Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Study Area
2.2. Data Collection and Processing
2.2.1. Discharge Data
2.2.2. Rain Gauges
2.2.3. Radar Rainfall Estimates
2.2.4. Adjustment of Radar Rainfall Estimates Using Weighted Multiple Regression (WMR) Method
- The spatial distribution of radar rainfall estimates corresponds to the rain gauge-based point measurements.
- Radar and rain gauge instruments perform the measurements at different heights, but the estimated rainfall from these instruments is assumed to be measured at the same level.
- The data analysis in each year is limited to the period from April to October to minimize the risk that radar would measure solid hydrometeors instead of liquid particles.
- Only simultaneous rainfall observations of rain gauge and radar are taken into further consideration; cases where only rain gauge or only radar registered rainfall were available are neglected.
- A semi-distributed hydrological model for each sub-catchment is assumed; mean value from all the radar estimates over the sub-catchment is assigned to its area. This mean value is accordingly adjusted and applied in the hydrological model.
2.2.5. IMERG GPM Satellite Rainfall Estimates
2.2.6. Digital Elevation Model and Land-Cover
2.3. HEC-HMS Hydrological Model
2.3.1. Model Set-up
2.3.2. Calibration and Validation
2.3.3. Simulation Time-Step Analysis
3. Results and Discussion
3.1. Adjustment of Radar Rainfall Estimates
3.2. Intercomparison of Precipitation Products
3.3. Simulation Results
3.3.1. Calibration and Validation of the Model
3.3.2. Simulation Time-Step Analysis
4. Conclusions
- (1)
- A good or very good model performance was obtained for most of the simulations during the calibration phase, but for the validation period, best results were obtained using the adjusted radar rainfall estimates and IMERG GPM data as precipitation data source.
- (2)
- Spatial and temporal distributions of rainfall estimated from different data sources vary significantly. As rainfall distribution in both time and space has a substantial impact on estimated values of model parameters a separate hydrological model should be applied for each source of the precipitation data.
- (3)
- Radar-estimated precipitation seems to be the most reliable source of information on the ‘real’ precipitation field. Precipitation interpolated from the rain gauge data seems to have a high degree of uncertainty, whereas IMERG GPM provides precipitation estimates of low spatial resolution.
- (4)
- Raw radar rainfall estimates seem to overestimate the observed rainfall significantly. Therefore, the radar data should be adjusted to minimize the bias between rain gauge measurement and radar estimation. When applied, the adjustment method for the radar rainfall estimates performed very well for event-based rainfall-runoff simulations in the mountainous area and can be easily adapted to other areas as it requires a relatively few data.
- (5)
- Short time of latency of IMERG GPM rainfall estimates makes it a valuable data source for near-real-time flood monitoring, but a rather sparse spatial resolution offsets this. Application of IMERG GPM rainfall estimates is challenging for small catchments as the satellite grids may cover the areas outside the sub-catchment or be partially common for several sub-catchment. If this is the case a weighting of rainfall should be done to account for the area of each sub-catchment covered by each grid.
- (6)
- Adequate choice of performance metrics is essential to evaluate the simulation results thoroughly. The evaluation criteria should allow judging the performance of the flow model regarding various flow characteristics (for the event-based modeling, these are predictive power of the model, timing of simulated and observed time series, tendency of over- or under-estimation of simulated flow, and accuracy in peak flow estimation). The applied evaluation metrics (Nash-Sutcliffe efficiency coefficient, Pearson’s correlation coefficient, percent bias, and relative peak flow difference) allowed to make a comprehensive assessment of simulation results regarding these characteristics.
- (7)
- Regardless of the values of the performance metrics, a visual analysis of the observed and simulated hydrographs should be performed. Sometimes the metrics can give satisfactory results even though the overall simulation results don’t fit the observed hydrograph.
- (8)
- Aggregation of simulation time-step up to 2 h improves the simulation results for radar- and satellite rainfall-based flow simulations. Further aggregation in time, up to 4 h, is valuable for simulations based on rain gauge precipitation data. The simulation results show that the time-step of simulations in a small catchment which have a short concentration time (like mountainous environments) should not exceed the response time of the catchment.
- (9)
- SCS Curve Number loss method applied in HEC-HMS is more adequate for simulations of flood events of unimodal distribution rather than of bimodal distribution. The method does not allow the regeneration of rainfall losses during the flood event and may lead to over- or underestimation of one of the flood event peaks.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Event | Start | End | Maximum Discharge (m3/s) |
---|---|---|---|
Event 1 | 14 May 2014 | 23 May 2014 | 211.1 |
Event 2 | 3 October 2014 | 7 October 2014 | 22.8 |
Event 3 | 21 May 2015 | 30 May 2015 | 26.8 |
Event 4 | 14 May 2016 | 17 May 2016 | 19.9 |
Event 5 | 17 July 2016 | 19 July 2016 | 23.2 |
Event 6 | 3 October 2016 | 9 October 2016 | 35.2 |
Rainfall Station | Acronym | Longitude | Latitude |
---|---|---|---|
Maków Podhalański | RS-1 | 19°40′ | 49°43′ |
Markowe Szczawiny | RS-2 | 19°30′ | 49°35′ |
Spytkowice Górne | RS-3 | 19°50′ | 49°34′ |
Zawoja | RS-4 | 19°34′ | 49°40′ |
Basin Model | Meteorological Model | ||
---|---|---|---|
Parameter Method | Selected Method | Parameter Method | Selected Method |
Loss | SCS Curve Number | Precipitation | Inverse Distance (for rain gauges) |
Transform | Snyder Unit Hydrograph | Specified Hyetograph (for radar and GPM) | |
Baseflow | Recession | ||
Routing | Muskingum-Cunge |
Performance Metrics | Value | Classification | Reference |
---|---|---|---|
Nash-Sutcliffe efficiency coefficient (NSE) | NSE ≤ 0.4 | Unsatisfactory | [66] |
0.40–0.50 | Acceptable | ||
0.50–0.65 | Satisfactory | ||
0.65–0.75 | Good | ||
0.75–1.00 | Very Good | ||
Pearson’s correlation coefficient (r) | r ≤ 0.4 | Unsatisfactory | [29] |
0.40–0.60 | Acceptable | ||
0.60–0.70 | Satisfactory | ||
0.70–0.85 | Good | ||
0.85–1.00 | Very Good | ||
Percent bias (PBias) Relative peak flow difference (rPFD) | >20% | Unacceptable | [63] |
≤20% | Acceptable |
Parameter | Rain Gauges 1 | Sub-Catchments 2 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
GS-1 | GS-2 | GS-3 | GS-4 | SC-1 | SC-2 | SC-3 | SC-4 | SC-5 | SC-6 | |
DR (km) | 82.8 | 84.9 | 101.7 | 80.8 | 91.6 | 94.5 | 100.8 | 102.4 | 96.9 | 90.4 |
HG (m a.s.l.) | 367 | 1184 | 525 | 604 | 713.4 | 684.7 | 588.2 | 509.8 | 524.9 | 578.8 |
MH (m) | 981.5 | 1434.1 | 1431.2 | 1011 | 1252.1 | 1482.4 | 1472.3 | 1384.8 | 1301.4 | 1067.1 |
Event | Total Precipitation Accumulation (mm) | |||
---|---|---|---|---|
Rain Gauges | Raw Radar | Adjusted Radar | IMERG GPM | |
Event 1 | 397 | 1680 | 692 | 678 |
Event 2 | 212 | 437 | 179 | 223 |
Event 3 | 227 | 577 | 236 | 293 |
Event 4 | 129 | 452 | 184 | 149 |
Event 5 | 175 | 424 | 175 | 190 |
Event 6 | 448 | 1108 | 456 | 783 |
Event | NSE | r | PBias (%) | rPFD | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Rain Gauges | Radar | IMERG | Rain Gauges | Radar | IMERG | Rain Gauges | Radar | IMERG | Rain Gauges | Radar | IMERG | |
Event 1 | −0.46 | 0.79 | 0.81 | 0.17 | 0.89 | 0.90 | −61.20 | −0.60 | −6.00 | −40.6 | −23.2 | −20.7 |
Event 2 | 0.75 | 0.91 | 0.74 | 0.87 | 0.95 | 0.88 | −2.30 | −4.50 | −4.50 | −3.5 | 3.9 | 8.7 |
Event 6 | 0.78 | 0.66 | 0.20 | 0.89 | 0.84 | 0.76 | −3.70 | −13.50 | 0.50 | 6.5 | 3.1 | 36.9 |
Sub-Catchment | Initial Abstraction (MM) | Curve Number (-) | ||||||
---|---|---|---|---|---|---|---|---|
Rain Gauges | Radar | IMERG | Rain Gauges | Radar | IMERG | |||
Initial | Optimized | Initial | Optimized | |||||
SC-1 | 25.23 | 19.42 | 26.57 | 21.83 | 43.02 | 48.63 | 70.68 | 54.27 |
SC-2 | 27.21 | 17.03 | 21.24 | 23.55 | 41.18 | 58.70 | 77.20 | 71.42 |
SC-3 | 17.77 | 13.73 | 15.23 | 15.38 | 51.73 | 86.64 | 87.11 | 75.52 |
SC-4 | 17.39 | 11.25 | 19.47 | 27.12 | 51.51 | 84.58 | 76.75 | 73.68 |
SC-5 | 17.01 | 16.48 | 12.92 | 21.83 | 52.82 | 63.07 | 81.71 | 73.49 |
SC-6 | 18.39 | 14.16 | 17.46 | 20.30 | 50.88 | 78.99 | 54.07 | 65.38 |
Sub-Catchment | Standard Lag (HR) | Peaking Coefficient (-) | ||||||
---|---|---|---|---|---|---|---|---|
Rain Gauges | Radar | IMERG | Rain Gauges | Radar | IMERG | |||
Initial | Optimized | Initial | Optimized | |||||
SC-1 | 1.74 | 1.54 | 2.75 | 2.07 | 0.4 | 0.49 | 0.36 | 0.19 |
SC-2 | 2.82 | 2.59 | 3.09 | 2.44 | 0.4 | 0.37 | 0.51 | 0.21 |
SC-3 | 2.82 | 2.61 | 2.96 | 2.86 | 0.4 | 0.40 | 0.26 | 0.20 |
SC-4 | 1.99 | 2.06 | 2.44 | 2.81 | 0.4 | 0.42 | 0.24 | 0.21 |
SC-5 | 1.77 | 2.31 | 2.16 | 2.58 | 0.4 | 0.37 | 0.32 | 0.22 |
SC-6 | 2.35 | 2.31 | 1.80 | 2.58 | 0.4 | 0.55 | 0.42 | 0.37 |
Sub-Catchment | Initial Discharge | Recession Constant | Threshold Discharge | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Rain Gauges | Radar | IMERG | Rain Gauges | Radar | IMERG | Rain Gauges | Radar | IMERG | ||||
Initial | Optimized | Initial | Optimized | Initial | Optimized | |||||||
SC-1 | 0.5 | 0.66 | 0.80 | 0.53 | 0.9 | 0.97 | 0.97 | 1.00 | 0.7 | 1.23 | 0.82 | 1.12 |
SC-2 | 0.5 | 0.59 | 0.67 | 0.66 | 0.9 | 1.00 | 1.00 | 1.00 | 0.7 | 1.23 | 0.70 | 1.00 |
SC-3 | 0.5 | 0.59 | 0.67 | 0.50 | 0.9 | 0.97 | 1.00 | 1.00 | 0.7 | 1.86 | 0.92 | 1.29 |
SC-4 | 0.5 | 0.51 | 0.59 | 0.66 | 0.9 | 0.97 | 1.00 | 0.97 | 0.7 | 1.00 | 0.94 | 0.82 |
SC-5 | 0.5 | 0.51 | 0.50 | 0.58 | 0.9 | 0.97 | 1.00 | 0.97 | 0.7 | 0.82 | 1.41 | 1.41 |
SC-6 | 0.5 | 0.93 | 0.80 | 0.58 | 0.9 | 0.97 | 0.97 | 1.00 | 0.7 | 0.93 | 1.00 | 1.12 |
Event | NSE | r | PBias (%) | rPFD | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Rain Gauges | Radar | IMERG | Rain Gauges | Radar | IMERG | Rain Gauges | Radar | IMERG | Rain Gauges | Radar | IMERG | |
Event 3 | 0.28 | 0.69 | 0.21 | 0.61 | 0.85 | 0.47 | 18.10 | 9.50 | 3.50 | −44.9 | 4.5 | −32.5 |
Event 4 | 0.71 | 0.88 | 0.74 | 0.85 | 0.97 | 0.92 | 1.50 | 10.40 | 10.80 | 8.7 | −5.4 | −17.5 |
Event 5 | −0.20 | 0.89 | 0.76 | 0.17 | 0.98 | 0.90 | 37.70 | 15.60 | 14.90 | −15.1 | −0.9 | 8.6 |
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Gilewski, P.; Nawalany, M. Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland. Water 2018, 10, 1665. https://doi.org/10.3390/w10111665
Gilewski P, Nawalany M. Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland. Water. 2018; 10(11):1665. https://doi.org/10.3390/w10111665
Chicago/Turabian StyleGilewski, Paweł, and Marek Nawalany. 2018. "Inter-Comparison of Rain-Gauge, Radar, and Satellite (IMERG GPM) Precipitation Estimates Performance for Rainfall-Runoff Modeling in a Mountainous Catchment in Poland" Water 10, no. 11: 1665. https://doi.org/10.3390/w10111665