Spatial‐temporal evaluation of different reference evapotranspiration methods based on the climate forecast system reanalysis data

2021 ◽  
Author(s):  
Tekalegn Ayele Woldesenbet ◽  
Nadir Ahmed Elagib
Keyword(s):  
Reference Evapotranspiration ◽  
Reanalysis Data ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

scholarly journals Assessment of applicability of mike 11-nam hydrological module for rainfall runoff modelling in a poorly studied river basin

Vestnik MGSU ◽  
2020 ◽  
pp. 1030-1046 ◽  
Author(s):  
Anghesom A. Ghebrehiwot ◽  
Dmitriy V. Kozlov
Keyword(s):  
River Basin ◽  
Reanalysis Data ◽  
Data Sets ◽  
Climate Forecast ◽  
Rainfall Runoff ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis ◽  
Mike 11 ◽  
Instrumental Observations

Introduction. The need to simulate hydrological processes is caused by, among other factors, the complexity of hydrological systems and data insufficiency due to the unavailability or a small number of instrumental observations. Recently, the reanalysis of the climate data supplied by the world’s leading meteorological centres has been used quite successfully in the regions that suffer from the deficit of instrumental information. This paper assesses the applicability of climate reanalysis data to rainfall runoff (“rainfall runoff”) modelling in the poorly studied river basin in Eritrea. Materials and methods. Climate Forecast System Reanalysis (CFSR) data generated by the National Centre for Environmental Prediction (USA) were used. Besides, high-resolution topographic information, generated by the SRTM international research project, was also applied to set the drainage area boundaries and to simulate the river network using such tools as MIKE and GIS. In addition, calibration and validation (evaluation) of the hydrological model (simulation quality) were performed using the Nash-Sutcliffe efficiency criterion, the determination coefficient, and the root mean square error of volumetric and peak flow rates. Results. The results suggest that a considerable overestimation of precipitation in the reanalysis data set, which in turn has a significant effect on other variables such as potential evapotranspiration, leads to a significant discrepancy between water balance values which are simulated and registered by the hydrographs. Conclusions. The applicability of Climate Forecast System Reanalysis (CFSR) data to river flow modelling in arid and semi-arid regions such as Eritrea is questionable. The incompatibility of spatial and temporal variations of initial variables (e.g. precipitation), derived from reanalysis data sets and instrumental observations, is undoubtedly the main reason for errors. Thus, the application of reanalysis data sets and development of hydrological models for the region under study requires further intensive research aimed at identifying most effective mechanisms designated for the harmonization of differences between reanalysis data and field observations. In the course of further research, CFSR information is to be converted into more realistic data; climate reanalysis indicators, provided by other sources and designated for different time scales in the context of the “rainfall runoff” model are to be assessed, and the efficiency of other software systems is to be compared with MIKE 11-NAM.

Prediction of Surface Flow by Forcing of Climate Forecast System Reanalysis Data

2016 ◽  
Vol 30 (8) ◽  
pp. 2627-2640 ◽  
Author(s):  
Milad Jajarmizadeh ◽  
Lariyah Mohd Sidek ◽  
Majid Mirzai ◽  
Sina Alaghmand ◽  
Sobri Harun ◽  
...  
Keyword(s):  
Reanalysis Data ◽  
Surface Flow ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

scholarly journals Erratum to: Prediction of Surface Flow by Forcing of Climate Forecast System Reanalysis Data

2016 ◽  
Vol 30 (10) ◽  
pp. 3651-3651
Author(s):  
Milad Jajarmizadeh ◽  
Lariyah Mohd Sidek ◽  
Majid Mirzaei ◽  
Sina Alaghmand ◽  
Sobri Harun ◽  
...  
Keyword(s):  
Reanalysis Data ◽  
Surface Flow ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

Climatic characteristics of drift ice in the northern Barents Sea

2019 ◽  
Vol 11 (3) ◽  
pp. 800-811
Author(s):  
Chenglin Duan ◽  
Sheng Dong ◽  
Zhifeng Wang ◽  
Zhenkun Liao
Keyword(s):  
Barents Sea ◽  
Ice Thickness ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis ◽  
Ice Velocity ◽  
Environmental Prediction ◽  
Climatic Characteristics

Abstract In this paper, a preliminary climatic description of the long-term offshore drift ice characteristics in the northern Barents Sea has been investigated from 1987 to 2016 based on the satellite ice motion datasets from National Snow and Ice Data Center (NSIDC) and reanalysis ice thickness datasets from National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR) and Climate Forecast System Version 2 (CFSv2). Both the ice velocity and thickness conditions have been studied at the three fixed locations from west to east. Annual and monthly drift ice roses indicate that the directions from WSW to SE are primarily prevailing, particularly in winter months. Besides, the annual ice speed extremums exceeding 40 cm s–1 mostly occur in the southerly directions from November to April. For the ice thickness, results reveal that it is prominently distributed in a thicker interval between 70 and 120 cm, and a thinner interval between 20 and 70 cm. The annual thickness maxima approximately range from 90 to 170 cm, primarily occurring from May to June, and demonstrate a light decreasing trend.

Validation of a thirty year wave hindcast using the Climate Forecast System Reanalysis winds

2013 ◽  
Vol 70 ◽  
pp. 189-206 ◽  
Author(s):  
Arun Chawla ◽  
Deanna M. Spindler ◽  
Hendrik L. Tolman
Keyword(s):  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis ◽  
Wave Hindcast

scholarly journals Evaluation of the Climate Forecast System Reanalysis weather data for the hydrological model in the Arctic watershed Målselv

2021 ◽  
Author(s):  
Minh Tuan Bui ◽  
Jinmei Lu ◽  
Linmei Nie
Keyword(s):  
High Resolution ◽  
Hydrological Model ◽  
The Arctic ◽  
Annual Rainfall ◽  
Climate Forecast ◽  
Hydrological Models ◽  
Forecast System ◽  
Climate Forecast System ◽  
Climate Forecast System Reanalysis

Abstract The high-resolution Climate Forecast System Reanalysis (CFSR) data have recently become an alternative input for hydrological models in data-sparse regions. However, the quality of CFSR data for running hydrological models in the Arctic is not well studied yet. This paper aims to compare the quality of CFSR data with ground-based data for hydrological modeling in an Arctic watershed, Målselv. The QSWAT model, a coupling of the hydrological model SWAT (soil and water assessment tool) and the QGIS, was applied in this study. The model ran from 1995 to 2012 with a 3-year warm-up period (1995–1997). Calibration (1998–2007), validation (2008–2012), and uncertainty analyses were conducted by the model for each dataset at five hydro-gauging stations within the watershed. The objective function Nash–Sutcliffe coefficient of efficiency for calibration is 0.65–0.82 with CFSR data and 0.55–0.74 with ground-based data, which indicate higher performance of the high-resolution CFSR data than the existing scattered ground-based data. The CFSR weather grid points showed higher variation in precipitation than the ground-based weather stations across the whole watershed. The calculated average annual rainfall by CFSR data for the whole watershed is approximately 24% higher than that by ground-based data, which results in some higher water balance components. The CFSR data also demonstrate its high capacities to replicate the streamflow hydrograph, in terms of timing and magnitude of peak and low flow. Through examination of the uncertainty coefficients P-factors (≥0.7) and R-factors (≤1.5), this study concludes that CFSR data are a reliable source for running hydrological models in the Arctic watershed Målselv.

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