THA 2017 Inte rnational Confe rence on “Water Management and Climate Change Towards Asia's Water-Energy-Food Nexus” 225 - 27January 2017, Bangkok, Thailand.
SWAT and MODFLOW Modeling of Spatio-Temporal Runoff and Groundwater Recharge Distribution J. Laonamsai 1, a and A. Putthi vi dhya2,a*
Abstract Thailand’s changing climate patterns has led to instability and challenges to the people and the nation’s economy. Drought caused by irregular rainfall has become a significant issue in Thailand in the most recent years as the central plain has no large water reservoirs of its own and is currently relying on dams in the lower Northern region for water. Effective water management needs to be practiced and conjunctive use of surface water and groundwater is being implemented. Interactions between groundwater and surface water in a basin have significant impacts on water management both at local and regional scales, particularly the spatial patterns of these interactions, as they provide basic knowledge on surface water and groundwater dynamic behavior. Th is paper aims to explore SWAT model for spatio-temporal surface water simulation and the estimation of groundwater recharge rates throughout Yom river basin during 1999-2012. Sensitivity analysis, calibration, validation, and uncertainty analysis are performed by SWAT-CUP software against streamflow and groundwater level. Due to the semi-distributed features of SWAT and the difficulty of calculating groundwater distributed parameters, recharge values estimated by SWAT are used in a MODFLOW model for groundwater simulation at steady and unsteady states. Although daily time steps are used to calculate groundwater discharge rates along the stream, modeling results are also averaged by month to determine seasonal trends. Results indicate high spatial variability in groundwater discharge. Average annual groundwater discharge is 24.73 m3 /s, with maximum and minimum rates occurring in September-October and March-April, respectively. Annual average rates increase by approximately 2.03 m3/s per year over the 14-year period, negligible compared with the average annual rate with 71.43% of the stream network experiences an increase in groundwater discharge rate between 1999 and 2012. Keywords SWAT, MODFLOW, groundwater recharge, runoff, conjunctive use A. Putthividhya Assistant Professor : Department of Water Resources Engineering Faculty of Engineering, Chulalongkorn University Bangkok, Thailand
[email protected] m
J. Laonamsai Graduate Student : Department of Water Resources Engineering Faculty of Engineering, Chulalongkorn University Bangkok, Thailand teeyoon100@g mail.co m Introduction Thailand’s changing climate patterns has led to instability and challenges to the people and the nation’s economy. Drought caused by irregular rainfall has become a significant issue in Thailand in the most recent years as the central plain has no large water reservoirs of its own and is currently relying on dams in the lower Northern region for water. Long periods of droughts are impacting rice and other cash crops production. Water scarcity is a global threat that is estimated to hit Thailand hard and the country is in need to develop a long-term plan to deal with these challenges. A thorough understanding of watershed hydrology is essential for sustainable water management, of particular importance are the interactions of groundwater and surface water. Without proper understanding of the hydrologic components for the planning of water resources, many problems may arise when attempting to establish water resource planning and security, optimize conjunctive use of groundwater and surface water (Sophocleous 2002); and evaluate the sensitivity of stream flows to changing climate or landuse alterations. These problems might eventually lead to complications and inaccurate predictions. Up till now, hydrologic component analysis in Thailand has concentrated on the management of surface water, while problems related to groundwater have not been managed in a proper manner. Furthermore, the groundwater model frequently used in Thailand was not adequately linked to surface water analysis. The main focus in many previous studies has been primarily on aquifer management. For example, groundwater recharge could not be considered in terms of hydrological processes, which are directly related to precipitation, evapotranspiration, and surface runoff. Groundwater recharge rate was then an output to the groundwater model and had therefore been determined from trial and error during calibration. Many techniques have been employed to estimate patterns of groundwater/surface water interactions at spatial and temporal scales, including water balance (Krause et al. 2007), Permeameter tests and seepage meters (Avery 1994), electrical resistivity surveys (Nyquist et al. 2008), and tracer
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tests (Harvey and Bencala 1993). Typically these previous studies have been undertaken at small spatial scales over a short time period. For assessment at larger spatio-temporal scales, numerical modeling approaches have frequently applied. The best modeling effort should rely on a long-term rainfall runoff simulation that can effectively produce an integrated analysis for both the groundwater and the surface runoff. It is also essential for the model to be able to examine the hydrologic effects while concurrently allowing hydraulic interaction between surface water and groundwater. SWAT model is widely used for long-term runoff and water quality simulations. It was originally developed from the CREAMS (Knisel 1980) and SWRRB (Williams et al. 1985) models with channel routing and groundwater components added for larger watersheds. One of the most essential components of an efficient groundwater model is the accuracy of recharge rates within the input data. The conventional groundwater flow analysis performed by MODFLOW often overlooks the accuracy of the recharge rates that are required to be calculated into the model. Consequently, there is considerable uncertainty in the simulated groundwater flow results. To enhance understanding of large-scale groundwater-surface water interactions, and to taken the accuracy of the groundwater recharge component into consideration, this study virtually integrate SWAT and MODFLOW models to explore the spatial patterns of surface water and groundwater discharge to the stream and river network of a relatively large basin as well as estimate the groundwater discharge rate based on the hydrologic analysis from the watershed. Our study area is focused at the Yom river basin located in the North of Thailand with large irrigation serviced fields scattered all over the place, leading to individual private groundwater wells installation to compensate the frequent shortage of surface water. Spatio-temporal surface water simulat ion and the estimation of groundwater recharge rates are being conducted. Sensitivity analysis, calibration, validation, and uncertainty analysis were performed by SWATCUP software against streamflow and groundwater level. Due to the semi-distributed features of SWAT and the difficulty of calculat ing groundwater distributed parameters, recharge values estimated by SWAT are used in a MODFLOW model for groundwater simu lation at steady and unsteady states. Although daily time steps are used to calculate groundwater discharge rates along the stream, modeling results are also averaged by month to determine seasonal trends. The combined use of both surface water and groundwater to meet the total local water demand provides a solution to this insecure situation as known as conjunctive water management (Chun 1964). Factors affecting the degree of interactions of surface water and groundwater include topography, underlying geology, subsurface hydraulic properties, temporal and spatial variation in precipitation, and local groundwater flow patterns (Cey et al. 1998). Determining the surface water-groundwater interactions and groundwater recharge sources is important for the effective management of groundwater resources, especially in drought threatening water resources management, and in the future
conditions under climate uncertainties as well as determination of migration pathways for contaminants (i.e., contaminated shallow aquifer recharges deep groundwater). Study area The upper Chao Phraya plain of Thailand shown in Figure 1 covers about 160,000 km2 with a population of 4 million people. The main landuse is 63% agriculture, out of which 21% is irrigated, and 24% is devoted for forestry. More than 90,000 groundwater wells exist in the region to serve as primary and secondary source of water supply.
Y.31
Y.14
Fig. 1 Study area (Upper Chao Phraya River Basin) Yom river basin is one of the eight sub-basins, stretching from latitude 15q 50’ N to 19q 25’ N and from longitude 99q 16’ to 100q 40’ E with a watershed area of 23,616 km2. The basin covers about 16.56% of the Chao Phraya river basin. The climate of the study area belongs to the tropical monsoon type. Terraced mountain mainly characterize the topography of upper Yom basin from Phayao province to Phrae province, and then followed by floodplain area at Sukhothai, Pichit, and parts of Phitsanulok provinces. The main landuse of the basin is predominantly rice cultivation and cattle grazing. Most surface water is utilized for agricultural purposes in rainy season. Domestic and industrial water rely on water supply and groundwater. Agricultural sector also uses groundwater in conjunction with surface water in dry period. Groundwater table decline is spotted in some parts of the irrigated serviced fields due to uncontrolled severe pumping, leading to the critical current and future groundwater accessibility problems for the entire public water users in the system. To understand the use of water resources in this basin, the fundamental characteristics and recharge sources of the groundwater aquifer need to be analyzed.
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Table 1 Groundwater storage and renewable water resources of the sub-basins (UNESCO) Groundwater Basin
Chaingmai-Lampoon Lampang Chiangrai-Prayao Prae Nan Upper Chao Phraya Lower Chao Phraya Total
Fig. 2 Study area (Sukhothai Sub-Groundwater Basin)
Groundwater Storage (million m3)
Renewable Water Resources (million m3)
485 295 212 160 200 6,400 6,470 14,222
97 59 42 32 40 1,280 1,294 2,844
Overview of SWAT and MODFLOW Hydrogeological Conditions of the Study Area Chao Phraya river basin is divided by 5 major rivers that flow from North to South, forming a depositional flood plain geological unit with average elevation of 40-60 m above MSL. On the Eastern and Western sides are surrounded by mountains of volcanic rocks. The basin drains directly into the lower basin in the South, though the free discharge is partially obstructed by crystalline rocks. Annual average rainfall of 900-1,450 mm is reported in the study region. The wet season starts from April to September and accounted for 81% of the total precipitation. On the other hand, 19% of the total rainfall occurs in the dry season between October and March. Irrigation water is diverted from Nan River at Naresuan diversion dam. The groundwater aquifer forms the geological basis as a depositional flood plain from North to Southeast with mountains of volcanic rocks surrounded in the West. More than 3,000 groundwater wells are present in this area with several groups of active intensive groundwater extraction. The aquifer systemwas defined as two-layered aquifer with the thickness of the upper, semi-confining layer varying between 10-70 m and lower confining layer between 100-300 m based on the hydrostratigraphic concepts that relying on the geological conditions of similar hydrogeologic properties and their confining boundaries. Hydrogeologically, the Chao Phraya river basin is comprised of seven groundwater sub-basins: ChiangmaiLampoon basin, Lampang basin, Payao basin, Prae basin, Nan basin, upper Chao Phraya basin, and lower Chao Phraya basin. Within these groundwater sub-basins, water is held in either confined or unconfined aquifers. Eight separate confined aquifers are located in the Upper Tertiary to Quaternary strata of the Bangkok area. Groundwater storage and renewable resources have been estimated for each groundwater sub-basin, as shown in Table 1.
SWAT was developed by the US Department of Agricultural Research Service to simulate water flow, nutrient mass transport and sediment mass transport at the watershed scale. It is a continuous, basin-scale, distributedparameter watershed model emphasizing surface processes, dividing the watershed into sub-basins which are then further divided into multiple unique combinations (HRUs) of land use, soil and slope for which detailed water, nutrient and sediment mass balance calculations are performed. These HRUs may or may not be spatially contiguous within a subbasin.
Fig. 3 Schematic demonstrating the SWAT-MODFLOW coupling and spatial interaction from SWAT Hydrologic Response Units (HRUs) to MODFLOW grid cells. Calculations in SWAT are performed for each HRU and then scaled up to the sub-basin outlet by the percent area of the HRU within the sub-basin. This approach results in the HRUs lacking the spatial relations typically seen in a fully distributed model, but yields a computationally efficient calculation scheme allowing for rapid watershed simulation over long time periods. Additionally, water, nutrient and sediment output fromeach HRU are routed directly to the sub-basin stream fro routing through the stream network. For groundwater-surface water interactions, therefore, system response variables such as groundwater discharge to streams or river seepage to the aqifer are available only at the sub-basin level. Furthermore, indicators of groundwater storage such as water table
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elevation are not geographically located. Because of the simplistic representation of subsurface processes, SWAT often performs poorly when applied to watersheds wherein groundwater discharge contributes significantly to streamflow (Perterson and Hamlet 1998; Spruill et al. 2000; Chu and Shirmohammadi 2004; Srivastava et al. 2006; Gassman et al. 2007). MODFLOW is a three-dimensional, physically based, distributed finite-difference groundwater model for variably saturated subsurface systems. A recent addition to MODFLOW is a Newtonian-based solver algorithm that better satisfies the complex non-linear drying and rewetting of grid cells in unconfined groundwater systems (Niswonger et al., 2011), a problem with previous model versions. Available processes to be simulated in MODFLOW include groundwater recharge, vadose zone percolation (UZF1 package) (Niswonger et al. 2006), evapotranspiration, pumping, discharge to subsurface drains and river-aquifer interactions. However, model application is limited to investigating management and climate effects on groundwater and groundwater-surface interactions, as MODFLOW does not simulate surface processes, such as land-atmosphere interactions, infiltration and surface runoff, nutrient cycling and transport, plant growth and the impacts of management practices on agricultural systems.
Fig. 4 Modular Groundwater Flow (MODFLOW) grid,showing the stream network, the river cells and the cells in which pumping wells are located. MODFLOW and SWAT were developed and calibrated separately for Sukhothai Sub-Groundwater basin. Later on, the calibrated SWAT and MODFLOW models developed after this study will be used to investigate the impacts of combined climate change and landuse pattern change on surface runoff and baseflow in the studying area. The original M ODFLOW model encompasses the entire Sukhothai Sub-Groundwater basin., an area of approximately 6,956 km2 . The region was discretized comp letely into fin ite difference grid cells with a lateral dimension of 1000 m by 1000 m, aligned in a grid consisting of 200 east-west oriented rows and 100 north-south oriented columns with the aquifer discretized vertically into 2 layers of varying thickness based on the local hydrogeologic units. The simu lation period of the model is fro m 1999 to 2012, but
was only calib rated for period between 2008 and 2012 at station well NT99 and NT92. Shown in Figure 5.
Fig. 5 Observed and SWAT-MODFLOW simulated time series of water table (m) for Sukhothai Province. The SWAT model used for Yom river basin included a 30 m National Elevation Dataset raster for land surface topography, a National Hydrography Dataset stream layer to delineate the stream network, a National Land Cover dataset 2006 for land use, and the national soil map provided by Land Development Department (LDD). The model was calibrated and tested during the 2004-2008 period using monthly stream discharge, with automated calibration. Model performance was generally acceptable for monthly stream flow with a Nash Sutcliff of greater than 0.7 in the calibration and validation periods at station Y.31 and Y.14 (basin outlets). The 30 river cells in Yom river basin are illustrated along the stream network in Figure 4.
Fig. 6 Observed and SWAT-MODFLOW simulated time series of stream discharge (m/s2) for The discharge gauge (Y.31) of The Yom River Basin.
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THA 2017 Inte rnational Confe rence on “Water Management and Climate Change Towards Asia's Water-Energy-Food Nexus” 225 - 27January 2017, Bangkok, Thailand.
Fig. 7 Observed and SWAT-MODFLOW simulated time series of stream discharge (m/s2) for The discharge gauge (Y.14) of The Yom River Basin. Fig. 9 Cell-wise water table elevation for MODFLOW grid in the Sukhothai sub-groundwater basin at the end of the simulation period (2012)
Results and discussion General model results
Fo r st reamflo w in the main Yo m river, observed and s imu lated st ream d ischarge at gaug ing stations along th e river (see Figu re 1 for locat ion ) is illust rat ed in Figu re 6 and 7, demonst rat ing fairly good trend o f the mod el simu lat ion resu lts with th e long -t erm hyd rog raph . Co mparison stat ist ics (Nash Sutcliff NSE; coefficient of det erminat ion R2 ) bet ween th e observed and s imu lat ed d ischarge rates are p resented in Tab le 2. Th e NSE fo r month ly discharg e rates are cons iderab ly acceptab le as they are g reat er than 0.5 (Mo rias i et al. 2007). A possib le cont ribut ion to the lo w fitt ing stat ist ical parameters is perh aps due t o the sparse g round wat er data at so me observat ion po ints.
Annual averag e recharg e (mm) calcu lat ed fro m the daily rech arge valu es in SWA T is shown in Figu re 8, demonstrat ing h igher rech arg e rates in th e upland fo rested areas and the watershed out let area, with lo w rech arg e rates along the main cent ral area of the floodp lain.
Table 2 Comparison statistics (NSE, R2) between the observed and simulated hydrograph at stream gauge (Y.31 and Y.14) of the Yom river basin for the SWAT-MODFLOW model. Fig. 8 Spatially-varying annual average recharge (mm) in Sukhothai sub-groundwater basin as simulated by the coupled model Simu lat ed groun d water hyd rau lic head in m at the end o f th e simu lat ion is shown in Figu re 9, with the h ighest water tab le elevat io n o f 472 m. (M SL) o ccu rring in the mountainous reg ions of Yo m river and lo w g round water level o f 181 m. (M SL) occu rring along th e Yo m river and tributaries . Overall, simu lat ed g round wat er head in th e No rth west reg io n o f th e b asin is abso lut ely h igh er than th e Sou theast reg ion .
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Stream Flow Calibration and Validation at Y.31
Index
Calibration (2004-2008)
Validation (1999-2003)
Validation (2009-2012)
R2
0.705
0.628
0.695
NSE
0.649
0.599
0.511
Stream Flow Calibration and Validation at Y.14 Index
Calibration (2004-2008)
Validation (1999-2003)
Validation (2009-2012)
R2
0.716
0.638
0.77
NSE
0.673
0.622
0.695
THA 2017 Inte rnational Confe rence on “Water Management and Climate Change Towards Asia's Water-Energy-Food Nexus” 225 - 27January 2017, Bangkok, Thailand.
References
Fig. 10 Simulated Average annual Groundwater discharge (m3/s) from the aquifer to the stream network.The principal groundwater discharge have between 3.21 m3/s to 35.64 m3/s. Figu re 10 sho ws the averag e annual ground water d ischarge (CM D) for various stat ions alo ng main Yo m river. As seen in the figu re, th e vast majo rity o f groun d water-su rface wat er int eract ion is disch arge fro m the aqu ifer to th e stream, wh ich match es prev ious study using isotop e fingerp rint ing techn iq ue.
Fig. 11 Plot of ɁD vs. Ɂ18O for groundwater samples in upper Chao Phraya river basin and LMWL (Chiang Mai province). LMWL represents the Local Meteoric Water Line. Acknowledgement
The authors thank the following for financial support: x Asahi Glass Foundation for their financial support in academic year 2015 x The Science and Technology Research Partnership for Sustainable Development, JST_JICA, Japan x The 90th Anniversary of Chulalongkorn University Rachadapisek Sompote Fund
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