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

The high-resolution Climate Forecast SystemReanalysis (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 performed by the Sequential Uncertainty Fitting Version 2 (SUFI-2) algorithm in the SWATCalibration Uncertainties Program 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 groundbased data, which indicate higher performance of the high-resolution CFSR data than the existing scattered ground-based data. TheCFSRweather grid points showedhigher variation in precipitation than theground-basedweather stations across thewholewatershed. Thecalculatedaverage annual rainfall by CFSR data for the whole watershed is approximately 24% higher than that by ground-based data, which results in somehigherwater balance components. TheCFSRdata also demonstrates its high capacities to replicate the streamflow hydrograph, in terms of timing and magnitude of peak and low flow. Through examinationof theuncertainty coefficientsP-factors ( 0.7) andR-factors ( 1.5), this study concludes that CFSR data is a reliable source for running hydrological models in the Arctic watershed Målselv.


INTRODUCTION
A watershed is a basic land unit for studying of hydrological cycle and for water resource management and planning (Edwards et al. ; Yu & Duffy ). It is defined as a land area where most of the precipitation drains to the same places, e.g., water bodies or low land areas (Edwards et al. ). The development of hydrological models has been a high target of the hydrologists (Ehret et  are not available. Due to these limitations of ground-based data, finding alternative sources of weather inputs for hydrological models is essential. This is especially crucial for the datasparse Arctic region (Lindsay et al. ; WMO ). An alternative source, which has recently been preferred by scientists, is to use the multiyear globally atmospheric reanalyzed data (Fuka et al. ).
Basically, the atmospheric reanalyzed data are generated through data assimilation, which is the process of integrating all available information, to estimate as accurately as possible the characteristics of a system (Talagrand ), from observed data (e.g., from the ground-based gauges, ships, aircraft, and satellites) and forecasted data (e.g., from numerical modeling of weather prediction) (Parker ). Reanalysis provides comprehensive features of climate at regular time steps over a long period usually from years to decades. Therefore, reanalysis data have been used in various fields, such as atmospheric dynamics (Kidston et al. ), investigation of climate variability (Kravtsov et al. ), evaluation of climate models (Gleckler et al. ), studying greenhouse gas fingerprints (Santer et al. ), and in the study of hydrology and hydrological models A comparison on the characteristics of the above seven well-known reanalysis products is shown in Table 1. Of them, the CFSR and ERA5 have the highest spatial resolution with a Gaussian grid (Washington & Parkinson ) of approximately 38 km (NCAR ) and approximately 31 km (Hersbach et al. ), respectively.
However, CFSR is the only one that covers all required input data (e.g., precipitation, maximum and minimum air temperature, relative humidity, solar radiation, and wind speed) for the hydrological model, the SWAT (soil and water assessment tool) model, used by this study. Therefore, the CFSR is selected for the evaluation of its performance for running the hydrological model in the Arctic conditions. The CFSR is the third generation of reanalysis product.
This dataset is the cooperation between the National Center for Atmospheric Research (NCAR ) and the NCEP (NCEP ). A coupling of atmosphere-ocean-land surfacesea ice systems in order to offer the best estimation of the weather pattern of those coupled areas is the great features of the CFSR product. The CFSR data have been verified as weather input for hydrological models in numerous studies at different climate conditions around the world (e.g., temperate, tropical, subtropical, Asian monsoon, and semi-arid) and provided reliable results. First of all, in the temperate climate zone, CFSR performed better than ground-based data for simulation of daily variation of streamflow in four watersheds in the USA, and CFSR could meet the challenge of hydrological simulation in ungauged watersheds (Fuka et al. ). In another study in the snow-dominated East River basin, Colorado, USA, CFSR was used as forcing data for the prediction of volumetric streamflow and returned good results (Najafi et al. ). Additionally, in a study of surface and atmospheric water budgets in the Upper Colorado River basin, CFSR showed its high capacity to capture the seasonal cycle of each water budget component (Smith & Kummerow ). CFSR was also used as weather input to detect the influences of atmospheric rivers on winter floods in nine river basins along the western coast of Great Britain and showed consistent results with other reanalysis products: the ERA-Interim, the 20CR, the MERRA, and the NCEP-NCAR (Lavers et al. ).
Secondly, in a tropical climate zone in Ethiopia, CFSR performed better than ground-based data for the prediction of daily streamflow in the Gumera watershed (Fuka et al. ) and for the prediction of monthly streamflow in the Awash watershed (Tolera et al. ). It is concluded that CFSR could perform better in large-scale basins (Tolera et al. ).
CFSR also demonstrated its high capacity for predicting potential evapotranspiration in the data-scarce Upper Mara Catchment in Kenya and Tanzania (Alemayehu et al. ).
Thirdly, in a study conducted over South America, with climate characteristics varying from tropical to subtropical zones, CFSR provided the smallest bias in results, compared with other reanalysis products (e.g., MERRA and NCEP-R2), for simulation of the hydrological cycle (Quadro et al. ).
Another study in the semi-arid climate of the Jaguaribe basin, Northeast Brazil, with CFSR as weather input for studying monthly streamflow variation, stated that CFSR's results were good to very good, and had the best performance compared to other weather input datasets (Bressiani et al. ).
Lastly, in the region dominated by the Asian monsoon climate, CFSR demonstrated good performance to simulate monthly streamflow variation in the largest river, the Yangtze River, in China, and was considered an alternative input for the large-scale basins (Lu et al. ). However, in some case studies, CFSR data performed worse than ground-based data and were not recommended (specifically for those study areas) as an alternative input to replace the high-quality  1948-present 1979-present 1979-2017 1871-2012 1980-2017 1950-2019 1979-2004 Temporal resolution this has yet to be verified well in the data-sparse Arctic region.
Therefore, to fill this knowledge gap, this paper aims: 1. Investigate the performance of the CFSR in running hydrological models in Arctic conditions, and 2. Examine whether CFSR data could be an alternative for weather input and could replace the limited groundbased data for hydrological models in the data-sparse Arctic region.

STUDY AREA
An Arctic watershed, Målselv, located in northern Norway, was chosen as the study area to investigate the performance of CFSR ( Figure 1).  Table S1).

SWAT model
The physically based (or process-based) ( where SW t is the soil water content at time t (mm), SW 0 is the initial soil water content (mm), R i is the amount of precipitation on day i (mm), Q i is the amount of surface runoff on day i (mm), E i is the amount of evapotranspiration on day i (mm), P i is the amount of percolation on day i (mm), and QR i is the amount of return flow on day i (mm).

Data acquisition
To run the SWAT model, several inputs are required: (1) spatial data, including Digital Elevation Map (DEM), soil, and land use; and (2) time-series data, including climate data and river discharge (Table 2).  Table S1) and soil types (Supplementary Material, Table S2) of the catchment based on the SWAT database.
The climate inputs used in this study are from two data sources, which are used to compare their performances: (1) the CFSR weather data (Figure 2(a)) and (2) Figure 3 illustrates the overview of methodologies used in this study.

Evaluation of model performance
The simulated results are compared with the observed data using the statistical coefficients, including (1) the coefficient of determination -R 2 (Equation (2)), measuring the fitness of the relationship between the simulated and observed values; (2) the Nash-Sutcliffe coefficient of efficiency - (3)); and (3) root mean square error, divided by the standard deviation -RSR (Equation (4)).

NSE (Equation
where Y obs i and Y sim i are the observed and simulated values at time i, Y obs mean and Y sim mean are mean observed and simulated data for the entire evaluation period, and n is the total number of observations/simulations.   Note: • The term 'a_' explains that a given value is added to the existing parameter value. • The term 'r_' explains that an existing parameter value is multiplied by (1þ a given value).
• The term 'v_' explains that the existing parameter value is replaced by a given value.  (Cuceloglu & Ozturk ), they also demonstrated that CFSR data were able to capture the seasonal trend of precipitation in ground-based data. Similar to findings from our study, the higher in magnitudes of monthly precipitation from the CFSR dataset compared with that from the groundbased dataset were also detected in those studies. However, in the tropical region (the study in upper Awash catchment, Ethiopia), the significant differences of monthly precipitation between the CFSR data and the ground-based data were mostly observed in summer time (July-August), while these were in wet seasons (December to April) in the temperate climate zone in the Back Sea catchment. In constrast, our study found the differences in monthly precipitation between two weather data sources from middle spring to beginning of summer (April to June).
The seasonal variation of precipitation (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) periods) is locally investigated at four co-located points (the points are closest together) between the ground-based weather stations and the CFSR weather grid points ( Figure 6). Of them, two co-located points are inside and the other two are outside of the watershed. As shown in Figure 6, the seasonal trends of precipitation of the CFSR data and ground-based data are almost similar at all the co-located points. However, the magnitude of precipitation from CFSR data is overestimated than that from the ground-based data. Especially, one co-located point locating inside the watershed (as in Figure 6(a)) has 8 months of a year, e.g., January, February, April-June, September, October, and December, when precipitation from the CFSR data is overestimated precipitation from the ground-based data. At other co-located points, the significant differences of precipitation between the CFSR data and the groundbased data are observed in the months of January, April-June, and December for co-located point 2 (Figure 6(b)), and in February, April, June, and December for co-located point 3 (Figure 6(c)), and in February, April-June, September, and December for co-located point 4 ( Figure 6(d)). In brief, the significant differences of monthly precipitation between the CFSR data and the ground-based data at the co-located points mostly occur in winter, from middle spring to the beginning of summer, and from the beginning to middle autumn. Figure 7 describes the boxplots of variation of total annual precipitation at four pairs of co-located points between ground-based weather stations and CFSR weather grid points. In general, at each pair of co-located points, the values of annual rainfall from the CFSR weather grid point are higher than that from the ground-based weather station. For example, the average annual rainfall from the CFSR data are higher approximately 49.70% (Figure 7(a)), 32.70% (Figure 7(b)), 31.60% (Figure 7(c)), and 36.90% (Figure 7(d)) compared with that from the gauge-based data.
It is obvious that precipitation from the high-resolution CFSR data is higher than that from the scattered groundbased data. Therefore, it is estimated that simulation results, e.g., streamflow or water balance components, would be higher by using the CFSR weather input compared with that by using the ground-based weather input.    (Table 6 and Supplementary Material, Figure S1). In addition, the R 2 values explain a good agreement between measured data and estimated results, in terms of timing for the runoff process occurring in the sub-basins, as well as the hydrograph of streamflow  According to model validation results, the high-resolution CFSR data (Table 7 and Supplementary Material, Figure S6) also demonstrate its higher performance than the scattered ground-based data (Table 7 and Supplementary Material, Figure S5). For example, CFSR performed very well at Lundberg and Skogly, and well at Lille Rostavatn, where the performance of ground-based data is only satisfactory.
Additionally, model performance is good at Målselvfossen, through the use of ground-based data, whereas it is very good through the use of CFSR data. Noticeably, simulation results at the Høgskarhus station in the validation period are worse than those in the calibration period for both weather datasets.
This could be partly because of gaps in the time-series data of river discharge used for validation (Supplementary Material, Figure S6c). However, the relatively good values of R 2 (Table 7 and  . Such studies concluded that the CFSR data were the potential sources for weather inputs to run the hydrological models in ungauged and large-scale catchments. According to outcomes from the present study, it could be concluded that the CFSR data not only perform well in temperate, tropical, semi-arid, and Asian monsoon climate zones, but also in Arctic conditions. However, findings from the present study also contradict findings from other studies (Dile & Srinivasan ; Roth & Lemann ), which stated that CFSR could not replace  the high-quality ground-based data. However, in the datasparse regions like the Arctic, reanalysis data, e.g., the CFSR, could be an alternative source, since there are not enough representative meteorological stations for the large catchment, or observed data often contain gaps or errors.

Comparison of the simulated streamflow hydrograph
According to the simulation results of the streamflow hydro- Regarding the calibration period, the magnitude of peak flow is almost captured at Skogly and Målselvfossen for both weather datasets. However, at Høgskarhus, peak flow is captured by using the ground-based data, but it is slightly underestimated by using the CFSR data. This could be explained by the fact that some sub-basins upstream of Høgskarhus have higher areal precipitation achieving from the ground-based data than from the CFSR data. On the contrary, most values of peak flow at the Lundberg station are captured by using CFSR data, but those are somewhat underestimated by using ground-based data. At the Lille Rostavatn station, both weather datasets slightly underestimate the magnitude of peak flow.
Regarding the validation period, the peak flows are almost captured at Skogly and Målselvfossen, but they are underestimated at Lille Rostavatn, for both weather datasets.
The differences in model performance between the two weather datasets are observed at Høgskarhus and Lundberg.
For instance, the model performs well in peak flow at Høgskarhus, but it performs worse at Lundberg from using the ground-based dataset, whereas the model performance at those stations shows the opposite behaviors through the use of the CFSR weather data.
In terms of low-flow simulation, a relatively good fitness between simulation and observation is achieved from the calibration and validation period by using both weather datasets. This finding is somewhat better than the finding from the study in Upper Awash Basin, Ethiopia (Tolera et al. ), since they concluded that simulation of low flow was underestimated/overestimated by using the CFSR data.

Comparison of the simulated water balance components
Rainfall is one of the major inputs of water balance components. In the SWAT, areal rainfall is calculated separately for every sub-basin. In particular, each sub-basin collects rainfall for itself from the stations (e.g., the ground-based weather stations or the CFSR grid points) that are closest to the centroid of the sub-basin by the method of the NNS. The results of spatial variation of areal rainfall calculated for every sub-basin, obtained from ground-based weather data and CFSR weather data, are displayed as in Figure 8. Generally, the total rainfall amount calculated for the whole watershed by CFSR data is approximately 24% higher than that by the ground-based data.
Approximately 88% of the watershed area has a rainfall ratio between ground-based data and CFSR data (rainfall ratio (Figure 8 1.32. This indicates that rainfall in some parts in the upstream calculated from the CFSR dataset is lower than that from the ground-based dataset. The higher rainfall amount from the CFSR dataset than from the ground-based dataset results in higher simulation results of some water balance components (Table 8) (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) between the ground-based weather data and the CFSR global weather data. data is around 11% higher than that from the ground-based data. Actual ET, lateral flow (LAT_Q), and amount of groundwater recharge (PERC) generated from the CFSR data are also higher than from the ground-based weather data. However, the groundwater amount (GW_Q) produced from the ground-based data is higher than that from CFSR data. Noticeably, the surface runoff component generated from the two weather datasets is almost similar.

Comparison of the simulation results of long-term average monthly streamflow
The simulated monthly streamflows, which are generated from ground-based data and CFSR data, are averaged for a 10-year period, 1998-2007, and the results are compared with the averaged values of observed data and shown in Figure 9. According to the graphs in Figure 9, both weather datasets simulate quite well the low value of the average monthly flow, except that slight overestimations are observed in September at Høgskarhus (Figure 9(b)) and Skogly (Figure 9(c)) from the ground-based data. However, the simulation of peak value of the average monthly flow differs somewhat between two weather datasets. For example, the CFSR replicates the peak flow at Lundberg (Figure 9(a)) and Skogly better than the ground-based data. In contrast, the ground-based data replicate the peak flow at Høgskarhus better than CFSR data. The ground-based data generated higher peak flows at Høgskarhus and Skogly than the CFSR data. This could be because of the contribution of higher areal rainfall in upstream sub-basins from the ground-based data, compared with that from the CFSR data. Interestingly, the graphs of long-term average monthly streamflows at Lille Rostavatn (Figure 9(d)) and Målselvfossen (Figure 9(e)) generated from both weather datasets are almost similar, excluding a slightly higher peak flow at  period, are !0.75, except that the value of P-factor at the Lille Rostavatn station calculated from the ground-based dataset is slightly under 0.70 (Figure 10(a)). Regarding the validation period, values of P-factors at most hydro-gauging stations, from both weather input datasets, are higher than 0.70, excluding the results at Skogly and Lille Rostavatn from the ground-based dataset (Figure 10(c)). The good values of P-factors achieved from the uncertainty analyses indicate that the measured river discharge is simulated well by the model, or the modeling error is low. The accuracy of modeling results by using the high-resolution CFSR dataset is higher than that by using the existing scattered ground-based dataset. Values of R-factors obtained from both weather input datasets are 1.50 for both calibration and the validation periods, except that R-factors at Høgskarhus and Skogly, which are obtained from the ground-based dataset, are higher than 1.50 (Figure 10(b) and 10(d)). Therefore, based on the analyzed results of R-factors, it could be concluded that using the high-resolution CFSR weather input to simulate river discharge in the Målselv watershed could produce a high certainty of modeling results. In contrast, using the available scattered ground-based data to simulate river discharge may produce uncertain results in upstream sections of the watershed, particularly the areas close to Høgskarhus and Skogly stations. This is because most of the available ground-based stations are located in the downstream of the watershed, and there is a lack of representative stations in the middle, as well as in the upstream, sections.
In brief, according to the above analyses of the statistical coefficients of model performance (e.g., R 2 , NSE, and RSR), the uncertainty measures (P-factor and R-factor), the simulation results of water balance components, monthly streamflow hydrograph, and long-term average monthly streamflow, the present study demonstrates that using the high-resolution CFSR weather input to run the SWAT model produces better modeling results than using the existing limited ground-based weather input, in the Arctic watershed, Målselv. It could be interpreted that one of the underlying reasons leading to lower model performance by using the ground-based weather input in this study area is that most of the available meteorological stations are located in the downstream sections, and there is a lack of representative stations in the middle, as well as in the upstream, sections. The Målselv watershed has characteristics of mountainous topography, where rainfall is high variant in space and time. Therefore, the scattered ground-based networks could not represent well the rainfall feature of the whole large watershed, unlike the denser grid points of the

CONCLUSIONS
Collecting enough weather input data to run hydrological models in the data-sparse Arctic region is a challenge for all modelers. In this study, the possibility of using the highresolution global reanalysis weather data, CFSR, as an alternative data input for the hydrological models was investigated in an Arctic watershed Målselv. The performance of CFSR data is compared with the ground-based (gauged) data through running the hydrological model QSWAT. Model performance with the high-resolution CFSR data is higher than that with the existing scattered ground-based data via the evaluation of the statistical coefficients. The NSE coefficient is in the range of 0.65-0.82 (good to very good) with the CFSR weather input, whereas it is in the range of 0.55-0.74 (satisfactory to good) with the ground-based weather input. The simulation results also demonstrate the high capacity of CFSR data to replicate the monthly average streamflow, in terms of monthly average hydrograph, peak and low-flow values, during a 10-year period, 1998-2007.
In contrast, the ground-based weather data showed lower performance than the CFSR data because the network of the ground-based weather station is scattered with only two stations inside and two stations outside the watershed.
In addition, most of the ground-based weather stations locate in the downstream. The representativeness of weather stations in the middle and upstream is missing. The higher rainfall amount and its spatial variation from the CFSR dataset than that from the ground-based dataset leads to higher simulation results of some water balance components, in terms of actual evapotranspiration, lateral flow, groundwater recharge, and water yield contributing to streamflow. By evaluating the uncertainty measures, P-factors (with results !0.70) and R-factors (with results 1.5), CFSR data demonstrated its capacity to produce a high certainty of modeling results in the Målselv watershed. The promising results from this study will open the chances for hydrological applications of the CFSR data in other watersheds in the Arctic region.