Evaluation of the Performance of CFSR Reanalysis Data Set for Estimating Potential Evapotranspiration (PET) in Turkey

Evapotranspiration is an important parameter for hydrological, meteorological and agricultural studies. However, the calculation of actual evapotranspiration is very challenging and costly. Therefore, Potential Evapotranspiration (PET) is typically calculated using meteorological data to calculate actual evapotranspiration. However, it is very difficult to get complete and accurate data from meteorology stations in, rural and mountainous regions. This study examined the availability of the Climate Forecast System Reanalysis (CFSR) reanalysis data set as an alternative to meteorological observation stations in the computation of potential annual and seasonal evapotranspiration. The PET calculations using the CFSR reanalysis dataset for the period 1987-2017 were compared to data observed at 259 weather stations observed in Turkey. As a result of the assessments, it was determined that the seasons in which the CFSR reanalysis data set had the best prediction performance were the winter (C'= 0.76 and PBias = -3.77) and the autumn (C' = 0.75 and PBias = -12.10). The worst performance was observed for the summer season. The performance of the annual prediction was determined as C'= 0.60 and PBias = -15.27. These findings indicate that the results of the PET calculation using the CFSR reanalysis data set are relatively successful for the study area. However, the data should be evaluated with observation data before being used especially in the summer models.

Application of meteorological element combination-driven SWAT model based on meteorological datasets in alpine basin

Thus far, reanalysis-based meteorological products have drawn little attention to the influence of meteorological elements of products on hydrological modeling. This study aims to evaluate the hydrological application potential of the precipitation, temperature, and solar radiation of the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and Climate Forecast System Reanalysis (CFSR) in an alpine basin. The precipitation, temperature, and solar radiation of the gauge-observed meteorological dataset (GD), CFSR, and CMADS were cross-combined, and 20 scenarios were constructed to drive the SWAT model. From the comprehensive comparisons of all scenarios, we drew the following conclusions: (1) among the three meteorological elements, precipitation has the greatest impact on the simulation results, and using GD precipitation from sparse stations yielded better performance than CMADS and CFSR; (2) although the SWAT modeling driven by CMADS and CFSR performed poorly, with CMADS underestimation and CFSR overestimation, the temperature and solar radiation of CMADS and CFSR can be an alternative data source for streamflow simulation; (3) models using GD precipitation, CFSR temperature, and CFSR solar radiation as input yielded the best performance in streamflow simulation, suggesting that these data sources can be applied to this data-scarce alpine region.

Predicting Tropical Monsoon Hydrology Using CFSR and CMADS Data over the Cau River Basin in Vietnam

To improve knowledge of this matter, the potential application of two gridded meteorological products (GMPs), the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and Climate Forecast System Reanalysis (CFSR), are compared for the first time with data from ground-based meteorological stations over 6 years, from 2008 to 2013, over the Cau River basin (CRB), northern Vietnam. Statistical indicators and the Soil and Water Assessment Tool (SWAT) model are employed to investigate the hydrological performances of the GMPs against the data of 17 rain gauges distributed across the CRB. The results show that there are strong correlations between the temperature reanalysis products in both CMADS and CFSR and those obtained from the ground-based observations (the correlation coefficients range from 0.92 to 0.97). The CFSR data overestimate precipitation (percentage bias approximately 99%) at both daily and monthly scales, whereas the CMADS product performs better, with obvious differences (compared to the ground-based observations) in high-terrain areas. Regarding the simulated river flows, CFSR-SWAT produced “unsatisfactory”, while CMADS-SWAT (R2 > 0.76 and NSE > 0.78) performs better than CFSR-SWAT on the monthly scale. This assessment of the applicative potential of GMPs, especially CMADS, may further provide an additional rapid alternative for water resource research and management in basins with similar hydro-meteorological conditions.

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

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.

USO DE DADOS METEOROLÓGICOS DE REANÁLISE PARA A SIMULAÇÃO DA VAZÃO NA BACIA HIDROGRÁFICA DO RIBEIRÃO DO PINHAL, LIMEIRA/SP

A grande preocupação com a preservação dos recursos hídricos demanda estudos capazes de avaliar e mitigar os impactos decorrentes das ações antrópicas. O uso de modelos hidrológicos constitui-se numa ferramenta importante, pois permitem simular diferentes cenários e seus impactos na disponibilidade hídrica. No entanto, a complexidade de obter dados meteorológicos observados, torna necessária a utilização de outras fontes de dados. Objetivou-se avaliar se com a utilização de dados meteorológicos de reanálise obtidos do modelo Climate Forecast System Reanalysis (CFSR) é possível calibrar o modelo Soil and Water Assessment Tool (SWAT) e simular a produção de água numa bacia hidrográfica. A área de estudo é a bacia hidrográfica do Ribeirão do Pinhal (BHRP), situada no município de Limeira-SP. Utilizou-se o modelo SWAT para simulação hidrológica com os dados meteorológicos observados e de reanálise. Foi possível calibrar o modelo SWAT utilizando os dados meteorológicos observados e de reanálise. Porém, o ajuste foi melhor quando se utilizou os dados meteorológicos observados. A vazão simulada utilizando dados de reanálise foi superestimada. Os dados meteorológicos de reanálise são adequados para simulações hidrológicas com o modelo SWAT, o que é evidenciado por meio dos índices estatísticos satisfatórios obtidos no procedimento de calibração do modelo SWAT.

Reanalysis dataset-based hydrologic predictions for ungauged basins

In the present study, Soil and Water Assessment Tool (SWAT) is employed to simulate streamflows from watershed with a semi-arid climate, using Climate Forecast System Reanalysis (CFSR) as forcing data input. To this end, two streamflow simulation scenarios, with and without readjustment of the reanalysis datasets, were investigated depending on available ground information. The findings indicate that the performance of the model is slightly improved when the former scenario, with readjustment of precipitation, is considered. Despite improvement in the overall model prediction, uncertainties during calibration and validation partially remained far less than the permissible limits. The reason seems to be associated with the mismatch between in-situ data and reanalysis datasets with respect to time and space. Irrespective of the sources of prediction uncertainties, the use of readjusted reanalysis datasets are deemed to be the best option for streamflow simulations in poorly gauged or ungauged watersheds. However, to underpin the findings with supportive and sound evidence, further investigation on the reanalysis datasets for hydrological predictions from similar regions with sufficient and reliable ground information becomes imminent. The study also underscores the need for undertaking pre-emptive measures to reverse the quantitative decline of hydrometric networks and existing management practices in the region.

PENDUGAAN CURAH HUJAN DENGAN TEKNIK STATISTICAL DOWNSCALING MENGGUNAKAN CLUSTERWISE REGRESSION SEBARAN TWEEDIE

Rainfall prediction is one of the most challenging problems of the last century. Statistical Downscaling Technique is one of the rainfall estimation techniques that are often used. The goal of this paper is to develop the modeling of cluster-wise regression with rainfall data set that has Tweedie distribution. The data used in this paper were the precipitation from Climate Forecast System Reanalysis (CFSR) version 2 as the predictor variables and rainfall from BMKG as the response variable. Data were collected from January 2010 to December 2019 on the Bogor, Citeko, Jatiwangi, and Bandung rain posts. The best result of this study is a Cluster-wise Regression model with 4 clusters and using Tweedie distribution in each rain post. The best model was evaluated by the Root Mean Square Error Prediction. RMSEP value on Bogor rain post is 17.11 (three clusters), Citeko rain post 14.85 (two clusters), Jatiwangi rain post 15.26 (three clusters), and Bandung rain post 14.33 (two clusters). This model was able to make models and clusters well on daily rainfall application.

Comparison of NCEP-CFSR and CMADS for Hydrological Modelling Using SWAT in the Muda River Basin, Malaysia

Identification of reliable alternative climate input data for hydrological modelling is important to manage water resources and reduce water-related hazards in ungauged or poorly gauged basins. This study aims to evaluate the capability of the National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) and China Meteorological Assimilation Driving Dataset for the Soil and Water Assessment Tool (SWAT) model (CMADS) for simulating streamflow in the Muda River Basin (MRB), Malaysia. The capability was evaluated in two perspectives: (1) the climate aspect—validation of precipitation, maximum and minimum temperatures from 2008 to 2014; and (2) the hydrology aspect—comparison of the accuracy of SWAT modelling by the gauge station, NCEP-CFSR and CMADS products. The results show that CMADS had a better performance than NCEP-CFSR in the climate aspect, especially for the temperature data and daily precipitation detection capability. For the hydrological aspect, the gauge station had a “very good” performance in a monthly streamflow simulation, followed by CMADS and NCEP-CFSR. In detail, CMADS showed an acceptable performance in SWAT modelling, but some improvements such as bias correction and further SWAT calibration are needed. In contrast, NCEP-CFRS had an unacceptable performance in validation as it dramatically overestimated the low flows of MRB and contains time lag in peak flows estimation.

CFSR- NCEP Performance for weather data forecasting in the Pernambuco Semiarid, Brazil

The present study aims to evaluate meteorological data -with real time actualization- from the Climate Forecast System Reanalysis (CFRS) of the National Centers for Environmental Prediction (NCEP), comparing them with data from local stations in two mesoregions: Sertão de Pernambuco (SP) and Sertão do São Francisco (SFF), semi-arid region of Pernambuco, Brazil. Statistical performance indicators were used for the period since 1979 to 2014 and the variables: precipitation (P), average, minimum and maximum temperature (Tm, Tn, Tx respectively), relative humidity (HR), wind speed (Vv), solar radiation (RS) and potential evapotranspiration (ETo). Tn, Tm and Tx showed the best results for the determination coefficient (R2), Willmott concordance index (d), Nash-Sutcliffe efficiency index (NSE) and percentage bias (PBIAS). ETo, P and HR obtained acceptable values for R2, d and NSE. CFSR data shows good performance with d values between 0.63 and 0.94.