# Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile

^{1}

^{2}

^{3}

^{4}

^{*}

^{†}

## Abstract

**:**

## 1. Introduction

## 2. Data and Methods

#### 2.1. Study Setting

^{2}with predominantly agricultural land cover and with clay to clay-loam as the prevailing soil type. The seasonal rainfall distribution of Gilgel Abbay is affected mainly by the location of the Intertropical Convergence Zone (ITCZ) with a rainy season, which coincides with the summer in the northern hemisphere (June–August). At short time scales (daily and sub-daily), rainfall distribution in this watershed is affected by orographic factors and the presence of Lake Tana [38,39]. The lowlands of Gigel Abbay receive more intense and short lasted rainfall as compared to its highlands [40].

#### 2.2. CMORPH and Local Gauge Data

#### 2.3. Bias Formulation and Estimation

#### 2.4. Schemes for Bias Correction

- (i)
- The first one allows for correcting the bias at a pixel based (i.e., space variable) and at a daily scale (i.e., time varying), and is based on the using the BF
_{TSV}factor estimated from Equation (1). To apply a correction that accounts for spatial and temporal variability in the CMORPH bias, the pixel-based daily BF_{TSV}factors were spatially interpolated using the inverse distance weight (IDW) method to yield a spatial and temporally varying field of BFs that cover the entire study area. We followed the approach of Haile et al. (2009) [38] in the same study area who showed good interpolation results by IWD. The CMOPRH daily rainfall fields were then multiplied by the BF_{TSV}bias fields for the respective time windows to result in a new set of CMORPH estimates that as such are bias-corrected in a temporally and spatially varying scheme. This procedure is similar to the local-bias correction algorithm developed by Seo and Breidenbach [19], which is adopted in the operational version of the National Weather System-Multisensor Precipitation Estimation (NWS-MPE) system. The use of Equation (1) applies a bias correction factor that varies in space and time domains. We refer to this formulation as time and space variant (TSV) bias correction. To assess the implications for ignoring or for accounting of variability of bias, two more bias estimation and correction schemes were tested: - (ii)
- Time and space fixed (TSF) bias correction: in this formulation the bias is obtained by using gauge and CMORPH estimates over the entire domain and over the total duration of the sample Equation (2):$$B{F}_{\mathit{TSF}}=\frac{{\sum}_{t=1}^{t=T}{\sum}_{i=1}^{i=n}S(i,t)}{{\sum}_{t=1}^{t=T}{\sum}_{i=1}^{i=n}G(i,t)}$$
_{TSF}, to result in a new set of CMORPH estimates that are bias-corrected in a spatially and temporally-lumped scheme. - (iii)
- Time variable (TV) bias correction: in this formulation the BF is spatially lumped over the entire domain but is still estimated for each daily time step Equation (3):$$B{F}_{\mathit{TV}}=\frac{{\sum}_{t=1}^{t=d-l}{\sum}_{i=1}^{i=n}S(i,t)}{{\sum}_{t=1}^{t=d-l}{\sum}_{i=1}^{i=n}G(i,t)}$$

_{TV}, to result in a new set of CMORPH estimates that are bias-corrected in a spatially-lumped but temporally-varying scheme.

#### 2.5. Hydrologiska Byråns Vattenbalansavdelning (HBV-96) Hydrologic Model

_{a}) is highest (i.e., reaches its potential value (E

_{p})) when SM reaches or exceeds a certain ratio of FC. The ratio, denoted as LP, is used as a calibration parameter. Otherwise, E

_{a}declines linearly as a function of soil moisture deficit represented by SM/FC:

_{q}) and slow (base) flow (Q

_{s}) are defined as follows:

_{q}is a recession coefficient for quick runoff, LZ is the actual storage in the lower zone store and K

_{s}is a recession coefficient for base flow. According to this formulation, the model has 8 parameters that can be used for model optimization and calibration, namely: FC, BETA, LP, ALPHA, K

_{q}, K

_{s}, PERC, and CFLUX.

^{2}.

#### 2.6. Model Calibration and Evaluation

_{Bias}, which measures systematic differences (bias) in the simulated streamflow volumes:

_{sim}and Q

_{obs}represent simulated and observed daily flows, respectively, at a certain day i, and n represents the number of days in the sample. The over-bar symbol denotes the mean statistical operation. The values of NS, which is dimensionless, can range between −∞ and 1, where a value of 1 indicates a perfect fit. Similarly, a Q

_{Bias}value of 1 reflects bias-free streamflow simulations whereas streamflow overestimation and underestimation are reflected by bias values that are larger or smaller than 1, respectively.

_{Bias}) are calculated. Following Rientjes et al. [37], the optimum parameters set is selected as the average value of the 25 parameter sets that are ranked highest in terms of the NS values. It is noted that a similar approach is followed in this study when calibrating the model in case CMORPH rainfall data is used as model input.

## 3. Results

#### 3.1. Evaluation of CMORPH Estimates

_{TSV}(Figure 2). The lowest, highest and mean values of BF

_{TSV}for a seven-day moving window are shown in the figure. These values are summarized based on BF

_{TSV}values calculated at the ten rain gauge stations within the study area. For each calendar day the minimum, maximum and mean are shown for the ensemble of network stations. The difference between the lowest and highest values shows the extent of the variation of the bias across the 10 stations in the study area. The mean values show pronounced seasonal variations and have different patterns throughout the two years. In general, CMORPH reports smaller rainfall amounts than gauge observations from mid-June to mid-August 2003, but reports larger rainfall amounts towards the end of the rainy season of 2003. This pattern is not shown in 2004, where positive and negative biases in CMORPH show lower variation in time. Overall, these results indicate that the bias in the CMORPH product exhibits pronounced variability in space and time over the study area. Possibly, this could be related to variations in rain generation mechanisms [11] but further investigations are needed for confirmation.

#### 3.2. Results on Rainfall Bias Correction

#### 3.3. Model Parameter Optimization Using Different Rainfall Inputs

_{Bias}= ∼0.9). All optimum parameter values obtained using the correction schemes are within the allowable value ranges. The values of the optimized model parameters are inter-compared and percent change of each parameter value is shown with respect to the reference case. To allow comparison of parameter values over a common scale, changes are calculated after normalizing the parameter values using their allowable minimum and maximum values, which are set equal for all simulations. We note that the results of parameter optimization are affected by the rainfall input as shown by the percentage errors in Table 2. In particular, parameters (FC, Beta and LP) which control the volume of the simulated hydrograph showed large changes of up to 81% compared to the parameters using the reference gauge data sets. There is also a significant change in the quick recession coefficient (K

_{q}), whereas those that control groundwater contributions (K

_{s}, PERC and CFLUX) are less affected.

#### 3.4. Effects of Rainfall Bias Corrections on Streamflow Simulations

^{−1}) between catchment-average daily rainfall estimates obtained from TSV and gauges. However, it is apparent that the large CMORPH bias in 2003 was substantially reduced. As a result, the patterns and volumes of the observed hydrographs were better captured when using the bias-corrected CMORPH estimates than the uncorrected ones. Some observed peak flows were better captured as a result of correcting for the rainfall bias. The improvements are particularly substantial for the 2003 hydrographs where the uncorrected CMORPH had large negative bias. We note that use of bias corrected rainfall data has some advantages over gauge only data; however, some aspects of observed hydrograph were better captured by using gauge only data (e.g., the second half of July 2003). The simulated hydrographs based on both rainfall inputs show smaller fluctuation than the observed hydrograph. Such mismatches could be caused by deficiencies in the HBV-96 model structure, poor rainfall representation by the low density of the rain gauge network, or errors in streamflow observations, among others.

_{TSV}). For example, for CMORPH TSV rainfall input, the bias increased from −13% to only −17% in 2003 while it increased from −8% to 20% in 2004. The observed changes of rainfall-to-streamflow biases are probably due to the non-linearity in the rainfall-runoff relation and subsequent runoff generation in the HBV-96 model. For instance, small bias in rainfall input can propagate to result in larger streamflow bias when the catchment is wet than when it is dry.

_{Bias}(Equation (9)), obtained using the uncorrected as well as the bias-corrected CMORPH rainfall inputs are shown in Figure 5 (see also Table 3). As compared to gauge-based simulations, the uncorrected and bias-corrected CMORPH data resulted in consistently smaller streamflow in the rainy season (June–August) of 2003 but larger streamflow towards the end of the rainy season. Note that this pattern has some resemblance to that of the rainfall biases (Figure 2). However, the temporal pattern of the streamflow biases in both 2003 and 2004 are smoother than those of the rainfall inputs. This possibly is a result of the filtering effect of the runoff model as it converts highly variable rainfall input to streamflow. The significantly large rainfall bias in October of 2004 is translated to a smaller streamflow bias probably as the model became relatively dry and therefore did not convert the excess rainfall input into surface runoff. CMORPH-based streamflow in 2003 is mostly 0.25 to 0.5 times the gauge observations showing underestimation though this streamflow differences became much smaller in 2004. Overall, these differences were reduced when the bias-corrected CMORPH rainfall amounts served as model inputs. An exception is that TSF, which is obtained using a space-time constant correction factor, only slightly altered the streamflow bias. For most parts of the wet season, the streamflow bias significantly decreased when time variable bias correction is applied. Accounting for both spatial and temporal variation of the CMORPH bias factor further reduced the streamflow bias.

## 4. Conclusions

^{−1}on average and by up to ±30 mm·d

^{−1}) with spatial as well as inter-annual and intra-annual variations in Gilgel Abbay catchment. Such biases could be related not only to rain generation mechanisms but also to the sampling and retrieval errors of satellite products [51]. We have showed through cross validation that it is not always the case that gauge-only, or satellite-only estimates, outperform one another. This suggests rainfall estimation can benefit from combined use of satellite and rain gauge data.

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Haile, A.T.; Habib, E.; Rientjes, T.H.M. Evaluation of the Climate Prediction Center (CPC) morphing technique (CMORPH) rainfall product on hourly time scales over the source of the Blue Nile River. Hydrol. Process
**2013**, 27, 1829–1839. [Google Scholar] - Kondragunta, C.; Shrestha, K. Automated real-time operational rain gauge quality controls in NWS hydrologic operations. In Proceedings of the 20th AMS Conference on Hydrology, Atlanta, GA, USA, 29 January–2 February 2006.
- AghaKouchak, A.; Mehran, A.; Norouzi, H.; Behrangi, A. Systematic and random error components in satellite precipitation data sets. Geophys. Res. Lett
**2012**, 39. [Google Scholar] [CrossRef] - Dinku, T.; Ceccato, P.; Grover-Kopec, E.; Lemma, M.; Connor, S.J.; Ropelewski, C.F. Validation of satellite rainfall products over East Africa’s complex topography. Int. J. Remote Sens
**2007**, 28, 1503–1526. [Google Scholar] - Habib, E.; Haile, A.T.; Tian, Y.; Joyce, R. Evaluation of the high-resolution CMORPH satellite-rainfall product using dense rain gauge observations and radar-based estimates. J. Hydrometeorol
**2012**, 13, 1784–1798. [Google Scholar] - Yilmaz, K.K.; Hogue, T.S.; Hsu, K.; Sorooshian, S.; Gupta, H.V.; Wagener, T. Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting. J. Hydrometeorol
**2005**, 6, 497–517. [Google Scholar] - Zhang, Y.; Seo, D-J.; Kitzmiller, D.; Lee, H.; Kuligowski, R.J.; Kim, D.; Kondragunta, C.R. Comparative strengths of SCaMPR satellite QPEs with and without TRMM ingest vs. gridded gauge-only analyses. J. Hydrometeorol
**2013**, 14, 153–170. [Google Scholar] - Bitew, M.M.; Gebremichael, M. Assessment of satellite rainfall products for streamflow simulation in medium watersheds of the Ethiopian highlands. Hydrol. Earth Syst. Sci
**2011**, 15, 1147–1155. [Google Scholar] - Bitew, M.M.; Gebremichael, M. Evaluation of satellite rainfall products through hydrologic simulation in a fully distributed hydrologic model. Water Resour. Res
**2011**, 47. [Google Scholar] [CrossRef] - Joyce, R.J.; Janowiak, J.E.; Arkin, P.A.; Xie, P.P. CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeorol
**2004**, 5, 487–503. [Google Scholar] - Habib, E.; ElSaadani, M.; Haile, A.T. Climatology-focused evaluation of CMORPH and TMPA satellite rainfall products over the Nile Basin. J. Appl. Meteorol. Climatol
**2012**, 51, 2105–2121. [Google Scholar] - Ebert, E.E.; Janowiak, J.E.; Kidd, C. Comparison of near-real-time rainfall estimates from satellite observations and numerical models. Bull. Am. Meteorol. Soc
**2007**, 88, 47–64. [Google Scholar] - Pereira, F.A.J.; Carbone, R.E.; Janowiak, J.E.; Arkin, P.; Joyce, R.; Hallak, R.; Ramos, C.G.M. Satellite rainfall estimates over South America—Possible applicability to the water management of large watersheds. J. Am. Water Resour. Assoc
**2010**, 46, 344–360. [Google Scholar] - Anagnostou, E.N.; Maggioni, V.; Nikolopoulos, E.I.; Meskele, T.; Hossain, F.; Papadopoulos, A. Benchmarking high-resolution global satellite rainfall products to radar and rain-gauge rainfall estimates. IEEE Trans. Geosci. Remote. Sens
**2010**, 48, 1667–1683. [Google Scholar] - Sapiano, M.R.P.; Arkin, P.A. An Intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data. J. Hydrometeorol
**2009**, 10, 149–166. [Google Scholar] - Smith, T.M.; Arkin, P.A.; Bates, J.J.; Huffman, G.J. Estimating bias of satellite-based precipitation estimates. J. Hydrometeorol
**2006**, 7, 841–856. [Google Scholar] - CMORPH Improvements: A Kalman Filter Approach to Blend Various Satellite Rainfall Estimate Inputs and Rain Gauge Data Integration. Available online: http://adsabs.harvard.edu/abs/2009EGUGA..11.9810J (accessed on 25 September 2013).
- Joyce, R.J.; Xie, P.; Janowiak, J.E. Kalman filter based CMORPH. J. Hydrometeorol
**2011**, 12, 1547–1563. [Google Scholar] - Seo, D.-J.; Breidenbach, J. Real-time correction of spatially nonuniform bias in radar rainfall data using rain gauge measurements. J. Hydrometeorol
**2002**, 3, 93–111. [Google Scholar] - Seo, D.-J.; Briedenbach, J.P.; Johnson, E.R. Real-time estimation of mean field bias in radar rainfall data. J. Hydrol
**1999**, 223, 131–147. [Google Scholar] - Zhang, J.; NOAA/NSSL; Norman, O.K.; Howard, K.; Vasiloff, S.; Langston, C.; Kaney, B.; Arthur, A.; van Cooten, S.; Kelleher, K.; et al. National Mosaic and QPE (NMQ) system—Description, results and future plan. In Proceedings of the 34th Conference on Radar Meteor, Williamsburg, VA, USA, 6 October 2009.
- Huffman, G.J.; Adler, R.F.; Bolvin, D.T.; Gu, G.J.; Nelkin, E.J.; Bowman, K.P.; Hong, Y.; Stocker, E.F.; Wolff, D.B. The TRMM multisatellite precipitation analysis (TMPA).Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol
**2007**, 8, 38–55. [Google Scholar] - Boushaki, F.I.; Hsu, K.-L.; Sorooshian, S.; Park, G.-H.; Mahani, S.; Shi, W. Bias adjustment of satellite precipitation estimation using ground-based measurement: A case study evaluation over the southwestern United States. J. Hydrometeorol
**2009**, 10, 1231–1242. [Google Scholar] - Li, M.; Shao, Q. An improved statistical approach to merge satellite rainfall estimates and raingauge data. J. Hydrol
**2010**, 385, 51–64. [Google Scholar] - Vila, D.A.; de Goncalves, L.G.G.; Toll, D.L.; Rozante, J. Statistical evaluation of combined daily gauge observations and rainfall satellite estimates over continental South America. J. Hydrometeorol
**2009**, 10, 533–543. [Google Scholar] - Hong, Y.; Hsu, K.; Moradkhani, H.; Sorooshian, S. Uncertainty quantification of satellite precipitation estimation and Monte Carlo assessment of the error propagation into hydrologic response. Water Resour. Res
**2006**, 42. [Google Scholar] [CrossRef] - Chiang, Y.-M.; Hsu, K.-L.; Chang, F.-J.; Hong, Y.; Sorooshian, S. Merging multiple precipitation sources for flash flood forecasting. J. Hydrol
**2007**, 340, 183–196. [Google Scholar] - Tobin, K.J.; Bennett, M.E. Adjusting satellite precipitation data to facilitate hydrologic modeling. J. Hydrometeorol
**2010**, 11, 966–978. [Google Scholar] - Tian, Y.; Peters-Lidard, C.D.; Eylander, J.B. Real-time bias reduction for satellite-based precipitation estimates. J. Hydrometeorol
**2010**, 11, 1275–1285. [Google Scholar] - Krakauer, N.Y.; Pradhanang, S.M.; Lakhankar, T.; Jha, A.K. Evaluating satellite products for precipitation estimation in mountain regions: A case study for Nepal. Remote Sens
**2013**, 5, 4107–4123. [Google Scholar] - Artan, G.; Gadain, H.; Smith, J.L.; Asante, K.; Bandaragoda, C.J.; Verdin, J.P. Adequacy of satellite derived rainfall data for stream flow modeling. Nat. Hazards
**2007**, 43, 167–185. [Google Scholar] - Zeweldi, D.A.; Gebremichael, M.; Downer, C.W. On CMORPH rainfall for stream flow simulation in a small, Hortonian watershed. J. Hydrometeorol
**2011**, 12, 456–466. [Google Scholar] - Behrangi, A.; Khakbaz, B.; Jaw, T.C.; AghaKouchak, A.; Hsu, K.; Sorooshian, S. Hydrologic evaluation of satellite precipitation products over a mid-size basin. J. Hydrol
**2011**, 397, 225–237. [Google Scholar] - Yong, B.; Ren, L.-L.; Hong, Y.; Wang, J.-H.; Gourley, J.J.; Jiang, S.-H.; Chen, X.; Wang, W. Hydrologic evaluation of Multisatellite Precipitation Analysis standard precipitation products in basins beyond its inclined latitude band: A case study in Laohahe basin, China. Water Resour. Res
**2010**, 46. [Google Scholar] [CrossRef] - Sorooshian, S.; AghaKouchak, A.; Arkin, P.; Eylander, J.; Foufoula-Georgiou, E.; Harmon, R.; Hendrickx, J.; Imam, B.; Kuligowski, R.; Skahill, B.; et al. Advanced concepts of remote sensing of precipitation at multiple scales. Bull. Am. Meteorol. Soc
**2011**, 92, 1353–1357. [Google Scholar] - Gebremichael, M.; Anagnostou, E.N.; Bitew, M. Critical steps for continuing advancement of satellite rainfall applications for surface hydrology in the Nile River basin. J. Am. Water Resour. Assoc
**2010**, 46, 361–366. [Google Scholar] - Rientjes, T.H.M.; Perera, B.U.J.; Haile, A.T.; Reggiani, P.; Muthuwatta, L.P. Regionalisation for lake level simulation—The case of Lake Tana in the Upper Blue Nile, Ethiopia. Hydrol. Earth Syst. Sci
**2011**, 15, 1167–1183. [Google Scholar] - Haile, A.T.; Rientjes, T.H.M.; Gieske, A.S.M.; Gebremichael, M. Rainfall variability over mountainous and adjacent lake areas: The case of Lake Tana basin at the source of the Blue Nile River. J. Appl. Meteorol. Climatol
**2009**, 48, 1696–1717. [Google Scholar] - Rientjes, T.H.M.; Haile, A.T.; Ayele, A.F. Diurnal rainfall variability over the Upper Blue Nile: A remote sensing based approach. Int. J. Appl. Earth Obs. Geoinf
**2013**, 21, 311–325. [Google Scholar] - Haile, A.T.; Rientjes, T.H.M.; Habib, E.; Jetten, V.; Gebremichael, M. Rain event properties at the source of the Blue Nile River. Hydrol. Earth Syst. Sci
**2011**, 15, 1023–1034. [Google Scholar] - Abdo, K.S.; Fiseha, B.M.; Rientjes, T.H.M.; Gieske, A.S.M.; Haile, A.T. Assessment of climate change impacts on the hydrology of Gilgel Abbay catchment in Lake Tana basin, Ethiopia. Hydrol. Process
**2009**, 23, 3661–3669. [Google Scholar] - Rientjes, T.H.M.; Haile, A.T.; Mannaerts, C.M.M.; Kebede, E.; Habib, E. Changes in land cover and stream flows in Gilgel Abbay catchment, Upper Blue Nile basin—Ethiopia. Hydrol. Earth Syst. Sci. Discuss
**2011**, 7, 9567–9598. [Google Scholar] - Wale, A.; Rientjes, T.H.M.; Gieske, A.S.M.; Getachew, H.A. Ungauged catchment contributions to Lake Tana’s water balance. Hydrol. Process
**2009**, 23, 3682–3693. [Google Scholar] - Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements; Irrigation and Drainage Paper; United Nations Food and Agriculture Organization: Rome, Italy, 1998; p. 300. [Google Scholar]
- Lindström, G.; Johansson, B.; Persson, M.; Gardelin, M.; Bergström, S. Development and test of the distributed HBV-96 hydrological model. J. Hydrol
**1997**, 201, 272–288. [Google Scholar] - Booij, M.J. Impact of climate change on river flooding assessed with different spatial model resolutions. J. Hydrol
**2005**, 303, 176–198. [Google Scholar] - Deckers, D.L.E.H.; Booij, M.J.; Rientjes, T.H.M.; Krol, M.S. Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall-runoff model. Water Resour. Manag
**2010**, 24, 3961–3985. [Google Scholar] - Merz, R.; Blöschl, G. Regionalisation of catchment model parameters. J. Hydrol
**2004**, 287, 95–123. [Google Scholar] - Rientjes, T.H.M.; Muthuwatta, L.P.; Bos, M.G.; Booij, M.J.; Bhatti, H.A. Multi-variable calibration of a semi-distributed hydrological model using streamflow data and satellite based evapotranspiration. J. Hydrol
**2013**, 505, 276–290. [Google Scholar] - Seibert, J. Estimation of parameter uncertainty in the HBV model. Nord. Hydrol
**1997**, 28, 4–5. [Google Scholar] - Gebremichael, M.; Krajewski, W.F. Characterization of the temporal sampling error inspace-time-averaged rainfall estimates from satellites. J. Geophys. Res
**2004**, 109. [Google Scholar] [CrossRef]

**Figure 1.**Study site showing the location of the Gilgel Abbay catchment and its eight sub-catchments within the Nile Basin. The locations of the ten (10) rain gauge stations and the streamflow gauge are indicated. Note that the unit of terrain elevation is meters. (

**a**) Geographic location of study area; (

**b**) Terrain elevation and rain gauge stations.

**Figure 2.**Mean, minimum and maximum of CMORPH daily bias factors (BF

_{TSV}, Equation (1)) evaluated for the ensemble of ten network stations.

**Figure 3.**(

**a**) Comparison of daily catchment-average gauge and uncorrected CMORPH rainfall. (

**b**) differences in daily rainfall estimates between gauge and CMORPH. (

**c**) and (

**d**) observed and simulated stream flow hydrographs for the year June 2003–December 2004 based on rainfall inputs from gauges and uncorrected CMORPH.

**Figure 4.**(

**a**) Comparison of daily catchment-average gauge rainfall and the corresponding TSV bias-corrected CMORPH. (

**b**) differences in rainfall estimates between gauge and TSV bias-corrected CMORPH. (

**c**) and (

**d**) observed and simulated stream flow hydrographs for the year June 2003–December 2004 based on rainfall inputs from gauges and TSV bias-corrected CMORPH.

**Figure 5.**Streamflow bias (Q

_{Bias}, Equation (9)) of streamflow simulations driven by different CMORPH rainfall inputs. Q

_{Bias}was calculated using a moving window of past 7 days. Streamflow simulations driven by gauge observations served as the reference.

**Table 1.**Ratios of monthly rainfall amounts of Climate Prediction Center-MORPHing (CMORPH) (without and with three bias correction schemes) to the corresponding gauge amounts.

Year | Rainfall Product | June | July | August | September | October |
---|---|---|---|---|---|---|

2003 | CMORPH | 0.69 | 0.63 | 0.88 | 1.25 | 0.74 |

CMORPH TSF | 0.71 | 0.64 | 0.9 | 1.28 | 0.76 | |

CMORPH TV | 0.74 | 0.82 | 0.94 | 0.9 | 0.63 | |

CMORPH TSV | 0.87 | 0.81 | 0.99 | 0.95 | 0.63 | |

2004 | CMORPH | 0.83 | 1.15 | 0.8 | 0.99 | 0.84 |

CMORPH TSF | 0.79 | 1.09 | 0.76 | 0.94 | 0.8 | |

CMORPH TV | 0.9 | 0.87 | 0.87 | 0.93 | 0.93 | |

CMORPH TSV | 0.95 | 0.86 | 0.87 | 0.96 | 0.98 |

**Table 2.**Calibrated values of the Hydrologiska Byråns Vattenbalansavdelning (HBV) model parameters using gauge and bias corrected CMORPH. Numbers in brackets represent percent changes in each parameter value (after normalizing with the allowable minimum and maximum range) with respect to the gauge-driven reference case. The last two rows show the NS and Q

_{Bias}values.

Parameter | Unit | Minimum | Maximum | Gauge | CMORPH with Bias Correction | ||
---|---|---|---|---|---|---|---|

Time-Space Fixed (TSF) | Space Fixed and Time Variable (TV) | Time-Space Variable (TSV) | |||||

FC | mm | 100 | 800 | 373 | 186 (−68) | 177 (−72) | 185 (−69) |

BETA | -- | 1 | 4 | 1.351 | 1.599 (71) | 1.562 (60) | 1.625 (78) |

LP | -- | 0.1 | 1 | 0.544 | 0.888 (77) | 0.905 (81) | 0.775 (52) |

ALPHA | -- | 0.1 | 3 | 0.271 | 0.242 (−17) | 0.236 (−20) | 0.269 (−1) |

K_{q} | day^{−1} | 0.0005 | 0.15 | 0.073 | 0.035 (−52) | 0.050 (−32) | 0.038 (−48) |

K_{s} | day^{−1} | 0.0005 | 0.15 | 0.087 | 0.086 (−1) | 0.083 (−5) | 0.074 (−15) |

PERC | mm·day^{−1} | 0.1 | 2.5 | 1.348 | 1.422 (6) | 1.208 (−11) | 1.339 (−1) |

CFLUX | mm | 0.0005 | 2.0 | 0.886 | 0.898 (1) | 0.805 (−9) | 0.892 (1) |

NS | -- | -- | -- | 0.8256 | 0.703 | 0.8038 | 0.8177 |

Q_{Bias} | -- | -- | -- | 0.995 | 0.982 | 0.988 | 0.982 |

**Table 3.**Ratios of catchment-average seasonal rainfall amounts of CMORPH (without and with three bias correction schemes) to the corresponding gauge amounts. The corresponding values for streamflow biases (Q

_{Bias}) are also presented. The NS efficiency values for the streamflow simulations are shown between brackets.

Year | Performance Measure | CMORPH | CMORPH (TSF) | CMORPH (TV) | CMORPH (TSV) |
---|---|---|---|---|---|

June–October 2003 | Rainfall Ratio | 0.818 | 0.819 | 0.806 | 0.869 |

Streamflow Q_{Bias} | 0.734 (0.19) | 0.762 (0.21) | 0.764 (0.71) | 0.831 (0.79) | |

June–October 2004 | Rainfall Ratio | 0.947 | 0.904 | 0.898 | 0.917 |

Streamflow Q_{Bias} | 0.726 (0.73) | 0.727 (0.73) | 0.792 (0.79) | 0.804 (0.80) |

© 2014 by the authors; licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

## Share and Cite

**MDPI and ACS Style**

Habib, E.; Haile, A.T.; Sazib, N.; Zhang, Y.; Rientjes, T.
Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile. *Remote Sens.* **2014**, *6*, 6688-6708.
https://doi.org/10.3390/rs6076688

**AMA Style**

Habib E, Haile AT, Sazib N, Zhang Y, Rientjes T.
Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile. *Remote Sensing*. 2014; 6(7):6688-6708.
https://doi.org/10.3390/rs6076688

**Chicago/Turabian Style**

Habib, Emad, Alemseged Tamiru Haile, Nazmus Sazib, Yu Zhang, and Tom Rientjes.
2014. "Effect of Bias Correction of Satellite-Rainfall Estimates on Runoff Simulations at the Source of the Upper Blue Nile" *Remote Sensing* 6, no. 7: 6688-6708.
https://doi.org/10.3390/rs6076688