scholarly journals GeoDAR: Georeferenced global dam and reservoir dataset for bridging attributes and geolocations

2021 ◽  
Author(s):  
Jida Wang ◽  
Blake A. Walter ◽  
Fangfang Yao ◽  
Chunqiao Song ◽  
Meng Ding ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Ecosystem Health ◽  
Energy Planning ◽  
Spatially Explicit ◽  
Environmental Significance ◽  
Hydrologic Modelling ◽  
Spatially Resolved ◽  
Spatial Features ◽  
Spatial Coverage ◽  
Attribute Accessibility

Abstract. Dams and reservoirs are among the most widespread human-made infrastructure on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GOODD) or detailed attributes for limited dam quantity or regions (e.g., GRanD and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD) maintained by the International Commission on Large Dams (ICOLD), documents nearly 60,000 dams with an extensive suite of attributes. Unfortunately, WRD records are not georeferenced, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dam And Reservoir (GeoDAR) dataset, created by utilizing online geocoding API and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at https://doi.org/10.6084/m9.figshare.13670527. GeoDAR v1.0 holds 21,051 dam points georeferenced from WRD, whereas v1.1 consists of a) 23,680 dam points after a careful harmonization between GeoDAR v1.0 and GRanD and b) 20,214 reservoir polygons retrieved from high-resolution water masks. Due to geocoding challenges, GeoDAR spatially resolved 40 % of the records in WRD which, however, comprise over 90 % of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we can assist in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. With a dam quantity triple that of GRanD, GeoDAR significantly enhances the spatial details of smaller but more widespread dams and reservoirs, and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modelling, water resource management, ecosystem health, and energy planning.

Predictably Predictable -  The Role of Catchment Characteristics and Complexity.

2021 ◽  
Author(s):  
Sophia Eugeni ◽  
Eric Vaags ◽  
Steven V. Weijs
Keyword(s):  
Water Resource Management ◽  
Performance Metrics ◽  
Complexity Analysis ◽  
Model Performance ◽  
Principal Component ◽  
The United States ◽  
Hydrologic Model ◽  
Hydrologic Models ◽  
Hydrologic Modelling

<p>Accurate hydrologic modelling is critical to effective water resource management. As catchment attributes strongly influence the hydrologic behaviors in an area, they can be used to inform hydrologic models to better predict the discharge in a basin. Some basins may be more difficult to accurately predict than others. The difficulty in predicting discharge may also be related to the complexity of the discharge signal. The study establishes the relationship between a catchment’s static attributes and hydrologic model performance in those catchments, and also investigates the link to complexity, which we quantify with measures of compressibility based in information theory. </p><p>The project analyzes a large national dataset, comprised of catchment attributes for basins across the United States, paired with established performance metrics for corresponding hydrologic models. Principal Component Analysis (PCA) was completed on the catchment attributes data to determine the strongest modes in the input. The basins were clustered according to their catchment attributes and the performance within the clusters was compared. </p><p>Significant differences in model performance emerged between the clusters of basins. For the complexity analysis, details of the implementation and technical challenges will be discussed, as well as preliminary results.</p>

scholarly journals Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada

2020 ◽  
Vol 24 (10) ◽  
pp. 4887-4902
Author(s):  
Fraser King ◽  
Andre R. Erler ◽  
Steven K. Frey ◽  
Christopher G. Fletcher
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Water Resource Management ◽  
Snow Water Equivalent ◽  
Bias Reduction ◽  
Machine Learning Techniques ◽  
Spatial Coverage ◽  
Snow Water ◽  
In Situ Observations

Abstract. Snow is a critical contributor to Ontario's water-energy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n=383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5∘ N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.

scholarly journals Trends and Persistence of Dry–Wet Conditions in Northeast Brazil

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1134
Author(s):  
Antonio Samuel Alves da Silva ◽  
Moacyr Cunha Filho ◽  
Rômulo Simões Cezar Menezes ◽  
Tatijana Stosic ◽  
Borko Stosic
Keyword(s):  
Time Scales ◽  
Water Resource Management ◽  
Standardized Precipitation Index ◽  
Kendall Test ◽  
Spatially Explicit ◽  
Fluctuation Analysis ◽  
Short Term ◽  
Agricultural Planning ◽  
Detrended Fluctuation

We analyze trend and persistence in Standardized Precipitation Index (SPI) time series derived from monthly rainfall data at 133 gauging stations in Pernambuco state, Brazil, using a suite of complementary methods to address the spatially explicit tendencies, and persistence. SPI was calculated for 1-, 3-, 6-, and 12-month time scales from 1950 to 2012. We use Mann–Kendall test and Sen’s slope to determine sign and magnitude of the trend, and detrended fluctuation analysis (DFA) method to quantify long-term correlations. For all time scales significant negative trends are obtained in the Sertão (deep inland) region, while significant positive trends are found in the Agreste (intermediate inland), and Zona da Mata (coastal) regions. The values of DFA exponents show different scaling behavior for different time scales. For short-term conditions described by SPI-1 the DFA exponent is close to 0.5 indicating weak persistency and low predictability, while for medium-term conditions (SPI-3 and SPI-6) DFA exponents are greater than 0.5 and increase with time scale indicating stronger persistency and higher predictability. For SPI-12 that describes long-term precipitation patterns, the values of DFA exponents for inland regions are around 1, indicating strong persistency, while in the shoreline the value of the DFA exponent is between 1.0 and 1.5, indicating anti-persistent fractional Brownian motion. These results should be useful for agricultural planning and water resource management in the region.

scholarly journals Deriving principal channel metrics from bank and long-profile geometry with the R package cmgo

2017 ◽  
Vol 5 (3) ◽  
pp. 557-570 ◽  
Author(s):  
Antonius Golly ◽  
Jens M. Turowski
Keyword(s):  
Water Resource Management ◽  
Software Tool ◽  
R Package ◽  
Landscape Patterns ◽  
Local Flow ◽  
Channel Dynamics ◽  
Spatial Features ◽  
Flow Hydraulics ◽  
Landscape Formation ◽  
Underlying Processes

Abstract. Landscape patterns result from landscape forming processes. This link can be exploited in geomorphological research by reversely analyzing the geometrical content of landscapes to develop or confirm theories of the underlying processes. Since rivers represent a dominant control on landscape formation, there is a particular interest in examining channel metrics in a quantitative and objective manner. For example, river cross-section geometry is required to model local flow hydraulics, which in turn determine erosion and thus channel dynamics. Similarly, channel geometry is crucial for engineering purposes, water resource management, and ecological restoration efforts. These applications require a framework to capture and derive the data. In this paper we present an open-source software tool that performs the calculation of several channel metrics (length, slope, width, bank retreat, knickpoints, etc.) in an objective and reproducible way based on principal bank geometry that can be measured in the field or in a GIS. Furthermore, the software provides a framework to integrate spatial features, for example the abundance of species or the occurrence of knickpoints. The program is available at https://github.com/AntoniusGolly/cmgo and is free to use, modify, and redistribute under the terms of the GNU General Public License version 3 as published by the Free Software Foundation.

scholarly journals Application of machine learning techniques for regional bias correction of SWE estimates in Ontario, Canada

2020 ◽  
Author(s):  
Fraser King ◽  
Andre R. Erler ◽  
Steven K. Frey ◽  
Christopher G. Fletcher
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Water Resource Management ◽  
Snow Water Equivalent ◽  
Evaluation Criteria ◽  
Bias Reduction ◽  
Machine Learning Techniques ◽  
Spatial Coverage ◽  
In Situ Observations

Abstract. Snow is a critical contributor to Ontario's water-energy budget with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n = 383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction however, only the RF method captures the nonlinearity in the bias, and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5° N and East (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs, and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.

scholarly journals Deriving principle channel metrics from bank and long-profile geometry with the R-package cmgo

2017 ◽  
Author(s):  
Antonius Golly ◽  
Jens M. Turowski
Keyword(s):  
Water Resource Management ◽  
Software Tool ◽  
R Package ◽  
Landscape Patterns ◽  
Local Flow ◽  
Channel Dynamics ◽  
Spatial Features ◽  
Flow Hydraulics ◽  
Landscape Formation ◽  
Underlying Processes

Abstract. Landscape patterns result from landscape forming processes. This link can be exploited in geomorphological research by reversely analyzing the geometrical content of landscapes to develop or confirm theories of the underlying processes. Since rivers represent a dominant control on landscape formation, there is a particular interest in examining channel metrics in a quantitative and objective manner. For example, river cross-section geometry is required to model local flow hydraulics which in turn determine erosion and thus channel dynamics. Similarly, channel geometry is crucial for engineering purposes, water resource management and ecological restauration efforts. These applications require a framework to capture and derive the data. In this paper we present an open-source software tool that performs the calculation of several channel metrics (length, slope, width, bank retreat, etc.) in an objective and reproducible way based on principle bank geometry that can be measured in the field or in a GIS. Furthermore, the software provides a framework to integrate spatial features, for example the abundance of species or the occurrence of knickpoints. The program is available https://github.com/AntoniusGolly/cmgo and is free to use, modify and redistribute under the terms of the GNU General Public License version 3 as published by the Free Software Foundation.

Electron Channeling Patterns

1977 ◽  
Vol 35 ◽  
pp. 12-13
Author(s):  
David C. Joy
Keyword(s):  
Electron Diffraction ◽  
Incidence Angle ◽  
Photographic Plate ◽  
Diffraction Effect ◽  
Angle Of Incidence ◽  
Bulk Single Crystal ◽  
Time Resolved ◽  
Electron Channeling ◽  
Spatially Resolved ◽  
Large Bulk

Electron channeling patterns (ECP) were first found by Coates (1967) while observing a large bulk, single crystal of silicon in a scanning electron microscope. The geometric pattern visible was shown to be produced as a result of the changes in the angle of incidence, between the beam and the specimen surface normal, which occur when the sample is examined at low magnification (Booker, Shaw, Whelan and Hirsch 1967).A conventional electron diffraction pattern consists of an angularly resolved intensity distribution in space which may be directly viewed on a fluorescent screen or recorded on a photographic plate. An ECP, on the other hand, is produced as the result of changes in the signal collected by a suitable electron detector as the incidence angle is varied. If an integrating detector is used, or if the beam traverses the surface at a fixed angle, then no channeling contrast will be observed. The ECP is thus a time resolved electron diffraction effect. It can therefore be related to spatially resolved diffraction phenomena by an application of the concepts of reciprocity (Cowley 1969).

FT-IR microspectroscopic analysis of diseased white matter brain tissues

1995 ◽  
Vol 53 ◽  
pp. 968-969
Author(s):  
Steven M. Le Vine ◽  
David L. Wetzel
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Twitcher Mouse ◽  
Grid Pattern ◽  
Marked Contrast ◽  
Spatially Resolved ◽  
Ft Ir ◽  
Ir Microspectroscopy ◽  
Chemical Evidence

In situ FT-IR microspectroscopy has allowed spatially resolved interrogation of different parts of brain tissue. In previous work the spectrrscopic features of normal barin tissue were characterized. The white matter, gray matter and basal ganglia were mapped from appropriate peak area measurements from spectra obtained in a grid pattern. Bands prevalent in white matter were mostly associated with the lipid. These included 2927 and 1469 cm-1 due to CH2 as well as carbonyl at 1740 cm-1. Also 1235 and 1085 cm-1 due to phospholipid and galactocerebroside, respectively (Figs 1and2). Localized chemical changes in the white matter as a result of white matter diseases have been studied. This involved the documentation of localized chemical evidence of demyelination in shiverer mice in which the spectra of white matter lacked the marked contrast between it and gray matter exhibited in the white matter of normal mice (Fig. 3).The twitcher mouse, a model of Krabbe’s desease, was also studied. The purpose in this case was to look for a localized build-up of psychosine in the white matter caused by deficiencies in the enzyme responsible for its breakdown under normal conditions.

Ultra-spatially resolved synchrotron powered FT-IR microspectroscopy of individual cells of biological material

1995 ◽  
Vol 53 ◽  
pp. 884-885
Author(s):  
David L. Wetzel ◽  
John A. Reffner ◽  
Gwyn P. Williams
Keyword(s):  
Thermal Noise ◽  
Spot Size ◽  
Acquisition Time ◽  
Thermal Source ◽  
Signal To Noise ◽  
Spatially Resolved ◽  
Good Spatial Resolution ◽  
Small Spot ◽  
Ft Ir ◽  
Ir Microspectroscopy

Synchrotron radiation is 100 to 1000 times brighter than a thermal source such as a globar. It is not accompanied with thermal noise and it is highly directional and nondivergent. For these reasons, it is well suited for ultra-spatially resolved FT-IR microspectroscopy. In efforts to attain good spatial resolution in FT-IR microspectroscopy with a thermal source, a considerable fraction of the infrared beam focused onto the specimen is lost when projected remote apertures are used to achieve a small spot size. This is the case because of divergence in the beam from that source. Also the brightness is limited and it is necessary to compromise on the signal-to-noise or to expect a long acquisition time from coadding many scans. A synchrotron powered FT-IR Microspectrometer does not suffer from this effect. Since most of the unaperatured beam’s energy makes it through even a 12 × 12 μm aperture, that is a starting place for aperture dimension reduction.

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