大误差小于3天;与无云光学遥感判别结果相比,4个湖泊的冻融参数误差为2~4天.结果表明,被动微波轨道亮温数据可实现青藏高原地区亚像元级中大型湖泊冻融信息的获取,历史卫星资料可为湖冰物候的监测提供重要的支撑.     

A new soil freeze/thaw discriminant algorithm using AMSR‐E passive microwave imagery

The soil freeze–thaw controls the hydrological and carbon cycling and thus affects water and energy exchanges at land surface. This article reported a newly developed algorithm for distinguishing the freeze/thaw status of surface soil. The algorithm was based on information from Advanced Microwave Scanning Radiometer Enhanced (AMSR‐E) which records brightness temperature (Tb) in the afternoon and after midnight. The criteria and discriminant functions were obtained from both radiometer observations and model simulations. First of all, the microwave radiation from freeze–thaw soil was examined by carrying out experimental measurements at 18·7 and 36·5 GHz using a Truck‐mounted Multi‐frequency Microwave Radiometer (TMMR) in the Heihe River of China. The experimental results showed that the soil moisture is a key component that differentiates the microwave radiation behaviours during the freeze–thaw process, and the differences in soil temperature and emissivity between frozen and thawed soils were found to be the most important criteria. Secondly, a combined model was developed to consider the impacts of complex ground surface conditions on the discrimination. The model simulations quite followed the trend of in situ observations with an overall relation coefficient (R) of approximately 0·88. Finally, the ratio of Tb18·7H (horizontally polarized Tb at 18·7 GHz) to Tb36·5V was considered primarily as the quasi‐emissivity, which is more reasonable and explicit in measuring the microwave radiation changes in soil freezing and thawing than the spectral gradient. By combining Tb36·5V to indicate the soil temperature variety, a Fisher linear discrimination analysis was used to establish the discriminant functions. After being corrected by TMMR measurements, the new discriminant algorithm had an overall accuracy of 86% when validated by 4‐cm soil temperature. The multi‐year discriminant results also provided a good agreement with the classification map of frozen ground in China. Copyright © 2011 John Wiley & Sons, Ltd.     

Determination of snow water equivalent over the eastern part of Turkey using passive microwave data

Information on regional snow water equivalent (SWE) is required for the management of water generated from snowmelt. Modeling of SWE in the mountainous regions of eastern Turkey, one of the major headwaters of Euphrates–Tigris basin, has significant importance in forecasting snowmelt discharge, especially for optimum water usage. An assimilation process to produce daily SWE maps is developed based on Helsinki University of Technology (HUT) model and AMSR‐E passive microwave data. The characteristics of the HUT emission model are analyzed in depth and discussed with respect to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed to suit models for snow over mountainous areas. Performance of the modified model is checked against the original, other modified cases and ground truth data covering the 2003–2007 winter periods. A new approach to calculate grain size and density is integrated inside the developed data assimilation process. An extensive validation was successfully performed by means of snow data measured at ground stations during the 2008–2010 winter periods. The root mean square error of the data set for snow depth and SWE between January and March of the 2008–2010 periods compared with the respective AMSR‐E footprints indicated that errors for estimated snow depth and predicted SWE values were 16.92 cm and 40.91 mm, respectively, for the 3‐year period. Validation results were less satisfactory for SWE less than 75.0 mm and greater than 150.0 mm. An underestimation for SWE greater than 150 mm could not be resolved owing to the microwave signal saturation that is observed for dense snowpack. Copyright © 2012 John Wiley & Sons, Ltd.     

Spatial variability of snowmelt timing from AMSR‐E and SSM/I passive microwave sensors,Pelly River,Yukon Territory,Canada

Spring snow melt run‐off in high latitude and snow‐dominated drainage basins is generally the most significant annual hydrological event. Melt timing, duration, and flow magnitude are highly variable and influence regional climate, geomorphology, and hydrology. Arctic and sub‐arctic regions have sparse long‐term ground observations and these snow‐dominated hydrologic regimes are sensitive to the rapidly warming climate trends that characterize much of the northern latitudes. Passive microwave brightness temperatures are sensitive to changes in the liquid water content of the snow pack and make it possible to detect incipient melt, diurnal melt‐refreeze cycles, and the approximate end of snow cover on the ground over large regions. Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) passive microwave brightness temperatures (T_b) and diurnal amplitude variations (DAV) are used to investigate the spatial variability of snowmelt onset timing (in two stages, ‘DAV onset’ and ‘melt onset’) and duration for a complex sub‐arctic landscape during 2005. The satellites are sensitive to small percentages of liquid water, and therefore represent ‘incipient melt’, a condition somewhat earlier than a traditional definition of a melting snowpack. Incipient melt dates and duration are compared to topography, land cover, and hydrology to investigate the strength and significance of melt timing in heterogeneous landscapes in the Pelly River, a major tributary to the Yukon River. Microwave‐derived melt onset in this region in 2005 occurred from late February to late April. Upland areas melt 1–2 weeks later than lowland areas and have shorter transition periods. Melt timing and duration appear to be influenced by pixel elevation, aspect, and uniformity as well as other factors such as weather and snow mass distribution. The end of the transition season is uniform across sensors and across the basin in spite of a wide variety of pixel characteristics. Copyright © 2007 John Wiley & Sons, Ltd.     

The application of microwave remote sensing technology in rock mechanics

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AMSR‐E algorithm for snowmelt onset detection in sub‐arctic heterogeneous terrain

The onset of snowmelt in the upper Yukon River basin, Canada, can be derived from brightness temperatures (T_b) obtained by the Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) on NASA's Aqua satellite. This sensor, with a resolution of 14 × 8 km² for the 36·5 GHz frequency, and two to four observations per day, improves upon the twice‐daily coverage and 37 × 28 km² spatial resolution of the Special Sensor Microwave Imager (SSM/I). The onset of melt within a snowpack causes an increase in the average daily 36·5 GHz vertically polarized T_b as well as a shift to high diurnal amplitude variations (DAV) as the snow melts during the day and re‐freezes at night. The higher temporal and spatial resolution makes AMSR‐E more sensitive to sub‐daily T_b oscillations, resulting in DAV that often show a greater daily range compared to SSM/I. Therefore, thresholds of T_b > 246 K and DAV > ± 10 K developed for use with SSM/I have been adjusted for detecting the onset of snowmelt with AMSR‐E using ground‐based surface temperature and snowpack wetness relationships. Using newly developed thresholds of T_b > 252 K and DAV > ± 18 K, AMSR‐E derived snowmelt onset correlates well with SSM/I observations in the small subarctic Wheaton River basin through the 2004 and 2005 winter/spring transition. In addition, the onset of snowmelt derived from AMSR‐E data gridded at a higher resolution than the SSM/I data indicates that finer‐scale differences in elevation and land cover affect the onset of snowmelt and are detectable with the AMSR‐E sensor. On the basis of these observations, the enhanced resolution of AMSR‐E is more effective than SSM/I at delineating spatial and temporal snowmelt dynamics in the heterogeneous terrain of the upper Yukon River basin. Copyright © 2007 John Wiley & Sons, Ltd.     

Active microwave remote sensing for soil moisture measurement: a field evaluation using ERS‐2

Active microwave remote sensing observations of backscattering, such as C‐band vertically polarized synthetic aperture radar (SAR) observations from the second European remote sensing (ERS‐2) satellite, have the potential to measure moisture content in a near‐surface layer of soil. However, SAR backscattering observations are highly dependent on topography, soil texture, surface roughness and soil moisture, meaning that soil moisture inversion from single frequency and polarization SAR observations is difficult. In this paper, the potential for measuring near‐surface soil moisture with the ERS‐2 satellite is explored by comparing model estimates of backscattering with ERS‐2 SAR observations. This comparison was made for two ERS‐2 overpasses coincident with near‐surface soil moisture measurements in a 6 ha catchment using 15‐cm time domain reflectometry probes on a 20 m grid. In addition, 1‐cm soil moisture data were obtained from a calibrated soil moisture model. Using state‐of‐the‐art theoretical, semi‐empirical and empirical backscattering models, it was found that using measured soil moisture and roughness data there were root mean square (RMS) errors from 3·5 to 8·5 dB and r² values from 0·00 to 0·25, depending on the backscattering model and degree of filtering. Using model soil moisture in place of measured soil moisture reduced RMS errors slightly (0·5 to 2 dB) but did not improve r² values. Likewise, using the first day of ERS‐2 backscattering and soil moisture data to solve for RMS surface roughness reduced RMS errors in backscattering for the second day to between 0·9 and 2·8 dB, but did not improve r² values. Moreover, RMS differences were as large as 3·7 dB and r² values as low as 0·53 between the various backscattering models, even when using the same data as input. These results suggest that more research is required to improve the agreement between backscattering models, and that ERS‐2 SAR data may be useful for estimating fields‐scale average soil moisture but not variations at the hillslope scale. Copyright © 2004 John Wiley & Sons, Ltd.     

Uncertainty of snow water equivalent retrieved from AMSR‐E brightness temperature in northeast Asia

Accurate estimation of snow water equivalent (SWE) has been significantly recognized to improve management and analyses of water resource in specific regions. Although several studies have focused on developing SWE values based on remotely sensed brightness temperatures obtained by microwave sensor systems, it is known that there are still a number of uncertainties in SWE values retrieved from microwave radiometers. Therefore, further research for improving remotely sensed SWE values including global validation should be conducted in unexplored regions such as Northeast Asia. In this regard, we evaluated SWE through comparison of values produced by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR‐E) from December 2002 to February 2011 with in situ SWE values converted from snow‐depth observation data from four regions in the South Korea. The results from three areas showed similarities which indicated that the AMSR‐E SWE values were overestimated when compared with in situ SWE values, and their Mean Absolute Errors (MAE) by month were relatively small (1.1 to 6.5 mm). Contrariwise, the AMSR‐E SWE values of one area were significantly underestimated when compared with in situ SWE values and the MAE were much greater (4.9 to 35.2 mm). These results were closely related to AMSR‐E algorithm‐related error sources, which we analyzed with respect to topographic characteristics and snow properties. In particular, we found that snow density data used in the AMSR‐E SWE algorithm should be based on reliable in situ data as the current AMSR‐E SWE algorithm cannot reflect the spatio‐temporal variability of snow density values. Additionally, we derived better results considering saturation effect of AMSR‐E SWE. Despite the demise of AMSR‐E, this study's analysis is significant for providing a baseline for the new sensor and suggests parameters important for obtaining more reliable SWE. Copyright © 2013 John Wiley & Sons, Ltd.     

基于光学与被动微波遥感的青藏高原地区土壤水分反演

青藏高原地区高精度的土壤水分反演对高原能水循环、全球大气循环研究有着极大的影响.因此,获取青藏高原土壤水分时空布信息是一个迫切需要解决的问题.温度植被干旱指数（TVDI）,是基于光学与热红外遥感通道数据反演土壤水分的重要方法,但在研究区域较大、地表覆盖格局差异显著时,TVDI模型反演精度会受到地表温度（Ts）等因素的影响.被动微波AMSR-E数据精确记录了像元内的土壤水分信息,但空间分辨率低.本文利用同时期的MODIS与被动微波数据,发展了针对青藏高原地区高精度土壤水分反演算法.首先,在TVDI模型中,利用修正型土壤调整植被指数（MSAVI）代替归一化植被指数（NDVI）,以改进NDVI易饱和的缺点;其次,利用ASTER GDEM数据,对地形高程和纬度差异引起的地表温度变化进行了校正;然后,通过神经网络训练建立基于TVDI、被动微波以及辅助气象数据的土壤水分反演模型,并应用该模型反演了青藏高原地区三个观测网（CAMP/Tibet、玛曲和那曲）的土壤水分;最后,利用实测土壤水分数据对反演结果进行验证,结果表明该模型的精度均方根误差（RMSE）数值可达到0.031~0.041 m³·m^-3.本文还应用该算法反演了青藏高原连续的土壤水分的空间分布,并比较了土壤水分的变化趋势与实测降水变化趋势,结果表明二者变化量的正负关系一致.

     

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.     

Permittivity in the microwave region of some natural minerals

The permittivity measured at a frequency of 10 GHz normalized to constant density is the same within the experimental error for talc, baryte and dolomite. In the case of clay and kaolin, it is about 10% higher. This is attributed to the high content of Al₂O₃ (30–40%) in them, which interpretation is proved by experiment.     

Intercomparison of snow water equivalent observations in the Northern Great Plains

In the Northern Great Plains, melting snow is a primary driver of spring flooding, but limited knowledge of the magnitude and spatial distribution of snow water equivalent (SWE) hampers flood forecasting. Passive microwave remote sensing has the potential to enhance operational river flow forecasting but is not routinely incorporated in operational flood forecasting. We compare satellite passive microwave estimates from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) to the National Oceanic and Atmospheric Administration Office of Water Prediction (OWP) airborne gamma radiation snow survey and U.S. Army Corps of Engineers (USACE) ground snow survey SWE estimates in the Northern Great Plains from 2002 to 2011. AMSR‐E SWE estimates compare favourably with USACE SWE measurements in the low relief, low vegetation study area (mean difference = ?3.8 mm, root mean squared difference [RMSD] = 34.7 mm), but less so with OWP airborne gamma SWE estimates (mean difference = ?9.5 mm, RMSD = 42.7 mm). An error simulation suggests that up to half of the error in the former comparison is potentially due to subpixel scale SWE variability, limiting the maximum achievable RMSD between ground and satellite SWE to approximately 26–33 mm in the Northern Great Plains. The OWP gamma versus AMSR‐E SWE comparison yields larger error than the point‐scale USACE versus AMSR‐E comparison, despite a larger measurement footprint (5–7 km² vs. a few square centimetres, respectively), suggesting that there are unshared errors between the USACE and OWP gamma SWE data.     

Erodibility of low‐compaction steep‐sloped reclaimed surface mine lands in the southern Appalachian region,USA

The use of loose spoils on steep slopes for surface coal mining reclamation sites has been promoted by the US Department of Interior, Office of Surface Mining for the establishment of native forest, as prescribed by the Forest Reclamation Approach (FRA). Although low‐compaction spoils improve tree survival and growth, erodibility on steep slopes was suspected to increase. This study quantified a combined K_C factor (combining the effects of the soil erodibility K factor and cover management C) for low compaction, steep‐sloped (>20°) reclaimed mine lands in the Appalachian region, USA. The combined K_C factor was used because standard Unit Plot conditions required to separate these factors, per Revised Universal Soil Loss Equation (RUSLE) experimental protocols, were not followed explicitly. Three active coal mining sites in the Appalachian region of East Tennessee, each containing four replicate field plots, were monitored for rainfall and sediment yields during a 14‐month period beginning June 2009. Average cumulative erosivity for the study sites during the monitoring period was measured as 5248.9 MJ·mm·ha^?1·h^?1. The K_C ranged between 0.001 and 0.05 t·ha·h·ha^?1·MJ^?1·mm^?1, with the highest values occurring immediately following reclamation site construction as rills developed (June – August 2009). The K_C for two study sites with about an 18–20 mm spoil D₈₄ were above 0.01 t·ha·h·ha^?1·MJ^?1·mm^?1 during rill development, and below 0.003 t·ha·h·ha^?1·MJ^?1·mm^?1 after August 2009 for the post‐rill development period. The K_C values for one site with a 40 mm spoil D₈₄ were never above 0.008 t·ha·h·ha^?1·MJ^?1·mm^?1 and also on average were lower, being more similar to the other two sites after the rill development period. Based on an initial K_C factor (K_e) measured during the first few storm events, the average C factor (C_e) was estimated as 0.58 for the rill development period and 0.13 for the post‐rill development period. It appears that larger size fractions of spoils influence K_C and C_e factors on low‐compaction steep slopes reclamation sites. Copyright © 2013 John Wiley & Sons, Ltd.     

Beryllium‐7 wet deposition variation with storm height,synoptic classification,and tree canopy state in the mid‐Atlantic USA

Short‐lived fallout isotopes, such as beryllium‐7 (⁷Be), are increasingly used as erosion and sediment tracers in watersheds. ⁷Be is produced in the atmosphere and delivered to the Earth's surface primarily in precipitation. However, relatively little has been published about the variation in ⁷Be wet deposition caused by storm type and vegetation cover. Our analysis of precipitation, throughfall, and sediments in two forested, headwater catchments in the mid‐Atlantic USA indicates significant variation in isotope deposition with storm type and storm height. Individual summer convective thunderstorms were associated with ⁷Be activity concentrations up to 5.0 Bq l^?1 in precipitation and 4.7 Bq l^?1 in throughfall, while single‐event wet depositional fluxes reached 168 Bq m^?2 in precipitation and 103 Bq m^?2 in throughfall. Storms originating from the continental USA were associated with lower ⁷Be activity concentrations and single‐event wet depositional fluxes for precipitation (0.7–1.2 Bq l^?1 and 15.8–65.0 Bq m^?2) and throughfall (0.1–0.3 Bq l^?1 and 13.5–98.9 Bq m^?2). Tropical systems had relatively low activity concentrations, 0.2–0.5 Bq l^?1 in precipitation and 0.2–1.0 Bq l^?1 in throughfall, but relatively high single‐event depositional fluxes due to large rainfall volumes, 32.8–67.6 Bq m^?2 in precipitation and 25.7–134 Bq m^?2 in throughfall. The largest sources of ⁷Be depositional variation were attributed to storm characteristics including precipitation amount and maximum storm height. ⁷Be activity associated with fluvial suspended sediments also exhibited the highest concentration and variability in summer (175–1450 Bq kg^?1). We conclude the dominant source of variation on event‐level ⁷Be deposition is storm type. Our results illustrate the complex relationships between ⁷Be deposition in precipitation and throughfall and demonstrate event‐scale relationships between the ⁷Be in precipitation and on suspended sediment. Copyright © 2015 John Wiley & Sons, Ltd.     

A change detection algorithm for terrain surface moisture mapping based on multi‐year passive microwave remote sensing (Examples of SSM/I and TMI Channels)

As an indication of the surface polarized emission, a polarization index (PI) of microwave radiance from the terrain surface (half‐space of canopy‐soil land) is derived from the radiative transfer model. This PI separates the radiance effects of the canopy‐soil moisture and interference from surface roughness and atmosphere, and is suitable to describe the change of terrain surface moisture, especially for extreme drought or flood conditions. As an example, the statistics of the monthly average < PI > from 6 years' data of the Defense Meteorological Satellite Program (DMSP) SSM/I observations at the lowest frequency 19·35 GHz channel as available are applied for the demonstration of the surface moisture status over a large and heterogeneous territory such as China. The deviation of the PI data at the same month from the average < PI > , i.e. Δ_nPI(≡(PI? < PI>)/ < PI>), gives prominence to focusing moisture variation of terrain surface, and its anomaly shows possible drought or flood occurrence in extreme conditions. The Δ_nPI mapping is validated by the typical examples of the drought in China's Shanxi area in May 2001 and the flood around China's Yangtze River in August 1998, respectively. Our approach is recommended for lower frequency channels to minimize the influence from vegetation canopy for future applications (such as the channels of the Advanced Microwave Scanning Radiometer [AMSR‐E] launched in May 2002 and microwave imaging radiometer of China's Fengyun satellite series). When the monthly < PI > and the ground truth of average volumetric moisture < m_v > of the region are correctly evaluated, it is tractable to retrieve the soil land surface moisture by using the PI data at the same month and the same region without much knowledge of surface roughness, vegetation canopy and other factors. As an example, the retrieval of m_v is favourably tested by using the Tropical Rainfall Measuring Mission (TRMM) Tropical Microwave Imager (TMI) data. Copyright © 2007 John Wiley & Sons, Ltd.     

Evaluation of irrigation water use efficiency using remote sensing in the middle reach of the Heihe river,in the semi‐arid Northwestern China

Irrigation of agricultural oases is the main water consumer in semi‐arid and arid regions of Northwestern China. The accurate estimation of evapotranspiration (ET) on the oases is extremely important for evaluating water use efficiency so as to reasonably allocate water resources, particularly in semi‐arid and arid areas. In this study, we integrated the soil moisture information into surface energy balance system (SEBS) for improving irrigated crop water consumption estimation. The new approach fed with the moderate resolution imaging spectro‐radiometer images mapped spatiotemporal ET on the oasis in the middle reach of the Heihe river. The daily ET outputs of the new approach were compared with those of the original SEBS using the eddy correlation observations, and the results demonstrate that the modified SEBS remedied the shortcoming of general overestimating ET without regard to soil water stress. Meanwhile, the crop planting structure and leaf area index spatiotemporal distribution in the studied region were derived from the high‐resolution Chinese satellite HJ‐1/CCD images for helping analyse the pattern of the monthly ET (ET_monthly). The results show that the spatiotemporal variation of ET_monthly is closely related to artificial irrigation and crop growth. Further evaluation of current irrigation water use efficiency was conducted on both irrigation district scale and the whole middle reach of the Heihe river. The results reveal that the average fraction of consumed water on irrigation district scale is 57% in 2012. The current irrigation water system is irrational because only 52% of the total irrigated amount was used to fulfil plant ET requirement and the rest of the irrigation water recharged into groundwater in the oasis in 2012. However, in view of the whole middle reach of the Heihe river, the irrigation water use efficiency could reach to 66% in 2012. But pumping groundwater for reused irrigation wastes mostly energy instead of water. An improved irrigation water allocation system according to actual ET requirement is needed to increase irrigation efficiency per cubic meter water resource in an effort to save both water and energy. Copyright © 2014 John Wiley & Sons, Ltd.     

Analysis of historical landslide time series in the Emilia‐Romagna region,northern Italy

A catalogue of historical landslides, 1951–2002, for three provinces in the Emilia‐Romagna region of northern Italy is presented and its statistical properties studied. The catalogue consists of 2255 reported landslides and is based on historical archives and chronicles. We use two measures for the intensity of landsliding over time: (i) the number of reported landslides in a day (D_L) and (ii) the number of reported landslides in an event (S_event), where an event is one or more consecutive days with landsliding. From 1951–2002 in our study area there were 1057 days with 1 ≤ D_L ≤?45 landslides per day, and 596 events with 1 ≤ S_event ≤ 129 landslides per event. In the first set of analyses, we find that the probability density of landslide intensities in the time series are power‐law distributed over at least two‐orders of magnitude, with exponent of about ?2·0. Although our data is a proxy for landsliding built from newspaper reports, it is the first tentative evidence that the frequency‐size of triggered landslide events over time (not just the landslides in a given triggered event), like earthquakes, scale as a power‐law or other heavy‐tailed distributions. If confirmed, this could have important implications for risk assessment and erosion modelling in a given area. In our second set of analyses, we find that for short antecedent rainfall periods, the minimum amount of rainfall necessary to trigger landslides varies considerably with the intensity of the landsliding (D_L and S_event); whereas for long antecedent periods the magnitude is largely independent of the cumulative amount of rainfall, and the largest values of landslide intensity are always preceded by abundant rainfall. Further, the analysis of the rainfall trend suggests that the trigger of landslides in the study area is related to seasonal rainfall. Copyright © 2010 John Wiley & Sons, Ltd.     

Concentration–discharge relationships in the coal mined region of the New River basin and Indian Fork sub‐basin,Tennessee, USA

For many basins, identifying changes to water quality over time and understanding current hydrologic processes are hindered by fragmented and discontinuous water‐quality and hydrology data. In the coal mined region of the New River basin and Indian Fork sub‐basin, muted and pronounced changes, respectively, to concentration–discharge (C–Q) relationships were identified using linear regression on log‐transformed historical (1970s–1980s) and recent (2000s) water‐quality and streamflow data. Changes to C–Q relationships were related to coal mining histories and shifts in land use. Hysteresis plots of individual storms from 2007 (New River) and the fall of 2009 (Indian Fork) were used to understand current hydrologic processes in the basins. In the New River, storm magnitude was found to be closely related to the reversal of loop rotation in hysteresis plots; a peak‐flow threshold of 25 cubic meters per second (m³/s) segregates hysteresis patterns into clockwise and counterclockwise rotational groups. Small storms with peak flow less than 25 m³/s often resulted in dilution of constituent concentrations in headwater tributaries like Indian Fork and concentration of constituents downstream in the mainstem of the New River. Conceptual two or three component mixing models for the basins were used to infer the influence of water derived from spoil material on water quality. Copyright © 2012 John Wiley & Sons, Ltd.     

Quantifying the impact of landscape changes on hydrological variables in the alpine and cold region using hydrological model and remote sensing data

Quantifying the impact of landscape on hydrological variables is essential for the sustainable development of water resources. Understanding how landscape changes influence hydrological variables will greatly enhance the understanding of hydrological processes. Important vegetation parameters are considered in this study by using remote sensing data and VIC-CAS model to analyse the impact of landscape changes on hydrology in upper reaches of the Shule River Basin (URSLB). The results show there are differences in the runoff generation of landscape both in space and time. With increasing altitude, the runoff yields increased, with approximately 79.9% of the total runoff generated in the high mountains (4200–5900 m), and mainly consumed in the mid-low mountain region. Glacier landscape produced the largest runoff yields (24.9% of the total runoff), followed by low-coverage grassland (LG; 22.5%), alpine cold desert (AL; 19.6%), mid-coverage grassland (MG; 15.6%), bare land (12.5%), high-coverage grassland (HG; 4.5%) and shrubbery (0.4%). The relative capacity of runoff generation by landscapes, from high to low, was the glaciers, AL, LG, HG, MG, shrubbery and bare land. Furthermore, changes in landscapes cause hydrological variables changes, including evapotranspiration, runoff and baseflow. The study revealed that HG, MG, and bare land have a positive impact on evapotranspiration and a negative impact on runoff and baseflow, whereas AL and LG have a positive impact on runoff and baseflow and a negative impact on evapotranspiration. In contrast, glaciers have a positive impact on runoff. After the simulation in four vegetation scenarios, we concluded that the runoff regulation ability of grassland is greater than that of bare land. The grassland landscape is essential since it reduced the flood peak and conserved the soil and water.     

Assessment of the geometry and volumes of small surface water reservoirs by remote sensing in a semi-arid region with high reservoir density

Water fluxes in highly impounded regions are heavily dependent on reservoir properties. However, for large and remote areas, this information is often unavailable. In this study, the geometry and volume of small surface reservoirs in the semi-arid region of Brazil were estimated using terrain and shape attributes extracted by remote sensing. Regression models and data classification were used to predict the volumes, at different water stages, of 312 reservoirs for which topographic information is available. The power function used to describe the reservoir shapes tends to overestimate the volumes; therefore, a modified shape equation was proposed. Among the methods tested, four were recommended based on performance and simplicity, for which the mean absolute percentage errors varied from 24 to 39%, in contrast to the 94% error achieved with the traditional method. Despite the challenge of precisely deriving the flooded areas of reservoirs, water management in highly reservoir-dense environments should benefit from volume prediction based on remote sensing.