Precipitation data are crucial to many applications related to human life and water management. Examples include estimating the hydrological water balance [1
], improving management practices, hydropower planning, development projects and flood controls. Conventionally, rain gauges provide the direct measurement of precipitation. However, rain gauges are often not enough to correctly resolve precipitation and precipitation-related processes. Moreover, the spatial coverage of the rain gauge over a large area is low, and they require huge investments [2
With the advancements in remote sensing and high computation facilities, several precipitation products from various sources are available. For instance, the National Center for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) [3
], Modern-Era Retrospective analysis for Research and Applications (MERRA) [4
] and Global Land Data Assimilation System (GLDAS) [5
] are different reanalysis data. Similarly, the Tropical Rainfall Measuring Mission (TRMM) [6
], Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [7
], Climate Prediction Center MORPHing (CMORPH) [8
], Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) [9
], Global Precipitation Measurement (GPM), Integrated Multi-Satellite Retrievals GPM (IMERG) [10
] are satellite dataset commonly available. A few gauge-gridded datasets are the Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) [12
], Indian Meteorological Department (IMD) gridded datasets [14
]. All these different precipitation products have been shown to be accurate and comparable with the ground observations in various circumstances. Moreover, remote sensing-based products [15
] and reanalysis data [16
] could be a viable alternative for ground-based observation [17
] in complex terrain where observed gauge data are of low quality, sparse or non-existent for flood forecasting, drought monitoring [15
] and water balance studies [21
Among different reanalysis products, NCEP-CFSR is one of the commonly used for precipitation application [3
], and various studies investigated the evaluation and intercomparison of the reanalysis data sets. Wang et al. [23
] reported that CFSR has better precipitation distribution in comparison with the NCEP- NCAR data and ERA data sets. Similarly, Rienecker et al. [4
] reported that the performance of CFSR and MERRA was similar in capturing the quantity of precipitation. Dile and Srinivasan [3
] tested the applicability of CFSR data for hydrological modeling of the Nile River Basin and concluded that the CFSR could be a valuable alternative for data-scarce regions. Roth and Lehmann [24
] compared the conventional weather data and CFSR data for simulating discharge and sediment volume for three catchments in Ethiopia and found that the CFSR simulation is not satisfactory. On similar lines, Tolera et al. [25
] showed that the CFSR data set could be reliably used for the streamflow simulation with a caution that CFSR estimates are higher than the observed rainfall.
Moreover, high-resolution satellite remote-sensing products are vital in driving hydrological models, especially in flood-prone complex terrain [26
]. Satellite data were heavily used for driving different hydrological models, including the SWAT model [15
] and variable infiltration capacity (VIC) [33
]. Furthermore, satellite data are useful in understanding the anthropogenic impacts on hydrology, evaluating the utility of watershed management practices, and predicting the future status of water quality and quantity. A multitude number of studies showed that the ability and reliability of the satellite-based precipitation products in driving the hydrological model fluctuate mainly due to variability in the catchment area, seasonality, topography, climatic characteristics, geographical location, satellite product type [15
]. Studies mentioned above reported satisfactory model performance by using different TRMM precipitation products. Specifically, Zhu et al. [32
] reported that performances were achieved on daily and monthly scales, respectively, for TRMM (3B42) data. Similarly, Li et al. [15
] investigated the adequacy of TRMM satellite rainfall data in driving a hydrological model for Tiaoxi catchment, Taihu lake basin, China. Yuan et al. [43
] used two TRMM products for statistical and hydrological assessment of these products at a sub-daily time scale in Myanmar using the SWAT model; they found all satellite data products showed acceptable results during the simulation of streamflow at the sub-daily scale. Apart from these studies, various other researchers also reported that the direct input of satellite-based precipitation products in hydrological models underperform in comparison with the observed ground-based measurements [29
]. However, it is to be noted that, by applying a suitable correction, the performance of these datasets is drastically improved. Zhang et al. [41
] found that the corrected TRMM multi-satellite precipitation analysis (TMPA 3B42V7) showed a better understanding than the original 3b42v7 data. Similar inferences were reported by Bitew et al. [29
] and Tuo et al. [2
] in their respective analysis.
All the studies mentioned above strengthened the need for thorough validation of these precipitation products and bias correction before using them as an input for the hydrological model. In addition, errors and uncertainties associated with inputs (precipitation products) have a high probability of inducing it in hydrologic simulations [45
]. A multitude of studies showed that the model recalibration using the precipitation product considerably increases the model performance [2
]. However, the model parameter ranges obtained through the recalibration with the precipitation data may be improbable, thereby questioning the model’s applicability for real-world applications. Further, in such recalibration of the models, it is essential to estimate the parameter uncertainty due to the precipitation input. Thus, validations and evaluations of these precipitation products are critical for any hydrologic modeling study [53
]. Further, the calibration and output of the hydrological model is a function of the input precipitation characteristics. Therefore, it is imperative to evaluate the effect of the different precipitation products on the parameter estimation (calibration) and the streamflow simulations. It can also be stated that there is enough scope for advancement in understanding the effect of the different precipitation products on the hydrologic model- based streamflow simulations, parameter estimation (Calibration), predictive uncertainty and its applicability in Indian subcontinents.
Even though there has been a plethora of work done in terms of comparison of the reanalysis and satellite dataset individually, there has been a limited number of studies on intercomparison of different classes of precipitation data sets. Therefore, in this study, we compared three different types of precipitation products that are commonly used, (i) gauge-based rainfall product (IMD), (ii) reanalysis data (NCEP-CFSR) and (iii) satellite precipitation (TRMM and TRMM corrected). This study aimed to (1) compare the statistical characteristics of four different precipitation data sets, (2) investigate the adequacy of these precipitation products in driving the semi-distributed SWAT hydrological model and (3) evaluate the parameter uncertainty involved. These precipitation inputs are used to develop a semi-distributed SWAT model for a semi-arid, forest dominated coastal basin, “Nagavali River Basin in India”.
3. Model Development and Evaluation
For simulating streamflow from different precipitation inputs, two different scenarios were considered, which are as follows:
Scenario A: calibrating the model with IMD gridded data and rerunning the model with other considered precipitation products; and
Scenario B: calibrating the model with each of the rainfall products.
Scenario A would allow investigating the impact of differences in rainfall products on streamflow simulation accuracy, whereas Scenario B (with each of rainfall products) will help in investigating the impact of input rainfall data on calibrated parameters, sensitivity analysis and streamflow simulation accuracy and in verifying whether these rainfall products can be used as an alternative source of input rainfall data for model calibration.
For both the scenarios, the sensitive parameters selection, automatic calibration and validation were performed by SUFI-2 optimization algorithms in the SWAT-CUP tool package. Sensitive parameters were identified using Latin-Hypercube One-factor-At-a-Time method with 2000 simulations. This procedure tests the model sensitivity by modifying only one parameter at a time and keeping the rest unchanged. In total, 18 parameters were identified from past literature (Setti et al. [63
], and sensitivity analysis was performed.
In this study, we considered the first two years, i.e., 1998–2000, as the warm-up period to reduce the effect of initial conditions. The monthly simulated streamflow of the Nagavali River Basin was calibrated over a time period of 2001–2008 and then validated in the period of 2009–2012 with observation streamflow at Srikakulam station (as shown in Figure 1
) using SWAT. For each iteration, 2000 simulations were run for the calibration period. Following each iteration, parameter ranges were modified (nearest to fitted value) with reference to the values suggested by the program and also by their reasonable physical limitations. Interested readers refer [78
] for more details about the protocol to calibrate the SWAT model.
We compared four different precipitation datasets of different categories, source and resolution, namely IMD, TRMM, bias-corrected TRMM and CFSR using a hydrological model. We investigated the impact of precipitation products on hydrological model calibration, model predictions for a forest-dominated river basin in India. The following are salient features obtained from the study.
All the considered precipitation products showed a good correlation with each other as well as the IMD gridded data on a daily scale. The CC of CFSR and bias-corrected TRMM with the IMD data were approximately 0.8. However, a quantitative comparison of the precipitation showed that CFSR and TRMM data have a slight tendency to overestimate precipitation. We also observed that bias-corrected TRMM data are comparable with the IMD data. The deviation is significant in the precipitation range from 30 mm/day to 60 mm/day.
We created two scenarios were for evaluating the precipitation using the SWAT model. In Scenario A, the model was calibrated using IMD precipitation data as input, and later this model was run with other data sets. In Scenario B, individual models were developed with each of the rainfall products as input. From these scenarios, we observed that the performance of streamflow modeling increased when the model was calibrated individually for each of the rainfall products.
The results from Scenario B showed that the different precipitation datasets resulted in multiple sets of sensitive parameters, parameter ranges and water balance components. The IMD data-based model yielded the best results in terms of streamflow simulation. The model based on the bias-corrected TRMM dataset produced closer results and thus can be used as an alternate for gauge precipitation. In summary, the choice of precipitation products has a vital role in model performance, prediction uncertainties and parameter uncertainties in streamflow simulations. Water balance estimation based on different precipitation datasets can lead to different conclusions, and therefore uncertainty generated by the use of different precipitation inputs must be taken into consideration.