The objective of this study is to assess the impacts of land cover change on
the hydrological responses of the Mahanadi river basin, a large river basin
in India. Commonly, such assessments are accomplished by using distributed
hydrological models in conjunction with different land use scenarios.
However, these models, through their complex interactions among the model
parameters to generate hydrological processes, can introduce significant
uncertainties to the hydrological projections. Therefore, we seek to further
understand the uncertainties associated with model parameterization in those
simulated hydrological responses due to different land cover scenarios. We
performed a sensitivity-guided model calibration of a physically
semi-distributed model, the Variable Infiltration Capacity (VIC) model, within a
Monte Carlo framework to generate behavioural models that can yield
equally good or acceptable model performances for subcatchments of the
Mahanadi river basin. These behavioural models are then used in conjunction
with historical and future land cover scenarios from the recently released
Land-Use Harmonization version 2 (LUH2) dataset to generate hydrological
predictions and related uncertainties from behavioural model
parameterization. The LUH2 dataset indicates a noticeable increase in the
cropland (23.3 % cover) at the expense of forest (22.65 % cover) by the
end of year 2100 compared to the baseline year, 2005. As a response,
simulation results indicate a median percent increase in the extreme flows
(defined as the 95th percentile or higher river flow magnitude) and mean
annual flows in the range of 1.8 % to 11.3 % across the subcatchments. The
direct conversion of forested areas to agriculture (of the order of 30 000 km2) reduces the leaf area index, which subsequently reduces the
evapotranspiration (ET) and increases surface runoff. Further, the range of
behavioural hydrological predictions indicated variation in the magnitudes
of extreme flows simulated for the different land cover scenarios; for
instance, uncertainty in scenario labelled “Far Future” ranges from 17 to 210 m3 s-1 across subcatchments. This study indicates that the
recurrent flood events occurring in the Mahanadi river basin might be
influenced by the changes in land use/land cover (LULC) at the catchment scale and suggests that
model parameterization represents an uncertainty which should be accounted
for in the land use change impact assessment.
Context and background
Land use/land cover (LULC) change induced by the rapid anthropogenic
activities is one of the major causes of change in hydrological and
watershed processes (Rogger et al., 2016). Alterations of
existing land cover types and land management practices in a catchment can
thereby significantly modify the rainfall path into runoff by changing the
hydrological dynamics such as surface runoff, baseflow, evapotranspiration
(ET), water holding capacity of the soil, interception and groundwater
recharge, thus reflecting a change in the water demand
(Berihun
et al., 2019; Bosch and Hewlett, 1982; Costa et al., 2003; Foley et al.,
2005; Garg et al., 2017; Hamman et al., 2018; Mao and Cherkauer, 2009;
Rogger et al., 2016; Zhang et al., 2014). For instance, developing countries
like India are facing rapid growth in population, which has prominent effects
on LULC dynamics through deforestation, rapid urbanization and agricultural
intensification, subsequently modifying the hydrological cycle in many river
basins of India. A recent analysis on global land cover changes for the
2000–2017 period (Chen et al., 2019a; IPCC, 2019) revealed 86 % changes in
land cover patterns in India with 82 % detected as croplands and the
remaining 4 % as forests (Chen et al., 2019a; IPCC,
2019). Therefore, a comprehensive understanding and evaluation of land cover
change impacts on hydrological processes are essential for decision makers
to plan environmental policies which focus on water resource allocation,
riparian ecosystem protection and river restoration (Chen
et al., 2019b; Chu et al., 2013).
Many studies have attempted to evaluate the hydrological responses to
different LULC patterns on specific geographic locations
(Abe
et al., 2018; Chu et al., 2013; Eum et al., 2016; Li et al., 2015; Ma et
al., 2010; Rodriguez and Tomasella, 2016; Viola et al., 2014; Woldesenbet et
al., 2017) including Indian river basins
(Babar
and Ramesh, 2015; Dadhwal et al., 2010; Das et al., 2018; Gebremicael et
al., 2019; Wilk and Hughes, 2002). Most of these studies used physically
distributed hydrological models (e.g., SWAT, VIC, MIKE-SHE) to simulate the
complex hydrological processes and to examine the impact of LULC changes on
those processes. Conventionally, this is done by calibrating and validating
the hydrological model against the observed data and then setting up that
single calibrated model for a baseline land cover scenario. The calibrated
model is then run for different land use scenarios, and subsequently the
differences in simulations are compared. However, it is widely recognized
that hydrological predictions obtained from a single calibrated model can be
biased; therefore, the measure of their reliability is always questionable
(Beven and Binley, 1992;
Huang and Liang, 2006). There may exist an “equally probable parameter set”
that can yield equally good or acceptable model predictions (also known as
behavioural models) which are identified due to the complex interactions
among the model parameters to represent the complex hydrological processes.
This is known as equifinality and is considered one of the main sources
of uncertainty in hydrological modelling (Her et
al., 2019). Recent climate change studies have acknowledged the
uncertainties stemming from model parameters; therefore, they take into
account these uncertainties while predicting the hydrological responses due
to climate change (Chaney
et al., 2015; Feng and Beighley, 2020; Her et al., 2019; Huang and Liang,
2006; Joseph et al., 2018; Mockler et al., 2016; Singh et al., 2014).
However, little is known about the contributions of model parameter
uncertainties to the land use change impacts; thus, very few studies
exist (Breuer
et al., 2006; Chen et al., 2019b) which reported that uncertainties
associated with the model parameters could significantly influence land
cover change impacts and hence should not be overlooked while modelling
hydrologic responses to LULC change.
This paper specifically focusses on the Mahanadi river basin, an easterly
flowing river basin in India. The eastern part of India is amongst the most
rapidly changing landscape over the country; specifically, Mahanadi river
basin has undergone drastic land cover changes in the last decades
(Behera
et al., 2018; Dadhwal et al., 2010). In this study, we address the following science
questions:
What are the expected impacts of LULC changes on the water balance of the
Mahanadi river basin?
How do these predicted impacts vary as a result of model parameter
uncertainties?
The major objectives of this study are
to predict the changes in hydrological processes owing to historical and
future changes in LULC and
to understand the contribution of uncertainty from hydrologic
parameterization to the hydrologic projections due to LULC change.
To this end, a large-scale physically semi-distributed hydrological model,
the Variable Infiltration Capacity (VIC)
(Liang et al., 1994), and historical and
future land cover scenarios from the Land-Use Harmonization 2 (LUH2)
database (Hurtt et al., 2018) are used to simulate the
discharge and other hydrological components at daily timescales in the
Mahanadi river basin. The ability of VIC to simulate the impacts of LULC
changes on hydrology are well documented in various research articles
(Garg
et al., 2017, 2019; Hurkmans et al., 2009; Mao and Cherkauer, 2009; Patidar
and Behera, 2019; Zhang et al., 2014).
We first perform sensitivity analysis of the model parameters and calibrate
the hydrological model using Monte Carlo simulations to identify behavioural
model simulations that implicitly account for the uncertainties from model
parameterization. Those behavioural models are then used to predict the
hydrological impacts due to different LULC scenarios. The land cover
scenarios used in this study are the most up-to-date scenarios, available from
version 2 of the Land-Use Harmonization (LUH2) dataset, which represents
future changes in the LULC based on Shared Socioeconomic Pathways (SSPs)
and climate radiative forcing outcomes (Representative Concentration Pathways, RCPs)
(Gidden et al., 2019). Previous studies (Breuer et
al., 2006; Chen et al., 2019b) have focussed only on the historical land use
scenarios to evaluate the hydrological impacts; however, and to our knowledge, this is the first study that uses applications of the VIC model in
conjunction with future land cover datasets produced under combined SSP and
RCP scenarios. While most past studies in other catchments used aggregated
(monthly) time steps to model the change, we use daily time steps to capture
the dynamics of daily flow variability. Moreover, analysis carried out in
most land use impact studies are typically limited to the streamflow,
missing an overall picture of the hydrological processes.
Research areaGeographical overview
The Mahanadi river basin is located in the eastern part of India (Fig. 1)
and drains an area of 141 589 km2, which nearly accounts for 4.3 % of
the total geographical area of India. The basin has a varying topography
with its lowest elevated area (-17 m) lying in the coastal reaches and the
highest elevated area (1323 m) in the northern hills. The basin is
characterized by tropical climate zone and receives rainfall from southwest
monsoons which commence in June and last till October. The average annual
rainfall is 1200 mm, with 90 % of the total annual rainfall occurring
during the monsoon months (Jin et
al., 2018). The mean annual discharge is 1895 m3 s-1. The basin is also
subjected to spatial variability in terms of receiving rainfall which has
resulted in floods in some parts of the basin and drought in others. Notice
that about 65 % of the basin is placed upstream of the Hirakud dam. The
Hirakud dam with a gross storage capacity of 8.136 km3 is the major
hydro-project in the river basin constructed in the year 1957 to alleviate
the flood problems and to serve multiple other purposes such as irrigation,
hydropower generation and supplying drinking water. Despite its significant
storage capacity, the large flows from upstream of the dam and middle reaches of
the catchment cause devastating floods during the monsoon in the deltaic
region of the basin.
The Mahanadi river basin boundary and the analysed flow gauges
and their catchments. Abbreviations for catchment names are Ba – Basantpur,
Ka – Kantamal, Ke – Kesinga, Su – Sundergarh and Sa – Salebhata.
About 48 % of the total area is under agriculture, out of which
30 % is cropped during the kharif season or monsoon (June–October), and 15 %
is under double or triple irrigation. The remaining 3 % of the area is
cropped during rabi and zaid seasons (winter and summer, respectively).
Deciduous Broadleaf Forest (DBF) being dominant among other forest types,
covers 25 % of the basin area (Fig. 2a). Built up, plantation,
grassland, shrubland, water bodies and other forest types constitute the
rest (22 %) of the basin area. Comparison of the local historical LULC maps
of 2005 and 2014, obtained from the National Remote Sensing Centre (NRSC),
shows an increase in the agricultural land from about 43 % to 48 % at the
expense of fallow land, built up areas and water bodies, while changes in
forest covers were insignificant. In addition, loamy and clayey are the
major soil types covering roughly 53 % and 42 %, respectively, of the
total basin area (NBBSS-LUP, India). Approximately 90 % of the basin has
moderately shallow to deep soil, having depths greater than 50 cm.
(a) LULC map of Mahanadi river basin from NRSC of year
2005. (b) Comparison of LAI values from MODIS, averaged over the
time period 2000–2015, and GLDAS.
Materials and methodsVariable Infiltration Capacity (VIC) model
The VIC model is a semi-distributed, land surface hydrologic model which
solves both water and energy balances within the grid cells
(Cherkauer and Lettenmaier, 1999). VIC maintains
sub-grid heterogeneity in land cover classes, i.e., divides each grid into
tiles based on the number of land cover classes, and also considers sub-grid
variability in the soil moisture storage capacity
(Liang et al., 1994). Surface runoff in
VIC is generated through an infiltration excess by using the Xiangjiang
formulation (Zhao et al., 1980) in the upper two soil layers.
Baseflow is generated from the third soil layer by applying the Arno
formulation (Franchini and Pacciani, 1991). Actual
evapotranspiration of each grid cell in VIC is obtained by summing up three
types of evaporation: evaporation from bare soil, evaporation from canopy
layer for each vegetation type and transpiration from different vegetation
types; it is then weighted by the fractional area of each vegetation class.
VIC computes potential evapotranspiration using the Penman–Monteith
equation. The amount of rainfall intercepted by the canopy is calculated as
a function of leaf area index (LAI).
To obtain the discharge at the basin outlet, the VIC model is coupled to a
stand-alone routing model (Lohmann et al., 1996). This routing
model follows a simple river routing scheme where runoff and baseflow are
first routed to the edge of the grid cells using an instantaneous unit
hydrograph and finally transported to the river/channel network using a
linearized St. Venant equation. More details about the structure and
formulations of the model can be found in the literature
(Gao
et al., 2010; Liang et al., 1994).
In this study, we implement the VIC model with three soil layers (known as
VIC-3L), version 4.2.d in the water balance mode at a daily time step and at
a grid resolution of 0.05∘ over the five subcatchments of the Mahanadi river
basin. Note that the VIC model is commonly employed at daily scales
especially when running with the water balance mode only
(Gou
et al., 2020; Hengade et al., 2018; Hurkmans et al., 2009). Flows are
routed to the subcatchments of Basantpur (Ba), Kantamal (Ka), Kesinga (Ke),
Sundergarh (Su) and Salebhata (Sa) (Fig. 1). We abstained from routing the
flow for the entire Mahanadi river basin due to the presence of a major
water management structure, Hirakud dam, at the middle reach of the basin.
Datasets
The key input data required by the VIC model are meteorological forcings
(precipitation, maximum temperature, minimum temperature and wind speed),
soil type, land cover information and topographic features. Topographical
features are determined using the 30 m CARTO-DEM (Cartosat-1 digital elevation model), a national DEM developed by ISRO (Indian Space Research
Organization) (Sivasena Reddy and Janga Reddy,
2015). The Mahanadi river basin is delineated and is converted into grid
format of resolution 0.05∘ constituting 4807 grids within the
basin area. Daily gridded precipitation (resolution 0.25∘) and
maximum and minimum temperature (resolution 1∘) for the time
period 1988–2010 are obtained from India Meteorological Department (IMD)
(Pai et al., 2014). Soil textures
are derived from the digitized soil map as provided by the National Bureau of
Soil Survey and Land Use Planning (NBSSLUP) (scale 1 : 250 000). Land cover
maps from two different sources, i.e., local and global, are used in this
study. The local LULC map is derived from National Remote Sensing Centre
(NRSC), India, of year 2005 (scale 1 : 250 000; resolution 56 m) and is
used in the model runs while performing sensitivity analysis, model
calibration and validation. Global land cover scenarios are obtained from
LUH2, which are used in model simulations for predicting impacts of land
cover changes on hydrological components. All LULC maps used in this study
are reformatted and reclassified into United States Geological Survey (USGS)
LULC types as required by the VIC model (Fig. 2a). The observed discharge
at daily scales at multiple gauges (Fig. 1) for the simulated time
(1988–2010) are obtained from the Central Water Commission (CWC), India, for
validating the simulated discharge.
Model parameters
We have selected 16 VIC model parameters (Table 1) for the sensitivity
analysis (SA). The choice of parameters was based on our preliminary
experiments and expected sensitive properties from previous studies (see
description below Table 1). Typical calibration in VIC involves only
streamflow-related parameters as also recommended by VIC model developers
(Gao
et al., 2010; Gou et al., 2020; Xie et al., 2007). However, a few studies
have reported that some vegetation parameters are sensitive to the runoff in
the VIC model (Demaria
et al., 2007; Joseph et al., 2018). Parameters subjected to SA in this study
include, among others, rarely implemented soil properties, such as bulk density
(BD) and fractional water content at wilting point (wpf) and at critical point
(wcrf); vegetation properties, such as architectural resistance (rarc) and
stomatal resistance (rmin); and routing parameters, such as velocity (v) and
diffusion (diff). A multiplier of wcrf is used to compute wpf to meet the
criteria that soil moisture at wilting point should always be less than soil
moisture at critical point, and the multiplier is tested for sensitivity
rather than the actual parameter. A similar approach is followed by
Rosolem et al. (2012) while testing
sensitivity of parameters in a land surface model. Feasible ranges (minimum
and maximum values) of soil parameters (BD, wcrf, ksat, Exp) are obtained based on
average hydraulic properties of USDA soil textural classes
(Cosby et al., 1984; Rawls et
al., 1998; Reynolds et al., 2000) considering only the dominant soil
textures within the basin. Ranges for the rest of the soil parameters are
based on suggestions from the VIC model developers and published studies.
Feasible ranges of the vegetation parameters are obtained based on the
recommended ranges provided in the Land Data Assimilation System (LDAS)
values for the dominant vegetation types in the basin. Our preliminary
experiments suggest canopy height is not sensitive; hence, roughness length
(RL) and displacement height (Disp), which are computed from canopy height,
are not accounted for in SA.
VIC and routing model parameters tested for sensitivity
analysis and feasible ranges.
ParametersDescriptionUnitsMinimumMaximumSoil parameters wcrfFraction of water content at critical pointb–0.400.60wpfc(wpf=M⋅ wcrf)Fraction of water content at wilting pointb–0.500.99BDBulk density of soil (used in VIC estimation of porosity)bkg m-313501550ksatSaturated hydraulic conductivitybmm d-1240840ExpParameter characterizing the variation of saturated hydraulic conductivity with soil moistureb–1030d1Thickness of first soil layeram0.010.3d2Thickness of second soil layeram0.313.5d3Thickness of third soil layeram0.313.5dsmaxMax velocity of baseflowamm d-110-4101.48dsFraction of max velocity of baseflowa–10-4100binfParameter to describe the Variable Infiltration Curvea–10-4100.6wsFraction of maximum soil moisture of the third layera–10-4100Vegetation parameters rarcArchitectural resistancebs m-12070rminMinimum stomatal resistancebs m-1100170routing vFlow velocitybm s-10.13diffFlow diffusivitybm2 s-15005000
Parameter names in bold are sampled on log domain. a indicates
parameters that are suggested by VIC model developers as the most sensitive
parameters (Gao et al., 2010). b indicates
parameters suggested in the literatures to be tested for sensitivity
(Demaria
et al., 2007; Gou et al., 2020; Joseph et al., 2018; Yanto et al., 2017). c wpf is analysed based on its multiplier (i.e., the M term in
wpf parameter's equation). Although description and units refer to
actual parameter in VIC, parameter range represents the multiplier values
(instead of actual parameter).
In addition, the LAI is an important vegetation factor, having substantial
control over the water balance by directly influencing the ET rates
(Gao et al., 2010;
Matheussen et al., 2002). LAI is specified at a mean monthly basis in VIC.
We compared the monthly-mean LAI averaged over the time period 2000–2015
from MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua/Terra with the LAI values from the GLDAS (Global Land Data Assimilation System) database for the river
basin. We observed that the monthly-mean LAI of all the LULC types from
MODIS captures the phenological characteristics more realistically than the
GLDAS LAI (Fig. 2b), which shall have further implications on water
balance. We find that the range of MODIS LAI obtained for each LULC type are well
in agreement with the LAI values obtained in the nearby Ganga river basin in
India (Patidar and Behera, 2019).
Another important factor linking vegetation characteristics to hydrological
processes in VIC is the root zone distribution. Typically, root zone
allocation in VIC requires user-defined root zone depths and fractions for
each land cover type that are kept fixed during the calibration process. We
derived root zone depths and estimated the fractions of roots in each zone
following Zeng (2002) for each vegetation
type, and we used a simplified approach to vary the root zone distributions
with respect to the soil depths during calibration. This ensures root zone
properties vary for different model calibration with a reduced number of
parameters, hence providing a more manageable calibration strategy. For
details on our root allocation approach, please refer to the Supplement
(Sect. S1).
Experimental designMorris method for sensitivity analysis
SA of the chosen 16 VIC-3L parameters (Table 1) is conducted using the
Morris (1991) method. This method requires Monte Carlo
simulations where the model is run with a specified number of samples and
measures the change in the model output by varying one parameter at a time.
We used the one-at-a-time Latin hypercube sampling (LHS-OAT) strategy to form a
total number of 1200 model parameter sets. This method proposed two
sensitivity measures: (1) the mean (μ) of the elementary effects,
which estimates the direct effect of the input parameter on model output, and (2) the standard deviation (σ) of the elementary effects, which estimates
the interaction between the input parameters on the model output. We tested
the sensitivity of model parameters on the Kling–Gupta efficiency (KGE)
metric (Eqs. 1–3) (Gupta et al.,
2009), computed using observed daily streamflow values over 20 years
(1990–2010) of simulation period.
1KGE=1-(r-1)2+(α-1)2+(β-1)2;2α=σsimσobs,3β=μsimμobs,
where r is the linear correlation between observed and simulated discharge,
α is an estimate of flow variability error and β is
a bias term. σsim and σobs are standard deviations in
simulated and observed discharge, respectively. μsim and μobs are mean of simulated and observed discharge, respectively.
We first visually inspect SA results and assume a screening threshold value
for the sensitivity index, below which the parameters can be regarded as
either completely insensitive or less influential. This is a common practice
followed in previous SA studies (Gou
et al., 2020; Sarrazin et al., 2016; Tang et al., 2007; Vanrolleghem et
al., 2015). Next, to achieve a more objective screening convergence result,
we compute the width of the 95 % confidence interval of the sensitivity
indices (Herman et
al., 2013; Wang and Solomatine, 2019) and then use maximum width of the
95 % confidence interval, as a statistic
(Sarrazin et al., 2016), across the lower
influential input to verify if the screening convergence has been reached. For a
detailed explanation about the steps we took for the SA experiments, please
refer to the Supplement (Sect. S2).
Model calibration and validation
Next, we calibrate sensitive parameters separately on a subbasin level for
the time 1990–2000 with a 2-year warm-up period (1988–1999), using a
sequence of Monte Carlo simulation, by generating 5000 near-random parameter
sets from within the specified range using the Latin hypercube sampling method (LHSM) with uniform distribution.
We use KGE (Eq. 1) as the objective function to assess the model performance
in the calibration period. The KGE metric balances the contribution to the
error coming from all three main components, namely correlation (e.g.,
timing/dynamics), variability (e.g., seasonality), and systematic bias, and
it is now a widely used metric in hydrometeorological studies
(Gupta et
al., 2009; Knoben et al., 2019). KGE ranges in [-∞, 1] with larger
values indicating better performance. Additionally, we use the percent bias (PBIAS) to
evaluate our model performance, especially to account for the high flow
conditions. We adopt a common practice of selecting the best model
simulations by using a top certain percentage of the total simulations
(Chaney
et al., 2015; Mockler et al., 2016). This is relevant in our study as
choosing model simulations based on a particular KGE score is subjective
given that the behavioural performance, as well as the behavioural
parameters, vary across the subcatchments. Therefore, we first assess the
performance of top 10 %, 5 % and 2 % of model simulations at every
subbasin and choose the top 2 % based on overall model performance across
the subcatchments, hence not compromising the performance quality and
also accounting for equifinality. These behavioural models are further used
to simulate streamflow in the validation period (2001–2010) for all the
subcatchments.
LULC scenarios
All the simulations in the calibration and validation period are performed
using a static local LULC map of year 2005 derived from NRSC. Simulations
using this land use map shall be termed NRSC2005 henceforth. Next, we
used a set of land use scenarios based on Shared Socioeconomic Pathways (SSPs) and
Representative Concentration Pathways (RCPs) from the recently released
Land-Use Harmonization project (LUH2) dataset (releases LUH2v2h and
LUH2v2f) for the time periods of 850–2005 and 2015–2100, respectively
(Hurtt et al., 2018) (see Table S2, Supplement). The LUH2
approach estimates the gridded land use fractions, annually at a resolution
of 0.25∘. The land use fraction maps are available for each land use type at a
resolution of 0.25∘. So, we have first obtained LUH2 fraction maps of
different LULC types for the Mahanadi basin extent at a resolution of 0.25∘ and
further regridded to VIC grid size of 0.05∘. Next, to run the VIC model, we
have prepared a vegetation parameter file where we included the fractional
coverage of all LULC types for each grid cell ensuring that each grid will
contain more than one vegetation type. The land use classes are reduced to
simplify our model application and consequently remapped to the VIC land
use classes by assuming all primary (forested or non-forested) and secondary
(forested and non-forested) land to Deciduous Broadleaf Forest (DBF);
managed pasture and rangeland are considered grassland, and all crops are
merged into a single class labelled “Cropland”. Urban land and water bodies are
retained (see Table S3, Supplement). It is worth mentioning that the
“potentially non-forested secondary land” class in the LUH2 datasets matched
to the forested areas in NRSC2005 and hence both mapped into DBF, which is the dominant forest type in the basin (Fig. S5 in the Supplement).
We used the behavioural models to simulate discharge for the baseline
scenario using land cover map from LUH2 of year 2005 so as to attain more
confidence in the future scenarios. We compare LULC maps, NRSC2005 and
LUH2005 (Fig. 3) and observe spatial patterns of the most dominant
land use classes; classes Cropland (CL) and Forest (F) show a similar spatial
distribution and have comparable aerial coverage. The only notable
difference in both maps is that the Barren Ground (BG) class is missing in
LUH2005. Table 2 shows the percentage of area covered by each land use
class in the basin. Note that we will refer to DBF as Forest (F)
henceforth.
Comparison of spatial patterns of land cover types from
NRSC and LUH2 for the baseline year, 2005. All land cover classes shown here
are resampled to the model grid resolution of 0.05∘. The colour bar represents
the fraction of area covered by each land cover type.
Percent of each land use type in NRSC2005 and LUH2005 in
the entire Mahanadi river basin (WB – Water Body; ENF – Evergreen
Needleleaf Forest; DBF – Deciduous Broadleaf Forest; GL – Grassland; CL –
Cropland; U – Urban; BG – Barren ground).
Among the future scenarios, owing to the large computational demand of our
simulations, we only considered the worst case scenario, RCP3.4 SSP4,
which resulted in maximum change in the land cover fractional area (Fig. 4). For our study, we have not taken into account the actual uncertainty due
to the land cover scenarios. However, the percentage of land cover change
relative to the baseline from other LUH2 scenarios is either negligible or
comparable to our chosen scenario. Therefore, our chosen scenario which
shows the maximum changes in land cover will likely produce the largest
impact.
(a) Fraction of catchment area occupied by land use
classes for scenario RCP3.4 SSP4. (b–d) Land cover scenarios from LUH2
(resolution – 0.25∘) for years 2015, 2050 and 2100 used in this study. LUH2
land cover classes shown here are resampled to the model grid resolution, and
only the predominant class is shown here for clarity. For actual model
simulations, VIC accounts for the individual proportion for each land cover
type at each grid point.
Land cover changes and fractional area covered in other future scenarios are
shown in Fig. S6 in the Supplement. Four distinct years (i.e., four
distinct land cover maps) have been chosen for this study: 2005 (Baseline),
2015 (Present), 2050 (Near Future) and 2100 (Far Future) to study the
impacts of LULC change in the Mahanadi river basin. A sharp decrease in the
forest cover is observed at the expense of agriculture in the years 2050 and
2100 (Fig. 4). We run the behavioural models three times using the
individual LUH2 datasets: (1) with land use map “LUH2015”, termed as the
“Present” (P) scenario; (2) with land use map “LUH2050”, termed as the “Near
Future” (NF) scenario; and (3) with land use map “LUH2100”, which is termed as
the “Far Future” (FF) scenario. To account for the extreme hydrological
effects that these changes could cause, two hypothetical scenarios are
framed: (1) the “All Cropland” (CL) scenario where all the grassland and forest
areas are transformed into cropland and (2) the “All Forest” (F) scenario where all
the cropland and grassland areas are transformed into forest. The urban and
water bodies in these hypothetical scenarios are retained as per the
baseline scenario. Notice that the daily meteorological forcing used in all
the model simulations is the same and obtained from the current climatology
(i.e., 1990–2010). Here, we focus on identifying the impacts on hydrological
responses mainly by applying individual land cover scenarios. Therefore, any
changes observed in the predicted hydrological components will be only
attributed to changes in LULC. It is also worth mentioning that running
model simulations with different land cover scenarios would not directly
impact the soil parameters identified in our chosen behavioural models. That
is because all soil-related parameter values in VIC are assigned solely
based on soil textures. The percent areas covered by each land use class
at all subcatchments across the scenarios are shown in Table 3.
Land cover area change across all subcatchments of
the Mahanadi river basin.
Ranges of percent change, change in flows, and uncertainty
(i.e., difference between max and min predicted flow) in extreme and mean
annual flows in all the scenarios with respect to the baseline scenario.
Mean annual extremeBaKaKeSuSaNear FutureChange (%)2.3 to 5.51.4 to 4.71.3 to 2.74.7 to 10.72.7 to 4.3Change (m3 s-1)132 to 28962 to 16642 to 7732 to 7527 to 41Uncertainty (m3 s-1)157104364114Far Future Change ( %)2.4 to 6.51.4 to 5.61.6 to 3.56 to 15.43 to 4.7Change (m3 s-1)137 to 34763 to 19551 to 10042 to 10928 to 45Uncertainty (m3 s-1)210132496717All Cropland Change (%)2.4 to 1.21.2 to 8.62.1 to 5.76.5 to 20.55 to 8.5Change (m3 s-1)124 to 49651 to 30167 to 16445 to 14749 to 81Uncertainty (m3 s-1)3722509710232All Forest Change (%)-4 to -14.4-2 to -11.4-2.6 to -6.6-15.8 to -41-13.5 to -22Change (m3 s-1)-218 to -712-85 to -400-86 to -190-109 to -289-131 to -213Uncertainty (m3 s-1)49431510418082Mean annual flowsBaKaKeSaSuNear FutureChange (%)3.7 to 7.62.5 to 6.132.4 to 4.24.9 to 9.73.4 to 4.6Change (m3 s-1)21 to 318.6 to 165.2 to 7.534 to 612.6 to 3.3Uncertainty (m3 s-1)107.42.3270.7Far Future Change (%)3.4 to 7.92.12 to 6.53.4 to 4.66 to 13.23.24 to 4.6Change (m3 s-1)19 to 32.67.3 to 16.85 to 8.84 to 8.32.4 to 3.3Uncertainty (m3 s-1)13.69.53.84.30.9All Cropland Change (%)2.8 to 8.51 to 7.72.1 to 5.66 to 164.1 to 6.6Change (m3 s-1)15.6 to 353.4 to 204.7 to 124.2 to 103.1 to 5Uncertainty (m3 s-1)19.416.67.35.82All Forest Change (%)-4.6 to -14.34-2.4 to -11.1-2.9 to -7.2-14.5 to -34.2-12 to -18.6Change (m3 s-1)-26.2 to -59-8.2 to -29-6.4 to -15.8-10.2 to -21.3-9.1 to -13.3Uncertainty (m3 s-1)3320.89.4114.2ResultsSensitivity analysis, model calibration and validation
It is to be noted that SA is conducted for all subbasins individually; hence,
the Morris screening results obtained for each subbasin are independent of
each other. However, we observe that the non-influential parameters match
closely with each other across subbasins (Fig. S2). Based on the Morris
sensitivity measures, there are six sensitive (or influential) parameters,
namely dsmax, d2, binf, v, ws and ds. The rest of the parameters (rmin, d3, wcrf, wpf, rarc, Exp, BD, diff, d1, ksat) are
either relatively non-influential or have negligible impact in the KGE
performance. d2 is the most important soil layer, probably because it is the
thickest soil layer where most of the roots are found, which is expected to
exert strong controls on ET. Dsmax, ds and ws are the baseflow-related parameters,
interlinked with each other, associated with the third soil moisture layer
d3, having a higher impact on low flows. We discard a common set of parameters
prior to the model calibration based on weighted average of the sensitivity
indices of the subbasins. The weights are assigned based on catchment area.
Figure 5 shows the influential and non-influential parameters for the entire
basin. The total number of model simulations performed is sufficient to
achieve the stability of the screening results (see Fig. S3, Supplement).
More details on the Morris screening results are given in the Supplement
that accompanies this paper (Sect. S2.2).
Sensitivity indices (mean and standard deviation) of
the Morris method for VIC-3L parameters for the Mahanadi river basin (computed based
on weighted average of all subcatchments). Parameters, top to bottom, listed
on the right side are in ranking order, highest to lowest influential
parameters, respectively, based on mean of EEs. The dashed red vertical line is
the screening threshold.
(a) Boxplot showing KGE range for calibrated, validated
and baseline scenario simulations. (b) Parallel coordinate plot representing VIC-3L
behavioural parameterization for all subcatchments obtained during model
calibration. Lines in black are simulations where KGE lies within top 2 %
i.e., behavioural simulations, and lines in grey are non-behavioural
simulations. Behavioural KGE values at Ba, Ka, Ke, Su and Sa range from 0.83–0.88, 0.85–0.88, 0.81–0.84, 0.74–0.76 and 0.62–0.66, respectively.
Parameters are defined in Table 1.
Figure 6 shows the performance of VIC with respect to KGE in the calibration
and validation period for all the subcatchments in the highest order of
their catchment size. The KGE ranges for the calibration and validation of
daily streamflow for all subcatchments are listed in Table S4 in
the Supplement. Overall, evaluation result suggests that the model
reproduced the observed flows remarkably well with the median KGE values of
0.85, 0.86, 0.82, 0.75, and 0.63 in calibration and 0.77, 0.82, 0.72, 0.60, and 0.59
in validation at Basantpur, Kantamal, Kesinga, Salebhata, and Sundergarh,
respectively. However, we observe a relative reduction in the daily KGE
values at the smaller subcatchments (Sundergarh and Salebhata) in both
calibration and validation periods. The PBIAS values obtained in the
calibration period (Fig. S8, Supplement) indicate that the model tends to be
more biased (positively) as the catchment size decreases and that the
largest catchment, Basantpur, is least biased. The median PBIAS values at
Sundergarh and Salebhata are +9 % and +23 %, respectively, in the
calibration period and +19 % and +55 % in the validation period. It
is to be noted that subbasins analysed are effected by human intervention, and observed
streamflow values are controlled by minor reservoirs and dams which will affect the
VIC simulations especially in the smaller subcatchments. Moreover,
non-consideration of groundwater recharge and irrigation in VIC can also
possibly affect performance at smaller subcatchments. Supplement Figure S7 shows that the models reproduced the daily and monthly flows
consistently when compared to the observed flows in both calibration and
validation periods.
Figure 6b shows that the distribution of behavioural parameters within their
respective variability ranges differs from one parameter to another as well
as across subcatchments. The behavioural models at all subcatchments are
scattered nearly across the entire range of parameter space for ds and ws,
reflecting high effect on modelled streamflow through their interaction with
other parameters. Contrarily, behavioural parameter ranges of binf, dsmax, d2 and v are
relatively constrained across subcatchments, towards either higher, mid or
lower values, indicating direct influence of these parameters on the
behavioural simulations. For instance, higher values of d2 and v, lower values of
dsmax and mid values of binf resulted in the behavioural model simulations at the
smaller subcatchment, Salebhata. Thickness of second soil layer, d2, is the
most identifiable parameter across all subcatchments.
Baseline scenario performance
We compare the performance of calibrated VIC models in the baseline scenario
(using LUH2005) against the validation performance (using the NRSC2005) for
the period 2001–2010. The boxplots in Fig. 6a show daily KGE values for
the baseline and validation simulations for all subcatchments studied here.
The median KGE values for the baseline at Ba, Ka, Ke, Su and Sa are 0.62,
0.64, 0.58, 0.62 and 0.72, respectively. The model performed relatively
well at the smaller subcatchments Sa and Su in the baseline, whereas
decline in the performance is observed at subcatchments Ba, Ka and Ke.
PBIAS values (Fig. S8, Supplement) indicates that baseline simulations
are more biased (negatively) than validation simulations at bigger
catchments. The median PBIAS values at Ba, Ka and Ke are -28 %, -29 %
and -33 %, respectively. This underestimation can be attributed to the
absence of 12 % Barren Ground in the baseline land cover, which is
replaced by croplands (4 %), forests (5.02 %), grasslands (4.57 %). The
increase in flows due to the increase in cropland is compensated by the
decrease in flows due to the increase in forest. Therefore, the
underestimation in the simulated flows using LUH2005 may result from the
increasing grasslands which increased LAI, thus resulting in an increase in
ET and decrease in surface runoff, respectively. Contrarily, a slight
positive bias of 3 % is observed at the smallest subcatchment (Sa) in the
baseline simulation, compared to +55 % in the validation simulation. KGE
values obtained across calibration, validation and baseline periods indicate
an overall good performance of the basin as per the existing studies using
KGE as a performance metric (Knoben et al.,
2019). Overall, baseline land cover map LUH2005 shows comparable model
performance against local land cover map NRSC in the historical period
with the model being able to capture the seasonality and land use/land
cover dynamics while simulating the daily flows.
LULC impacts and uncertainties
Figure 7 shows percent change in annual average of extreme flows (i.e., 95th
percentile or higher) for the time 2001–2010 in scenarios NF, FF, All
Cropland (CL) and All Forest (F) with respect to baseline scenario for the
behavioural models. The range of percent change represents the related
uncertainty in model predictions arising from the behavioural model
parameters. We observe an insignificant positive change in projected extreme
flows in the present (P) scenario despite a major increase, 6 % to 36 %,
in croplands replacing forests across four out of five subcatchments (not
shown Fig. 7). We observe a prominent increase in the extreme flows at all
subcatchments in both future scenarios (NF and FF). The projected change in
extreme flows in NF ranges between 1.3 % and 10.7 % across the
subcatchments. The median percent change in the NF scenarios at
subcatchments Ba, Ka, Ke, Su and Sa are 3.6 %, 2.6 %, 1.8 %, 8.1 %
and 3.8 %, respectively. This increase in extreme flows in NF can be
attributed to the reduction in forest cover (-20 % to -42 %) at the
expense of cropland (+7 % to +48 %) across the subcatchments.
Percent increases of slightly higher magnitudes are observed in the FF scenario
in response to further increase in croplands. The projected changes in
extreme flows in FF ranges between 1.4 % and 15.4 % across the
subcatchments. The median percent change in the FF scenario at subcatchments Ba,
Ka, Ke, Su and Sa are 4 %, 2.8 %, 2.3 %, 11.3 % and 4.1 %,
respectively, in response to reduction in forest cover (-19 to -50 %) at
the expense of cropland (+19 % to +54 %) across the subcatchments. As
anticipated, maximum percent increases in the extreme flows (1.2 % to 20.5 %)
are observed in the hypothetical All Cropland scenario where all forests and
grasslands are replaced by cropland and maximum reduction (-2 % to -41 %)
observed in the All Forest scenario where all the croplands and grasslands are
converted to forests. The projected percent changes in mean annual flows are
slightly higher than the extreme flows across all scenarios and
subcatchments. The median values in both future (FF and NF) and CL scenarios show
slightly higher positive percent change in the range of 3 % to 11 % and
higher negative percent change, -5 % to -25 %, in the F scenario.
(a, left) Percent change in extreme flows (i.e., 95th
percentile or higher).(a, right) Change in extreme flows (in m3 s-1).(b, left) Percent change in mean flows. (b, right) Change in mean flows (in
m3 s-1), averaged annually over 2001–2010 in the Near
Future (NF), Far Future (FF), All Cropland (CL) and All Forest (F) scenarios with
respect to baseline scenario for all the subcatchments. Note that the daily
meteorological forcing used in all the model simulations are obtained from
the current climatology (i.e., 1990–2010). The results are shown for the
behavioural model simulations obtained through calibration.
Maximum increments in extreme flows and annual flows across all scenarios are recorded at the largest subcatchment Basantpur, which are in the range of 194
to 496 and 31 to 35 m3 s-1, respectively. The maximum
reduction of 712 and 59 m3 s-1 is observed in the All
Forest scenario at Basantpur. Much less change in terms of magnitudes is
observed in the annual flows compared to the extreme flows. This can be
explained by the fact that the basin receives approximately 85 % of the
total annual rainfall during the monsoon months (June–September). Therefore, with
negligible changes occurring during the rest of the year, changes in extreme
flows occurring only during the monsoon months are masked out when computed
for the entire year. We further computed the difference between maximum and
minimum values (ranges) of projected extreme flows as a measure of the
amount of uncertainty contained in ensemble predictions made using land
cover scenarios and multiple (behavioural) parameter sets (Table 4).
Uncertainty in Far Future scenario ranges from 17 to 210 m3 s-1
across subcatchments. Among all the scenarios, maximum uncertainty is
observed in the hypothetical All Forest scenario (-82 to -494 m3 s-1) followed by All Cropland scenario (32 to 372 m3 s-1). Overall the uncertainty of hydrological
model parameterization is observed at the largest subcatchment Basantpur and
decreases with respect to the decrease in the catchment size.
We analysed the water balance components to understand the factors causing
changes in the streamflow. Overall, we found that the increase in the
mean annual flows is caused by the increment in runoff and reduction in
ET across all subcatchments. Positive median changes are observed in runoff
(NF, FF and CL), ranging between (2.8 to 14) % and negative
changes of (-4 to -37) % in the F scenario. Negative median changes are
observed in ET in scenarios (NF, FF and CL) ranging between (-1.4 to -3.4) % and positive changes of (1.9 to 7.8) % in the F scenario. Removal of
forests decreases the LAI of the natural vegetation and hence decreases ET.
Moreover, the removal of forest cover reduces the root water uptake by
plants, which increases the water content of the second and third layer of
the soil. The top, thin soil layer in the VIC model helps in partitioning the
rainfall amount into direct runoff and the amount entering the soil.
Therefore, the increase in the cropland results in more direct runoff, thus
reducing the soil moisture content in the first soil layer. The increase in
runoff is not significant, despite the occurrence of major deforestation in
the future scenarios. This is because the decrease in ET due to forest
removal is compensated as increment in croplands also leads to a major
increase in ET rates, which is why we do not see a sharp reduction in the ET
rates. Negligible changes are observed in baseflow, while slight increase in
total soil moisture is noticed across the subcatchments (not shown). The
water balance indicates that 15 % to 21 % of precipitation is direct runoff
and 64 % to 80 % is ET across all subbasins and all land cover scenarios,
whereas negligible baseflow and soil moisture changes are observed. This is
probably because the third soil moisture layer in the model does not reach
saturation to cause the non-linear baseflow, as precipitation in the basin
is highly concentrated in only 3 to 4 months in monsoon, and the rest of
the year remains dry.
Discussions
Performing a comprehensive sensitivity analysis and model calibration
enhances the accuracy of hydrological predictions, which subsequently
improves the representations of changes in the hydrological regime due to
land cover changes. Our SA results are in agreement with existing studies
conducted on several basins using VIC, which show binf and d2 are the most
sensitive parameters
(Demaria
et al., 2007; Gou et al., 2020; Lilhare et al., 2020; Yeste et al., 2020).
Moreover, not all the parameters recommended for calibration by VIC model
developers (binf, d1, d2, d3, ds, dsmax and ws) are sensitive to the basin runoff, which is also in line with
findings of Bao
et al. (2011), Demaria et al. (2013) and Gou et al. (2020) for other
basins. For instance, first- and third-layer soil depths (d1 and d3) are not
found sensitive in this study. d1 is the thinner topmost soil layer, having not
much control on ET and subsurface processes. d3 is probably not sensitive as
most of the roots are present in the second soil layer, hence not
contributing to the soil moisture uptake through the roots. We found that
soil properties impose greater control on model performance than the
vegetation parameters. However, while varying soil depth influences the ET
rates by posing indirect influences on both timing and magnitude of the soil
water available for ET, varying root depth and fractions (using our root
zone allocation approach) has provided substantial control over the water
balance by directly influencing the ET rates, thereby improving KGE (not
shown). The weakness in reproducing flows at smaller subcatchments in
Mahanadi basin is also reported previously in some studies
(Kneis
et al., 2014; Mishra et al., 2008).
LUH2 is a new dataset that is not yet extensively used in basin-scale hydrology. A
recent study by Krause et
al. (2019) predicted worldwide increment in runoff (67 %) and a variable
response of ET across different land use scenarios using LUH2 dataset. The
major land cover changes in the future scenarios in Mahanadi basin (as
predicted by LUH2) agrees with Behera et al. (2018), wherein they reported
a prominent conversion of DBF to croplands in year 2025.
Our findings indicate an increase of 27–496 m3 s-1 in extreme flows
and 2.6–35 m3 s-1 in annual mean flows due to deforestation, across
the subbasins and scenarios (including the hypothetical cropland scenario).
These increasing trends are consistent with other studies in the Mahanadi
river basin in India (Dadhwal
et al., 2010), neighbouring basins
(Das
et al., 2018; Kundu et al., 2017) and elsewhere (Abe
et al., 2018; Berihun et al., 2019; Cornelissen et al., 2013; Costa et al.,
2003). Kundu et al. (2017)
found an increase in runoff and decrease in ET due to the expansion in
projected agricultural land in Narmada river basin in India.
Das et al. (2018) predicted
that deforestation, urbanization and cropland expansion in eastern river
basins of India in the future would increase runoff and baseflow and
decrease ET%. It should be noted that 15 % of the agricultural land in
the basin is under irrigation effects; however, this version of VIC
(version 4.2.d) does not represent irrigation. Therefore, reduction in ET
rates due to conversion of forest to cropland could be compensated by the
moisture available due to the irrigation during the non-monsoon season.
However, this may not have a significant effect on the assessments of
impacts on runoff, especially on extreme flows, because those events are
likely to be related to the monsoon season, where the effect of irrigation
is minimum.
Percent change in (a) mean runoff (b) mean ET
averaged annually over the time (2001–2010) in the Near Future (NF), Far
Future (FF), Cropland (CL) and Forest (F) scenarios with respect to baseline
scenario for all the subcatchments. Note that the daily meteorological
forcing used in all the model simulations is obtained from the current
climatology (i.e., 1990–2010). The results are shown for the behavioural
model simulations obtained through calibration.
We found a small change in mean annual discharge as well as in water balance
components despite a major change in land cover. Our results correlate well
with several research studies (Ashagrie
et al., 2006; Fohrer et al., 2001; Hurkmans et al., 2009; Kumar et al.,
2018; Patidar and Behera, 2019; Rogger et al., 2016; Viglione et al., 2016;
Wagner et al., 2013; Wilk and Hughes, 2002), wherein they have reported that
the impacts of land cover change on water balance components in a
large-scale river basin are too small to be detected due to the compensation
effects. Wilk and Hughes (2002) showed that removal of large
forests led to little or no changes in annual runoff in large heterogeneous
catchments in South India. Patidar and Behera (2019)
in a recent study in a large river basin in India reported that the
conversion of forest to agriculture may not alter the water balance
significantly as the impacts on ET and runoff cancel out at the basin scale.
The range of these hydrological estimates (Figs. 7, 8 and Table 4)
provides more straightforward and explicit quantification of uncertainty
than other statistical measures such as variance or interquartile ranges
(Her et al., 2019). Our results suggest that even a
small set of calibrated models can predict a wide range of flows through
different hydrological processes occurring within the basin; therefore,
the impacts of uncertainty derived from model parameters on the relative
changes cannot be neglected. The uncertainty due to model parameters did not
alter the trend of changes in extreme flow, mean annual flow and
hydrological components due to land use change in comparison to the baseline
simulations. However, a considerable variation is observed especially in the
magnitudes of extreme flows simulated for the different land cover
scenarios. For instance, the competing interactions among ds and ws led to the
varying hydrological processes occurring within the basin, thereby affecting
the partition of water in the soil column. Similar conclusions are outlined
in Chen et al. (2019b) that
the projected monthly and annual flows simulated for different land use
scenarios were having significant uncertainty due to model parameterization.
In addition, we found that the trends within the scenarios especially in the
mean annual flows, runoff and ET are not consistent. For instance, we expect
the increase in flows to be more in Far Future scenarios than Near Future,
given that the increase in agricultural land in the Far Future is relatively
more. However due to different parametrization, some models predicted
decrease in Far Future flows relative to Near Future (Fig. 7). This
clearly indicates that the impact of land use could be biased when a single
model prediction is used, as the impacts could be potentially hidden within
simulation uncertainty derived from model (Chen et al., 2019b).
Only a small percentage of model simulations (2 %; 100 model simulations)
with relatively high daily KGE scores (KGE > 0.8 at 3 out of 5
subcatchments) were used for assessing the impacts, yet significant
variations in extreme flow magnitudes and trends (in some cases) are
observed. Therefore, selecting models with relatively lower KGE values might
have led to larger uncertainty bounds and inconsistent trends in the relative
change. Equifinality in hydrological modelling and its influence on
hydrological analysis of climate change has been discussed in several
studies. However, its influence on hydrological analysis of land cover
change has not been studied enough to provide a clear idea about the
contributions of model parameter uncertainty to hydrological projections.
Our results thus underline the importance of considering model uncertainty
and consequently equifinality while modelling the land cover change impacts.
Conclusions
In this study an attempt is made to quantify the hydrologic response of the
subcatchments of the Mahanadi river basin, owing to different land cover
scenarios obtained from the LUH2 dataset, through the implementation of a
sensitivity-based calibrated semi-distributed hydrological model. Our
findings offer insights into the plausible hydrological scenarios in future at a
river basin level, which is a crucial step forward for a developing country
in the context of today's increasing focus on integrated water resources
management (IWRM) in river basins. Overall, VIC captured the observed
daily flows well in calibration, validation and baseline periods across
subcatchments. Deforestation at the expense of cropland dominated the land
cover change processes across all scenarios and subcatchments, which has led
to an increase in the extreme flows and mean annual flows. Analysis of other
hydrological components have shown that the increase in flows is caused by
the increase in runoff and decrease in ET. The uncertainties due to model
parameterization in land use change impacts varies from one subcatchment to
another. The uncertainties did not alter the trend of changes when compared
to the baseline; however, a considerable variation is observed especially in
the magnitudes of extreme flows simulated for the different land cover
scenarios. This result suggests a significant constraint on the usage of
hydrological models for the variations of extreme flows due to land cover
change, even with high KGE values at daily time step as the impacts could be
potentially hidden within simulation uncertainty derived from the model
parameters. The uncertainties from model parameters thus should be
considered in land use change impact assessment for a more robust and reliable
analysis, which shall make the land cover change mitigation strategies and
water resources management plans more effective.
This study indicates that the recurrent flood events occurring in the
Mahanadi river basin might be influenced by the changes in LULC at the
catchment scale. However, projected increase in precipitation due to climate
change might have more pronounced effect on the streamflow on this basin,
especially extreme flows (Asokan and Dutta, 2008;
Ghosh et al., 2010; Jin et al., 2018), thereby hiding the hydrological
impacts of LULC changes. Future studies shall focus on modelling the
combined impacts of climate and land cover changes on hydrology of the Mahanadi
river basin, considering the uncertainties from model parameterization,
which is currently lacking in many studies.
Data availability
The DEM was acquired from Bhuvan, Indian Geo-Platform (https://bhuvan-app3.nrsc.gov.in/data/download/index.php, Bhuvan, 2021) of Indian Space Research Organisation, last access: 30 November 2021. Values of Unit Hydrograph are obtained from the Variable Infiltration Capacity (VIC) model; Routing: Unit Hydrograph (UH) file (https://vic.readthedocs.io/en/vic.4.2.d/Documentation/Routing/UH/, UH-VIC, 2021). Daily gridded rainfall, maximum and minimum temperature data used in this study can be obtained from the Indian Meteorological Department (IMD) (https://www.imd.gov.in, India Meteorological Department, 2021, home page/rainfall and temperature information). Wind speed data used in this study can be obtained from NCEP/NCAR reanalysis (https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.pressure.html, NOAA Physical Sciences Laboratory, 2021). The source code for VIC-3L version 4.2.d is available from GitHub (https://github.com/UW-Hydro/VIC/releases/tag/VIC.4.2.d, UW-Hydro, 2021). For downloading LUH2 datasets (https://luh.umd.edu/data.shtml, Land Use Harmonization, 2021), please refer to Hurtt et al. (2020), 10.5194/gmd-13-5425-2020. Observed discharge data are obtained from the Central Water Commission, India (http://www.cwc.gov.in/, Central Water Commission, Ministry of jal shakti, Department of Water Resources, River Development and Ganga Rejuvenation, GoI, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/hess-25-6339-2021-supplement.
Author contributions
SN, MARR and RR designed this study.
SN performed the model simulations. MARR and RR assisted SN in analysing and
discussing the results. SN wrote the article, and MARR and RR commented on
the article.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This work was funded by the BEMUSED project, which is funded by UK Natural Environment
Research Council (NERC; grant number NE/R004897/1).
Financial support
This research has been supported by the Natural Environment Research Council (grant no. NE/R004897/1).
Review statement
This paper was edited by Thom Bogaard and reviewed by two anonymous referees.
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