A REVISED VERSION OF PnET‐II TO SIMULATE THE HYDROLOGIC CYCLE IN SOUTHEASTERN FORESTED AREAS1

ABSTRACT: The PnET‐II model uses hydroclimatic data on maximum and minimum temperatures, precipitation, and solar radiation, together with vegetation and soil parameters, to produce estimates of net primary productivity, evapotranspiration (ET), and runoff on a monthly time step for forested areas. In this study, the PnET‐II model was employed to simulate the hydrologic cycle for 17 Southeastern eight‐digit hydrologic unit code (HUC) watersheds dominated by evergreen or deciduous tree species. Based on these control experiments, model biases were quantified and tentative revision schemes were introduced. Revisions included: (1) replacing the original single soil layer with three soil layers in the water balance routine; (2) introducing calibrating factors to rectify the phenomenon of overestimation of ET in spring and early summer months; (3) parameterizing proper values of growing degree days for trees located in different climate zones; and (4) adjusting the parameter of fast‐flow (overland flow) fraction based on antecedent moisture condition and precipitation intensity. The revised PnET‐II model, called PnET‐II3SL in this work, substantially improved runoff simulations for the 17 selected experimental sites, and therefore may offer a more powerful tool to address issues in water resources management.     

Growth of the Decision Tree: Advances in Bottom‐Up Climate Change Risk Management

There has recently been a return in climate change risk management practice to bottom‐up, robustness‐based planning paradigms introduced 40 years ago. The World Bank's decision tree framework (DTF) for “confronting climate uncertainty” is one incarnation of those paradigms. In order to better represent the state of the art in climate change risk assessment and evaluation techniques, this paper proposes: (1) an update to the DTF, replacing its “climate change stress test” with a multidimensional stress test; and (2) the addition of a Bayesian network framework that represents joint probabilistic behavior of uncertain parameters as sensitivity factors to aid in the weighting of scenarios of concern (the combination of conditions under which a water system fails to meet its performance targets). Using the updated DTF, water system planners and project managers would be better able to understand the relative magnitudes of the varied risks they face, and target investments in adaptation measures to best reduce their vulnerabilities to change. Next steps for the DTF include enhancements in: modeling of extreme event risks; coupling of human‐hydrologic systems; integration of surface water and groundwater systems; the generation of tradeoffs between economic, social, and ecological factors; incorporation of water quality considerations; and interactive data visualization.     

Does More Storage Give California More Water?

Increasing reservoir storage is commonly proposed to mitigate increasing water demand and provide drought reserves, especially in semiarid regions such as California. This paper examines the value of expanding surface reservoir capacity in California using hydroeconomic modeling for historical conditions, a future warm‐dry climate, and California's recently adopted policy to end groundwater overdraft. Results show expanding surface storage capacity rarely provides sizable economic value in most of California. On average, expanding facilities north of California's Delta provides some benefit in 92% of 82 years modeled under historical conditions and in 61% of years modeled in a warm‐dry climate. South of California's Delta, expanding storage capacity provides no benefits in 14% of years modeled under historical conditions and 99% of years modeled with a warm‐dry climate. Results vary across facilities between and within regions. The limited benefit of surface storage capacity expansion to statewide water supply should be considered in planning California's water infrastructure.     

1990—2014年武汉市空气质量时空变化

基于武汉市1990—2014年空气污染物(SO2,NOx/NO2,TSP/PM10)年均值浓度数据,采用Daniel趋势检验和空间统计方法研究污染物浓度时间变化趋势和空间变异。研究发现武汉市近20年SO2浓度变化平稳且满足国家二级标准限值,而氮氧化物(NOx/NO2)浓度显著上升并超过标准限值,颗粒物(TSP/PM10)浓度虽然有下降趋势,但依然未达到标准限值。武汉市近10年SO2污染空间变化最明显,重污染区域由城区向郊区转移;NO2污染空间差异最显著,二环线内监测点NO2浓度一直高于二环线外且平均高9.2%,而二环线内监测点SO2、PM10浓度低于二环线外。     

Stream Temperature Patterns over 35 Years in a Managed Forest of Western Washington

Stream temperature changes as a result of forest practices have been a concern in the Pacific Northwest for several decades. As a result of this concern, stream protection requirements for forest lands were first adopted in the early 1970s and have become progressively more stringent. While there have been multiple studies examining the effects of stream protection buffers on water temperature, there are few studies examining temperature patterns over long periods on intensively managed forests. Water temperature in the upper Deschutes River watershed, Washington has been monitored since 1975 and represents one of the longest studies of water quality on managed forests in the Pacific Northwest. This data record, collected from basins of varying sizes, has enabled us to examine the combined effects of hydro‐climatic patterns and forest management on stream temperature. Effects of harvest conducted prior to buffer regulations were clearly identifiable and most pronounced on smaller streams. We were not able to detect any response on larger channels to more recent timber harvest where riparian buffers were required. This analysis also emphasizes that it is critical to account for changing climate when examining long‐term temperature patterns. We found that in many cases the temperature improvements associated with more stringent buffer requirements implemented over the last 35 years in the Deschutes watershed have been offset by warming climatic conditions.     

生物覆盖层属性对气体输运过程的影响

为更好地理解生物覆盖介质属性对甲烷减量化过程中气体通量分布的影响,将熟化时间为24个月和2个月的新旧绿色废物分别用于建设1#和2#生物覆盖单元.通过分布在其表面的12个静态箱测量混合气体的组成和通量,结合生物覆盖层含水率和干密度对示踪气体进行回归分析,并利用数值模拟工具模拟示踪气体的输运过程.测量结果表明,两个覆盖单元表面气体通量分布呈现高度变异性,其中,1#单元中气体通量的最大值与最小值的差别达9倍,在2#单元中,这种差异放大到20倍.生物覆盖层中含水率和干密度(孔隙度)也同样呈现出高度变异性,多元回归分析表明,含水率和干密度的分布与气体通量的分布明显线性关系,其中,含水率的分布总体上决定了气体的输运路径.数值模拟结果表明,气体在2#单元中的输运受到扩散过程的影响较大,结构性因子变异程度更高导致了预测的不确定性.     

近52a长江中下游地区极端降水的时空变化特征

长江中下游地区是我国主要农业区,同时也是降水异常,洪涝灾害频繁发生的地区之一,对长江中下游地区极端降水变化的研究,可以为该区农业生产及防洪减灾提供参考依据。利用1961~2012年间的长江中下游地区84个站点的逐日降水观测资料,基于年最大日降水(AM)序列与超门限峰值降水(POT)序列,通过滑动平均、Mann-Kendall检验法、线性倾向估计等方法,分析了该地区极端降水事件的时空变化特征。结果表明:(1)长江中下游地区近52a来极端降水量呈现为较明显的增加趋势,且极端降水量速率为9.3mm/10a,存在较为明显的年代际波动变化特征,1990年以后进入极端降水量偏多的时期;(2)AM与POT序列多年平均值大值主要分布在江西省大部、湖北东南部以及安徽南部;AM与POT序列多年标准差大值主要分布江西东南部与北部,湖北东南部以及湖南西北部;AM序列多年平均值与标准差均高于POT序列,AM序列年际间振幅要明显强于POT序列,极端降水年际变化幅度大于年内变化;(3)长江中下游沿岸地区年最大日降水量主要表现为增加趋势,长江以北的西部地区则主要表现为减少趋势;长江沿岸地区以及中东部地区的极端降水量主要表现为增加趋势,西部地区则主要表现为减少趋势。     

Pragmatic Approaches for Water Management Under Climate Change Uncertainty1

Stakhiv, Eugene Z., 2011. Pragmatic Approaches for Water Management Under Climate Change Uncertainty. Journal of the American Water Resources Association (JAWRA) 47(6):1183–1196. DOI: 10.1111/j.1752‐1688.2011.00589.x Abstract: Water resources management is in a difficult transition phase, trying to accommodate large uncertainties associated with climate change while struggling to implement a difficult set of principles and institutional changes associated with integrated water resources management. Water management is the principal medium through which projected impacts of global warming will be felt and ameliorated. Many standard hydrological practices, based on assumptions of a stationary climate, can be extended to accommodate numerous aspects of climate uncertainty. Classical engineering risk and reliability strategies developed by the water management profession to cope with contemporary climate uncertainties can also be effectively employed during this transition period, while a new family of hydrological tools and better climate change models are developed. An expansion of the concept of “robust decision making,” coupled with existing analytical tools and techniques, is the basis for a new approach advocated for planning and designing water resources infrastructure under climate uncertainty. Ultimately, it is not the tools and methods that need to be revamped as much as the suite of decision rules and evaluation principles used for project justification. They need to be aligned to be more compatible with the implications of a highly uncertain future climate trajectory, so that the hydrologic effects of that uncertainty are correctly reflected in the design of water infrastructure.     

On the Relationship Between Mean and Variance of Soil Moisture Fields1

Abstract: The objective of this work was to explain an apparent contradiction in the literature related to the relationship between mean and variance (or standard deviation) of soil moisture fields. Some studies found an increase in soil moisture variance with decreasing mean soil moisture, while others showed a decrease. The evidence of maximum variance in the mid‐range of mean soil moisture was also reported in the literature. In this paper, we focus on the effects of spatial variability of soil texture on the relationship between variance and mean of soil moisture during soil dry‐down processes. Soil texture influences soil moisture mean and variance through its direct effects on evaporation and drainage, which are two main factors controlling soil drying. A differential equation describing soil moisture dry down is proposed and studied. Our study shows that as mean soil moisture is greater than a threshold, variance increases with decreasing mean soil moisture. If mean soil moisture is less than the threshold, variance decreases with decreasing mean soil moisture. The threshold depends on soil texture and is between the field capacity and the wilting point. The soil moisture dry‐down equation is also applied to explain the apparent contradiction with regard to the relationship between mean and variance of soil moisture fields reported in the literature.     

Variability in Population Abundance and the Classification of Extinction Risk

Abstract: Classifying species according to their risk of extinction is a common practice and underpins much conservation activity. The reliability of such classifications rests on the accuracy of threat categorizations, but very little is known about the magnitude and types of errors that might be expected. The process of risk classification involves combining information from many sources, and understanding the quality of each source is critical to evaluating the overall status of the species. One common criterion used to classify extinction risk is a decline in abundance. Because abundance is a direct measure of conservation status, counts of individuals are generally the preferred method of evaluating whether populations are declining. Using the thresholds from criterion A of the International Union for Conservation of Nature (IUCN) Red List (critically endangered, decline in abundance of >80% over 10 years or 3 generations; endangered, decline in abundance of 50–80%; vulnerable, decline in abundance of 30–50%; least concern or near threatened, decline in abundance of 0–30%), we assessed 3 methods used to detect declines solely from estimates of abundance: use of just 2 estimates of abundance; use of linear regression on a time series of abundance; and use of state‐space models on a time series of abundance. We generated simulation data from empirical estimates of the typical variability in abundance and assessed the 3 methods for classification errors. The estimates of the proportion of falsely detected declines for linear regression and the state‐space models were low (maximum 3–14%), but 33–75% of small declines (30–50% over 15 years) were not detected. Ignoring uncertainty in estimates of abundance (with just 2 estimates of abundance) allowed more power to detect small declines (95%), but there was a high percentage (50%) of false detections. For all 3 methods, the proportion of declines estimated to be >80% was higher than the true proportion. Use of abundance data to detect species at risk of extinction may either fail to detect initial declines in abundance or have a high error rate.