What Matters Most: Are Future Stream Temperatures More Sensitive to Changing Air Temperatures,Discharge, or Riparian Vegetation?

Simulations of stream temperatures showed a wide range of future thermal regimes under a warming climate — from 2.9°C warmer to 7.6°C cooler than current conditions — depending primarily on shade from riparian vegetation. We used the stream temperature model, Heat Source, to analyze a 37‐km study segment of the upper Middle Fork John Day River, located in northeast Oregon, USA. We developed alternative future scenarios based on downscaled projections from climate change models and the composition and structure of native riparian forests. We examined 36 scenarios combining future changes in air temperature (ΔT_air = 0°C, +2°C, and +4°C), stream discharge (ΔQ = ?30%, 0%, and +30%), and riparian vegetation (post‐wildfire with 7% shade, current vegetation with 19% shade, a young‐open forest with 34% shade, and a mature riparian forest with 79% effective shade). Shade from riparian vegetation had the largest influence on stream temperatures, changing the seven‐day average daily maximum temperature (7DADM) from +1°C to ?7°C. In comparison, the 7DADM increased by 1.4°C with a 4°C increase in air temperature and by 0.7°C with a 30% change in discharge. Many streams throughout the interior western United States have been altered in ways that have substantially reduced shade. The effect of restoring shade could result in future stream temperatures that are colder than today, even under a warmer climate with substantially lower late‐summer streamflow.     

Climatic Controls on Watershed Reference Evapotranspiration Varied during 1961–2012 in Southern China

Reference evapotranspiration (ET_o) is an important hydrometeorological term widely used in understanding and projecting the hydrological effects of future climate and land use change. We conducted a case study in the Qinhuai River Basin that is dominated by a humid subtropical climate and mixed land uses in southern China. Long‐term (1961–2012) meteorological data were used to estimate ET_o by the FAO‐56 Penman–Monteith model. The individual contribution from each meteorological variable to the trend of ET_o was quantified. We found basin‐wide annual ET_o decreased significantly (p < 0.05) by 3.82 mm/yr during 1961–1987, due to decreased wind speed, solar radiation, vapor pressure deficit (VPD), and increased relative humidity (RH). However, due to the increased VPD and decreased RH, the ET_o increased significantly (p < 0.05) in spring, autumn, and annually at a rate of 2.55, 0.56, and 3.16 mm/yr during 1988–2012, respectively. The aerodynamic term was a dominant factor controlling ET_o variation in both two periods. We concluded the key climatic controls on ET_o have shifted as a result of global climate change during 1961–2012. The atmospheric demand, instead of air temperature alone, was a major control on ET_o. Models for accurately predicting ET_o and hydrological change under a changing climate must include VPD in the study region. The shifts of climatic control on the hydrological cycles should be considered in future water resource management in humid regions.     

Adaptive Management and Climate Change Adaptation: Two Mutually Beneficial Areas of Practice

Adaptive management (AM) is a rigorous approach to implementing, monitoring, and evaluating actions, so as to learn and adjust those actions. Existing AM projects are at risk from climate change, and current AM guidance does not provide adequate methods to deal with this risk. Climate change adaptation (CCA) is an approach to plan and implement actions to reduce risks from climate variability and climate change, and to exploit beneficial opportunities. AM projects could be made more resilient to extreme climate events by applying the principles and procedures of CCA. To test this idea, we analyze the effects of extreme climatic events on five existing AM projects focused on ecosystem restoration and species recovery, in the Russian, Trinity, Okanagan, Platte, and Missouri River Basins. We examine these five case studies together to generate insights on how integrating CCA principles and practices into their design and implementation could improve their sustainability, despite significant technical and institutional challenges, particularly at larger scales. Although climate change brings substantial risks to AM projects, it may also provide opportunities, including creating new habitats, increasing the ability to quickly test flow‐habitat hypotheses, stimulating improvements in watershed management and water conservation, expanding the use of real‐time tools for flow management, and catalyzing creative application of CCA principles and procedures.     

近40年来长江干流水质变化研究

为掌握长江水质状况及其变化趋势,开展1981—2019年长江干流水质变化特征研究.系统总结了39年间长江干流地表水环境监测情况,以COD_Mn、NH₃-N和TP为研究因子,探讨了长江干流水环境质量变化规律;同时,选取有连续监测结果的断面,分析了长江上游、中游和下游不同断面近40年来的水质变化特征.结果表明:①1981—2019年,我国水环境监测迅速发展,长江干流水环境质量监测在监测点位、监测频次、监测项目和水环境质量等方面都发生了较大变化.②长江干流地表水水质总体相对较好,上游水质好于中下游,上游水体中ρ(COD_Mn)、ρ(NH₃-N)和ρ(TP)均低于中下游.③1981—2005年各江段ρ(COD_Mn)和ρ(NH₃-N)年均值变化特征不同,在2006年之后大体呈逐渐降低的变化趋势.④2006年以来,长江干流水质呈好转态势,水体中ρ(COD_Mn)、ρ(NH₃-N)和ρ(TP)均呈逐年下降趋势.⑤近年来,长江干流断面中TP的污染程度高于COD_Mn和NH₃-N,应引起重视.研究显示,政府的相关管理措施对长江干流水质改善具有正面推动作用,极大改善了长江流域总体水质,也促进了长江干流水质的进一步好转.      

1961—2017年青藏高原极端降水特征分析

基于青藏高原78个气象站点的逐日降水数据,采用百分位阈值法确定极端降水阈值,计算极端降水指数并分析其时空分布特征,以期为区域气候变化预测及防灾减灾对策的制定提供参考。结果表明：（1）1961—2017年青藏高原年降水量表现出上升趋势,上升速率为8.06 mm/10 a,多年平均降水量达472.36 mm。78个站点的年降水量倾向率最小值为-25.46 mm/10 a,最大值为43.02 mm/10 a,有15.38%的站点降水在下降,较为集中地分布在高原的东部和南部,其余84.62%的站点降水量在上升。（2）青藏高原各站点极端降水阈值的平均值为23.11 mm,取值范围为7.84~51.90 mm。高值中心出现在横断山区的贡山和木里,低值中心出现在柴达木盆地及昆仑山北翼区。（3）青藏高原各站点的极端降水量、极端降水日数和极端降水贡献率均表现出了明显的上升趋势,极端降水强度虽然也在上升但趋势并不明显,表明青藏高原极端降水量的上升并非是极端降水的强度引起的,而是由极端降水频次的上升引起的。柴达木盆地的极端降水量和极端降水日数虽然并没有表现出高值水平,但该地区的极端降水贡献率却表现出较高水平,表明该区域虽然降水量较少,但是降水往往以极端降水的形式产生。     

中国资源型城市房价时空变化与影响因素分析

基于禧泰数据库的全国城市二手房价监测数据,利用描述统计和GIS空间分析方法详细分析了2011—2018年126个中国资源型城市房价时空变化特征,并采用空间杜宾模型进一步揭示了其影响因素。研究结果表明：（1）2011年和2018年中国资源型城市平均房价分别为4105元/m²和5675元/m²,再生型城市、成熟型城市、成长型城市和衰退型城市的平均房价依次递减;（2）2011—2018年中国资源型城市平均房价呈现出波动上升的态势,房价增长率为38.2%,远低于全国城市平均房价增长幅度55.3%,且不同类型资源型城市房价的增长幅度有所差异,以成熟型和再生型城市的房价增幅相对较大;（3）中国资源型城市房价和变化存在显著的空间集聚特征,房价热点区主要集中在东部地区和中部地区城市,房价冷点区主要以东北地区和西部地区城市为主;（4）空间杜宾模型显示,人均GDP、人均住房开发投资、多样化指数、专业化指数和工业废水排放强度是影响中国资源型城市房价空间差异的主要因素。     

2001—2018年基于MODIS遥感影像的青海湖湖冰物候变化特征分析及其影响因素

青海湖是青藏高原上最大的咸水湖,研究该区域冬季湖泊冻融时间的变化趋势及其与气候变化之间的关系,可以为预测未来气候对青海湖水情变化提供重要的见解。根据冰的亮度温度值高于水的亮度温度值这一差异,使用2001—2018年MODIS MOD02QKM数据产品和Landsat TM/ETM+遥感影像分别提取了青海湖开始冻结、完成冻结、开始消融和完成消融四个时间点的数据,综合分析青海湖湖冰物候特征变化,并结合气象数据,得出湖冰物候变化对气候的响应。结果表明：青海湖每年11月左右进入冰期,12月开始形成稳定的冰盖,次年3月或4月开始消融。湖冰覆盖时长和封冻期的变化趋势基本相同,整体上呈现出缩短的趋势,湖冰消融期整体上呈现出先缩短后增加的趋势;2001—2018年,平均首日冻结面积为8.15%,平均冻结速率为192.02 km²?d^?1,开始冻结和完成冻结的日期略有延迟,开始消融和完成消融的日期已经大大提前;冬季温度越高,青海湖湖冰封冻时间越短,日照时数越长湖冰覆盖时长越短,对于湖冰消融期来说,降水量越多湖冰消融速度越慢,平均风速越大湖冰消融速度越快。初步认为,气温是湖冰冻融的主要因素,预测未来1—2 a青海湖冬季气温仍会呈现上升趋势,湖冰封冻时长也会出现缩短趋势。     

大靖河径流变化特征及其对气候变化和人类活动的响应

流域径流变化及其影响因素的研究对流域水资源的可持续开发利用以及改善流域的生态环境具有重要意义。以大靖河流域1956—2016年水文气象数据为基础,分析了近61 a来大靖河径流的年内、年际变化特征,并定量评估了气候变化和人类活动对径流变化的影响。结果表明：大靖河径流年内分配不均,主要集中在7—10月,占全年径流的60.98%;1956年以来大靖河径流以1.1×10⁵ m³?a^?1的速率递减,Hurst指数为0.69,表明大靖河径流在未来一段时间将继续呈递减趋势;大靖河径流存在4个震荡周期,分别为2—5 a、8—13 a、17—24 a及35—45 a左右,降水的震荡周期（8—13 a、17—24 a及35—47 a）与径流周期具有较好的一致性;气候变化对大靖河径流减少的贡献率为4.7%,人类活动的贡献率为95.3%,这可能与研究区土地利用方式变化有关。     

2000 — 2018年哈尔里克山冰川高程遥感变化分析

冰川高程变化是冰川形态变化的重要特征之一,能为推算冰川体积变化及物质平衡提供基本参数。利用Sentinel-1A雷达数据,与SRTM数据进行差分干涉,得到2000—2018年哈尔里克山冰川高程变化结果。为进一步提高监测结果的可靠性,以裸地区域的高程变化量作为系统误差修正值,对冰川高程变化结果进行第一次优化。由于差分干涉结果受地形影响较大,为降低地形因素对高程变化的影响,分析地形因子与高程变化标准差之间的关系,去除标准差过大的冰川区域,提高冰川高程变化可靠性,对冰川高程变化结果进行第二次优化,并在此基础上进行精度评定。结果表明：过去18年来,哈尔里克山冰川高程平均下降(8.74±0.14) m;分析冰川高程变化的空间差异性,发现西北部冰川高程减少程度较东南部小;分析冰川高程变化与海拔的关系,发现总体上冰川高程下降程度随海拔升高而减小。     

2016~2018年中国城市臭氧浓度时空聚集变化规律

为揭示中国O₃浓度的时空格局及聚集变化规律,通过对2016~2018年全国338个城市1144个监测站点的O₃浓度观测数据,使用空间插值及空间自相关等方法进行分析研究.结果表明：2016~2018年全国O₃浓度（第90百分位数）总体呈现上升趋势（由2016年的141.54μg/m³上升到2018年的153.21μg/m³）,污染态势逐年加重,且华北及长江中下游等人口稠密地区O₃浓度最高,O₃浓度的空间分布呈现显著的聚集性和相似性规律,且聚集性逐年增强,O₃浓度的年聚集区主要呈现北高南低的分异,高高值聚集区主要集中在北方（城市占比22.19%~29.59%）,低低值聚集区则主要集中在南方（城市占比15.98%~22.19%）,此外,O₃浓度高高值聚集区与低低值聚集区空间分布的季节变化规律以顺时针周期性变化为主：3a来,春季集聚区分布与年集聚情况相同,夏季高高值,低低值聚集区逐渐向西扩大聚集范围,秋季则顺时针转变为东高西低的分异情况,随后高高值（低低值）聚集区沿顺时针方向南（北）移动,到冬季则转变为南高北低的空间分异情况.