IJERD – International Journal of Environmental and Rural Development (2021) 12-2

Research article
erdApplication of APEX Model in Evaluating Streamflow and
Sediment Yield in Stung Chinit Catchment
NARETH NUT*
Faculty of Agricultural Engineering, Royal University of Agriculture, Phnom Penh, Cambodia
Email: nnareth@rua.edu.kh

MANUEL R. REYES
Sustainable Intensification Innovation Lab (SIIL), Kansas State University, Kansas, USA

GILBERT SIGUA
United States Department of Agriculture, Agriculture Research Service, Coastal Plains
Research Center, South Carolina, USA

MACHITO MIHARA
Faculty of Regional Environment Science, Tokyo University of Agriculture, Tokyo, JapanSAKDANUPHOL CHAN
Faculty of Agricultural Engineering, Royal University of Agriculture, Phnom Penh, Cambodia

TAINGAUN SOURN
Department of International Cooperation and ASEAN, Ministry of Tourism, Phnom Penh,
Cambodia
Received 31 December 2020

Accepted 18 October 2021(*Corresponding Author)

Abstract Streamflow and sediment yield are important indicators to understand thealterations in hydrological processes as well as the supply and transformation of nutrients andbiological productivity in the ecosystem. The main objective of this study was to evaluate theperformance and applicability of the APEX model in estimating streamflow, sediment yield,
and quantifying the soil erosion in Stung Chinit Catchment. The result indicates that the
APEX model performed well in accurately simulating the monthly streamflow and annual
sediment yield in the watershed. The overall statistical indicators (NSE, R², and PBIAS) of
streamflow calibration (1997-2015) were 0.60, 0.62, and 2.50%, respectively. The validation
statistical indicators (NSE, R², and PBIAS) for streamflow were 0.71, 0.80, and 28.22%,
respectively. The mean annual surface runoff was 212.03 mm; varied from 72.56 mm to435.59 mm. The average annual sediment yield varied from 10.21 tons/ha/year in thelowlands of the Stung Chinit catchment main river channel to 68.2 tons/ha/year in thehighlands, with an annual average of 29.2 tons/ha/year. Most of the sediment yield came fromthe eastern part and near the outlet of the Stung Chinit catchment.
Keywords APEX model, sediment yield, streamflow, Stung Chinit catchment

INTRODUCTION
Soil erosion is a major environmental problem which affects people by degrading water quality,
depositing sediment in the channel, decreasing the reservoir effective capacity and increasing therisk of flooding (Umit et al. 2018). Due to some human activities such as forest burning, overgrazing,
deforestation, recreation, soil erosion rates have been increased above natural levels, a phenomenon
known as accelerated erosion. Accelerated surface erosion is a severe matter that reduces agricultural
productivity, finite arable lands, and reservoir capacity. Erosion and sedimentation in a catchment
are closely associated with natural processes which is mainly driven by rainfall and runoff processes.
Erosion is the movement and detachment of soil particles by natural forces, primarily caused byⒸ ISERD

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water and wind. Sediment yield is the amount of soil that is transported to surface water bodies withina time scale over a specific area (Issaka and Ashraf, 2017).
Sediment yield in a watershed varies spatially, depending on several contributing factors suchas topography, soil types, catchment area, climate (i.e., precipitation, wind, temperature, etc.),
vegetation cover, human-influenced soil erosion, forest fires, river discharge (Francipane et al. 2015).
In a watershed, the amount of sediment transported by a river system depends on the supply of
sediment and transport capacity of the flow. Therefore, the accuracy of the estimate of the sedimentyield from any watershed relies on understanding and representation of the multiple contributing
factors such as rainfall, runoff, and erosion processes. More discussion on sedimentation processes
can be found in earlier publications (Wilkinson and McElroy 2007; Francipane et al. 2015). Most
physical models normally require hydro-meteorological, topographical, soil, and land use data as the
input data for the model. Besides these data, models such as APEX, DSSAT, EPIC, and SWAT
(Ayele et al, 2017; Jeong et al. 2010) also require crop management data. APEX is capable ofevaluating the effects of various water and land management practices on watershed hydrology,
sediment yields and water quality at various environmental issues (Luo and Wang, 2019; Assefa etal. 2018; Van Liew et al., 2017; Ayele et al, 2017; Tuppad et al. 2010)
The Stung Chinit catchment is one of the major tributaries of Tonle Sap Lake. There is anintensified economic activity in the catchment, including major land concessions, infrastructuredevelopments and demographic pressures (CNMC, 2012). As a result, some soil erosion may occur
eroded from the upstream to the downstream of the catchment due to agricultural land expansion byencroaching to the forest land. Moreover, farmers suffer abnormal storms, floods and multiple kindsof droughts (meteorological, hydrological and agricultural), making them and their communities
highly vulnerable to water scarcity.
OBJECTVIESThe research aims to evaluate the performance and applicability of the APEX model in estimating
streamflow and sediment yield as well as identifying the soil erosion in Stung Chinit Catchment,
Cambodia.
MATERIALS AND METHODS
Study Area
Stung Chinit catchment covers an area of about 8,236 km2 and composed of the Stung Chinit andStung Taing Krasaing Rivers and other small streams that drain from the north. The mainstream,
Stung Chinit, flows 264 km south-westwards to the gentler slopes downstream before discharging
into the Tonle Sap Lake (CNMC, 2012). A measuring stream gauge is located in the middle of theriver along National Road 6, Kampong Thmar (Fig. 1).
Rainfall in the catchment increases with elevation, while the spatial distribution of annual
average rainfall ranges from 1200 to 1500 mm. Over 90% of the catchment’s annual rainfall isreceived during the wet season, from May to October, and the highest rainfall occurs in August(MOWRAM, 2014). Daily temperatures vary from 20°C in the coolest months of December toJanuary up to 35°C during the hottest months of April and May (CNMC, 2012). Farmers in Stung
Chinit and Taing Krasaing mainly cultivate traditional wet season rice and some dry season rice. A
large proportion of the catchment has poor quality soil, Acrisols, which covers 60.75% of the entire
catchment (CNMC, 2012). Agricultural land occupies 28.9% of the total catchment area (238,020ha), located mostly on poor Acrisols. Another 46.3% of the catchment area (381,327 ha) is occupied
by forestland, grassland, shrubland, soil and rock, urban settlements and water bodies. Of the total
agricultural land, rice takes up 154,014 ha, annual crops 49,197 ha, perennial crops 22,938 ha andvillage garden crops 7331 ha (CNMC, 2012).

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Fig. 1 Topography, hydrological and meteorological stations, and rivers of the study area
The APEX Model Application, Data Inputs and Model Setup to Predict Hydrology
The APEX model is an extension of the Environmental Policy Integrated Climate (EPIC) model(Williams, 1998), which is capable of evaluating the effects of various soil and water management
practices on the hydrology of the system, crop growth, and other environmental factors (Wang et al.,2014; Steglich et al., 2018). The APEX simulates watershed processes based on weather data, soil
characteristics, topography, vegetation, and management practices (Wang et al., 2012). Multiple
options are available in the APEX model in estimating evapotranspiration, surface runoff, peakrunoff rate, and available soil water capacity to derive hydrology of the system (Wang et al., 2012).
The APEX model requires some input data, including Geographic Information System (GIS)
data layers, climatic data, and management practices. The GIS data layers are digital elevation model(DEM), soil, and land use or crop covers (Table 1). A 30 m DEM was obtained from the UnitedStates Geographical Survey (USGS) website. The land use data was received from the Cambodia
National Mekong Committee (CNMC), 2015 (Fig. 2d). A harmonized world soil map prepared by
the Food and Agricultural Organization (FAO) with two levels were used for soil database
preparation of APEX (Fig. 2b).
During the model setup process, the APEX version 1501, developed by Texas A&M AgriLife
Research, Temple, Texas, USA, was used and divided into three steps. The first step was the processof setting up the APEX model and began with the processing of GIS data layers to delineate thewatershed boundary, subareas, and derive watershed characteristics from the Digital Elevation Model(DEM). The land use, soil data and slope was overlaid and used the dominant landuse/soil/slope,
while the land use of paddy rice was modified in the management file of APEX file based on theschedule of tradition rice cropping system which people mostly cultivate in the catchment (Table 2).
The dominant land uses in the catchment are evergreen forest (52.94%), paddy rice (26.82%),
grassland (6.23%), marsh/swarm (4.56%), shrubland (4.36%), agricultural land (3.6%) anddeciduous forest (1.49%) (Fig. 2d), while The dominant soils are Acrisols (55.13%), Gleysols(34.92%) and Vertisols (9.95%) (Fig. 2b). The second step was to integrate weather data of 12
catchments of Stung Chinit provided by WinRock International (Fig. 1) through the Arc-APEX
model interface. The third step was the process of performing an initial model run and complete
model setup procedures to create APEX model output files for further analysis.
The APEX can simulate all the key water balance components of the system. Precipitation, snow
melts, and irrigation are the main inputs to the system, which are then disseminated into various
components: surface runoff, subsurface/tile drainage flow, soil water, percolation, and evapoⒸ ISERD

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IJERD – International Journal of Environmental and Rural Development (2021) 12-2transpiration (Williams, et al. 2006). In APEX, the key landscape processes across hydrologicalconnected units are called subareas. The subareas are the smallest unit in APEX with homogenous
watershed characteristics, such as soil types, land use/crop cover, slope, and management. There are
two options to estimate the runoff volume (Williams, et al. 2006) which are the modified Soil
Conservation Service (SCS) (NRCS, 2004) curve number (CN) and Green and Ampt infiltration(Green and Ampt, 1911) methods used in the APEX model. The SCS-CN runoff estimate method isa function of rainfall and retention parameter. The curve number is a function of land use, hydrologic
soil group and management practices. The subsurface flow is a function of the vertical and horizontal
flow and simulated as a simultaneous process (Wang et al., 2012). The horizontal flow consists of alateral flow, whereas the vertical flow (percolation) adds to groundwater storage, which is thensubjected to return flow or deep percolation. The vertical component of percolation is calculated asa function of soil water content, field capacity, and travel time. There are five options available toestimate the potential evapotranspiration such as Penman, Penman–Monteith, Baier and Robertson,
Priestly and Taylor, and Hargreaves methods (Williams, et al. 2006). The Hargreaves method is
dynamic and requires a lower data, and is a function of solar radiation, latent heat of vaporization,and temperature.
Table 1 Some required input data for APEX model setup
Data

SourceDEM (30×30m) resolution
Soil type FAO/UNESCO in 1984

ASTER-GDEM, USGS
http://www.fao.org/soils-portal/data-hub

Land use and Land Cover
Meteorological data (1990 – 2017)
Streamflow data (1997 – 2017)

Cambodia National Mekong Committee (CNMC), 2015
Rainfall data of 12 catchments and streamflow data
provided by Soparith Tes from Winrock International.

Table 2 Major crop management activity and cropping pattern in Stung Chinit catchment
Types of crop

Medium wet season rice

Management practices

Date

1st tillage
2nd tillage
3rd tillage
DAP fertilizer application
Sowing25-April
5-May
15-May15-May15-May1st stage fertilizer application after planting
2nd stage fertilizer application after planting
Harvest30-June30-July
10-November

Sensitivity Analysis, Model Calibration, and Validation
Model sensitivity analysis is a method of identifying key parameters that affect model performanceand are essential for model parametrization. The APEX model has huge sets of parameters related tohydrology, sediment, nutrients, crops, and other environmental factors. Sensitivity analysis is thefirst step for hydrological models, which helps to diagnose and narrow down the enormous sets ofparameters for calibration. Model calibration is a process in which model parameters are modified
so that a model output mimics observed data, whereas validation is the use of modified parameters
to simulate another set of observed data. The APEX auto-calibration and uncertainty estimator(APEX-CUTE) was used to perform sensitivity analysis and auto-calibration for the APEX
hydrology model (Wang et al., 2014), followed by manual adjustment of a few parameters. The firststep was to examine the APEX hydrology model outputs for modifications. Some default methods
and input parameters might need modification to get better simulation prior to sensitivity analysis
and calibration (Williams, et al. 2008). The second step included a sensitivity analysis, calibration,Ⓒ ISERD

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and validation of the APEX hydrology model. Streamflow was recorded at Kampong Thmar Station
located in the middle of Stung Chinit catchment from 1990 - 2017, while the sediment yield wasdone in 2005 - 2008. Model warm-up period (1990-1996) was used to initialize model parameters
and obtain better predictions. Streamflow records were split into two periods: calibration (1997 2015) and validation (2016 - 2017). The APEX hydrology model was verified in monthly basis for
the Stung Chinit catchment from the measuring gauge to the upstream of the watershed. Most of theparameters considered during calibration were related to soil properties and climate. The third step
includes evaluating the APEX model. The APEX model performance in predicting hydrology of thesystem was evaluated using commonly used statistical measures such as Nash–Sutcliffe efficiency(NSE), determination of coefficient (R2), and percent bias (PBIAS). NSE is a normalized statisticalmeasure that was proposed in Reference (Chad et al. 2015). PBIAS measures the deviation of model
prediction as an under- or overestimation from observation, while R2 is a statistic that will give some
information about the goodness of fit of a model.

(a)

(b)

(c)

(d)

Fig. 2 Model input data in the Stung Chinit catchment(a) meteorological and hydrological stations, rivers, outlets, and subareas;(b) FAO soil; (c) slope classifications;(d) land use and land cover in 2015 from CNMCRESULTS AND DISCUSSION
APEX Sensitivity Analysis, Calibration, and Validation for Streamflow
All relevant parameters for APEX hydrology components were included in the sensitivity analysis
in accordance with the reference (Wang et al., 2014). The results of the sensitivity analysis in the
Stung Chinit catchment illustrated that streamflow was sensitive to the following parameters: Return
flow ratio (RFPO), Groundwater residence day (RFTO), Hargreaves potential evapotranspiration(PET) equation exponent (PARM-34), Groundwater residence day (PARM-40), SCS curve number
index coefficient (PARM-42), runoff volume adjustment factor (PARM-92), runoff CN initial
abstraction (PARM-20) and soil evaporation coefficient (PARM-12), in order of decreasing
influence (Table 3). The most sensitive parameters were associated with soil characteristics,
groundwater residence day and climatic conditions. The parameter PARM-42 was found to be themost sensitive parameter for streamflow followed by PARM-34, possibly because ET was the second
most-dominant hydrological process after rainfall. Feng et al. (2015) showed that ET affecting theⒸ ISERD

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water yield of the catchment in their scenario analysis, while Assefa et al. (2018) depicted that ET
could be able to impact on hydrology. Variable CN nonlinear CN/SW with depth soil water weighting
method (NVCN = 0), which is a function of soil water content and is directly linked with ET, wasused. Parameters PARM-92 and PARM-20 were found to be the third and fourth most sensitive
parameters for streamflow prediction. Parameters APM and PARM-90 and PARM-92 were lesssensitive and thus not used for calibration.
Table 3 APEX sensitive parameters and final calibrated values for streamflow calibration
Description

Range

Defaultvalue

Optimal
value

RFPO

Return flow ratio: (Return flow)/(Return flow + Deep
percolation)

0.05 – 0.95

0.5

0.85

RFTO

Groundwater residence day

10 – 50

30

60

PARM (12)Soil evaporation coefficient

1-2

2

1.5

PARM (17)Soil evaporation plant cover factor

1.5 – 2.5

1.5

2.5

PARM (20)

Runoff CN initial abstraction

0.8 – 1.5

1

1

PARM (34)

Hargreaves PET equation exponent

0.5–0.6

00

PARM (40)Groundwater residence day

10 - 50

30

60

PARM (42)

SCS curve number index coefficient

0.3 -2.5

0.4

0.4

PARM (49)

Groundwater storage threshold

0.001 - 1.0

0.25

0.99

PARM (90)Subsurface flow factor

1–100

1

1

PARM (92)

Runoff volume adjustment factor

0.1–2.0

1

1

Parameters

The APEX hydrology model was calibrated by using the 18 years of the measured streamflow
data (1997 - 2015) (Fig. 4a) followed by validation (2016 - 2017) using the monthly parameters in
Fig. 4b. Model parameter initialization was carried out prior to calibration (warm-up period: 19901996). Final calibrated values of sensitive parameters are listed in Table 3. Based on statistical
performance measure ratings of Moriasi et al, (2007) as shown in Table 4, the simulation of APEX
model in identifying the water discharge showed very good agreement with the observed monthly
streamflow both calibration and validation for a monthly time step of NSE = 0.60, R 2 = 0.62 and
PBIAS = 2.50%, and NSE = 0.71, R2 = 0.80 and PBIAS = 28.22% for model calibration andvalidation, respectively (Table 5 and Fig. 3).
Table 4 Model performance evaluation rating
Statistic
R2
NSE
PBIAS

Unsatisfactory< 0.50< 0.50> ±25

Evaluation rating
SatisfactoryGood
0.50 ‒ 0.60
0.60 ‒ 0.70
0.50 ‒ 0.65
0.65 ‒ 0.75
±15 < PBIAS < ±25±10 < PBIAS < ±25

Very good
0.70 ‒ 1
0.75 ‒ 1< ±10

Table 5 APEX model performance on a monthly basis of observed and simulated
streamflow calibration (1997–2015) and validation (2016–2017) in Stung Chinit
Catchment
Station Name
Kampong Thmar

Component
Calibration
Validation

NSE
0.60
0.71

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R2
0.62
0.80

PBIAS (%)
2.50
28.22

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Fig. 3 The monthly comparison of time series measured and simulated streamflow
and corresponding precipitation data for Stung Chinit Catchment

(a)

(b)

Fig. 4 The monthly comparison of measured and simulated streamflow for (a) calibration(1997-2015), and (b) validation (2016-2017) periods for Stung Chinit Catchment
We illustrated a scatter plot with line 1:1 and regression lines to compare the results between
the observed and simulated monthly streamflows during calibration and validation (Fig. 4). The
model overpredicted the flow when the observed values were less than approximately 320 m3/s (Fig4a). The model had a large error of prediction when it predicted a monthly streamflow peak greaterthan 490 m3/s. During validation, the low-flow values (<150 m3/s) were scattered near the 1:1 line,
but most of the high-flow values (> 250 m3/s) were underpredicted (Fig. 4b).
APEX Annual Sediment Simulation
Due to the limited data of sediment yield for the model calibration, we could not do the calibrationon the sediment yield. The annual suspended sediment load was just estimated as shown in (Table
6). The annual sediment yield is mainly varied with the amount of surface runoff in the catchment.
The severe sediment rate of 68.14 ton/ha/year occurred in 1999. This can be the result of forest
decline due to the land encroachment and forest-logging in the catchment. Since then, the sedimentyield is declining dramatically. The huge erosion occurred in 2011 and 2013 because the country
experienced the flooding across the study area at that time; resulted in severe sediment occurrence.
Variability of Surface Runoff and Sediment Yield in the CatchmentⒸ ISERD

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The mean annual surface runoff was 212.03 mm (varied from 72.56 mm to 435.59 mm). The
sediment yield varied from 10.21 ton/ha/year in the riparian lowlands of the Stung Chinit main river
channel to 68.14 ton/ha/year primarily in the mountain highlands from, with an average sedimentyield rate of 29.62 ton/ha/year for the entire basin (Table 6).
Table 6 Water balance and sediment components
Year
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
Mean

PRECIP (mm)
1279
1182
1910
1550
1521
1196
1261
1271
1019
1581
1517
1412
1640
1468
1592
1447
1616
1352
931
1167
1095
1381

QSS (mm)
176.33
192.46435.59243.49209.23112.45
126.65172.6197.83
313.98261.03171.64284.64217.74347.19234.73355.71
189.3872.56
134.26103.16212.03QSW (mm)
176.28192.39435.43243.36209.11
112.4
126.59172.4897.79
313.76260.86171.55284.47217.60346.97234.61355.49189.2472.52
134.19103.09211.91

QTS (mm)
286.97306.70708.31495.76439.94243.95301.3
489.18385.16659.87566.53363.78532.77446.53641.92475.51652.86442.01184.78295.92257.83437.03YW (t/ha)
16.95
21.88
68.14
37.46
26.88
11.18
16.13
29.90
10.21
40.67
29.34
20.75
40.07
30.06
52.52
26.57
58.56
35.66
12.76
16.00
20.25
29.62

PRECIP = Precipitation; QSS= Surface runoff; QSW = Watershed outflow; QTS= Total flow from all subarea and
YW= Watershed sediment yield

CONCLUSION
Our results showed that the Stung Chinit catchment experienced soil loss during 2011 and 2013 dueto the big flooding occurrence in the catchment area. A calibrated and validated APEX model wasable to estimate streamflow and sediment yield. The model also provided a good qualitativedescription on the effects of land uses and geographic indicators on streamflow and sedimentestimation. The mean annual surface runoff was 212.03 mm varied from 72.56 mm to 435.59 mm,
while the sediment yield varied from 10.21 tons/ha/year in the lowlands of the Stung Chinit
catchment main river channel to 68.2 tons/ha/year in the highlands, with an annual sediment yield of
29.2 tons/ha/year. Most of the sediment yield came from the eastern part and near the outlet of thethe Stung Chinit catchment. Land use management in lowlands could be improved while practisingsome soil erosion control methods in highlands and minimizing inappropriate tillage practices inareas with slopes greater than 25 to prevent soil loss. Due to our data limitations, we did not compare
the impact of land use change on streamflow and sediment load in this watershed, but it is important
to determine how the drivers of streamflow and sediment load will be changed in response to land
use change and climate change in the watershed. Nevertheless, this research should be able to develop
a reliable physically-based streamflow model, which is capable of illustrating and defining thecritical source areas and conditions of sediment yield.

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ACKNOWLEDGEMENTS
This research and publication are made possible by the generous support of the American People
provided to the Royal University of Agriculture through Partnerships for Enhanced Engagement in
Research (PEER) Cycle 7 funded by the United States Agency for International Development(USAID), implemented by the National Academy of Sciences (NAS) under Sponsor Cooperative
Agreement No. AID-OAA-A-11-00012 of Subaward Number: 2000010295. The technical expertiseand contributions of his USDA-ARS partner is also appreciated. The contents are the sole
responsibility of the authors and do not necessarily reflect the views of NAS, USAID or the UnitedStates Government. Moreover, the research and publication are made possible by the generous
support of the American people through support by the United States Agency for International
Development to the Feed the Future Innovation Labs for Collaborative Research on Sustainable
Intensification (Cooperative Agreement No. AID-OAA-L-14-00006, Kansas State University).
Thanks to Kelly Robbins, Senior Program Officer, the National Academy of Sciences (NAS), for her
support and guidance on the research grant implementation. The author also profoundly thank to Dr.
Abeyou W. Worqlul from Texas A&M AgriLife Research and the United State Department ofAgriculture (USDA) for technical support, assistance, and consultation as well as the Institute ofTechnology of Cambodia (ITC) and International Fertilizer Development Center (IFDC) for data
provision.
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