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Aneesh Goly, Ramesh S. V. Teegavarapu, and Arpita Mondal

have been assessed in the literature in both space and time domains, the large number of predictors used, and the different proposed evaluation metrics used for assessing model performances. Hence, based on the recommendations in the literature provided above the following methods are chosen for this study: multiple linear regression with a seasonal component, stepwise regression, and support vector machine are used in this study along with the introduction of positive coefficient regression to

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Pennan Chinnasamy and Jason A. Hubbart

(1996) , Lautz and Seigel (2006) , and Schilling et al. (2006) . During the calibration period, the estimated error interval was set to ±0.01 m, with a confidence interval of 95%. The residual (difference between observed and modeled head) was calculated to assess the performance of the model. Thus, following calibration, a residual near zero was achieved and the model was validated from July to September 2010. To quantify model bias, observed and modeled head values were evaluated using the Nash

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A. H. M. Siddique-E-Akbor, Faisal Hossain, Safat Sikder, C. K. Shum, Steven Tseng, Yuchan Yi, F. J. Turk, and Ashutosh Limaye

state variables for the GBM basin using a large-scale hydrological model forced with satellite meteorological datasets? 2) How can we advance the application of satellite datasets, notably precipitation, to improve the hydrologic modeling for decision making on GBM basin water management? Our study therefore had two key objectives: 1) to develop, calibrate, and validate a macroscale, spatially distributed hydrologic model for the GBM basins and 2) to evaluate the performance of key satellite

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Virnei Silva Moreira, Luiz Antonio Candido, Debora Regina Roberti, Geovane Webler, Marcelo Bortoluzzi Diaz, Luis Gustavo Gonçalves de Gonçalves, Raphael Pousa, and Gervásio Annes Degrazia

.2470 . 10.2134/jeq2003.2470 Kucharik , C. J. , and T. E. Twine , 2007 : Residue, respiration, and residuals: Evaluation of a dynamic agroecosystem model using eddy flux measurements and biometric data . Agric. For. Meteor. , 146 , 134 – 158 , . 10.1016/j.agrformet.2007.05.011 Kucharik , C. J. , and Coauthors , 2000 : Testing the performance of a dynamic global ecosystem model: Water balance, carbon balance, and vegetation structure . Global

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Gretchen Keppel-Aleks, Samantha J. Basile, and Forrest M. Hoffman

evaluate models against observations. When evaluating coupled ESMs, simulated ecosystem properties may disagree with observational metrics due either to misparameterization of the relevant biogeochemical or biogeophysical processes or to biases in the physical climate drivers thereof. Model development and improvement, therefore, requires evaluating simulations using metrics that constrain functional responses—in other words, the relationships between driver and response variables—rather than simply

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Thomas Stanley, Dalia B. Kirschbaum, George J. Huffman, and Robert F. Adler

TMPA-RT and IMERG-L rainfall estimates. In quantile mapping, a value from one product is used to look up the value of the second product at the same quantile. For example, if a TMPA-RT precipitation threshold was 180 mm day −1 , the equivalent IMERG-L value would be 231 mm day −1 . Fourth, the quantile-mapped version of LHASA was run with IMERG-L data. The performance of the adapted rainfall thresholds was evaluated by a comparison to the original TMPA-based model. The true positive rate (TPR) was

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Xiaolei Fu, Lifeng Luo, Ming Pan, Zhongbo Yu, Ying Tang, and Yongjian Ding

river catchments over part of the Arkansas–Red River basin in the United States as an example, in hopes of estimating finer soil moisture distribution based on the coarse-resolution atmosphere forcing data. An evaluation of the simulated results is performed against simulations from several other state-of-the-art land surface models (Mosaic, Noah, and VIC) and in situ observations to examine the performance of the TOPLATS. 2. Study area and data description The study area ( Figure 1 ) covers part of

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Mohammad H. Mokhtari, Ibrahim Busu, Hossein Mokhtari, Gholamreza Zahedi, Leila Sheikhattar, and Mohammad A. Movahed

total data were thus used for training, testing the performance of the model, and cross validation, respectively. The output of the network was evaluated using MSE and MAE, 3. Results and discussion 3.1. Primary evaluation of the input variables As shown in Table 2 , the data analysis proved a significant positive linear correlation between the spectral bands and albedo at the significance level ( p value) of 0.01. The interrelationship of the spectral bands also suggested a high correlation

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Rosa Lasaponara, Antonio Lanorte, and Stefano Pignatti

obtained for the investigated region at the same time as MIVIS data acquisition. Fieldwork fuel typing occurred during and after the acquisition of remote sensing data. A global positioning system (GPS) was used for collecting geoposition data (latitude and longitude). Aerial photos and fieldwork fuel types were used as a ground-truth dataset, first to identify the fuel types defined in the context of the Prometheus system, and second, to evaluate performance and results obtained for the considered

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David M. Mocko and Y. C. Sud

circulation models (GCMs) and/or regional models. SSiB has been calibrated for a number of biomes using observational data taken from several regions of the world. In these calibrations and/or evaluations, atmospheric data serve as external forcing, while the model simulates soil/vegetation temperature(s), soil moisture(s), and surface fluxes that are compared with observations. Thus far, validation datasets include the Russian soil moisture data ( Robock et al., 1995 ; Schlosser et al., 1997 ; Xue et

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