scholarly journals Improving the Applicability of Hydrologic Models for Food–Energy–Water Nexus Studies Using Remote Sensing Data

2020 ◽  
Vol 12 (4) ◽  
pp. 599 ◽  
Author(s):  
Akash Koppa ◽  
Mekonnen Gebremichael
Keyword(s):  
Remote Sensing ◽  
Multivariate Calibration ◽  
Remote Sensing Data ◽  
Hydrologic Model ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Hydrologic Models ◽  
Food Energy ◽  
Trade Offs ◽  
Spatially Distributed

Food, energy, and water (FEW) nexus studies require reliable estimates of water availability, use, and demand. In this regard, spatially distributed hydrologic models are widely used to estimate not only streamflow (SF) but also different components of the water balance such as evapotranspiration (ET), soil moisture (SM), and groundwater. For such studies, the traditional calibration approach of using SF observations is inadequate. To address this, we use state-of-the-art global remote sensing-based estimates of ET and SM with a multivariate calibration methodology to improve the applicability of a widely used spatially distributed hydrologic model (Noah-MP) for FEW nexus studies. Specifically, we conduct univariate and multivariate calibration experiments in the Mississippi river basin with ET, SM, and SF to understand the trade-offs in accurately simulating ET, SM, and SF simultaneously. Results from univariate calibration with just SF reveal that increased accuracy in SF at the cost of degrading the spatio-temporal accuracy of ET and SM, which is essential for FEW nexus studies. We show that multivariate calibration helps preserve the accuracy of all the components involved in calibration. The study emphasizes the importance of multiple sources of information, especially from satellite remote sensing, for improving FEW nexus studies.

scholarly journals Possibilities of the Practical Application of Remote Sensing Data in Real Property Appraisal

2015 ◽  
Vol 23 (2) ◽  
pp. 68-76
Author(s):  
Rafał Dąbrowski ◽  
Dorota Latos
Keyword(s):  
Remote Sensing ◽  
Real Estate ◽  
Remote Sensing Data ◽  
Real Estate Market ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Remotely Sensed Data ◽  
Real Property ◽  
Sensing Data ◽  
Wide Range

Abstract In the process of real estate evaluation, there are many important elements that should be taken into consideration: for example the type of property which is under evaluation, real estate market analysis, and the selection of appropriate evaluation approaches, methods and techniques. In each of the approaches, different parameters are important. Most importantly, however, is the appropriate selection and identification of real estate characteristics, both physical and economic, as well as the determination of the importance of these qualities so that, when necessary, it will be possible to make the appraisal and select those characteristics which have the greatest impact on value of property. To acquire information about real estate, one can analyze data from multiple sources of information, such as: the land and mortgage register, land and buildings register, local town and country planning, industry data and other. Integrating data from all these sources allows comprehensive and up-to-date knowledge on the assessed property to be developed. One of best sources of data describing the characteristics of real estate is remote sensing. This method offers a high spectrum of information and, therefore, allows a wide range of analyses. As a result, one can obtain highly accurate facts about the property. Remote sensing data, however, are not a panacea for all problems and have some limitations that determine their applicability. This paper describes the possibilities of specifying some characteristics of the property under appraisal based on remote sensing data. For this purpose, the qualitative - quantitative analysis of the four most "common" types of remote sensing data were made, along with determining selected characteristics of 30 properties located in the Warsaw - Bemowo suburb area. The results of this research made it possible to answer the question of whether it is practicable and, if so, to what extent, to use remotely sensed data as the basis for determining the characteristics of a property as well as connecting the types of data to individual properties. The paper focuses on defining the characteristics of two kinds of real estate: land and buildings.

scholarly journals Monitoring of glaciers in the Polar Urals using remote sensing Data

2020 ◽  
Vol 217 ◽  
pp. 04003
Author(s):  
Natalia Martynova ◽  
Valentina Budarova ◽  
Artem Sheremetinsky ◽  
Nikita Mezentsev
Keyword(s):  
Remote Sensing ◽  
Technological Progress ◽  
Satellite Images ◽  
Research Question ◽  
Remote Sensing Data ◽  
Field Work ◽  
Sources Of Information ◽  
Sensing Data ◽  
Polar Urals ◽  
The Polar Urals

The development of technological progress provides more opportunities for indirect monitoring of changes in the environment. Remote sensing is one of The most accessible and reliable sources of information. In this work, we used satellite images from the Landsat family. The theoretical justification of the research question is given. The research methodology was developed. Collection and processing of satellite images for various time periods. A series of schematic maps based on remote sensing Data has been created. As a result of digitization of satellite images, 9 glacier contours were obtained by year. We determined the area of the Romantics glacier and found that it lost at least 60% of its original area. These studies were used to build a series of cartographic schemes that clearly show the reduction of the glacier area. It is concluded that the use of remote sensing allows you to solve problems, monitoring the object. The use of this method allows not only to save time for field work, but also material costs for expedition equipment and various equipment. This method can be tested on any objects.

scholarly journals Multi-Source Neural Variational Inference

2019 ◽  
Vol 33 ◽  
pp. 4114-4121
Author(s):  
Richard Kurle ◽  
Stephan Günnemann ◽  
Patrick Van der Smagt
Keyword(s):  
Latent Variables ◽  
Information Sources ◽  
Information Source ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Learning Framework ◽  
Trade Offs ◽  
Variational Autoencoder ◽  
Learning Research ◽  
Inference Methods

Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference methods that take into account the complementarity and redundancy of various information sources. In this paper we formulate a variational autoencoder based multi-source learning framework in which each encoder is conditioned on a different information source. This allows us to relate the sources via the shared latent variables by computing divergence measures between individual source’s posterior approximations. We explore a variety of options to learn these encoders and to integrate the beliefs they compute into a consistent posterior approximation. We visualise learned beliefs on a toy dataset and evaluate our methods for learning shared representations and structured output prediction, showing trade-offs of learning separate encoders for each information source. Furthermore, we demonstrate how conflict detection and redundancy can increase robustness of inference in a multi-source setting.

scholarly journals Technical note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models

2013 ◽  
Vol 10 (4) ◽  
pp. 4275-4299 ◽  
Author(s):  
J. D. Herman ◽  
J. B. Kollat ◽  
P. M. Reed ◽  
T. Wagener
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Hydrologic Model ◽  
Technical Note ◽  
Diagnostic Methods ◽  
Laboratory Research ◽  
Storage Space ◽  
Hydrologic Models ◽  
Global Sensitivity ◽  
Spatially Distributed

Abstract. The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) model over a six-month period in the Blue River Watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly identify sensitive and insensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. Method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models.

scholarly journals Monitoring temporary ponds dynamics in arid areas with remote sensing and spatial modelling

2010 ◽  
Vol 7 (1) ◽  
pp. 103-133
Author(s):  
V. Soti ◽  
C. Puech ◽  
D. Lo Seen ◽  
A. Bertran ◽  
C. Vignolles ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Pond Water ◽  
Temporal Variations ◽  
Hydrologic Model ◽  
Meteorological Station ◽  
Rainfall Data ◽  
Temporary Ponds

Abstract. A hydrologic pond model was developed that simulates daily spatial and temporal variations (area, volume and height) of temporary ponds around Barkedji, a village located in the Ferlo Region in Senegal. The model was tested with rainfall input data from a meteorological station and from Tropical Rainfall Measuring Mission (TRMM) satellites. During calibration phase, we used climatic, hydrologic and topographic field data of Barkedji pond collected daily during the 2002 rainy season. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Digital Elevation Model (DEM) and a QuickBird satellite image acquired in August 2005 (2.5 m pixel size) were used to apply the hydrologic model to all ponds (98 ponds) of the study area. With input rainfall data from the meteorological station, simulated water heights values for years 2001 and 2002 were significantly correlated with observed water heights for Furdu, Mous 2 and Mous 3 ponds, respectively with 0.81, 0.67 and 0.88 Nash coefficients. With rainfall data from TRMM satellite as model input, correlations were lower, particularly for year 2001. For year 2002, the results were acceptable with 0.61, 0.65 and 0.57 Nash coefficients for Barkedji, Furdu and Mous 3 ponds, respectively. To assess the accuracy of our model for simulating water areas, we used a pond map derived from Quickbird imagery (August 2007). The validation showed that modelled water areas were significantly correlated with observed pond surfaces (r2=0.90). Overall, our results demonstrate the possibility of using a simple hydrologic model with remote sensing data (Quickbird, ASTER DEM, TRMM) to assess pond water heights and water areas of a homogeneous arid area.

scholarly journals ROAD AND ROADSIDE FEATURE EXTRACTION USING IMAGERY AND LIDAR DATA FOR TRANSPORTATION OPERATION

2015 ◽  
Vol II-3/W4 ◽  
pp. 239-246 ◽  
Author(s):  
S. Ural ◽  
J. Shan ◽  
M. A. Romero ◽  
A. Tarko
Keyword(s):  
Remote Sensing ◽  
Cross Sections ◽  
Road Networks ◽  
Remote Sensing Data ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Multiple Sources ◽  
The Road ◽  
Average Grade ◽  
Road Projects

Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information needed for a robust evaluation of road projects includes road centerline, width, and extent together with the average grade, cross-sections, and obstructions near the travelled way. Remote sensing is equipped with a large collection of data and well-established tools for acquiring the information and extracting aforementioned various road features at various levels and scopes. Even with many remote sensing data and methods available for road extraction, transportation operation requires more than the centerlines. Acquiring information that is spatially coherent at the operational level for the entire road system is challenging and needs multiple data sources to be integrated. In the presented study, we established a framework that used data from multiple sources, including one-foot resolution color infrared orthophotos, airborne LiDAR point clouds, and existing spatially non-accurate ancillary road networks. We were able to extract 90.25% of a total of 23.6 miles of road networks together with estimated road width, average grade along the road, and cross sections at specified intervals. Also, we have extracted buildings and vegetation within a predetermined proximity to the extracted road extent. 90.6% of 107 existing buildings were correctly identified with 31% false detection rate.

scholarly journals Uncertainty reduction and parameters estimation of a~distributed hydrological model with ground and remote sensing data

2014 ◽  
Vol 11 (6) ◽  
pp. 6215-6271
Author(s):  
F. Silvestro ◽  
S. Gabellani ◽  
R. Rudari ◽  
F. Delogu ◽  
P. Laiolo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Remote Sensing Data ◽  
Hydrologic Model ◽  
Parameters Estimation ◽  
State Variables ◽  
Test Bed ◽  
Sensing Data

Abstract. During the last decade the opportunity and usefulness of using remote sensing data in hydrology, hydrometeorology and geomorphology has become even more evident and clear. Satellite based products often provide the advantage of observing hydrologic variables in a distributed way while offering a different view that can help to understand and model the hydrological cycle. Moreover, remote sensing data are fundamental in scarce data environments. The use of satellite derived DTM, which are globally available (e.g. from SRTM as used in this work), have become standard practice in hydrologic model implementation, but other types of satellite derived data are still underutilized. In this work, Meteosat Second Generation Land Surface Temperature (LST) estimates and Surface Soil Moisture (SSM) available from EUMETSAT H-SAF are used to calibrate the Continuum hydrological model that computes such state variables in a prognostic mode. This work aims at proving that satellite observations dramatically reduce uncertainties in parameters calibration by reducing their equifinality. Two parameter estimation strategies are implemented and tested: a multi-objective approach that includes ground observations and one solely based on remotely sensed data. Two Italian catchments are used as the test bed to verify the model capability in reproducing long-term (multi-year) simulations.

scholarly journals Observed and simulated hydroclimatology using distributed hydrologic model from in-situ and multi-satellite remote sensing datasets in Lake Victoria region in East Africa

2010 ◽  
Vol 7 (4) ◽  
pp. 4785-4816 ◽  
Author(s):  
S. I. Khan ◽  
P. Adhikari ◽  
Y. Hong ◽  
H. Vergara ◽  
T. Grout ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Water Cycle ◽  
Lake Victoria ◽  
Remote Sensing Data ◽  
Hydrologic Model ◽  
East African ◽  
Catchment Scale ◽  
Distributed Hydrologic Model

Abstract. Floods and droughts are common, recurring natural hazards in East African nations. Studies of hydro-climatology at daily, seasonal, and annual time scale is an important in understanding and ultimately minimizing the impacts of such hazards. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5- and 10-year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia Basin. The spatiotemporal variability of the water cycle components were quantified using a physically-based, distributed hydrologic model, with in-situ and multi-satellite remote sensing datasets. Moreover, the hydrologic capability of remote sensing data such as TRMM-3B42V6 was tested in terms of reconstruction of the water cycle components. The spatial distribution and time series of modeling results for precipitation (P), evapotranspiration (ET), and change in storage (dS/dt) showed considerable agreement with the monthly model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The hydrologic model captured the spatial variability of the soil moisture storage. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. Relatively high flows were experienced near the basin outlet from previous rainfall, with a new flood peak responding to the rainfall in the upper part of the basin. The monthly peak runoff was observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. The model was found to be useful in poorly gauged catchments using satellite forcing data and showed the potential to be used not only for the investigation of the catchment scale water balance but also for addressing issues pertaining to sustainability of the resources within the catchment.

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