DTSGUI: A Python Program to Process and Visualize Fiber-Optic Distributed Temperature Sensing Data

Fiber-optic distributed temperature sensing (FO-DTS) has proven to be a transformative technology for the hydrologic sciences, with application to diverse problems including hyporheic exchange, groundwater/surface-water interaction, fractured-rock characterization, and cold regions hydrology. FO-DTS produces large, complex, and information-rich datasets. Despite the potential of FO-DTS, adoption of the technology has been impeded by lack of tools for data processing, analysis, and visualization. New tools are needed to efficiently and fully capitalize on the information content of FO-DTS datasets. To this end, we present DTSGUI, a public-domain Python-based software package for editing, parsing, processing, statistical analysis, georeferencing, and visualization of FO-DTS data.     

Implementation of a linear optimization water allocation algorithm into a fully integrated physical hydrology model

Connections between groundwater and surface water are well-established and remain the focus of significant ongoing research. Within the water management community, stream-aquifer interactions are widely acknowledged and often subject to regulation. Yet, the tools most commonly used by water managers often have limited physical hydrology and a resulting inability to represent feedbacks between elements of the physical system. This paper presents the development of a water allocation module (WAM) for an integrated hydrologic model, ParFlow. The module presented here is designed to maximize the satisfaction of demand subject to a system of priorities, preferences and constraints. Because ParFlow solves the groundwater and surface water systems simultaneously, the coupled management model can explicitly incorporate groundwater surface water interactions into management decisions. Proof of concept for the new module is demonstrated for a hypothetical water management scenario, utilizing moisture dependent irrigation, in a real world basin, the Little Washita.     

Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan

ABSTRACT

“Panta Rhei – Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurring at the interface of hydrology and society, and their role in influencing future hydrologic system change. It calls for strategic research effort focused on the delivery of coupled, socio-hydrologic models. In this paper we explore and synthesize opportunities and challenges that socio-hydrology presents for data-driven modelling. We highlight the potential for a new era of collaboration between data-driven and more physically-based modellers that should improve our ability to model and manage socio-hydrologic systems. Crucially, we approach data-driven, conceptual and physical modelling paradigms as being complementary rather than competing, positioning them along a continuum of modelling approaches that reflects the relative extent to which hypotheses and/or data are available to inform the model development process.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned     

Long-term database of Beasley Lake Watershed with 25 years of agricultural conservation practices

Beasley Lake Watershed is an agriculturally influenced drainage basin in western Mississippi that has been intensively studied for 25 years. As part of the USDA Conservation Effects Assessment Project (CEAP), the watershed has archived hydrology, precipitation, and water quality data in order to measure the effects of multiple USDA Natural Resources Conservation Service conservation practices on lake water quality. The long-term database is available to researchers using a web-based application, Sustaining the Earth's Watersheds, Agricultural Research Data System (STEWARDS). STEWARDS is a GIS-based data retrieval application that encompasses spatial and temporal data collected from multiple sites within the watershed. This data note describes information located in the STEWARDS Beasley Lake Watershed database, including hydrology, precipitation, and water quality data. This information is valuable to researchers and agencies beyond the USDA as an available and useful database to improve the understanding of how land-use practices affect the water quality of shallow lake systems.     

Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan

ABSTRACT

“Panta Rhei – Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurring at the interface of hydrology and society, and their role in influencing future hydrologic system change. It calls for strategic research effort focused on the delivery of coupled, socio-hydrologic models. In this paper we explore and synthesize opportunities and challenges that socio-hydrology presents for data-driven modelling. We highlight the potential for a new era of collaboration between data-driven and more physically-based modellers that should improve our ability to model and manage socio-hydrologic systems. Crucially, we approach data-driven, conceptual and physical modelling paradigms as being complementary rather than competing, positioning them along a continuum of modelling approaches that reflects the relative extent to which hypotheses and/or data are available to inform the model development process.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned     

Visualization of Aqueous Geochemical Data Using Python and WQChartPy

Graphical methods have been widely used for visualization, classification, and interpretation of aqueous geochemical data to obtain a better understanding of surface and subsurface hydrologic systems. This method note presents WQChartPy, an open-source Python package developed to plot a total of 12 diagrams for analysis of aqueous geochemical data. WQChartPy can handle various data formats including Microsoft Excel, comma-separated values (CSV), and general delimited text. The 12 diagrams include eight traditional diagrams (trilinear Piper, Durov, Stiff, Chernoff face, Schoeller, Gibbs, Chadha, and Gaillardet) and four recently proposed diagrams (rectangle Piper, color-coded Piper, contour-filled Piper, and HFE-D) that have not been implemented in existing graphing software. The diagrams generated by WQChartPy can be saved as portable network graphics (PNG), scalable vector graphics (SVG), or portable document format (PDF) files for scientific publications. Jupyter and Google Colab notebooks are available online to illustrate how to use WQChartPy with example datasets. The geochemical diagrams can be generated with several lines of Python codes. Source codes of WQChartPy are publicly available at GitHub ( https://github.com/jyangfsu/WQChartPy ) and PyPI ( https://pypi.org/project/wqchartpy/ ).     

Application of deep learning in ecological resource research: Theories,methods, and challenges

Ecological resources are an important material foundation for the survival, development, and self-realization of human beings. In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society. Advances in observation technology have improved the ability to acquire long-term, cross-scale,massive, heterogeneous, and multi-source data. Ecological resource research is entering a new era driven by big data. Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data. Deep learning is a method for automatically extracting complex high-dimensional nonlinear features, which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data. To promote the application of deep learning in the field of ecological resource research, here, we first introduce the relationship between deep learning theory and research on ecological resources, common tools, and datasets. Second, applications of deep learning in classification and recognition,detection and localization, semantic segmentation, instance segmentation, and graph neural network in typical spatial discrete data are presented through three cases: species classification, crop breeding, and vegetation mapping. Finally, challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning. It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data,improve the universality and interpretability of algorithms, and enrich applications with the development of hardware.     

Two kinds of moment ratio diagrams and their applications in hydrology

We refocus attention on moment ratio diagrams and their uses in hydrology with four major objectives: (1) to summarize the information available in the literature about possible uses of the traditional moment ratio diagram introduced by Karl Pearson, which uses the coefficient of skewness and of kurtosis to compare the shapes of various distributions commonly used in hydrology; (2) to complete this traditional MRD by integrating into it the regions occupied by the log-Pearson Type III and generalized gamma distributions which are more and more used in hydrology; (3) to present another MRD which uses ratios of moments of orders –1 (harmonic mean), quasi zero (geometric mean) and 1 (arithmetic mean); (4) to stress the need to consider the different MRD's (along with the more recently introduced L-moment ratio diagrams) as complementary tools for choosing between distributions fitted to hydrologic data. Finally, using Monte Carlo simulation we compare the two types of diagrams as tools to identify and discriminate between different distributions.     

A recipe for why and how to set up and sustain an experimental catchment

Experimental catchments are fundamental elements in hydrological sciences as they provide key data for putting forward and testing hypotheses, developing theories, constraining models, and making predictions. Significant progress in catchment hydrology stemmed from field measurements but increasing costs and risks associated with field work and the availability of big data based on remote sensing, machine learning, and a plethora of models, as well as observations deriving from previous and current sites, raises questions on whether running an experimental catchment still provides individual and community benefits as in the past. In this commentary, I highlight the advantages of keeping experimental catchments alive and propose a personal 10-step “recipe” to set up a new experimental catchment and manage and sustain it in the long term. These suggestions can be useful both to young and less young researchers who are open to facing the challenge of measuring processes in the field and are willing to offer the scientific community new experimental evidence for advancing our current knowledge in catchment hydrology.     

The Fernow Experimental Forest,West Virginia,USA: Insights,datasets, and opportunities

Long-term experimental watershed studies have significantly influenced our global understanding of hydrological processes. The discovery and characterization of how stream water quantity and quality respond to a changing environment (e.g. land-use change, acidic deposition) has only been possible due to the establishment of catchments devoted to long-term study. One such catchment is the Fernow Experimental Forest (FEF) located in the headwaters of the Appalachian Mountains in West Virginia, a region that provides essential freshwater ecosystem services to eastern and mid-western United States communities. Established in 1934, the FEF is among the earliest experimental watershed studies in the Eastern United States that continues to address emergent challenges to forest ecosystems, including climate change and other threats to forest health. This data note describes available data and presents some findings from more than 50 years of hydrologic research at the FEF. During the first few decades, research at the FEF focused on the relationship between forest management and hydrological processes—especially those related to the overall water balance. Later, research included the examination of interactions between hydrology and soil erosion, biogeochemistry, N-saturation, and acid deposition. Hydro-climatologic and water quality datasets from long-term measurements and data from short-duration studies are publicly available to provide new insights and foster collaborations that will continue to advance our understanding of hydrology in forested headwater catchments. As a result of its rich history of research and abundance of long-term data, the FEF is positioned to continue to advance understanding of forest ecosystems in a time of unprecedented change.     

地球物理测井反问题机器学习数据集的构建方法研究

基于数据驱动机器学习的智能地球物理测井有望显著提高测井资料处理与解释的效率, 具有广阔的应用前景.但是, 数据驱动的测井反演如储层参数预测面临小样本、少标签和可解释性差等困难.通常, 人工解释实测数据集是测井机器学习标签的主要来源.由于井下油气储层复杂多样, 测井反演具有多解性,且地层具有非均质性, 实测数据集构建的标签体系不仅量少, 可靠性也存疑.本文提出基于地质领域知识和岩石物理机理模型, 通过正演模拟构建测井反问题机器学习数据集的方法.从地质约束出发, 综合考虑井眼环境、测井仪器、地层模型及流体分布等影响, 由测井领域知识正演生成测井数据以弥补实测数据集的不足, 以此实现机理模型与数据驱动的融合.数值实验结果表明, 正演生成的测井数据集有效扩充了样本和标签数量, 其参与储层参数预测及储层划分深度神经网络训练, 对发展数据驱动及数据与机理混合驱动的方法、提升测井储层评价参数预测模型效果, 成效显著.

     

Geospatial knowledge-based verification and improvement of GlobeLand30

Assuring the quality of land-cover data is one of the major challenges for large- area mapping projects. Although the use of geospatial knowledge and ancillary data in improving land-cover classification has been studied since the early 1980s, mature methods and efficient supporting tools are still lacking. This paper presents a geospatial knowledge-based verification and improvement approach for global land cover (GLC) mapping at 30-m resolution. A set of verification rules is derived from three types of land cover and its change knowledge (natural, cultural and temporal constraints). A group of web-based supporting tools is developed to facilitate the integration of and access to large amounts of ancillary data and to support online data manipulation and analysis as well as collaborative verification workflows. With this approach, two 30-m GLC datasets (GlobeLand-2000 and GlobeLand-2010) were verified and modified. The results indicate that the data quality of GlobeLand30 has been largely improved.     

Understanding the relative impacts of natural processes and human activities on the hydrology of the Central Rift Valley lakes,East Africa

Significant changes have been observed in the hydrology of Central Rift Valley (CRV) lakes in Ethiopia, East Africa as a result of both natural processes and human activities during the past three decades. This study applied an integrated approach (remote sensing, hydrologic modelling, and statistical analysis) to understand the relative effects of natural processes and human activities over a sparsely gauged CRV basin. Lake storage estimates were calculated from a hydrologic model constructed without inputs from human impacts such as water abstraction and compared with satellite‐based (observed) lake storage measurements to characterize the magnitude of human‐induced impacts. A non‐parametric Mann–Kendall test was used to detect the presence of climatic trends (e.g. a decreasing or increasing trends in precipitation), while the Standard Precipitation Index (SPI) analysis was used to assess the long‐term, inter‐annual climate variability within the basin. Results indicate human activities (e.g. abstraction) significantly contributed to the changes in the hydrology of the lakes, while no statistically significant climatic trend was seen in the basin, however inter‐annual natural climate variability, extreme dryness, and prolonged drought has negatively affected the lakes. The relative contributions of natural and human‐induced impacts on the lakes were quantified and evaluated by comparing hydrographs of the CRV lakes. Lake Abiyata has lost ~6.5 m in total lake height between 1985 and 2006, 70% (~4.5 m) of the loss has been attributed to human‐induced causes, whereas the remaining 30% is related to natural climate variability. The relative impact analysis utilized in this study could potentially be used to better plan and create effective water‐management practices in the basin and demonstrates the utility of this integrated methodology for similar studies assessing the relative natural and human‐induced impacts on lakes in data sparse areas. Copyright © 2015 John Wiley & Sons, Ltd.     

Modeling and assessing hydrologic processes for historical and potential land-cover change in the Duoyingping watershed,southwest China

Land-cover change significantly influences hydrologic processes at the watershed level. The mountainous Duoyingping watershed in upstream Yangtze River, China, has undergone dramatic land-cover change in the past three decades. It is likely to maintain this trend in the future, inevitably altering hydrologic processes in the region to a certain degree. Therefore, the effects of land-cover change on runoff, evapotranspiration (ET), and soil moisture in the watershed were assessed using a large-scale Variable Infiltration Capacity (VIC) hydrologic model.To minimize the effect of climate change on simulation results, we used detrended climate data over the period 1980–2005 in forcing the VIC model. The dynamics in the spatial distribution of land-cover types in the Duoyingping watershed from 1980 to 2000 were first examined, revealing that reforestation and deforestation were the major change patterns. On the basis of various land-use policies, potential land-cover scenarios for 2030 were established using an integrated land-use change model (CLUE-S). The scenarios were developed using 2000 land-use data as bases. Finally, the calibrated VIC model was applied in the scenarios to assess land-cover effects on hydrology. Hydrologic simulations showed that the effects of historical land-cover change on hydrology were discernible in the sub-watersheds of Nanba, Yingjing, and Yuxi. The annual ET was projected to decrease by 0.8–22.3% because of deforestation, and increase by 2.3–27.4% because of shrubland–forest conversion. Different potential land-cover scenarios play various roles in the effect on hydrology because of various land-use policies. In the scenario concerning forest protection policy, annual ET increased by more than 15%, while annual runoff decreased by 6%. However, a negligible effect on hydrology was found under the scenario involving cropland expansion. On the basis of the results, it is concluded that ET is more sensitive to land-cover change than are other hydrologic components. Hydrologic alteration caused by reforestation and deforestation during the dry season was more significant than that during wet season. Generally, the proposed approach in the study can be a useful means of assessing hydrologic responses to land-cover change.     

Development of a SWAT extension module to simulate riparian wetland hydrologic processes at a watershed scale

Using a mass balance algorithm, this study develops an extension module that can be embedded in the commonly used Soil and Water Assessment Tool (SWAT). This module makes it possible to assess effects of riparian wetlands on runoff and sediment yields at a watershed scale, which is very important for aquatic ecosystem management but rarely documented in the literature. In addition to delineating boundaries of a watershed and its subwatersheds, the module groups riparian wetlands within a subwatershed into an equivalent wetland for modelling purposes. Further, the module has functions to compute upland drainage area and other parameters (e.g. maximum volume) for the equivalent wetland based on digital elevation model, stream network, land use, soil and wetland distribution GIS datasets. SWAT is used to estimate and route runoff and sediment generated from upland drainage area. The lateral exchange processes between riparian wetlands and their hydraulically connected streams are simulated by the extension module. The developed module is empirically applied to the 53 km² Upper Canagagigue Creek watershed located in Southern Ontario of Canada. The simulation results indicate that the module can make SWAT more reasonably predict flow and sediment loads at the outlet of the watershed and better represent the hydrologic processes within it. The simulation is sensitive to errors of wetland parameters and channel geometry. The approach of embedding the module into SWAT enables simulation of hydrologic processes in riparian wetlands, evaluation of wetland effects on regulating stream flow and sediment loading and assessment of various wetland restoration scenarios. Copyright © 2008 John Wiley & Sons, Ltd.     

Knowledge gaps in our perceptual model of Great Britain's hydrology

There is a no lack of significant open questions in the field of hydrology. How will hydrological connectivity between freshwater bodies be altered by future human alterations to the hydrological cycle? Where does water go when it rains? Or what is the future space–time variability of flood and drought events? However, the answers to these questions will vary with location due to the specific and often poorly understood local boundary conditions and system properties that control the functional behaviour of a catchment or any other hydrologic control volume. We suggest that an open, shared and evolving perceptual model of a region's hydrology is critical to tailor our science questions, as it would be for any other study domain from the plot to the continental scale. In this opinion piece, we begin to discuss the elements of and point out some knowledge gaps in the perceptual model of the terrestrial water cycle of Great Britain. We discuss six major knowledge gaps and propose four key ways to reduce them. While the specific knowledge gaps in our perceptual model do not necessarily transfer to other places, we believe that the development of such perceptual models should be at the core of the debate for all hydrologic communities, and we encourage others to have a similar debate for their hydrologic domain.     

Evaluation of infiltration‐based stormwater management to restore hydrological processes in urban headwater streams

Urbanization threatens headwater stream ecosystems globally. Watershed restoration practices, such as infiltration‐based stormwater management, are implemented to mitigate the detrimental effects of urbanization on aquatic ecosystems. However, their effectiveness for restoring hydrologic processes and watershed storage remains poorly understood. Our study used a comparative hydrology approach to quantify the effects of urban watershed restoration on watershed hydrologic function in headwater streams within the Coastal Plain of Maryland, USA. We selected 11 headwater streams that spanned an urbanization–restoration gradient (4 forested, 4 urban‐degraded, and 3 urban‐degraded) to evaluate changes in watershed hydrologic function from both urbanization and watershed restoration. Discrete discharge and continuous, high‐frequency rainfall‐stage monitoring were conducted in each watershed. These datasets were used to develop 6 hydrologic metrics describing changes in watershed storage, flowpath connectivity, or the resultant stream flow regime. The hydrological effects of urbanization were clearly observed in all metrics, but only 1 of the 3 restored watersheds exhibited partially restored hydrologic function. At this site, a larger minimum runoff threshold was observed relative to the urban‐degraded watersheds, suggesting enhanced infiltration of stormwater runoff within the restoration structure. However, baseflow in the stream draining this watershed remained low compared to the forested reference streams, suggesting that enhanced infiltration of stormwater runoff did not recharge subsurface storage zones contributing to stream baseflow. The highly variable responses among the 3 restored watersheds were likely due to the spatial heterogeneity of urban development, including the level of impervious cover and extent of the storm sewer network. This study yielded important knowledge on how restoration strategies, such as infiltration‐based stormwater management, modulated—or failed to modulate—hydrological processes affected by urbanization, which will help improve the design of future urban watershed management strategies. More broadly, we highlighted a multimetric approach that can be used to monitor the restoration of headwater stream ecosystems in disturbed landscapes.     

Modelling surface‐water depression storage in a Prairie Pothole Region

In this study, the Precipitation‐Runoff Modelling System (PRMS) was used to simulate changes in surface‐water depression storage in the 1,126‐km² Upper Pipestem Creek basin located within the Prairie Pothole Region of North Dakota, USA. The Prairie Pothole Region is characterized by millions of small water bodies (or surface‐water depressions) that provide numerous ecosystem services and are considered an important contribution to the hydrologic cycle. The Upper Pipestem PRMS model was extracted from the U.S. Geological Survey's (USGS) National Hydrologic Model (NHM), developed to support consistent hydrologic modelling across the conterminous United States. The Geospatial Fabric database, created for the USGS NHM, contains hydrologic model parameter values derived from datasets that characterize the physical features of the entire conterminous United States for 109,951 hydrologic response units. Each hydrologic response unit in the Geospatial Fabric was parameterized using aggregated surface‐water depression area derived from the National Hydrography Dataset Plus, an integrated suite of application‐ready geospatial datasets. This paper presents a calibration strategy for the Upper Pipestem PRMS model that uses normalized lake elevation measurements to calibrate the parameters influencing simulated fractional surface‐water depression storage. Results indicate that inclusion of measurements that give an indication of the change in surface‐water depression storage in the calibration procedure resulted in accurate changes in surface‐water depression storage in the water balance. Regionalized parameterization of the USGS NHM will require a proxy for change in surface‐storage to accurately parameterize surface‐water depression storage within the USGS NHM.