The present study developed an integrated hydrologic model for sustainable utilisation and water management in two complex watersheds with varying physioclimatic features and reservoirs. The soil and water assessment tool (SWAT) is used for predicting integrated inflows into the Hatnur reservoir from the Burhanpur and Purna watersheds of the Upper Tapi River basin, while outflows are simulated using a rule curve. The influence of watershed complexities on hydrological model parameters and the watershed processes are investigated using extensive multisite and multivariable calibration (1998–2007) and validation (2008–2013) approaches, including sensitivity and uncertainty analyses. The sensitive parameters are related to curve number (CN), groundwater, slope, and main channel characteristics. The annual streamflow (m3/km2/mm of rainfall) in the Burhanpur watershed is 568.7, which is 4.2 times higher than the Purna watershed's streamflow of 136.2. The hypsometric analysis, areal rainfall, and flow duration curves revealed a substantially different streamflow pattern and a larger coefficient of variation in the spatial distribution of water balance components over sub-watersheds in the Burhanpur watershed compared to the Purna watershed due to diverse topographic features. The developed model would be useful for planning controlled releases from the terminal reservoir to mitigate hazards in the downstream reaches of the Tapi River basin.

  • Integrated hydrological modelling of two complex watersheds and terminal reservoir.

  • Extensive multisite and multivariable model calibration and uncertainty analyses.

  • Assimilating watershed's physioclimatic heterogeneity through sensitive parameters.

  • Role of physiographic diversity on spatial patterns of hydrologic water balance.

Water is vital for life but is limited and unevenly distributed in space and time. Understanding the movement of water and its interactions are challenging tasks in various phases of hydrological processes. Nearly one-third of the world's population may face water shortage by the year 2050 due to qualitative and quantitative water reductions caused by rapid industrialisation, urbanisation, population increase, desertification, global warming, etc. (Costa et al. 2003; McColl & Aggett 2007). Climate change–induced extreme weather events (floods/droughts) and seasonality of monsoon rainfall, especially in developing nations like India, inflict substantial socioeconomic disruptions and question the temporal availability of water (Monirul & Mirza 2003). To deal with these multifaceted water resources management issues, the dams and reservoirs evolved with human civilisation, embarking more adaptability on the one hand; on the other, they interfered with the natural hydrological processes (Zhang et al. 2012). This necessitates undertaking an integrated hydrological modelling (IHM) approach, including hydrological processes of watersheds, reservoirs, and water exchange more holistically and comprehensively to better understand water resource systems for their sustainable and efficient water usage.

The computer-based numerical model invariably represents complex physical hydrologic processes and their interactions which is used to analyse water movement, distribution, and behaviour in the watersheds (Kim & Parajuli 2012). For instance, Hydrological Simulation Program-Fortran (HSPF; Donigian et al. 1995), MIKE-Système Hydrologic Europeèn (MIKE-SHE; DHI 2017), variable infiltration capacity (VIC; Hamman et al. 2018) model, soil and water assessment tool (SWAT; Neitsch et al. 2011), etc. are the models used by hydrologists for a variety of applications. The SWAT is a widely used physically based, computationally efficient, continuous hydrological model to mimic watershed processes (Neitsch et al. 2011). The calibration of the hydrological model is one of the most crucial processes to ensure an accurate representation of actual watershed processes (Sharma et al. 2022). Researchers have emphasised multisite and multivariable calibration approaches owing to their numerous hydrological improvements over single-site calibration (Daggupati et al. 2015; Shah et al. 2021). Though the SWAT is extensively explored for simulating streamflow, sediment, and pollutant loading for different hydrological conditions across the globe (Thampi et al. 2010; Piniewski et al. 2017; Setyorini et al. 2017; Abbaspour et al. 2018; Munoth & Goyal 2019, 2020a; Wang & Cao 2021; Rostami et al. 2022), the literature lacks a comprehensive application of the SWAT for IHM of watershed hydrology and reservoir processes using multisite and multivariable calibration approaches.

The Upper Tapi River basin (UTRB) in India is a physio-climatically heterogeneous region due to its unique geographical setting and relief (Sharma et al. 2019a). In IHM, the model complexities increase when the watersheds encompass large-scale physioclimatic heterogeneity (Birkel & Barahona 2019). In recent decades, the Hatnur reservoir, a terminal reservoir of the UTRB, and the upstream watersheds have observed a shift towards increasing water stress conditions (Sharma et al. 2019b). The head watershed, i.e., Burhanpur, having high runoff coefficients, generates very high runoff, making the UTRB susceptible to flooding (Sharma et al. 2019a) and prone to excessive erosion (Resmi et al. 2020; Ramani et al. 2021). Notably, the 2006 flood in Surat city in the Lower Tapi basin was responsible for the death of 150 people and losses of 210 billion Rupees (Jibhakate et al. 2023b) due to heavy runoff contributions from the UTRB. Gehlot et al. (2023) are also concerned about the transition of the UTRB towards dry-warm climatology in the near future (2021–2050), which would escalate the present water stress conditions in the region. Chandra et al. (2014, 2016) and Munoth & Goyal (2020b, 2022) studied the hydrologic response of the headwater regions without considering the hydrologic responses of its largest tributary and terminal reservoir.

Therefore, by considering the limitations of previous studies, as stated above, and the importance of the regional hydrologic assessments, the present study adds another dimension to the existing knowledge on the hydrologic behaviour of two contrasting watersheds and their terminal reservoir using an IHM approach at a daily timescale. The primary objective of the present study is to develop an integrated framework of the SWAT model and calibrate the same to capture the complex and diverse characteristics of the UTRB, Burhanpur and Purna watersheds, with their terminal reservoir, i.e., Hatnur reservoir. The study also emphasises the simulation of the reservoir in the IHM framework and spatial variability of water balance components for harnessing the water resources potential of the study region. Such analyses would help in effectively planning and managing the water resources for meeting the irrigation and domestic demands in the command area of the reservoir in the Middle Tapi River basin (MTRB) and the prevention of floods in the Lower Tapi River basin (LTRB).

Study area

The Tapi River is the sixth largest river in the Indian Peninsula and the second largest westward flowing river draining into the Arabian Sea. The Tapi basin, which is situated in western-central India, drains 65,145 km2, about 2% of the geographical area of India. The basin is sub-divided into three sub-basins (Central Water Commission (CWC) 2014; Figure 1), namely, the UTRB, from the origin of the Tapi River to Hatnur dam (29,430 km2); the MTRB, from Hatnur dam to Ukai dam (32,925 km2), and the LTRB, from Ukai dam to the Arabian Sea (2,790 km2). The Purna River, the longest tributary of the Upper Tapi River, having 379 km length, forms two physio-climatically contrasting watersheds, namely, Burhanpur and Purna watersheds (Sharma et al. 2019a). The Purna and Suki Rivers have their confluence with the Upper Tapi River at 8 and 2.5 km upstream of the Hatnur dam. The Burhanpur and Purna watersheds have a drainage area of 10,600 and 18,490 km2, respectively, up to their confluence point (CWC 2014). Primarily, the UTRB is characterised as a hot semi-arid eco-region with shallow and medium-deep black soils except for the plateau region of the headwater region, which is a hot subhumid region (CWC 2014). The Burhanpur watershed is covered with Deccan trap lava flows. In contrast, the Purna watershed comprises predominately impermeable and poorly permeable Deccan basalts and porous quaternary alluvial deposits (Loliyana & Patel 2020). The UTRB consists of black soil with medium to fine texture and moderately low to high productivity.
Figure 1

Index map of the Upper Tapi basin showing its geographical setting, topographical variation, and hydrometeorological stations/grid locations: (a) India; (b) Tapi basin; (c) Upper Tapi basin; and (d) Upper Tapi basin at Hatnur dam.

Figure 1

Index map of the Upper Tapi basin showing its geographical setting, topographical variation, and hydrometeorological stations/grid locations: (a) India; (b) Tapi basin; (c) Upper Tapi basin; and (d) Upper Tapi basin at Hatnur dam.

Close modal

The Hatnur reservoir is a terminal reservoir of the UTRB, which was constructed in 1982 with a gross and live storage capacity of 388.0 and 255.0 million cubic meters (MCM), respectively. The reservoir was commissioned in 1983 to serve the purpose of irrigation and water supply in the basin. The gross command area and the culturable command area of the Hatnur reservoir are 59,150 and 47,350 ha, respectively. The full reservoir level (FRL) at RL 214.0 m corresponds to the gross storage capacity of 388.0 MCM. The new operation strategy of the Hatnur reservoir advises no water impoundment until August 21, with a minimum reservoir level of RL 209.5 m and a corresponding storage capacity of 180.0 MCM. The reservoir is usually filled up to the FRL by October 15.

Input datasets for the SWAT model

Watershed topography and its climatological details, i.e., digital elevation model (DEM), soil properties, land use and land cover (LULC), slope, rainfall, minimum and maximum air temperature, solar radiation, relative humidity, wind speed, etc., are principally required datasets for the development of any hydrological model. Based on the availability of the datasets (shown in Table 1), their frequency, and resolution, the SWAT model is used to map the watershed hydrology of the UTRB and simulate its terminal reservoir. The shuttle radar topographic mission (SRTM) DEM is used to derive the topographic features of the UTRB. The LULC details are obtained from the National Remote Sensing Centre (NRSC), Hyderabad. These LULC maps were reclassified into six classes, namely, agriculture, barren land, built-up, current fallow, forest, and water, and their corresponding percentages over the Burhanpur (Purna) watershed are 31.8% (66.0%), 5.7% (4.0%), 0.7% (2.2%), 5.4% (11.5%), 54.9% (14.6%), and 1.4% (1.8%), respectively (Figure 2(a)). The soil maps were prepared by digitising the physically surveyed soil maps procured from the National Bureau of Soil Survey & Land Use Planning (NBSS&LUP), Nagpur. The associated soil properties were prepared from the soil database of the said agency. The Purna watershed majorly comprises clay (72.0%) and sandy clay (27.9%), while the Burhanpur watershed comprises clay (71.5%), sandy clay loam (16.9%), and sandy clay (10.6%). These soils are primarily a part of B, C, and D soil hydrologic groups, and their corresponding percentage over the Burhanpur (Purna) watershed is 1.0% (0.0%), 43.2% (48.4%), and 53.2% (51.6%), respectively (Figure 2(b)).
Table 1

Data used in the present study and their sources

Data typeFrequency/resolutionPeriodSource/agency
SRTM DEM 30 m 2000 https://earthexplorer.usgs.gov 
LULC map 56 m 2005–06 NRSC, Hyderabad 
Soil map 1:2,50,000 – NBSS&LUP, Nagpur 
Rainfall Daily (station) 1994–2013 IMD, Pune 
Temperature Daily (0.5° × 0.5°) 
Streamflow Daily (station) CWC, Surat Division 
Inflow and outflow @ Hatnur Daily TIDC, Jalgaon 
Data typeFrequency/resolutionPeriodSource/agency
SRTM DEM 30 m 2000 https://earthexplorer.usgs.gov 
LULC map 56 m 2005–06 NRSC, Hyderabad 
Soil map 1:2,50,000 – NBSS&LUP, Nagpur 
Rainfall Daily (station) 1994–2013 IMD, Pune 
Temperature Daily (0.5° × 0.5°) 
Streamflow Daily (station) CWC, Surat Division 
Inflow and outflow @ Hatnur Daily TIDC, Jalgaon 
Figure 2

(a) LULC map; (b) soil hydrologic group map; (c) slope map; and (d) soil texture map of the UTRB (Purna and Burhanpur watersheds).

Figure 2

(a) LULC map; (b) soil hydrologic group map; (c) slope map; and (d) soil texture map of the UTRB (Purna and Burhanpur watersheds).

Close modal

The climate data were procured from the India Meteorological Department (IMD), Pune. The station-based rainfall datasets have been used for the model simulation after infilling the missing rainfall data using the inverse distance weighting method (Gehlot et al. 2021). The gridded temperature data of IMD (Srivastava et al. 2009), available at 1.0° × 1.0°, have been used after bilinear interpolation to 0.5° × 0.5° (Jibhakate et al. 2023a). The streamflow data at Burhanpur and Yerli gauging stations were procured from the CWC, Surat Division. The inflow/outflow to/from the Hatnur reservoir was obtained from the Tapi Irrigation Development Corporation (TIDC), Jalgaon. These were used for the calibration and validation of the developed hydrologic model.

Methodology

Integrated hydrologic modelling using the SWAT

The Upper Tapi River flows up to the Hatnur reservoir, and the Purna River merges into it around 8 km upstream of the reservoir. The Hatnur reservoir was simulated in an integrated manner by extending the SWAT model of the Burhanpur watershed to the reservoir by defining a terminal reservoir and an inlet point for adding discharge of the Purna watershed at the confluence of the Upper Tapi River and the Purna River. The SWAT model for the Purna watershed was developed separately to account for large-scale topographical, morphological, and hydroclimatic heterogeneity in the UTRB.

The SWAT is a deterministic, semi-distributed, and continuous hydrological model that operates at a daily/sub-daily time step and can simulate hydrological and water quality processes at the basin level (Neitsch et al. 2011). The brief outline describing the sequential processes and datasets involved in developing the SWAT hydrologic model is shown in Figure 3. Shivansh & Patel (2023) extensively analysed the impact of drainage area threshold on hydrologic simulations of the UTRB and recommended a drainage area threshold of 200 km2. Based on the adopted drainage area threshold of 200 km2, the SWAT model created the river network, and the UTRB was divided into smaller units called sub-watersheds, leading to the formation of 20 and 38 sub-watersheds for the Burhanpur and Purna watersheds, respectively. Each sub-watershed is further divided into hydrological response units (HRUs), which are defined as a unique combination of land cover, soil, slope, and management practices in a watershed and are considered lumped. The hydrological parameters (i.e., precipitation, infiltration, surface runoff, evapotranspiration, lateral flow, and percolation) for each HRU are simulated using a water balance equation (Equation (1)) in the soil profile:
formula
(1)
where () is the final (initial) soil water content; t is the time; and , , , , and are the amount of precipitation, surface runoff, evapotranspiration, percolation, and groundwater return flow/baseflow, respectively. The unit of all variables is mm/day, except for t whose unit is in days. Surface runoff from HRUs is estimated using the soil conservation service (SCS) curve number (CN) method from daily rainfall (USDA-SCS 1972). The modification option is opted for HRU slope because CN values are sensitive to the slope. The UTRB holds a significant proportion of black cotton clayey soil, which is susceptible to form cracks during the dry period. Thus, the crack flows are also considered while modelling surface runoff. Due to weather data scarcity, the Hargreaves method estimates potential evapotranspiration. All these processes are accounted for in the land phase of the hydrological cycle. The SWAT model simulates the amount of water, sediment, nutrient/pesticide, and bacterial loading to the main channels in each sub-basin. On the other hand, the routing phase governs the movement of water, sediments, and nutrients/pesticides to the outlet through the channel network. The flow is routed through the channel using the variable storage coefficient method (Williams 1969). More information on the SWAT hydrological model is available in Neitsch et al. (2011).
Figure 3

A systematic framework describing the sequential processes and datasets involved in developing the SWAT hydrologic model.

Figure 3

A systematic framework describing the sequential processes and datasets involved in developing the SWAT hydrologic model.

Close modal
The SWAT allows the simulation of reservoir processes, i.e., water, sediments, nutrients, and pesticide routing at the watershed outlet (Arnold et al. 1998). The water balance for reservoirs includes inflow, outflow, rainfall on the surface, evaporation, seepage from the reservoir bottom, and diversions. The water balance for a reservoir in the SWAT model is computed using Equation (2):
formula
(2)
where V and are the volume of water in the impoundment at the end and beginning of the day (in m3); , , , , and are the volume of water entered and released from the reservoir, precipitation over the water body, evaporation from the water body, and volume of water lost from the water body by seepage during the day (in m3), respectively. is calculated by using the hydraulic conductivity of the reservoir bottom surface and its surface area (Neitsch et al. 2011). The SWAT model offers the controlled outflow with a target release, in which the volume of outflow from the reservoir is calculated as a function of the desired target storage. The user may specify this target reservoir storage monthly, or it can be calculated as a function of flood season and soil water content. A detailed explanation of the reservoir component in the SWAT can be found in Neitsch et al. (2011).

Since the Hatnur reservoir is a controlled reservoir, the target release method was used to simulate the reservoir outflows. The reservoir parameters were obtained from the field agency, i.e., TIDC, Jalgaon. The parameters defined for the reservoir module in the SWAT are shown in Table 2. The monthly target storage for the flood period (July–October) was provided based on the existing rule curve of the reservoir. In contrast, the target storage of the FRL was used for the remaining months (non-flood period) to maximise the reservoir storage. The discharges from the reservoir for municipal and agricultural needs are abstracted from the storage for consumptive use.

Table 2

Reservoir module parameters for target release–based simulation of the Hatnur reservoir

VariableDefinitionValue
MORES Month the reservoir became operational 
IYRES Year the reservoir becomes operational 1,982 
RES_ESA Reservoir surface area when the reservoir is filled to the emergency spillway (ha) 6,503 
RES_EVOL Volume of water needed to fill the reservoir to the emergency spillway (104 m338,800 
RES_PSA Reservoir surface area when the reservoir is filled to the principal spillway (ha) 2,290 
RES_PVOL Volume of water needed to fill the reservoir to the principal spillway (104 m312,800 
RES_VOL Initial reservoir volume (104 m312,800 
RES_K Hydraulic conductivity of the reservoir bottom (mm/h) 10 
IRESCO Outflow simulation options 
STARG Target reservoir volume specified for a given month (m3Rule curve 
IFLODR1 Beginning month of the non-flood season 11 
IFLODR2 Ending month of the non-flood season 
NDTARGR Number of days to reach target storage from the current reservoir storage 
VariableDefinitionValue
MORES Month the reservoir became operational 
IYRES Year the reservoir becomes operational 1,982 
RES_ESA Reservoir surface area when the reservoir is filled to the emergency spillway (ha) 6,503 
RES_EVOL Volume of water needed to fill the reservoir to the emergency spillway (104 m338,800 
RES_PSA Reservoir surface area when the reservoir is filled to the principal spillway (ha) 2,290 
RES_PVOL Volume of water needed to fill the reservoir to the principal spillway (104 m312,800 
RES_VOL Initial reservoir volume (104 m312,800 
RES_K Hydraulic conductivity of the reservoir bottom (mm/h) 10 
IRESCO Outflow simulation options 
STARG Target reservoir volume specified for a given month (m3Rule curve 
IFLODR1 Beginning month of the non-flood season 11 
IFLODR2 Ending month of the non-flood season 
NDTARGR Number of days to reach target storage from the current reservoir storage 

Model calibration and performance evaluation

The Sequential Uncertainty Fitting algorithm (SUFI-2; Abbaspour 2015) in SWAT-CUP (Calibration and Uncertainty Program) calibrates the SWAT model. A multimeric approach is used to calibrate and validate the developed model for Burhanpur using the observed streamflow at the Burhanpur gauging site, inflow/outflow to/from the Hatnur reservoir. At the same time, the model for the Purna watershed is calibrated and validated using the measured streamflow at the Yerli gauging site. The warm-up, calibration, and validation periods are 1994–1997, 1998–2007, and 2008–2013, respectively. To better represent the watershed hydrology of the heterogeneous catchments of the UTRB, the parameters related to the basin, main and tributary channels, soil, HRUs, groundwater, and watershed management were identified, and a subset of these parameters is used to calibrate the developed models for two watersheds.

SUFI-2 can ascertain all sources of uncertainties involved in simulating hydrological processes. The uncertainty assessment estimates the P-factor and R-factor based on the 95% prediction uncertainty (95PPU) band. The 95PPU band is a block represented by the 97.5th and 2.5th percentile values of the simulated output variable(s). The P-factor is the percentage of the observed data bracketed by the 95PPU band and represents the capability of the model to capture uncertainties in the simulations. The R-factor, on the other hand, is the measure of the quality of the model calibration and is estimated as the ratio of the thickness of the 95PPU band to the standard deviation in the observations. The ideal values of the P-factor and R-factor are 1 and 0, respectively. Model performance is assessed using statistical indicators like the coefficient of determination (R2; Equation (3)), Nash–Sutcliffe efficiency (NSE; Nash & Sutcliffe 1970; Equation (4)) model, percentage bias (PBIAS; Gupta et al. 1999; Equation (5)), and root-mean-square error–observations standard deviation ratio (RSR; Equation (6)) by comparing the model simulated output with the observed data:
formula
(3)
formula
(4)
formula
(5)
formula
(6)
where , ,, and represent measured, simulated, average of measured, and average of simulated discharge, respectively, and n is the data length. Moriasi et al. (2007) proposed the performance criterion/rating of the hydrological model based on the values of these statistical indicators.

Physiographic and hydroclimatic heterogeneity in the UTRB

Hypsometric curves (HCs, shown in Figure 4(a)) for two contrasting watersheds of the UTRB with similar elevation ranges show substantially different areal distributions. The mean elevation (coefficient of variation (CV)) for the Burhanpur and Purna watersheds are 487.2 m (34.8%) and 372.1 m (36.1%), respectively. The corresponding areas below mean elevations are 54.2 and 68.4%, respectively. The higher mean elevation and lower CV show the consistent and gradually varying steep terrain of the Burhanpur watershed surrounded by the Satpura ranges in the north and the Gwaligarh hills in the south (Figure 1). It is also evident from the steep slope of the HC (Figure 4(a)) for Burhanpur throughout the elevation range. On the contrary, the Purna watershed shows the persistence of flat terrain in the alluvium plains, except in the foothills of the bounding hill ranges, i.e., Gwaligarh hills in the north, the Ajanta and Satmala range in the south, and the Mahadeo hills in the east, and reflected in the HC of the Purna watershed (Figure 4(a)). The interquartile range of elevation for the Purna watershed is 117 m. On the other hand, the Burhanpur watershed has an elevation range of 281 m, which is 140% higher than the elevation range of the Purna watershed.
Figure 4

(a) Hypsometric curves; (b) area rainfall; (c) streamflow of Burhanpur and Purna watersheds; and (d) inflow and outflow of the Hatnur reservoir. The inner subplots show the variations for exceedance probability less than equal to 10.

Figure 4

(a) Hypsometric curves; (b) area rainfall; (c) streamflow of Burhanpur and Purna watersheds; and (d) inflow and outflow of the Hatnur reservoir. The inner subplots show the variations for exceedance probability less than equal to 10.

Close modal

The areal rainfall duration curve (Figure 4(b)) signifies that the Burhanpur watershed is more susceptible to high-magnitude rainfall extremes and more rainy days (when daily rainfall equals or exceeds 2.5 mm) than the Purna watershed. The average annual rainfall in the Burhanpur and Purna watersheds is 994.5 and 817.1 mm, respectively. The seasonal distribution of annual rainfall in the former (latter) watershed during monsoon (JJAS), post-monsoon (ON), winter (DJF), and pre-monsoon (MAM) is 92.7% (87.5%), 5.0% (8.7%), 1.4% (2.2%), and 1.0% (1.7%), respectively. The narrow valley and the elevated Betul Plateau of the Burhanpur watershed led to a strong orographic effect, resulting in higher rainfall during the southwest monsoon. Interestingly, the Purna watershed has a higher post-monsoon contribution than the Burhanpur watershed. The higher elevation of Satpura hills can be a plausible reason for preventing the northeast monsoon in the Burhanpur watershed.

The varying rainfall characteristics of these watersheds lead to substantially different streamflow characteristics (Figure 4(c)). The Burhanpur watershed has dominant forest cover, while the Purna watershed has agricultural land. Both catchments have nearly three-quarters of the soil cover of clayey texture. The steep topography of the Burhanpur watershed leads to large streamflow peaks compared to the flatter Purna watershed. The Upper Tapi River flows nearly 85% of the time of the year, showing the significant contribution of the baseflow. The persistence of the forest land cover helps detain the runoff, leading to percolation and, thus, prolonging the baseflow assistance in the Burhanpur watershed. On the other hand, the alluvium plains of the Purna watershed provide good potential for groundwater reserve in shallow aquifers. The large number of detention structures in the Purna watersheds is also responsible for the availability of the flows in the Purna River (Sharma et al. 2019a). The storage capacity of the downstream Hatnur reservoir has reduced significantly due to the reservoir sedimentation with prime contribution from the Burhanpur watershed due to high erosion-prone areas in the watershed (Resmi et al. 2020; Ramani et al. 2021). The reservoir greatly influences the low flows in the river, which are evident from the flow duration curves of inflow and outflow from the Hatnur reservoir, as shown in Figure 4(d). Due to the limited storage capacity of the terminal reservoir, i.e., 388.0 MCM, the reservoir does not affect the high flows and passes on to the downstream MTRB without much interference. Sharma et al. (2021) showed moderate alterations in the natural regime of the Tapi River due to the construction of the Hatnur dam. Because of physiographic and hydroclimatic heterogeneity and the influence of human artefacts on the hydrological regime, IHM and investigations are required to understand the watershed dynamics of the UTRB.

Integrated hydrologic modelling and assessment

Parametric sensitivity analysis

The Purna watershed has more human interventions in the form of detention structures. In contrast, the Burhanpur watershed resembles a virgin catchment. This enables more parameterisation efforts in calibrating the hydrologic model for the former watershed vis-à-vis the latter. The hydrologic modelling of the headwater catchment of the UTRB, i.e., Burhanpur watershed, was performed by integrating the Hatnur reservoir. Additional parameters related to the reservoir are considered during the calibration process to account for the integrated multisite and multivariable calibration processes. The multisite and multivariable calibration approach is adopted, considering its numerous advantages over single-site calibration. It considers the impacts of spatial heterogeneity in the watershed characteristics during semi-automated calibration and parametric sensitivity assessments of the hydrological models.

The parameters in Table 3 are related to the fundamental processes of water circulation in the hydrological cycle, namely, groundwater (.gw), land management (.mgt), soil (.sol), main channel (.rte), hydrological response units (.hru), tributary channels (.sub), and basin parameters (.bsn). Sensitive parameters, at a 5% significance level, comprise the groundwater return flow/base flow, land management, soil, primary and tributary channel, basin, and HRU processes (Table 3). However, the top five sensitive parameters for the Purna watershed are the threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN.gw), groundwater ‘revap’ coefficient (GW_REVAP.gw), effective hydraulic conductivity in the main channel in mm/hr (CH_K2.rte), SCS runoff curve number (CN2.mgt), and deep aquifer percolation fraction (RCHRG_DP.gw). It can be observed that the parameters from the baseflow and main channel dominate among the family of sensitive parameters. On the other hand, hydrologically sensitive parameters for the Burhanpur watershed (Table 3) are RCHRG_DP.gw, GW_DEALY.gw, CN2.mgt, SLSOIL.hru, and ALPHA_BF.gw. The groundwater and soil slope parameters were sensitive because a large portion of this sub-basin is covered with forest area and has clay loamy and clay soils. Also, the upper part of the catchment has a fractured rock area, which is favourable to return flow. The clay particles can attract and retain water molecules due to the polar nature of water molecules. These types of soils have larger amounts of water for plant uptake. Therefore, there may be a chance that the deep-rooted plants can uptake water from the shallow aquifer itself. The CN2 is a function of soil permeability, slope, land use, and antecedent moisture conditions, and its values show the runoff characteristics of the basin.

Table 3

Calibrated model parameter values and sensitivity rank for two contrasting watersheds

ParametersDescriptionMinimum valueMaximum valueFitted valueSensitivity rank
Purna watershed 
v__ALPHA_BF.gw Baseflow alpha factor (days) 0.35 0.86 0.36 15 
v__GW_DELAY.gw Groundwater delay (days) 2.62 49.13 16.41 13 
v__GWQMN.gw Threshold depth of water in the shallow aquifer required for return flow to occur (mm) 2,027.03 5,881.32 5,215.49 
v__GW_REVAP.gw Groundwater ‘revap’ coefficient 0.09 0.23 0.16 
v__RCHRG_DP.gw Deep aquifer percolation fraction 0.02 0.16 0.03 
r__CN2.mgt SCS runoff curve number −0.09 0.14 −0.02 
r__SOL_AWC().sol Available water capacity of the soil layer −0.21 0.16 0.11 
r__SOL_BD().sol Moist bulk density (g/cm3−0.06 0.22 −0.01 14 
v__CH_N2.rte Manning's ‘n’ value for the main channel 0.03 0.03 0.03 10 
v__CH_K2.rte Effective hydraulic conductivity in main channel (mm/h) 1.52 129.83 2.77 
v__ALPHA_BNK.rte Baseflow alpha factor for bank storage 0.23 0.49 0.38 
v__ESCO.hru Soil evaporation compensation factor 0.02 0.51 0.49 
v__OV_N.hru Manning's ‘n’ value for overland flow 0.026 0.194 0.031 12 
v__CH_K1.sub Effective hydraulic conductivity in tributary channel 67.01 199.05 94.18 
v__CNCOEF.bsn Plant evapotranspiration (ET) curve number coefficient 0.79 1.38 1.23 11 
Burhanpur watershed, including the Hatnur reservoir 
v__ALPHA_BF.gw Baseflow alpha factor (days) 0.72 
r__CN2.mgt SCS runoff curve number −0.2 0.2 −0.04 
v__ESCO.hru Soil evaporation compensation factor 0.25 
v__GW_DELAY.gw Groundwater delay (days) 50 2.67 
r__HRU_SLP.hru Average slope steepness 0.08 
v_SLSOIL.hru Slope length for lateral subsurface flow 150 75.34 
r__SOL_AWC().sol Available water capacity of the soil layer 0.02 0.3 0.17 
v__RCHRG_DP.gw Groundwater ‘revap’ coefficient 0.11 
v_RES_K.res Hydraulic conductivity of the reservoir bottom 15 10 
ParametersDescriptionMinimum valueMaximum valueFitted valueSensitivity rank
Purna watershed 
v__ALPHA_BF.gw Baseflow alpha factor (days) 0.35 0.86 0.36 15 
v__GW_DELAY.gw Groundwater delay (days) 2.62 49.13 16.41 13 
v__GWQMN.gw Threshold depth of water in the shallow aquifer required for return flow to occur (mm) 2,027.03 5,881.32 5,215.49 
v__GW_REVAP.gw Groundwater ‘revap’ coefficient 0.09 0.23 0.16 
v__RCHRG_DP.gw Deep aquifer percolation fraction 0.02 0.16 0.03 
r__CN2.mgt SCS runoff curve number −0.09 0.14 −0.02 
r__SOL_AWC().sol Available water capacity of the soil layer −0.21 0.16 0.11 
r__SOL_BD().sol Moist bulk density (g/cm3−0.06 0.22 −0.01 14 
v__CH_N2.rte Manning's ‘n’ value for the main channel 0.03 0.03 0.03 10 
v__CH_K2.rte Effective hydraulic conductivity in main channel (mm/h) 1.52 129.83 2.77 
v__ALPHA_BNK.rte Baseflow alpha factor for bank storage 0.23 0.49 0.38 
v__ESCO.hru Soil evaporation compensation factor 0.02 0.51 0.49 
v__OV_N.hru Manning's ‘n’ value for overland flow 0.026 0.194 0.031 12 
v__CH_K1.sub Effective hydraulic conductivity in tributary channel 67.01 199.05 94.18 
v__CNCOEF.bsn Plant evapotranspiration (ET) curve number coefficient 0.79 1.38 1.23 11 
Burhanpur watershed, including the Hatnur reservoir 
v__ALPHA_BF.gw Baseflow alpha factor (days) 0.72 
r__CN2.mgt SCS runoff curve number −0.2 0.2 −0.04 
v__ESCO.hru Soil evaporation compensation factor 0.25 
v__GW_DELAY.gw Groundwater delay (days) 50 2.67 
r__HRU_SLP.hru Average slope steepness 0.08 
v_SLSOIL.hru Slope length for lateral subsurface flow 150 75.34 
r__SOL_AWC().sol Available water capacity of the soil layer 0.02 0.3 0.17 
v__RCHRG_DP.gw Groundwater ‘revap’ coefficient 0.11 
v_RES_K.res Hydraulic conductivity of the reservoir bottom 15 10 

Note: Bold-faced parameters are sensitive parameters at a 5% significance level.

Performance evaluation of the model

The capabilities of the integrated hydrological model for the prediction of daily inflows/outflows into/from the Hatnur reservoir can be evaluated using numerous statistical indicators. Table 4 shows the statistical performance indicators at Yerli, Burhanpur, and inflow/outflow into/from the Hatnur reservoir. Apart from their flow hydrographs, the observed and simulated streamflow scatter plots are also shown in Figure 5. At Yerli, it resulted in R2 ≥ 0.70, NSE ≥ 0.70, and PBIAS (%) ≤ ±25 for both calibration and validation periods. Such model performance can be considered very good in simulating the streamflows (Moriasi et al. 2007). The uncertainty in the model prediction shows that the model encapsulated nearly 54% (32%) values of the observations during calibration (validation) at a relative thickness of 0.58 (0.63). One of the catastrophic floods, i.e., the 2006 flood, had a significant contribution from the Purna watershed with a daily average of 11,140 m3/s, resulting in an inflow volume of 962.5 MCM into the Hatnur reservoir, which is approximately 2.5 times its gross storage capacity. It is worth noting that the Purna watershed has many check dams/detention structures not incorporated in the present study due to the non-availability of their data. These structures affect the entry of the overland flows into the main streams. As a result, the developed model for the Purna watershed is relatively inferior to Burhanpur, particularly for the low flows (Figure 5(a)).
Table 4

Performance evaluation of multisite and multivariable (streamflow, inflow, and outflow) calibration and validation of the SWAT model

Performance indicatorsYerliBurhanpurInflow @ HatnurOutflow @ Hatnur
Calibration period (1998–2007) 
P-factor 0.54 0.56 0.31 0.70 
R-factor 0.58 0.07 0.09 0.07 
R2 0.75 0.71 0.78 0.77 
NSE 0.72 0.62 0.76 0.76 
PBIAS (%) −8.3 20.4 −22.5 4.80 
RSR 0.53 0.62 0.49 0.49 
Validation period (2008–2013) 
P-factor 0.32 0.40 0.31 0.80 
R-factor 0.63 0.16 0.12 0.09 
R2 0.71 0.73 0.74 0.75 
NSE 0.71 0.73 0.74 0.75 
PBIAS (%) −7.2 10.0 −16.7 11.5 
RSR 0.54 0.52 0.51 0.50 
Performance indicatorsYerliBurhanpurInflow @ HatnurOutflow @ Hatnur
Calibration period (1998–2007) 
P-factor 0.54 0.56 0.31 0.70 
R-factor 0.58 0.07 0.09 0.07 
R2 0.75 0.71 0.78 0.77 
NSE 0.72 0.62 0.76 0.76 
PBIAS (%) −8.3 20.4 −22.5 4.80 
RSR 0.53 0.62 0.49 0.49 
Validation period (2008–2013) 
P-factor 0.32 0.40 0.31 0.80 
R-factor 0.63 0.16 0.12 0.09 
R2 0.71 0.73 0.74 0.75 
NSE 0.71 0.73 0.74 0.75 
PBIAS (%) −7.2 10.0 −16.7 11.5 
RSR 0.54 0.52 0.51 0.50 
Figure 5

Observed and simulated discharge/streamflow hydrographs and scatter plots at the (a) Yerli gauging station in the Purna watershed, (b) the Burhanpur watershed, (c) inflow into the Hatnur reservoir, and (d) outflows from the Hatnur reservoir during calibration and validation periods.

Figure 5

Observed and simulated discharge/streamflow hydrographs and scatter plots at the (a) Yerli gauging station in the Purna watershed, (b) the Burhanpur watershed, (c) inflow into the Hatnur reservoir, and (d) outflows from the Hatnur reservoir during calibration and validation periods.

Close modal

It is evident from the performance indicators (Table 4) and the flow hydrographs (Figure 5) that the developed hydrological model is reliable for predicting inflow into the terminal reservoir of the UTRB, i.e., the Hatnur reservoir. The outflow simulated using the target storage approach in the reservoir module of the SWAT matched well with observed releases from the reservoir. Also, the uncertainty in the model simulated outflows shows that the model encapsulated nearly 70% (80%) values of the observations during calibration (validation) at a relative thickness of 0.07 (0.09). Since the reservoir module of the SWAT simulated the watershed processes on a daily timescale, the provision for target storage in the reservoir module is only at the end of the month. It could be one of the significant factors influencing the simulation of the outflow from the Hatnur reservoir. In addition, the model underestimates the streamflow in the Burhanpur watershed, particularly the peak flows. Since the SWAT simulates the watershed processes at the HRU scale, the meteorological variables are considered uniformly at the sub-watershed scale, restricting the ability of the model to account for the local scale weather patterns. It can also be due to the non-availability of sufficient rain gauge stations to capture the orographic effect in the Burhanpur watershed.

The Burhanpur watershed has steep topography and rocky strata with prominent geological units like the Deccan trap. In contrast, the Purna watershed has a flat topography, with alluvium, favouring agricultural practices in the watershed (Sharma et al. 2019a). The relief features of these catchments enable quick (delayed) disposal of the runoff generated in response to the rainfall event, leading to a steep (flat) recession of the flow hydrographs in the Burhanpur (Purna) watershed, leading to high (low) runoff coefficient. The higher CN of the Burhanpur watershed represents the high runoff potential of the catchment with relatively lower evapotranspiration. The vice versa holds equally good for the Purna watershed. Therefore, the calibrated parameters of the two watersheds of the UTRB represent their physiographic heterogeneity. The hydrological sensitivity of these parameters can be attributed to their (Burhanpur vis-à-vis Purna watersheds) subhumid vis-à-vis semi-arid climatology, narrow-steep-rocky valley vis-à-vis wide-plain-alluvium surface, and forest-dominated land cover vis-à-vis agricultural land use, respectively. For instance, the ALPHA_BF, representing the baseflow parameter, is high for the Burhanpur watershed. At the same time, GW_DELAY is lower. On the contrary, the values of ALPHA_BF are lower, and GW_DELAY is higher in the Purna watershed. The deep percolation represented by RCHRG_DP is increased in the Burhanpur watershed compared to the Purna watershed due to forest cover in the former watershed. These contrasting parameters agree with the contrasting physiography of these two catchments.

Potential of water balance components

The hydrological water balance components of a watershed include the spatial and temporal distribution of rainfall in the form of evapotranspiration, surface runoff, water yield, baseflow/groundwater runoff, shallow and deep aquifer percolation/recharge, and revap, among others. The spatial heterogeneity of the watershed characteristics inherits spatial variability of these water balance components. Figure 6 shows the spatial variability in the different water balance components, i.e., rainfall, evapotranspiration, surface runoff, and water yield. The rain over the sub-watersheds of the Burhanpur watershed varied between 705.5 and 1,248.5 mm, with a mean rainfall of 958.3 mm. The CV for rain is 23.3%. On the other hand, the Purna watershed has a mean rainfall of 797.3 mm, with a CV of 9.0%. Nearly 45.0 and 42.1% of the annual rainfall are lost in evapotranspiration from the water bodies and vegetation surfaces in Purna and Burhanpur watersheds, respectively. The yearly average evapotranspiration depth over the sub-watersheds in the Purna (Burhanpur) watershed is 359.0 mm (403.1 mm), with a CV of 16.3% (30.1%). The sub-watersheds with dense vegetation/forest cover and water bodies, i.e., reservoir, show high evapotranspiration in the UTRB (Figures 6(b) and 2(a)).
Figure 6

Spatial variation of water balance components: (a) rainfall; (b) evapotranspiration; (c) surface runoff; and (d) water yield.

Figure 6

Spatial variation of water balance components: (a) rainfall; (b) evapotranspiration; (c) surface runoff; and (d) water yield.

Close modal

The surface runoff and water yield in the Purna (Burhanpur) watershed is 12.8% (17.7%) and 16.0% (37.3%) of the annual rainfall. It shows that the Burhanpur watershed has a significant contribution from the baseflow, which was evidenced through the flow duration curve (Figure 4(c)). The surface runoff and water yield in the sub-watersheds are directly proportional to the areal rainfall (Figure 6). The Pearson's correlation coefficient between areal rainfall and surface runoff (water yield) is 0.8 (0.9). The annual average streamflow potential of the Burhanpur watershed (expressed as m3/km2/mm of rainfall) is 568.7, 4.2 times higher than the streamflow potential of the Purna watershed. The mean annual water yield depth and its CV in the Purna (Burhanpur) watershed are 127.7 mm (357.2 mm) and 36.4% (57.6%), respectively. The high CV value in the Burhanpur watershed directly influences its diverse topography from the origin of the river to the terminal reservoir. The watershed in the Satpura ranges' foothills has more rainfall than on the foothills of the Gwaligarh hills. It is due to their presence on the windward side where moisture-laden southwest monsoon trade winds strike off.

The outcomes of the present study would have direct relevance to field applications. The IHM of the upstream watershed and terminal Hatnur reservoir would help predict daily inflows (outflows) into (from) the Hatnur reservoir for the forecasted rainfall. The anticipated inflows into the reservoir would be very useful for supplying the water into the canal for fulfilling the domestic and irrigation requirements of the command area in the MTRB. Also, the releases from the Hatnur reservoir would be useful for predicting inflows into the downstream Ukai reservoir. Knowing the inflows into the Ukai reservoir would help the dam authority to have planned releases to prevent excessive flooding in the downstream regions of the LTRB, especially Surat City. It is worth noting that Surat city is one of the fastest-growing cities in the world, and the 2006 flood alone led to the loss of 210 billion Rupees. The developed integrated model also provides spatial and temporal distributions of rainfall based on existing watershed characteristics. This would help plan watershed management practices and their impacts on the water balance components. Such information would alleviate the water stress conditions in the region and attain sustainable development goals. Thus, the present study brings invaluable insight into the current understanding of the hydrological processes with contrasting watershed characteristics.

The hydrological processes in the UTRB, having two physio-climatically heterogeneous watersheds, i.e., Purna and Burhanpur, and their terminal Hatnur reservoir, have been modelled in an integrated manner using the SWAT to predict the daily inflow (outflow) into (from) the Hatnur reservoir. The hydrologic modelling of the headwater catchment of the UTRB, i.e., the Burhanpur watershed, was performed by integrating the Hatnur reservoir and outflows from the Purna watershed. Extensive multisite and multivariable (streamflow, inflow/outflow) calibration (1998–2007) and validation (2008–2013) approaches have been employed to investigate the hydrological influence of model parameters on the watershed processes, including their sensitivity and uncertainty analyses. The key conclusions drawn from the present study are as follows:

  • The areal rainfall over the Burhanpur and Purna watersheds deviates nominally, except for the extremes; however, their flow duration curves varied significantly in high-, medium-, and low-flow regimes. The sizable change in the model output (i.e., streamflow) for nearly similar inputs (i.e., rainfall and other meteorological variables) highlighted the substantial influence of the contrasting nature of these two watersheds in terms of topography, geology, soil, and LULC.

  • The parametric sensitivity assessment for the contrasting watersheds reflected the physiographical heterogeneity of the two watersheds. It is also revealed that hydrological processes in the Burhanpur and Purna watersheds are primarily affected by RCHRG_DP.gw (deep aquifer percolation fraction), GW_DEALY.gw (groundwater delay), CN2.mgt (curve number), SLSOIL.hru (slope length for lateral subsurface flow), and ALPHA_BF.gw (baseflow alpha factor); and GWQMN.gw (the threshold depth of water in the shallow aquifer required for return flow to occur), GW_REVAP.gw (groundwater ‘revap’ coefficient), CH_K2.rte (effective hydraulic conductivity in the main channel), CN2.mgt, and RCHRG_DP.gw, respectively.

  • The hydrological sensitivity of these parameters can be attributed to their (Burhanpur vis-à-vis Purna watersheds) subhumid vis-à-vis semi-arid climatology, narrow-steep-rocky valley vis-à-vis wide-plain-alluvium surface, and forest-dominated land cover vis-à-vis agricultural land use, respectively.

  • The graphical and quantitative evaluation of the integrated SWAT model has shown satisfactory simulation capability (NSE and R2 ≥ 0.71) under the multisite and multivariable calibration and validation framework for the prediction of daily inflow into and outflow from the Hatnur reservoir, including the hydrological response of Burhanpur and Purna watersheds.

  • The Burhanpur watershed showed a large CV, compared to the Purna watershed, in the spatial distribution of water balance components over sub-watersheds due to its diverse topography from the origin of the river to the terminal reservoir.

Nonetheless, integrating sediment yield, reservoir sedimentation modelling, and details of minor hydraulic and detention structures with the existing model would make the simulation of hydrological processes more realistic. Including more rain gauges would alleviate the modelling of better-watershed dynamics of the Burhanpur watershed. These limitations can be considered as a future direction for the present study.

The authors acknowledge the financial support received from the Indian National Committee on Climate Change (INCCC) sponsored research project ‘Impact of Climate Change on Water Resources of Tapi Basin’ under the Department of Water Resources, River Development & Ganga Rejuvenation, Ministry of Jal Shakti, Government of India. The authors also appreciate the infrastructural support provided by the Centre of Excellence (CoE) on ‘Water Resources and Flood Management’, TEQIP-II, Ministry of Education, Government of India. The authors are also thankful to India Meteorological Department (IMD), Pune; Central Water Commission (CWC), Tapi Division-Surat, National Remote Sensing Centre (NRSC), Hyderabad; National Bureau of Soil Survey and Land Use Planning (NBSS&LUP), Nagpur; Tapi Irrigation Development Corporation (TIDC), Jalgaon; and USGS EarthExplorer portal for providing the required data for the present study. Acknowledgements are due to the esteemed editor and reviewers for providing valuable and thoughtful suggestions in improving the quality of this manuscript.

P.M. and L.K.G.: Conceptualization, methodology, model development and data analysis, writing-original draft; P.L.P., S.K., P.V.T., and R.G.: Conceptualization, data curation, software, supervision, writing-review and editing.

This work was supported by the Indian National Committee on Climate Change (INCCC), Department of Water Resources, River Development & Ganga Rejuvenation, Ministry of Jal Shakti, Government of India vide their letter no. 16/22/2016-R&D/3059-3076 dated November 7, 2016.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

Abbaspour
K. C.
2015
SWAT-CUP: SWAT Calibration and Uncertainty Programs – A User Manual. EAWAG – Swiss Federal Institute of Aquatic Science and Technology, Dubendorf, Switzerland, pp. 1–103
.
Arnold
J. G.
,
Srinivasan
R.
,
Muttiah
R. S.
&
Williams
J. R.
1998
Large area hydrologic modeling and assessment part I: Model development 1
.
JAWRA Journal of the American Water Resources Association
34
(
1
),
73
89
.
https://doi.org/10.1111/j.1752-1688.1998.tb05961.x
.
Birkel
C.
&
Barahona
A. C.
2019
Rainfall-runoff modeling: A brief overview
. In:
Reference Module in Earth Systems and Environmental Sciences
.
https://doi.org/10.1016/B978-0-12-409548-9.11595-7
.
Chandra
P.
,
Patel
P. L.
,
Porey
P. D.
&
Gupta
I. D.
2014
Estimation of sediment yield using SWAT model for Upper Tapi basin
.
ISH Journal of Hydraulic Engineering
20
(
3
),
291
300
.
https://doi.org/10.1080/09715010.2014.902170
.
Chandra
P.
,
Patel
P. L.
&
Porey
P. D.
2016
Prediction of sediment erosion pattern in Upper Tapi basin, India
.
Current Science
110
(6),
1038
1049
.
Costa
H. M.
,
Botta
A.
&
Cardille
A. J.
2003
Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia
.
Journal of Hydrology
283
,
206
217
.
https://doi.org/10.1016/S0022-1694(03)00267-1
.
CWC
2014
Tapi Basin Watershed Atlas
.
Central Water Commission
,
New Delhi
.
Daggupati
P.
,
Yen
H.
,
White
M. J.
,
Srinivasan
R.
,
Arnold
J. G.
,
Keitzer
C. S.
&
Sowa
S. P.
2015
Impact of model development, calibration and validation decisions on hydrological simulations in West Lake Erie Basin
.
Hydrological Processes
29
(
26
),
5307
5320
.
https://doi.org/10.1002/hyp.10536
.
DHI (Danish Hydraulic Institute)
2017
User Guide and Reference Manual to MIKE SHE: An Integrated Hydrological Modeling Framework
.
Software Manual, DHI
,
Denmark
.
Donigian
A. S.
,
Bicknell
B. R.
&
Imhoff
J. C.
,
1995
Hydrological simulation program – Fortran
. In:
Computer Models of Watershed Hydrology
(
Singh
V. P.
, ed.).
Water Resource Publications
,
Highlands Ranch, CO
, pp.
395
442
.
Gehlot
L. K.
,
Jibhakate
S. M.
,
Sharma
P. J.
,
Patel
P. L.
&
Timbadiya
P. V.
2021
Spatio-temporal variability of rainfall indices and their teleconnections with El Niño-Southern oscillation for Tapi basin, India
.
Asia-Pacific Journal of Atmospheric Sciences
57
(
1
),
99
118
.
https://doi.org/10.1007/s13143-020-00179-1
.
Gehlot
L. K.
,
Patel
P. L.
&
Timbadiya
P. V.
2023
Spatial varying and co-occurring future climate changes over a heterogeneous river basin: A multivariate approach
.
Journal of Water and Climate Change
.
https://doi.org/10.2166/wcc.2023.206
.
Gupta
H. V.
,
Sorooshian
S.
&
Yapo
P. O.
1999
Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration
.
Journal of Hydrologic Engineering
4
(
2
),
135
143
.
https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135)
.
Hamman
J. J.
,
Nijssen
B.
,
Bohn
T. J.
,
Gergel
D. R.
&
Mao
Y.
2018
The Variable Infiltration Capacity model version 5 (VIC-5): Infrastructure improvements for new applications and reproducibility
.
Geoscientific Model Development
11
,
3481
3496
.
https://doi.org/10.5194/gmd-11-3481-2018
.
Jibhakate
S. M.
,
Gehlot
L. K.
,
Timbadiya
P. V.
&
Patel
P. L.
2023a
Spatiotemporal variability of extreme temperature indices and their implications over the heterogeneous river basin, India
.
Environmental Monitoring and Assessment
195
(
6
),
664
.
https://doi.org/10.1007/s10661-023-11196-8
.
Jibhakate
S. M.
,
Timbadiya
P. V.
&
Patel
P. L.
2023b
Flood hazard assessment for the coastal urban floodplain using 1D/2D coupled hydrodynamic model
.
Natural Hazards
116
(
2
),
1557
1590
.
https://doi.org/10.1007/s11069-022-05728-7
.
Kim
H.
&
Parajuli
P. B.
2012
Impacts of reservoir outflow estimation methods in SWAT model calibration
.
Transactions of the ASABE
57
(
4
),
1029
1042
.
https://doi.org/10.13031/trans.57.10156
.
Loliyana
V. D.
&
Patel
P. L.
2020
A physics based distributed integrated hydrological model in prediction of water balance of a semi-arid catchment in India
.
Environmental Modelling & Software
127
,
104677
.
https://doi.org/10.1016/j.envsoft.2020.104677
.
McColl
C.
&
Aggett
G.
2007
Land-use forecasting and hydrologic model integration for improved land-use decision support
.
Journal of Environmental Management
84
,
494
512
.
https://doi.org/10.1016/j.jenvman.2006.06.023
.
Monirul
M.
&
Mirza
Q.
2003
Climate change and extreme weather events: Can developing countries adapt?
Climate Policy
3
(
3
),
233
248
.
doi:10.3763/cpol.2003.0330
.
Moriasi
D. N.
,
Arnold
J. G.
,
Van Liew
M. W.
,
Bingner
R. L.
,
Harmel
R. D.
&
Veith
T. L.
2007
Model evaluation guidelines for systematic quantification of accuracy in watershed simulations
.
Transactions of the ASABE
50
(
3
),
885
900
.
doi:10.13031/2013.23153
.
Munoth
P.
&
Goyal
R.
2019
Effects of DEM sources, spatial resolution and drainage area threshold values on hydrological modeling
.
Water Resources Management
33
(
9
),
3303
3319
.
https://doi.org/10.1007/s11269-019-02303-x
.
Munoth
P.
&
Goyal
R.
2020a
Impacts of land use land cover change on runoff and sediment yield of Upper Tapi River sub-basin, India
.
International Journal of River Basin Management
18
(
2
),
177
189
.
doi:10.1080/15715124.2019.1613413
.
Munoth
P.
&
Goyal
R.
2020b
Hydromorphological analysis of Upper Tapi River sub-basin, India, using QSWAT model
.
Modeling Earth Systems and Environment
6
(
4
),
2111
2127
.
https://doi.org/10.1007/s40808-020-00821-x
.
Munoth
P.
&
Goyal
R.
2022
Effects of area threshold values and stream burn-in process on runoff and sediment yield using QSWAT model
.
ISH Journal of Hydraulic Engineering
28
(
1
),
40
48
.
doi:10.1080/09715010.2019.1670107
.
Nash
J. E.
&
Sutcliffe
J. V.
1970
River flow forecasting through conceptual models part I – A discussion of principles
.
Journal of Hydrology
10
(
3
),
282
290
.
https://doi.org/10.1016/0022-1694(70)90255-6
.
Neitsch
S. L.
,
Arnold
J. G.
,
Kiniry
J. R.
&
Williams
J. R.
2011
Soil and Water Assessment Tool Theoretical Documentation Version 2009
.
Texas Water Resources Institute
.
Piniewski
M.
,
Szcześniak
M.
,
Kardel
I.
,
Berezowski
T.
,
Okruszko
T.
,
Srinivasan
R.
,
Vikhamar Schuler
D.
&
Kundzewicz
Z. W.
2017
Hydrological modelling of the Vistula and Odra River basins using SWAT
.
Hydrological Sciences Journal
62
(
8
),
1266
1289
.
doi:10.1080/02626667.2017.1321842
.
Ramani
R. S.
,
Patel
P. L.
&
Timbadiya
P. V.
2021
Key morphological changes and their linkages with stream power and land-use changes in the Upper Tapi River basin, India
.
International Journal of Sediment Research
36
(
5
),
602
615
.
https://doi.org/10.1016/j.ijsrc.2021.03.003
.
Resmi
S. R.
,
Patel
P. L.
&
Timbadiya
P. V.
2020
Impact of land use-land cover and climatic pattern on sediment yield of two contrasting sub-catchments in Upper Tapi basin, India
.
Journal of the Geological Society of India
96
,
253
264
.
https://doi.org/10.1007/s12594-020-1545-6
.
Rostami
A.
,
Raeini-Sarjaz
M.
,
Chabokpour
J.
,
Azamathulla
H. M.
&
Kumar
S.
2022
Determination of rainfed wheat agriculture potential through assimilation of remote sensing data with SWAT model case study: ZarrinehRoud Basin, Iran
.
Water Supply
22
(
5
),
5331
5354
.
https://doi.org/10.2166/ws.2022.160
.
Shah
S.
,
Duan
Z.
,
Song
X.
,
Li
R.
,
Mao
H.
,
Liu
J.
,
Ma
T.
&
Wang
M.
2021
Evaluating the added value of multivariable calibration of SWAT with remotely sensed evapotranspiration data for improving hydrological modeling
.
Journal of Hydrology
603
,
127046
.
https://doi.org/10.1016/j.jhydrol.2021.127046
.
Sharma
P. J.
,
Patel
P. L.
&
Jothiprakash
V.
2019a
Assessment of variability in runoff coefficients and their linkages with physiographic and climatic characteristics of two contrasting catchments
.
Journal of Water and Climate Change
10
(
3
),
464
483
.
https://doi.org/10.2166/wcc.2018.139
.
Sharma
P. J.
,
Patel
P. L.
&
Jothiprakash
V.
2019b
Impact of rainfall variability and anthropogenic activities on streamflow changes and water stress conditions across Tapi basin in India
.
Science of the Total Environment
687
,
885
897
.
https://doi.org/10.1016/j.scitotenv.2019.06.097
.
Sharma
P. J.
,
Patel
P. L.
&
Jothiprakash
V.
2021
Impact assessment of Hathnur reservoir on hydrological regimes of Tapi River, India
.
ISH Journal of Hydraulic Engineering
27
(
supp 1
),
433
445
.
10.1080/09715010.2019.1574616
.
Sharma
A.
,
Patel
P. L.
&
Sharma
P. J.
2022
Influence of climate and land-use changes on the sensitivity of SWAT model parameters and water availability in a semi-arid river basin
.
Catena
215
,
106298
.
https://doi.org/10.1016/j.catena.2022.106298
.
Shivansh
,
Patel
P. L.
,
2023
Effect of drainage area threshold on stream flow modelling using Arcswat
. In:
Hydrology and Hydrologic Modelling. HYDRO 2021. Lecture Notes in Civil Engineering
, Vol.
312
(
Timbadiya
P. V.
,
Patel
P. L.
,
Singh
V. P.
&
Sharma
P. J.
, eds).
Springer
,
Singapore
.
https://doi.org/10.1007/978-981-19-9147-9_4
.
Srivastava
A. K.
,
Rajeevan
M.
&
Kshirsagar
S. R.
2009
Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region
.
Atmospheric Science Letters
10
(
4
),
249
254
.
https://doi.org/10.1002/asl.232
.
Thampi
S. G.
,
Raneesh
K. Y.
&
Surya
T. V.
2010
Influence of scale on SWAT model calibration for streamflow in a river basin in the humid tropics
.
Water Resources Management
24
(
15
),
4567
4578
.
https://doi.org/10.1007/s11269-010-9676-y
.
USDA-SCS (United States Department of Agriculture–Soil Conservation Service)
1972
Section 4 Hydrology, Chapters 4–10
. In:
National Engineering Handbook
. USDA-SCS, Washington, DC, USA.
Wang
Z.
&
Cao
J.
2021
Spatial-temporal pattern study on water conservation function using the SWAT model
.
Water Supply
21
(
7
),
3629
3642
.
https://doi.org/10.2166/ws.2021.127
.
Williams
J. R.
1969
Flood routing with variable travel time or variable storage coefficients
.
Transactions of the ASAE
12
(
1
),
100
0103
.
doi:10.13031/2013.38772
.
Zhang
C.
,
Peng
Y.
,
Chu
J.
,
Shoemaker
C. A.
&
Zhang
A.
2012
Integrated hydrological modelling of small-and medium-sized water storages with application to the upper Fengman Reservoir Basin of China
.
Hydrology and Earth System Sciences
16
(
11
),
4033
4047
.
https://doi.org/10.5194/hess-16-4033-2012
.

Author notes

These authors contributed equally to this work as first authors.

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