Proceedings of the 19th

IAHR-APD Congress 2014, Hanoi, Vietnam

ISBN 978604821338-1

1

ASSESSMENT OF RICE PRODUCTIVITY UNDER CLIMATE CHANGE FOR CA MAU PROVINCE, VIETNAM

Proloy Deb*1, Duong Anh Tran2 and Nguyen Mai Dang3,4

1Water Engineering and Management, School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4 KlongLuang, Pathumthani 12120, Thailand

2Lehrstuhl für Wasserbau und Wasserwirtschaft Technische Universität München 21, D-80333 München, Germany

3Center for International Education, Hanoi, Vietnam

4Institute for Hydrology, Environment and Climate Change, Hanoi, Vietnam

*Corresponding author. Tel.: +662524 5793. E-mail address: debproloy@gmail.com (P.Deb)

ABSTRACT

The present study analyzes the spatial and temporal impacts of climate change on paddy yield for autumn-winter (AW) and summer-autumn (SA) cropping pattern. Climate variables for the future time windows were derived from general circulation model (HadCM3) which were further downscaled using SDSM 4.2 at three different stations for three future time periods (2025s, 2055s and 2085s). AquaCrop 4.0 was used to project the future paddy yield for the considered stations. Our results illustrate that climate change will elevate the maximum and minimum temperatures for all the stations ranging from 1.60 to 2.65°C and 2.08 to 2.97°C respectively for 2085s. An increase in average annual rainfall is also observed ranging from 8.8 to 35.3% for the three stations for the corresponding time window. Simulation for rice productivity shows a decline of paddy yield by -13.75 and -9.76% by 2085s relative to baseline climate for SA cropping season in case of A2 and B2 scenarios respectively. However, an increasing trend in paddy yield was observed in AW season with a boost of 12.5 and 8.12% for corresponding scenarios for period.

Keywords:Climate change; rice yield;SDSM v 4.2;AquaCrop v 4.0;Ca Mau;Vietnam

1. INTRODUCTION Increase in agricultural production is a leading challenge in present context due to the dependence of economy on agriculture for developing nations like Vietnam. In spite of the major agricultural developmental plans of the Vietnamese government over the past 20 years, a significant fluctuation in the productivity of rice has been detected (IRRI 2013) in addition, more than 30 million children have been identified mal-nourished in the country (NIN 2010). In terms of GDP, agriculture accounts 22% share of Vietnam’s economy (World Bank 2012). Being the second largest exporter of rice in the world, production is intensified and is expected to be more in forthcoming years (APN 2007). Additionally, rice is also the staple food of the people of Vietnam. Although a major proportion of agriculture is rainfed, in addition to the available rain water, rice productivity is also dependent on accessible water of acceptable quality for irrigation (Trinh et al. 2013). The overwhelming dependence of Vietnam’s economy on rainfedagriculture makes it sensitive to climatic variables. Vietnam is identified to be in the top five developing nations which are impressionably susceptible to the impacts of climate change. In addition to high intensity

droughts in recent years Vietnam has also experienced sea level rise by approximately 20 cm in past 50 years (Sadoff& Muller 2009; IPCC 2007a). It is also anticipated that the nation will experience a sea level rise of 1 m by the end of this century affecting 10% of GDP in terms of economic loss; approximately 31% of land area in Mekong delta including 9800 km2 agricultural area (MONRE 2009). Due to the intensified agricultural policy in Vietnam, the extensive use of groundwater resources in Mekong delta for farming operations during dry season has enforced the saline water intrusion at an overwhelming rate (IUCN 2011). Additionally it has also been estimated that, considering today as the baseline, there is an expected reduction in total volume of freshwater by 4, 9 and 14% for the year 2025, 2070 and 2100 respectively (DWRM 2008). This will enforce the farmers to use the invaded saline groundwater for rice cultivation in the dry season. Climate change is expected to have a wide-ranging effect on the environment and socio-economic sectors particularly in the water and agriculture (IPCC 2007b; Mestre-Sanchís&Feijoo-Bello 2009). It affects the spatial and temporal pattern of precipitation and temperature which actively changes the plant phenological process and consecutively affects productivity (Tao et al. 2003). Specifically change in temperature can affectively alter

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ISBN 978604821338-1

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the evaporative demand of crop (Tao et al. 2003; Roderick & Farquhar 2002). High intense temperature for short intervals can lead to sterility of the spikelet in rice (Masutomi et al. 2009). Nelson et al. (2009) further contended climate change will contribute to yield reduction of the staple crops in South and Southeast Asian developing countries. The yield reduction is mostly anticipated due to the shortened growth period along with reduction in photosynthesis rate and enhanced respiration, thus increase in water requirement. Many parts of the world have already experienced significant reduction in rice yield due to climate change and therefore it is of major concern (Babel et al. 2011; Masutomi et al. 2009; Krishnan et al. 2007). Although climate projections by general circulation models (GCMs) have been accepted globally as an essential tool for impact assessment studies (Ramirez-Villegas et al. 2013;Zhi-Qing and Da-Wei 2008) yet the recent studies shows the impacts on agricultural sector is still under altercation (Asseng et al. 2013). Global studies illustrate an increase of 4˚C can cause massive food insecurity in the world (Thornton et al. 2011). In many countries studies have reported that varying climatic variables also has led to progressive loss in yield compared to the potential (Laux et al. 2010; Falloon and Betts 2010; Meza et al. 2008). Additionally, modelling studies also have shown altered agricultural productivity in many countries in tropical and sub-tropical areas where agriculture is highly sensitive to climate variables (Osborne et al. 2013; Babel et al. 2011;Lane and Jarvis 2007;Inthavon et al. 2004; Buddhaboon et al., 2004) and leads to restrain the adaptation strategies (Thornton et al. 2011). Based on literature, even though it is known that climate change will have severe impacts on paddy yield, however a local scale study integrating the climate change and its implications on double season cropping (summer-autumn and autumn-winter) is of utmost importance in a coastal region of Vietnam. The study can be divided into two major objectives; first, projection of future climate while the second part deals with the prediction of the future rice yield under climate change. The outputs of this study can be used as a basis for identifying various agro-adaptation measures that

farmers can possibly follow. In addition it is also useful for the policy makers to device proper management alternatives for water resources under climate change. 2. MATERIALS AND METHODS 2.1 Study area

The study was carried out in the coastal region of Ca Mau province in Vietnam. It is located within 8° 33́-9° 35́ N latitudes and 104° 42ʹ - 105° 24ʹ Elongitudes (Fig. 1). The province covers an area of 5,331 km2, making up 13.13% of the Mekong River Deltawith an average elevation of 0.5amsl.It also consists of 254 km long coastline which has caused prominent sea water intrusion in recent years. Three representative stations Tran Van Thoi (9° 09ʹ N; 104° 54ʹ E), Ca Maucity (9° 11ʹ N; 105° 09ʹ E) and Nam Can (8° 46ʹ N; 104° 57ʹ E) were considered for the study which have subequatorial tropical climate. Temperature and precipitation at the three stations considered are shown in Table 1 and 2 respectively. It can be observed that>70% average annual rainfall occurs in the wet period (May to November) for all the stations which implies higher application of water for dry period (December to April) paddy cultivation. Soil in the study site is mostly alkaline and alum poisoned and hence it is termed as fertile-limited soil; therefore fertilizers are necessary to be applied for agricultural production. Being in the delta region, the site has abundant surface and groundwater resources, yet the latter is mostly supplied for irrigation purpose as surface water is basically applied to other sectors. In the study area farmers usually prefer double cropping system with the summer-autumn (SA) and the autumn-winter (AW) cropping season. The commonly grown rice cultivar is OM6976 which has a growing cycle of 88 days from transplanting to maturity was considered for this study. For the SA season the common transplanting period is mid-May and harvesting is done at mid-August followed by the AW season where the paddy is transplanted on 1st week of September and harvested by 1st week of December.

Table 1.Maximum and minimum temperature at the study sites for baseline period

Station Annual Tmax (°C) Average annual Tmax (°C)

Annual Tmin (°C) Average annual Tmin (°C)

Tran Van Thoi 33.21 29.61 18.17 24.52 Ca Mau city 34.47 31.64 19.11 26.96 Nam Can 37.19 34.31 19.35 27.65

Table 2.Temporal distribution of precipitation in the study sites

Station Annual (mm) Wet (mm) Dry (mm)

Tran Van Thoi 2864 2004 860 Ca Mau city 2427 1705 722 Nam Can 3112 2371 741

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2.2 Methodology

Outputs of two climate scenarios derived from the special report on emission scenarios (SRES), A2 and B2 from the global climate model (GCM) HadCM3 (Gordon et al. 2000) were downscaled statistically for three representative stations to forecast the future temperature and precipitation. Observed climate data were obtained from Department of Hydrology and Meteorology for three stations which were used in the downscaling process. The outputs of climate downscaling were further used as input for the calibrated crop simulation model AquaCrop 4.0 (Steduto et al. 2009) in order to forecast the rice yield for the summer-autumn (SA) and autumn-winter (AW) seasons. Fig. 2 shows the methodological framework applied for projection of yield under climate change and salinity in this study. The methodological flowchart was developed from the existing one used by Mishra et al. 2013 and Babel et al. 2011 where they applied to assess the impacts of climate change on rice yield at Indian Ganga Basin and Northeast Thailand respectively:

2.3 Climate change scenario

Forecasting future meticulously is very difficult especially when the predictions can generate feedback and change in human behavior. Hence, in order to assess the potential impacts of climate change on agriculture, scenarios of plausible future changes are used. They serves as convenient tool for analyzing how different driving forces and responses may influence future outcomes and has been used successfully applied in many studies all over the world (Sanabria and Lhomme 2013; Shrestha et al. 2013; Chung and Nkomozepi 2012). Although there are four climate scenarios that has been described by IPCC, but for this study, as suggested by MONRE 2009, the most probable scenarios in context to Vietnam are A2 and B2 and hence were used. The A2 scenario depicts a heterogeneous world with continuously increasing population and regionally focussed economic growth; in addition to less concern with rapid economic development. Whereas, B2 scenario represents continuously increasing population with emphasis on local rather than global solutions to economic, social and environmental sustainability.

2.4 Downscaling climate variables from GCM to station level

Although GCMs predict the future climate variables but at a very coarse resolution. Moreover, regional features like topography, vegetation, cloudiness and local climate effects are not incorporated by GCMs (IPCC 2007b) and therefore to make regional studies, it is necessary to transfer

the coarse resoultion to a finer scale by downscaling technique. Two most commonly used downscaling techniques are statistical and dynamic methods. Dynamic method requires high computation capacity and in addition nesting of GCM is done which is a complex process. However, statistical downscaling presumes relating large-scale predictor variables to station scale predictand and the relationship is assumed to be constant for the future climate. Due to the simplicity in steps and less computational time required for statistical downscaling, it was conducted for the present study.

Though there are several techniques available for statistical downscaling, SDSM v 4.2 (Wilby and Dawson 2007) has been used succesfully on a global scale (Babel et al. 2013; Rehana and Majumdar 2012; Toews and Allen 2009) was used for this study. The model develops multiple linear regression transfer function between GCM predictors and local climate variable. The principle of spatial downscaling is given in follows:

R = F(L) [1]

where R represents the predictand (local climate variable), L represents predictors (a set of large scale climate variables) and F represents deterministic/stochastic relation by L. Baseline period here defined as the past climate (1973-2000 for Tran Van Thoi; 1971-2000 for Ca Mau city and 1982-2000 for Nam Can stations) for which the observed data was available. The climate projection done were divided into three time windows for 21st century, each of 30 years period: 2011-2040 (2025s), 2041-2070 (2055s) and 2071-2099 (2085s).

2.5 Crop model used AquaCrop

The crop simulation model used for this study was AquaCrop v 4.0. It is a windows based programme designed to simulate biomass and yield responses of field crops to different degrees of water availability under various soil conditions and climate change. It incorporates the soil-crop-atmosphere components by its soil and water balance, atmosphere (rainfall, temperature, evapotranspiration and carbon dioxide concentration), crop characteristics (canopy cover, root depth, biomass production and yield) and field management practices (irrigation, fertility and agronomic practices) components (Raes et al. 2009a). It is also capable to calculate the daily water balance and can separate evapotranspiration into evaporation and transpiration. The model calculates the above ground biomass based on the Eq. 2 where it is a function of normalized water productivity, transpiration (factor of canopy cover), reference evapotranspiration (RE) and air temperature stress

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coefficient. The cumulative above ground biomass calculated is then converted into yield based on Equation 3.

𝐵 = 𝐾𝑠𝑏 × 𝑊𝑃∗ × ∑𝑇𝑟

𝐸𝑇𝑜 [2]

𝑌 = 𝑓𝐻𝐼 × 𝐻𝐼𝑂 × 𝐵 [3]

where, B is above ground biomass in kg m-2, Ksb is air temperature stress coefficient, WP* is normalized water productivity in kg m-2 mm-1 which is normalized for CO2, type of product synthesized and soil fertility which is suggested to be kept 15 and 20 kg m-2 mm-1 for rice (Raes et al. 2009b), Tr is transpiration in mm and ETorefers to evapotranspiration in mm. Y is referred as yield in kg m-1, fHIis adjustment factor for all the stress that affects the yield of crop, HIO is the reference harvest index and B is the above ground biomass mentioned earlier in kg m-2. AquaCrop being a simple and less data intensive model was selected for this study because of its robust performance in various regions of the world (Abedinpour et al. 2012; Mkhabela and Bullock 2012; Geerts et al. 2009) moreover, intensive crop phenology associated data availability being an issue for the study site. The model has several default values for crop parameters including rice, however, handful of parameters need to be tuned based on the local conditions, cultivars and management practices.

3. RESULTS AND DISCUSSION

3.1 Calibration and validation of SDSM

The calibration process derives relationship among the meso-scale screened predictor variables and observed station data. The summary of the selected predictor variables and respective predictands for the selected stations are presented in Table 3. From the table it is evident that various local variables

influence the atmospheric global variables. Geopotential height at 500 hpa influences maximum and minimum temperature for all the stations however, near surface relative humidity and relative humidity at 500 hpa is observed to have a significant relationship for precipitation at all the three stations. Moreover, it can also be noted, at different stations various predictors have good relationship with the predictands. For instance, at Tran Van Thoi station air low strength at 850 hpa is more sensitive to maximum temperature whereas, mean sea level pressure and zonal veocity at 500 hpa is more sensitive to the corresponding predictand at Ca Mau city and Nam Can stations respectively. Due to influence of predictors to predictands are different for various stations considered, calibration of SDSM was done for the three stations seperately. The observed historic data from 1973 – 1990, 1971 – 1990 and 1982 – 1994 were used for calibration for the Tan Van Thoi, Ca Mau city and Nam Can stations respectively.

Following the calibrated transfer functions, the climate variables (Tmax, Tmin and Precp.) were generated for the three selected stations. The generated values were further compared to the observed station data to validate the model. Validation period considered for this study were 1991 – 2000 for Tran Van Thoi and Ca Mau city whereas for Nam Can station 1995 – 2000. The comparison of the model with observed data for validation was done based on the average monthly temperature and precipitation. Table 4 represents the performance of SDSM during calibration and validation process. The values indicates the model simulates Tmax and Tmin in good agreement with the observed data however, the model estimated Precp. with poor accuracy although it is in acceptable range as demonstrated by Shrestha et al. 2013.

Table 3.Summary of the selected predictor variables and predictands respectively

Stations Predictand Predictors

Tran Van Thoi Max. temp 500 hpageopotential height (p500) Relative humidity at 500 hpa (r500)

Airflow strength at 850 hpa (8_f) Min. temp Near surface relative humidity (rhum)

Zonal velocity at 500 hpa (5_u) Precp. Near surface relative humidity (rhum)

Surface specific humidity (shum) Mean Sea Level Pressure (mslp)

Ca Mau city Max. temp Mean sea level pressure (mslp) 500 hpageopotential height (P500)

Near surface relative humidity (rhum) Relative humidity at 850 hpa (r850)

Min. temp Airflow strength at 850 hpa (8_f) Relative humidity at 500 hpa (r500) Relative humidity at 850 hpa (r850)

Precp. 850 hpageopotential height (P850)

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Relative humidity at 500 hpa (r500) 850 hpageopotential height (P850)

Nam Can Max. temp Zonal velocity at 500 hPa (5_u) Airflow strength at 850 hPa (8_f)

Relative humidity at 500 hpa (r500) Relative humidity at 850 hpa (r850)

Min. temp Mean sea level pressure (mslp) 500 hpageopotential height (P500)

Surface zonal velocity (P_u) Precp. Surface meridonial velocity )P_u)

Near surface relative humidity (rhumas)

Figure 1.Location of the study area along with the salinity and meteorological station.

Figure 2. Methodological flowchart to assess the impact of climate change and salinity in irrigation water on rice yield

Crop data

Future

precipitation

and temperature

Downscaling

Soil data

SDSM Observed

precipitation

and temperature

Management

data

AquaCrop

Yield for future

climate under

salinity

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Table 4.Performance evaluation of SDSM during calibration and validation

Predictand

Tran Van Thoi Ca Mau city Nam Can

Tmax Tmin Precp. Tmax Tmin Precp. Tmax Tmin Precp.

Calibration R2 0.68 0.88 0.46 0.78 0.84 0.54 0.72 0.78 0.66

RMSE 1.74 1.32 109.34 1.88 1.17 79.52 2.44 1.63 127.46

Validation R2 0.60 0.82 0.46 0.75 0.71 0.33 0.77 0.82 0.48

RMSE 2.83 2.24 163.20 2.44 2.16 117.11 2.31 2.40 136.40

Table 5. Projected future changes in maximum temperature at Tran Van Thoi, Ca Maucity and Nam Can stations

Station

Baseline (°C)

Time period

Maximum temperature (Tmax)

Scenario A2 Scenario B2

Tmax (°C) Change (°C) Tmax (°C) Change (°C)

Tran Van Thoi

29.61

2025s 30.22 0.61 29.72 0.11 2055s 31.61 2.00 30.78 1.17 2085s 32.26 2.65 31.66 2.05

Ca Mau city

31.64

2025s 32.54 0.90 32.16 0.52 2055s 33.82 2.18 33.07 1.43 2085s 34.15 2.51 33.61 1.97

Nam Can

34.31

2025s 34.62 0.31 34.44 0.13 2055s 35.75 1.44 35.22 0.91 2085s 36.51 2.20 35.91 1.60

Table 6. Projected future changes in minimum temperature at Tran Van Thoi, Ca Maucity and Nam Can stations

Station

Baseline (°C)

Time period

Minimum temperature (Tmin)

Scenario A2 Scenario B2

Tmin (°C) Change (°C) Tmin (°C) Change (°C)

Tran Van Thoi

24.52

2025s 25.43 0.91 24.95 0.43 2055s 26.37 1.85 25.83 1.31 2085s 27.43 2.91 26.65 2.13

Ca Mau city

26.96

2025s 27.34 0.38 27.15 0.19 2055s 28.79 1.83 28.26 1.30 2085s 29.93 2.97 29.14 2.18

Nam Can

27.65

2025s 28.42 0.77 28.01 0.36 2055s 29.21 1.56 28.76 1.11 2085s 30.07 2.42 29.73 2.08

Table 7. Projected future changes in precipitation at Tran Van Thoi, Ca Mau city and Nam Can stations

Station

Baseline

(mm)

Time period

Precipitation (Prec.)

Scenario A2 Scenario B2

Prec. (mm) Change (%) Prec. (mm) Change (%)

Tran Van Thoi

2864

2025s 2917 1.9 2946 2.9 2055s 3022 5.6 3034 6 2085s 3216 12.3 3196 11.6

Ca Mau city

2427

2025s 2458 1.3 2442 0.7 2055s 2688 10.8 2864 18.1 2085s 3283 35.3 3152 29.9

Nam Can

3112

2025s 3274 5.3 3177 2.1 2055s 3462 11.3 3294 5.9 2085s 3556 14.3 3384 8.8

3.2 Climate change impact on temperature The projection results for maximum temperature shows an increasing trend for the three stations considered. The late part of the century is expected to have highest increase in maximum temperature. The maximum temperature changes observed from analyzing both A2 and B2 scenarios for Tran Van

Thoi, Ca Mau city city and Nam Can are 2.65, 2.51 and 2.20 ̊C respectively under A2 scenario (Table 5). In addition, it can also be noted although, for both scenarios the expected increase in maximum temperature is higher for 2085s at Tran Van Thoi as compared to Ca Mau city station, yet for 2025s and 2055s reverse is observed. The analysis also illustrates that for all the three stations A2 scenario

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projects higher increase in magnitude of the maximum temperature. Remarkable variation in minimum temperature is also predominant for all the stations. A rise equivalent to 3 ̊C is expected for Ca Mau city station by the end of the century which will have implications on crop yield (discussed later sections). Table 6 illustrates the projected change in minimum temperature for the three stations. Although highest temperature is observed at Nam Can station for the baseline period, yet the expected increase in minimum temperature is least for the future time windows. From the analysis of the two tables, it can be concluded that the increase in maximum and minimum temperatures at various stations are irrespective of each other for future time periods. 3.3 Climate change impact on precipitation

A remarkable increase in precipitation is observed for all three stations at all the time windows considered (Table 7). Although the highest magnitude in precipitation is expected for Nam Can station for A2 scenario (3556 mm), yet the predicted maximum change is anticipated for Ca Mau city station (35%; A2 scenario). Highest change in magnitude is also expected for Ca Mau city station for the mid and late century for both scenarios. The baseline precipitation indicates southern region (Nam Can station) receives more rainfall compared to the other stations. The same trend is also observed from the results obtained by downscaling for future climate. The phenomenon of higher evaporation from sea travelling to landmass in the form of cloud in the coastal areas can be attributed to this.

Figure 3 represents the projected monthly precipitation compared to the baseline period for the three stations and two scenarios considered. Results indicates a significant late shift in the precipitation pattern for both A2 and B2 scenarios and all stations corresponding to all time windows compared to the baseline period. It can also be noticed, due to shift in onset of monsoon longer span of dry season is expected for Tran Van Thoi and Ca Mau city stations. Downscaled results suggests, in case of Nam Can station precipitation follow similar trend for both scenarios although higher magnitude is dominant for A2 scenario. It can also be postulated that due to the longer dry period for Tran Van Thoi and Ca Mau city stations, higher irrigation water is required for the SA cropping season. It is also noteworthy that due to the projected increase in magnitude of precipitation, more intense flood is expected during monsoon season.

3.4 Climate change impact on rice yield Table 8 represents the projected impacts of climate change on paddy yield for SA cropping season. A univocal negative trend was observed in yield for all stations at the three time periods considered. Higher declination in yield is also significant for A2 scenario in all cases. In addition to the immense increase in temperature, greater shift in rainfall pattern for A2 scenario can be attributed to the projected altercation of paddy yield. It is anticipated that, Ca Mau city site will experience the most severe impact of climate change leading to 13.44 to 20.61 % reduction in yield by the end of the century compared to the baseline. Furthermore, at Nam Can site, climate change will have severe impact on paddy yield at the end of the century compared to early part where the loss in yield not significant. Nevertheless, a reduction in paddy yield ranging from 5.63 to 13.75 % and 4.12 to 9.76% is expected from 2025s to 2085s under A2 and B2 scenario respectively at Ca Mau city province for SA cropping season. A contradictory increasing trend in paddy yield was predicted for all stations in case of AW cropping season compared to SA (Table 9). Significant increase in magnitude of the

Precipitation during the AW season can be attributed to the observed yield pattern. Maximal increase in paddy yield (16.48%) was observed for Tran Van Thoi site under A2 scenario. Surprisingly, a remarkable boost of 10% in paddy yield was observed for the corresponding station and scenario for 2025s. Additionally at the Nam Can site, a phenomenal increase of 11.9 and 4.41% in paddy yield was noted for 2055s for A2 and B2 scenarios respectively compared to baseline paddy yield. In average, an escalation of 4.23, 6.51 and 12.5% compared to baseline yield for 2025s, 2055s and 2085s respectively for A2 scenarios were observed. Also a boost of 4.92, 5.62 and 8.12% were observed for the corresponding time periods and for B2 scenario. The above mentioned results can be compared to those derived by Kim et al. 2012 whose results suggests, an increase of 12.6 and 22% for 2050 and 2100 respectively which can be attributed to CO2 elevation. In addition, for China yield is expected to decrease by 22.1 and 35.0% for the corresponding scenarios compare to baseline yield. Erda et al. 2005 predicted an alteration of -12.90, -13.60 and -28.60% in the rainfed paddy yield in China for 202s, 2050s and 2080s under A2 scenario. Similarly study conducted by Babel et al. 2011 indicated paddy yield reduction of 17.81, 27.59 and 24.34% in Northeast Thailand under A2 scenario.

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Table 8 Projected paddy yield under climate change for the SA cropping season

Sites

Baseline

yield (t/ha)

Scenario

2025s 2055s 2085s

Yield (t/ha)

Percent change

Yield (t/ha)

Percent change

Yield (t/ha)

Percent change

Tran Van Thoi 3.836 A2 3.642 -5.06 3.577 -6.75 3.516 -8.34 B2 3.714 -3.18 3.646 -4.95 3.584 -6.60

Ca Mau city 3.794 A2 3.492 -7.96 3.274 -13.71 3.012 -20.61 B2 3.558 -6.22 3.482 -8.22 3.284 -13.44

Nam Can 3.739 A2 3.594 -3.88 3.466 -7.30 3.278 -12.30 B2 3.628 -2.97 3.529 -5.62 3.394 -9.23

Average 3.790 A2 3.576 -5.63 3.439 -9.25 3.269 -13.75 B2 3.633 -4.12 3.552 -6.26 3.421 -9.76

Figure 3. Projected monthly precipitation with the baseline period at (a) Tran Van Thoi, (b) Ca Mau city and (c) Nam Can under

A2 and B2 scenarios for 2025s, 2055s and 2085s

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Table 9 Projected paddy yield under climate change for the AW cropping season

Sites

Baseline

yield (t/ha)

Scenario

2025s 2055s 2085s

Yield (t/ha)

Percent change

Yield (t/ha)

Percent change

Yield (t/ha)

Percent change

Tran Van Thoi

3.836 A2 4.224 +10.11 4.286 +11.73 4.468 +16.48 B2 4.086 +6.52 4.112 +7.19 4.294 +11.94

Ca Mau city 3.794 A2 3.816 +0.58 4.018 +5.90 4.116 +8.49 B2 3.942 +3.90 3.994 +5.27 4.046 +6.64

Nam Can 3.739 A2 3.814 +2.00 4.184 +11.90 4.208 +12.54 B2 3.790 +1.36 3.904 +4.41 3.956 +5.80

Average 3.790 A2 3.951 +4.23 4.163 +6.51 4.264 +12.50 B2 3.939 +4.92 4.003 +5.62 4.099 +8.12

4. CONCLUSIONS

The present study can be divided into two parts, first one deals with projection of climatic variables at basin scale, followed by impact assessment on paddy yield for two different cropping seasons at Ca Mau province of Vietnam. Based on the climate projections by SDSM, it can be concluded that climate projections are essential tool for proper water management at basin scale. The magnitude of temperature change illustrates the future is expected to be more warmer with an increase of 1.60 to 2.65°C and 2.08 to 2.97°C for 2085s respectively for all three stations. Furthermore, a significant increase in the future precipitation is also observed ranging from 8.80 to 35.3% for both scenarios and all stations. A late shift in onset of monsoon is also expected for all three stations andtime windows considered which can significantly lead to prolonged increase in the length of dry spell including higher extent of sea water intrusion. Also, shorter wet spell with high intense rainfall may lead to more frequent flash floods in the region.

Yield simulation by AquaCrop indicates increased annual temperature and precipitation is expected to lower the yield of SA paddy cultivation by 5.63 to 13.75% and 4.12 to 9.76% by the end of the century for A2 and B2 scenarios respectively. However, contradictorily yield is expected to enhance by 4.23 to 12.50% and 4.92 to 8.12% for the corresponding time and scenarios respectively compared to the baseline yield.Hence, from the present study it can be recommended that for paddy cultivation in SA season is negatively influenced by the climate change whereas, the AW cropping is benefitted and therefore further research on uncertainty analysis of paddy yield under climate change and evaluation of several adaptation measures is recommended for the study area to enhance the paddy yield under climate change.

ACKNOWLEDGEMENTS

The authors acknowledge the financial help provided by APEC climate center, Busan, South

Korea and Swedish International Development Agency (SIDA). Wholehearted thanks are given to Department of Agriculture and Rural Development of Ca Mau city, Southern Regional Hydro-meteorological Center and Can Tho University, Vietnam for providing all necessary data.

REFERENCES

Abedinpour M, Sarangi A, Rajput TBS, Singh M, Pathak H, Ahmad T (2012) Performance evaluation of AquaCrop model for maize crop in a semi-arid environment. Agric Water Manage110: 55-56;

APN (2007) Asia-Pacific Network for Global Change Research 2007 Linking Climate Change Adaptation in Sustainable Development in Southeast Asia. Project Reference Number: ARCP2007-05CMY.2007;

Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL et al (2013) Uncertainty in simulating wheat yields under climate change. Nature Climate Change3 (9): 827-832;

Babel MS, Agarwal A, Swain DK, Herath S (2011) Evaluation of climate change impacts and adaptation measures for rice cultivation in Northeast Thailand. Clim Res46: 137-146;

Babel MS, Bhusal SP, Wahid SM, Agarwal A (2013) Climate change and water resources in the Bagmati River Basin, Nepal. TheorApplClimatoldoi: 10.1007/s00704-013;

Buddhaboon C, Kongton S, Jintrawet A (2004) Climate scenario verification and impact on rainfed rice production. Report of APN CAPABLE Project, Southeast Asia START Regional Center, Chulalongkorn University, Bangkok;

Chung SO, Nkomozepi T (2013) Uncertainty of paddy irrigation requirement estimated from climate change projections in the Geumho river basin, Korea. Paddy Water Environ 10: 175-185;

DWRM (2008) Water resources and major challenges, Available on

Proceedings of the 19th

IAHR-APD Congress 2014, Hanoi, Vietnam

ISBN 978604821338-1

10

http://dwrm.gov.vn/en/index.php?cires=News&in=viewst&sid=12 Accessed on 23rd July 2012;

Erda L, Wei X, Hui J, Yinlong X, Yue L, Liping B, Liyong X (2005) Climate change impacts on crop yield and quantity with CO2 fertilization in China. Philos Trans R Soc B 2005: 2149-2154;

Falloon P, Betts R (2010) Climate impacts on European agriculture and water management in the context of adaptation and mitigation – The importance of an integrated approach. Sci Total Environ 408: 5667-5687;

Gordon C, Cooper C, Senior CA, Banks H, Gregory JM, Johns TC, Mitchell JFB, Wood RA (2000) The simulation of sea surface temperature, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. ClimDyn16: 147-168;

Greets S, Raes D, Garcia M, Miranda R, Cusicanqui JA, Taboada C, Mendoza J, Huanca R, Mamani A, Condori O, Mamani J, Morales B, Osco V, Steduto P (2009) Simulating yield response of Quinoa to water availability with AquaCrop. Agron J101: 499-508;

Intavon T, Jintrawet A, Chinvanno S, Snidvongs A (2004) Impact of climate change on rainfed lowland rice production in SavannakhetProvience, Lao PDR. Report of APN CAPABLE Project, Southeast Asia START Regional Center, Chulalongkorn University, Bangkok;

IPCC (2007a) Climate change 2007: Synthesis Report Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Geneva;

IPCC (2007b) Climate change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the 4th assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge;

International Rice Research Institute (2013) World Rice Statistics. http://ricestat.irri.org:8080/wrs. Accessed on 25 July 2013;

IUCN (2011) Mekong Water Dialogues: Groundwater in the Mekong Delta. Discussion Paper of IUCN. International Union for Conservation of Nature. Hanoi;

Kim HY, Ko J, Kang S, Tenhunen J (2013) Impacts of climate change on paddy rice yield in a temperate climate. Global Change Biology 19: 548–562 doi: 10.1111/gcb.12047;

Kramer PJ (1983) Water Relations of Plants. Academic Press, Inc., New York;

Krishnan P, Swain DK, Bhaskar BC, Nayak SK, Dash RN (2007) Impact of elevated CO2 and temperature on rice yield and methods of

adaptation as evaluated by crop simulation studies. AgricEcosyst Environ122: 233-242;

Lane A, Jarvis A (2007) Changes in climate will modify the geography of crop suitability: agricultural biodiversity can help with adaptation. J Semi-Arid Trop 4 (1): 1-12;

Laux P, Jackel G, Tingem RM, Kunstmann H (2010) Impact of climate change on agricultural productivity under rainfed conditions in Cameroon – A method to improve attainable crop yield by panting date adaptations. Agric Forest Meteor150: 1258-1271;

Masutomi Y, Takahashi K, Harasawa H, Matsuoka Y (2009) Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models. AgricEcosyst Environ 131: 281-291;

Mestre-Sanchís F, Feijoo-Bello ML (2009) Climate change and its marginalizing effect on agriculture. Ecol Econ68: 896-904;

Meza FJ, Silva D, Vigil H (2008) Climate change impacts on irrigated maize in Mediterranean climates: Evaluation of double cropping as an emerging alternative. Agric Sys98: 21-30;

Ministry of Natural Resources and Environment, MONRE (2009) Climate Change, Sea Level Rise Scenarios for Vietnam. MONRE, Ha Noi;

ftp://ftp.gfzpotsdam.de/pub/home/hydro/guentner/hohmann/Climate%20Change%20Scenarios%20for%20Vietnam.pdf. Accessed on 24 August 2013;

Mishra A, Singh R, Raghuwanshi NS, Chatterjee C, Froebrich J (2013) Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin. Sci Tot Environ xxx, xxx-xxx

Mkhabela MS, Bullock PR (2012) Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada. Agric Water Manage110: 16-24;

Munns R (1993) Physiological processes limiting plant growth in saline soils: some dogmas and hypotheses. Plant Cell Environ16: 15-24;

National Institute of Nutrition (2010) Summary report of General Nutrition Survey 2009 – 2010. Hanoi;

Nelson GC, Rosegrant MW, Koo J, Robertson R et al (2009) Climate change: impact on agriculture and costs of adaptation. International Food Policy Research Institute, Washington DC;

Osborne T, Rose G, Wheeler T (2013) Variation in the global-scale impacts of climate change on crop

Proceedings of the 19th

IAHR-APD Congress 2014, Hanoi, Vietnam

ISBN 978604821338-1

11

productivity due to climate model uncertainty and adaptation. Agric Forest Meteor 170: 183-194;

Raes D, Steduto P, Hsiao TC, Fereres E (2009a) AquaCrop – The FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron J101: 438-447;

Raes D, Steduto P, Hsiao TC, Fereres E (2009b) Crop Water Productivity. Calculation Procedures and Calibration Guidance. AquaCrop version 4.0. FAO, Land and Water Development Division, Rome;

Ramirez-Villegas J, Jarvis A, Laderach P (2013) Empirical approaches for assessing impacts of climate change on agriculture: The EcoCrop model and a case study with grain sorghum. Agric Forest Meteor170: 67-78;

Rehana S, Mujumdar PP (2012) Regional impacts of climate change on irrigation water demands. Hydrol Processes doi: 10.1002/hyp.9379;

Rezaei M, Davatgar N, Khaledian MR, Pirmoradian N (2013) Effect of intermittent irrigation with saline water on rice yield in Rasht, Iran. ActaagriculturaeSlovenica 101(1): 49-57;

Sadoff C, Muller M (2009) Water management, Water Security and Climate Change Adaptation: Early Impacts and Essential Responses, Technical Committee background Paper 14. Global Water Partnership, Stockholm;

Sannbria J, Lhomme JP (2013) Climate change and potato cropping in the Peruvian Altiplano. TheorApplClimatol 112: 683-695;

Shrestha S, Gyawali B, Bhattarai U (2013) Impacts of climate change on irrigation water requirements for rice-wheat cultivation in Bagmati River Basin, Nepal. J Water Clim Change in press;

Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop – the FAO crop model to simulate yield response to water: 1. Concepts and underlying principles. Agron J101: 426-437;

Tao F, Yokozawa M, Hayashi Y, Lin E (2003) Future climate change, the agricultural water cycle and agricultural production in China. AgricEcosyst Environ95: 203-215;

Thornton PK, Jones PG, Ericksen PJ, Challinor AJ (2011) Agriculture and food systems in sub-Saharan Africa in a 4˚C world. Philo Trans Royal Soc A: Math, Phy and EnginSci369 (1934): 117-136;

Toews MW, Allen DM (2009) Evaluating different GCMs for predicting spatial recharge in an irrigated arid region. J Hydrol 374: 265-281;

Trinh LT, Duong CC, Steen PVD, Lens PNL (2013) Exploring the potential for wastewater reuse in agriculture as a climate change adaptation measure

for Can Tho City, Vietnam. Agric Water Manage 128: 43-54;

Wilby RL, Dawson CW (2007) SDSM 4.2 – a decision support tool for the assessment of regional climate change impacts, user manual. Nottingham, UK;

World Bank (2012) Vietnam Poverty Assessment. Report number 74910. Hanoi http://www-wds.worldbank.org. Accessed on 21 October 2013;

Zhi-Qing J, Da-Wei Z (2008) Impacts of changes in climate and its variability on food production in Northeast China. ActaAgron Sin34 (9): 1588-1597;

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