基于MODIS的积雪时空变化与CMADS气象因子相关性研究——以塔什库尔干河流域为例

积雪是塔什库尔干河流域宝贵的资源,了解流域融雪时空变化规律及其与气象、地形因素的相关关系具有重要意义。基于不同高程带、坡度和坡向的Arc GIS解译积雪覆盖数据和CMADS数据,采用方差分析和Pearson相关性分析等方法,研究不同高程带、坡度和坡向雪盖时空变化规律及其与气象因子的相关关系。结果表明:平均气温、太阳辐射和降水是影响塔什库尔干河流域积雪的主导气象因子,同时还受地形(高程、坡度、坡向)的限制;积雪覆盖率在各地形上存在明显季节差异性及月差异性,积雪覆盖率与气象因子相关度从高到低依次排序为:平均气温太阳辐射降水风速相对湿度,积雪覆盖率与前3个因素存在显著负相关关系,风速次之,与相对湿度的相关性最小。     

Impacts of topographic factors on regional snow cover characteristics

At a local scale, snow cover is influenced by terrain properties, and it affects water availability across some arid and semiarid regions. This study aimed to quantify the spatial heterogeneity of snow cover due to topographic effects based on moderate-resolution image spectroradiometer (MODIS) daily snow cover products, processed with spatial and backward temporal filters. A snow-dominant region in the middle section of the northern Tianshan Mountains in China was selected, and the snow cover ratio (SCR) and the number of snow cover days (SCD) were investigated. The results suggest that MODIS images are biased toward underestimation of the snow cover in the study region, and the error is primarily manifested within the elevation band of 1 500–2 500 m. The snow cover is mainly affected by elevation, and snow mostly accumulates above 3 800 m. In addition, the differences in SCR and SCD between the south- and north-facing slopes are more significant than those between the east- and west-facing slopes. Notably, the north-facing slopes have the maximum values of SCR and SCD, whereas the south-facing slopes have the minimum values of SCR and SCD. Furthermore, the impact of slope gradients on snow cover varies across seasons. Snow cover on a sloped surface decreases with the slope gradient during winter, while it tends to increase with the slope gradient during the other seasons. Overall, this study presents a useful perspective on the variance in regional snow cover and provides guidance for the water resources management of snow meltwater with different terrain features.     

1979—2018年雅砻江中上游积雪时空变化及影响因素分析

基于1979—2018年积雪深度卫星数据及同期的格点型降水和气温观测资料,分析了雅砻江中上游积雪时空分布及其影响因子,利用相关分析法分析气候因子与地形因子对积雪深度时空分布的影响及贡献程度,采用趋势分析法研究积雪特征和气候因子的时间变化规律。结果表明：雅砻江中上游降水、气温、高程、坡度和坡向对年均雪深空间分布的贡献率分别为0.218、0.453、0.206、0.080和0.043,气候因子的贡献程度明显高于地形因子;研究区积雪期主要集中在10月至次年5月,年降水量和年均气温呈显著上升趋势,西北部和东南部积雪期变暖现象明显,绝大部分区域年均雪深表现为不明显的减少趋势;积雪深度与降水量的相关系数多为正值,与气温的相关系数均为负值,在区域中下游表现的相关性较强,各影响因子与积雪深度的相关程度由强到弱依次为气温、高程、坡度、降水和坡向。     

KM河流域融雪径流与积雪面积-气温关系分析

为更好地探明气候变化下高寒山区积雪的消融规律及其对径流的影响,以及积雪面积、气温、径流之间存在的关系,本文根据2006-2009年的8日合成MODIS积雪产品数据,以及同时期出山口水文站的径流、气温数据,分析了KM河流域的融雪径流特征,定量分析了积雪面积、气温与融雪径流的关系。结果表明:进入3月份,海拔≤2 000 m积雪已经消融完结;在3月10日前后,海拔2 000～3 500 m积雪开始消融,到融雪期末积雪基本消融完结;在4月初,海拔3 500～4 500 m才开始融雪,到融雪期末积雪没有消融完结;海拔≥4 500 m积雪面积在融雪期内变化相对比较稳定。流域各高程带积雪面积与气温都呈抛物线关系,且相关性随高程的上升而减弱。     

A Stream Water Availability Model of Upper Indus Basin Based on a Topologic Model and Global Climatic Datasets

Integrated water resources management at river basin scales and evaluation of effects of climate change on regional water resources require quantitative estimates of space-time variability of monthly discharges within a river network. This study demonstrates that such estimates, which can be called stream water availability, for regional river basins with meager or nonexistent gauge data, can be obtained by combining continuity models of hydrological processes, flow routing, and topology of the river basin. The hydrologic processes can be adequately modeled using high quality databases of hydrologic significance. A stream water availability model is presented for Upper Indus Basin (UIB) utilizing the most up-to-date datasets for topography, temperature, precipitation, net radiation, land cover, soil type, and digital atlas. Multiple datasets have been evaluated and the ones with best accuracy and temporal coverage have been selected for the final model. Upper Indus River and its major tributaries are highly significant in regional water resources management and geopolitics. However, UIB is a poorly studied and largely ungauged river basin with an area of 265,598 km² and extremely rugged topography. Several factors, the chief ones being the challenging terrain and the trans-boundary nature of the basin, have contributed to this knowledge gap. Hydro-climatologically it is a complex basin with a significant cryospheric component. The spatial and temporal variation of the principal climatic variables, namely precipitation, net radiation, and temperature has been thoroughly accounted for in the development of a stream water availability model based on a process model coupled with a topologic model and a linear reservoir model of river flow routing. Model calculations indicate that there are essentially two hydrologic regimes in UIB. The regime that is truly significant in contributing stream flows, originates from the UIB cryosphere containing outstanding glaciers and snowfields. The other regime, generated from wet precipitation and melt water from seasonal snow covers is insignificant due to high rates of infiltration and evaporation in the semi-desert environment prevailing at elevations below perennial snow and ice covers. In general, the modeled stream flow characteristics match with the sparse discharge measurements that are available. Flow in the Indus considerably increases at its confluence with Shyok River and further downstream where other tributaries form the north join the main stem. At or near the outlet of the basin stream flow can vary from less than 800 m³ s^− 1 in the winter and spring to nearly 8,000 m³ s^− 1 in the peak summer and can persist to over 1,500 m³ s^− 1 in the autumn. The importance of snow and glacial melt in Indus River discharge is apparent and any global or regional climate change affecting the equilibrium line elevation of the snow fields in the Karakoram will have a profound influence on the water availability in the Indus. Estimates are made for per capita water availability in Ladakh and Gilgit-Baltistan territories, controlled by India and Pakistan respectively. Geopolitical significance and climate change effects are discussed briefly.     

Assessment of Snowmelt Runoff Using Remote Sensing and Effect of Climate Change on Runoff

Runoff regimes in Himalayan basins are controlled mainly by melting of snow and ice cover. The air temperature is the principal variable to estimate the importance of the melting of the snow cover when using snowmelt runoff model. Changes in temperature will ultimately affect stream flow and snow/ice melt runoff in particular. Global atmospheric general circulation models (GCMs) have been developed to simulate the present climate and used to predict future climatic changes and its effect. These GCMs have certain disadvantages, therefore another simple approach of hypothetical scenarios have been developed and successfully demonstrated in this study to investigate the effect of changes in temperature. Adopted plausible climate scenarios included three temperature scenarios (T + 1, T + 2, T + 3°C). The effect of these changes has been studied on the stream flow which has contribution from snowmelt, rainfall and base flow in the Satluj basin. It was observed that with the increase in temperature there is not much change in total stream flow, but the distribution of stream flow have changed. More snowmelt runoff occurred earlier due to increased snow melting however, reduced in the monsoon months.     

Impact of renewed solar dimming on streamflow generation in monsoon dominated tropical river basins

From 1950s to 1980s, various observational studies around the globe found a significant decrease in surface solar radiation (SSR), which reversed in late 1980s for most of the countries including India. SSR observations at 12 stations located across India revealed that a much stronger dimming has reappeared during the last decade (2006–2015) after a brightening during 1996–2005. In the present study, effects of renewed solar dimming on actual evapotranspiration and runoff were analyzed using a semi-distributed hydrological model, Soil and Water Assessment Tool (SWAT) in 24 river basins (ranging from 1260 to 40000 km²) located in peninsular India. For these river basins, calibration (2003–2009) and validation (2010–2014) were performed using the observed daily discharge data, obtained from water resources information system (WRIS) of India, with a 3 year warm up period (2000–2002). The sequential uncertainty domain parameter fitting algorithm (SUFI-2) of SWAT-CUP (calibration and uncertainty program) was used with modified Nash–Sutcliffe efficiency (MNS) as the objective function to calibrate 13 model parameters, which can potentially affect streamflow. In nearly all the river basins, the p- and r-factor of 95 percentage prediction uncertainty (PPU) were more than 0.7 and less than 1, respectively. At daily timescale, MNS values were more than 0.5 in most of the river basins, reaching up to 0.66 and 0.71 during calibration and validation periods, respectively. Calibrated model was used to analyze the water balance of these river basins and different sets of experiments (with observed SSR trends) were performed to find SSR impacts on it. The model was simulated with and without the observed declines in SSR trends. The average change in SSR (in terms of evaporation equivalent) was −267.93 ± 100.92 mm/day/year (−5.62 ± 2.12%) with maximum reaching up to −417.12 mm/day/year (−8.99%). Due to this SSR change, actual evaporation was reduced resulting in 18.97 ± 9.78 mm/day/year (4.13 ± 2.50%) change in percolation. The percolation changes were higher for river basins having areas covered by forests and cropland/woodland, and having loam and sandy-clay soils. The increase in runoff generated was 6.90 ± 3.42 mm/day/year (2.14 ± 1.58%) with a maximum of 15.25 mm/day/year (7.56%) whereas corresponding increase in streamflow was found to be 9.93 ± 5.27 mm/day/year(4.21 ± 2.38%) with a maximum of 26.71 mm/day/year (11.86 %). The study reveals that the recent observed SSR changes are significant enough to have resulted in increased streamflow in the monsoon dominated tropical river basins of India.     

Monitoring changes of snow cover,lake and vegetation phenology in Nam Co Lake Basin (Tibetan Plateau) using remote SENSING (2000–2009)

The changes of environmental factors such as snow cover, vegetation and hydrologic regime of lakes can reflect ecosystem responses to changing climate. A series of satellite imagery-based environmental data archives including variations in snow cover, vegetation phenology and lake level were mapped in the Nam Co Lake Basin for the period 2000–2009. Results of the synthesis indicate that throughout this period, the average annual snow cover was 19.87% of the total basin, and there is an obvious relation between the elevation and a clear decreasing southeast–northwest trend in snow-cover persistence. Snow mainly happens from October to May. The multi-year mean water storage of Nam Co Lake is 86.40 × 10⁹ m³, with a lake level increase of approximately 2.06 m during the study period. Vegetation phenology showed obvious variation with advanced start of season (SOS) and slightly extended duration of season (DOS). The mean DOS for the Nam Co Lake Basin was 154 days from 2000 to 2009. Affected by air temperature, the SOS dates coincided with snowmelt. The seasonal-variability of climate factors was also studied. The satellite-derived continuous and multiple datasets offer the advantage of monitoring the temporal and spatial trends of each of these metrics and mapping extensive, remote in mountainous areas with no in-situ data such as represented by the Tibetan Plateau.     

Modeling the Linkage Between River Water Quality and Landscape Metrics in the Chugoku District of Japan

The present study was conducted using secondary database, remote sensing, geographical information system (GIS) and multivariate analysis tools in order to develop Multiple Linear Regression (MLR) models that could be able to predict level of water quality variables using compositional and spatial attributes of land cover in the river basins. The study encompasses 21 river basins with 32 000 Km² area, located in the Chugoku district in West Japan. Biological Oxygen Demand (BOD), pH, Dissolved Oxygen (DO), Suspended Solid (SS), Total Nitrogen (TN), and Total Phosphorus (TP) were considered as water quality variables of the stream. Satellite data was used to generate the land cover map of the study area. MLR models were developed using the compositional (%) and spatial attributes (landscape metrics) of the land cover at watershed and class levels for representing the land cover-stream water quality linkage. The results of the MLR modeling using the land cover data at the class level revealed that 92%, 74% and 62% of the total variations in concentration of DO, pH and TP were explained by changes in the measure of the spatial attributes of the land cover at the class level in the study area. These models can help local and regional land managers to understand the relationships between the compositional attribute (%) and the spatial features of the land cover and river water quality and would be applied in formulating plan for watershed-level management.     

NDVI对NDSI积雪信息提取阈值的影响研究

雪盖面积是水文,气候模型的重要输入参数之一。通过遥感可以实时获取大尺度范围内的积雪覆盖信息,弥补地面观测在空间范围上的不足。中等分辨率光谱成像仪(MODIS)数据因其具有高光谱、高时间分辨率的特点,越来越多的应用在积雪信息提取上。本文在总结现有方法的基础上,分析了现有方法的优劣,建立了一个新的积雪信息、归一化植被指数(NDVI)、归一化雪盖指数(NDSI)的线性关系模型。对该模型进行了精度验证。结果表明:在植被覆盖率较高的地区,该方法减少了8.7%的误分类像元。     

Impact of Declining Trend of Flow on Harike Wetland,India

Harike wetland in the Indian state of Punjab is formed by a barrage built downstream on the confluence of rivers Satluj and Beas, with the idea of storing and providing irrigation and drinking water to parts of Southern Punjab and adjoining Rajasthan. Due to decrease in flow at Harike and deforestation etc. in the catchment area, the wetland is reducing in the last few years. In this study, the analysis of rainfall/runoff data has been carried out to see the effect of decreasing trend of runoff on wetland area. Wetland area has been delineated using remote sensing technique. The analysis of rainfall, discharge and ground water level showed that the flow pattern is decreasing at Harike. The remote sensing data revealed that the wetland area has reduced approximately 30% over the last 13 years.     

Spatial Distribution and Seasonal Variability of Rainfall in a Mountainous Basin in the Himalayan Region

The average distribution of precipitation provides essential input for understanding the hydrological process. The role of complex topography in mountainous basins makes the spatial distribution of precipitation different than the plain areas. Besides the rugged topography, the Himalayan basins also face the problem of limited physical accessibility and data availability. In this study, seasonal and annual distribution of rainfall with elevation and distance from the lower most station (Akhnoor) has been studied for the Chenab basin (western Himalayas). The study basin covers all the three ranges i.e. outer, middle and greater Himalayas. The rainfall stations are grouped into windward and leeward categories. The trends of spatial distribution of rainfall are discussed in detail. Attempts are also made to investigate the impact of reduced network on the mean annual rainfall of the Chenab basin. A reduction in rain gauges from 42 to 19 has resulted in an increase in the estimate of mean annual rainfall by 14% with respect to the estimate obtained using 42 stations network.     

三江并流区德钦县积雪面积时空变化分析

德钦县积雪储量是滇西北重要的淡水资源,对三江并流区的河流补给、缓解水资源压力起着重要的调节作用。基于10景TM、ETM+、OLI影像、DEM和气象数据,采用NDSI指数提取了德钦县1989,1996,2000,2009,2015年5个时期的积雪信息,分析了积雪面积时空变化特征,探究了积雪与气候变化的关系。结果表明:时间上,积雪面积呈现逐年衰退的变化趋势;26 a来,积雪面积由819.884 7 km~2减少到619.115 4 km~2,减少了24.5%,年平均减少7.44 km~2;空间上,不同海拔带内积雪面积都在减少,在海拔1 464～3 000 m积雪面积衰退最为激烈和明显,海拔3 001～4 000 m积雪面积变化也较为迅速,积雪主要分布在海拔4 001～6 740m,且缩减速度较为缓慢;26 a来区域温度升高和降水减少加速了积雪面积减少。     

Estimation of Peak Flood Discharges at Ungauged Sites Across Turkey

The reliable forecasting of the peak flood discharge at river basins is a common problem, and it becomes more complicated when there is inadequate recorded data. The statistical methods commonly used for the estimation of peak flood discharges are generally considered to be inadequate because of the complexity of this problem. Recently, genetic programming (GP) which is a branch of soft computing methods has attracted the attention of the hydrologists. In this study, gene-expression programming (GEP) and linear genetic programming (LGP), which are extensions to GP, in addition to logistic regression (LR) were employed in order to forecast peak flood discharges. The study covered 543 ungauged sites across Turkey. Drainage area, elevation, latitude, longitude, and return period were used as the inputs while the peak flood discharge was the output. Model comparison results revealed that GEP predicted the peak flood discharges with R ²?=?57.4?% correlation, LGP with 56?% and LR model with 42.3?%, respectively. The peak flood discharges in all river basins can now be determined using the single equation provided by the GEP model.     

Operational River Discharge Forecasting with Support Vector Regression Technique Applied to Alpine Catchments: Results,Advantages, Limits and Lesson Learned

The main objective of this study is to derive a flexible approach based on machine learning techniques, i.e. Support Vector Regression (SVR), for monthly river discharge forecasting with 1-month lead time. The proposed approach has been tested over 300 alpine basins, in order to explore advantages and limits in an operational perspective. The main relevant input features in the forecast performances are the snow cover areas and the discharge behavior of the previous years. Forecasts obtained by training SVR machine on single gauging stations show better performances than the average of the previous 10 years, considered as benchmark, in 94% of the cases, with a mean improvement of about 48% in root mean square error. In case of poorly gauged basins, to increase the number of training sample, multiple basins have been considered to train the SVR machine. In this case, performances are still better than the benchmark, even if worse than those of SVR machine trained on single basins, with a decrease of the performances ranging from 13% to 54%.     

Runoff Changes in the Šumava Mountains (Black Forest) and the Foothill Regions: Extent of Influence by Human Impact and Climate Change

The main aim of our research project was to determine the extent to which the outflow can be influenced by human interventions in three selected water basins in the Šumava Mountains (Black Forest) and its foothills. The rainfall-runoff analyses using both the single-mass and double-mass curves over the period of hydrologic observations were taken as a preliminary methodology. Standard statistical testing methods Wilcoxon and Mann–Kendall non-parametric tests were applied to detect the trends. Besides mean discharge, precipitation, snow and air temperature trends, analysis of land cover change and human impact on the river network and development of drainage areas were also carried out. The greatest deviations were widely observed in the period between the second half of the 1970s and the first half of the 1980s. The whole system came slowly back to its initial condition in the early 1990s. The runoff trend deviation was related to natural and human factors, mainly to current climatic changes, river network modification and changes of land cover.     

湖北竹山县植被覆盖度与地形因子的空间关系分析

以湖北省竹山县为研究区,运用RS和GIS技术,并借助空间叠加分析,研究区内植被覆盖度分别随不同地形因子变化的分布特征及相关性。研究结果表明:①研究区植被覆盖度整体情况良好,以高覆盖度为主,占比为80.45%。②植被覆盖度随高程的升高呈增加趋势,在高程大于2 000 m时达到最大值,其值为91.99%;植被覆盖度随坡度增大呈先增大后减少趋势,在35°～45°坡度带达最大值;在坡向上,阴坡植被覆盖度整体稍高于阳坡。③不同植被覆盖度与各地形因子级的相关性强度不同,对于各级植被覆盖度,坡度和坡向对其影响明显高于高程,植被覆盖度越高,坡度和坡向两者对其影响越明显;高程与各级植被覆盖度的相关性无明显的规律。     

闽赣自然植被覆盖格局及其驱动机制分析

自然植被覆盖格局的研究对生态环境可持续发展具有十分重要的意义。以福建和江西2省为研究区,基于30 m分辨率的植被指数数据,运用地理加权回归模型,结合自然环境和人类活动因素,探索分析闽赣地区自然植被覆盖格局特征及其驱动机制。研究表明:①地形是影响闽赣自然植被覆盖程度的主要因素,江西和福建2省自然植被覆盖程度分别在高程[100,300)m和[300,500)m区间,与高程相关性最大;在坡度25°和10°以上,与坡度的相关性减弱。②江西主干河流附近农田和城镇交错,福建主干河流附近城镇和林地嵌套,2省自然植被覆盖程度与距河流距离的相关性差异显著。③距离城镇居民点5 km和4 km分别为江西和福建2省自然植被覆盖程度与距居民点距离相关性最弱处。     

中国积雪时空演变特征及其与海气环流因子的时序关联性

积雪时空变化作为表征增暖背景下冰冻圈的重要指标之一,在全球气候变化科学研究中具有重要作用。采用1961—2016年中国545个气象观测站的积雪日数、积雪深度日值数据和3个海气环流因子,通过多种统计方法,诊断中国积雪的时空演变特征及积雪与海气环流因子的时频相关性。结果表明:(1)1961—2016年中国积雪日数和深度在波动中呈缓慢增加趋势,且具有明显的年代分段变化特征。中国积雪日数和深度均具有30a和50a的周期振荡特征,其中积雪深度在1969年发生突变。(2)1961—2016年中国气候态积雪日数和深度具有明显的南低北高的空间分异特征,尤其是东北、内蒙古东部和新疆北部的积雪较多分布。中国积雪多寡具有明显的年代和区域分异特征。(3)在变化趋势上,1961—2016年中国积雪日数在东北和内蒙古东部趋于增多;而积雪深度除上述区域外,在华北、西北和江淮东部也趋于增多。在波动特征上,中国积雪日数和深度在华南南部、云南和四川东部波动较大,其它地区波动相对较小。(4)与海气因子时频关联性上,中国积雪与不同海气因子关联性不同,且与同一因子在不同时段的关联性也有一定差异。     

Developing a Snowmelt Forecast Model in the Absence of Field Data

In data poor regions predicting water availability is a considerable challenge for water resource managers. In snow-dominated watersheds with minimal in situ measurements, satellite imagery can supplement sparse data networks to predict future water availability. This technical note presents the first phase of an operational forecast model in the data poor Elqui River watershed located in northern Central Chile (30°S). The approach applies remotely-sensed snow cover products from the Moderate Resolution Imaging Spectrometer (MODIS) instrument as the first order hydrologic input for a modified Snowmelt Runoff Model. In the semi-arid Elqui River, snow and glacier melt are the dominant hydrologic inputs but precipitation is limited to up to six winter events annually. Unfortunately winter access to the Andean Cordillera where snow accumulates is incredibly challenging, and thus measurements of snowpack are extremely sparse. While a high elevation snow monitoring network is under development, management decisions regarding water resources cannot wait as the region is in its eighth consecutive year of drought. Our model applies a Monte Carlo approach on monthly data to determine relationships between lagged changes in snow covered area and previous streamflow to predict subsequent streamflow. Despite the limited data inputs the model performs well with a Nash-Sutcliffe Efficiency and R² of 0.830 and 0.833 respectively. This model is not watershed specific and is applicable in other regions where snow dominates hydrologic inputs, but measurements are minimal.