Hydrological modeling of the Mun River basin in Thailand

Journal of Hydrology 452–453 (2012) 232–246

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Hydrological modeling of the Mun River basin in Thailand Aysha Akter a,⇑, Mukand S. Babel b a b

Department of Civil Engineering, Chittagong University of Engineering & Technology, Chittagong 4349, Bangladesh Water Engineering and Management, Asian Institute of Technology (AIT), Klong Luang, Pathumthani 12120, Thailand

a r t i c l e

i n f o

Article history: Received 10 January 2012 Received in revised form 20 May 2012 Accepted 28 May 2012 Available online 4 June 2012 This manuscript was handled by Laurent Charlet, Editor-in-Chief, with the assistance of P.J. Depetris, Associate Editor Keywords: Nutrient HSPF Hydrology Land use River water quality Mun River basin

s u m m a r y Sources of pollution in river basins are increasing due to rapid changes in land uses and excessive nutrient application to crops which lead to degraded instream water quality. In this connection, the Mun River basin, one of the important and largest river basins in Thailand, has been studied. Comparative figures of nutrients in the Mun’s water over a decade showed an increased total nitrogen (TN) and phosphorus (TP) ratio in the Lower Mun region (TN:TP > 14). Laboratory analysis of weekly water samples showed a realistic nutrient response when daily rainfall was compared to the seasonal water quality data collected by the Pollution Control Department (PCD). The Hydrologic Simulation Program – FORTRAN (HSPF) was calibrated and used to assess the effects of different land uses on river water quality. Model parameters related to hydrology and sediment were calibrated and validated using relevant measurements by the Royal Irrigation Department (RID). With a reasonable and acceptable model performance (r2 = 0.62), the highest simulated runoff was observed in urban areas. The trend of agricultural land (as a percentage of total area) – total nitrogen showed a linear relationship of a good correlation (i.e. r2 = 0.85). Based on the findings, it can be concluded that this model is expected to provide vital information for developing suitable land management policies and strategies to improve river water quality. Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction Land use changes and excessive application of nutrients (Nitrogen and Phosphorus) in agricultural river basins, as well as in mixed type basins, influence instream river water quality. Severe consequences include runoff of excess nutrients from the basin. Basin characteristics such as drainage density, channel slope and basin relief ratio are correlated with discharge and nitrogen loss from the basin. According to Hill, both annual loss and mean annual concentrations of nitrates are correlated with land use patterns. However, land use variables were not considered in the analysis of nutrient export from point and non-point sources in the study (Hill, 1978). USEPA, in Section 502(14) of the Clean Water Act, has defined the term ‘point source’ as ‘any discernible, confined and discrete conveyance, including but not limited to any pipe, ditch, channel, tunnel, conduit, well, discrete fissure, container, rolling stock, concentrated animal feeding operation, or vessel or other floating craft, from which pollutants are or may be discharged’. Pollution sources, especially those failing to meet these criteria, are in the ‘Nonpoint’ source category generally resulting from land runoff, precipitation, atmospheric deposition, drainage, seepage or hydrologic modifications (USEPA, 2001). ⇑ Corresponding author. Tel.: +880 1713018512. E-mail addresses: [email protected], [email protected] (A. Akter), [email protected] (M.S. Babel). 0022-1694/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jhydrol.2012.05.059

Seasonal variations in climate, like in wet and dry years, have significant influence on nitrogen fluxes (Correll et al., 1999). In the rainy season, the peak pesticides and sediment concentrations have significant impact on stream fauna and drinking water sources (Williams et al., 1995). Land use change, through influencing storage patterns and water discharge transforms the hydrological cycle and its surrounding aquatic systems (Keitt et al., 1997; Richardson and McCarthy, 1994). Several studies have attempted to find out the influencing factors of storage and movement of nitrogen and nutrients in a fluvial system. A majority of these studies show that land use activity significantly influences nutrient loading and discharge (Beaulac and Reckhow, 1982; Correll et al., 1999). For instance, it has been concluded that an agricultural watershed discharges a higher amount of nutrients than forested watershed (Vanni et al., 2001). Nutrient exports from pasture and grazing activities are not significantly different from the export from forestland use (Beaulac and Reckhow, 1982), but the amounts of discharge of nitrogen and phosphorus significantly increase with the incremental percent of cropland. Along with this, row-cropped watersheds export a higher amount of nutrients compared to forests/animal feedlots/manual storage with relatively little change in the amount even if the percentage of pastureland increases (Correll et al., 1999). It seems that row-cropped, nonrow-cropped or pastured watersheds, in reality, are not very different in nitrogen exporting. Changes in land use can also affect stream

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chemistry (Reynolds et al., 1995), leading to an adverse impact on aquatic organisms (Harriman et al., 1987; Stueber et al., 2002). Previously, point sources were addressed as the most easily identifiable sources of water quality degradation. Due to increased environmental awareness, there is now more control on the point sources. But due to the increase in chemical (fertilizers, insecticides etc.) usage in agricultural lands as well as other land use changes, nonpoint sources account for a majority of water quality problems in present times (Alm, 1990; Walton and Hunter, 2009). Studies show that nonpoint sources alone contribute 79–88% of Nitrogen (N) and 74–87% of Phosphorus (P) that enter estuarine systems (Alm, 1990; Gilliland and Baxter-Potter, 1987). The development of different types of land uses and their resultant loading of nonpoint source pollutants such as sediments and pesticides into surface water has become a great concern for adjacent natural resources and the environment. Such changes have a long term effect on the social and economic system of the basin’s population. Population density also significantly influences the fixed N and P concentrations in river systems (Caraco and Col, 1999). Urban watersheds are major contributors in terms of increased nutrient loading rates with an increased percentage of impervious land area

(Beaulac and Reckhow, 1982; Bledsoe and Watson, 2001; Dow and DeWalle, 2000). This is notably important for today’s rapidly urbanizing river basins in case of higher N and P loading. Modeling of environmental deterioration to better understand and manage natural resources, such as river basins and watersheds, is a continuous process. Basin scale models that incorporate weather data and watershed characteristics like land use assist in the analyses of point and non-point sources’ pollutant loading and are tools for the development of management strategies in a watershed and river basins. One of the first comprehensive watershed models is the Stanford Watershed Model, which was developed in the early 1960s (Crawford and Linseley, 1966). Enhancing the model by constantly including sediment transport and water quality simulation components finally resulted in the development of the Hydrocomp Simulation Program (HSP), the Agricultural Runoff Management Model (ARM), and the Nonpoint Source Pollutant Loading Model (NPS). In 1976, the U.S. Environmental Protection Agency (EPA) combined all the functions performed by HSP, ARM and NPS models, which resulted in the Hydrologic Simulation Program FORTRAN (HSPF). In its initial stage, HSPF required extensive data input, leading to a complex

N

Mun River Mun Basin Basin's Boundary

0

100

200

233

Kilometers

Fig. 1. Study area.

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procedure. For the user’s convenience, the EPA commissioned Tera tech Inc. to develop the Better Assessment Science Integrating Point and Non-point Sources (BASINS) watershed management system. BASINS integrates an ArcView based GIS package. At present, BASINS comprises of the latest HSPF. HSPF is the most complete model (Laroche et al., 1996) and it has been in use for 30 years (USEPA, 2001; Whittemore and Beebe, 2000). HSPF module of BASINS is an analytical tool designed to allow simulation of the hydrology and water quality in natural and man-made systems, and it helps to predict possible environmental problems in the watershed. HSPF allows the prediction of runoff and nonpoint source constituent loads and enables modeling of the edge-of-field and in-stream water quality (Moore et al., 1988). HSPF is designed to be applicable to many different watersheds, and parameter adjustment enables the user to adjust the model to account for site-specific variation. HSPF is composed of three application modules: PERLAND, IMPLAND and RCHRES for simulating hydrologic and water quality processes on pervious and impervious lands and in reaches respectively, as well as in well mixed reservoirs (Donigian et al., 1999; Laroche et al., 1996). Engelmann et al. (2002) used the HSPF model using the BASINS database to predict discharge and sediment concentration at the outlet of the 103 km2 Ohio Watershed. HSPF has also been applied for large basins where the whole basin was divided into sub-basins (Hayashi et al., 2004). Sangjun et al. (2003) used the HSPF model in the BASINS environment to assess the effects of various land development scenarios on water quality in the Polecat Creek watershed in Caroline county, Virginia. Similarly, the HSPF model has also been used for modeling discharge (Albek et al., 2004; Hayashi et al., 2004; Mishra et al., 2009; Ryu, 2009), sediment loads (Hayashi et al., 2004; Mishra et al., 2009), water quality (Jeon et al., 2011; Ribarova et al., 2008; Walton and Hunter, 2009), instream flow considering snow melt (Kourgialas et al., 2010) and other hydrological processes and phenomena.

The 10 year trend of land use change in the Mun River basin showed an increase in hazardous industries, i.e. type-3 industries at the upstream (PCD, 1995), and this was verified by the data from the Department of Industrial Works (2004). The statistics of rice cultivation area from 1994 to 2004 showed that the rice planted area was around twice compared to previous years at the downstream of the basin (PCD, 1995) and as confirmed by data from the Department of Agricultural Extension (DoAE, 2004). So, the discharges from the increasing point and non-point sources may have significant impact on the water quality of the Mun River. Besides, the poorly consolidated sandy soils dominate the Mun basin and more than 40% of the basin soils suffer from erosion during the intense rain of the southwestern monsoon (Binnie and Partners, 1995). These factors may have a significant influence on sediment and water quality in the Mun River. The aim of this study therefore is to demonstrate the correlation between the hydrologic/water quality parameters and the land use variables in the Mun River basin. The study consists of two parts: the first part is devoted to the analytical results of historical water quality and hydrological parameters for the study area. The second part establishes a relationship between hydrological and water quality parameters and the land use of the basin through the application of the HSPF model in the Mun River.

2. Study area 2.1. Location The Mun River is the right bank tributary of the Mekong River, situated in the northeastern part of Thailand. It lies between latitudes 14° and 16°, and longitudes 101°30 and 105°30 (Toda et al., 2004) (Fig. 1). It originates from the Khao Yai National Park

N

Rice Planted Area Planted Area'94 Planted Area' 04 Mun 3- Parts Partition boundary Mun River Mun Basin

Amnat Chroen 2.79% 4.80% 40.73% MIDDLE MUN

19.47% 16.31%

20.56% 14.43%

18.54% 14.15%

16.53%

UPPER MUN

LOWER MUN

Nakhon Ratchathani

0

22.11%

9.58%

Buri Ram

100

Surin

Si Sa Ket

Ubon Ratchathani

200 Kilometers

Fig. 2. Province wise percentage of total rice planted area in the Mun River Basin (Data Source: (PCD, 1995) and DoAE, 2004).

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750000

102° 800000 850000

103° 900000

17° 104° 1000000 1050000

950000

1100000

105° 1150000

1200000

17°

Industry numbers (provincewise) Type 1 '94 Type 1 '04 1850000 Type 2 '94 Type 2 '04 Type 3 '94 Type 3 '04 1800000 Mun 3- Parts Partitionboundary Mun River 16° Mun Basin

N 1850000

1800000 16° 1750000

1750000 12329

1700000

1700000

4782

233186 640 1344 3027 231 317 86 141

1750

15° 1650000

610 4871 650 1332

3630 1508 1115 77

678 244 392

15°

2092 62 98

1650000

3279 123 198

202

1600000

1600000

14° 1550000

1550000 14°

0

100

200 Kilometers 1500000

1500000 750000

800000

850000 102°

900000

950000 103°

1000000

1050000 104°

1100000

1150000 105°

1200000

Fig. 3. Province wise No. of industries in the Mun River Basin (Data Source: (PCD, 1995) and DIW, 2004).

Thailand varies greatly temporally and spatially. The main mechanism for such a rainfall pattern is the southeastern monsoon, and as a result, the rainfall period lasts 5 months from May to September in Thailand (Patamatamkul, 2004). In an average year, the

near Nakhon Ratchasima and flows through southern Isan for 673 km until it joins the Mekong at Khong Chiam in the Ubon Ratchathani province, Thailand (a province being the second higher administrative division in Thailand). The rainfall in northeastern

Table 1 Summary list of date collected for the study. Data Type

Criteria

Duration

Site

Source

Spatial data DEM (Scale:1:250,000) Elevation River network Basin boundary (Scale:1:500,000) Land use Soil classification Water quality Forty parameters Hydrology Precipitation Evaporation River flow Cross-section (at flow gauge station) Dam Location HYV curve Meteorology Precipitation Temperature Wind speed Sunshine duration Sediment conc. Industry numbers Rice planted area

1000 km HYDRO1 K (Raster format)

–

Mun Basin

USGS websites

Vector format

–

Mun Basin

RID

DLD

Seasonal

1993–2004

Eighteen stations

PCD

Daily

1990–2003 (Water year)

Fifteen stations

RID

Daily Annual

1990–2002 (Water year)

Thirty-five stations

Daily

1995–2004 (Water year)

Six numbers

RID

Daily

1995–2003

Six stations

MET

Daily – –

1998–2002 (Water year) 2004 2004

Nine stations Province wise Province wise

RID DIW DoAE

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Mun’s contribution to the Mekong is approximately 25  109 m3, which is equivalent to an annual runoff of 210 mm or 800 m3/s (Binnie and Partners, 1995). From the literature, it has been found that for study purposes, the Mun River basin is often divided into three parts, namely: Upper Mun (101°300 –102°300 ), Middle Mun (102°300 –104°300 ) and Lower Mun (104°300 –105°300 ) (Figs. 2 and 3). 2.2. Land use Data on rice harvested land were obtained at two times (1994 and 2004) (Fig. 2). According to the data, the planted area was 38.16% of the total basin area in 1994 but in 2004 it became 45.32% of the total basin area (PCD, 1995). Fig. 2 shows that the planted areas in the upper and middle parts of the basin have decreased over the time, whereas a remarkable increase in the planting area is seen in the lower part of the Mun River Basin. The fertilizer and pesticide applied area is about 80% of the total planted area in each part of the basin whereas the total consumption of fertilizers is 265,220 tons and of total pesticide is 7958 tons (PCD, 1995). This data was used to estimate the fertilizer and pesticide consumptions for this study. The data of province-wise type-3 industries showed an overall increase in the industries throughout the basin (Fig. 3). An estimate of wastewater generation made by a survey in 1994 (PCD, 1995) was considered in this study with the 2004s available data. 3. Data collection The data required for the study included water quality data, hydro-meteorological data, point and non-point pollutant sources,

land use patterns, sediment and river flow data. Data sources were the Pollution Control Department (PCD), Royal Irrigation Department (RID), Thai Meteorological Department (TMD), Land Development Department (LDD), Department of Industrial Works (DIW) and Department of Agricultural Extension (DoAE) (Table 1). 3.1. Hydrologic data The daily rainfall records for all stations in the project area were obtained from RID. Through a critical review of rainfall records, 15 stations were considered for the estimation of mean areal rainfall. Geographical locations of the stations were also considered during the selection. Based on the hyetograph (rainfall over time) and the total number of rainy days per month in the14 years’ record (1990–2003), a year was divided into two seasons: the wet season (May to October) and the dry season (November to April). Using the 14 years (1990–2003) of data, it was found that the mean annual precipitation was 1131 mm for the whole basin whereas it was 1082 mm in the upper, 1197 mm in the middle and 1436 mm in the lower parts of the basin. The mean annual evaporation as recorded by RID was 5678 mm in the upper, 4072 mm in the middle, and 2881 mm in the lower parts of the basin. Similarly, other hydrologic parameters that were evaluated include air temperature, cloud cover, wind speed, precipitation, solar radiation, evaporation and evapotranspiration (Akter and Babel, 2005). 3.2. Water quality data 3.2.1. Seasonal data Historical data was collected from the Pollution Control Department (PCD) for 18 stations (Fig. 4) for 40 parameters: depth of river, water temperature, pH, turbidity, conductivity, salinity, DO,

Fig. 4. Four water sampling stations (this study) along with the PCD 18 sampling stations in the Mun River.

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237

Fig. 5. Variation of total phosphate along the Mun River (Data Source: (PCD, 1995)).

Fig. 6. Variation of ammonia nitrogen along the Mun River (Data Source: (PCD, 1995)).

BOD, COD, total coliform, faecal coliform, total phosphate, nitrate– nitrogen, nitrite–nitrogen, ammonia–nitrogen, total Kjeldahl nitrogen (TKN), suspended solids, total dissolved solids, hardness, alkalinity, acidity, phenol, metals (Fe, Cl, Cd, Cr, Mn, Ni, Pb, Zn, Cu, Hg, As, CN), and pesticides (alpha-BHC, aldrin, endrin, pp-DDT, heptachlor and heptachlor exposure). From the seasonal data, it is seen that in the wet season, the total phosphate measured in 1994 and 2004 showed an incremental trend almost all over the river (Fig. 5). One of the driest parts of Thailand, Nakhon Ratchasima, exists at the upstream of the river and is also one of the largest industrialized provinces (Binnie and Partners, 1995). Industrial wastewater is one of the major sources for the ammonia released into river water and in the Mun river, the variation also seems to be influenced by different types of land uses (Fig. 6).

3.2.2. Weekly data For weekly water quality variation, water sampling and laboratory tests were carried out from April to October 2004 following APHA methods (APHA, 1998). The water sampling stations were Pak Chong (latitude 14°580 longitude 102°050 ), Nakhon Ratchasima (first station), Piman (latitude 15°110 longitude 102°300 ), Naknon Ratchasima (second station), Satuk (latitude 15°180 longitude 103°170 ), Buri Ram (third station) and Ubon Ratchathani (latitude 15°150 longitude 104°520 ) (fourth station) (Fig. 4). There are many factors affecting the storage and transformation of nutrients (both nitrogen and phosphorus) in the river basin. Hence, for the weekly water sampling at the four stations (Fig. 4) the water quality analysis was carried out for seven selected parameters: Suspended Solids (SS), Total Phosphate (T-P), Total Nitrogen (T-N), Ammonium Nitrogen (NH4–N), Nitrite Nitrogen

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(NO2–N), Nitrate Nitrogen (NO3–N) and Phosphorus Phosphate (PO4–P). 4. Model setup 4.1. HSPF input data The requirements to build an HSPF project are – watershed boundary, land use and soil data, reach/river network files, elevation data and locations of gauging stations. After building the

Table 2 Input time series dataset for HSPF model setup. Name of Dataset

Computing equation

Input

Cloud cover

Hamon et al. (1954) Percent cloud cover from daily percent sunshine

Solar Radiation

Total daily solar radiation based on empirical curves of radiation as a function of latitude Hamon et al. (1954)

Percent sunshine= (Actual hr of sunshine  Possible hrs of sunshine) Latitude

Potential Evapotranspiration (PET)

Wind travel Temperature

Precipitation

Hamon (1961) formula

=average daily wind speed x 24 Distribution for 24 h

Triangular distribution (centered around the mid day)

Daily cloud cover (Tenths) Daily maximum air temperature Latitude Daily minimum air temperature Average daily wind speed Daily maximum air temperature Daily minimum air temperature Observation hours (24 h) Daily precipitation

project, the studied basin need to be delineated into two or more hydrologically connected sub-basins to characterize the basin and modeling. The HSPF requires six meteorological data time series for simulation (1995–2004); these are air temperature, cloud cover, wind speed, precipitation, solar radiation and evapotranspiration (Table 2). WDMUtil provided the functionality of summarizing, listing and graphing datasets in the Watershed Data Management (WDM) format. Input datasets can be retrieved in HSPF form and output datasets (simulated streamflow and water quality) written to WDM file format. All of the daily datasets were disaggregated to hourly values using WDMUtil. Point source of pollution data was calculated based on per capita water consumption. 4.2. Sub-basin delineation The selection of sub-basins was based on each sub-basin boundary’s outlet point’s requirement where each outlet point corresponded to (i) a flow gauge which had been used for hydrologic calibration or validation; (ii) a water quality monitoring station, and (iii) a confluence with a major tributary (Fig. 7, Table 3). A total of 23 sub-basins were delineated. There are 12 categories of land use in the Mun River basin. For modeling purposes, to represent the sub-basin’s land use, these were reclassified into six categories of agriculture, forest, industrial area, urban area, mixed land use (bushes and wetlands), and water body (Fig. 8). 4.3. Model reach network Using the BASINS manual delineation tool, the sub-basin and stream themes (Fig. 9) were imported to launch HSPF. To create the HSPF project along with the BASINS watershed, WDM files were imported. Data for the basin’s individual reaches was calculated from the river’s cross-section at the gauge stations and enclosed in a function table (F-Table) (USEPA, 2001) with stagedischarge, which was adopted from the rating curve for each station within the modeling duration of 1997–2004.

Fig. 7. Delineated sub-basins and reaches considering gauging stations.

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land flow (pervious land). For calibrating the model for river flow, both temporal (upstream and downstream boundary conditions) and spatial (reach network and cross-sections) input data was required. The model boundary conditions were set as:

5. Modeling approach The 8-year study period (1997–2004) was divided into four phases, i.e. the simulation period, initialization period, calibration period and validation period. These phases are defined as follows:

 Upstream: Stage hydrograph obtained from stream-elevation (F-Table).  Downstream: Discharge computed from rating curves at the downstream boundary streamflow gauging stations, namely M.2, M.5 and M.7 to represent the hydrological conditions and parameters in the three parts of the Mun Basin.

 Simulation period: January 1, 1997–December 31, 2004.  Initialization period: 1997 and 1998.  Calibration period: Water year 1999 i.e. April 1999–March 2000.  Validation period: Water years 2000–2003.

On the other hand, calibration for overland flow requires only spatial data (topography, rain gauge locations). Overland flow was calculated through the adjustment of the four hydrologic parameters which were initially estimated by using the methods of Donigian et al. (1999) and these were subsequently adjusted in model calibration process (Table 4). The overall statistical performance of hydrological calibration and validation show that the Middle and Lower Mun demonstrate a reasonable correlation (Table 5).

The hydrological modeling consists of two basic model components for the model setup and these are (i) River flow and (ii) Over-

Table 3 Model sub basin areas and their stream characteristics. Sub basin

Instream

Sub basin ID

Area (km2)

Name

Length (km)

00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23

2591.45 1837.18 367.01 2995.58 3953.49 3938.88 4654.96 2161.28 243.26 4378.01 3520.62 778.38 13045.68 3595.14 4481.42 4730.33 345.00 3130.92 3738.04 17.89 2859.57 356.75 1633.34 886.93

Mun River Mun River Chi River Mun River Mun River Mun River Mun River Mun River Mun River Mun River Mun River Mun River Mun River Mun River Mun River Lam Ta Khong Lam Ta Khong Mun River Mun River Mun River Mun River Mun River Mun River Mun River

13.01 84.94 629.48 550.12 190.40 177.81 32.40 331.58 116.61 174.99 351.00 444.34 142.01 127.31 148.09 69.28 471.97 337.18 257.89 444.05 220.82 265.42 523.42 401.63

6. Water quality analysis 6.1. Seasonal data analysis Mean seasonal variation of water quality parameters for three major categories, namely physical, chemical, biological and heavy metal, showed maximum values during the wet season and also  The average value showed a rising trend of pH towards alkalinity from upper to Middle and then a slight decline in the Lower Mun (Ubon Ratchathani).  In 1998 and 1999, the average observations showed a decreasing trend of DO from Nakhon Ratchasima to Buri Ram and then a constant trend going up to Ubon Ratchathani —which had the lowest DO level (2.60–2.90 mg/L)—all the way up to Nakhon Ratchasima (MU.17 and MU.16). The same trend was observed during the 2004 wet season with the lowest DO of 3.60 mg/L (at MU.15).

Subwatersheds3.shp Agriculture Forest Industrial Area Urban Mixed (Bushes & Wetlands) Water Body

N

12 11

15

13 18

17

0

8

19 100

6

1

7

5

0

4

9

14

16

2

3

10

20

22

23

21

200 Kilometers

Fig. 8. Proportion of different land uses at selected sub-basins for HSPF model set up.

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Fig. 9. View of study area at HSPF interface.

Table 4 The hydrological calibration parameters and values. Model Parameter

Initial estimate

Final calibrated value

Comments

LZSN, Lower Zone Storage Nominal

Forest, cropland and pasture: 24 cm Wetland, paddy land & urban: 16 cm 4.57 cm for all sub basins and all land uses Forest: 0.063 cm/h Wetland, cropland, Pasture, Paddy, Urban: 0.41 cm/h 0.98 for all sub basins and all land uses

39 cm for all sub basins and all land uses

The lower zone is related to soil moisture conditions and annual cycle of rainfall and evapotranspirationa

5.33 cm for all sub basins and all land uses 0.3 cm/h for all sub basins and all land uses

USZN is related to LZSN and basin’s topography, estimated as 14% of LZSN Most of the Mun Basin’s soil is loamy soil which is the B group of SCS hydrological soil groups in each basin

0.99 for all sub basins and all land uses

The ratio of active groundwater flow to a stream on 1 day to a stream on 1 day to the same ratio of the previous day

UZSN, Upper Zone Storage Nominal INFILT, index to the infiltration capacity of the soil AGWRC, Groundwater recession rate constant a

The Mun basin is categorized as arid part of Thailand, the LZSN is estimated as: (Annual Mean Rainfall  4) + 4 i.e. (1162.22  4) + 4.

 The average trend of BOD showed reasonable values. However, some stations showed a higher value of BOD, particularly in the wet seasons.  For NO3–N, the overall higher values were found in Lower Mun (MU.01–MU.08).  The average NH3–N values were the highest in Lower Mun, a class 4 or 5 category water (as per PCD classifications).  Observations showed that TKN was absent after 1994; however, unfortunately after a while, some measure of TKN was found during the dry season of 2004.

Table 5 Statistical performance of the hydrologic measures for the model. Station No.

M.2 M.5 M.7

Calibration perioda (Water year 1999)

Validation period (Water year 2000–2003)

EI

r

EI

r

0.38 0.42 1.61

0.53 0.55 0.70

0.24 0.31 0.60

0.67 0.60 0.79

a EI = Nash Suttcliffe coefficient; r = Coefficient of correlation; r2 = Coefficient of determination.

A. Akter, M.S. Babel / Journal of Hydrology 452–453 (2012) 232–246 Table 6 Reclassification of Mun River water quality for some stations. Sampling stations

The observed results only for 2004 Parameters

Value (mg/L)

Classification

MU.06 MU.14 MU.17 MU.02 MU.03 MU.04 MU08 MU17 MU01 MU05

BOD

2.10 4.6 3.2 0.80 0.70 0.60 0.13 0.36 0.23 0.46– 0.1 0.26 0.27– 0.1 0.58

Class Class Class Class

0.67 0.23 0.26 1.30

Class 5

NH3–N

Mn Cr

MU07 MU08 MU17

Ni

MU05 MU07 MU08 MU17

Cu

4 5 4 5

Class 5 Class 5

Class 5

Comments (Based on Thailand’s standard)

Up to Class 4 the standard is 4 mg/L

241

 The trend of total nitrogen to total phosphorus ratio for the three representing stations was calculated to be 3:1. However, overall, there was an upward trend, with 2004 showing the highest values recorded.  Most of the heavy metals were observed only at a few stations, namely MU. 17, MU. 11, MU. 08, MU. 07, MU. 05 and MU01. As for the biological and pesticide measures:

Up to Class 4 the standard is 0.5 mg/L Up to Class 4 the standard is 1.0 mg/L Up to Class 4 the standard is 0.05 mg/L

Up to Class 4 the standard is 0.1 mg/L Up to Class 4 the standard is 0.1 mg/L

 The representation of mean annual variation (from 1993 to 2004) for nitrate, ammonia and total phosphate for all the seasonal stations portrayed the overall view of nutrients in the Mun River’s water. The Lower Mun contained the highest amount of nitrate whereas Middle Mun had the maximum concentration of ammonia in the basin and the upper Mun had the greatest concentration of phosphate.  The yearly variation of total nitrogen as seen in the earlier period in the Upper Mun consisted of nominal amounts which were in an increasing trend from 1999 onwards, whereas the Middle Mun displayed a constant trend and the Lower Mun recorded its highest value in 2004.  The yearly variation of total phosphate showed its highest amount during 1998–2000 across the Mun River and after that the trend is seen to be in a decreasing mode.

 The variations in faecal coliform (220–2400 MPN/100 ml in the Upper Mun and 330–92,000 MPN/100 ml in the Lower Mun) and total coliform (230–7900 MPN/100 ml in the Upper Mun and 220–92,000 MPN/100 ml in the Lower Mun) showed higher values in the wet season.  Pesticides were found in only nominal traces in the river water. Those found were alpha-BHC (<0.005 mg/L), Heptachlor (<0.01 mg/L), Aldrin (<0.02 mg/L), Heptachlor- epoxide (<0.005 mg/L), Dieldrin (<0.01 mg/L), Endrin (<0.01 mg/L) and DDT (<0.01 mg/L). As per these statistics, the overall water quality of the Mun River belongs to Class 3, but some stations need further reclassification as they mostly follow the limits for Class 4 or Class 5 (Table 6). 6.2. Weekly data analysis The statistical results of the water quality’s parameters based on the weekly monitoring from April to October 2004 showed (Akter and Babel, 2005):  Most of the higher values were observed during May to October; however, temperature, pH, nitrate and total nitrogen are more dominant in the dry season (i.e. April).  Middle Mun had the highest total nitrogen in both seasons.  Lower Mun showed the highest total phosphate during the whole duration of monitoring. The results for water quality of the continually updated upper part of the basin showed total phosphate to be the most significant in both seasonal and weekly water sampling measures. Such

Fig. 10. Isohyetal map for the Mun River Basin based on 15 years (1990–2004) of annual rainfall (in mm) data at 20 gauging stations.

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responses also authenticate population growth as one of the major factors in the change of the river water’s constituent. The presence of heavy metals was dominant as well as suspended pesticide was also found in the upper part of the basin.

7. Hydrologic/water quality parameters and land use The overall mean seasonal hydrologic parameters for the duration of the water years 1990–2003 showed the average annual

rainfall at the basin varying from 825 to 1670 mm, as shown in the isohyetal map’s station wise rainfall depth variation in the entire basin (Fig. 10). The historical data analysis demonstrates that For the rainfall-runoff relationship, the coefficient of determination was 0.66 for three stations in the mainstream, namely M.38 C, M.2 and M.7.  The rainfall-total nitrogen trend and the rainfall-total phosphate trend showed a poor correlation (r2 = 0.01) overall but

Weekly Total Nitrogen Variation at Upper Mun 4

60 Mean Rainfall, RID station M.38,M.145, M.146 and M.147

50

3.5

Weekly TN

Rainfall (mm)

2.5 2

30

1.5 20 1 10

0.5 0 pr -0 64 M ay 13 -M 0 4 ay 20 -0 4 -M ay 27 -M 0 4 ay -0 4 3Ju 1 0 n- 0 4 -J un -0 17 4 -J un -0 24 4 -J un -0 4 1Ju l-0 4 8Ju 1 5 l-04 -J ul 2 2 -0 4 -J ul 2 9 -0 4 -J ul -0 5Au 4 12 g-0 4 -A u 19 g-0 4 -A u 26 g-0 4 -A ug -0 24 Se p0 9Se 4 16 p-0 4 -S e 23 p-0 4 -S ep -0 30 4 -S ep -0 4 7O c 1 4 t- 0 -O 4 c 2 1 t- 0 4 -O c 2 8 t- 0 4 -O ct -0 4

4 -0 pr

-A

29

-A

4

-0

r -0

pr

-A

15

22

4 r -0

Ap

Ap 1-

4

0 8-

Total Nitrogen (mg/L)

3 40

Duration (April to October) Weekly Total Nitrogen Variation at Middle Mun 10 Mean Rainfall, RID station M.6A and M.91 Weekly TN

100

8

Total Nitrogen (mg/L)

120

Rainfall (mm)

80 6 60 4 40 2

20

0 -0 pr

-A 29

-A

pr -0 64 M ay 13 -M 0 4 ay 20 -0 4 -M ay 27 -0 4 -M ay -0 4 3Ju 1 0 n- 0 4 -J un -0 17 4 -J un -0 24 4 -J un -0 4 1Ju l-0 4 8Ju 1 5 l-04 -J ul 2 2 -0 4 -J ul 2 9 -0 4 -J ul -0 5Au 4 1 2 g -0 4 -A u 1 9 g -0 4 -A ug -0 26 4 -A ug -0 24 Se p0 9Se 4 1 6 p -0 4 -S e 2 3 p -0 4 -S ep -0 30 4 -S ep -0 4 7O c 1 4 t- 0 -O 4 ct -0 21 4 -O c 2 8 t- 0 4 -O ct -0 4

4

4 -0

r -0

pr

Ap

15

-A

Ap 1-

8-

22

r-0

4

4

0

Duration (April to October) Weekly Total Nitrogen Variation at Lower Mun 3

Rainfall, RID station M.123 Weekly TN

Rainfall (mm)

100

80

2

60

1.5

40

1

20

0.5

0

Ap

-A

8-

15

Ap

r-0

4 r-0 4 pr -0 22 4 -A p 29 r -0 4 -A pr -0 64 M a 1 3 y -0 -M 4 ay 20 -M 0 4 ay 27 -M 0 4 ay -0 34 Ju 1 0 n -0 4 -J un -0 17 4 -J u 2 4 n -0 4 -J un -0 4 1Ju l8- 0 4 Ju 1 5 l -0 4 -J u 2 2 l- 0 4 -J u 2 9 l- 0 4 -J u 5 - l- 0 4 Au 12 g -0 4 -A u 1 9 g -0 4 -A ug 26 -A 0 4 ug -0 2Se 4 p0 9Se 4 16 p -0 4 -S ep 23 -S 0 4 e 3 0 p -0 4 -S ep -0 74 O c 1 4 t-0 -O 4 c 2 1 t- 0 4 -O c 2 8 t- 0 4 -O ct -0 4

0

1-

2.5

Duration (April to October) Fig. 11. Variation of total nitrogen in the Mun River (weekly monitoring).

Total Nitrogen (mg/L)

120

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specifically so in the Lower Mun where the runoff was also found quite high. The selected stations for representing Lower Mun are on the confluence of the Chi-Mun River basin. Thus, due to absence of the three measured parameters in the adjacent Chi basin possibly resulted in such poor relationship.  The graphical representation of daily rainfall (April to October 2004) and total nitrogen and total phosphate showed a proportional relationship of these nutrients with rainfall events

(Fig. 11 for total nitrogen and Fig. 12 for total Phosphorus).  For sediment concentration, a multivariate statistical representation showed good agreement with the observed data (r2 = 0.89) in the Upper Mun and for the Middle Mun also a good correlation with the observed data (r2 = 0.67) was found.  The agricultural land (as a percentage of total area) and total nitrogen showed a linear relationship with a very good correlation (r2 = 0.85).

Weekly Total Phosphate Variation at Upper Mun 0.5

60 Mean Rainfall, RID station M.38,M.145,M.146 and M.147

0.35 0.3 0.25

30

0.2 20

0.15 0.1

10

Total Phosphate (mg/L)

0.4

40

Rainfall (mm)

0.45

Weekly TP

50

0.05 0

r- A 04 p 22 r - 04 -A p 29 r - 04 -A pr -0 64 M a 13 y -0 -M 4 ay 20 - M 04 ay 27 - M 04 ay -0 34 Ju 10 n -0 4 -J un -0 17 4 -J u 24 n -04 -J un -0 4 1Ju l -0 4 8Ju 1 5 l -0 4 -J u 22 l- 04 -J u 29 l- 04 -J u 5- l- 04 Au 12 g - 0 -A 4 u 1 9 g- 0 4 -A ug 26 - A 04 ug -0 2Se 4 p 9 - - 04 Se 16 p- 0 -S 4 e 23 p- 0 4 -S ep 30 - S 04 ep -0 4 7O c 14 t- 0 -O 4 ct -0 21 4 -O ct -0 28 4 -O ct -0 4 15

8-

1-

Ap

Ap

r -0

4

0

Duration (April to October) Weekly Total Phosphate Variation at Middle Mun Mean Rainfall, RID station M.6A and M.91 Weekly TP

100

0.35 0.3 0.25

Rainfall (mm)

80 0.2 60 0.15 40 0.1 20

0.05 0

r -0 4 pr -0 4 pr -0 29 4 -A pr -0 64 M 1 3 a y -0 -M 4 ay 20 -M 0 4 ay 27 -M 0 4 ay -0 34 Ju 1 0 n -0 4 -J un -0 17 4 -J u 2 4 n -0 4 -J un -0 4 1Ju l8- 04 Ju 1 5 l-0 4 -J u 2 2 l- 0 4 -J u 2 9 l- 0 4 -J u 5 - l- 0 4 Au 1 2 g -0 -A 4 u 19 g -0 4 -A ug 26 -A 04 ug -0 2Se 4 p0 9Se 4 1 6 p -0 -S 4 ep 23 -S 04 ep 30 -S 04 ep -0 74 O c 1 4 t- 0 -O 4 c 2 1 t- 04 -O c 2 8 t- 04 -O ct -0 4 -A

-A

15

22

Ap 8-

Ap

r -0

4

0 1-

Total Phosphate (mg/L)

120

Duration (April to October)

Weekly Total Phosphate Variation at Lower Mun 0.5

120 Rainfall, RID station M123 Weekly TP

100

0.35 0.3 0.25

60

0.2 40

0.15 0.1

20

0.05 0

r -0 4 pr -A 0 4 pr 29 -A 0 4 p 6 - r -0 4 M 1 3 a y-M 0 4 2 0 a y- 0 -M 4 2 7 a y -0 -M 4 ay 0 3Ju 4 n 10 -J 04 u 1 7 n -0 4 -J u 2 4 n -0 4 -J un 1- 04 Ju l8- 04 Ju 1 5 l-0 -J 4 u 2 2 l- 0 4 -J u 2 9 l- 0 4 -J u 5 - l- 0 4 A 12 ug-0 -A 4 u 1 9 g -0 -A 4 ug 26 -A 0 4 ug -0 2Se 4 p904 S 16 ep-0 -S 4 ep 23 -S 0 4 e 3 0 p -0 -S 4 ep 7 - -0 4 O 1 4 c t- 0 -O 4 2 1 c t-0 -O 4 2 8 c t-0 -O 4 ct -0 4 -A

15

22

Ap 1-

8-

Ap

r -0

4

0

Duration (April to October) Fig. 12. Variation of total phosphorus in the Mun River (weekly monitoring).

Total Phosphate (mg/L)

0.4

80

Rainfall (mm)

0.45

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Weekly observations showed that the total nitrogen was highest in April. The total nitrogen in water samples was found to be even as high as 8.06 mg/L at Satuk on 10 April 04 and 2.72 mg/L at Ubon Ratchathani. During this period, the daily rainfall records for the first 10-day (can be considered as first flash rain) data range were 0.1–51.8 mm/day and the highest range was found in Lower Mun. Also, high phosphate concentration was observed in June, when the mean rainfall was 393.3 mm, about 32% of the mean annual rainfall. The observed high value of water quality parameters in the sample of 10 April, 2004 was due to the first flash rain events or the application of fertilizers during the early part of the rainy season or a combination of the two. However, there were no records for individual rainfall events. Observations during the short monitoring period indicated that in the lower part of the basin, a slightly increasing trend of nutrients was found in June although no rain was recorded at the time. Even so, no specific relationship can be drawn for the short sampling test data for 2004 due to a lack of flow data and the recent years’ sediment concentration data.

8. Hydrologic/physical behavior of the basin The model used for this study, BASINS 3.1, showed good agreement with the observed flow at stations M.2, M.5 and M.7 that represent Upper Mun, Middle Mun and Lower Mun respectively. The detailed statistical performance of the hydrologic calibration shows that most of the figures are reasonable (Fig. 13) and the model’s validation has a correlation of 0.67, 0.60 and 0.79 for the Upper, Middle and Lower parts of the Mun basin (Table 5, Fig. 14). The calibrated and validated hydrologic model gave a Nash Suttcliffe coefficient of 0.60 and r2 of 0.62 at M.7 and hence it was selected for sediment calibration. The statistics for hydrologic parameters are reasonable when compared to a similar study done by Engelmann et al. (Engelmann et al., 2002) at the Ohio watershed with the Nash Suttcliffe coefficient of 0.50 during the validation period. The detailed statistical performance for sediment calibration shows most of the error evolution results to be reasonable and the model’s validation has a correlation of 0.44 for the upper part of the basin (Table 7).

Hydrologic calibration at station M.7 0

5000

100 Observed at M.7

Precipitation,Ubon

200

1000

400

0

500 9 pr -9

27

-M

-A 29

13

r -9 9 pr -9 -A

Ap 1-

15

ay -M 9 9 a 10 y-9 9 -J un 2 4 - 99 -J un -9 8Ju 9 l-9 22 9 -J ul 5- -99 Au g 19 -99 -A u 2- g-9 Se 9 p 16 -99 -S e 30 p-9 -S 9 e 1 4 p -9 -O 9 c 2 8 t- 9 9 -O ct 1 1 - 99 -N o 25 v-9 -N 9 ov 9D e 99 c 23 -99 -D e 6- c-9 9 Ja n20 0 0 -J an -0 3Fe 0 b 17 -00 -F e 2- b- 0 0 M ar 16 -00 -M a 30 r- 0 -M 0 ar -0 0

300

9

2000

600

-1000

Date Fig. 13. Calibration of hydrologic parameters in the Mun River.

Validation of hydrologic parameters at M.7 (Water Year 2000 to 2003) 8000

0

7000

5000

100

200 4000 3000 300 2000 1000

400

0 -1000

500 2 0 1 3 0 2 2 1 2 1 3 2 1 0 1 1 0 2 0 -0 ay-0 ul-0 p-0 v-0 n-0 ar-0 y-0 ul-0 p-0 v-0 n-0 ar-0 ay-0 ul-0 p-0 v-0 n-0 ar-0 r a -J o Ja M o Ja M o Ja M e e -J -J Ap Se 1- 31-M 30 28- 27-N 26- 27- 26-M 25 23-S 22-N 21- 22- 21-M 20 18-S 17-N 16- 17-

Date Fig. 14. Validation of hydrologic parameters in the Mun River.

Precipitation (mm)

Observed Flow at M.7 Simulated Flow, Reach 1 Precipitation, mm

6000

Precipitation (mm)

Simulated, Reach 1

3000

Flow (Cubic meter per second)

Flow (cubic meter per second)

4000

A. Akter, M.S. Babel / Journal of Hydrology 452–453 (2012) 232–246 Table 7 Statistical performance of the sediment measures for the model. Station No.

M.89 M.5

245

Acknowledgements

Calibration period (Water year 1999)

Validation period (Water year 2000)

EI

r

EI

r

11.13 2.46

0.41 0.26

1.69 0.42

0.44 0.16

The erodible soil property of the Mun basin generally posed a higher detachment tendency, and from the application of HSPF through the BASINS 3.1 model, comparative measures showed that paddy land is the highest contributor as far as detached soils are concerned among all the six different land uses. The best set of parameter values was validated for sediment concentration for the water year 2000 and resulted in a negative Nash Suttcliffe coefficient of 0.42 and r2 of 0.03. Engelmann et al. (2002) had also found the Nash Suttcliffe coefficient to be 0.88 and r2 to be 0.36 at the Ohio watershed, and Carson (Carson, 1986) had found r2 of 0.36 in the same watershed.

9. Conclusions The parameters used to measure water quality show seasonal variation which can be concluded as: (i) most of the physical, chemical and all of the coliform parameters showed higher values in the wet season; (ii) heavy metals, moreover, appeared during the wet season and (iii) although the presence of pesticides was very nominal, higher values were detected in the wet season. The TN:TP ratio showed an increasing trend and, for the year 2004, it was as high as greater than 14 in the Lower Mun. The weekly monitoring provided a realistic variation in nutrients and suspended solids along the river. The isohyetal map provided the overall hydrological features of the basin based on the rainfall data of 15 years. Consequently, the mean seasonal hydrologic statistics showed that the upper basin had the least rainfall and highest evaporation, leading it to be the driest part of the basin. The relationship of sediment concentration and flow and rainfall showed that insofar as land use is concerned, the upper part showed a good correlation (r2 = 0.97). The hydrological and sediment components of the Hydrologic Simulation Program FORTRAN (HSPF) module under the Better Assessment Science Integrating Point and Nonpoint Sources (BASINS 3.1) model were calibrated and validated using 48 months and 24 months of observed data respectively. The two limitations of the data needed to be noted are:  Lack of data on land use change provided only a sketchy representation of the hydrological responses in the basin.  Inadequate information about the discharge of pollutants at the point and non-point sources restricted the proper presentation of variation in nutrient in case of the establishment of the HSPF model. The model performance indicators ensure the acceptable applicability of the HSPF model to simulate flow and nutrients transport in the river and to establish the relationship between the basin’s land use and hydrologic parameters. Given a better performance from weekly sampling measures, it can be inferred that the frequent water quality sampling approach needs to be the fundamental consideration in decision making for improved river basin management. And this can be done through the establishment of land use data and hydrologic parameters’ responses in conjunction with river water’s quality parameters.

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