蒸散发是土壤-植被-大气系统中水循环和能量交换的主要组成部分,准确估算区域蒸散发对农业用水调度与水资源的管理至关重要。利用MODIS数据产品结合地面气象站的观测资料,基于能量平衡原理建立的SEBAL(Surface Energy Balance Algorithms for Land)模型对西北农牧交错带2015年生长季(4-10月)的地表蒸散发量进行反演研究,并用Penman-Monteith(P-M)公式结合作物系数对模型的估算结果进行对比,结果表明:SEBAL模型估算结果与P-M公式之间的平均绝对误差为0.79mm/d,均方根误差为0.94mm/d,R^2=0.76,整体反演值偏高,但基本能满足本地区的研究需求。生长季区域日均蒸散发的变化范围为0.12-10.66mm/d,日蒸散量均值为4.31mm/d,呈东北、西南部较高,西部偏低的空间分布特征。将蒸散发估算值与地表特征参数统计分析发现蒸散发与NDVI和地表净辐射之间呈正相关,与地表温度和地表反照率之间呈负相关;不同土地利用/覆被类型的日蒸散发量由大到小依次为:耕地、林地、未利用地与草地。
Over the past decades,a number of water sciences and management programs have been developed to better understand and manage the water cycles at multiple temporal and spatial scales for various purposes,such as ecohydrology,global hydrology,sociohydrology,supply management,demand management,and integrated water resources management(IWRM).At the same time,rapid advancements have also been taking place in tracing,mapping,remote sensing,machine learning,and modelling technologies in hydrological research.Despite those programs and advancements,a water crisis is intensifying globally.The missing link is effective interactions between the hydrological research and water resource management to support implementation of the UN Sustainable Development Goals(SDGs)at multiple spatial scales.Since the watershed is the natural unit for water resources management,watershed science offers the potential to bridge this missing link.This study first reviews the advances in hydrological research and water resources management,and then discusses issues and challenges facing the global water community.Subsequently,it describes the core components of watershed science:(1)hydrological analysis;(2)water-operation policies;(3)governance;(4)management and feedback.The framework takes into account water availability,water uses,and water quality;explicitly focuses on the storage,fluxes,and quality of the hydrological cycle;defines appropriate local water resource thresholds through incorporating the planetary boundary framework;and identifies specific actionable measures for water resources management.It provides a complementary approach to the existing water management programs in addressing the current global water crisis and achieving the UN SDGs.
The study of snow and ice melt （SIM） is important in water-scarce arid regions for the assessment of water supply and quality. These studies involve unique difficulties, especially in the calibration of hydro-models because there is no direct way to continuously measure the SIM at hydrostations. The recursive digital filter （RDF） and the isotopic hydro-geochemical method （IHM） were coupled to separate the SIM from eight observed series of alpine streamflows in northwestern China. Validation of the calibrated methods suggested a good capture of the SIM characteristics with fair accuracy in both space and time. Applications of the coupled methods in the upper reaches of the Hei River Basin （HRB） suggested a double peak curve of the SIM fraction to streamflow for the multi-component recharged （MCR） rivers, while a single peak curve was suggested for the rainfall-dominant recharged （RDR） rivers. Given inter-annual statistics of the separation, both types of the alpine rivers have experienced an obvious decrease of SIM since 196os. In the past 10 years, the SIM in the two types of rivers has risen to the levels of the 1970s, but has remained lower than the level of the 1960s. The study provided a considerable evidence to quantify the alpine SIMbased on the separation of observed data series at gauge stations. Application of the coupled method could be helpful in the calibration and validation of SIM-related hydro-models in alpine regions.
LI Chang-binQI Jia-guoYANG Lin-shanYANG Wen-jinZHU Gao-fengWANG Shuai-bing
This paper presents a new correction method, "instant correction method(ICM)", to improve the accuracy of numerical prediction products(NPP) and provide weather variables at grid cells. The ICM makes use of the continuity in time of the forecast errors at different forecast times to improve the accuracy of large scale NPP. To apply the ICM in China, an ensemble correction scheme is designed to correct the T213 NPP(the most popular NPP in China) through different statistical methods. The corrected T213 NPP(ICM T213 NPP) are evaluated by four popular indices: Correlation coefficient, climate anomalies correlation coefficient, root-mean-square-errors(RMSE), and confidence intervals(CI). The results show that the ICM T213 NPP are more accurate than the original T213 NPP in both the training period(2003–2008) and the validation period(2009–2010). Applications in China over the past three years indicate that the ICM is simple, fast, and reliable. Because of its low computing cost, end users in need of more accurate short-range weather forecasts around China can benefit greatly from the method.
