CREAMS/GLEAMS: Model Use, Calibration, and Validation

Geographic information system (GIS) based tools have become popular for solar photovoltaic (PV) potential estimations, especially in urban areas. There are readily available tools for the mapping and estimation of solar irradiation that give results with the click of a button. Although these tools capture the complexities of the urban environment, they often miss the more important atmospheric parameters that determine the irradiation and potential estimations. Therefore, validation of these models is necessary for accurate potential energy yield and capacity estimations. This paper demonstrates the calibration and validation of the solar radiation model developed by Fu and Rich, employed within ArcGIS, with a focus on the input atmospheric parameters, diffusivity and transmissivity for the Netherlands. In addition, factors affecting the model’s performance with respect to the resolution of the input data were studied. Data were calibrated using ground measurements from Royal Netherlands Meteorological Institute (KNMI) stations in the Netherlands and validated with the station data from Cabauw. The results show that the default model values of diffusivity and transmissivity lead to substantial underestimation or overestimation of solar insolation. In addition, this paper also shows that calibration can be performed at different time scales depending on the purpose and spatial resolution of the input data.

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Fracture Parameters Calibration and Validation for the High Strength Steel based on the Mesoscale Failure Index

Theoretical and Applied Fracture Mechanics ◽

10.1016/j.tafmec.2021.102929 ◽

2021 ◽

pp. 102929

Author(s):

Haohui Xin ◽

José A.F.O. Correia ◽

Milan Veljkovic ◽

Filippo Berto

Keyword(s):

High Strength Steel ◽

High Strength ◽

Calibration And Validation ◽

Failure Index ◽

Fracture Parameters

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Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales

Remote Sensing ◽

10.3390/rs13020257 ◽

2021 ◽

Vol 13 (2) ◽

pp. 257 ◽

Cited By ~ 1

Author(s):

Shaun R. Levick ◽

Tim Whiteside ◽

David A. Loewensteiner ◽

Mitchel Rudge ◽

Renee Bartolo

Keyword(s):

Remote Sensing ◽

Vegetation Structure ◽

Laser Scanning ◽

Canopy Cover ◽

Structural Diversity ◽

Point Clouds ◽

Large Area ◽

Calibration And Validation ◽

Savanna Vegetation ◽

Landscape Scales

Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.

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Calibration and Validation of the TEMPEST-D CubeSat Radiometer

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3018999 ◽

2020 ◽

pp. 1-11

Author(s):

Wesley Berg ◽

Shannon T. Brown ◽

Boon H. Lim ◽

Steven C. Reising ◽

Yuriy Goncharenko ◽

...

Keyword(s):

Calibration And Validation ◽

The Tempest

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A unique vadose zone model for shallow aquifers: the Hetao irrigation district, China

Hydrology and Earth System Sciences ◽

10.5194/hess-23-3097-2019 ◽

2019 ◽

Vol 23 (7) ◽

pp. 3097-3115 ◽

Cited By ~ 1

Author(s):

Zhongyi Liu ◽

Xingwang Wang ◽

Zailin Huo ◽

Tammo Siert Steenhuis

Keyword(s):

Field Experiment ◽

Irrigation Water ◽

Yellow River ◽

Shallow Groundwater ◽

Groundwater Table ◽

Irrigation District ◽

Zone Model ◽

Calibration And Validation ◽

Irrigation Water Use ◽

Hetao Irrigation District

Abstract. Rapid population growth is increasing pressure on the world water resources. Agriculture will require crops to be grown with less water. This is especially the case for the closed Yellow River basin, necessitating a better understanding of the fate of irrigation water in the soil. In this paper, we report on a field experiment and develop a physically based model for the shallow groundwater in the Hetao irrigation district in Inner Mongolia, in the arid middle reaches of the Yellow River. Unlike other approaches, this model recognizes that field capacity is reached when the matric potential is equal to the height above the groundwater table and not by a limiting soil conductivity. The field experiment was carried out in 2016 and 2017. Daily moisture contents at five depths in the top 90 cm and groundwater table depths were measured in two fields with a corn crop. The data collected were used for model calibration and validation. The calibration and validation results show that the model-simulated soil moisture and groundwater depth fitted well. The model can be used in areas with shallow groundwater to optimize irrigation water use and minimize tailwater losses.

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