Huiseong Noh 1 and Jongso Lee 2 and Narae Kang 2 and Dongryul Lee 1 and Hung Soo Kim 2 and Soojun Kim 3
Academic Editor:Gwo-Fong Lin
1, Water Resources Research Division, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang 10223, Republic of Korea
2, Department of Civil Engineering, Inha University, Incheon 22212, Republic of Korea
3, Columbia Water Center, Earth Institute, Columbia University, New York, NY 10027, USA
Received 23 February 2016; Revised 17 June 2016; Accepted 19 July 2016; 24 August 2016
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
With the influence of climate change and climate variability, the magnitude and frequency of extreme hydrological events increase, which makes the world suffer from natural disasters such as floods, droughts, landslides, avalanches, and so forth. Particularly in Korea, people are suffering from localized heavy rainfall and flash flood in some years and from severe drought in other years. Therefore, a study on a more accurate and comprehensive analysis and prediction is required in order to adapt to climate change and climate variability and reduce human life and property damages from floods or droughts caused by extreme hydrological events. To this end, the necessity of high-resolution hydrometeorological data with high accuracy is emphasized. In particular, rainfall is used as basic data for all interpretations related to hydrologic cycle and the water resources plan and management and also has a nonlinear relationship with other hydrological factors and environmental ones (i.e., runoff, soil moisture, erosion, water quality, etc.) [1]. For this reason, acquiring exact rainfall data is very important.
In a runoff simulation for the analysis of floods and droughts, rainfall is used as input data for rainfall-runoff model and many studies have been conducted for measuring exact rainfall in terms of time and space. Particularly for improving the accuracy of the spatiotemporal rainfall, hydrological and meteorological fields have recently paid more interest in estimating the precipitation data with a new Remote Sensing Technology including rain or weather radar (in the following, we will frequently omit the term "rain" or "weather") and artificial satellite.
In particular, radar data has advantages in that it can continue to provide rainfall information with high spatial and temporal resolutions. In early times of rainfall observation using radar, the rainfall intensity can be determined from the horizontal reflectivity (ZH ) observed using single-polarization radar. However, there is difficulty in measuring rainfall because its changing characteristics can vary depending on the type of clouds or developmental conditions, temporal and spatial location, and type of hydrometer. It has since become possible to measure various dual-polarization variables (ZH , ZV , ZDR , KDP , ρHV , etc.) using dual-polarization radar. In this process, precipitation estimation techniques can be developed.
Due to the advantages of radar, many studies in hydrological and meteorological fields that utilize radar data are being actively carried out. Utilization of radar data can be largely divided into two aspects. The first is general radar image data which analyzes the current status of rainfall and the second is grid-type rainfall distribution data for calculating flood flow [2]. In hydrological fields, two major research areas include hydrologic phenomena in various basins (including natural basins and urban basins) [2-15] and the spatial and temporal variability of rainfall [16, 17]. To evaluate previous studies which applied radar rainfall data to the Soil & Water Assessment Tool (SWAT) rainfall-runoff model used in this study, Di Luzio and Arnold [18] applied NEXRAD Stage III data to the SWAT model as part of the Distributed Model Intercomparison Project (DMIP) to carry out the first simulation of daily runoff and Jayakrishnan et al. [19] used WSR-88D data obtained from the Sondu River in Kenya to carry out a simulation of runoff and water quality. Recently, Jeong et al. [20] used CAPPI data of single-polarization radar to measure the optimal grid size for radar reflectivity and used the SWAT model and as a result of the runoff simulation proposed the optimal grid size of radar rainfall data in the applicable basin to be 4-8 km. Furthermore, Sexton et al. [21] and Price et al. [22] used the SWAT model to compare the runoff discharge from NEXRAD data with that from rain gauge data and suggested that radar rainfall data could be utilized in a useful way in basins where rain gauge data is insufficient.
As shown above, previous studies have presented various evaluation results of the hydrological applicability of radar rainfall data depending on the radar data used, target area, and hydrological model applied. However, most of the studies have largely focused on short duration rainfall events or long-term runoff simulation for about a month or a season using radar data. In other words, there are few studies on the hydrological applicability in terms of long-term runoff for water resource management. However, the accumulated rain radar rainfall data are considered to be used sufficiently for a long-term hydrologic analysis.
Therefore, this study aims to review the hydrological applicability of radar rainfall data in a long-term runoff analysis for more than one year with the characteristics of single- and dual-polarization radar rainfall data.
To this end, the procedure employed in this study consists of the following steps:
(1) Collect hydrometeorological input data on hydrological systems in the basin.
(2) Create single-polarization rain radar-derived rainfall (RZ(P) ) and dual-polarization rain radar-derived rainfall (RKDP (P) ) and compare/analyze rain gauge data (Rgauge ).
(3) Build the SWAT model and conduct calibration and validation as well as runoff simulation over the analysis period utilizing Rgauge , RZ(P) , and RKDP (P) as input data.
(4) Analyze the characteristics and applicability of Rgauge , RZ(P) , and RKDP (P) in long-term runoff analysis.
2. Study Area and Rainfall Data
2.1. Study Area
In this study, the Gamcheon stream basin of the Nakdong River in Korea (see Figure 1) was selected as the area of study to evaluate the hydrologic applicability of radar rainfall data. The Gamcheon stream is the first tributary of the Nakdong River and its stream length is 69 km. The area of the Gamcheon stream basin is about 1,005.3 km2 and it occupies about 4.3% of the Nakdong River basin. The basin is a dendritic form basin. Relatively wider alluvial plains are developed midsteam and downstream in the form of a basin surrounded by hilly mountains. The average channel width is about 230-350 m and the bed slope is about 0.0021-0.0014. Toward the upper stream, the bed slope tends to increase. The average annual rainfall is about 1,100 mm. The Gamcheon stream basin has no influx of streamflows from other basins and there are few impacts due to artificial variations of the streamflow.
Figure 1: Study basin.
[figure omitted; refer to PDF]
2.2. Rain Gauge Data (Rgauge ) and Radar Rainfall Data (RZ(P) ,RKDP (P) )
To compare the applied hydrological simulations which utilized Rradar , rainfall data at 5 rain gauge stations within the basin were collected from the Water Management Information System (WAMIS) operated by the Ministry of Land, Infrastructure, and Transport (MOLIT) from 2010 to 2012 (Figure 1). In Korea, MOLIT, Korea Meteorological Administration (KMA), Korea Water Resources Corporation (K-water), and Korea Rural Community Corporation (KRC) carry out hydrologic observations for their own purposes, but the hydrological observation is largely supervised by MOLIT, which is responsible for flood forecasting and warning as well as water management.
In this study, the radar data used was the Mt. Bisl rain radar data from MOLIT (Figure 1). MOLIT constructs a radar rainfall estimation system by carrying out a series of processes including quality control of radar data and application of Quantitative Precipitation Estimation (QPE) algorithms and provides rain radar-derived rainfall data.
The major procedures includes (1) import of observed radar data, (2) data quality control (removal of nonmeteorologic echoes), (3) creation of radar rainfall field (3 types of spatial fields: LEMAP (Lowest Elevation MAP), PPI (Plan Position Indicator), and CAPPI (Constant Altitude Plan Position Indicator)), (4) radar rainfall estimation (3 types of algorithms: RZ , RZDR , and RKDP ), (5) radar rainfall adjustment using ground rainfall, (6) calculation of point rainfall and areal rainfall of subwatershed using adjusted radar rainfall, and (7) storage of rainfall data in a DB system. In particular, LEMAP (Lowest Elevation MAP) shows a radar Rrinfall field based on the radar reflectivity data observed at a very close altitude from the ground. Figure 2 represents the procedure of the radar rainfall estimation system currently employed by MOLIT.
Figure 2: Radar data quality control and rainfall estimation procedures employed by MOLIT.
[figure omitted; refer to PDF]
In this study, we applied the following rainfall estimation algorithms to compare the hydrological applicability of Rradar . First, RZ and RKDP are calculated using (1) and (2). Equation (1) has the same shape as the existing single-polarization with the so-called Z-R relationship (Z=aRb ): [figure omitted; refer to PDF]
The inverse of this equation is Z=300R1.4 . Herein, Z is the reflectance of radar in the horizontal direction observed with single-polarization radar, ZH . Equation (1) is an empirical equation appropriate for rainfall types between straight form rainfall and convective rainfall, which are at intermediate level or higher in terms of rainfall intensity according to the characteristics of rainfall in the summer [23].
Next, (2) and (3) were proposed by Ryzhkov et al. [24] as a prototype for dual-polarization radar of WSR-88D in the United States. Generally, the radar reflectivity (Z), differential reflectivity (ZDR ), and specific differential phase shift (KDP ) are used in dual-polarization radar rainfall estimation. Also, the drop size distribution (DSD) depends on the rain intensity. Therefore, to adjust for any errors in the process of obtaining RKDP , each formula used depends on the rain intensity calculated by RZ , as shown in (3) [24]. One has [figure omitted; refer to PDF]
Here, f1 and f2 , the functions of RZ and RKDP , are determined by utilizing (1), (2), and (4) and the reflectivity, f1 (ZDR ¯) and f2 (ZDR ¯), where Zdr ¯ is an adjustment factor depending on the shape of the drop size.
In this study, single-polarization rain radar-derived rainfall which considers the reflectivity (Z) and can be described by (1) alone and dual-polarization rain radar-derived rainfall which involves all of the dual-polarization variables shown in (1) to (4) expressed as RKDP , RZ , and RKDP were utilized. The rainfall intensity (mm/hr) had an observational radius of 150 km, a temporal resolution of 2.5 min, and a spatial resolution of 125 m × 125 m while applying the rainfall estimation algorithms shown above. To utilize as input data in the SWAT model, RZ and RKDP data were converted to daily rainfall by multiplying the rate of the observational cycle (2.5 min) and time. In this case, radar-derived point rainfall data (RZ(P) ,RKDP (P) ) that belongs to the subbasin were recreated (Figure 4).
3. SWAT Model
3.1. SWAT Model and Input Data Buildup
The SWAT (Soil and Water Assessment Tool) model is a unit model of a basin developed by the USDA Agricultural Research Service (ARS) [26, 27]. In particular, the SWAT model has advantages in that it can allow a hydrologic analysis of ungauged basins by conducting a predictive simulation of long-term rainfall-runoff and sediment movement within the basin. It also has the ability to quantify relative effects of water quality depending on forms of cultivation and climate/vegetation changes. To make a temporal/spatial analysis of hydrology and water quality using the SWAT model, it is necessary to obtain meteorological data that changes over time (daily amount of precipitation, temperature, wind speed, amount of sunshine, and relative humidity), the current status of land use spatially, soil attributes, and the Digital Elevation Model (DEM). The SWAT model is widely used because it is easy to generate major input values and it is possible to analyze the runoff of rainfall in basins, the occurrence of nonpoint pollution, and temporal/spatial changes.
In this study, the DEM was set at 30 m × 30 m so that runoff of rainfall in the basins and actual stream within the basins can be well reproduced. As a land use map, the 1 : 25,000 classification land use map provided by WAMIS was used. As a soil map, the 1 : 50,000 reconnaissance soil map provided by WAMIS was used. Meteorological data including the mean daily wind speed (m/sec), daily average relative humidity (%), daily maximum/minimum temperature (°C), and daily quantity of horizontal solar radiation (MJ/m2 ) were obtained from western meteorological observing stations. Table 1 summarizes the input and output data of the SWAT model.
Table 1: Input and output data of the SWAT model.
SWAT input data | |
Temporal analysis | Precipitation |
Temperature | |
Wind speed | |
Solar radiation | |
Relative humidity | |
| |
Spatial analysis | Land use |
Soil | |
Topography | |
| |
SWAT output data | |
| |
Daily/monthly/yearly | Runoff/soil erosion/water quality for HRU |
Runoff/soil erosion/water quality for subwatershed | |
Runoff/soil erosion/water quality for each segment |
3.2. Model Parameter Calibration and Validation
To correct the parameters, we applied a trial-and-error method and calibration tool to increase the predictive accuracy of runoff discharge in the SWAT model. If the calibration procedure is properly planned, daily data collected over the course of one year is sufficient for the model calibration to obtain conceptually realistic estimates. In addition, the use of older data does not greatly influence the adjustment of parameters [28]. The periods of correction and calibration were 2010 and 2011, respectively, and the ground rainfall data and daily discharge data within basins used for the periods of correction, calibration, and simulation were provided by WAMIS and the Korea Hydrological Survey Center (KHSC).
As CANMX, CN2, ESCO, GW_REVAP, SOL_AWC, SOL_K, REVAPMN, and GWQMN among the parameters related to runoff discharge in the SWAT model react sensitively, CN2 was adjusted to correct observational values of the runoff discharge. In addition, to correct the base runoff, the parameters related to underground water (GW_REVAP, REVAPMN, and GWQMN) were calibrated. In other words, if the base runoff is simulated to be higher, GW_REVAP and GWQMN are increased and REVAPMN is reduced. If base runoff is simulated to be lower, the coefficients are calibrated reversely. The range of parameters and the shape of the input data are summarized in Table 2.
Table 2: Range and input data of the SWAT model parameters.
Variable name | Definition | Range | Input file |
GW_DELAY | Groundwater Delay time | 0-500 | [low *] .gw |
ALPHA_BF | Baseflow Alpha Factor | 0-1 | [low *] .gw |
GW_REVAP | Groundwater "revap" coefficient | 0.02-0.2 | [low *] .gw |
GWQMN | Threshold depth of water in the shallow aquifer required for return flow to occur | 0-5,000 | [low *] .gw |
REVAPMN | Threshold depth of water in the shallow aquifer for "revap" to occur | 0-500 | [low *] .gw |
ESCO | Soil evaporation compensation factor | 0.01-1.0 | [low *] .hru |
SLSOIL | Slope length for lateral subsurface flow | 0-10 | [low *] .hru |
LET_TIME | Lateral flow travel time | 0-10 | [low *] .hru |
LET_SED | Sediment concentration in lateral and groundwater flow | 0-10 | [low *] .hru |
CH_K(2) | Effective hydraulic conductivity in main channel alluvium | -0.01-150 | [low *] .rte |
CH_N(2) | Manning's "n" value for the main channel | 0-0.3 | [low *] .rte |
CN2 | SCS Curve Number | 30-98 | [low *] .mgt |
SOL_AWC | Available water capacity | 0-1 | [low *] .sol |
SOL_K | Saturated hydraulic conductivity | 0-2,000 | [low *] .sol |
MSK_CO2 | Calibration coefficient used to control the impact of the storage time constant for low flow | 0-10 | [low *] .bsn |
SURLAG | Surface runoff lag time | 1-24 | [low *] .bsn |
SFTMP | Snowfall temperature | -5-5 | [low *] .bsn |
SMTMP | Snow melt base temperature | -5-5 | [low *] .bsn |
SMFMX | Melt factor for snow on June 21 | 1.7-6.5 | [low *] .bsn |
SMFMN | Melt factor for snow on December 21 | 1.7-6.5 | [low *] .bsn |
3.3. Model Applicability Evaluation Index
In this study, to evaluate the applicability of the SWAT model for the calibration, validation, and simulation periods, the Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS (%)), and RMSE-observations standard deviation ratio (RSR) were used. To determine the optimal value of each index, NSE = 1, PBIAS = 0, and RSR = 0, as shown in (3)-(5). The NSE is a normalized statistic that determines the relative magnitude of the residual variance ("noise") compared to the measured data variance ("information") [29]. PBIAS measures the average tendency of the simulated data to be larger or smaller than their observed counterparts [30]. RSR was calculated as the ratio of the RMSE and standard deviation of measured data [25]: [figure omitted; refer to PDF]
Herein, Qiobs is the ith observed streamflow, Qisim is the ith simulated streamflow, Qmean is the mean of the observed streamflow, and n is the total number of observations.
Ramanarayanan et al. [31] suggested that if R2 is 0.5 or higher and NES is 0.4 or higher, the model simulates natural phenomenon well. Moriasi et al. [25] claimed that, based on examples of existing various models and research data, index values of the model simulation of NSE > 0.50, RSR < 0.70, and PBIAS ± 25% are satisfactory. In particular, Moriasi et al. [25] proposed the criteria for setting the general performance rating in the model of runoff discharge, as shown in Table 3. This criteria is based on monthly unit runoff discharge, but the model simulation is poorer with a shorter time step than a longer time step (e.g., daily versus monthly or yearly) [32]. Therefore, these criteria can be used to evaluate the results of calibration, validation, and simulation obtained in this study.
Table 3: General performance ratings for a monthly time step.
Performance rating | RSR | NSE | PBIAS (%) |
Very gooda | 0.00 <= RSR <= 0.50 | 0.75 < NSE <= 1.00 | PBIAS < ±10 |
Gooda | 0.50 < RSR <= 0.60 | 0.65 < NSE <= 0.75 | ±10 <= PBIAS < ±15 |
Satisfactorya | 0.60 < RSR <= 0.70 | 0.50 < NSE <= 0.65 | ±15 <= PBIAS < ±25 |
Unsatisfactorya | RSR > 0.70 | NSE <= 0.50 | PBIAS ≥ ±25 |
a [25].
3.4. Application of Radar Rainfall Data in the SWAT Model
A rainfall station should be installed to represent the local distribution of rainfall in a basin. In this case, five rainfall stations (Seonsan, Gimcheon, Jirye, Buhang 1, and Buhang 2) are located in the Gamcheon stream basin of the Nakdong River and the density of rainfall station is about 201.1 km2 /station (basin area is 1,005.3 km2 ). This density of the rainfall station is above the minimum criteria recommended by World Metrological Organization (WMO) (mountains and hills: 250-575 km2 /station), but it is not sufficient for the criteria for flood forecasting and warning (generally, 50 km2 /station) recommended by the Design Criteria Rivers Commentary of Korea Water Resources Association (KWRA) [33]. This study aimed to propose the method(s) to compensate the problems occurring during hydrologic analysis by using a semidistributed model, which might appear due to intermittence of rain gauge. In other words, it is to generate rain radar data at the applicable site by using rain radar rainfall data if rain radar rainfall data at the site without rain gauge data are required.
Radar-derived rainfall data can be used in a useful way in basins where ground observation data (rainfall station) is not sufficiently guaranteed. Therefore, this study creates radar-derived point rainfall data (RZ(P) ; RKDP (P) ) at central points of 42 subbasins divided when building the SWAT model to demonstrate the advantages of radar-derived rainfall data.
In other words, the SWAT model uses the closest rain gauge station (Buhang 1 station) to interpret #7 subbasin (#7 basin is at the utmost bottom of the figure), as shown in Figure 3(b). Therefore, it is difficult to simulate appropriate runoff discharge if there is difficulty in taking into account the temporal and spatial characteristics of rainfall because the size of basin is large or there are not many rain gauge stations in the basin.
Figure 3: Creation of virtual radar-derived point rainfall (RZ(P) ,RKDP (P) ).
(a) [figure omitted; refer to PDF]
(b) [figure omitted; refer to PDF]
Figure 4: Comparison of basin average rainfalls for the different rainfall data types.
[figure omitted; refer to PDF]
4. Results and Discussion
In this study, we utilized 2012 as the year for simulation taking into account the observation period and accuracy of each data set, established the SWAT model in the basins prior to runoff simulation, and created RZ(P) and RKDP (P) , as shown in Figure 3. In addition, we made a simple comparative analysis of RZ(P) , RKDP (P) , and Rgauge .
4.1. Comparison of Rainfall Data
Figure 4 represents the results of the comparison of the basin average rainfall accumulated for the period of simulation (2012).
As a result of comparing the average basin rainfall accumulated during the period of simulation (2012), Rgauge is 1,281.4 mm and RZ(P) and RKDP (P) are 1,272.6 mm and 1,450.6 mm, respectively. Compared to the ground observation rainfall, RZ(P) is underestimated by about 0.7% (8.8 mm) and RKDP (P) is overestimated about 13.2% (169.2 mm). In the scatter plot which shows the accumulated rainfall and correlation of Rgauge and RZ(P) or RKDP (P) (inside Figure 4), the correlation coefficients and root-mean-square deviation error values between RZ(P) or RKDP (P) and Rgauge are 0.968 and 0.976 and 2.926 and 2.848, respectively. This suggests that the values obtained for RKDP (P) are better. However, as a result of comparing the rainfall of the Nile depending on the period, RZ(P) and RKDP (P) represent the characteristics of rainfall in the rainy or wet season (heavy rainfall and in the summer when typhoons occur frequently: Jun. to Sept.) relatively well but are overestimated in the dry season (periods other than the rainy or wet season: spring, fall, and winter). In particular, in the dry season, the PBIAS results utilizing RZ(P) and RKDP (P) are in the range of about 11% to 37% (overestimated). Given that the annual PBIAS is about 0.7% to 13.2%, RZ(P) and RKDP (P) are very low in terms of accuracy for rainfall estimation in dry seasons (Table 4). Tables 5 and 6 show data for the ten (10) days in which daily rainfall data of RZ(P) and RKDP (P) are overestimated and underestimated the most compared to Rgauge . As shown in the overestimation list, RKDP (P) has higher variations except for the abnormal observation at RZ(P) (Sept. 12, 2012) and in the underestimation list, RZ(P) has higher variations.
Table 4: Summary of the statistics of basin average rainfall for the different rainfall data types.
Classification | Accumulated rainfall (mm) | CC | RMSE | R 2 | PBIAS (%) | ||||||
R g a u g e | R Z ( P ) | R K D P ( P ) | R Z ( P ) | R K D P ( P ) | R Z ( P ) | R K D P ( P ) | R Z ( P ) | R K D P ( P ) | R Z ( P ) | R K D P ( P ) | |
Simulation period(2012/1-2012/12) | 1,281.4 | 1,272.6 | 1,450.6 | 0.968 | 0.976 | 2.926 | 2.848 | 0.936 | 0.953 | 0.69 | - 13.20 |
Rainy period(2012/6, 7, 8, 9) | 940.8 | 906.9 | 1,001.2 | 0.977 | 0.988 | 3.880 | 3.251 | 0.955 | 0.975 | 4.52 | - 5.41 |
Dry period(2012/3, 4, 5, 10, 11) | 263.6 | 293.0 | 361.3 | 0.877 | 0.902 | 2.620 | 2.904 | 0.769 | 0.813 | - 11.14 | - 37.07 |
Winter period(2012/1, 2, 12) | 68.0 | 72.7 | 88.1 | 0.932 | 0.945 | 1.253 | 1.247 | 0.868 | 0.893 | -6.99 | - 29.54 |
Table 5: List of the ten days where RZ(P) and RKDP (P) results overestimated the rain gauge rainfall data the most.
Date | Daily rainfall (mm) | Difference (mm) | Date | Daily rainfall (mm) | Difference (mm) | ||
Rain gauge rainfall data (Rgauge ) | Rain radar rainfall data (RZ(P) ) | Rain gauge rainfall data (Rgauge ) | Rain radar rainfall data (RKDP (P) ) | ||||
2012-09-12 | 0.0 | 29.2 | -29.2 | 2012-04-25 | 15.8 | 32.4 | -16.6 |
2012-05-28 | 5.2 | 19.1 | -13.9 | 2012-03-30 | 11.4 | 27.9 | -16.5 |
2012-05-08 | 5.4 | 18.8 | -13.4 | 2012-11-04 | 6.0 | 21.7 | -15.7 |
2012-12-14 | 15.4 | 28.3 | -12.9 | 2012-12-14 | 15.4 | 29.8 | -14.4 |
2012-11-04 | 6.0 | 18.3 | -12.3 | 2012-03-22 | 5.2 | 18.3 | -13.1 |
2012-08-16 | 25.2 | 33.2 | -8.0 | 2012-07-06 | 30.6 | 43.4 | -12.8 |
2012-04-25 | 15.8 | 22.8 | -7.0 | 2012-11-11 | 17.0 | 26.0 | -9.0 |
2012-11-11 | 17.0 | 23.6 | -6.6 | 2012-08-20 | 1.6 | 9.7 | -8.1 |
2012-07-15 | 19.2 | 25.7 | -6.5 | 2012-08-16 | 25.2 | 33.1 | -7.9 |
2012-03-22 | 5.2 | 11.5 | -6.3 | 2012-07-15 | 19.2 | 27.0 | -7.8 |
Table 6: List of the ten days where RZ(P) and RKDP (P) results underestimated the rain gauge rainfall data the most.
Date | Daily rainfall (mm) | Difference (mm) | Date | Daily rainfall (mm) | Difference (mm) | ||
Rain gauge rainfall data (Rgauge ) | Rain radar rainfall data (RZ(P) ) | Rain gauge rainfall data (Rgauge ) | Rain radar rainfall data (RKDP (P) ) | ||||
2012-09-17 | 176.0 | 143.6 | 32.4 | 2012-08-30 | 69.0 | 51.9 | 17.1 |
2012-08-28 | 63.0 | 46.2 | 16.8 | 2012-03-23 | 22.0 | 11.9 | 10.1 |
2012-08-30 | 69.0 | 53.1 | 15.9 | 2012-08-28 | 63.0 | 54.2 | 8.8 |
2012-03-23 | 22.0 | 11.1 | 10.9 | 2012-09-13 | 9.6 | 2.6 | 7.0 |
2012-05-14 | 20.6 | 9.8 | 10.8 | 2012-09-17 | 176.0 | 170.4 | 5.6 |
2012-09-13 | 9.6 | 1.1 | 8.5 | 2012-09-16 | 69.0 | 64.2 | 4.8 |
2012-09-16 | 69.0 | 61.1 | 7.9 | 2012-07-07 | 5.2 | 0.7 | 4.5 |
2012-04-21 | 40.2 | 34.4 | 5.8 | 2012-06-12 | 9.2 | 5.9 | 3.3 |
2012-08-23 | 34.2 | 28.6 | 5.6 | 2012-08-11 | 9.6 | 6.3 | 3.3 |
2012-07-07 | 5.2 | 0.3 | 4.9 | 2012-03-04 | 6.2 | 3.0 | 3.2 |
4.2. Simulated Streamflow Results
As mentioned earlier, the entire period of analysis (2010 to 2013) was divided into periods of calibration (2010), validation (2011), and simulation (2012). The parameters were corrected through runoff simulation for the period of correction using ground rainfall data. Using these parameters, the runoff analysis was carried out based on the SWAT model for the periods of calibration and simulation. The results from the runoff analysis were analyzed based on the general applicability evaluation criteria of the model presented by Moriasi et al. [25] in Section 3.3.
Figure 5 compares the observed and simulated streamflows for the periods of correction and calibration. The simulated streamflows obtained using the observed streamflow and Rgauge during the period of correction are 20.7 m3 /sec and 20.5 m3 /sec, respectively, and the means of the simulation streamflows using the observed streamflow and Rgauge during the period of calibration are 22.7 m3 /sec and 22.4 m3 /sec, respectively. As shown in Figure 5, the results of the runoff charge using Rgauge for the periods of correction and calibration describe the entire characteristics of the daily unit runoff discharge relatively well. However, 2010/8/10 and 2010/8/15~16 during the period of calibration and 2011/8/9 during the period of validation are underestimated because the run-off discharge is relatively high. Moreover, as a result of evaluating the applicability of the SWAT model during the periods of correction and calibration, as presented in Table 7, the NSE values are 0.97 and 0.78, respectively, the PBIAS (%) values are 1.44 and -24.13, respectively, and the RSR values are nearly 0.47. The results from the runoff analysis during the periods of correction and calibration are a natural result of the application of the optimized model parameters of the correction period to the calibration period.
Table 7: Evaluation of the model applicability in the streamflow analysis.
Classification | Type of rainfall data | NSE | RSR | PBIAS (%) |
Calibration (2010) | R g a u g e | 0.97 (very good) | 0.47 (very good) | 1.46 (very good) |
| ||||
Validation (2011) | R g a u g e | 0.78 (very good) | 0.47 (very good) | -24.13 (satisfactory) |
| ||||
Simulation (2012) | R g a u g e | 0.74 (good) | 0.51 (good) | -27.50 (unsatisfactory) |
R Z ( P ) | 0.69 (good) | 0.55 (good) | -42.74 (unsatisfactory) | |
R K D P ( P ) | 0.72 (good) | 0.53 (good) | -44.79 (unsatisfactory) | |
| ||||
Simulation (rainy or wet season) | R g a u g e | 0.74 (good) | 0.51 (good) | -25.59 (unsatisfactory) |
R Z ( P ) | 0.73 (good) | 0.52 (good) | -30.23 (unsatisfactory) | |
R K D P ( P ) | 0.75 (good) | 0.50 (very Good) | -29.80 (unsatisfactory) | |
| ||||
Simulation (dry season) | R g a u g e | -8.91 (unsatisfactory) | 3.15 (very good) | -35.00 (unsatisfactory) |
R Z ( P ) | -24.10 (unsatisfactory) | 5.01 (good) | -80.51 (unsatisfactory) | |
R K D P ( P ) | -23.40 (unsatisfactory) | 4.94 (very good) | -92.49 (unsatisfactory) |
Figure 5: Calibration and validation of the SWAT model.
[figure omitted; refer to PDF]
Figure 6 shows the results of daily streamflow hydrographs which applied RZ(P) and RKDP (P) to the model that completed its validation using the parameters calibrated earlier.
Figure 6: The results of the daily streamflow simulation (2012).
[figure omitted; refer to PDF]
The mean of the observation streamflow during the period of simulation (2012) is 19.8 m3 /sec. The mean of the runoff discharge using the ground observation data is about 25.2 m3 /sec and the mean values of the runoff discharge using RZ(P) and RKDP (P) are about 27.7 m3 /sec and 28.1 m3 /sec, respectively. In other words, the average streamflows were higher when RZ(P) and RKDP (P) were used compared to when Rgauge was used during the period of simulation (2012). The NSE, RSR, and PBIAS (%) values, which are evaluation indexes for the applicability of the SWAT model during the period of simulation, showed more significant simulation results when Rgauge was used compared to when RZ(P) and RKDP (P) were used (Table 7).
Similar to the rainfall comparison (Section 4.1), the runoff discharge analysis was carried out by classifying the season to either the rainy or wet season or the dry season depending on the characteristics of the rainfall and seasonal period. As a result, the NSE, RSR, and PBIAS (%) values, which are evaluation indexes for the applicability of the SWAT model, during the rainy or wet season are 0.74, 0.51, and -25.59, respectively, when Rgauge was used and 0.73, 0.52, and -30.23 and 0.75, 0.50, and -29.80 when RZ(P) and RKDP (P) were used. In other words, it is significant to use RZ(P) and RKDP (P) in the rainy or wet season (Jun. to Sept.) when convection precipitation, which is at an intermediate level or higher (relatively high) in terms of rainfall intensity including heavy rains and typhoons, occurs frequently. Such a result is attributable to the fact that the QPE algorithms (Section 2.2) used in this study are optimized for rainfall observation in the form of convection precipitation with a high rainfall intensity for the purpose of forecasting and warning floods. If QPE algorithms are applied to observation strategies and algorithms for the purpose of stratiform rainfall or winter season observation, opposite results from those obtained in this study are expected.
Therefore, to increase the applicability of rain radar-derived rainfall data in analyzing more than one year of long-term runoff (both daily and monthly time steps), it is necessary to take the development and application of algorithms into account to improve QPE algorithms as well as the accuracy of radar data.
5. Conclusions
In this study, we evaluated the applicability of long-term runoff simulation on a daily basis using rain radar-derived rainfall data with rain gauge data (Rgauge ).
The SWAT model, which is a semidistribution hydrologic model, was applied to the Gamcheon stream basin of the Nakdong River from 2010 to 2012. In addition, radar-derived point rainfall data (RZ(P) and RKDP (P) ) and rain gauge data (Rgauge ) were compared prior to runoff simulation.
As a result of comparing the annual average accumulated amount of precipitation during the period of simulation (2012), RZ(P) and RKDP (P) underestimated Rgauge by about 0.7% and overestimated Rgauge by 13.2%, respectively. The annual data were classified into the rainy or wet season and dry season depending on the characteristics of rainfall and seasonal period taking into account the typical rainfall distribution in Korea in Jun. to Sept. when heavy rain and typhoons occur in order to compare the average accumulated rainfall in the basin. As a result, RZ(P) and RKDP (P) had relatively lower errors compared to Rgauge in the summer (rainy or wet season), but there were large errors because such values are overestimated in the dry season.
Based on the correlation coefficient (R), runoff simulations using Rgauge , RZ(P) , and RKDP (P) were carried out. As a result, the simulations described the characteristics of the changes of runoff on a daily basis well. The NSE, RSR, and PBIAS (%) values, which are evaluation indexes for the applicability of the SWAT model, were evaluated in this study. As a result, the NSE and RSR values were appropriate, but PBIAS (%) was negative in most cases. This suggests that the PBIAS (%) is overestimated. In particular, as in the rainfall analysis, the runoff discharge analysis was classified into the rainy or wet season and dry season. As a result of the comparison of both seasons, the NSE, RSR, and PBIAS (%) values in the rainy or wet season showed similar or more significant values when Rgauge was used compared to when RZ(P) and RKDP (P) were used. But, in the dry season, the analysis did not match the ground observation rainfall data well (Rgauge ). Based on the results obtained to date, the QPE algorithms used in this study are highly applicable in runoff simulation from Jun. to Sept. (summer; rainy season) and less applicable in other periods (dry season; winter).
The results from this study suggest that it is necessary to select radar observation strategies and algorithms appropriately depending on the intended purpose of radar rainfall data. Therefore, further studies are needed to improve the bias correction and rainfall algorithms (in real time) to increase the usability of radar data in analyzing long-term runoff for more than one year (both daily and monthly time steps). Still, there is a limit to the accuracy of Quantitative Precipitation Estimation. But if the accuracy of Quantitative Precipitation Estimation can be improved sufficiently, the hydrological application scope of rain radar rainfall will be expanded sufficiently and more exact hydrologic analysis will become possible.
Acknowledgments
This research was supported by a grant from a Strategic Research Project (Development of Flood Warning and Snowfall Estimation Platform using Hydrological Radars) funded by the Korea Institute of Construction Technology.
[1] A. Berne, W. F. Krajewski, "Radar for hydrology: unfulfilled promise or unrecognized potential?," Advances in Water Resources , vol. 51, pp. 357-366, 2013.
[2] M. Borga, "Accuracy of radar rainfall estimates for streamflow simulation," Journal of Hydrology , vol. 267, no. 1-2, pp. 26-39, 2002.
[3] B. Anderl III, W. Attmannspacher, G. A. Schultz, "Accuracy of reservoir inflow forecasts based on radar rainfall measurements," Water Resources Research , vol. 12, no. 2, pp. 217-223, 1976.
[4] I. D. Cluckie, M. D. Owens, V. Collinge, C. Kirby, "Real-time radar-runoff models and use of weather radar information," Weather Radar and Flood Forecasting , pp. 171-190, John Wiley & Sons, New York, NY, USA, 1987.
[5] N. Kouwen, G. Garland, "Resolution considerations in using radar rainfall data for flood forecasting," Canadian Journal of Civil Engineering , vol. 16, no. 3, pp. 279-289, 1989.
[6] J. Wyss, E. R. Williams, R. L. Bras, "Hydrologic modeling of New England river basins using radar rainfall data," Journal of Geophysical Research , vol. 95, no. 3, pp. 2143-2152, 1990.
[7] G. S. Schell, C. A. Madramootoo, G. L. Austin, R. S. Broughton, "Use of radar measured rainfall for hydrologic modelling," Canadian Agricultural Engineering , vol. 34, no. 1, pp. 41-48, 1992.
[8] W. P. James, C. G. Robinson, J. F. Bell, "Radar-assisted real-time flood forecasting," Journal of Water Resources Planning and Management , vol. 119, no. 1, pp. 32-44, 1993.
[9] K. P. Georgakakos, J. A. Sperfslage, A. K. Guetter, "Operational GIS based models for NEXRAD radar data in the US," in Proceedings of the International Conference on Water Resources and Environmental Research, vol. 1, of Water Resources and Environmental Research Center, pp. 603-609, Kyoto University, Kyoto, Japan, October 1996.
[10] V. A. Bell, R. J. Moore, "A grid-based distributed flood forecasting model for use with weather radar data: part 2. Case studies," Hydrology and Earth System Sciences , vol. 2, no. 2-3, pp. 283-298, 1998.
[11] B. E. Vieux, P. B. Bedient, "Estimation of rainfall for flood prediction from WSR-88D reflectivity: a case study, 17-18 October 1994," Weather and Forecasting , vol. 13, no. 2, pp. 407-415, 1998.
[12] M. R. Knebl, Z.-L. Yang, K. Hutchison, D. R. Maidment, "Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event," Journal of Environmental Management , vol. 75, no. 4, pp. 325-336, 2005.
[13] J. N. Diaz-Ramirez, W. H. McAnally, J. L. Martin, "Sensitivity of simulating hydrologic processes to gauge and radar rainfall data in subtropical coastal catchments," Water Resources Management , vol. 26, no. 12, pp. 3515-3538, 2012.
[14] H. Noh, N. Kang, B. Kim, H. Kim, "Flood simulation using vflo and radar rainfall adjustment data by statistical objective analysis," Journal of Korean Wetlands Society , vol. 14, no. 2, pp. 243-254, 2012.
[15] A. Fares, R. Awal, J. Michaud, P.-S. Chu, S. Fares, K. Kodama, M. Rosener, "Rainfall-runoff modeling in a flashy tropical watershed using the distributed HL-RDHM model," Journal of Hydrology , vol. 519, pp. 3436-3447, 2014.
[16] F. Olivera, J. Choi, D. Kim, M.-H. Li, "Estimation of average rainfall areal reduction factors in Texas using NEXRAD data," Journal of Hydrologic Engineering , vol. 13, no. 6, pp. 438-448, 2008.
[17] J. Choi, F. Olivera, S. A. Socolofsky, "Storm identification and tracking algorithm for modeling of rainfall fields using 1-h NEXRAD rainfall data in Texas," Journal of Hydrologic Engineering , vol. 14, no. 7, pp. 721-730, 2009.
[18] M. Di Luzio, J. G. Arnold, "Formulation of a hybrid calibration approach for a physically based distributed model with NEXRAD data input," Journal of Hydrology , vol. 298, no. 1-4, pp. 136-154, 2004.
[19] R. Jayakrishnan, R. Srinivasan, C. Santhi, J. G. Arnold, "Advances in the application of the SWAT model for water resources management," Hydrological Processes , vol. 19, no. 3, pp. 749-762, 2005.
[20] C. Jeong, K. Joo, W. Lee, H. Shin, J.-H. Heo, "Estimation of optimal grid size for radar reflectivity using a SWAT model," Journal of Hydro-Environment Research , vol. 8, no. 1, pp. 20-31, 2014.
[21] A. M. Sexton, A. M. Sadeghi, X. Zhang, R. Srinivasan, A. Shirmohammadi, "Using NEXRAD and rain gauge precipitation data for hydrologic calibration of SWAT in a northeastern watershed," Transactions of the ASABE , vol. 53, no. 5, pp. 1501-1510, 2010.
[22] K. Price, S. T. Purucker, S. R. Kraemer, J. E. Babendreier, C. D. Knightes, "Comparison of radar and gauge precipitation data in watershed models across varying spatial and temporal scales," Hydrological Processes , vol. 28, no. 9, pp. 3505-3520, 2014.
[23] R. S. Sekhon, R. C. Srivastava, "Doppler radar observations of drop-size distributions in a thunderstorm," Journal of the Atmospheric Sciences , vol. 28, no. 6, pp. 983-994, 1971.
[24] A. V. Ryzhkov, S. E. Giangrande, T. J. Schuur, "Rainfall estimation with a polarimetric prototype of WSR-88D," Journal of Applied Meteorology , vol. 44, no. 4, pp. 502-515, 2005.
[25] D. N. Moriasi, J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, T. L. Veith, "Model evaluation guidelines for systematic quantification of accuracy in watershed simulations," Transactions of the ASABE , vol. 50, no. 3, pp. 885-900, 2007.
[26] J. G. Arnold Spatial scale variability in model developmentand parameterization [Ph.D. dissertation] , Purdue University, West Lafayette, Ind, USA, 1992.
[27] J. G. Arnold, R. Srinivasan, R. S. Muttiah, J. R. Williams, "Large area hydrologic modeling and assessment part I: model development," Journal of the American Water Resources Association , vol. 34, no. 1, pp. 73-89, 1998.
[28] S. Sorooshian, V. K. Gupta, V. P. Singh, "Model calibration," Computer Models of Watershed Hydrology , pp. 23-68, Water Resources Publications, Fort Collins, Colo, USA, 1995.
[29] J. E. Nash, J. V. Sutcliffe, "River flow forecasting through conceptual models: part I--a discussion of principles," Journal of Hydrology , vol. 10, no. 3, pp. 282-290, 1970.
[30] H. V. Gupta, S. Sorooshian, P. O. Yapo, "Status of automatic calibration for hydrologic models: comparison with multilevel expert calibration," Journal of Hydrologic Engineering , vol. 4, no. 2, pp. 135-143, 1999.
[31] T. S. Ramanarayanan, J. R. Williams, W. A. Dugas, L. M. Hauck, A. M. S. McFarland, "Using APEX to identify alternative practices for animal waste management: part II. Model application," ASAE Paper , no. 97-2209, ASAE, St. Joseph, Mich, USA, 1997.
[32] B. Engel, D. Storm, M. White, J. G. Arnold, "A hydrologic/water quality model application protocol," Journal of the American Water Resources Association , vol. 43, no. 5, pp. 1223-1236, 2007.
[33] Korea Water Resources Association Design Criteria Rivers Commentary , 2009.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Copyright © 2016 Huiseong Noh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In recent years, with the increasing need for improving the accuracy of hydrometeorological data, interests in rain-radar are also increasing. Accordingly, with high spatiotemporal resolution of rain-radar rainfall data and increasing accumulated data, the application scope of rain-radar rainfall data into hydrological fields is expanding. To evaluate the hydrological applicability of rain-radar rainfall data depending on the characteristics of hydrological model, this study applied [subscript]Rgauge[/subscript] and [subscript]Rradar[/subscript] to a SWAT model in the Gamcheon stream basin of the Nakdong River and analyzed the effect of rainfall data on daily streamflow simulation. The daily rainfall data for [subscript]Rgauge[/subscript] , [subscript]RZ[/subscript] , and [subscript]R[subscript]KDP[/subscript] [/subscript] were utilized as input data for the SWAT model. As a result of the daily runoff simulation for analysis periods using [subscript]RZ(P)[/subscript] and [subscript]R[subscript]KDP[/subscript] (P)[/subscript] , the simulation which utilized [subscript]Rgauge[/subscript] reflected the rainfall-runoff characteristics better than the simulations which applied [subscript]RZ(P)[/subscript] or [subscript]R[subscript]KDP[/subscript] (P)[/subscript] . However, in the rainy or wet season, the simulations which utilized [subscript]RZ(P)[/subscript] or [subscript]R[subscript]KDP[/subscript] (P)[/subscript] were similar to or better than the simulation that applied [subscript]Rgauge[/subscript] . This study reveals that analysis results and degree of accuracy depend significantly on rainfall characteristics (rainy season and dry season) and QPE algorithms when conducting a runoff simulation with radar.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer