Fine-temporal resolution rainfall is a prerequisite for flood risk-related studies. In recent years, satellite precipitation products (SPPs) have gained some attention in the hydro-meteorology field, but the application of these products for hydrological studies is still uncertain. This study discusses the methodology for temporal disaggregation of daily rain gauge (DRG) data using SPP for improved accuracy in hydrologic simulation. The objectives of this research article are (i) to temporally dis-aggregate the DRG data using 3-h SPP TRMM_3B42RT; (ii) to suggest the best approach in terms of hydrologic simulation capabilities using the compound factor (CF) technique. The hydrologic simulation in HEC-HMS was performed for various temporally disaggregated rain gauge (TDRG) data and results were analyzed using 10 efficiency criteria. The best approach was selected using the CF technique. The lowest CF of 2.4 reveals that peak discharge can be computed with less than 8% deviation using the TDRG data, which utilizes the least error tile/grid/pixel from rain gauge rainfall value out of four nearby tile/grid/pixel. This study concluded that flood volume can be computed with less than 5% deviation using DRG data but it failed to reproduce the flood peak. This approach has overcome the insufficiency of sub-daily rainfall observation.

  • Evaluation of SPP TRMM_3B42RT for a major flood event using statistical measurements like RB (%), CC, and RMSE (mm/day).

  • Temporal disaggregation of the DRG data using 3-h SPP TRMM_3B42RT.

  • Identification of the best approach in terms of hydrologic simulation by using 10 efficiency criteria and the use of the CF technique.

Rainfall is the primary variable required to drive the hydrologic model. Rainfall has a strong influence on the hydrologic response (Hou et al. 2020) due to its ample spatial-temporal variability (Cho 2020). Hence, it becomes essential to capture the spatial-temporal variability of rainfall for accurate runoff prediction. Generally, the conversion of daily rainfall to sub-daily rainfall relies on the characteristics of observed sub-daily rainfall in the past. There are some temporal disaggregation approaches like multiplicative cascade models, multivariate techniques and simple methods of fragments that preserve the rainfall characteristics like rainfall intermittency, rainfall asymmetry at finer temporal resolution and autocorrelation. The availability of historically recorded sub-daily rainfall of sufficient length is still very limited in most of the regions.

In recent years, the application of satellite precipitation products (SPPs) has grown due to their fine spatial-temporal resolutions. Microwave- and infrared sensor-based satellites for precipitation retrievals are available with global coverage (Ghimire et al. 2022). The error produced in SPPs subjected to the interpolation technique used (Nerini et al. 2015; Chen et al. 2016). In addition, SPP is an alternative or supplementary data source to ground-based rain gauge data to drive the hydrologic model. The SPPs have gained significant momentum in hydrologic modeling due to their high spatial-temporal coverage. Furthermore, the SPPs are helpful for the temporal disaggregation of daily rainfall to sub-daily rainfall (Yaswanth et al. 2023).

There are studies (Ryo et al. 2014; Shrestha et al. 2019; Acharya et al. 2022) related to improving the temporal resolution of ground-based rain gauge rainfall for hydrologic simulation. The findings of these studies suggested that temporally disaggregated rain gauge (TDRG) data increases the simulation performance of flood peaks.

Multiple hydrological applications like flood forecasting, rainfall–runoff modeling, water balance modeling and computer-based models for transport of pollutants require high temporal resolution rainfall data (Güntner et al. 2001). The hydrological model's application for practical purposes is often limited by the temporal resolution of rainfall data (Aronica et al. 2005). The accuracy of hydrological models is largely influenced by the spatial resolution of input data (Yang et al. 2001). The temporal disaggregation approaches have appreciable benefits. They can increase the resolutions of hydrological processes (Koutsoyiannis et al. 2003).

Hazarika et al. (2007) compared the observed point gauge rainfall data with TRMM_3B42RT rainfall data for real-time flood forecasting in the Bagmati River in Nepal. The pixel-based comparison of TRMM_3B42RT rainfall and observed rainfall showed that TRMM_3B42RT rain fields closely correspond with aerial average rainfall. It was also found that TRMM_3B42RT underestimated the rain for peak monsoon period for the wet rain regimes whereas it overestimated the rain for dry rain regimes. Dönmez & Tekeli (2017) tested TRMM_3B42RT rainfall data for Gaziantep, a city located in a semi-arid region of Turkey for the flood predictions. Their research demonstrated that TRMM_3B42RT rainfall data indicated high rainfall rates during the flood events. Yuan et al. (2019) proposed a framework for statistical and hydrological assessment of the TRMM and GPM SPPs in both near and post-real-time versions at sub-daily temporal scales in a poorly gauged watershed in Chindwin River basin, Myanmar. Statistical assessment at grid and basin scales shows that TRMM_3B42RT rainfall data generally presents higher quality among various SPPs. The TRMM_3B42RT obtains satisfactory hydrological performance in a 3-h flood simulation, with a Nash–Sutcliffe model efficiency of 0.868.

This research discusses the different approaches for the conversion of daily to sub-daily rain gauge (DRG) rainfall values and implications related to hydrologic simulation. An effort is devoted to improving the hydrologic simulation capabilities by using TDRG data by SPP. The objectives of this research article are (i) to evaluate SPP TRMM_3B42RT for a major flood event using statistical measurements; (ii) to temporally disaggregate the DRG data using 3-h SPP TRMM_3B42RT; and (iii) to suggest the best approach in terms of hydrologic simulation capabilities using compound factor (CF) technique.

The study area is located between 73°22′32″ E to 75°14′3″E and 24°19′15″ N to 25°24′27″ N in the southern part of Rajasthan, India. This area mainly covers four districts, namely Bhilwara, Chittorgarh, Rajsamand, and Udaipur. The area is classified as a semi-arid region and has a tropical climate. The geographical area is about 13,634 km2. Banas River is ephemeral and originates from the Khamnor hills of the Aravali range. The area shows minimum and maximum elevations of 337 and 1,311 m, respectively. The index map of the study area with various attributes is shown in Figure 1. There are 18 rain gauge stations dispersed over 13 TRMM tiles with each tile containing at least one rain gauge station. The maximum discharge of 6,500 m3/s was reported between 2005 and 2020 during the 2016 flood event.
Figure 1

Index map of Banas sub-basin showing rain gauge, TRMM grid point location, and stream network.

Figure 1

Index map of Banas sub-basin showing rain gauge, TRMM grid point location, and stream network.

Close modal

The weblinks to access the DRG rainfall, SPP TRMM_3B42RT, digital elevation model, land use land cover, and hydrologic soil group data and related information are provided in the ‘Data availability statement’. The 2-h discharge data at the stream gauge station was collected from the Bisalpur dam authority, which is located downstream of the stream gauge station.

In this research article, a methodology for temporal disaggregation of DRG data using SPP to improve the hydrologic simulation accuracy is discussed. To begin with, the DRG data were temporally disaggregated into 3-h data by using the 3-h TRMM_3B42RT product using different approaches. These approaches are discussed in Figure 2. Tile-1, -2, -3, and -4 are the four nearby tiles to the rain gauge station.
Figure 2

Schematic diagram to address (a) approach-1, -2, -3; (b) approach-4; and (c) approach-5.

Figure 2

Schematic diagram to address (a) approach-1, -2, -3; (b) approach-4; and (c) approach-5.

Close modal

Approach-1 (Figure 2(a)): The ground-based rain gauge station is located in tile-2. So, tile-2 is utilized for the extraction of rainfall values. This scenario is abbreviated as S-1 in the later part of the article.

Approach-2 (Figure 2(a)): The arithmetic average of P1, P2, P3, and P4 is utilized for extraction of rainfall values as per Equation (1). This scenario is abbreviated as S-2 in the later part of the article.
(1)
where P1, P2, P3, and P4 are 3-h rainfall of SPP TRMM_3B42RT.

Approach-3 (Figure 2(a)): The R1 denotes DRG rainfall. P1, P2, P3, and P4 are TRMM_3B42RT aggregated daily rainfall values. A tile that shows the minimum deviation from R1 is utilized for the extraction of rainfall values. So, the tile which represents the minimum of {|R1P1|, |R1P2|, |R1P3|, |R1P4|} is utilized for extraction of rainfall values. This scenario is abbreviated as S-3 in the later part of the article.

Approach-4 (Figure 2(b)): In this approach the dry period was identified from the rainfall series generated from S-1. After, the identification of the dry period the rainfall was uniformly distributed in the wet period. The dry period and wet period were identified using the threshold of 0 and >0 mm/h rainfall, respectively. This scenario is abbreviated as S-4 in the later part of the article.

Approach-5 (Figure 2(c)): In this approach bilinear interpolation technique is used. This method uses the distance-weighted average of four nearby tile values to estimate the new value. Equations (2) and (3) represents the linear interpolation parallel to the x-axis, whereas Equation (4) represents the linear interpolation parallel to the y-axis. This scenario is abbreviated as S-5 in the later part of the article.
(2)
(3)
(4)
The rainfall series was extracted as per the approaches discussed above. The TDRG data were derived for approaches S-1, S-2, S-3, and S-5 using Equation (5).
(5)
where R3h is 3-h TDRG rainfall (mm), T3h is TRMM_3B42RT 3-h rainfall (mm), R24h is DRG rainfall (mm) and T24h is TRMM_3B42RT aggregated 24-h rainfall (mm). T24h was derived by adding the T3h values for a day. The DRG rainfall is recorded at 08:30 (IST). TRMM tile which includes this time is 01:30–04:30 (UTC), i.e., 07:00–10:00 (IST). For the temporal synchronization between these two datasets this tile is divided into two halves to make T24h and R24h compatible for comparison, i.e., 07:00–08:30 and 08:30–10:00 (IST). The same rainfall intensity (mm/h) was considered in these tiles.
In each scenario rainfall at RGS was extracted using a different method. To understand the rainfall attributes of different scenarios, i.e., S-1, S-2, S-3 (S-4 is not included here, because it directly generates TDRG data) and S-5 statistical parameters like relative bias (RB) %, correlation coefficient (CC) and root mean square error (RMSE) are computed using Equations (6)–(8). Here, RB and CC depict the agreement between satellite and gauge rainfall. RMSE is a measure of the average magnitude of the error.
(6)
(7)
(8)
where Ti is the TRMM_3B42RT daily rainfall estimate (mm/day), Ri is the DRG rainfall (mm/day), is the average of the TRMM_3B42RT daily rainfall estimate (mm/day), are the average of the DRG rainfall (mm/day) and n represents the total number of data.
To understand the hydrologic response of TDRG data, a hydrologic model was developed in HEC-HMS version 4.9 (Figure 3). This semi-distributed model comprised 13 sub-basins and 8 reaches.
Figure 3

The HEC-HMS model for the study area.

Figure 3

The HEC-HMS model for the study area.

Close modal
The methods used in HEC-HMS with parameters are enlisted in Table 1. The curve number map was generated in Arc-GIS 10.8 using land use land cover, hydrologic soil group and curve number lookup table. The land use land cover and hydrologic soil group map are shown in Figure 4. Initial abstraction was considered as 0.2 times of potential maximum retention. Lag time was derived using Kirpich's (1940) formula. The storage-time constant (k) denotes the average time taken by a flood wave to pass through the reach, which was computed by considering a flood wave velocity of 2.25 m/s and reach length. To initialize the model an initial value for the weighting factor, recession constant and ratio to peak were assigned as 0.25, 0.5 and 0.1, respectively. A Thiessen polygon map was prepared and Thiessen weights were assigned based on the contributing area of sub-catchment for a rain gauge station. The developed model was calibrated using the extreme events of 2011, 2012, 2013 and 2014. The parameters curve number, lag time, storage-time constant, weighting factor, recession constant and ratio to peak were calibrated using the simplex method, peak weighted RMSE objective function at 0.01 tolerance under optimization trials. The average of the obtained parameters corresponds to the selected four events considered in the final model. The hydrologic simulation of the 2016 flood event was performed from 05 August 2016, 10:00 to 15 August 2016, 13:00. An initial discharge of 0.01203 m3/s/km2 was assigned based on the observed hydrograph. The simulation was performed at 1-h time intervals.
Table 1

Selected methods in HEC-HMS simulation

ModelMethodParameter
Loss SCS curve number Initial abstraction
Curve number 
Transform SCS unit hydrograph Lag time 
Routing Muskingum Storage-time constant (K)
Weighting factor (X
Baseflow Recession Initial discharge
Recession constant
Ratio to peak 
Rain gauge data Thiessen polygon Thiessen weight 
ModelMethodParameter
Loss SCS curve number Initial abstraction
Curve number 
Transform SCS unit hydrograph Lag time 
Routing Muskingum Storage-time constant (K)
Weighting factor (X
Baseflow Recession Initial discharge
Recession constant
Ratio to peak 
Rain gauge data Thiessen polygon Thiessen weight 
Figure 4

Land use land cover and hydrologic soil group map of the study area.

Figure 4

Land use land cover and hydrologic soil group map of the study area.

Close modal

To strengthen the persuasiveness of the results an additional event from 04 August 2019 to 21 August 2021 with an observed peak discharge of 2,309 m3/s was also selected. The hydrologic simulation was performed on a daily scale (as per the availability of daily runoff data during this period) which shows an NSE and R2 value of 0.81 and 0.90, respectively.

Furthermore, the sensitivity analysis was also performed to assess the model parameter's sensitivity to the results. Sensitivity analysis provides a comprehensive understanding of physical processes within the complex hydrologic systems. The one value at a time, i.e., local sensitive analysis was performed with parameters variation from +30 to −30% at an increment of 10%. The results of sensitivity analysis revealed that curve number and storage-time constant have the highest and ratio to peak has the lowest sensitivity for peak discharge. Whereas, curve number and recession constant have the highest and weighting factor has the lowest sensitivity for flood volume.

To check the efficiency of various TDRG data to reproduce the flood hydrograph, a total of ten efficiency criteria were tested as listed in Table 2.

Table 2

Efficiency criteria for hydrologic simulation

StatisticEquationOptimal value
Percent difference in runoff volume  
Percent difference in peak flow  
Difference in flood peak time (h 
Coefficient of determination  
Nash–Sutcliffe efficiency  
Index of agreement  
Kling-Gupta efficiency  
Root mean square error  
Normalized root mean square error  
Mean absolute error  
StatisticEquationOptimal value
Percent difference in runoff volume  
Percent difference in peak flow  
Difference in flood peak time (h 
Coefficient of determination  
Nash–Sutcliffe efficiency  
Index of agreement  
Kling-Gupta efficiency  
Root mean square error  
Normalized root mean square error  
Mean absolute error  

Vs is simulated runoff volume (mm), Vo is observed runoff volume (mm), Qs is simulated discharge (m3/s), Qo is observed discharge (m3/s), Qsp is simulated peak discharge (m3/s), Qop is observed peak discharge (m3/s), Qoa is average observed discharge (m3/s), Qsa is average simulated discharge (m3/s), TPS is simulated flood peak time, TPO is observed flood peak time, cc is correlation coefficient, σs is the standard deviation of simulated discharge (m3/s), and σo is the standard deviation of observed discharge (m3/s).

Furthermore, the CF model was utilized to rank the best TDRG data in terms of overall hydrologic efficiency. The CF was calculated as per Equation (9).
(9)
where n is the total number of efficiency criteria used and R is the rank assigned to the efficiency criteria.
The DRG data were compared with aggregated daily TRMM_3B42RT rainfall using extracted values as discussed in S-1, S-2, S-3, and S-5 for the 2016 flood event (Figure 5). The higher DRG rainfall was underestimated by all approaches referring to RGS-1, RGS-5, RGS7, and RGS-10. The lower DRG rainfall was overestimated by all approaches referring to RGS-11, RGS-12, RGS-14, RGS-15, RGS-16, RGS-17, and RGS-18. Only at RGS 13, the rainfall of S-1 and S-3 coincided with DRG rainfall, S-2 and S-5 slightly overestimated the rainfall.
Figure 5

Rainfall plot for DRG and TRMM_3B42RT using S-1, S-2, S-3, S-4, and S-5 for total rainfall of 2016 flood.

Figure 5

Rainfall plot for DRG and TRMM_3B42RT using S-1, S-2, S-3, S-4, and S-5 for total rainfall of 2016 flood.

Close modal
The comparison of aggregated daily TRMM_3B42RT and RGS rainfall was accessed using statistical measurements. The scatter plot of TRMM_3B42RT and RGS are shown in Figure 6. The least RB of 5.5% was given by approach S-2. The highest RB of 62.20% was given by approach S-5. There was not much difference in CC and RMSE values for all scenarios.
Figure 6

Scatter plots of daily rainfall recorded by TRMM_3B42RT and RGSs.

Figure 6

Scatter plots of daily rainfall recorded by TRMM_3B42RT and RGSs.

Close modal

The evaluation of TRMM_3B42RT for the 2016 flood event revealed that biases are present. The total rainfall depth was underestimated and overestimated for higher rainfall and lower rainfall, respectively. Hence, the direct use of TRMM_3B42RT data for hydrologic simulation can produce misleading results. So, temporal disaggregation of existing rain gauge data is adopted as discussed in methodology sections by using Equation (5). TDRG data were prepared considering S-1, S-2, S-3, S-4, and S-5 approach.

The maximum 3-h rainfall during the 2016 flood event was compared for each approach as shown in Figure 7. DRG rainfall was also plotted by assuming a uniform temporal variation of daily rainfall as a reference to compare the rainfall intensity variation for S-1, S-2, S-3, S-4, and S-5 approach.
Figure 7

Rainfall plot for DRG and temporally disaggregated rain gauge data using S-1, S-2, S-3, S-4, and S-5 for maximum 3-h rainfall.

Figure 7

Rainfall plot for DRG and temporally disaggregated rain gauge data using S-1, S-2, S-3, S-4, and S-5 for maximum 3-h rainfall.

Close modal

The S-1 approach has given the maximum 3-h rainfall at RGS7 with a magnitude of 111.41 mm, which is 299.78% higher than the DRG rainfall. There was a significant difference in the maximum 3-h rainfall in all approaches. So, the method of rainfall extraction at any point location using four nearby tiles plays a significant role.

TDRG data were produced by using approaches S-1, S-2, S-3, S-4, and S-5. The hydrologic simulation was performed using these TDRG rainfall series, resulting in the flood hydrograph as shown in Figure 8. The flood volume (mm), peak discharge (m3/s) and time of peak for simulated and observed hydrograph are enlisted in Table 3. The least deviation of flood volume, peak discharge and time of peak were found in DRG, S-3, and S-1, respectively. The scatter plot of simulated and observed discharge is shown in Figure 9. The highest coefficient of determination (R2) value of 0.7764 was associated with the S-1 approach.
Table 3

Comparison of observed and simulated values for DRG, S-1, S-2, S-3, S-4, and S-5 rainfall

RainfallFlood volume (mm)
Peak discharge (m3/s)
Time of peak (dd-mm-yyyy hh:mm)
ObservedSimulatedObservedSimulatedObservedSimulated
DRG 87.81 91.69 6,500 5,366.7 8-8-2016 8-8-2016 
     18:00 12:00 
S-1 87.81 115.55 6,500 8,062.5 8-8-2016 8-8-2016 
     18:00 16:00 
S-2 87.81 93.22 6,500 5,464.4 8-8-2016 9-8-2016 
     18:00 12:00 
S-3 87.81 107.04 6,500 7,017.9 8-8-2016 9-8-2016 
     18:00 7:00 
S-4 87.81 53.37 6,500 3,555.3 8-8-2016 9-8-2016 
     18:00 7:00 
S-5 87.81 102.95 6,500 8,472.8 8-8-2016 9-8-2016 
     18:00 3:00 
RainfallFlood volume (mm)
Peak discharge (m3/s)
Time of peak (dd-mm-yyyy hh:mm)
ObservedSimulatedObservedSimulatedObservedSimulated
DRG 87.81 91.69 6,500 5,366.7 8-8-2016 8-8-2016 
     18:00 12:00 
S-1 87.81 115.55 6,500 8,062.5 8-8-2016 8-8-2016 
     18:00 16:00 
S-2 87.81 93.22 6,500 5,464.4 8-8-2016 9-8-2016 
     18:00 12:00 
S-3 87.81 107.04 6,500 7,017.9 8-8-2016 9-8-2016 
     18:00 7:00 
S-4 87.81 53.37 6,500 3,555.3 8-8-2016 9-8-2016 
     18:00 7:00 
S-5 87.81 102.95 6,500 8,472.8 8-8-2016 9-8-2016 
     18:00 3:00 
Figure 8

Flood hydrograph of 2016 using DRG, S-1, S-2, S-3, S-4, and S-5 rainfall.

Figure 8

Flood hydrograph of 2016 using DRG, S-1, S-2, S-3, S-4, and S-5 rainfall.

Close modal
Figure 9

Scatter plots of simulated and observed discharge for DRG, S-1, S-2, S-3, S-4, and S-5 rainfall.

Figure 9

Scatter plots of simulated and observed discharge for DRG, S-1, S-2, S-3, S-4, and S-5 rainfall.

Close modal

The efficiency criteria were evaluated as represented in Table 2. The efficiency criteria values for DRG, S-1, S-2, S-3, S-4, and S-5 are listed in Table 4. DRG rainfall outperformed in terms of a percent difference in runoff volume criteria. S-1 rainfall outperformed in terms of the difference in flood peak time, coefficient of determination, Nash–Sutcliffe efficiency and index of agreement criteria. S-2 rainfall outperformed in terms of Kling-Gupta efficiency and RMSE criteria. S-3 rainfall outperformed in terms of a percent difference in peak flow and normalized RMSE criteria. S-4 rainfall fails to outperform in selected efficiency criteria. S-5 rainfall outperformed in terms of mean absolute error criteria.

Table 4

Efficiency criteria for hydrologic simulation using DRG, S-1, S-2, S-3, S-4, and S-5 rainfall

RainfallDv (%)Dp (%)Tp (h)R2NSEdKGERMSE (m3/s)NRMSEMAE (m3/s)
DRG 4.419 –17.435 –6.00 0.604 0.526 0.874 0.767 1,024.940 0.720 603.811 
S-1 31.591 24.038 2.00 0.776 0.722 0.910 0.668 1,031.142 0.575 581.713 
S-2 6.161 –15.932 18.00 0.673 0.609 0.900 0.800 919.808 0.635 533.661 
S-3 21.900 7.968 13.00 0.717 0.677 0.908 0.755 952.817 0.573 576.304 
S-4 –39.221 –45.303 13.00 0.482 –1.549 0.680 –0.192 1,282.580 1.547 749.029 
S-5 17.242 30.351 9.00 0.696 0.676 0.901 0.751 1,008.664 0.631 523.767 
RainfallDv (%)Dp (%)Tp (h)R2NSEdKGERMSE (m3/s)NRMSEMAE (m3/s)
DRG 4.419 –17.435 –6.00 0.604 0.526 0.874 0.767 1,024.940 0.720 603.811 
S-1 31.591 24.038 2.00 0.776 0.722 0.910 0.668 1,031.142 0.575 581.713 
S-2 6.161 –15.932 18.00 0.673 0.609 0.900 0.800 919.808 0.635 533.661 
S-3 21.900 7.968 13.00 0.717 0.677 0.908 0.755 952.817 0.573 576.304 
S-4 –39.221 –45.303 13.00 0.482 –1.549 0.680 –0.192 1,282.580 1.547 749.029 
S-5 17.242 30.351 9.00 0.696 0.676 0.901 0.751 1,008.664 0.631 523.767 

Bold values indicate the best results.

To suggest the best approach, we have used the multi-criteria decision-making-based CF technique. A study by Shaikh et al. (2022) also used the CF technique for the ranking of synthetic unit hydrograph methods. This method converts the qualitative understanding of a phenomenon into a quantitative one (Hembram & Saha 2020). However, the CF technique gives lumped value by considering equal weightage to selected efficiency criteria, resulting in the exaggeration of the final output. The ranks were assigned based on the optimal value of efficiency criteria. A lower rank is assigned to the best-performing scenario, i.e., scenario which shows the closest value to the optimal value of efficiency criteria.

Assigned rank and CF values (Equation (9)) are listed in Table 5. The lowest value of CF was 2.4 for approach S-3. So, the first rank was assigned to approach S-3. The second rank was assigned to approaches S-1 and S-2 due to the tie between CF values.

Table 5

Ranking of DRG, S-1, S-2, S-3, S-4, and S-5 rainfall

RainfallDv (%)Dp (%)Tp (h)R2NSEdKGERMSE (m3/s)NRMSEMAE (m3/s)CFOverall rank
DRG 3.7 
S-1 2.9 
S-2 2.9 
S-3 2.4 
S-4 5.8 
S-5 3.1 
RainfallDv (%)Dp (%)Tp (h)R2NSEdKGERMSE (m3/s)NRMSEMAE (m3/s)CFOverall rank
DRG 3.7 
S-1 2.9 
S-2 2.9 
S-3 2.4 
S-4 5.8 
S-5 3.1 

The following conclusions can be drawn from this research article.

  • 1.

    Higher rainfall values were underestimated and lower rainfall values were overestimated by SPP TRMM_3B42RT.

  • 2.

    The rainfall results of different scenarios suggest that satellite precipitation tile/grid/pixel to point estimate using four nearby tiles/grids/pixels is sensitive to the extraction technique used.

  • 3.

    The CF technique revealed that the approach S-3 performed excellently. For the temporal disaggregation of rain gauge rainfall, approach S-3 utilizes a tile/grid/pixel representing the least deviation from observed rainfall at RGS out of four nearby tiles/grids/pixels.

  • 4.

    The DRG data were found to be capable of capturing the runoff volume with less than a 5% deviation in runoff volume.

  • 5.

    The approach S-3 was found to be capable of capturing the peak discharge with less than 8% deviation in peak discharge.

  • 6.

    It can be concluded that flood volume can be computed accurately using DRG data but it failed to reproduce the flood peak. The peak discharge can be computed accurately using the S-3 approach.

These results were derived using the 2016 flood event, which is characterized by a bimodal hydrograph. This study strengthens the findings of Yaswanth et al. (2023), in which improved hydrologic simulation results were reported by the use of TDRG data using an approach similar to S-3. This approach will be useful in remote areas where rain gauge is limited in number and available at daily temporal resolution. Real-time TRMM_3B42 data are utilized, which in the application can simulate the real-time stream flow. In the future, this methodology can be verified and modified with an enhanced understanding of different SPPs.

The authors acknowledge the Bisalpur dam authority for providing the discharge data at the Bigod stream gauging station.

Daily rain gauge data can be downloaded from http://www.water.rajasthan.gov.in/wrd. 3-Hourly TRMM rainfall data can be downloaded from https://disc.gsfc.nasa.gov/datasets/TRMM_3B42RT_7/summary?keywords=TRMM_3B42RT_7. Digital elevation model can be downloaded from https://portal.opentopography.org/raster?opentopoID=OTSRTM.082015.4326.1. Land use land cover can be downloaded from https://esri.maps.arcgis.com/apps/instant/media/index.html?appid=fc92d38533d440078f17678ebc20e8e2. Hydrologic soil group can be downloaded from https://daac.ornl.gov/Soils/guides/Global_Hydrologic_Soil_Group.html. The discharge data collected from Bisalpur dam authority cannot be made publicly available.

The authors declare there is no conflict.

Acharya
S. C.
,
Nathan
R.
,
Wang
Q. J.
&
Su
C. H.
2022
Temporal disaggregation of daily rainfall measurements using regional reanalysis for hydrological applications
.
Journal of Hydrology
610
,
127867
.
https://doi.org/10.4225/41/5993927b50f53
.
Aronica
G.
,
Freni
G.
&
Oliveri
E.
2005
Uncertainty analysis of the influence of rainfall time resolution in the modelling of urban drainage systems
.
Hydrological Processes
19
,
1055
1071
.
https://doi.org/10.1002/hyp.5645
.
Chen
H.
,
Yu
R.
&
Shen
Y.
2016
A new method to compare hourly rainfall between station observations and satellite products over Central-Eastern China
.
Journal of Meteorological Research
30
,
737
757
.
https://doi.org/10.1007/s13351-016-6002-5
.
Dönmez
S.
&
Tekeli
A. E.
2017
Comparison of TRMM-based flood indices for Gaziantep, Turkey
.
Natural Hazards
88
,
821
834
.
https://doi.org/10.1007/s11069-017-2892-5
.
Ghimire
U.
,
Akhtar
T.
,
Shrestha
N. K.
,
Paul
P. K.
,
Schürz
C.
,
Srinivasan
R.
&
Daggupati
P.
2022
A long-term global comparison of IMERG and CFSR with surface precipitation stations
.
Water Resources Management
36
,
5695
5709
.
https://doi.org/10.1007/s11269-022-03328-5
.
Güntner
A.
,
Olsson
J.
,
Calver
A.
&
Gannon
B.
2001
Cascade-based disaggregation of continuous rainfall time series: the influence of climate
.
Hydrology and Earth System Sciences
5
(
2
),
145
164
.
https://doi.org/10.5194/hess-5-145-2001
.
Hazarika
M. K.
,
Kafle
T. P.
,
Sharma
R.
,
Karki
S.
,
Shrestha
R. M.
&
Samarkoon
L.
2007
Statistical approach to discharge prediction for flood forecasts using TRMM data
. In:
Proceedings of the 5th Annual Mekong Flood Forum
,
Ho Chi Minh City, Vietnam
, pp.
17
18
.
Hembram
T. K.
&
Saha
S.
2020
Prioritization of sub-watersheds for soil erosion based on morphometric attributes using fuzzy AHP and compound factor in Jainti River basin, Jharkhand, Eastern India
.
Environment, Development and Sustainability
22
(
2
),
1241
1268
.
https://doi.org/10.1007/s10668-018-0247-3
.
Hou
J.
,
Wang
N.
,
Guo
K.
,
Li
D.
,
Jing
H.
,
Wang
T.
&
Hinkelmann
R.
2020
Effects of the temporal resolution of storm data on numerical simulations of urban flood inundation
.
Journal of Hydrology
589
,
125100
.
https://doi.org/10.1016/j.jhydrol.2020.125100
.
Koutsoyiannis
D.
,
Onof
C.
&
Wheater
H. S.
2003
Multivariate rainfall disaggregation at a fine timescale
.
Water Resources Research
39
,
7
.
https://doi.org/10.1029/2002WR001600
.
Nerini
D.
,
Zulkafli
Z.
,
Wang
L. P.
,
Onof
C.
,
Buytaert
W.
,
Lavado-Casimiro
W.
&
Guyot
J. L.
2015
A comparative analysis of TRMM-rain gauge data merging techniques at the daily time scale for distributed rainfall-runoff modeling applications
.
Journal of Hydrometeorology
16
,
2153
2168
.
https://doi.org/10.1175/JHM-D-14-0197.1
.
Ryo
M.
,
Saavedra Valeriano
O. C.
,
Kanae
S.
&
Ngoc
T. D.
2014
Temporal downscaling of daily gauged precipitation by application of a satellite product for flood simulation in a poorly gauged basin and its evaluation with multiple regression analysis
.
Journal of Hydrometeorology
15
,
563
580
.
https://doi.org/10.1175/JHM-D-13-052.1
.
Shaikh
M. P.
,
Yadav
S. M.
&
Manekar
V. L.
2022
Assessment of the empirical methods for the development of the synthetic unit hydrograph: A case study of a semi-arid river basin
.
Water Practice & Technology
17
(
1
),
139
156
.
https://doi.org/10.2166/wpt.2021.117
.
Yang
D.
,
Herath
S.
&
Musiake
K.
2001
Spatial resolution sensitivity of catchment geomorphologic properties and the effect on hydrological simulation
.
Hydrological Processes
15
,
2085
2099
.
https://doi.org/10.1002/hyp.280
.
Yaswanth
P.
,
Kannan
B. A. M.
,
Bindhu
V. M.
,
Balaji
C.
&
Narasimhan
B.
2023
Evaluation of remote sensing rainfall products, bias correction and temporal disaggregation approaches, for improved accuracy in hydrologic simulations
.
Water Resources Management
1
24
.
https://doi.org/10.1007/s11269-023-03486-0
.
Yuan
F.
,
Zhang
L.
,
Soe
K.
,
Ren
L.
,
Zhao
C.
,
Zhu
Y.
&
Liu
Y.
2019
Applications of TRMM-and GPM-era multiple-satellite precipitation products for flood simulations at sub-daily scales in a sparsely gauged watershed in Myanmar
.
Remote Sensing
11
(
2
),
140
.
https://doi.org/10.3390/rs11020140
.
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