Abstract

Due to unavailability of sufficient discharge data for many rivers, an appropriate approach is required to provide accurate data for estimating discharge in ungauged watersheds. In this study, Global Land Data Assimilation System (GLDAS) datasets were integrated with Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) to simulate the outlet river discharge in Polroud watershed, located in the North of Iran. Temperature and precipitation products generated by GLDAS were calibrated using regression analysis based on observation data for the period of 2004–2006. Then, river discharge was simulated by using HEC-HMS based on two different datasets (GLDAS meteorological product and gauged data) on the scale of the basin for the same period. The results clearly indicated that the forcing of GLDAS data into HEC-HMS model leads to promising results with acceptable correlation with observed data. Although, in comparison with direct GLDAS runoff products, the proposed approach improved the accuracy of river discharge, the problem of underestimation still reduces the expected accuracy. Because of global accessibility, GLDAS datasets would be a good alternative in ungauged or poorly gauged watersheds.

INTRODUCTION

Flooding is the most common natural disaster in snow-dominated catchments such as Polroud river basin in the North of Iran. Therefore, accurate data for river discharge (in time and space) is very important for flood early warning and proper water resources management. Precipitation, air temperature and runoff are key determinants of the river discharge and the origin of devastating floods in some cases. However, ground-based approaches for data collection are very costly and time consuming. In addition, these hydrological parameters have significant gaps over remote and ungauged regions. Notwithstanding the recent decline in the number of active river gauges, simulation of hydrological parameters such as river discharge continues to be one of the most widely measured elements.

Simulation of river discharge is not usually an easy task since the hydrological data required for simulation are not available in such basins. However, global land surface models provide a potential alternative source of forcing data for hydrological models in regions where the hydrological data required for simulation are not readily available.

The Global Land Data Assimilation System (GLDAS) (Rodell et al. 2004) is an important tool for hydrology and water resources due to the provision of hydrological parameters on a global scale. GLDAS was designed to provide optimal estimates of land surface fluxes and storages of water and energy. Therefore, many of the problems related to the acquisition of land data would be eliminated by providing the accurate data from this system (Fang et al. 2009).

The GLDAS rainfall product is available at 0.25-degree spatial resolution and 3-hourly time intervals for entire latitudes (approximately 60°S–60°N), which is suitable for flood risk analysis studies (Seyyedi et al. 2015). GLDAS integrates satellite and ground-based observations for parameterizing, forcing and constraining global offline simulations of advanced land surface models (LSMs). GLDAS includes four LSMs: Mosaic (Koster & Suarez. 1992), the Community Land Model (CLM) (Dai et al. 2003), the Variable Infiltration Capacity (VIC) model (Liang et al. 1994) and Noah (Koren et al. 1999; Ek et al. 2003). Noah was developed by the National Centers for Environmental Prediction (NCEP) through the cooperation of public and private institutions. Because of the importance of GLDAS outputs, these data have been applied and evaluated in many studies.

Numerous estimations and applications have been carried out to provide a better and deeper understanding about GLDAS/Noah products over different regions (Yang & Koike 2008; Zhang et al. 2008; Zaitchik et al. 2010; He et al. 2015; Van Loon et al. 2016).

Seyyedi et al. (2015) compared TRMM satellite rainfall product and the GLDAS reanalysis precipitation datasets. Results from that study suggest the use of downscaling and error correction for the GLDAS reanalysis precipitation dataset before implementing it for runoff simulations.

In ungauged watersheds, the accurate extraction of physiographic characteristics has a great influence on estimating flood discharge in ungauged basins; however, the overall accuracy of GLDAS runoff outputs are not suitable for estimation of river discharge (Zaitchik et al. 2010). The subsequent approach may be entering the precipitation and near surface air temperature data generated by GLDAS imported into hydrological models such as the Hydrologic Engineering Center Hydrologic Modeling System (HEC-HMS) (US Army Corps of Engineers (USACE) 2000).

HEC-HMS has been successfully used by many researchers to simulate runoff and river discharge in many basins worldwide (Oloche Oleyiblo & Zhi-jia. 2010; Kure et al. 2013; Bhuiyan et al. 2017; Gao et al. 2018; Quedraogo et al. 2018).

In this study, an alternative approach was introduced to simulate the river discharge based on forcing GLDAS datasets into the HEC-HMS model (US Army Corps of Engineers (USACE) 2000) in two cases of continuous simulation using soil moisture accounting (SMA) and single-event simulation using the Soil Conservation Services (SCS) loss method in the Polroud basin.

MATERIALS AND METHODS

Study area

Polroud basin, with an area of 1,688 square kilometers located in north latitude from 36°32′ to 37°01′ and east longitude from 49°45′ to 50°33′, is the largest watershed in the east of Guilan province. The drainage basin is mostly mountainous, with its maximum and average height relative to sea level being 3,800 and 1,928 meters, respectively. Long-term average annual precipitation is equivalent to 929 mm and its average annual temperature is 15.5 °C.

The physiographic parameters of the Polroud sub-basins consist of area, slope, height, as well as the length and slope of the main rivers, which are effective in the rainfall-runoff simulation; these are presented in Table 1.

Table 1

The physiographic parameters of the Polroud sub-basins from WMS model

WatershedArea (km)2Mean basin elevation (m)Slope (%)
Main stream (km)Perimeter (km)
StreamBasin
Sub-basin 1 1.25 1,632.10 0.28 0.4 0.3 7.44 
Sub-basin 2 243.95 1,127.20 0.06 0.41 34.3 111.3 
Sub-basin 3 101.02 1,463.10 0.13 0.46 18.10 59.49 
Sub-basin 4 156.75 1,507.50 0.07 0.38 21.73 81.31 
Sub-basin 5 476.20 1,790.40 0.02 0.24 30.77 154.96 
Sub-basin 6 144.51 2,240.80 0.07 0.37 30.77 74.18 
Sub-basin 7 565.26 2,327.60 0.05 0.40 48.94 159.12 
WatershedArea (km)2Mean basin elevation (m)Slope (%)
Main stream (km)Perimeter (km)
StreamBasin
Sub-basin 1 1.25 1,632.10 0.28 0.4 0.3 7.44 
Sub-basin 2 243.95 1,127.20 0.06 0.41 34.3 111.3 
Sub-basin 3 101.02 1,463.10 0.13 0.46 18.10 59.49 
Sub-basin 4 156.75 1,507.50 0.07 0.38 21.73 81.31 
Sub-basin 5 476.20 1,790.40 0.02 0.24 30.77 154.96 
Sub-basin 6 144.51 2,240.80 0.07 0.37 30.77 74.18 
Sub-basin 7 565.26 2,327.60 0.05 0.40 48.94 159.12 

In the Polroud basin, there are three hydrometric stations: ‘Samosh’, ‘Musa Kalayeh’ and Tul-lat. However, because of lack of data, only data of Tul-lat station were used in this study to simulate the river discharge (Figure 1).

Figure 1

Location of Polroud basin with its sub-basins and the HEC-HMS hydrographic network.

Figure 1

Location of Polroud basin with its sub-basins and the HEC-HMS hydrographic network.

Figure 2

Schematic of soil moisture accounting algorithm (US Army Corps of Engineers 2000).

Figure 2

Schematic of soil moisture accounting algorithm (US Army Corps of Engineers 2000).

GLDAS

GLDAS (Rodell et al. 2004) combined satellite and ground-based observations with the aim of estimating terrestrial water and energy storages. GLDAS raster outputs for the period 2004–2006, including rainfall, near surface air temperature, snow water equivalent, surface and subsurface runoff, were downloaded from the Earthdata portal and then meteorological outputs, including rainfall and near surface air temperature, were forced to the calibrated HEC-HMS model to simulate the river discharge in Tul-lat outlet. The GLDAS outputs implemented in this study have been summarized in Table 2 (Fang et al. 2009).

Table 2

Main characteristic of GLDAS outputs implemented for river discharge simulation

ParameterUnitFormatSpatial resolutionTemporal resolutionPeriod
Average surface temperature GRIB 0.25° 3-hourly 2004–2013 
Snow water equivalent kgm−2 GRIB 0.25° 3-hourly 2004–2013 
Rainfall rate kgsm−2 GRIB 0.25° 3-hourly 2004–2013 
Surface runoff, subsurface runoff kgm−2 GRIB 0.25° 3-hourly 2004–2013 
ParameterUnitFormatSpatial resolutionTemporal resolutionPeriod
Average surface temperature GRIB 0.25° 3-hourly 2004–2013 
Snow water equivalent kgm−2 GRIB 0.25° 3-hourly 2004–2013 
Rainfall rate kgsm−2 GRIB 0.25° 3-hourly 2004–2013 
Surface runoff, subsurface runoff kgm−2 GRIB 0.25° 3-hourly 2004–2013 

Simulation of river discharge

To simulate the river discharge during the period of 2004–2006, both the gauge data and calibrated GLDAS data were separately entered into the HEC-HMS model. The watershed was first divided into homogeneous sub-basins (Figure 1) using WMS software. Then, hydrological modelling was developed in HEC-HMS for the watershed using the sub-basin geometric data (Table 1). The model takes into account the influences of physical parameters of the watershed such as climatic, topography, land use and soil data representing the boundary condition over the watershed to simulate runoff. The hydrology parameters needed in the rainfall-runoff modelling were generated using GIS layers and WMS functions. Runoff processes were simulated for each sub-basin system from the upstream to the watershed outlet throughout the streamflow network.

In this study, the constant monthly base flow method was used to simulate the base discharge. The constant monthly base flow method allows the specification of a constant base flow for each month of the year (US Army Corps of Engineers 2000). Therefore, the 3-year average of river baseflow for each 12 months was entered to the model as constant monthly baseflow data. Simulations were performed using both the SMA loss method (for continuous simulation) and SCS loss method (for single-event simulation).

The SCS-CN loss method is used in runoff estimation to specify the amount of infiltration rates of soils. The method uses an integration of land use and soil data to determine curve number (CN) values of the watershed. The CN values were adopted from Technical Release 55 (USDA 1986) based on hydrologic soil groups (HSGs). The surface storage coefficient (S) was calculated for each sub-basin via CN value based on Equation (1) as follows: 
formula
(1)
The initial abstraction (Iα) for each sub-basin was considered as 20 percent of basin storage (Tassew et al. 2019): 
formula
(2)

HMS also includes the SMA method to define rainfall, runoff, storage and losses relationships and simulate the continuous simulation. As shown in Figure 2, in the SMA algorithm five storage zones are considered. For the simulation of water movement through the various storage zones, 12 parameters including the maximum capacity (maximum depth) of each storage zone, initial storage condition in terms of percentage of the filled portion of each zone, and the transfer rates, such as the maximum infiltration rate, are required (Fleming & Neary 2004). The whole 12 parameters needed for the SMA were taken into consideration in this simulation. The maximum infiltration rate and the maximum soil depth as well as the percolation rates and groundwater components had significant influence on the simulated flow discharges. The remaining parameters were also adjusted to match the simulated and observed peak flows, volumes, time to peaks and hydrograph shape.

The expression used in the SCS method for estimating runoff may be presented as: 
formula
(3)
where Q is accumulated storm runoff in m and P is accumulated storm rainfall in mm.
Routing of the runoff process from the upstream to the watershed outlet throughout the streamflow network was conducted using lag time value calculated for each sub-basin as follows: 
formula
(4)
where Tl shows the basin lag time, the interval between the precipitation center to the peak point of the hydrograph (hours), L indicates the length of the main river (feet), y is the average slope of the basin (percentage).
The accuracy assessments were performed based on statistical criteria including the coefficient of determination (R2), Nash-Sutcliffe coefficient (E), Bias, root mean square error (RMSE), as follows: 
formula
(5)
 
formula
(6)
 
formula
(7)
 
formula
(8)
 
formula
(9)
where Qobs and Qsim show the observed and simulated discharges, respectively; n is the number of observations; and indicates the average observed discharge.

RESULTS AND DISCUSSION

Simulation of river discharge based on meteorological data from station

To improve the accuracy of simulation, internal parameters in both functions of SCS and SMA were calibrated manually using gauge data during the period of 2004–2006. The calibrated parameters for the loss methods including SCS with seven sub-basins and SMA for whole basin have been demonstrated in the Tables 3 and 4, respectively.

Table 3

The calibrated parameters for the SCS loss method with seven sub-basins

Sub-basinWatershed (km2)Curve numberLag time (min)Initial abstraction (Ia)
Sub-basin 1 1.25 40 438 76.2 
Sub-basin 2 223.9 40 3,888 76.2 
Sub-basin 3 101.02 38 2,526 82 
Sub-basin 4 136.75 40 2,958 76.2 
Sub-basin 5 421.20 48 3,744 50.8 
Sub-basin 6 134.3 48 2,196 50.8 
Sub-basin 7 516.2 48 4,230 50.8 
Sub-basinWatershed (km2)Curve numberLag time (min)Initial abstraction (Ia)
Sub-basin 1 1.25 40 438 76.2 
Sub-basin 2 223.9 40 3,888 76.2 
Sub-basin 3 101.02 38 2,526 82 
Sub-basin 4 136.75 40 2,958 76.2 
Sub-basin 5 421.20 48 3,744 50.8 
Sub-basin 6 134.3 48 2,196 50.8 
Sub-basin 7 516.2 48 4,230 50.8 
Table 4

The calibrated parameters for the SMA loss method for the whole basin

SMA parameterValue
Canopy storage (mm) 1.2 
Surface storage (mm) 
Max rate of infiltration (mm/hr) 12 
Soil storage (mm) 120 
Tension storage (mm) 110 
Soil percolation (mm/hr) 
Groundwater 1 storage (mm) 250 
Groundwater 1 percolation (mm/hr) 1.2 
Groundwater 1 coefficient (hr) 120 
Groundwater 2 storage (mm) 200 
Groundwater 2 percolation (mm/hr) 0.8 
Groundwater 2 coefficient (hr) 150 
SMA parameterValue
Canopy storage (mm) 1.2 
Surface storage (mm) 
Max rate of infiltration (mm/hr) 12 
Soil storage (mm) 120 
Tension storage (mm) 110 
Soil percolation (mm/hr) 
Groundwater 1 storage (mm) 250 
Groundwater 1 percolation (mm/hr) 1.2 
Groundwater 1 coefficient (hr) 120 
Groundwater 2 storage (mm) 200 
Groundwater 2 percolation (mm/hr) 0.8 
Groundwater 2 coefficient (hr) 150 

The results of river discharge simulation using the HEC-HMS model based on the SMA loss method and meteorological data from Tul-lat station have been presented in Table 5 as well as Figure 3. The simulation was performed from 1 January 2004 to 30 December 2006 (36 months). The period of 2004–2005 was used for the calibration process and the period of 2006 was assigned for model verification. The results demonstrated that simulated discharge agree significantly well with the gauge observation. The correlation between simulated discharge station and gauged discharge was in the range of 0.75 and 0.86 with RMSE of 6.9–10.3 respectively. As shown in Table 5, 2005 (calibration period) was identified as the best performance with R2, Nash and RMSE equal to 0.81, 0.80 and 6.9, respectively.

Table 5

The statistical assessment of simulated river discharge station using HEC-HMS based on the SMA loss method in Polroud basin

PeriodR2NashBiasRMSE
2004 0.75 0.64 6.3 10.3 
2005 0.81 0.80 −0.12 6.9 
2006 0.86 0.85 1.69 8.0 
PeriodR2NashBiasRMSE
2004 0.75 0.64 6.3 10.3 
2005 0.81 0.80 −0.12 6.9 
2006 0.86 0.85 1.69 8.0 
Figure 3

Comparisons between daily simulated discharge station (hollow circles for 2006, black circles for 2005 and gray circles for 2004), gauged discharge (black line) and gauged precipitation (gray line) in the Polroud basin for the period of 2004–2006.

Figure 3

Comparisons between daily simulated discharge station (hollow circles for 2006, black circles for 2005 and gray circles for 2004), gauged discharge (black line) and gauged precipitation (gray line) in the Polroud basin for the period of 2004–2006.

In order to evaluate the performance of simulation based on the SCS loss method, the rainfall-runoff model was implemented for single flooding events. Therefore, analyzing the errors for such cases was performed independently without considering the base flow. Based on gauge records, flooding events were recognized in each year separately and flooding was simulated based on the SCS loss method. For accuracy assessment, index of Percentage Error Parameter (PEP) was implemented between the simulated and the observed data values. Table 6 presents the error rate for the simulation of 44 flooding cases in the Polroud basin. The values of minimum, maximum and average error are 2%, 35% and 15.6%, respectively. The results indicate that the accuracy of the SCS loss method for simulation of flooding events were favorable and acceptable.

Table 6

The error rate for the simulation of 44 flooding cases (period of 2004–2006) using SCS loss method in the Polroud basin

Year200420052006Period of 2004–2006
Number  14 15 15 44 
 Minimum 6.9 
PEP (%) Maximum 35 25.6 21.6 35 
Average 22 10.9 14.1 15.6 
Year200420052006Period of 2004–2006
Number  14 15 15 44 
 Minimum 6.9 
PEP (%) Maximum 35 25.6 21.6 35 
Average 22 10.9 14.1 15.6 

Simulation of river discharge based on calibrated GLDAS data

To see the possibility of river discharge simulation in the ungauged catchments using a global dataset, calibrated meteorological GLDAS data was entered into the HEC-HMS model to simulate river discharge during the period of 2004–2006. Meteorological GLDAS datasets were first calibrated using bias correction based on the gauge measurements from 2004 to 2006. We studied the correlation between GLDAS products and gauge records, performed the regression analysis and applied the bias corrections to the GLDAS dataset.

Spatial distribution of both annual accumulated precipitation and annual averaged air temperature as well as their scatter plot with gauged data have been indicated in Figure 4. The results showed that GLDAS near surface air temperature in daily time steps have a good correlation with gauge records, however, calibrated GLDAS precipitation indicates a rather low accuracy and a range of underestimation compared to the similar gauged data. In Figure 4(a), the areas with the highest temperature are colored light brown and the areas with the lowest temperature are colored dark brown. The scatter plot in this figure indicates high correlation between GLDAS and gauged temperature after calibration. Spatial distribution of precipitation provides a better insight into how precipitation is distributed over the catchment. As clearly demonstrated in Figure 4(b), the highest precipitation occurs in the coastline area of the Caspian Sea with dark blue color. The scatter plot between GLDAS and gauged precipitation indicates rather low correlation between these two datasets.

Figure 4

(a) the spatial distribution of annual near surface air temperature (°C) from GLDAS product averaged over the period of 2004–2006, Scatter plot of GLDAS temperature and gauge temperature for the years of 2004–2006, (b) the spatial distribution of annual accumulated GLDAS precipitation product (mm) averaged over the period of 2004–2006, scatter plot of GLDAS precipitation and gauge rainfall for the years of 2004–2006. The GLDAS dataset has been calibrated with gauged data.

Figure 4

(a) the spatial distribution of annual near surface air temperature (°C) from GLDAS product averaged over the period of 2004–2006, Scatter plot of GLDAS temperature and gauge temperature for the years of 2004–2006, (b) the spatial distribution of annual accumulated GLDAS precipitation product (mm) averaged over the period of 2004–2006, scatter plot of GLDAS precipitation and gauge rainfall for the years of 2004–2006. The GLDAS dataset has been calibrated with gauged data.

Figure 5, compares the results of river discharge simulation derived by running HEC-HMS using calibrated GLDAS datasets with GLDAS runoff product and gauged discharge data.

Figure 5

Comparison of daily discharge from GLDAS runoff product (gray line and gray circles) with observed discharge (black line), simulated discharge based on forcing GLDAS data (dotted line and black circles) and simulated discharge based on station data (hollow circles) for the years of 2004 (a), 2005 (b) and 2006 (c).

Figure 5

Comparison of daily discharge from GLDAS runoff product (gray line and gray circles) with observed discharge (black line), simulated discharge based on forcing GLDAS data (dotted line and black circles) and simulated discharge based on station data (hollow circles) for the years of 2004 (a), 2005 (b) and 2006 (c).

Although GLDAS datasets have simplified the way we access data in ungauged catchments, there is evidence that we may not able to use them without data calibration. The primary result indicated that direct use of GLDAS runoff products without initial calibration will lead to low accuracy (R2 = 0.14–0.3). The comparison results shown in the Figure 5 indicates that forcing GLDAS dataset (temperature, rainfall) into the simulation process led to promising results comparing to the observation data. The results confirmed that more than 50% improvement in accuracy of simulation after calibration process of forcing GLDAS dataset, but the problem of underestimation still exists.

Table 7 indicates the statistical assessment of simulated river discharge derived by forcing GLDAS data into the HEC-HMS model instead of using gauge data. The correlation between simulated and observation data was in the range of 0.36 and 0.66 with RMSE of 12–14 respectively. Based on Table 7, the best performance was observed in the year 2006 (R2 = 0.66, Nash = 0.63, Bias = −1.6 and RMES = 13).

Table 7

Statistics analysis of simulated daily discharge (a) using forcing GLDAS datasets into the HEC-HMS (b) direct GLDAS runoff product in the Polroud river basin during the period of 2004–2006

Simulated discharge with forcing GLDAS dataset
GLDAS runoff product
PeriodR2NashRMSEError %R2NashRMSEError %
2004 0.38 −0.04 14.1 −40 0.19 −1.1 21 −77 
2005 0.36 0.34 12.9 −20 0.33 0.064 15.48 −53 
2006 0.66 0.63 13 −19 0.14 −0.16 23.5 −58 
Simulated discharge with forcing GLDAS dataset
GLDAS runoff product
PeriodR2NashRMSEError %R2NashRMSEError %
2004 0.38 −0.04 14.1 −40 0.19 −1.1 21 −77 
2005 0.36 0.34 12.9 −20 0.33 0.064 15.48 −53 
2006 0.66 0.63 13 −19 0.14 −0.16 23.5 −58 

As shown in Figure 5, both the GLDAS runoff products or discharge simulated by forcing GLDAS dataset into the HEC-HMS has an underestimation problem compared to gauge data, especially from January to June because of snow dominance. From July to December, the difference was less marked and it can ensure the accuracy and usefulness GLDAS runoff products.

CONCLUSIONS

Accurate data with proper resolution (both spatial and temporal) are essential for river discharge studies. However, due to lack of data in many basin, GLDAS datasets produced by global land surface models can be implemented for hydrological modeling as a substitute approach in an ungauged catchment.

In this study, the SMA and SCS loss method in the HEC-HMS model was calibrated using available gauge observations in the Polroud basin. The SCS loss method was used to simulate 44 flooding events during the period of 2004–2006 and SMA was implemented for continuous simulation of river discharge in the same period. The results indicated that the simulated river discharge agreed well with gauge records.

To evaluate the application of GLDAS products directly and indirectly for estimation of river discharge in an ungauged catchment, GLDAS data including average near surface air temperature and rainfall was calibrated using available gauge observations in the basin. Then, GLDAS data were forced into the HEC-HMS to continuously simulate the river discharge during the period of 2004–2006 based on the SMA loss method.

The results clearly indicated that the forcing of GLDAS data into the HEC-HMS model leads to promising results with acceptable correlation with observed data. Although, in comparison with direct GLDAS runoff products, the proposed approach improved the accuracy of river discharge, the problem of underestimation still reduces the expected accuracy. GLDAS underestimates most of the parameters (rainfall, runoff and temperature) (Zaitchik et al. 2010), while this model is able to predict the process of changes in parameters over time correctly.

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