The Soil and Water Assessment Tool (SWAT) is a globally applied river basin
ecohydrological model used in a wide spectrum of studies, ranging from land
use change and climate change impacts studies to research for the development
of the best water management practices. However, SWAT has limitations in
simulating the seasonal growth cycles for trees and perennial vegetation in
the tropics, where rainfall rather than temperature is the dominant plant
growth controlling factor. Our goal is to improve the vegetation growth
module of SWAT for simulating the vegetation variables – such as the leaf area
index (LAI) – for tropical ecosystems. Therefore, we present a modified SWAT
version for the tropics (SWAT-T) that uses a straightforward but robust soil
moisture index (SMI) – a quotient of rainfall (
The Soil and Water Assessment Tool (SWAT; Arnold et al., 1998) is a process-oriented, spatially semi-distributed and time-continuous river basin model. SWAT is one of the most widely applied ecohydrological models for the modelling of hydrological and biophysical processes under a range of climate and management conditions (Arnold et al., 2012; Bressiani et al., 2015; Gassman et al., 2014; van Griensven et al., 2012; Krysanova and White, 2015). SWAT has been used in many studies in tropical Africa to investigate the basin hydrology (e.g. Dessu and Melesse, 2012; Easton et al., 2010; Mwangi et al., 2016; Setegn et al., 2009) as well as to study the hydrological impacts of land use change (e.g. Gebremicael et al., 2013; Githui et al., 2009; Mango et al., 2011) and climate change (Mango et al., 2011; Mengistu and Sorteberg, 2012; Setegn et al., 2011; Teklesadik et al., 2017). Notwithstanding the high number of SWAT model applications in tropical catchments, only a few studies discussed the limitation of its plant growth module for simulating the growth cycles of trees and of perennial and annual vegetation in this region of the world (Mwangi et al., 2016; Strauch and Volk, 2013; Wagner et al., 2011).
It is worthwhile to note that phenological changes in vegetation affect the biophysical and hydrological processes in the basin and thus play a key role in integrated hydrologic and ecosystem modelling (Jolly and Running, 2004; Kiniry and MacDonald, 2008; Shen et al., 2013; Strauch and Volk, 2013; Yang and Zhang, 2016; Yu et al., 2016). The leaf area index (LAI) – the area of green leaves per unit area of land – is a vegetation attribute commonly used in ecohydrological modelling, as it strongly correlates with the vegetation phenological development. Thus, an enhanced representation of the LAI dynamics can improve the predictive capability of hydrologic models, as already noted in several studies (Andersen et al., 2002; Yu et al., 2016; Zhang et al., 2009). Arnold et al. (2012) underscored the need for a realistic representation of the local and regional plant growth processes to reliably simulate the water balance, the erosion and the nutrient yields using SWAT. For instance, the LAI and canopy height are needed to determine the canopy resistance and the aerodynamic resistance to subsequently compute the potential plant transpiration in SWAT. Therefore, inconsistencies in the vegetation growth simulations could result in uncertain estimates of the actual evapotranspiration (ET), as noted in Alemayehu et al. (2015).
SWAT utilizes a simplified version of the Environmental Policy Impact Climate (EPIC) crop growth module to simulate the phenological development of plants, based on accumulated heat units (Arnold et al., 1998; Neitsch et al., 2011). It uses dormancy, which is a function of day length and latitude, to repeat the annual growth cycle for trees and perennials. Admittedly, this approach is suitable for temperate regions. However, Strauch and Volk (2013) showed that the temporal dynamics of the LAI are not well represented for perennial vegetation (savanna and shrubs) and evergreen forest in Brazil. Likewise, Wagner et al. (2011) reported a mismatch between the growth cycle of deciduous forest and the SWAT dormancy period in the Western Ghats (India), and they subsequently shifted the dormancy period to the dry season.
Unlike temperate regions where the vegetation growth dynamics are mainly
controlled by the temperature, the primary controlling factor in tropical
regions is the rainfall (i.e. the water availability) (Jolly and Running,
2004; Lotsch, 2003; Pfeifer et al., 2012, 2014; Zhang, 2005). A study of
Zhang et al. (2005) explored the relationship between the rainfall
seasonality and the vegetation phenology across Africa. They showed that the
onset of the vegetation green-up can be predicted using the cumulative
rainfall as a criterion for the season change. Jolly and Running (2004)
determined the timing of leaf flush in an ecosystem process simulator
(BIOME-BGC) after a defined dry season in the Kalahari, using events where
the daily rainfall (
The Mara Basin
The main objective of this study is to improve the vegetation growth module of SWAT for trees and perennials in the tropics. Towards this, the use of the SMI as a dynamic trigger for a new vegetation growth cycle within a predefined period will be explored. The modified SWAT (SWAT-T) model will be evaluated for the Mara River basin using 8-day MODIS LAI and remote-sensing-based ET (Alemayehu et al., 2017). Additionally, the model will be evaluated using observed daily streamflow data.
The Mara River, a transboundary river shared by Kenya and Tanzania, drains an
area of 13 750 km
Rainfall varies spatially mainly due to its equatorial location and the topography. The rainfall pattern in most part of the basin is bimodal, with a short rainy season (October–December) driven by convergence and southward migration of the Intertropical Convergence Zone (ITCZ) and a long rainy season (March–May) driven by south-easterly trades. In general, rainfall decreases from west to east across the basin, while temperature increases southwards. The Mara Basin is endowed with significant biodiversity features, including moist montane forest on the escarpment, dry upland forest, scattered woodland and extensive savanna grasslands (Fig. 1b). The upper forested basin is dominated by well-drained volcanic origin soils, while the middle and lower parts of the basin are dominated by poorly drained soil types with high clay content.
SWAT (Arnold et al., 1998, 2012; Neitsch et al., 2011) is a comprehensive,
process-oriented and physically based ecohydrological model for river
basins. It requires specific information about weather, soil properties,
topography, vegetation and land management practices in the watershed to
directly simulate physical processes associated with water movement, sediment
movement, crop growth, nutrient cycling, etc. In SWAT, a basin is partitioned
into sub-basins using topographic information. The sub-basins, in turn, are
subdivided into hydrological response units (HRUs) that represent a unique
combination of land use, soil type and slope class. All the hydrologic
processes are simulated at HRU level on a daily or sub-daily time step. The
flows are then aggregated to sub-basin level for routing into a river network
(Neitsch et al., 2011). SWAT considers five storages to calculate the water
balance: snow, the canopy storage, the soil profile (with up to 10 layers),
a shallow aquifer and a deep aquifer. The global water balance is
expressed as
SWAT provides three options for estimating ET
In this study, we use the Penman–Monteith method (Monteith, 1965) to compute
the ET
SWAT simulates the annual vegetation growth based on the simplified version of the EPIC plant growth model (Neitsch et al., 2011). The potential plant phenological development is hereby simulated on the basis of accumulated heat units under optimal conditions; however, the actual growth is constrained by temperature, water, nitrogen or phosphorous stress (Arnold et al., 2012; Neitsch et al., 2011).
Plant growth is primarily based on temperature, and hence each plant has its own temperature requirements (i.e. minimum, maximum and optimum). The fundamental assumption of the heat unit theory is that plants have a heat unit requirement that can be quantified and linked to the time of planting and maturity (Kiniry and MacDonald, 2008; Neitsch et al., 2011). The total number of heat units required for a plant to reach maturity must be provided by the user. The plant growth modelling includes the simulation of the leaf area development, the light interception and the conversion of intercepted light into biomass, assuming a plant species-specific radiation-use efficiency (Neitsch et al., 2011). The plant growth model assumes a uniform, single plant species community; thereby, plant mixtures such as trees and grass cannot be simulated in SWAT (Kiniry and MacDonald, 2008).
During the initial period of the growth, the optimal leaf area development is
modelled (Neitsch et al., 2011) as
Afterwards, the leaf senescence becomes the dominant growth process, and hence
the LAI follows a linear decline (Neitsch et al., 2011). However, Strauch and
Volk (2013) suggested a logistic decline curve instead, in order to avoid
the LAI dropping to zero before entering the dormancy stage. We adopted
this change in SWAT2012, whereby the LAI during leaf senescence for trees and
perennials is calculated as (Strauch and Volk, 2013)
As detailed in Neitsch et al. (2011), the daily LAI calculations for perennials and trees are slightly different, as for the latter the years of development are considered.
For perennials, the LAI for a day
Dormancy is the period during which trees and perennials do not grow. It is commonly considered to be a function of latitude and day length. It is assumed that dormancy starts as the day length nears the minimum day length of the year. At the beginning of the dormancy period, a fraction of the biomass is converted to residue and the leaf area index is set to the minimum value (Neitsch et al., 2011) and thereby resets the annual growth cycle. Also, SWAT offers two management settings options for the start and the end of the growing season, either based on a calendar date scheduling or based on heat units (the default).
The moisture index (SMI) derived from historical precipitation
observations (
In the tropics, however, dormancy is primarily controlled by precipitation (Bobée et al., 2012; Jolly and Running, 2004; Lotsch, 2003; Zhang et al., 2010; Zhang, 2005). Hence, the default growth module of SWAT cannot realistically represent the seasonal growth dynamics for trees and perennials in the tropics.
As several studies demonstrated (Jolly and Running, 2004; Zhang, 2005; Zhang
et al., 2006), the water availability in the soil profile is one of the
primary governing factors of the vegetation growth in the tropics. Thus, we
propose to implement a soil moisture index (SMI) to trigger a new growth
cycle for tropical ecosystems in SWAT within a predefined period. The SMI is
computed as
Figure 2 presents the seasonal pattern of SMI, based on long-term
precipitation for several gauge stations in the Mara Basin and
ET
Summary of the inputs of the SWAT model and the evaluation datasets.
To avoid false starts of the new growing cycle during the dry season due to
short-spell rainfall, the end of the dry season and the beginning of the
rainy season (SOS
Based on the rationale elaborated in the preceding sections, we modified the
standard SWAT2012 (revision 627) plant growth subroutine for basins located
between 20 If the simulation day is within SOS If the SMI exceeds or equals a user-defined threshold, a new growing cycle
for trees and perennials is initiated. Subsequently,
fr In the case where the SMI is still below a user-defined threshold at the end of month
SOS
It is worth noting that the SMI threshold can be set depending on the
climatic condition of the basin.
The SWAT-T executable and the associated changes can be found in the Supplement.
The 8-day raw-median LAI time series for evergreen forest
The remote sensing LAI data used in this study are based on the MODIS TERRA sensor (Table 1). The LAI product retrieval algorithm is based on the physics of the radiative transfer in vegetation canopies (Myneni et al., 2002) and involves several constants (leaf angle distribution, optical properties of soils and wood, and canopy heterogeneity) (Bobée et al., 2012). The theoretical basis of the MODIS LAI algorithm and the validation results are detailed in Myneni et al. (2002). Kraus (2008) validated the MOD15A2 LAI data at the Budongo Forest (Uganda) and Kakamega Forest (Kenya) sites and reported an accuracy level comparable to the accuracy of field measurements, indicating the reliability of MOD15A2 LAI.
We selected relatively homogeneous representative sample sites (i.e.
polygons) for evergreen forest (174 km
ET is one of the major components of a basin water balance that is influenced by the seasonal vegetation growth cycle. Thus, remote-sensing-based ET estimates can be used to evaluate (calibrate) the SWAT-T model. Alemayehu et al. (2017) estimated ET for the Mara River basin using several MODIS thermal imageries and the Global Land Data Assimilation System (GLDAS) (Rodell et al., 2004) weather dataset from 2002 to 2009 at an 8-day temporal resolution based on the operational simplified surface energy balance (SSEBop) algorithm (Senay et al., 2013). The latter mainly depends on the remotely sensed land surface temperature and the grass reference evapotranspiration (Senay et al., 2013). Alemayehu et al. (2017) demonstrated that the SSEBop ET for the study area explained about 52, 63 and 81 % of the observed variability in the MODIS NDVI at 16-day, monthly and annual temporal resolution. Also, they suggested that the estimated ET can be used for hydrological model parameterization. Therefore, we used this remote-sensing-based ET estimate (hereafter ET-RS) to evaluate the SWAT-T-simulated ET at a land cover level.
Due to the limited availability of observed streamflow, we used daily
observed streamflow series (2002–2008) for the headwater region
(700 km
The Mara River basin was delineated using a high-resolution (30 m) digital
elevation model (DEM) (NASA, 2014) in ArcSWAT2012 (revision 627). The basin
was subdivided into 89 sub-basins to spatially differentiate areas of the
basin dominated by different land use and/or soil type with dissimilar impact
on hydrology. Each sub-basin was further discretized into several HRUs. The
model was set up for land use conditions representing the period 2002–2009.
The land cover classes for the basin were obtained from the FAO Africover
project (FAO, 2002). As shown in Fig. 1b, the dominant portion of the basin
is covered by natural vegetation including savanna grassland, shrubland and
evergreen forest. These land cover classes were assigned the characteristics
of RNGE, RNGB and FRSE, respectively, in the SWAT plant database (Neitsch et
al., 2011). We extracted the soil classes for the basin from the Harmonized
Global Soil Database (FAO, 2008). A soil properties database for the Mara
River basin was established using the soil water characteristics tool (SPAW;
The daily LAI as simulated standard SWAT plant growth module with different management settings and by the modified plant growth module (SWAT-T) for evergreen forest (FRSE) using default SWAT parameters. The vertical lines (black) denote monthly rainfall (see management settings explanations in the text).
The list of hydroclimatological and spatial data used to drive the SWAT
model is presented in Table 1. In situ measurements of rainfall and other
climate variables are sparse, and thus bias-corrected multi-satellite
rainfall analysis data from Roy et al. (2017) were used. The bias correction
involves using historical gauge measurements and a downscaling to a 5 km
resolution. Detailed information on the bias-correction and downscaling
procedures can be found in Roy et al. (2017). The ET
The main purpose of this study is to explore the potential of the SMI to trigger a new vegetation growth cycle for tropical ecosystems. To evaluate the effect of the modification on the SWAT vegetation growth module, we initially intercompared simulated LAI from the modified (i.e. SWAT-T) and the standard plant growth module with varying management settings. This analysis involved uncalibrated simulations with the default SWAT model parameters, whereby the models thus only differ regarding the way the vegetation growth is simulated, and in terms of the management settings. It is worth noting that the aim of these simulations is mainly to expose the inconsistencies in the vegetation growth module structure of the original SWAT model. Afterwards, we calibrated the parameters related to the simulation of the LAI, the ET and the streamflow by trial and error, and expert knowledge for the SWAT-T model. Firstly, the SWAT parameters that control the shape, the magnitude and the temporal dynamics of LAI were adjusted to reproduce the 8-day MODIS LAI for each land cover class. Then, we adjusted the parameters that mainly control the streamflow and ET simulation, simultaneously using the daily observed streamflow and the 8-day ET-RS. One may put forward that the manual adjustment may not be as robust as an automatic calibration as the latter explores a larger parameter space. However, the manual calibration is believed to be apt to illustrate the impact of the modification of the vegetation growth cycle and its effect on the water balance components. The SWAT-T model calibration and validation were done for 2002–2005 and 2006–2009, respectively.
The daily LAI as simulated standard SWAT plant growth module with different management settings and by the modified plant growth module (SWAT-T) for grass (RNGE) using default SWAT parameters. The vertical lines (black) denote monthly rainfall (see management settings explanations in the text).
The Pearson correlation coefficient (
To highlight the added value of the modified vegetation growth module in SWAT-T for simulating the seasonal growth pattern of trees and perennials, we compared the daily simulated LAI of the standard SWAT2012 (revision 627) model and SWAT-T model. At this stage, the models were uncalibrated (i.e. based on default SWAT parameters).
Figures 4 and 5 present the monthly rainfall along with SWAT-simulated daily
LAI for FRSE and RNGE using the standard vegetation growth module under
different management settings as well as the modified version (i.e. SWAT-T).
In the standard plant growth module, whereby the heat unit management option
is selected (“heat unit” in Figs. 4 and 5), the start and the end of
the vegetation growth cycle occur at the default
fr
As shown in Figs. 4 and 5, the simulation with the standard SWAT module can be partly improved by using a date scheduling (“date”) for the start and the end of the vegetation growth cycle (i.e. instead of heat unit). Alternatively, all the management settings can be removed (“no mgt”) and vegetation can growing from the start of the simulation. It is worthwhile noting the low LAI values during and following the rainy months (i.e. March–May), suggesting unrealistic growth cycle simulation. Additionally, regardless of the management setting, the vegetation growth cycle resets annually on 28 June due to dormancy. In contrast, the simulated LAI with the modified vegetation growth module (“SWAT-T”) corresponds with the monthly rainfall distribution, for FRSE and RNGE (see Figs. 4 and 5). We noted similar results for tea and RNGB.
Comparison of Penman–Monteith-based daily potential transpiration simulated by the SWAT-T and the standard SWAT models for grassland. Note that the heat unit scheduling is used in the standard SWAT model.
The MODIS LAI and the SWAT-T model-simulated, HRU-weighted, aggregated 8-day LAI time series (2002–2009). The grey shading indicate the boundaries of the 25th and 75th percentiles. The vertical line marks the end of the calibration period and the beginning of the validation period.
In SWAT, the LAI is required to compute the potential transpiration, the potential soil evaporation and the plant biomass, among others. For instance, to compute the daily potential plant transpiration, the canopy resistance and the aerodynamic resistance are determined using the simulated LAI and the canopy height, respectively (Neitsch et al., 2011). Therefore, the aforementioned limitations of the annual vegetation growth cycle in the standard SWAT model growth module also influence the simulation of the transpiration. Figure 6 shows a comparison of the daily potential transpiration for RNGE as simulated by the SWAT model with the standard and modified vegetation growth modules, based on the Penman–Monteith equation. We observe 12 % of the standard SWAT-simulated daily potential transpiration time series (2002–2009) for RNGE equal to zero, suggesting a considerable inconsistency. The inconsistency is considerably reduced when the modified vegetation growth module (SWAT-T) is used (i.e. less than 2 % zero values). Similar results are noted for FRSE and RNGB.
List of SWAT parameters used to calibrate LAI, ET and streamflow with their default and calibrated values.
Summary of the performance metrics for the SWAT-T for simulating LAI, ET and streamflow. Note that the performance for LAI and ET refers to 8-day aggregated data, whereas daily streamflow data are considered.
The long-term (2002–2009) average monthly LAI pooled scatter plot
The interannual and spatial variation of the start of the rainy season for the savanna vegetation in the Mara River basin for 2002–2005. Note that HRU level Julian dates are used and the sub-basins are overlaid.
The comparison of remote-sensing-based evapotranspiration (ET-RS) and SWAT-T-simulated ET (ET-SWAT-T) aggregated per land cover class. Note that for SWAT-T HRU level ET is aggregated per land cover. The vertical black lines mark the end of the calibration period and the beginning of the validation period.
These findings should not come as a surprise as several studies have shown
the effect of the selection of the ET
We also notice the SWAT-T-simulated potential transpiration is consistent
regardless of the ET
Table 2 presents the SWAT model parameters that are adjusted during the
manual calibration process. Initially, the minimum LAI (ALAI_MIN) for each
land cover class was set based on the long-term MODIS LAI. Also, the PHU
was computed using the long-term climatology, as suggested in Strauch and
Volk (2013). The shape coefficients for the LAI curve (FRGW
SWAT-T-simulated monthly ET
Figure 7 presents the comparison of 8-day MODIS LAI with the calibrated SWAT-T-simulated LAI aggregated over several land cover classes for the calibration and validation periods. We evaluated the degree of agreement qualitatively (by visual comparison) and quantitatively (by statistical measures). From the visual inspection, it is apparent that the intra-annual LAI dynamics (and hence the annual growth cycle of each land cover class) from the SWAT-T model corresponds well with the MODIS LAI data. This observation is supported by correlations as high as 0.94 (FRSE) and 0.92 (RNGB) during the calibration period (Table 3). As shown in Table 3, the model also shows a similar performance during the validation period, with low average bias and correlation as high as 0.93 (FRSE). Overall, the results indicate that the SMI can indeed be used to dynamically trigger a new growing season within a predefined period.
Despite the overall good performance of SWAT-T in simulating the LAI, we observed biases for FRSE and tea, mainly during the rainy season (see top row of Fig. 7). This is partly attributed to the cloud contamination of the MODIS LAI in the mountainous humid part of the basin, as shown in Fig. 3a and b. Similar observations were also made by Kraus (2008). Also, the senescence seems to occur slightly early for tea (see Fig. 3b), whereby we note a mismatch between the SWAT-simulated LAI and the MODIS LAI. This suggests the need to further adjust the fraction of total PHU when the leaf area begins to decline (DLAI).
The seasonal patterns of the LAI for FRSE, tea, RNGE and RNGB are analysed using 8-day aggregated LAI data time series (2002–2009) from the calibrated SWAT-T model and MODIS LAI. Generally, and not surprisingly, the seasonal dynamics of the SWAT-T-simulated LAI and the MODIS LAI agree well (Fig. 8a) with a pooled correlation of 0.97.
As shown in Fig. 8b, the SWAT-T-simulated monthly average LAI shows a higher seasonal variation as compared to the variation observed from MODIS LAI for FRSE; the peak-to-trough difference of the SWAT-T data is about 48 % of the average annual MODIS LAI, while the amplitude is 31 % for the MODIS data. The seasonal variation from MODIS LAI is comparable to the results of Myneni et al. (2007), who noted 25 % seasonal variation in the Amazon forest. We also notice a correlation of 0.66 between the seasonal LAI and the rainfall in the humid part of the basin. Our observations are in agreement with Kraus (2008), who reported an association of the LAI dynamics for forest sites located in Kenya and Uganda with interannual climate variability.
In the part of the basin where there is a marked dry season, the LAI exhibits
a notable seasonal variation, with an amplitude that is up to 79 % of the
mean annual LAI (1.4 m
In addition to improving the seasonal dynamics of LAI in SWAT without the need for management settings, the SMI accounts for the year-to-year shifts in the SOS due to climatic variations. This is particularly important for long-term land use change and climate change impact studies. Figure 9 demonstrates the year-to-year shifts as well as the spatial variation of the SOS dates for part of the Mara River basin dominated by savanna grassland. Generally, the season change tends to occur in the month of October (i.e. Julian date 278–304). Yet, we acknowledge the need of further verification studies in basins with sufficient forcing data and field measurements.
As presented in Table 2, several SWAT parameters were calibrated by comparing SWAT-T model simulated ET with ET-RS. The higher water use by FRSE as compared to other land cover classes is reflected by a lower ESCO and a higher GW_REVAP and GSI (Table 2). The lower ESCO indicates an increased possibility of extracting soil water to satisfy the atmospheric demand at a relatively lower soil depth. Also, the higher GW_REVAP points to an increased extraction of water by deep-rooted plants from the shallow aquifer or pumping. Similar findings were reported by Strauch and Volk (2013).
The average seasonal and spatial distribution of ET (2002–2009) in the Mara Basin, as simulated by the SWAT-T model at HRU level.
Figure 10 presents the comparison of 8-day ET-RS and SWAT-T-simulated ET for the calibration (2002–2005) and validation (2006–2009) periods for FRSE, tea, RNGE and RNGB. Visually, the ET simulated by the SWAT-T fairly agrees with the ET-RS for all the covers. As shown in Table 3, the statistical performance indices show a modest performance in simulating ET for the dominant cover types in the basin. The average model biases for the simulated ET range from 7.8 % (RNGE) to 1.2 % (RNGB) during the calibration period. Additionally, the correlation between 8-day ET from the SWAT-T and the ET-RS varies from 0.67 (tea) to 0.72 (grassland). Overall, we notice similar performance measures during the calibration and validation periods, suggesting a fair representation of the processes pertinent to ET.
The variability of the ET is controlled by several biotic and abiotic factors. The 8-day ET time series as simulated by the SWAT-T model illustrates the variation of the temporal dynamics of ET in the study area. For land cover types located in the humid part of the basin (FRSE and tea), there is no clear temporal pattern (Fig. 10). In contrast, the areas covered by RNGE and RNGB show a clear seasonality of the simulated ET. These observations are consistent with the seasonality of the simulated LAI, as discussed in Sect. 3.2.2.
To shed light on the consistency of SWAT-T-simulated LAI and ET, we selected
simulation outputs at HRU level for April and August (Figs. 11 and 12).
Figure 11a exhibits the monthly ET at HRU level for the wet month
(April) and the dry month (August) in 2002. The lower portion of the basin,
with dominant savanna cover, experiences a monthly ET between 16 and
63 mm month
Observed and simulated flows for the Nyangores River at Bomet.
Figure 13 presents the comparison of daily SWAT-T-simulated streamflow with observed streamflow, for the calibration and validation periods. Visually, the simulated hydrograph fairly reproduced the observations. The average biases of the SWAT-T-simulated streamflow as compared to observations amount to 3.5 and 15.5 % during the calibration and validation periods, respectively (Table 3). The correlation is about 0.72 (0.76) during calibration (validation) period. A KGE of 0.71 points to the overall ability of the calibrated SWAT-T model to reproduce the observed streamflow. However, the model tends to underestimate the baseflow and this is more pronounced during the validation period. This is partly associated with the overestimation of the ET for evergreen forest (6.6 %) during the validation, since ET has a known effect on the groundwater flow.
We presented an innovative approach to improve the simulation of the annual
growth cycle for trees and perennials – and hence improve the simulation of
the evapotranspiration and the streamflow – for tropical conditions in SWAT.
The robustness of the changes made to the standard SWAT2012 revision 627 have
been assessed by comparing the model outputs with remotely sensed 8-day
composite MODIS LAI data, as well
as with remote-sensing-based evapotranspiration (ET-RS) and observed
streamflow data. Towards this, we presented a straightforward but robust SMI, a quotient of rainfall (
The structural improvements of the LAI simulation have been demonstrated by comparing uncalibrated SWAT model simulations of the LAI using the modified (i.e. SWAT-T) and the standard SWAT vegetation growth module. The results indicate that the modified module structure for the vegetation growth exhibits temporal progression patterns that are consistent with the seasonal rainfall pattern in the Mara Basin. Further, we note a better consistency of the SWAT-T-simulated potential transpiration for perennials and trees, suggesting the usefulness of the vegetation growth module modification in reducing the model structural uncertainty. Our calibrated SWAT-T model results also show that the calibrated SWAT-T-simulated LAI corresponds well with the MODIS LAI for various land cover classes with correlations of up to 0.94, indicating the realistic representation of the start of the new growing season using the SMI within a predefined period. The improvement of the vegetation growth cycle in SWAT is also supported by a good agreement of the simulated ET with ET-RS, particularly for the grassland. Additionally, the daily streamflow simulated with the SWAT-T mimic well the observed streamflow for the Nyangores River. Therefore, the SWAT-T developed in this study can be a robust tool for simulating the vegetation growth dynamics in a consistent way in hydrologic model applications.
The modified SWAT model for tropics is provided in the Supplement.
The authors declare that they have no conflict of interest.
We would like to thank Tirthankar Roy (University of Arizona), for providing bias-corrected satellite rainfall products. We also would like to thank the Water Resource Management Authority (WRMA) of Kenya for the provision of streamflow data. The critical review by two anonymous reviewers, Timo Brussée and the editor helped substantially in streamlining the paper. Edited by: Xuesong Zhang Reviewed by: two anonymous referees