RETRACTED ARTICLE. Hydrologic evaluation on the ERA-Interim output using observed river discharge data

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1 DOI /s ORIGINAL ARTICLE Hydrologic evaluation on the ERA-Interim output using observed river discharge data L. A. Djiotang Tchotchou A. Tompkins F. Mkankam Kamga Received: 13 September 2013 / Accepted: 26 March 2014 Ó The Author(s) This article is published with open access at Springerlink.com Abstract The simulated output of ERA-Interim GCM model was evaluated from a hydrologic point of view, using a distributed hydrologic model and observed river discharges data. The ERA-Interim output for the presentday climate should be able to provide a current river flow pattern when it is converted into river discharge information using a reasonably well-prepared hydrologic model. For this evaluation, the Cetemps hydrological model (CHYM) was performed for various sub-basins in the Nyong river, Cameroon, and calibrated in advance, using observed discharge data of each sub-basin. All sub-basins in the study area provide 7 years of observed discharge data, and the evaluations were conducted by comparing seasonal and inter-annual patterns of simulated and observed discharges. The result shows that the GCM provides excellent discharge output over the lower Nyong river, while its utilization into the upper Nyong river remains limited. The proposed hydrologic evaluation showed that the CHYM model can be used to study the extreme flow events, especially over Messam catchment, a lower Nyong river sub-basin. Keywords ERA-Interim CHYM model Nyong river River discharge L. A. Djiotang Tchotchou (&) F. Mkankam Kamga Laboratory for Environmental Modelling and Atmospheric Physics, Department of Physics, Faculty of Sciences, University of Yaoundé 1, Yaounde, Cameroon angennes@yahoo.fr A. Tompkins Physics of Weather and Climate Group, Earth System Physics Section, International Centre for Theoretical Physics, Trieste, Italy Introduction General circulation models (GCMs) evolution in the last three decades allows to expect a reasonable hydrologic dataset from the model output. However, the evaluation of the simulated model output is necessary to achieve reasonable confidence in the model performances and then be able to use on the evaluation of the impact of climate change in the future. Performances of GCMs have been continuously evaluated through validation of controlled run output by comparing them with observation of parameters, such as precipitation, temperature and other atmospheric variables. Climate change research results with GCMs have been devoted on model output evaluation through several models: Intercomparison Project such as the Atmospheric Model Intercomparison [AMIP; Gates et al. (1998)] and the Coupled Model Intercomparison Project [CMIP; Covey et al. (2003)]. However, for large-scale hydrological applications in data sparse regions and especially for climate change studies, numerical climate simulations have to be utilized. Nevertheless, additional evaluation of the model output is necessary to properly utilize it for a designed purpose, such as assessing the impact of climate change on the hydrologic cycle. Most of the use of GCM outputs in the hydrologic field is to predict problems related to water such as flooding and the water resources condition. Therefore, it would be interesting to evaluate GCM from a hydrologic viewpoint, namely basin scale and river discharge-based standpoint. So, the controlled simulation output of a GCM should provide a river flow pattern similar to the observation when it is converted into river discharge information through a well-prepared hydrologic model. The main focus of this paper is the evaluation of the output of the new ERA-Interim GCM using a distributed

2 hydrologic model and observed river discharge data. The study area is divided into four catchments corresponding to four sub-basins, each catchment provides 7 years of observed discharge data, which can allow to evaluate the ERA-Interim output. For the evaluation of this work, a distributed hydrologic model was prepared and calibrated in advance for various sub-basins within the Nyong river, Cameroon. Because the hydrologic model is well calibrated with output data (river discharges), it is possible to see the characteristics of the ERA-Interim output into a river discharge format, which is more direct and easy-to-understand information for hydrologists. The study area is described in Description of study area. Hydrological model description, boundary forcing and experiment design of the paper starts with a description of the hydrological model, the lateral boundary forcing fields, and the experiment design. The validation Fig. 1 Nyong river basin and its sub-basin (in the bottom the river basin as simulated by CHYM model) results of Cetemps hydrological model (CHYM) are presented in Results and analysis and recommendations are given in Concluding remarks. Description of study area The Nyong river is the second largest river in Cameroon, with drainage basin of 27,800 Km 2. The entire basin is within tropical latitudes, between N and Nof latitude and E and E of longitude. In this study area (Fig. 1), fourth nested catchments have been followed up, i.e., from upstream to downstream: the Awout watershed (tributary of the So o) at Messam, the So o watershed (tributary of the Nyong) at Pont So o, the upper Nyong basin at Mbalmayo and at Olama station (after the confluence with the So o).

3 The vegetation cover in the entire watershed is dominated by semi-deciduous forests on hill slopes and by raffian in the low-lying swampy zones (Boeglin et al. 2003). In the eastern most portion of the basin, the Nyong river flows through permanent swamp forests and permanently and seasonally inundates grass swamps 2 3 km in width. Downstream the basin is dominated by rounded hills covered in semi-deciduous forest (60 %) and cropland (40 %), predominantly tubers, manioc, palms and plantain (Letouzey 1985). The climate of the region is of a Guinean type with four seasons. There is a continuous severe wet season from September to November, and a continuous severe dry season from December to March. The small wet season begins in April and lasts until June. Afterward, the small dry season begins and lasts for a period of 2 months (July and August). Mean annual air temperature is about 24 C over the entire Nyong basin. Boeglin (2002) reports average annual values for precipitation and runoff at Olama as 1,631 and 343 mm, respectively. The Nyong watershed is predominantly covered by tropical rain forest and is free of anthropogenic disturbance (industrial or agricultural) except in the region of Yaounde. Hydrological model description, boundary forcing and experiment design Hydrological model description The CHYM (Tomassetti 2005; Verdecchia et al. 2008) is used in this study. CHYM is a grid-based distributed model that can be run in any geographical domain and with any resolution up to the resolution of the implemented digital elevation model (DEM), i.e., about 300 m; for operational purpose, the capability of simulating an arbitrary domain corresponds to the need to run the model for those river basins that are more stressed by the current meteorological events. The architectural characteristics of model have been designed to assimilate different data sources for the estimation of rainfall field on the model grid. For the operational activities, rainfall is estimated on CHYM grid at hourly time step using observed data for the past and meteorological model simulation for the future. The observed data are taken from a rain gauge network operating in the Central Italy and also rain estimation is obtained using radar measurements and infrared signal from geostationary satellite platform. When different data sources are available at same time step, these are assimilated with different priorities using cellular automata (CA)- based algorithm. The CA are discrete dynamical system. Space, time and states of the system are discrete quantities. Each point in a regular spatial lattice, called a cell, can have anyone of a finite number of states. The state of the cells in the lattice is updated according to a local set of rules. All the cells in the lattice are updated synchronously. According to the minimum energy principle the flow direction is assumed that for which the slope is maximum, this corresponds to the assumption that flows occur with a strong preferential direction. A complete description of the model can be found in Tomassetti (2005) and Verdecchia et al. (2008). Boundary forcing The European Centre for Medium-Range-Weather Forecast (ECMWF) Interim Reanalysis (ERA-Interim) hourly data gridded are used to initialize CHYM model. The new ERA-Interim (Simmons et al. 2007; Uppala et al. 2008) is the third generation ECMWF reanalysis product. The main advances in this reanalysis compared to ERA-40 are a higher horizontal resolution ( but available also at and ), four-dimensional variational analysis, a better formulation of background error constraint, a new humidity analysis, an improved model physics, variational bias correction of satellite radiance data, and an improved fast radiative transfer model. ERA-Interim uses mostly the sets of observations acquired for ERA-40 with a few exceptions: acquisition of a new altimeter wave-height that provides data of more uniform quality, use of reprocessed meteosat data for wind and clearsky radiance, and new ozone profile information from 1995 onwards. Several problems experienced in the ERA-40 reanalysis were eliminated or substantially improved in the ERA-Interim. In particular, an important improved performance was seen in the humidity and hydrologic cycle over the Tropics. The quality of the analysis was also validated by additional means: fit of background forecasts to the observations, fit of surface winds to independent buoyancy winds, agreement with independent tropical-cyclone track data, and comparison of precipitation with independent estimates from the Global Precipitation Climatology Project (GPCP). All pointed to a systematic edge in favor of the ERA-Interim reanalysis (Uppala et al. 2008). Experiment design The hydrological model is integrated over the Nyong river basin for 7-year period from January 1999 to December 2005 at a spatial resolution of m and daily time scale. The study period was chosen due to the availability of observed discharge data. The side in Zonal direction is and km in longitudinal direction. The world USGS DEM is used in this study. Simulated discharge is validated against stations discharge data of Olama, Pont So o, Messam and Mbalmayo provided by the program ORE BVET

4 (Observatory for Research in Environment Experimental Tropical Catchments). Era-Interim precipitation is compared to rain gauge data sets which covered our study area. Results and analysis Seasonal patterns reproducibility Evaluation of the simulated river discharges was carried out by comparing it with the observed river discharges. (a) (c) First, the seasonal patterns of those two river discharge data set were compared in each sub-basin. The observed discharge data in all basins were available in daily resolution and also the hydrologic model was running in the same time step. Thus, the seasonal pattern was estimated by averaging the daily discharge of the two data sets to obtain the monthly discharge figures. As shown in Fig. 2, simulated and observed river discharge from Messam, the seasons are well marked, with rapid variations in flow, mainly in response to precipitation. During the big wet season (September to November), the maximum discharge was (b) (d) Fig. 2 Observed discharges and simulated discharges by CHYM model using the ERA-Interim output (both are average of 7 years) at the Messam, Pont So o, Olama and Mbalmayo stations

5 observed and simulated in October. The model was able to simulate the low flow discharge observed between February and August although the overestimation of the quantity. Similar annual patterns were simulated over Pont So o subbasin but the high flow discharge between October and December was underestimated by the model. Olama and Mbalmayo simulated discharge shows similar annual patterns with similar quantities and poor performance levels in comparison with the observed discharge. The maximums discharges are observed in November but the simulated were obtained 1 month before. Although there are couple of basins showing good performances in the annual discharge pattern, such as Messam and Pont So o, the (a) (c) simulation results from the upper Nyong at Mbalmayo and Olama did not show reasonable performances. Inter-annual patterns reproducibility In this section, a year to year analysis is done between simulated and observed river discharge in each sub-basin. For this purpose, the monthly averaged discharge was added for each year to obtain the cumul averaged discharge of those two river discharge data set. Figure 3 presents the time series of discharge during the 7-year period of this study. These time series show the variation of the averaged cumul*** discharge for both the CHYM and stations (b) (d) Fig. 3 Interannual discharge of model (dashed line) and observed (dashed line with triangle) during the years

6 observations. In most of the years, the CHYM simulates the correct direction of the discharge for all sub-basins except Olama; however, the magnitude of the discharge is not always accurately simulated especially over Messam and Pont So o where the discharge is overestimated by the model. Nevertheless, the model still demonstrates a good performance over Mbalmayo in simulating the inter-annual variability of discharge. Extreme events reproducibility The analysis of the seasonal pattern that has been done in previous section (see Fig. 2) can allow to define the period of maximum peak discharge of each year between September and December (SOND). The annual flood peak of (a) (c) each catchment during that period was also evaluated using the observed daily discharge data. The daily average discharge of the 7 years of observation and the simulation was selected and passed through a low pass band lanczos filter to cutoff 90 days low frequency and 10 days high frequency. That filter allows to remove the synoptic variability lower to 10 days and the seasonal cycle upper to 90 days. Then, as shown in Fig. 4, scattergrams were plotted with observed discharges (x-axis) and the simulated discharges (y-axis), after sorting those data in filter. As a way of estimating a regression coefficient of the scattergrams and the correlation coefficient between the two datasets, they were able to check the overall performance level of the simulation results for extreme flood events. A regression coefficient of approximately 1.0 is our desirable (b) (d) Fig. 4 Scattergrams of simulated September to December during the years to observed daily discharge both filtered by a lanzcos low pass-band filter and its regression and correlation coefficients

7 value, and a coefficient of \1.0 means an overestimated simulation result and vice versa. From Fig. 4, which shows the regression and correlation coefficients of each subbasin, it is possible to see that the simulated discharges show underestimated extreme values for Pont So o and Olama when compared to the observation with Pont So o performs slightly better than Olama (correlations 0.65 against 0.49). In the other side, the simulated discharges for Messam and Mbalmayo show overestimated extreme values compared to the observation with Messam does better than Mbalmayo (correlations 0.89 against 0.20). A similar analysis is done in Fig. 5 but in this time for the extreme drought events. The daily average discharge of the 7 years of observation and the simulation was selected for the (a) (c) period December to March (DJFM) and the low pass-band lanczos filter applies in the same way as in the extreme flood events. As shown in Fig. 5, one can see that the simulated discharges show generally overestimated extreme values when compared to the observation except for Olama. All sub-bassins do very poorly (correlations under 0.2) with only Messam showing a good performance (correlation equals to 0.82). Precipitation reproducibility ERA-Interim precipitation output from the controlled run can now be evaluated. A seasonal patterns of precipitation used to drive CHYM model is compared to observed data (b) (d) Fig. 5 Scattergrams of simulated December to March during the years to observed daily discharge both filtered by a lanzcos low pass-band filter and its regression and correlation coefficients

8 Fig. 6 Observed rainfall versus ERA-Interim rainfall over the study region between N and N of latitude and E and E of longitude sets. The seasonal patterns were obtained by averaging all the three data sets over the region drained by the Nyong river (between N and N of latitude and E and E of longitude). As shown in Fig. 6, all data sets exhibited the fourth seasons found in the Guinean climate. They captured well the big dry seasons DJFM but ERA- Interim failed in reproducing the small dry season July August. The figure clearly demonstrated that ERA-Interim underestimated the precipitation during the big rainy season SON and overestimated it during the big dry season. These results were confirmed when compared Era-Interim precipitation to GPCP and Tropical Rainfall Measuring Mission (TRMM) data sets over the study region (not shown). The overestimation of simulated discharge that has been found on this study can be attributed to the quality of rainfall used as boundary forcing although it is an atmospheric reanalysis system that assimilates observations several times a day. Dee et al. (2011) reported that ERA- Interim shows more rainfall than GPCC in most of the Northern Hemisphere (this is particularly true for our study region), and in parts of South America. And then explained that the large differences over Central Africa are indicative of higher uncertainties due to the sparse radiosonde coverage there, particularly during the first decade of the averaging period. So, this is evidence in the decadal change estimates, where ERA-Interim probably overestimates the decrease in rainfall in the Central African region. And finally that a possible explanation is the presence of a substantial warm bias in the model associated with underestimated aerosol optical depth in the region. A sensitivity test was conducted using another initial boundary condition and this confirmed the overestimation of simulated discharge coming from ERA-Interim. Concluding remarks In this study, the ERA-Interim output for the present climate day was evaluated using a distributed hydrologic model and observed river discharge data. The distributed hydrologic model was run on the Nyong river basin; it was calibrated using observed discharge data. The calibrated hydrological model performed a runoff simulation using the ERA-Interim output, and converted it into river discharge in formation. The simulated river discharges at the four checking points in the basin were evaluated in three aspects, seasonal variation, inter-annual variation and extreme events discharge. While the simulated river discharge of basins in the upper Nyong river show various types of discrepancies to observed discharges, the discrepancy is diminished as one goes along the lower Nyong river. The simulated discharges show improved performances for Messam and Pont So o than Mbalmayo and Olama catchments. The model shows good performance in simulating the inter-annual variability of discharge over all sub-basins except Olama. It was able to understand that the ERA-Interim output does not show high accuracy over the upper Nyong river; however, the output shows reasonable reproducibility when it is analyzed on the lower Nyong river in the case study of the Nyong river. Daily flood peak also shows better performance for Pont So o and Messam sub-bassins, while daily drought peak was good only for Messam sub-bassin. To generalize the result, it is recommended to use a bias correction method to correct the Era- Interim rainfall in this study area. A linear biais correction method can be used by separating Era-Interim output value into a climatological term plus an anomaly term. This means that the climatological terms are the mean of all values across all years for a given month and sub daily period and the anomaly terms are calculated by subtracting the appropriate observed rainfall term (for example TRMM rainfall which has the same temporal tine scale with Era- Interim) from the value of ERA-Interim. This means that the observed rainfall data sets should be interpolated to Era-Interim grid point to really correct the bias over the spatial scale. Acknowledgments This work was supported by International Centre for Theoretical Physics (ICTP), Trieste, Italy under the Associate Scheme. I am grateful to the program ORE BVET for providing the observed river discharge.

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