Effects of Climate and Land Cover on Hydrology in the Southeastern U.S.: Potential Impacts on Watershed Planning

The hydrologic response to statistically downscaled general circulation model simulations of daily surface climate and land cover through 2099 was assessed for the Apalachicola‐Chattahoochee‐Flint River Basin located in the southeastern United States. Projections of climate, urbanization, vegetation, and surface‐depression storage capacity were used as inputs to the Precipitation‐Runoff Modeling System to simulate projected impacts on hydrologic response. Surface runoff substantially increased when land cover change was applied. However, once the surface depression storage was added to mitigate the land cover change and increases of surface runoff (due to urbanization), the groundwater flow component then increased. For hydrologic studies that include projections of land cover change (urbanization in particular), any analysis of runoff beyond the change in total runoff should include effects of stormwater management practices as these features affect flow timing and magnitude and may be useful in mitigating land cover change impacts on streamflow. Potential changes in water availability and how biota may respond to changes in flow regime in response to climate and land cover change may prove challenging for managers attempting to balance the needs of future development and the environment. However, these models are still useful for assessing the relative impacts of climate and land cover change and for evaluating tradeoffs when managing to mitigate different stressors.     

Using GIS to Delineate Headwater Stream Origins in the Appalachian Coalfields of Kentucky

Headwater streams have a significant nexus or physical, chemical, and/or biological connection to downstream reaches. Generally, defined as 1st‐3rd order with ephemeral, intermittent, or perennial flow regimes, these streams account for a substantial portion of the total stream network particularly in mountainous terrain. Due to their often remote locations, small size, and large numbers, conducting field inventories of headwater streams is challenging. A means of estimating headwater stream location and extent according to flow regime type using publicly available spatial data is needed to simplify this complex process. Using field‐collected headwater point of origin data from three control watersheds, streams were characterized according to a set of spatial parameters related to topography, geology, and soils. These parameters were (1) compared to field‐collected point of origin data listed in three nearby Jurisdictional Determinations, (2) used to develop a geographic information system (GIS)‐based stream network for identifying ephemeral, intermittent, and perennial streams, and (3) applied to a larger watershed and compared to values obtained using the high‐resolution National Hydrography Dataset (NHD). The parameters drainage area and local valley slope were the most reliable predictors of flow regime type. Results showed the high‐resolution NHD identified no ephemeral streams and 9 and 65% fewer intermittent and perennial streams, respectively, than the GIS model.     

Development and Operational Testing of a Super‐Ensemble Artificial Intelligence Flood‐Forecast Model for a Pacific Northwest River

Coastal catchments in British Columbia, Canada, experience a complex mixture of rainfall‐ and snowmelt‐driven contributions to flood events. Few operational flood‐forecast models are available in the region. Here, we integrated a number of proven technologies in a novel way to produce a super‐ensemble forecast system for the Englishman River, a flood‐prone stream on Vancouver Island. This three‐day‐ahead modeling system utilizes up to 42 numerical weather prediction model outputs from the North American Ensemble Forecast System, combined with six artificial neural network‐based streamflow models representing various slightly different system conceptualizations, all of which were trained exclusively on historical high‐flow data. As such, the system combines relatively low model development times and costs with the generation of fully probabilistic forecasts reflecting uncertainty in the simulation of both atmospheric and terrestrial hydrologic dynamics. Results from operational testing by British Columbia's flood forecasting agency during the 2013‐2014 storm season suggest that the prediction system is operationally useful and robust.     

Combining geodiversity with climate and topography to account for threatened species richness

Understanding threatened species diversity is important for long‐term conservation planning. Geodiversity—the diversity of Earth surface materials, forms, and processes—may be a useful biodiversity surrogate for conservation and have conservation value itself. Geodiversity and species richness relationships have been demonstrated; establishing whether geodiversity relates to threatened species’ diversity and distribution pattern is a logical next step for conservation. We used 4 geodiversity variables (rock‐type and soil‐type richness, geomorphological diversity, and hydrological feature diversity) and 4 climatic and topographic variables to model threatened species diversity across 31 of Finland's national parks. We also analyzed rarity‐weighted richness (a measure of site complementarity) of threatened vascular plants, fungi, bryophytes, and all species combined. Our 1‐km² resolution data set included 271 threatened species from 16 major taxa. We modeled threatened species richness (raw and rarity weighted) with boosted regression trees. Climatic variables, especially the annual temperature sum above 5 °C, dominated our models, which is consistent with the critical role of temperature in this boreal environment. Geodiversity added significant explanatory power. High geodiversity values were consistently associated with high threatened species richness across taxa. The combined effect of geodiversity variables was even more pronounced in the rarity‐weighted richness analyses (except for fungi) than in those for species richness. Geodiversity measures correlated most strongly with species richness (raw and rarity weighted) of threatened vascular plants and bryophytes and were weakest for molluscs, lichens, and mammals. Although simple measures of topography improve biodiversity modeling, our results suggest that geodiversity data relating to geology, landforms, and hydrology are also worth including. This reinforces recent arguments that conserving nature's stage is an important principle in conservation.     

Implications of Conceptual Channel Representation on SWAT Streamflow and Sediment Modeling

Hydrologic modeling outputs are influenced by how a watershed system is represented. Channel routing is a typical example of the mathematical conceptualization of watershed landscape and processes in hydrologic modeling. We investigated the sensitivity of accuracy, equifinality, and uncertainty of Soil and Water Assessment Tool (SWAT) modeling to channel dimensions to demonstrate how a conceptual representation of a watershed system affects streamflow and sediment modeling. Results showed the amount of uncertainty and equifinality strongly responded to channel dimensions. On the other hand, the model performance did not significantly vary with the changes in the channel representation due to the degree of freedom allowed by the conceptual nature of hydrologic modeling in the parameter calibration. Such findings demonstrated good modeling performance statistics do not necessarily mean small output uncertainty, and partial improvements in the watershed representation may neither increase modeling accuracy nor reduce uncertainty. We also showed the equifinality and uncertainty of hydrologic modeling are case‐dependent rather than specific to models or regions, suggesting great caution should be used when attempting to transfer uncertainty analysis results to other modeling studies, especially for ungauged watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

无常规水文监测高寒湿地纳帕海水量波动模拟分析

论文以无常规水文监测高寒湿地纳帕海为例,基于流域产汇流的时滞效应,建立了湿地区气候因子(日累计降水量)与水文因子(湿地明水量)之间的经验关联模型,以模拟湿地水文情势的波动。研究首先通过对1990-2011 年不同时相的48 期纳帕海湿地Landsat TM/ETM+遥感数据进行解译,提取明水景观变化信息;再利用研究区1988-2011 年逐日降水数据经过统计计算后生成的不同时间步长日累计降水量与48 期明水面积序列进行回归分析,筛选出最佳时间步长日累计降水量并获得其与明水面积之间的经验关联模型;进而借助纳帕海湿地明水面积与明水量之间的经验方程建立湿地明水量-最佳日累计降水量关联模型。模拟结果表明:8-9 月是纳帕海湿地的主汛期,9 月多年平均明水量可达866.11×10⁴ m³;干、湿季明水量差异较大,干季平均明水量为95.91×10⁴ m³。上述模拟结果能够与遥感影像解译所获取的信息和实地调查信息保持基本一致,说明此模型能够较好地模拟研究区水文情势波动,为无监测高寒湿地水文情势研究提供了一种新的思路,同时也为该地区湿地洪灾防范提供了数据基础和方法指导。     

HSPF水文水质模型应用研究综述

HSPF(Hydrological Simulation Program-Fortran)模型采用FORTRAN语言编写,以Stanford水文模型为基础,能够综合模拟径流、土壤流失、污染物传输、河道水力等过程,并大量应用于气候变化与土地利用变化的流域水环境效应情景模拟.该模型是半分布式水文水质模型的优秀代表,在国外得到广泛的应用.HSPF模型包括PERLND、IMPLND与RCHRES等3个主要模块,分别实现对透水地段、不透水地段与地表水体的水文水质模拟.总体来看,HSPF模型在国外水文、水质过程模拟,以及涉及气候变化和土地利用影响的情景分析中发挥重要作用,但是国内该模型的应用非常有限.HSPF模型存在的主要问题包括:①模型中某些方案和算法还有改进和完善的空间;②模型对数据输入要求较高,模拟的精度受到空间和属性等数据的限制;③模型只限于均匀混合的河流、水库和一维水体模拟,对于复杂流域或水体的模拟研究,需要与其它模型整合以解决更加综合的问题.目前,针对发展与完善HSPF模型的研究仍在继续,包括模型平台开发、模型功能扩展、模型校正方法研究、参数敏感性研究等方面.随着我国基础数据的积累及共享程度的提高,HSPF模型在我国的应用也将更加广泛.     

气温变化对西南山区流域森林水文效应影响的模拟

为了揭示气温变化对西南山区流域森林水文效应的影响,用生物物理/动态植被模型SSiB4/TRIFFID与流域地形指数水文模型TOPMODEL的耦合模型SSiB4T/TRIFFID模拟了西南山区长江上游梭磨河流域森林水文效应对气温变化的响应,分析了气温变化对植被不同演替阶段的流域总径流和总蒸发以及冠层截流蒸发、植被蒸腾和土壤蒸发的影响。结果表明,（1）梭磨河流域森林（常绿针叶林）蒸腾与草和灌木差异小,森林蒸腾潜热比草和灌木仅高1~4 W.m-2,森林冠层截留蒸发高于草和灌木,但土壤蒸发明显低于草和灌木覆盖,森林覆盖流域总蒸发低于草和灌木覆盖甚至低于裸土蒸发,因此增加了流域总径流量,但森林增加径流的作用随土壤蒸发的减小而减小。（2）气温减小1℃将通过减小森林冠层截留蒸发和蒸腾而使森林增加流域总径流量的作用增加;相反,气温增加将增加森林冠层截留蒸发和蒸腾而使森林增加总径流量的作用减小。（3）当温度增加4℃,由于森林总蒸发较草和灌木明显增加,对于较高的土壤蒸发,森林增加总径流量的作用已不明显;对于较低的土壤蒸发,森林减小了流域总径流量。     

Modeling Flow Through a One-Dimensional Multi-Layered Soil Profile Using the Method of Lines

The vertical flow of water through horizontal layers of soil is considered using the Method of Lines. Continuity of mass principles are used to develop the interface boundary conditions, by introducing fictitious points at the interface, and the boundary conditions are handled using explicit and iterative approximations. Both the pressure based, and the water content based forms of Richards' equation are solved using the Method of Lines. The solutions obtained are compared with some particular analytic solutions obtained from the literature, and the results show that good accuracy can be achieved. It is also shown that the water content model can handle a large discontinuity at the interface when compared against the analytical solution. This result is also confirmed against a numerical example from the literature, and was effective for relatively dry initial conditions.     

Spatiotemporal Variability of Modeled Watershed Scale Surface‐Depression Storage and Runoff for the Conterminous United States

This study explores the viability of using simulated monthly runoff as a proxy for landscape‐scale surface‐depression storage processes simulated by the United States Geological Survey’s National Hydrologic Model (NHM) infrastructure across the conterminous United States (CONUS). Two different temporal resolution model codes (daily and monthly) were run in the NHM with the same spatial discretization. Simulated values of daily surface‐depression storage (treated as a decimal fraction of maximum volume) as computed by the daily Precipitation‐Runoff Modeling System (NHM‐PRMS) and normalized runoff (0 to 1) as computed by the Monthly Water Balance Model (NHM‐MWBM) were aggregated to monthly and annual values for each hydrologic response unit (HRU) in the CONUS geospatial fabric (HRU; n = 109,951) and analyzed using Spearman’s rank correlation test. Correlations between simulated runoff and surface‐depression storage aggregated to monthly and annual values were compared to identify where which time scale had relatively higher correlation values across the CONUS. Results show Spearman’s rank values >0.75 (highly correlated) for the monthly time scale in 28,279 HRUs (53.35%) compared to the annual time scale in 41,655 HRUs (78.58%). The geographic distribution of HRUs with highly correlated monthly values show areas where surface‐depression storage features are known to be common (e.g., Prairie Pothole Region, Florida).