Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

Spatiotemporal Evaluation of Simulated Evapotranspiration and Streamflow over Texas Using the WRF‐Hydro‐RAPID Modeling Framework

This study assesses a large‐scale hydrologic modeling framework (WRF‐Hydro‐RAPID) in terms of its high‐resolution simulation of evapotranspiration (ET) and streamflow over Texas (drainage area: 464,135 km²). The reference observations used include eight‐day ET data from MODIS and FLUXNET, and daily river discharge data from 271 U.S. Geological Survey gauges located across a climate gradient. A recursive digital filter is applied to decompose the river discharge into surface runoff and base flow for comparison with the model counterparts. While the routing component of the model is pre‐calibrated, the land component is uncalibrated. Results show the model performance for ET and runoff is aridity‐dependent. ET is better predicted in a wet year than in a dry year. Streamflow is better predicted in wet regions with the highest efficiency ~0.7. In comparison, streamflow is most poorly predicted in dry regions with a large positive bias. Modeled ET bias is more strongly correlated with the base flow bias than surface runoff bias. These results complement previous evaluations by incorporating more spatial details. They also help identify potential processes for future model improvements. Indeed, improving the dry region streamflow simulation would require synergistic enhancements of ET, soil moisture and groundwater parameterizations in the current model configuration. Our assessments are important preliminary steps towards accurate large‐scale hydrologic forecasts.     

Conceptual Framework of Connectivity for a National Agroecosystem Model Based on Transport Processes and Management Practices

There is a critical need for a national agroecosystem model for conservation policy and environmental planning, driven by issues including harmful algal blooms, water scarcity, flooding, and other weather‐related extremes. In this study, we illustrate the feasibility of a national agroecosystem model that will downscale processes to individual fields and first‐order channels. We propose to conceptually divide the conterminous United States (U.S.) into process domains as a framework for simulating processes and management at relevant scales. Specifically, we are proposing five domains: field (1–50 ha), transition (0.2–2.0 km²), headwater (1–15 km²), tributaries (15–150 km²), and main river (>150 km²). The proposed conceptual framework hydrologically connects fields across the U.S. using the National Hydrography Dataset (NHDPlus version 2). Parameterizing the Soil and Water Assessment Tool for the national agroecosystem model resulted in 4,880,000 agricultural fields, 2,250,000 non‐agricultural hydrologic response units, and 7,130,000 transition, 1,610,000 headwater, 591,000 tributary, and 432,400 main channels. Application of this framework was shown for Hydrologic Unit Code 07120002 in central Illinois and Indiana to demonstrate the feasibility of the approach using data that is readily available across the U.S. The new connectivity framework has the potential to dramatically improve national conservation and environmental assessments performed by U.S. Department of Agriculture and U.S. Environmental Protection Agency.     

室内颗粒物在玻璃表面的沉积与黏附特性

为研究室内颗粒物的自然沉降和黏附玻璃表面的规律,将普通硅酸盐玻璃载玻片以与水平面呈不同夹角(0°,45°和90°)放置于实验室中,研究载玻片表面在未处理、经玻璃清洗剂或TiO₂处理条件下,不同放置时间(1,3,10和30 d)内其黏附颗粒物的特性.结果表明:经TiO₂预处理的载玻片表面黏附的颗粒物质量与放置时间呈正比,与水平面夹角呈反比;而未处理和经玻璃清洗剂处理的载玻片的上述关系不明显.经清洗剂预处理的载玻片表面的颗粒物数浓度、遮光比相对较小,说明可以通过表面预处理防止玻璃表面黏尘.载玻片表面黏附颗粒物的平均粒径不随放置时间、与水平面夹角和预处理试剂种类变化而明显改变,粒径主要集中在15～25 μm.      

电镀厂下游水体中重金属的分布特征及其风险评价

以苏南某电镀厂下游地表水和浅层地下水为研究对象,探讨电镀厂废水对其造成的影响,采用ICP-AES分析了地表水与地下水样品中重金属Zn、Mn、Cr、Cu和Ni的含量.结果表明,电镀厂向河流中排放Zn、Mn、Cr、Cu和Ni等酸性污染物,浓度分别达到1.34、3.77、28.1、6.40和9.37 mg·L-1,pH为2.32,除Zn外皆超过国家污水综合排放标准,是构成下游河流中重金属沿程分布的主要原因.重金属在井水中的分布特征,未能说明其受到河水重金属的影响,表明重金属在土壤中迁移率较低.风险评价结果显示,地表水水质明显下降,河水中Ni和Mn超标严重,Cr和Cu也有部分样点不同程度地超过Ⅲ类标准限值.以14口井水为地下水中,除Mn元素有4个样点属于Ⅳ类地下水之外,其它重金属皆达到生活饮用水标准.     

离子色谱法应用于杭州地表水中有机酸及无机阴离子分布研究

建立了离子色谱-抑制电导法同时检测杭州市地表水中的有机酸及无机阴离子,并对地表水中有机酸及无机阴离子的含量进行探讨.该方法的重现性(RSD)为0.15％ ～4.92％,相关系数r2为0.9990 ～ 0.9999,加标回收率为84.36％～111.05％,最低检出限为0.003～0.028 mg/L.利用该建立的方法,...     

An Application of the T‐TEL Assessment Method to Evaluate Connectivity in a Lake‐Dominated Watershed after Drought

Lakes are landscape features that influence connectivity of mass and energy by being foci for the reception, mixing, and provision of water and material. Where lake fractions are high, they influence hydrological connectivity. This behavior was exemplified in the Baker Creek watershed in Canada's Northwest Territories during a two‐year drought in which many lake levels declined below outlet elevations. This study evaluated how lakes controlled surface runoff connectivity reestablishment following the drought using a new assessment method, T‐TEL (time scales — thresholds, excesses, losses). Analysis of daily data showed that during a summer period following the drought, connectivity occurred between 0% and 41% of the time. The size of run‐of‐the‐river lakes relative to their upstream watershed area, and the upstream lake fraction, are two factors for connectivity. These terms represent a lake's ability to control the size of storage deficits relative to rainfall, and evaporation and storage losses along pathways. The connectivity magnitude–duration curve only aligned with the watershed flow duration curve during high‐water conditions, implying lakes functioned as individuals rather than as part of a perennial watercourse during much of the study. The T‐TEL method can be used to quantify consistent metrics of hydrologic connectivity that can be used for regionalization exercises and understanding hydrologic controls on material transport.     

Representing the Connectivity of Upland Areas to Floodplains and Streams in SWAT+

In recent years, watershed modelers have put increasing emphasis on capturing the interaction of landscape hydrologic processes instead of focusing on streamflow at the watershed outlet alone. Understanding the hydrologic connectivity between landscape elements is important to explain the hydrologic response of a watershed to rainfall events. The Soil and Water Assessment Tool+ (SWAT+) is a new version of SWAT with improved runoff routing capabilities. Subbasins may be divided into landscape units (LSUs), e.g., upland areas and floodplains, and flow can be routed between these LSUs. We ran three scenarios representing different extents of connectivity between uplands, floodplains, and streams. In the first and second scenarios, the ratio of channelized flow from the upland to the stream and sheet flow from the upland to the floodplain was 70/30 and 30/70, respectively, for all upland/floodplain pairs. In the third scenario, the ratio was calculated for each upland/floodplain pair based on the upland/floodplain area ratio. Results indicate differences in streamflow were small, but the relative importance of flow components and upland areas and floodplains as sources of surface runoff changed. Also, the soil moisture in the floodplains was impacted. The third scenario was found to provide more realistic results than the other two. A realistic representation of connectivity in watershed models has important implications for the identification of pollution sources and sinks.     

Drivers of Interbasin Transfers in the United States: Insights from Sampling

Interbasin transfers (IBTs) are manmade transfers of water that cross basin boundaries. In an analysis of 2016 data, this work identified 2,161 reaches crossing United States (U.S.) Geological Survey hydrologic unit code 6 boundaries in the U.S. The objectives of this study were to characterize and classify IBTs, and examine the development drivers for a subset of 109 (~5%) of the IBT reaches through examination of samples from different climate regions of the U.S. The IBTs were classified as being near irrigated agricultural lands, near cities, or rural IBTs not near cities or irrigated land. IBTs near both cities and irrigated agricultural land were designated as city + irrigated agriculture. The 109 samples were selected, based on approximate proportional distribution to the total number of IBTs within each climate region, with representation of areas having a high density of IBTs. Analysis of the samples revealed that in the U.S., there have been four major drivers for basin transfers: irrigation for agriculture, municipal and industrial water supply, commercial shipping or navigation, and drainage or flood management. The most common has been drainage or flood management, though IBTs at least partially driven by agricultural needs are also prevalent. The majority of the sampled IBTs were constructed between 1880 and 1980, with peaks in development between 1900–1910 and 1960–1970. The samples also showed the drivers of IBT development evolved over time, reflecting changes in regional economies, populations, and needs.     

Hydrography Change Detection: The Usefulness of Surface Channels Derived From LiDAR DEMs for Updating Mapped Hydrography1

Abstract: The 1:24,000‐scale high‐resolution National Hydrography Dataset (NHD) mapped hydrography flow lines require regular updating because land surface conditions that affect surface channel drainage change over time. Historically, NHD flow lines were created by digitizing surface water information from aerial photography and paper maps. Using these same methods to update nationwide NHD flow lines is costly and inefficient; furthermore, these methods result in hydrography that lacks the horizontal and vertical accuracy needed for fully integrated datasets useful for mapping and scientific investigations. Effective methods for improving mapped hydrography employ change detection analysis of surface channels derived from light detection and ranging (LiDAR) digital elevation models (DEMs) and NHD flow lines. In this article, we describe the usefulness of surface channels derived from LiDAR DEMs for hydrography change detection to derive spatially accurate and time‐relevant mapped hydrography. The methods employ analyses of horizontal and vertical differences between LiDAR‐derived surface channels and NHD flow lines to define candidate locations of hydrography change. These methods alleviate the need to analyze and update the nationwide NHD for time relevant hydrography, and provide an avenue for updating the dataset where change has occurred.