Characteristics of snow cover in the Hindukush,Karakoram and Himalaya region using Landsat satellite data

Estimation of snow cover characteristics (snow grain size, snow contamination, snow depth and liquid water content) from satellite data are important components for many hydrological models used for the water resource management. This research aimed to use Landsat ETM+ (Enhanced Thematic Mapper Plus) data to estimate the snow pack characteristics in northern Pakistan. The Normalized Difference Snow Index (NDSI), Snow Contamination Index (SCI) and Snow Grain Size Index (SGI) are applied to estimate the snow cover characteristics in northern Pakistan for the first time. Qualitative maps are generated to show the snow cover distribution, snow contamination concentration and snow grain size distribution over snow cover area. Our results show that NDSI, SCI and SGI can be effectively used to identify snow area, contaminated snow and ageing snow. Furthermore, the results of the current study indicate that in the HKH region 99.8% of the snow is least contaminated whereas 94.50% of the area has fine snow grain size. As no such attempt in the past has been made in northern Pakistan, it is envisaged that the results of this study will be helpful in planning remote sensing data for the water resource management and in characterizing the snow cover for the climate change applications in the region. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluation of snow models in terrestrial biosphere models using ground observation and satellite data: impact on terrestrial ecosystem processes

Snow is important for water management, and an important component of the terrestrial biosphere and climate system. In this study, the snow models included in the Biome‐BGC and Terrestrial Observation and Prediction System (TOPS) terrestrial biosphere models are compared against ground and satellite observations over the Columbia River Basin in the US and Canada and the impacts of differences in snow models on simulated terrestrial ecosystem processes are analysed. First, a point‐based comparison of ground observations against model and satellite estimates of snow dynamics are conducted. Next, model and satellite snow estimates for the entire Columbia River Basin are compared. Then, using two different TOPS simulations, the default TOPS model (TOPS with TOPS snow model) and the TOPS model with the Biome‐BGC snow model, the impacts of snow model selection on runoff and gross primary production (GPP) are investigated. TOPS snow model predictions were consistent with ground and satellite estimates of seasonal and interannual variations in snow cover, snow water equivalent, and snow season length; however, in the Biome‐BGC snow model, the snow pack melted too early, leading to extensive underpredictions of snow season length and snow covered area. These biases led to earlier simulated peak runoff and reductions in summer GPP, underscoring the need for accurate snow models within terrestrial ecosystem models. Copyright © 2007 John Wiley & Sons, Ltd.     

Retrieval of suspended sediment concentrations in large turbid rivers using Landsat ETM+: an example from the Yangtze River,China

Suspended sediment concentration (SSC) is a critical parameter in the study of river sediment transport and water quality variation, but traditional measurement methods are costly and time‐consuming. This paper is focused on presenting a methodology that may be useful in estimating SSC which is of key importance in process geomorphology and hydrology. In previous studies, remote sensing has been applied to estimate the SSC of sea waters as well as low turbid inland waters like lakes, reservoirs and short river reaches visible within a single Landsat satellite image coverage. Rivers, especially highly turbid large rivers, have largely been ignored. The dataset used in this paper includes measured SSC and multi‐temporal Landsat ETM+ images covering most part of the Yangtze River. Using an effective easy‐to‐use atmospheric correction method that does not require in situ atmospheric conditions, retrieved water reflectance of Band 4 was found to be a good SSC indicator within the large SSC range 22–2610 mg l^–1. The newly developed regression relation between SSC and water reflectance of Band 4 appears to be able to provide a relatively accurate SSC estimate directly from Landsat ETM+ images for the Yangtze River from the upper, the middle to the lower reaches. With the relation it is possible to estimate or map out SSC dynamics of large rivers which lack SSC data due to constraints of conventional measurements. Copyright © 2009 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.     

Estimation of irrigation return flow on monthly time resolution using SWAT model under limited data availability

ABSTRACT

This study is an assessment of the return flow ratio of an irrigation abstraction using the flow records of a downstream stream-gauging station, with the example of the Kozluk scheme irrigated by diverted water from Garzan Creek flowing through the southeastern region of Turkey. In the planning reports of the major dam projects of the region, an unverified return ratio was assumed in eliminating the influence of this irrigation on the flow measurements of Garzan Creek. The correctness of this assumed return ratio is evaluated by analysing the monthly streamflow measurements of the Besiri station through a Soil and Water Assessment Tool (SWAT) model constructed with the coarse-scale topography, land use and soil data from open source databases. The results show the necessity of irrigation project-based return flow analyses using regional fine-scale datasets, instead of rule-of-thumb assumptions, to determine the effects of irrigation activities on flow regimes more accurately.     

基于多源卫星数据的小型水体蓝藻水华联合监测——以天津于桥水库为例

近年来水体富营养化呈扩张趋势,蓝藻水华不仅在太湖等大型湖泊频发,水面面积较小的天津于桥水库等也形势严峻,亟需加强卫星遥感监测.但是,以往在太湖等业务化使用非常成功的MODIS等卫星数据(约500 m),由于空间分辨率较低,难以满足小型水体的监测要求;而Landsat-8等空间分辨率较高的卫星数据(30 m),通常重返周期较长,无法满足水华高频监测需求.本文以天津市于桥水库(面积约80 km2)为研究区,针对常用的卫星数据,从空间、时间、光谱范围和数据可获取性共4个方面,评价不同卫星数据蓝藻水华监测能力和算法,同时对不同卫星监测结果一致性进行评估.结果表明:(1)筛选出国产HJ-1A\B CCD、GF-1 WFV和美国Landsat-8 OLI这3种卫星波段合适,空间分辨率较高,适用于桥水库蓝藻水华监测,但考虑到其重返周期较长,建议多星联合观测;(2)各个卫星监测结果与卫星影像目视解译结果基本一致,均方根误差和相对误差均分别控制在0.78 km2和4.9%以内;(3)不同卫星监测结果一致性良好,一致性精度达到99.5%;(4)根据历史影像结果,发现于桥水库2016年水质开始呈富营养化,藻华现象在夏、秋两季最为严重.研究表明,针对小型水面水体蓝藻水华监测,利用较高分辨率数据联合监测,是一种有效的替代策略,今后可在更多小型水域推广.     

Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach

Hydrologic model development and calibration have continued in most cases to focus only on accurately reproducing streamflows. However, complex models, for example, the so‐called physically based models, possess large degrees of freedom that, if not constrained properly, may lead to poor model performance when used for prediction. We argue that constraining a model to represent streamflow, which is an integrated resultant of many factors across the watershed, is necessary but by no means sufficient to develop a high‐fidelity model. To address this problem, we develop a framework to utilize the Gravity Recovery and Climate Experiment's (GRACE) total water storage anomaly data as a supplement to streamflows for model calibration, in a multiobjective setting. The VARS method (Variogram Analysis of Response Surfaces) for global sensitivity analysis is used to understand the model behaviour with respect to streamflow and GRACE data, and the BORG multiobjective optimization method is applied for model calibration. Two subbasins of the Saskatchewan River Basin in Western Canada are used as a case study. Results show that the developed framework is superior to the conventional approach of calibration only to streamflows, even when multiple streamflow‐based error functions are simultaneously minimized. It is shown that a range of (possibly false) system trajectories in state variable space can lead to similar (acceptable) model responses. This observation has significant implications for land‐surface and hydrologic model development and, if not addressed properly, may undermine the credibility of the model in prediction. The framework effectively constrains the model behaviour (by constraining posterior parameter space) and results in more credible representation of hydrology across the watershed.     

联合GNSS/RS多源数据反演三维大气水汽分布研究

大气水汽作为重要的温室气体之一, 其在全球气候变化研究及极端天气预警中起到重要作用.全球导航卫星系统(Global Navigation Satellite System, GNSS)水汽层析技术作为重构高时空分辨率大气水汽三维分布的重要手段之一, 目前得到了快速的发展和应用.但受GNSS观测信号几何特征的限制, 传统水汽层析模型仍无法解决观测分布不均和空余体素块多的问题.本文首次利用遥感(Remote Sensing, RS)卫星高水平分辨率信号的观测几何优势, 提出一种联合GNSS/RS多源数据的水汽层析方法, 用于反演高精度的三维大气水汽分布.利用徐州地区GNSS观测数据与MODIS、MERSI两种遥感水汽数据对该方法的可行性及精度进行验证.结果表明: 相比于传统GNSS层析模型, GNSS/RS层析模型的观测信号数量和网格穿刺率分别提高了38.41%和55.56%.分别以研究区域的探空站(58027)水汽廓线和ERA5再分析资料为参考值, 发现GNSS/RS层析模型的各类精度指标均优于传统模型, 其中平均均方根误差分别提高了29.68%和20.31%, 并且在不同高度层上GNSS/RS层析水汽产品质量也优于传统模型, 这表明GNSS/RS层析模型可有效改善层析结果质量, 反演出精确、可靠的大气水汽三维分布信息.

     

Evaluation of remote sensing‐based evapotranspiration estimates using a water transfer numerical simulation under different vegetation conditions in an arid area

Daily actual evapotranspiration (AET) and seasonal AET values are of great practical importance in the management of regional water resources and hydrological modelling. Remotely sensed AET models and Landsat satellite images have been used widely in producing AET estimates at the field scale. However, the lack of validation at a high spatial frequency under different soil water conditions and vegetation coverages limits their operational applications. To assess the accuracies of remote sensing‐based AET in an oasis‐desert region, a total of 59 local‐scale daily AET time series, simulated using HYDRUS‐1D calibrated with soil moisture profiles, were used as ground truth values. Of 59 sampling sites, 31 sites were located in the oasis subarea and 28 sites were located in the desert subarea. Additionally, the locally validated mapping evapotranspiration at high resolution with internalized calibration surface energy balance model was employed to estimate instantaneous AET values in the area containing all 59 of the sampling sites using seven Landsat subimages acquired from June 5 to August 24 in 2011. Daily AET was obtained using extrapolation and interpolation methods with the instantaneous AET maps. Compared against HYDRUS‐1D, the remote sensing‐based method produced reasonably similar daily AET values for the oasis sites, while no correlation was observed for daily AET estimated using these two methods for the desert sites. Nevertheless, a reasonable monthly AET could be estimated. The correlation analysis between HYDRUS‐1D‐simulated and remote sensing‐estimated monthly AET values showed relative root‐mean‐square error values of 15.1%, 12.1%, and 12.3% for June, July, and August, respectively. The root mean square error of the summer AET was 10.0%. Overall, remotely sensed models can provide reasonable monthly and seasonal AET estimates based on periodic snapshots from Landsat images in this arid oasis‐desert region.