Short Paper Proceedings on Climate Change Adaptation in Agricultural Sector, Thailand

15 September, 2017 Institute of Industrial Science, the University of Tokyo

PREFACE

Meteorological Uncertainty caused by Global Climate Change would have significant impact on agricultural sector, because agricultural systems are strongly related with local climate condition. In Thailand, agriculture is major sector which producing 11% of GDP and 40% of employment. Ratio of irrigated agricultural land is about 30% and other 70% is rain-fed so that climate change makes agricultural production more unstable and also makes significant damage to the societies and economics in local area. To mitigate these issues, it is desirable to develop and disseminate enhanced adaptation systems including new varieties that are effective together with new cultivation methods, crop growth monitoring system to detect water stress condition, soil and water management to improve crop production. Furthermore, it is necessary to conduct reliable research to find ways to achieve economic betterment through agriculture under CC condition. In this workshop, we aim to review the current state and relevant research of above topics and to discuss about framework of CC adaptation strategies in agricultural sector, in Thailand.

CONTENTS:

1. Effect of Water Salinity on Growth and Photosynthesis of Dendrobium Sonia ‘Earsakul’ 1 Patchareeya Boonkorkaew , Napasorn Chiewchookul , Poonpipope Kasemsap Praderm Wanichananan and Sudsaisin Kaewrueng

2. Establishment of Satellite-Based Drought Monitoring Platform in Thailand 4 Mongkol Raksapatcharawong, Watcharee Veerakachen, Kazuo Oki, Peerapon Prompitakporn, Chinnapoj Wongsripisant

3. Variability of Salinity Degree in Sal-Affected Soil in Northeast Thailand 8 Supranee Sritumboon, Somsak Ssukchan, Roengsak Katawatin, Mallika Srisutham, Pontip Phontusang, Koshi Yoshida, Kazuo Oki, Koki Homma, Masayasu Maki

4. Shallow Groundwater Channel Constrained by EM and Resistivity Techniques at Western Margin of Chao Phraya Basin, Suphanburi Province. 12 Desell Suanburi, Koshi Yoshida, Weerakaset Suanpaga, Naruekamon Janjirawuttikul and Sutthisak Manyon

5. Contributing Adaptation Strategy for Climate Change by Evaluating Agronomic Conditions through Measurement of LAI in rice 16 Koki Homma

6. Development of SDT Drought Index using Satellite Images 18 Kazuo Oki

7. Evaluation of the relationships between electric conductivity, sodium adsorption ratio before planting and leaf area index during growth period in Khon Kaen, Thailand 20 Masayasu Maki, Koki Homma, Taiki Saito, Koshi Yoshida, Kazuo Oki, Taichi Tebakari, Roengsak Katawatin, Mallika Srisutham, Supranee Sritumboon, Somsak Sukchan

8. Land Use Pattern and Population Dynamics in Flood Area in Thailand 22 Hiroaki Shirakawa

9. The Livelihood Strategies of Farming Households under Drought Stress in Rural Areas of Khon Kaen Province in the Northeastern Part of Thailand 24 Nao Endo

10. Basin Modelling for Evaluation of Available Water Resources and Nitrogen Runoff in Northeast Thailand 27 Yuki Jikeya and Koshi Yoshida

Effect of Water Salinity on Growth and Photosynthesis of Dendrobium Sonia ‘Earsakul’

Patchareeya Boonkorkaew 1, Napasorn Chiewchookul 1, Poonpipope Kasemsap 1, Praderm Wanichananan 2 and Sudsaisin Kaewrueng 3 1 Department of Horticulture, Faculty of Agriculture, Kasetsart University, Postal Address 10900, Thailand Tel: +66 257 90308, Fax: +66 257 91951 ext. 112, E-mail: [email protected] 2 Nation Center for Genetic Engineering and Biotechnology, Postal Address 12120, Thailand 3 Department of Farm Mechanics, Faculty of Agriculture, Kasetsart University, Postal Address 10900, Thailand

Abstract The effect of water salinity (250, 1000, 2000, 3000 and 4000 µS cm-1 of electrical conductivity (EC)) on growth and photosynthesis of 18-month-old Dendrobium Sonia ‘Earsakul’ plants during their flowering stage and grown in a 60% shading greenhouse is examined. The results showed that the number of leaves, number of roots, new shoots, new inflorescences, CO2 exchange rate and stomatal conductance decreased as the salt concentration in the water increased. Meanwhile, salinity had no effect on flower appearance. Therefore, D. Sonia ‘Earsakul' at the flowering stage can be irrigated with 40 ml of water with a 2,000 µS cm-1 EC per plant at 2-day intervals for 2 months.

Keywords: Electrical conductivity, NaCl, Orchid, Saline water

1 INTRODUCTION filled with coconut husk. Two weeks before the experiment Thailand is the world’s largest producer and exporter commenced, the plants were acclimatized under the 60% of tropical orchids, including species such as shading greenhouse at Department of Horticulture, Faculty of Dendrobium, Ascocenda and Vanda. The production area Agriculture, Kasetsart University, Bang Khen, Bangkok, is mainly in Central Thailand, i.e. Samut Sakhon, Thailand. On average, air temperature was 30°C and Bangkok, Nakhon Pathom, Ratchaburi, Phra Nakhon Si relative humidity was 70%. The photosynthetic photon −2 −1 Ayutthaya, Pathum Thani and Nonthaburi provinces [1]. flux (PPF) ranged from 200 - 400 µmol m s . Plants -1 Dendrobium Sonia ‘Earsakul’ is one of the most important were fertilized once a week with 4 g L of N-P-K (21-21- -1 orchid plants, grown as cut- flowers and potted plants in 21) and 4 g L of N-P-K (10-52-13). Plants with a 1-5 cm Thailand. Meanwhile, high water quality is vital for orchid inflorescence length and 4-5 stems per plant were used in growth and development, ideally with a pH of 5.2-6.2 and this experiment. Observation occurred from September to electrical conductivity (EC) not exceeding 750 µS cm-1 December 2016. The experiment was conducted using a (0.75 dS m-1) or 0.5 g L-1 NaCl [2]. Good water resources completely randomized design with 15 replicates per are rain, tap water, canals and rivers. treatment. Irrigation water treatments included 5 different levels of salinity in EC, at 250 (tap water as a control Due to low water levels in Bhumibol dam and treatment), 1000, 2000, 3000 and 4000 µs cm-1. Saline Srinagarindra dam, the Royal Irrigation Department (RID) solutions were prepared using instant ocean sea salt cannot release water into Chao Phraya River, one of the (NaCl) mixed with tap water for each treatment. and two main sources of Bangkok’s tap water. Low water Irrigated saline water was sprayed on the entirety of each levels in the Chao Phraya River cause seawater intrusion, plant using 40 ml per plant at 2-day intervals for 2 leading to saline water. In 2014, Thailand suffered the months. worst water salinity levels in more than a century; in February 15-16, 2014, saline levels downriver (1.80 g L-1) were much higher than the standard salt concentration of 2.2 Measurements of growth and flower quality water that supplies homes, which must not exceed 0.25 g 2.2.1 Growth: counted the number of leaves (whole -1 L [3]. In 2015, the salt concentration of water in Tha plant), number of fresh roots and number of new -1 Chin River was much higher (7.0 g L ), and the estimated inflorescences in the same pseudobulb. Data were orchid cultivation area affected by this water salinity was collected once a month for 2 months approximately 1,600 ha (50% of total production area) [4]. 2.2.2 Flower: recorded the number of open flowers Salinity, in irrigation water, is one of the major per inflorescence, inflorescence length, flower width and environmental limiting factors for growth and productivity flower height of orchids. Since orchid farmers do not know much about the effect of salinity, they are afraid of using saline water 2.2.3 Percentage of new shoot and percentage of to irrigate mature plants, especially in the flowering stage. new inflorescence Therefore, the objective of this research was to determine the interaction between water salinity level and duration of 2.3 Photosynthesis parameters measurement growth, flower quality and photosynthesis parameters of Photosynthesis parameters of Dendrobium Sonia Dendrobium Sonia ‘Earsakul’. ‘Earsakul’ such as CO2 exchange rate (CER) and stomatal conductance (gs) were monitored using a 2 MATERIAL AND METHODS Portable Photosynthesis System; LI-6400XT (LI-COR 2.1 Plant material Inc., Lincoln, NE, USA). The CO2 concentration in the chamber was 400 ± 10 ppm. The leaf surface was 18-month-old plants (flowering stage) of Dendrobium measured using a Standard 2 x 3 cm Chamber (6400-08 Sonia ‘Earsakul’ were propagated by tissue culture and Clear Chamber Bottom, LI-COR, USA). Leaf samples grown in plastic pots measuring 3½ inches, which were were measured from the third leaf (from the top) of a front

1 pseudobulb, which was flowering, with 3 data measurements per leaf and 3 leaves per treatment every 10 a 250 1000 2000 3000 4000 (A) 2 hours from 2 to 6 a.m. after being treated with saline a water for 1 and 2 months. 8 a

6 b 2.4 Statistical analysis b The data were analyzed using SAS and the mean 4 results were compared using Duncan’s multiple range test b 2 c

(DMRT) at a 0.01 and 0.05 probability level. roots of Number c b 0 3 RESULTS AND DISCUSSION 18 0 1 2 250 1000 2000 3000 4000 16 Time (month) (B) The results show that, after irrigating with treatments of saline water for one month at 1,000, 2,000, 14 a ab a 12 3,000 and 4,000 µS cm-1, the number of new roots were b b reduced, to 2.3, 3.6, 5.8 and 6.4 roots, respectively, 10 producing fewer than that of the control (Fig.1A). 8 6 Additionally, the number of leaves decreased when the EC was higher than 3000 µS cm-1 (Fig.1B). After 2 4 Number of leaves of leaves Number months, leaf number was decreased under 3,000 and 2 4,000 µS cm-1 saline water, as compared with 2.4 and 2.5 0 0 1 2 leaves under the control. New shoot sprouting similarly decreased, at 42.9, 71.4 and 71.4% under 2,000, 3,000 Time (month) and 4,000 µS cm-1, respectively (Fig.2). However, there was no significant difference in flower appearance; Fig.1 Effect of water salinity on the number of roots (A) flowers openings were 5.8 - 6.2 cm, inflorescence length and leaves (B) of D. Sonia ‘Earsakul’ after treating for 1 and 2 ranged from 29.4 - 32.2 cm, flower width was between 8.0 months. - 8.1 cm and flower height was between 7.1 - 7.3 cm (Table 1, Fig.3). 120 These results were similar to those observed by [5], in which both 9 and 24 month-old plants of D. Sonia 100 Percentage of new shoot ‘Earsakul’ were exposed to saline water at EC 2 dS m-1 80 every day for 3 months, resulting in no significant Percentage of new difference in growth and flowering as compared with the 60 inflorescence control. It is apparent from the results that salinity in

shoot shoot (%) 40 irrigated water primarily affects the number of roots, and plants show some symptoms that are similar to drought 20 stress [6] when irrigated with a high concentration of

New inflorescence New inflorescence new and 0 saline water for a long period of time. control 1,000 2,000 3,000 4,000 -1 Meanwhile, effects of saline water and growing salinity (µS cm ) media on growth and flowering of Phalaenopsis revealed Fig.2 Effect of water salinity on new shoots and new that flower size was smaller when the salt concentration inflorescence after treating for 2 months. in water increased. Moreover, saline water that exceeded 0.75 dS m-1 (750 μS cm−1) affected leaf dropping and fresh weight. Therefore, water for cultivation of Table 1 Effect of saline water on flower quality of D. Sonia Phalaenopsis should not exceed 750 μS cm−1 [7]. ‘Earsakul’ after treating for 2 months. Photosynthesis parameters, such as CO2 exchange Flower Flower Salinity Opened Inflorescence -1 width height rate (CER) and stomatal conductance (gs) decreased as flower length (cm) the salt concentration in the water increased. After being (µS cm ) (cm) (cm) treated with saline water for 2 months, CER was an 250 6.2 31.1 8.1 7.25 average of 3.44, 2.37, 2.59, 1.28 and 0.53 μmol m−2 s−1, (control) while gs was an average of 14.13, 7.32, 6.92, 4.34 and 1000 6.1 30.7 8.1 7.1 4.50 mmol m−2s−1 at 250 (control), 1,000, 2,000, 3,000 2000 6.2 32.2 8.0 7.3 and 4,000 μS cm−1, respectively (Table 2). 3000 6.0 30.9 8.0 7.3 From these results, we can apply and transfer knowledge to orchid growers in Central Thailand who face 4000 5.8 29.4 8.0 7.1 water salinity problems from seawater intrusion; we will F-test ns ns ns ns address these issues through introduction of the EC CV (%) 18.73 16.80 5.49 3.92 meter for monitoring EC values in water reservoirs. Currently, referring to the EC as in this experiment can allow farmers to adjust water before irrigating to orchid plants. For example, in 2015, farmers could not use rivers that had an EC level of about 5,000 μS cm−1. They bought fresh water for irrigation, therefore increasing costs. In this case, however, we transferred this knowledge by training farmers to mix fresh water and saline water from their reservoirs, diluting the concentration of salt to 2,000 μS cm−1. Furthermore, we transferred the knowledge by giving interviews to Agricultural magazines.

2 per plant at 2,000 µS cm-1 EC at 2-day intervals for 2 months , while at EC levels of 3,000 – 4,000 µS cm-1, can be irrigated at 2-day intervals for only 1 month.

5 REFERENCE [1] Thai Customs Department, 2014, Trade Statistics, Available Source: http://www.customs.go.th, April 13, 2015. [2] Department Of Agriculture, 2004, Orchid, The Agricultural Co-operative Federation of Thailand, Fig.3 Effect of saline water on D. Sonia ‘Earsakul’ after Ltd., Bangkok. treating with 250 (T1), 1000 (T2), 2,000 (T3), 3,000 (T4), [3] Daily News, 2014, What areas have the highest salt and 4,000 (T5) µS cm-1 for 2 months. concentration in tap water?, Available Source: https: //www.dailynews.co.th, June 5, 2015. [4] komchadluek, 2015, ‘Khlong Chinda Floodgate’ Table 2 Effect of water salinity on CO2 exchange rate, Prevents Saltwater Intrusion, Available Source: http: CER and stomatal conductance (gs) of D. Sonia ‘Earsakul’ after treating for 1 and 2 months. //www.komchadluek.net, June 5, 2015. [5] Sonsud, T., 2015, Effect of Water Salinity on Growth −2 −1 g (mmol m−2s−1) Salinity CER (μmol m s ) s and Photosynthesis in Dendrobium Sonia ‘Earsakul’, -1 (µS cm ) 1 month 2 months 1 month 2 months M.S. Thesis, Kasetsart University. 250 2.31 bc 3.44 a 31.55 a 14.13 a [6] Chaudhuri, K. and Choudhuri, M.A., 1997, Effect of (control) short-term NaCl stress on water relations and gas 1000 3.15 a 2.37 ab 25.82 b 7.32 b exchange of two jute species, Biologia Plantarum, 40: 373-380. 2000 2.84 ab 2.59 b 25.98 b 6.92 b [7] Wang, Y.T., 1998. Impact of salinity and media on 3000 2.25 c 1.28 c 19.19 c 4.34 c growth and flowering of a hybrid Phalaenopsis 4000 2.45 bc 0.53 d 16.83 c 4.50 c orchid. HortScience, 33(2): 247-250. F-test ** ** ** * CV (%) 19.38 32.83 16.77 40.91 6 ACKNOWLEDGEMENT This research was partially supported by a grant from 4 CONCLUSION Japan International Cooperation Agency (JICA) under the Saline water affected the number of leaves, roots, project ADAP-T. new shoots, new inflorescences, CO2 exchange rate and stomatal conductance, but did not affect flower appearance. Dendrobium Sonia ‘Earsakul’ at the flowering stage can be irrigated with 40 ml saline water .

3 Establishment of Satellite-Based Drought Monitoring Platform in Thailand

Mongkol Raksapatcharawong1, Watcharee Veerakachen1, Kazuo Oki2, Peerapon Prompitakporn1, Chinnapoj Wongsripisant1 1Chulabhorn Satellite Receiving Station, Faculty of Engineering, Kasetsart University, Thailand Tel: +66 2940-7052, Fax: +66 2940-7052 ext. 1001, E-mail: [email protected] 2The University of Tokyo, 4-6-1, Komaba, Meguro, Tokyo 1538505, Japan Tel: +81 03-5452-6382, E-mail: [email protected]

Abstract

Drought is a major disaster resulted from climate change. Capability of monitoring drought condition and assessment drought risk can effectively increase the potential of drought adaptation plan, especially for agricultural country like Thailand. This research aims to provide consistent and countrywide drought information by establishing a drought monitoring platform which utilizes multiple satellite data sources received by Chulabhorn Satellite Receiving Station (CSRS), Thailand. The analysis of drought vulnerability maps, based on IPCC conceptual framework, is adopted for monitoring and assessment for agricultural drought. Three drought aspects are involved in creating the vulnerability map: exposure, sensitivity and adaptive capacity. Satellite data are used to generate indices called Standardized Precipitation Index (SPI), Standardized Difference Temperature (SDT) and Drought Severity Index (DSI) which provide a relationship for drought exposure. Sensitivity is determined by agriculture area consisting of farmland and livestock. Both aspects will be combined to show a potential impact of drought on human. The irrigation area is used to indicate adaptive capacity against drought. This geographical information data and satellite-based drought indices will be merged into drought vulnerability map which can be frequently updated. Our preliminary result shows that the exposure map of Sukhothai province matches with the description of drought situation announced in Mar 2016 by the Department of Disaster Prevention and Mitigation (DDPM), Thailand.

Keywords: Adaptive Capacity, Drought Exposure, Drought Sensitivity, Drought Severity Index (DSI), Standardized Precipitation Index (SPI), Standardized Difference Temperature (SDT), Vulnerability

1 INTRODUCTION Unfortunately, to our best knowledge, there is no such system to provide comprehensive drought information for According to the land use data in 2015-2016 provided stakeholders to prepare themselves for drought situation. by Land Development Department and the Labor status For example, the DDPM announced drought area in a form and number of employed/ unemployed data in 2016 of Na- of map (in pdf format) while Thai Meteorological Depart- tional Statistical Office, it is found that Thailand has about ment (TMD) releases meteorological drought information 277,550 square kilometers of agricultural area, which ac- on their website as an image. Since these retroactive out- counts for 53.62% of the total area of the country. Also, In puts cannot be further processed with geographical infor- terms of occupation, it is found that the labor force in ag- mation system (GIS) data, they can only somewhat miti- ricultural sector is the highest comparing to other occupa- gate the problem but cannot effectively support adaptation tions. The total number of labor force in the agricultural measure which is considered as more sustainable solution. sector is 10.72 million, which accounts for 28.44% of the total population aged 15 and over with jobs classified by Recent progress in satellite remote sensing technol- occupation. Therefore, "agriculture" is important to the ogy and data processing has enabled observation of large country in the utilization of land such as food production surface area with reasonably temporal resolution. Multiple base, renewable energy, etc. and major occupation affect- satellite data sources can provide different (but related) ing most of the population. So, If there has some factor aspects of the same area so that they can be combined and affecting the agricultural sector, it will affects the economy processed for more information. In conjunction with GIS, and most of the people. And drought is one of the major interactions among human activities, weather conditions, factor. and geography are comprehensible and adaptation for changes can be devised. This research proposes a study of Climate change (CC), as a result of Global Warming, these interactions and establishes a drought monitoring has significantly affected the weather in Thailand in a way platform based on this idea. The rest of the paper is orga- such that extreme rainfalls and droughts are frequently nized as follow. Section 2 describes material and research observed. These haphazard events are much complicated methodology. Section 3 shows results and discussion. Sec- than local wisdoms can handle, and hence, call for a more tion 4 concludes this work. systematic and technological data processing to tackle.

4 2 MATERIAL AND METHODS Exposure, which is an index used to indicate the inten- 2.1 Agricultural Drought Vulnerability Map sity of drought in different areas. When combined with Sensitivity, which is an index used to indicate the sensitiv- To define activities for adaptation according to ity level from the effects of drought in different areas for drought situation, the analytical framework for vulnerabil- example urban area is less sensitive than rice field. The re- ity assessment is developed as a conceptual framework for sult is a severity level of drought followed by sensitive area managing and dealing with events such as climate change for example if there is drought in the urban area, it will be adaptation, disaster management, poverty management ignored. But if there is drought in the rice field, it will pay and development, etc. [1] [2]. The framework has been de- attention. The result is called Potential Impact, then Poten- veloped as a process to determine the degree of vulnerabil- tial Impact combined with Adaptive Capacity, which is an ity or ability to cope with the effects of variability and index used to indicate the factors that can be Adapta- change according to the situation. It is based on three tion/Prevention/Cope to drought. The result is a severity drought aspects which are exposure, sensitivity and adap- level of drought followed by sensitive area and adaptive tive capacity [3]. area for example irrigation area is better adapted to drought Vulnerability = f(Exposure, Sensitivity, Adaptive Capacity) (1) than outside the irrigation area. Therefore, if drought occurs in the irrigation area. That areas will be able to adapt. The Exposure is determined by the risk factors of various As a result, the intensity of drought is decreased. The result disasters such as cyclone risk map, flood risk map, land- is called Vulnerability Map slide risk map, etc. The Sensitivity is determined by the 2.2 Data and processing sensitivity factors that affect the human and ecosystem such as population density, protected area, etc. The Adap- Nowadays, remote sensing technology or the use of tive Capacity is determined by socio-economic/technol- satellite imagery data to obtain spatial data is an effective ogy/infrastructure factors that can be adaptation/preven- approach because it is fast and has very wide spatial result. tion/cope to drought such as irrigation area, road area, etc. Therefore in this research, satellite imagery was used and processed to produce high-level products that corre- sponded to the factors of calculating the vulnerability of

agricultural drought and the GIS information is also included.

Table 1 Description of data used in this study Index Parameter Data Source Exposure SPI Rainfall from FY-2 CSRS SDT LST MODIS CSRS DSI NDVI MODIS CSRS Sensitivity Agricultural Field survey and LDD area Landsat Livestock Field survey and LDD area Landsat Figure 1 The general structure of vulnerability analysis. Adaptive Irrigation - RID It can be seen that the process has the advantage of Capacity area being considered and analyzed from a variety of factors and multi-dimensional. It can use many factors and ana- Standardized Precipitation Index (SPI) [6] is the num- lyzed them together to get the result and that can be used ber of standard deviations that observed cumulative pre- to make decisions or to continue to analyze. Based on these cipitation deviates from the climatological average. The advantages, this type of analysis has been applied to a wide SPI is computed for several time scales, ranging 1 month range of applications. And one of them is the analysis of to 48 months, to capture the various scales of both short- drought in agriculture [4] [5]. term and long-term drought. The Land Surface Temperature (LST) is the radiative The vulnerability analysis of agricultural drought is skin temperature of the land surface. LST is a mixture of considered in the following aspects: Exposure is consid- vegetation and bare soil temperatures. Because both re- ered as risk factors that cause drought and enhance drought spond rapidly to changes in incoming solar radiation due severity; Sensitivity is considered as factors that are sensi- to cloud cover and aerosol load modifications and diurnal tive to the effects of drought; Adaptive Capacity is consid- variation of illumination, the LST displays quick varia- ered as factors that supporting the adaptation to drought tions too. severity or prevention from drought damage or cope with Standardized Difference Temperature (SDT) [7] is cal- drought severity. The three factors will be considered and culated from the difference of land surface temperature be- calculated as follows. tween day and night.

5 Normalized Difference Vegetation Index (NDVI) [8] is used to determine the density of green on a patch of land, researchers must observe the distinct colors (wavelengths) of visible and near-infrared sunlight reflected by the plants. The Drought Severity Index (DSI) [9] is calculated the difference between the Normalized Difference Vegetation 2016-01 2016-02 2016-03 2016-05 Index (NDVI) for the current month and a long-term mean Figure 4 DSI Results NDVI for this month. Table 2 Detail of satellite data Para- Reso- Satel- me- Sensor lu- Period lite ter tion SPI FY-2 Infra- 5 km Jan 09 – Aug 17 2016-01 2016-02 2016-03 2016-05 red (59,459 hourly images) Figure 5 Exposure Map SDT Terra Modis 1 km Jan 01 – Aug 17 (6,024 daily images) 3.2 Potential Impact Map DSI Terra Modis 1 km Jan 01 – Aug 17 It is a combination of Exposure and Sensitivity Maps. (200 monthly images) The Sensitivity will be used as factor that are directly sensitive to drought which, in this research, focuses on agri- 3 RESULTS AND DISCUSSION cultural area (farmland and livestock). Therefore, non-agricultural area will be masked out for further processing The calculation results of each index such as Exposure and will be considered as no data. Map, Potential Impact Map and Vulnerability Map ranges from -2 to 2, which can be divided into 7 levels with the following meanings: 1) the value ≤ -2.00 (red color) is extreme drought 2) the value from -1.99 to -1.50 (orange color) is severe drought 3) the value from -1.49 to -1.00 (yellow color) is moderately drought 4) the value from - 0.99 to 0.99 (light green color) is near normal 5) the value 2016-01 2016-02 2016-03 2016-05 from 1.00 to 1.49 (green color) is moderately wet 6) the value from 1.50 to 1.99 (cyan color) is very wet 7) the Figure 6 Potential Impact Map value ≥ 2.00 (purple color) is extremely wet. 3.1 Exposure Map 3.3 Vulnerability Map Based on the assumption that agricultural drought is It is combination of Potential Impact and Adaptive Ca- related to 3 main factors which are rainfall, soil moisture, pacity Maps. Adaptive Capacity assumes irrigation area as and plant cover and fertility. So to calculate Exposure we a major factor in adaptation to drought. Thus, in the areas use SPI, which is related to rainfall, SDT, which is related where irrigation is considered, it is likely that the area will to soil moisture and DSI, which is related to plant cover be able to adapt, thus reducing the severity of drought. As and fertility as a parameter, respectively. The result will be a result, most of the country were quite normal during Jan a severity level of drought in the areas. –Feb 2016 and started to exhibit drought in the following month (Mar 2016), especially in the central region. Rainy season started in May 2016 ameliorated the drought situa-

tion as the dry areas were diminishing.

2016-01 2016-02 2016-03 2016-05 Figure 2 SPI Results

2016-01 2016-02 2016-03 2016-05 Figure 7 Vulnerability Map 3.4 Verification We verify our result, the Exposure Map, with drought

2016-01 2016-02 2016-03 2016-05 situation announced by the Department of Disaster Prevention and Mitigation (DDPM), Ministry of Interior. Figure 3 SDT Results From disaster daily report on Mar 23, 2016, 112 districts

6 in 22 provinces are declared emergency from drought; for duction. Second, considering additional factors for Sensi- example Sawan Khalok District, Si Nakhon District, Si tivity and Adaptive Capacity, e.g., socio-economics, de- Samrong District, Si Satchanalai District, Muang mographic attributes, etc. Third, taking into account of cli- Sukhothai District, Khiri Mat District, Thung Saliam Dis- mate change dataset for scenario analysis and adaptation trict and Bandan Lanhoi District of Sukhothai Province. measures. When compared with our Exposure Map for Mar 2016, we found that most of Si Satchanalai District, Si Nakhon ACKNOWLEDGEMENT District and Thung Saliam District is moderate to extreme This work is supported by Japan International Coop- drought, in accordance with the disaster report from eration Agency (JICA) under the Advancing co-design of DDPM as shown in Figure 8. integrated strategies with adaptation to climate change in Thailand (ADAP-T) Project.

REFERNCE [1] IPCC, "Contribution of Working Group II to the fourth assessment report of the intergovernmental panel on climate change," Cambridge University Press, Cambridge, UK, 2007. [2] S. L. Cutter, C. T. Emrich, J. J. Webb and D. Morath, "Social Vulnerability to Climate Variability Hazards: A Review of the Literature," University of South Carolina, Columbia, 2009. [3] A. A. Yusuf and H. Francisco, "Climate Change Vulnerability Mapping for Southeast Asia," Economy and Environment Program for Southeast Asia, 2009. [4] C. S. Murthy, B. Laxman, M. R. Sesha Sai and P. G. Diwakar, "ANALYSING AGRICULTURAL DROUGHT VULNERABILITY AT SUBDISTRICT LEVEL THROUGH EXPOSURE, SENSITIVITY AND ADAPTIVE CAPACITY BASED COMPOSITE INDEX," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 65-70, 2017. [5] X. LIU, Y. WANG, J. PENG, A. K. BRAIMOH and H. YIN, "Assessing Vulnerability to Drought Based on Exposure, Sensitivity and Adaptive Capacity: A Case Study in Middle Inner Mongolia of China," Chinese Geographical Science, vol. Figure 8 Exposure Map for Sukhothai Province (Mar 16) 23, no. 1, pp. 13-25, 2013. 4 CONCLUSION [6] T. B. McKee, N. J. Doesken and J. Kleist, "The relationship of drought frequency and duration to time scales," In Proceedings This research establishes drought monitoring platform of the 8th Conference on Applied Climatology, American in Thailand based on IPCC framework. The platform uti- Meterological Society, 17–22 January 1993. lizes remote sensing data from satellites to generate indices called SPI, SDT, and DSI which are combined to provide [7] O. Kazuo, "Development of Drought Index Using Satellite the Exposure Map. We then used agricultural area ex- Images," The University of Tokyo, Tokyo, 2015. tracted from land use GIS data as a sensitivity factor to de- [8] J. W. Rouse, R. H. Haas, J. A. Schell and D. W. Deering, termine the impact of drought on agriculture. To demon- "Monitoring vegetation systems in the Great Plains with ERTS," strate adaptive capacity, irrigation areas were selected as Third ERTS Symposium, NASA SP-351 I, pp. 309-317, 1973. adaptive measure to counter drought effects as depicted in [9] Q. Mu, M. Zhao, J. S. Kimball, N. G. McDowell and S. W. the Vulnerability Map. Hence, the agricultural areas with Running, "A Remotely Sensed Global Terrestrial Drought rainfall shortage and no irrigation shall be given highest Severity Index," Bulletin of the American Meteorological priority. Preliminary results from Exposure Map of Su- Society, vol. 94, no. 1, pp. 83-98, 2013. khothai Province were satisfactorily compared with drought information from DDPM.

Since this research is still in early stage, the platform development will be enhanced in the following aspects. First, assessing the accuracy of the Exposure Map with more data sources water consumption and agricultural pro-

7 Variability of Salinity Degree in Sal-Affected Soil in Northeast Thailand

Supranee SRITUMBOON1/, Somsak SUKCHAN1/, Roengsak KATAWATIN2/, Mallika SRISUTHAM2/, Pontip PHONTUSANG2/, Koshi YOSHIDA3/, Kazuo OKI4/, Koki HOMMA5/, Masayasu MAKI6/ 1/Land Development Department, Thailand; 2/Land Resources and Environment Department, Faculty of Agriculture, Khon Kaen University; 3/Ibaraki University, Japan; 4/University of Tokyo, Japan; 5/Tohoku University, Japan; Tohoku Technology Institute, Japan. Corresponding author: [email protected]

Abstract

The study has been investigated in the salt-affected soil in Ban Phai District, Khon Kaen Province,Thailand from October 2016 to March 2017. This study aims to investigate the changes of salinity in soils at different time intervals. Soil samples were taken at the depth of 0-30 cm in every 2 weeks from 4 classes of salt-affected soil; including class 1 very severely (salt crust >50 %, ECe >16 dS/m), class 2 severely (salt crust 10-50%, ECe 8-16 dS/m), class 3 moderately (salt crust 1-10%, ECe 4-8 dS/m), and class 4 slightly (salt crust < 1%, ECe 2-4 dS/m) and there were analyzed ECe, SAR and soil moisture content (%w/w) at LDD5’s laboratory. The result presented that soil moisture content (>80% of area) was the highest in October 2016 (15-30 %w/w). It was decreasing in December2016 and March 2017. Moreover, salt- affected soil has ECe values range from 0.23-112.70 dS/m. Thus, ECe was slightly increasing from October 2016 to March 2017 that it was a negative relate to soil moisture content. More than 98 % of area in class 1 was classified as a very severely degree in October 2016 – March 2017. While, ECe variation in class 2 was similar trend with class 1. For class 3, most of the study areas (94.21 %) in October 2016 was classified as a slightly level. But On the other hand, ECe was classified as moderately level (4-8 dS/m) in December 2016 and March 2017. Significant, most of the areas in class 4 had ECe range from 0-2 dS/m (non-saline) in October 2016 and March 2017. But ECe in December 2016 was classified as a moderately. As SAR value, most area in class 1 had SAR higher than 45. While class 2 had SAR range from 13-25 in October 2016. And SAR was increasing in December 2016 and March 2017. For class 3 and class 4, there was a significant differences from class 1 and class 2. Thus, SAR value was the highest in December 2016. The results indicated that not only soil moisture content could effects on the salt migration but also soil properties, importantly, soil texture should be considered due to soil texture strongly determines water movement that related to salt level changing in soil profile. However, the study involve the salt migration and accumulation in soil profile in year round that requires further study.

Keywords: variability, salinity, salt-affected soil, Northeast, Thailand

1. INTRODUCTION 2017 as the representative of wet-dry seasons. The electrical conductivity (ECe), Sodium Adsorption Ratio (SAR), soil Salt-affected soils are soils that contain sufficient moisture content (%w/w), and soil textures were analyzed at salt to impair the growth of crops that refer to the soils have Land Development Department Regional Office 5’s enough salt in the root zone to give an electric conductivity laboratory. Also, field study was classified into 4 classes in the saturation extract (ECe) of more than 4 mS/cm at 25C based on the salinity soil map of Land Development [1]. Most of the inland salt-affected soils of Thailand is department [3] including class 1 very severely (salt crust >50 Northeast part that area 2.85 million ha [2]. However, it is %, ECe >16 dS/m), class 2 severely (salt crust 10-50%,ECe difficult to define salt-affected soils precisely. Due to soil 8-16 dS/m), class 3 moderately (salt crust 1-10%,ECe 4-8 salinity level can be easily altered, with many factors dS/m), and class 4 slightly (salt crust < 1%, ECe 2-4 dS/m). influencing such as soil moisture, rainfall, soil, etc. Therefore, In addition, three weather stations were installed in the field this study aims to investigate the changes in soil salinity at study that ranged 1.61-60.49 mm (Fig.1, Fig.2, and different time intervals. Fig.3).Meanwhile, soil texture for each class were shown by Table 1.

2. MATERIAL AND METHOD

The study area is was conducted in the salt-affected area in Ban Phai District, Khon Kaen Province, Northeast of Thailand. 50 soil samples were collected by auger every 2 weeks at the depths of 0-30 cm. from October 2016 to March

8 Class 3 salt crust 1-10 Table 1 Soil textures of salt-affected soil in the study area. % Class 2 salt crust 10-50 Class %sand %silt %clay Texture % class1 26.3-75.5 17.4-54 7.1-26.8 sandy loam, severely loam, silty loam Class 1 salt crust class2 36.2-72.2 22.2-47.6 5.7-22 sandy loam, loam >50% strongly Class 4 salt crust class3 33.1-67.4 18.5-35.4 12.2-40.5 sandy loam, clay moderately loam, sandy clay <1% loam class4 5.8-78.1 12.9-35.9 5.8-41.3 sandy loam, slightly loamy sand,clay loam, sandy clay loam

Fig. 1 Study area at Ban Phai district, Khon Kaen province, 3. RESULT AND DISCUSION Thailand. The soil analysis were presented that most of area

(>80% of area) had soil moisture content 15-30 %w/w in October 2016. It was slightly decreasing in December 2016 Class 1 Class 2 that value 15-20 %w/w. While, soil moisture content in March 2017 was the lowest that ranged 10-15 %w/w (>90 % of area) (Fig.4). Moreover, the ECe analysis showed that salt- affected soil has ECe values ranged from 0.23-112.70 dS/ m. Thus, ECe in class 1was ranging from 4.18-112.70 dS/m that is the highest. The ECe was slightly increasing from October 2016 Class 3 Class 4 to March 2017. However, more than 98 % of area in class 1 were classified as a very severely of salinity degree that ECe value higher than 16 dS/m. As class 2, it had ECe ranged from 0.81-23.10 dS/m. Also, ECe variation is similar trend with class 1 that it is the lowest in October and ECe is the highest in March. Notable, the salinity degree in October Fig. 2 Characteristic of salt-affected soil of classes 1, 2, 3 2016 was a moderately that 66.89 % of area had ECe ranged and 4. 4-8 dS/m. While, most area in December 2016 and March 2017 had ECe about 8-16 dS/m that was classified as a severely level. Meanwhile, class 3 had ECe 0.60-34.90 Monthly Rainfall dS/m.Thus, 94.21% of area in October 2016 had ECe ranged from 2-4 dS/m that was a slightly degree of salinity. But ECe 70.00 60.00 was increased to 4-8 dS/m. that covered 91.29 % and 50.00 95.91% of area in December 2016 and March 2017, 40.00 respectively. Regrading to class 4, the ECe was ranging from

mm. 30.00 0.23-25.50 dS/m. Thus, most of area in October 2016 and 20.00 March 2017 had ECe 0-2 dS/m (non salinity). But ECe in 10.00 December 2016 ranged from 4-8 dS/m that covered 62.28 % 0.00 of area in class4 (Fig.5,6,7 and 8). Sep-16 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 As SAR data, it had a positive relation to ECe, resulting in SAR has a similar changing trend with ECe. That, SAR in class 1 about 21.27 – 189.69 and most of area had SAR Fig.3 Monthly rainfall from September 2016 to July 2017. higher than 45. While class 2 had SAR range from 0.85- 89.01, and SAR in this class averaged 13-25 in October

2016. But, SAR was increasing in December 2016 and March 2017 that ranged 25-45. For class 3 and class 4, there was a significant difference from class 1 and class 2. The SAR value was the highest in December 2016. But, SAR value was lower in October 2016 and March 2017. Thus, class 3 had SAR value range from 1.05-114.62. And most areas in December 2016 had SAR values between 25-45.

While, October 2016 and March 2016 had a SAR value 13-

9 25. For class 4, this class was the lowest of SAR that was

Variation of ECe vs SMC in Class 1 less than 13 (Fig.5, 9,10 and 11). Variation of ECe vs SMC in Class 2 110.00 25.00 However, the salinity degree was slightly increasing in 100.00 90.00 20.00 December 2016 (transition wet to dry). While, in March 2017 80.00 70.00 15.00 60.00 (dry) the salt was increasing in class 1 and class 2. But the 50.00 10.00 ECe (dS/m) ECe ECe (dS/m) 40.00 30.00 5.00 ECe and SAR in class 3 and class 4 were variable that 20.00 10.00 salinity degree in December 2016 is higher than in March - - 0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30

Soil Moisture Content (%w/w) soil moisture content (%w/w) 2017, although, soil moisture content in December 2016 is Oct 2016 (0.82-14.92) Dec 2016 (0.9-23.10) Mar 2017 (0.81-22.10) higher. The major factor that could effect on salinity is soil Oct 2016 (4.18-94.00) Dec 2016 (6.80-93.40) Variation of ECe vs SMC in Class 3 Variation of ECe vs SMC in Class 4 moisture content. Thus, salinity degree in December (flooded 40.00 20.00

soil) is the lowest. Owing to the salt can be leached to deeper 30.00 15.00 soil layers when the capillary barrier disappear in the rainy 20.00 10.00 ECe ECe (dS/m)

season [4].Furthermore, the result indicated that not only soil ECe (dS/m) 10.00 moisture content could effect on the salt migration but also 5.00 - soil properties in the unsaturated condition and the pore size 0 5 10 15 20 25 30 35 - 0 5 10 15 20 25 30 35 soil moisture content (%w/w) soil moisture content (%w/w) [5]. Importantly, soil texture should be considered. Due to Oct 2016 (0.60-12.82) Dec 2016 (0.88-34.90) Mar 2017 (0.87-19.71) Oct 2016 (0.26-4.87) Dec 2016 (0.79-15.5) soil texture strongly determines water movement in soil profile that related to mechanism of salt moving in soil profile Fig.6 Variation of ECe vs soil moisture content in salt- [6] and the pore size distribution [7].Notable, the soil texture affected soil in October, December 2016, and March 2017 analysis in class 3 and class 4 was higher of percent clay -1 particle than class 1 and class 2. Thus, the pore size is too EC (dS.m ) small such as clay soils, resulting in retarded the upward 0-2 movement of groundwater [5]. Class1 2-4

4-8

8-16 100 Class2 >16 SMC(%w/w) 80 0-5 60 5-10 10-15 40 % area 15-20 Class3 20 20-30 >30 0 oct dec mar oct dec mar oct dec mar oct dec mar class1 class2 class3 class4 Class4

Fig. 4 Percent of changing area for soil moisture content in Oct 2016 Dec 2016 Mar 2017 October 2016, December 2016, and March 2017 Fig.7ECe changing for each class in October 2016,

December2016, and March 2017

Variation of ECe vs SMC (October 2016-March 2017) Variation of SAR vs SMC (October 2016 - March 2017)

120.00 200.00

100.00 150.00 100.00 80.00 ECe (dS/m) SAR 60.00 100.00 80.00

ECe ECe (dS/m) 0-2 40.00

50.00 2-4 20.00 60.00 4-8 - 0.00 0 10 20 30 40 area% 8-16 0 10 20 30 40 40.00 Soil Moisture Content (%w/w) Soil Moisture Content (%w/w) >16 class1 (4.18-112.70) class 2 (0.80-32.10) class 1 (21.27-189.69) class 2 (0.85-89.01) class 3 (0.56-34.90) class 4 (0.23-25.50) class 3 (1.05-114.62) class 4 (0.23-41.17) 20.00

- Fig.5 Variation of ECe and SAR vs soil moisture content class1 class2 class3 class4 class1 class2 class3 class4 class1 class2 class3 class4 from October 2016 – March 2017 in salt-affected soil Oct-2016 Dec-2016 Mar-2017

Fig.8 Percent of changing area for salinity class in October 2016, December2016, and March 2017

Variation of SAR vs SMC in Class 1 Variation of SAR vs SMC in Class 2 200.00 100.00 4. CONCLUSION 80.00 150.00 60.00 100.00 40.00 SAR It is quite difficult to predict the change of salinity SAR 50.00 20.00 degree in soil due to several factors. Soil moisture content is 0.00 0.00 0 5 10 15 20 25 30 0 5 10 15 20 25 30 35 40 Soil Moisture Content (%w/w) Soil Moisture Content (%w/w) one of the major factors have been focusing on changing of Oct 2016 (26.74-118.28) Dec 2016 (28.17-189.69) Mar 2017 (21.27-166.42) Oct 2016 (0.85-56.32) Dec 2016 (8.83-61.81) Mar 2017 (9.06-63.82) the salinity degree. However, soil properties are another factor that must be taken into account, especially, soil Variation of SAR vs SMC in Class 3 Variation of SAR vs SMC in Class 4 140.00 50.00 texture. Because the soil texture is important factor to on 120.00 40.00 100.00 water upward and downward movements in the soil profile.

80.00 SAR30.00 60.00 Thus, the water movement in the soil as capillary rise and SAR 20.00 40.00 10.00 20.00 leachate is an important mechanism for the salinity migration

0.00 0.00 0 5 10 15 20 25 30 35 0 10 20 30 40 and accumulation in a soil layer. Moreover, the influence of Soil Moisture Content (%w/w) Soil Moisture Content (%w/w) Oct 2016 (1.45-18.70) Dec 2016 (1.02-41.17) Mar 2017 (0.23-12.38) Oct 2016 (1.38-60.02) Dec 2016 (3.63-114.62) Mar 2017 (8.62-59.92) soil texture on the movement of salt in the soil profile has less study. Hence, further study is needed to measure and Fig.9 Variation of SAR vs soil moisture content in investigate. In addition, soil properties can be related to salt-affected soil in October, December 2016, appropriate of soil management for controlling salinity. Also, and March 2017 the future study should be concern to the new method to easier prediction and the impact of climate change on salinity SAR Class1 changing in year round. Importantly, the research relate to >45 soil management cloud be ameliorated the salt-affected soil. 25-45 Addition, the investigation involves the cropping system/crop calendar and alternative crops for planting in salt-affected 13-25 area should be deeply studied. 0-13 Class2

5. REFERENCE

Class3 [1] http://www.ldd.go.th/ldd_en/en-US/inland-salt- affected-soils/. [2] Land Development Department. 2017. Management and Improvement the saline soil in Thailand. Available on http://www.ldd.go.th/Lddwebsite/web_ord/ Class4 Technical/pdf/P_Technical03001_5.pdf. [3] Land Development Department. 2004. Northeast Saline Soil Map. Available on http://oss101.ldd.go.th Oct 2016 Dec 2016 Mar 2017 /web_salt_NE/ne_thailand.htm. [4] Guo,G.,K.Araya, H.Jia,Z.Zhang,K.Ohomiya,and Fig.10 SAR changing for each class in October 2016, J.Matsuda.2006.Improvement of salt-affected soils: 1. December2016, and March 2017 Interception of capillarity. Biosystems Eng. 94(1): 139– 150. doi:10.1016/j.biosystemseng.2006.01.012.

100.00 [5] Xiaopeng L., X.C.Scott, and K. F. Salifu. 2014.Soil SAR texture and layering effects on water and salt 80.00 0-13 dynamics in the presence of a water table: a review. 13-25 60.00 Environ. Rev. 22: 41–50 (2014) dx.doi.org/10.1139/er- 25-45 2013-0035. % area% 40.00 >45 [6] Wösten, J.H.M., Y.A. Pachepsky, and W.J.Rawls. 2001.

20.00 Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic - class1 class2 class3 class4 class1 class2 class3 class4 class1 class2 class3 class4 characteristics. J. Hydrol. 251(3–4): 123–150. Oct-2016 Dec-2016 Mar-2017 doi:10.1016/S00221694(01)00464-4. [7] Campbell, G.S. 1985. Soil physics with Basic. Transport Fig.11 Percent of changing area for SAR in October 2016, models for soil–plant systems: Developments in soil December2016, and March 2017 sciences 14. Elsevier, Amsterdam, the Netherlands.

11 Shallow Groundwater Channel Constrained by EM and Resistivity Techniques at Western Margin of Chao Phraya Basin, Suphanburi Province.

Desell Suanburi1, Koshi Yoshida2, Weerakaset Suanpaga1, Naruekamon Janjirawuttikul3 and Sutthisak Manyon4 1Kasetsart University, 3Land Development Department, 4Department of Groundwater Resources Tel: +66 814969744, Fax: +662 5793711, E-mail: [email protected] 2 Ibaraki University

Abstract Due to geological structure in the basement of Chao Phraya basin in NW-SE and alluvial fan deposit occurrence at the western side, old sand channel deposit which presented as significant shallow groundwater (SGW) for support as water supply in agriculture utilities during dry season. Analysis on secondary data such as remote sensing and sandy soil map including field soil sampling and shallow groundwater well observing, the research area A1 is located at Dan Chang - Nong Ya Sai region of Suphanburi province. The trend of sedimentation was found as following the lineament of surface streams from west to east (same as groundwater flow from west to east direction). EM technique was successfully introduced to regional investigated for mapping the zone of sand deposit presenting two main zones sand channels. Then 2D resistivity imaging technique was performed at the mid-northern portion in more detail scale which mapping SGW zones show comparable direction as EM results. 2D resistivity inversion model scan be displayed sand deposit zone (or high potential of SGW) as slightly high resistivity with depth of about 10 m, varying in width of 40m - 150m and with lineament in West to East direction. SGW is found as an effective water supply for local agriculture activities e.g. sugar cane and rice paddy with cheap investment. To observe systematically in long term of the change of SGW level in relation with significant factors e.g. rainfall, air pressure, moisture, soil conductivity etc. may help to prediction the environmental condition of climate change i.e. the warning for drought or flood situation including for planning proper planting in the future. Keywords: Shallow groundwater, sand deposit channel, EM and resistivity

1 INTRODUCTION

Background Shallow groundwater may occure due to concentrated sand sedimentation channel during the basin developing tracing from the basement high plateaus passing high terrest deposit to Chao Phraya river at the middle of basin (e.g. west to east for this case study) which plays as a significant role of water supply for local agriculture use. The target area of SGW covers some parts of the Middle part of Central Plain Region occupied in six provinces i.e. Suphan Buri, Ang Thong, Chainat, Singburi and UThai Thani. The area copes with this paper presenting at Fig 1 Location map of research area A1 Northwestern region of Suphanburi province called Nong Topography Yaisai-Danchang area (or named area A1) located on the west margin of Chao phraya basin where the majority of The area A1 located at the Middle Central Plain (Chao agriculture land use is sugar cane plantation. Phraya Basin) is undulating terrain and made up of With primary assumsion, sandy soil zone may infer to old residues from erosion and decomposition of sand and channels of sand deposit that present as high potential of gravels (old channel) that present as shallow groundwater shallow groundwater zones. Known shallow groundwater zone which categoried as folows: position may help for effective water supply management 1. River basin, the area along the banks of Chao Praya for local agriculture. River and Watercourses that meet at Chao Praya River, appearing as smooth and averages 5-25 m.s.l. Aims 2. High Terrace Deposits (Old Terrace or old sand The aims of application in integrated techniques to map deposit channel), located adjacent to the river basin SGW in Dan Chang – Nong Ya Sai area, Suphanburi located about 30-50 km from Chao Praya river with province, for support water supply information in effective elevation of about 25-80 m.s.l. (this area should be most use for local agriculture activités and may predict drought interested in SGW studies) crisis circumstance due to climate change in the future 3. Mountains and Plateaus, the basement of Central Location of Research Area A1 Plain basin occurring at the margin of the basin with elevation of about 100 - 500 m.s.l. The area A1 is located at between Dan Chang and Nong Ya Sai district, Suphanburi province, (named Dan Chang - Nong Ya Sai area) traveling from Bangkok by distance of about 170 km to NW.

Fig 2 Geology and groundwater map of Suphanburi of research area A1

Geology The area is mainly occupied with thick Quanternary sediments (Pleistocene Age) generally composed of gravel, sand, and clay. Groundwater

There area two main unconsolidated aquifers i.e. Alluvial deposit (Qaf) contains gravel and sand of flood plain and lower terrace deposits and Terrace/Colluviul aquifer

(Qht/cl) stores in voids of gravel, sand rock fragments and weathered /decomposed rocks with average depth of 10- 40 meters.

2 MATERIAL AND METHOD Fig 3 Principles of EM operating to distinguish sand (low Data and Field Instruments conductivity) and clay (higher conductivity) zones. 1. Secondary data e.g. soil map geology map groundwater map including remote sensing etc. 2. geophysical instrument 2.1 Multi-Frequency EM conductivity meter, Profiler EMP- 400 2.2 Multi-electrode resistivity meter, Geomative (GD-10 model). Research Methodogy 1. Area analysis using GIS management as fallows. 1.1. RS processing in case of classifying alluvial sediments deposit try color ban RGB by 311. Fig 4 EM (Profiler EMP-400 equipment) field operating. 1.2. Create sandy soil map from soil map (scale 1:25,000) 3.2 Detailed 2D resistivity imaging measurement was 2. Field checking by soil sampling and classifying performed follows EM result showing the sand deposit including shallow groundwater well observation. zone. 3. Geophysical surveying Principle of resistivity surveying 3.1 Regional EM is conducted in regional scale area. In theoretically, Electromagnetic multi-frequencies Resistivity reading is represented by an apparent technique was attempted with two coil configurations by resistivity, ρa calculated from observed potential value, ∆V, concept of first coil transmits oscillating electromagnetic injected current, I and geometric factor of the electrode energy penetrating into subsurface then induces configuration. The current is injected into the ground at secondary EM fields in conductive bodies and receiving at electrodes (CA, CB) and the potential change in second coil) underground can be detected by electrode (PM, PN). as seen in Fig. 5. (Telford et al., 1990).

Fig. 5 General resistivity array positioning current electrode CA-CB and voltage probes PM-PN.

2D resistivity imaging with automatic 60-120 multi- electrode measurement is applied at the target location which obtain a geo-eletrical pseudo-section along survey lines. The measured data are found as continuous and detailed subsurface feature in both vertical and horizontal direction. Measuring practise is design by using Dipole- Dipole, Schlumberger electrode configuration with reading Fig. 8 Processed RS map displays sand station spacing of 5 m. (Explained in Fig 5 deposit zone at the area A1

Fig.6 Explanation for subsurface reading point obtained 2D resistivity imaging technique

Fig. 9 Sandy soil map at area A1 3. After field checking, Ban Chaeng Gnam area was selected for geophysical investigating. The result of EM mapping can clearly shows two main sand deposit zones with high resistive boundary laying in about West to East with about 500 m wide (as seen in Fig. 10(a)). The result of 2D resistivity measuring at the middle of upper resistive boundary (of EM result) by resistivity mapping (Fig 10 (b)) can show more detail of sand deposit zones with same direction of EM mapping. The dimension of sand deposit Fig. 7 Taking 2D resistivity measuring at channel can be identified as about 10 m deep and 40 – location of resistive zone resulted from EM 150m wide (can be seen in Fig 10(c)). result. Sand deposits zone found from both EM and resistivity 4. Setting sensor and data logger for long period techniques was presented as slightly higher resistivity monitoring i.e. groundwater level. Rainfall, air temperature value but can clearly separated from surrounding clay and moisture ground conductivity and moisture etc. layer. Known sand deposit zone can infer as high potential of SGW zones which can be evidenced by lots of apparent 3 RESULT AND DISCUSSION local SGW wells. The result to study SGW can be shown as follows : SGW location map must be very beneficial for local farmer to know the location of drilling wells for effective drilling and 1. The processed RS map can be presented in Fig. 8 by safe invest budget. considering red color zone which may reflected image of sand deposit zone, tracing from sources of sediments at The same procedure in SGW mapping should be effective the West toward the thick sediments at the East (close to technique for future studying SGW at another sites i.e. Chao Phraya river). Chainat province.

2. Sandy soil map displays sandy soil area occupied as fan shape extending from West to East (Fig. 9) which may related to types of sediments, slope and surface water flow direction.

(a)

Fig. 10 The result of EM mapping (a) displays two main resistive zones which may infer sand deposit channel. Resistivity mapping in (b) confirm sand deposit zones in detail scale by 2D resistivity imaging technique. Sand deposit channels can be identified with sligthly high resistivity value zone (c) with depth of about 10 m and vary width of about 40 – 150 m.

5 EFERENCE 4 CONCLUSION Department of groundwater resources (DGR) In conclusion, to locate high potential SGW zone should do Initial locating the research area by applying RS and soil Department of mineral resources (DMR) data then EM and resistivity techniques are performed for Suanburi, D. & Wathanaku, P. (2009). Subsurface subsurface SGW feature. Investigation by Resistivity Scanning Technique for SGW is found in the area A1 is significant strategic factor Groundwater Management at Seashore Developing related to climate change, in particular, drought crisis Site, Pkuket, Thailand. The proceeding in World City situation warning. Then monitoring water laver in SGW Water Forum 2009, August 18~21, 2009, Incheon, wells in long period (up to two years) were set up Korea. corresponde to the location of SGW channel systematically Suanburi, D. (2010). Resistivity Scanning Technique: A including sensors of air temperature, pressure and New Approach for Effective Groundwater moisture, soil moisture and conductivity, and rainfall. All Investigation, proceeding of the 5th International significant factors may use for water balance of the area Conference on Applied Geophysics 11-13 November A1. 2010 Phuket Thailand. It is recommendation that this success EM and resistivity Telford, W.M., Geldart, L.P. & Sheriff, R.E. (1990). Applied practise for SGW mapping, can be further modified for a Geophysics, 2ed. Cambridge: Cambridge University similar propose such as deep salinity soil mapping in the Press, USA. 770p. North Eastern area.

15 Short paper

Contributing Adaptation Strategy for Climate Change by Evaluating Agronomic Conditions through Measurement of LAI in rice

Koki Homma 1 Graduate School of Agricultural Science, Tohoku University, Aoba, Sendai, 980-8045, Japan Tel: +81 22 757 4083, Fax: +81 22 757 4087, E-mail: [email protected]

Abstract To contribute development of adaptation strategy for climate change, evaluation of the response of crop against environment and management is firstly recommended. For the purpose, the author have conducted several studies in relation to leaf area, which is the major organ to produce biomass and reflects crop responses. This paper briefly explains some of the results and discusses the utilization of LAI measurement in rice.

Keywords: Canopy analyser, leaf area index (LAI), remote sensing, simulation model, statistic evaluation

1 INTRODUCTION years [6]. The difference between TIPS and NDVI Leaf is one of the most important organ which captures light (Normalized difference vegetation index) is associated and synthesize carbohydrate. The leaf area determines the with plant morphology, such as angle of leaf and number amount of light captured and then firstly restricts biomass of tillers. Accordingly, cultivar features, such as traditional production. The leaf area is ordinary proportional to and improved, are possibly distinguished by remote biomass and reflects plant status. Accordingly evaluation sensing based on the analysis of TIPS and NDVI [7]. of leaf area is used to assess growth and production of Most rice production in Southeast Asia is conducted in the plant. rainy season, during which cloudy conditions often The leaf area is ordinary expressed by the unit per land interrupt satellite observation in visible and near-infrared area, then called leaf area index (LAI, m2 m-2). This paper range. Accordingly, remote sensing based on synthetic briefly explains our achievement in relation to LAI aperture radar (SAR) is proposed as a more suitable measurement in rice and how to contribute to develop the method to evaluate rice growth in this area because the adaptation strategy for climate change. observation is independent from cloud and solar illumination. Although the evaluation of LAI based on SAR still needs further improvement, LAI growth rate was 2 UTILIZING CANOPY ANALYZER associated with increase rate of back scattering coefficient LAI is usually determined with destructive method, which in consecutive SAR images (Fig. 1) [8]. involve cutting green leaf blades from plant samples and measure leaf area with an area meter. However, the Table 1. Results of ANCOVA for effects of cultivation method presents the disadvantage of requiring relatively management and environment on LAI growth rate in laborious work to collect and measure the samples. farmers’ fields in Pursat, Cambodia [3]. Alternatively, non-destructive measurement methods employing plant canopy analyzers such as the LAI-2000 (LI-COR,Inc., Lincoln, NE; LI-COR). The canopy analyser method requires several tens of seconds to evaluate LAI but shows relatively large measurement error against the destructive method (sometimes about 30%). Hrooka et al. [1] shows that frequent measurement of LAI, which can be possible by non-laborious measurement by canopy analyser, together with statistical regression analysis decrease the effect of measurement error and characterize the dynamics of leaf growth. The method was applied in Lao PDR [2] and Cambodia (Table 1) [3] to evaluate cultivation management and environment. It was also applied in a field experiment to evaluate genotypic difference in LAI growth characteristics for 60 rice cultivars [4].

3 EVALUATION BY REMOTE SENSING METHOD To monitor LAI in a wider range of area, evaluation of LAI by remote sensing method is recommended. Hashimoto et al. [5] proposed a new index to evaluate LAI called the time- series index of plant structure (TIPS), of which estimation stability was confirmed by field experiment for several

16 parameterized, the model would assess its impacts under future climate. Although further salinity problems has been anticipated in Northeast Thailand, the model doesn’t incorporate effect of salinity on rice growth and yield. We are now conducting investigation of dynamics of EC in soil solution and its effect on rice growth and yield in farmers’ fields in Khon Kaen with ST2-R3. The results will be incorporated into the model in a few years.

6 REFERENCE [1] Hirooka, Y., Homma, K., Shiraiwa, T., Kuwada, M. 2016. Parameterization of leaf growth in rice (Oryza sativa L.) utilizing a plant canopy analyser. Field Crops Res. 186: 117-1223. [2] Hirooka, Y., Homma, K., Maki, M., Sekiguchi, K., Shiraiwa, T., Yoshida, K. 2017. Evaluation of the dynamics of the leaf area index (LAI) of rice in farmer’s fields in Vientiane Province, Lao PDR. J. Agric. Meteorol. 73: 16-21.

Fig. 1 Relationship between LAI growth rate and back [3] Hirooka, Y., Homma, K., Kodo, T., Shiraiwa, T., scattering coefficient (BSC) increase rate [8]. The line was Soben, K., Chann, M., Tsujimoto, K., Tamagawa, K., regressed by the field where BSC and days after Koike, T. 2016. Evaluation of cultivation environment transplanting (DAT) had a significant correlation (S). and management based on LAI measurement in farmers’ paddy fields in Pursat province, Cambodia. Field Crops Res. 199,: 150-155. 4 SIMULATION MODEL BASED ON LAI EVALUATION [4] Hirooka, Y., Irie, T., Homma, K., Shiraiwa, T., We developed a model for simulating rice growth and yield Toriumi, A. 2013. Analysis of genotypic variation of (SIMRIW-RS) that requires several LAI measurement [9]. leaf canopy dynamics in rice by using plant canopy The calibration based on LAI produces more accurate analyzer. J Crop Res. 58: 51-56. (In Japanese with estimation of yield, and simultaneously provides English abstract) information of fields such as soil fertility and water [5] Hashimoto, N.; Maki, M.; Tanaka, K.; Tamura, M. availability. The model was validated in Vientiane, Lao PDR Study of a method for extracting LAI time-series with SAR (Fig. 2) [10]. patterns for estimation of crop phenology. J. Remote Sens. Soc. Jpn. 2009, 29, 381–391. (In Japanese with English abstract) [6] Maki, M., Homma, K. 2014. Empirical regression models for estimating multiyear leaf area index of rice from several vegetation indices at the field scale. Remote Sensing 6, 4764-4779. [7] Kambayashi, M., Homma, K., Maki, M., Hirooka, Y., Shiraiwa, T. 2012. Research on detection of rice ecotypes by canopy spectral reflectance. The 33rd Asian Conference on Remote Sensing, PS1-24, 1-8. [8] Hirooka, Y., Homma, K., Maki, M., Sekiguchi, K. 2015. Applicability of synthetic aperture radar (SAR) to evaluate leaf area index (LAI) and its growth rate Fig. 2 Time-series LAI simulation by SIMRIW-RS before of rice in farmers’ fields in Lao PDR. Field Crops Res. and after readjustment of field parameters, using accurate 176, 119-122. estimated LAIs from COSMO-SkyMed (CSK) data [10]. [9] Homma, K., Maki, M., Hirooka, Y. 2017. Development of a rice simulation model for remote- 5 STRATEGY TO CONTRIBUTE CLIMATE CHANGE sensing (SIMRIW-RS). J. Agric. Meteorol. 73, 9-15. ADAPTATION [10] Maki, M., Sekiguchi, K., Homma, K., Hirooka, Y., Oki, The model, SIMRIW-RS, was developed on the basis of K. 2017. Estimation of rice yield by SIMRIW-RS, a SIMRIW and SIMRIW-Rainfed. SIMRIW was used to model that integrates remote sensing data into a crop evaluate rice productivity under future climate although the growth model. J. Agric. Meteorol. 73, 2-8. variable factors were solar radiation and temperature [11]. [11] Matthews, R. B., M.J. Kropff, T. Horie and D. SIMRIW-Rainfed was developed based on the farmers’ Bachelet 1997. Simulating the impact of climate fields data in Ubon Ratchathani, Northeast Thailand, where change on rice production in Asia and evaluating soil fertility and water availability limit rice productivity [12]. options for adaptation. Agricultural Systems 54: 399- Accordingly, SIMRIW-RS would assesses rice productivity 425. under future climate with calibrated parameters under [12] Homma, K., Horie, T. 2009. The present situation and present management of farmers if ST1 in ADAP-T provides the future improvement of fertilizer applications by weather and soil moisture data. farmers in rainfed rice culture in Northeast Thailand. A system to evaluate rice productivity with SIMRIW-RS is In Elsworth, L.R., Paley, W.O. (Eds.) Fertilizers: being developed in Thailand by ST2-R2. If new cultivation Properties, Applications, and Effects. Nova Science methods for climate change adaptation by ST2-R1 is Publishers, New York, 147-180.

DEVELOPMENT OF SDT DROUGHT INDEX USING SATELLITE IMAGES

Kazuo OKI (Institute of Industrial Science, The University of Tokyo)

1.Introduction the difference in ground surface temperature during the day Drought is the most serious disaster in the world and the and night, we propose a spatially evaluable drought index. estimation of the United Nations Food and Agriculture Organi- zation (FAO) causes damage of 6 to 8 billion dollars each 2. Method year. Since 1900, 11 million people have died and more than In this research, we propose SDT (Standardized Difference 2 billion people were affected by drought. The drought is a Temperature) as a drought index focusing on day and night disaster that affects a very large number of people. The difference of ground surface temperature. SDT is expressed drought frequency, influence and duration will be increased by the following equation. by climate change in the future, and human and economic damage will also be expanded . The appropriate drought monitoring is required to reduce the damages. Drought index is used as a method of drought evaluation, :Difference in ground surface temperature between and analysis. WMO (1992) defines the drought index as "in- daytime and night at k time at point i. dex on the long-term and extraordinary water shortage accumulation", but since the definition of abnormal water shortage : Average of ground surface temperature difference be- varies from region to region, the climatic conditions of individu- tween day and night at point i al regions should be considered fully. For example, in deserts : Standard deviation of ground surface temperature differ- with little precipitation, water shortage is occurring continuously, but the severity of drought is not necessarily large. This is be- ence between day and night at point i cause the the ecosystem and the social system adapted to and are already formed in areas where the amount of precipi- SDT evaluates the degree of drought by evaluating soil tation is originally small like the desert. By considering the time moisture content by day and night ground surface tempera- deviation and cumulative situation, the drought index should ture difference. The larger the value of SDT is the greater the be evaluated the droughts reflecting the climatic conditions of difference in ground surface temperature during the day and individual areas. night, which means that the amount of soil moisture is small, SPI (Standardized Precipitation Index) , which is a drought and the degree of drought is large. Conversely, when the index expressed only by precipitation and is characterized by value of SDT is negative, the degree of drought is small , being able to calculate easily using precipitation observation which means the soil moisture is large. data such as meteorological observatories.The SPI also takes There are three features of SDT. First, because the ground account of time deviation by standardization, reflects the cli- surface temperature data is acquired from the satellite, it can matic conditions of individual regions, and makes it easy to be digitized everywhere. The second one is easy to calculate. compare regions. There are many drought indices widely The third one is standardized, so it can be compared with used besides SPI, but since many drought indices use values at different places. ground observation data like SPI, the places where quantifica- In this study, the ground surface temperature uses data of tion can be done are limited and evaluation of spatial deviation Terra / MODIS (Moderate Resolution Imaging Spectrometer), is inadequate. and the spatial resolution is 1 km. In addition, the ground sur- Therefore, for more detailed drought monitoring, it is re- face temperature in the daytime is observed at 10:30 and the quired to be able to evaluate droughts everywhere. In this ground surface temperature at night is observed at 22:30. . research, we develop a method to evaluate droughts spatially by using satellite remote sensing. Specifically, by focusing on

18 3. Results Fig. 1 shows the 3-month average SDT map for the whole world from 2000 to 2013. The larger the positive value, the more drought-affected area. Conversely, the larger the negative value, the more wetness-affected area.

(d) Fig. 1. the 3-month average SDT map for the whole world from 2000 to 2013. (a) DJF: December, January, February, (b) MAM: March, April, May, (c) JJA: June, July, August, (d) SON: September, October, November. (a) From Figure 1, we can see that droughts and floods are taking place all over the world. Figure 2 shows the correlation between the soil moisture map (with ERA-Reanalysis data) and the SDT map in various parts of the world. It shows a high correlation with the soil moisture map and the SDT map, and it can be said that the SDT map shows the degree of drought by evaluating soil moisture influence.

(b)

Fig.2 the correlation between the soil moisture map (with ERA-Reanalysis data) and the SDT map in various parts of the world.

4. Conclusions In this research, we developed a method to evaluate droughts spatially by using satellite remote sensing. Specifical- ly, by focusing on the difference in ground surface temperature during the day and night, we proposed a spatially evaluable drought inde (c )

19 Short paper

Evaluation of the relationships between electric conductivity, sodium adsorption ratio before planting and leaf area index during growth period in Khon Kaen, Thailand

Masayasu Maki1, Koki Homma2, Taiki Saito2, Koshi Yoshida3, Kazuo Oki4, Taichi Tebakari5, Roengsak Katawatin6, Mallika Srisutham6, Supranee Sritumboon7, Somsak Sukchan7 1 Tohoku Institute of Technology, Postal Address 982-8577, Japan Tel: +81 22 3053918, Fax: +81 22 3053918, E-mail: [email protected] 2 Tohoku University, Japan; 3Ibaraki University, Japan; 4University of Tokyo, Japan; 5Toyama Prefectural University, Japan; 6Khon Kaen University,Thailand; 7Land Development Department, Thailand

Abstract Geospatial distribution map that indicates degree of salt injury is required for rice growth management in Khon Kaen. However, the methodology for generating the map does not exist. As the first stage to develop the methodology, evaluation of the relationships between electric conductivity (ECe), sodium adsorption ratio (SAR), which are the indicators of salt injury, before planting and leaf area index during growth period was conducted in this study.To measure ECe and SAR, soil sampling was conducted in Ban Phai in Khon Kaen province during Apr. 6-7. To generate geospatial map of LAI, Spectral measurement using drone and field measurement of LAI were conducted in same region during Sep. 6-8. Spectral images were obtained by multi-spectral camera attached to drone. LAI of rice was measured by canopy analyzer. Firstly, evaluation of the relationships between LAI and several vegetation indices derived from UAV image was conducted for finding the best vegetation index to estimate LAI. As the result, Green- red ratio Vegetation Index (GRVI) was the best vegetation index to estimate LAI. Then, evaluation of the relationships between LAI derived from GRVI image and ECe, SAR was conducted. As the results, it was confirmed that LAI during growth period and ECe, SAR before planting had negative relation. This result indicates that mapping the geospatial distributions of ECe and SAR is very important to manage rice growth.

Keywords: electric conductivity, sodium adsorption ratio, leaf area index, vegetation index, unmanned air vehicle

1 INTRODUCTION relationships between ECe, SAR before planting and LAI Salt affected soil is widely distributed in Northeast Thailand. during growth period were evaluated. Salt affected soil cause low rice productivity. In order to manage rice growth in Northeast Thailand, geospatial Table 1 Vegetation indices used in this study distribution map that indicates the degree of salt injury is required. Electric conductivity (ECe) and sodium Vegetation Index Fomula adsorption ratio (SAR) are very important indices for SR NIR / Red evaluating rice growth. However, the methodology to NDVI (NIR – Red) / (NIR + Red) create the maps of Ece and SAR is not exist. GRVI (Green – Red) / (Green + Red) In this study, as the first stage to develop the G×(NIR – Red) / (NIR + C×Red +L) methodology, evaluation of each relationship between Ece, EVI2 SAR before planting and leaf area index (LAI) during (C=2.4, G=2.5, L=1.0) growth period was conducted. CIred_Edge (NIR / Red Edge)−1

NDRE (NIR−Red Edge) / (NIR + Red Edge) 2 MATERIAL AND METHOD Soil sampling for ECe and SAR measurement (6 points) was conducted in Ban Phai in Khon Kaen province during 3 RESULT AND DISCUSSION Apr. 6-7 2016 (before planting). LAI measurement was As the results of evaluation of the relationship between LAI conducted in same region during Sep. 6-8 (during growth and each vegetation index, GRVI was the best index for period). At the same time, multi-spectral images were obtained by multi-spectral camera (Sequoia, Parrot) attached to drone (Solo, 3DR). LAI of rice (36 points) was measured by canopy analyzer (LAI-2200, Li-Cor). Several indices for estimating LAI were firstly calculated using multi-spectral camera. In this study, SR (Simple Ratio), NDVI (Normalized Difference Vegetation Index), GRVI (Green-red ratio Vegetation Index), EVI2 (Enhanced Vegetation Index 2), CIred_edge (Red edge Chlorophyll Index), and NDRE (Normalized Difference Red Edge) were used. These indecis were calculated using the formulas in Table 1. Green, Red, Red Edge and NIR in table 1 indicate reflectances of wavelength in green, red, red edge and near infrared, respectively. Then the relationship between LAI and each vegetation index was evaluated and geospatial distribution map of LAI was created using the best index for estimating LAI. Finally, Fig. 1 Relationship between LAI and GRVI

20 Fig. 2 Geospatial distribution map of rice LAI during Sep. 6-8 derived from drone images estimating LAI during growth period in this area. Fig. 1 show the relationship between LAI and GRVI and Fig. 2 shows the geospatial distribution map of LAI during Sep. 6- 8 derived from drone images. Next, Evaluation of the relationships between LAI derived from GRVI image and ECe, SAR was conducted. Fig. 3 shows the relationship between ECe before planting and LAI during growth period.

This figure indicates that there is strong negative correlation between LAI during growth period and ECe before planting. Fig. 4 shows the relationship between SAR before planting and LAI during growth period. This figure also indicates the strong negative correlation between SAR Fig. 4 relationship between LAI during growth period and before planting and LAI during growth period. This means SAR before planting rice that growth amount was lowered where the values of ECe and SAR were high before planting. Therefore, it is considered that evaluation of the values of ECe and SAR before planting rice is very important for suitable rice growth management.. 4 CONCLUSION In this study, as the first step to develop the methodology for creating geospatial distribution map that shows degree of salt injury, evaluation of the relationship between LAI during growth period and both ECe and SAR before planting was conducted. As the result, there were strong negative correlations between both ECe and SAR and LAI. This indicates that evaluation of values of ECe and SAR before planting is very important for suitable rice growth management. This means that geospatial distribution maps of ECe and SAR before planting is useful for planning of rice planting and growth management after planting. Therefore, as the future study, it is considered that evaluation of relationship between the indices of salt injury (ECe and SAR) and spectral information derived from drone before planting is required for mapping of geospatial distribution of salt injury available for planning Fig. 3 relationship between LAI during growth period and of rice planting. Ece before planting rice

21 Short paper

Land Use Pattern and Population Dynamics in Flood Area in Thailand

Hiroaki Shirakawa1 1 Graduate School of Environmental Study, Nagoya University, 464-8601, Furo-Cho Chikusa-ku Nagoya, Japan Tel: +81 (0)52 789 4654, Fax: +81 (0)52 789 3840, E-mail: [email protected]

Abstract The purpose of the study is to examine land use pattern and population dynamics in flooded area in Thailand. Frequently flooded area is mainly used for agriculture. Between 1996 to 2015, the population increased in the area where flooding has not occurred relatively, however the population in the frequently flooding are is almost stable. The results of this study show flooding gives great impact to land use and population distribution in Thailand.

Keywords: Flooded frequency, land use, population

1 INTRODUCTION（TARGET ISSUE） Development Department). Population data is collected in The flood that occurred in 2011 had very serious impacts “tambon” (sub-district) level from 1996 to 2015. Analysis on society and economy in Thailand. Especially the results were tabulated by region. Region classification is agricultural damage in the Chao Phraya River basin was shown in Fig.2. serious. The rice production in rainy season in 2011 decreased more than 50% comparing with the previous year, and it decreased more than 90% in the most severe area. In Thailand, it is difficult to construct a dam because of topographically characteristics, so it is important to secure a water spill area to alleviate the impact of the flood. Agricultural land has been used as such a water spill area. At present, there is concern that the flood damage will expand due to the development of the water spill area. This study examined land use and population dynamics by flood occurrence frequency.

Fig.2 Regional category

3 RESULT AND DISCUSSION Fig 1 shows the flooded frequency from 2005 to 2016. The area where has been flooded at least one time between 2005 and 2016 is 183,837km2. Within the area, the area where has been flooded more than 9 times is 2,167km2 and it is equal to 1.2 % total flooded area. Fig.1 Production growth of major rice (2010-2011) Frequently flooded area is mainly used for agriculture (Table 1). On the other side, total population (in 2015) of “tambon” 2 MATERIAL AND METHOD where flooding is occurred at least one time between 2005 Flooded frequency between 2005 to 2016 is calculate by and 2016 is about 55 million. Within the population, the using the data which is created by GISDA(Geo-Informatics total population of “tambon” where flooding has occurred and Space Technology Development Agency). Land use more than 9 times is about 5.7 million, and it is equal to data is used the data which is created by LDD (Land 10.5% of total population in the flooded area. Between

22 1996 to 2015, the population increased in the area where Table 2 Flood frequency and population flooding has not occurred relatively, however the (thousand) population in the frequently flooding are is almost Flooded frequency(2005 - 2016) stable(Table2). Y ear R egion 1-4 5-8 9-12 Total N orth Eastern 9,384 6,105 2,284 17,773 N orthern 5,758 3,116 1,991 10,864 Southern 3,152 1,997 344 5,493 1996 Eastern 1,485 938 101 2,524 (A ) W estern 1,712 444 411 2,566 C entral 781 1,290 662 2,733 B M R 2,228 6,604 0 8,832 Total 24,500 20,493 5,792 50,784 N orth Eastern 9,801 6,337 2,288 18,426 N orthern 5,676 2,977 1,941 10,594 Southern 3,535 2,148 347 6,031 2010 Eastern 1,693 1,013 96 2,802 (B ) W estern 1,798 449 405 2,651 C entral 810 1,352 642 2,804 B M R 2,848 7,310 0 10,158 Total 26,160 21,587 5,719 53,466 N orth Eastern 9,895 6,443 2,287 18,625 N orthern 5,834 2,989 1,948 10,771 Southern 3,666 2,234 359 6,259 Fig.1 flooded frequency 2015 Eastern 1,787 1,056 97 2,940 (C ) W estern 1,847 451 406 2,704 Table 1 Land use pattern and flood frequency C entral 822 1,379 638 2,839 (Km2) B M R 3,074 7,470 0 10,543 Flooded frequency (2005 - 2016) Total 26,924 22,022 5,735 54,682 1-4 5-8 9-12 Total Agriculture 35,106 3,627 145 38,878 N orth Eastern 512 338 3 852 Build-up land 1,187 45 6 1,237 N o rth e rn 7 6 -127 -43 -93 Forest 946 22 0 968 North Eastern Water 2,485 326 14 2,826 Southern 514 238 15 766 Others 4,926 936 52 5,914 Eastern 302 118 -3 417 Sub total 44,650 4,956 217 49,823 (C )-(A ) Agriculture 43,578 8,482 1,432 53,492 W estern 135 7 -5 138 Build-up land 1,861 84 9 1,954 Forest 422 11 0 433 C e n tra l 4 1 9 0 -24 107 Northern Water 1,375 271 33 1,680 B M R 846 865 0 1,711 Others 1,004 109 9 1,122 Total 2,425 1,530 -57 3,897 Sub total 48,240 8,958 1,483 58,681 Agriculture 8,013 1,163 0 9,176 Build-up land 216 5 0 221 Forest 271 19 0 290 Southern Water943098 Others 795 62 0 857 Sub total 9,389 1,252 0 10,641 Agriculture 12,338 2,209 0 14,547 Build-up land 889 49 0 938 Forest300030 4 CONCLUSION Eastern Water 236 31 0 267 The results of this study show flooding gives great impact Others 607 57 0 664 Sub total 14,100 2,345 0 16,446 to land use and population distribution in Thailand. And it Agriculture 8,263 3,055 137 11,456 also means that if the time and place of flooding will be Build-up land 482 30 0 512 changed by climate change, damage by flooding may Forest403043 Western Water709079 increase drastically in the country. Others 98 10 0 108 Sub total 8,953 3,107 138 12,198 Agriculture 18,905 4,651 327 23,883 Build-up land 1,582 50 1 1,634 Forest692071 Central Water 289 25 1 315 Others 228 9 0 238 Sub total 21,074 4,737 329 26,140 Agriculture 6,949 715 0 7,664 Build-up land 1,762 10 0 1,772 Bangkok Forest2002 Metropolitan Water892091 Region Others 373 7 0 380 Sub total 9,175 734 0 9,909 Agriculture 133,152 23,902 2,042 159,096 Build-up land 7,980 272 16 8,268 Whole Forest 1,780 57 0 1,837 country Water 4,639 666 48 5,353 Others 8,031 1,191 62 9,284 Sub total 155,582 26,088 2,167 183,837

23 The Livelihood Strategies of Farming Households under Drought Stress in Rural Areas

of Khon Kaen Province in the Northeastern Part of Thailand

Nao ENDO Faculty of Education, Kochi University 2-5-1 Akebono-cho, Kochi-shi, Kochi, 780-0938, Japan Tel: +81 88 844 8373, E-mail: [email protected]

ABSTRACT The purpose of this study is to clarify the livelihood strategies of farming household under drought stress in Khon Kaen Province. In this research, household surveys were conducted in two villages of the province in December 2016 and May 2017. The study villages are located in a rain-fed cultivation area and a salt accumulation area respectively. The items of the surveys included jobs of household members, the condition of farm management from Dec. 2015 to Nov. 2016 and the effects of drought. The results of our survey showed that non-agricultural activities of farming household members played an important role in coping with drought in the study areas.

Keywords: Household livelihood, farming, coping strategy, drought, Thailand

1. INTRODUCTION near Route 2 which is the main road in northeastern It has been said that the northeastern part of Thailand Thailand. Therefore, they have relatively easy accessibility was a difficult area to farm, because it had no large rivers to the cities. and a relatively small amount of rainfall in the country. In this research, household surveys were conducted in Moreover, recently, extreme climate events such as floods these villages in December 2016 and May 2017 respectively. (2011) and droughts (2014, 2015) often occurred in this The number of sample farming household was 21 in Wang area. Meanwhile, non-agricultural employment opportunities Wa Village and 25 in Doo Village. The items of the surveys were increasing inside and outside of northeastern Thailand included characteristics of household members, jobs of due to the economic growth in cities of the area, and the household members, the status of land ownership, the launch of low-cost carriers and the extension of interurban condition of farm management from Dec. 2015 to Nov. 2016 bus lines between cities in Thailand. In these circumstances, and the effects of drought. livelihood strategies which take into consideration non-agricultural activities of agricultural household members 3. RESULT AND DISCUSSION are significant factors affecting seriousness of damages 1) The Characteristics of Farming Household Livelihoods from extreme climate events. Therefore, the purpose of this in Ordinary Years study is to clarify the livelihood strategies of farming Regarding household type, the nuclear family and the household livelihoods under drought stress in rural areas of three-generation family accounted for over half of all Khon Kaen Province in the northeastern part of Thailand. households in both villages. More than 70 % of all This study is a socio-economic part of a KAKEN research household heads were in their 60s and over. The average project “Estimation of Agricultural Damage Function under number of household members was 4.5 in Wang Wa Village Extreme Climate Condition in Central Indochina Peninsula and 3.5 in Doo Village. However, more than 50 % of all (15H05254)”. households had family members who were engaged in non-agricultural activities outside the villages for more than 2. STUDY AREA AND METHODS a month (Table 1). Households which were supported by The study sites in this research were Wang Wa Village this income occupied 25 % in Wang Wa Village and 40 % in and Doo Village near Khon Kaen City. Wang Wa Village is Doo Village. Moreover, farming households which had located about 20 km south of the city and in a rain-fed non-agricultural income sources accounted for more than cultivation area. Cassava and rice were mainly cultivated in 80 % of all households in both villages. These findings show the village. Doo Village is about 50 km south of the city. The that farming households maintained their livelihoods by village is located in a salt accumulation area, and therefore, using non-agricultural income sources inside and outside farmers there had to select salt-tolerant crops. Jasmine rice, villages in this area. Meanwhile, 70 % of households in which can be categorized as salt-tolerant, was cultivated in Wang Wa Village and 54.2 % in Doo Village made most of most of the paddy field in this village. These two villages are their money from farming in ordinary years (Table 2).

24 Table 1. Characteristics of the farming household income in study villages

Average Households which Households Households number of had members who which had which had Total number working were engaged in income from non- of sample household non-agricultural members agricultural households members per activities outside outside the income household the villages (%) villages (%) sources (%)

Wang Wa 20 2.6 50.0 25.0 87.5

Doo 25 2.2 52.0 40.0 80.0 (Source: agricultural household survey data)

Table 2. The experience of being damaged from 2) Livelihood Strategies of Farming Household in Drought droughts which farming households Years in study villages had Table 3 shows the experience for farming households Househol to be damaged from droughts in study villages. More than Househol ds which 90 % of farming households had experienced some damage Househol ds which have ever from droughts in both villages. In Wang Wa Village and Doo Total ds which have ever decrease number have ever Village, 85 % and 91.7 % of all households were damaged given up d the of sample been from the 2015 drought respectively (Table 4). The main crop planting planted househol dameged damaged by the drought was rice cultivated in the rainy rice due area of ds from season in both villages. However, about 50 % of all to rice due droughts households did not cope with the drought by agricultural droughts to droughts techniques. Some households could not harvest any rice in the year. On the other hand, more than 50 % of all (n) (n) (%) (n) (%) (n) (%) households in both villages made most of their money from Wang non-agricultural activities in drought years (Table 5). 20 18 90.0 4 20.0 7 35.0 Wah Moreover, when household income decreased due to a bad harvest, many households used their savings, debt from Doo 24 22 92 4 17 0 0 relatives and income from non-agricultural activities as the (Source: agricultural household survey data) money source for the next cropping season and their living cost. From the findings described above, it is inferred that farming households coped with a drought by using non-agricultural activities and their social capital rather than Table 3. The experience of being damaged from the techniques on agricultural lands in this area. 2015 drought which farming households

in study villages had Table 4. Main income sources of farming Househol households in study villages in regular years Total ds which number have The kind of crops Wang Wa Doo of sample been damaged by the househol dameged drought (n) (%) (n) (%) ds from the Agriculture 14 70.0 13 54.2 drought Non-agriculture 5 25.0 7 29.2 (n) (n) (%) (n) Agriculture Rice: 7 1 5.0 0 0 Wang + non-agriculture 20 17 85.0 Rice + Cassava: 9 Wah Agriculture Cassava: 1 0 0 3 12.5 + remittance Rice: 21 Non-agriculture Doo 24 22 92 0 0 1 4.2 Rice + Chili pepper: 1 + remittance (Source: agricultural household survey data) Total 20 100 24 100 (Source: agricultural household survey data)

25 Table 5. Main income sources of farming households in study villages in drought years Wang Wa Doo (n) (%) (n) (%)

Agriculture 6 30.0 9 37.5

Non-agriculture 12 60.0 8 33.3

Remittance 0 0 3 12.5 Agriculture 2 10.0 0 0 + non-agriculture Agriculture 0 0 3 12.5 + remittance Non-agriculture 0 0 1 4.2 + remittance Total 20 100 24 100 (Source: agricultural household survey data)

4. CONCLUSION The results of our survey show that non-agricultural activities of farming household members played an important role when people in the study areas coped with drought in the study areas. Moreover, family members living outside their village also contributed to farming household livelihoods in ordinary years and drought years. This study does not analyze the relation between the composition of household livelihoods and the coping strategy under drought stress because the number of sample households is small. This is an issue for future research.

26 Short paper

Basin Modelling for Evaluation of Available Water Resources and Nitrogen Runoff in Northeast Thailand

Yuki Jikeya1 and Koshi Yoshida1 1 Ibaraki University, Postal Address 300-0393, JAPAN Tel: +81 29 888 8600, Fax: +81 29 888 8600, E-mail: [email protected],ac,jp

Abstract Meteorological Uncertainty caused by Global Climate Change would have significant impact on agricultural sector, because agricultural systems are strongly related with local climate condition. Especially in northeast Thailand, the ratio of irrigated agricultural land is only 8% and others are rain-fed so that climate change makes agricultural production more unstable and also makes crucial damage to the societies and economics in local area. To mitigate these issues, it is desirable to develop and disseminate enhanced adaptation systems. In this study, water and nitrogen-load estimation model was developed to assess the spatial distribution of available water resources and nitrogen runoff, and applied to Northeast Thailand. Keywords: TOPMODEL, Water balance, Nitrogen dynamics, spatial distribution

1 INTRODUCTION（TARGET ISSUE） the basin (Yoshida, 2011). TOPMODEL was employed for Meteorological Uncertainty caused by Global Climate the rainfall-runoff analysis. This distributed model can Change would have significant impact on agricultural include the spatial distributions of topography, land use, sector, because agricultural systems are strongly related and soil characteristics. Therefore, TOPMODEL is used with local climate condition. Especially in northeast widely for hydrological characteristic analysis, water Thailand(Fig.1), the ratio of irrigated agricultural land is management, water quality analysis, and future only 8% and others are rain-fed so that climate change forecasting. TOPMODEL was proposed by Beven and makes agricultural production more unstable and also Kirkby (1979) based on the contributing area concept in makes crucial damage to the societies and economics in hillslope hydrology. This model is based on the local area. To mitigate these issues, it is desirable to exponential transmissivity assumption, which leads to the develop and disseminate enhanced adaptation systems. topographic index ln(a/To/tanb), where a is the upstream In this study, water and nitrogen-load estimation model catchment area draining across a unit length, To is the was developed to assess the spatial distribution of lateral transmissivity under saturated conditions, and b is available water resources and nitrogen runoff, and the local gradient of the ground surface. Fig.2 illustrates applied to Northeast Thailand. the conceptual structure of the water cycle as estimated by TOPMODEL.

Fig.2 TOPMODEL structure

TOPMODEL includes three soil layers: the root zone, Fig.1 Study Area unsaturated zone, and saturated zone. The water contents of the root zone and unsaturated zone are calculated by distributed parameters, whereas the water content of the saturated zone is normally calculated by 2 MATERIAL AND METHOD lumped parameters. Because TOPMODEL requires only (STUDY AREA, EXPERIMENT, METHODOLOGY) three parameters (i.e., m, To, and Srzmax), the model is easy to link with GIS data (for details, see Ao et al. 1999 and Nawarathna et al. 2001). In addition, a dam operation 2.1 TOPMODEL model was combined with TOPMODEL to calculate water To evaluate nitrogen transportation according to the river storage in the reservoirs (Hanasaki et al. 2007, Juthithep, water flow, a distributed water-cycling model was 2014). developed and applied to analyse the water balance in

2.2 Nitrogen balance model For a nitrogen dynamics analysis in soil, Lin, et.al.(2000) or Suga (2005) mentioned that three type of nitrogen such as organic, ammonium, nitrate should be considered. In this study, a conceptual nitrogen balance model was developed by considering three pools in soil, organic N, ammonium N, and nitrate N, as shown in Fig.3. In this study, organic N here is assumed as nitrogen contained in relatively firstly decomposed organic matter which can be obtained by autoclave-extractable nitrogen test. The soil N, present mainly in organic form, is almost unavailable for plants. The vegetation uses mostly inorganic forms of N, which are made available by decomposition of organic matter. Soil microorganisms convert the N contained in the organic matter through the process of mineralization. Although plants can use both forms of inorganic N, nitrate is used in preference to ammonium because of its greater solubility in water. In other words, nitrates dissolve quickly in pore solution, which is taken up by plants. On the other hand, this also means that nitrate is easily leached to groundwater. Ammonium N is less mobile because it is strongly adsorbed on clay minerals because of its positive charge. Fig.3 Nitrogen Balance Model Denitrification is the anaerobic microbial reduction of N, which is used as an electron acceptor, resulting in a transfer of soil nitrogen to the atmosphere. 3 RESULT AND DISCUSSION

By using the proposed model, water and nitrogen balances in Northeast Thailand were calculated at a resolution of 1 km × 1 km. The calculated river discharge was in good agreement with the amounts observed at Yasoton and Rasisalai stations from 1987 to 2003 (Fig.4).

Fig.4 Observed and calculated river discharge at Yasothon and Rasisali stations

The first 5 years of data were used for parameter calibration, and the latter 12 years of data were used for validation. At the Yasothon station, Nash-Sutcliff efficiency (NSE) were 081 and 0.76 in the calibration and validation periods respectively. Model performance can be evaluated as “good”, if NSE > 0.75 (Moriasi et. al., 2007). At the Rasisalai station, Nash-Sutcliff efficiency

(NSE) were 0.86 and 074 in the calibration and validation periods respectively. The observed and calculated daily nitrogen load at the Yasoton and Rasisalai station is shown in Fig.5. Estimated correlation coefficients R were 0.69, and 0.58 respectively. The data of total nitrogen concentration in the Mun-Chi River Basin were measured only once per month; therefore, there is a possibility that peak nitrogen load might not be observed in such data. The current monitoring system of water quality data is manual, so if the frequency of monitoring were to be improved by using an automatic monitoring system, the model parameters also could be calibrated through better fitting. To make clear the characteristics of nitrogen load from different land use, the histograms of nitrogen load from each land use were evaluated (shown in Fig.6). In urban grid cell, nitrogen load ranged very widely compare to other land use, however average nitrogen load was 15.6 kg/ha/year, and it was larger than other land use. On the other hand, nitrogen load in forest was relatively small

(0.84 kg/ha/year).

4 CONCLUSION

(INCLUDDING POSSIBLE MEASURE FOR CLIMATE CHANGE ADAPTATION) In this study, conceptual nitrogen balance model combined with TOPMODEL was developed and applied to Northeast Thailand as the quantitative evaluation tool of the spatial distribution of nitrogen loading from different land use. Nash-Sutcliffe efficiency of river discharge at observed stations were more than 0.75 which model performance can be evaluated as “good”. The correlation coefficients of estimated average annual nitrogen-load at Fig.5 Obs and Cal nitrogen load at 2 stations Yasothon and Rasisalai stations were 0.69 and 0.58. By

Fig.6 Histgram of estimated nitrogen load from each grid cell

29 using the proposed model, spatial distribution of annual methods (in Japanese). Annual Journal of Hydraulic nitrogen load was estimated and histograms of nitrogen Engineering, JSCE, 43, 7–12. load from each land use was evaluated. As a result, Beven, K.J. and Kirkby, M.J., 1979: A physically based nitrogen load from large cities was large. variable contributing area model of hydrology. To improve the model accuracy, further research is Hydrological Sciences Bulletin, 24(1), 43–69. needed both in field observation and model development. Hanasaki, N., Kanae, S., Oki T and K. Mushiake 2003: In northeast Thailand, long-term observed water quality Development of globally applicable reservoir data is not available after 2004, however even in short- operation model (in Japanese). Annual Journal of term observation, basin wide monitoring data in tributary Hydraulic Engineering, JSCE, 47, 181–186. level may be helpful for more accurate parameter calibration. In this study, we employed many assumptions Juthitep V., T. Masumoto, H. Minakawa and R. Kudo, to simplify the nitrogen balance model. For example, we 2014: Application of a DWCM-AgWU Model to the neglected N uptake in forest, assuming that most of forest Chao Phraya River Basin with Large Irrigation are mature with no net accumulation of biomass. This Paddy Areas and Dams. Applied Hydrology, 26, 11- would be changed for well-managed, semi-natural or 22. disturbed natural forest where uptake of N occurs. Lin B.L., Sakoda A., Shibasaki R., Goto N. and Suzuki N. Because of sparsity of data, we ignored the spatial and 2000: Modelling a global biogeochemical nitrogen temporal heterogeneity in fertilizer management within the cycle in terrestrial ecosystems. Ecological Modelling, basin (e.g. difference of N application rate and timing of 35(1), 89–110. application). These problems may cause errors in the Moriasi, D.N. , Arnold, J.G. , Van Liew, M.W. , Bingner, calculation of N transformation processes. Despite all the R.L. , Harmel, R.D. and Veith, T.L. 2007: Model uncertainties, our results provide a first insight in the evaluation guidelines for systematic quantification of magnitude and spatial distribution of nitrogen loading in accuracy in watershed simulations. Transactions of northeast Thailand, and this kind of model can be used in the American Society of Agricultural and Biological the impact assessment of different management Engineers, vol.50(3), 885-900. strategies for sanitation systems or farming practices. Nawarathna, N.M.N.S.B., Ao, T.Q., Kazama, S., In the future, climate change will accelerate the water Sawamoto, M. and Takeuchi, K., 2001: Influence of cycle and severe droughts often occur in northeast human activity on the BTOPMC model runoff Thailand. Especially in dry season, nutrient concentration simulations in large-scale watersheds. 29th IAHR in large cities will increase due to the shortage of Congress Proceedings, Theme a, Beijing, pp. 93– available water resource which dilute the nutrient 99. concentration in drainage canal. And water environment Suga Y., Hirabayashi Y., Kanae S. and Oki, T., 2005: in city will be degraded. Therefore, suitable sanitation Changes in river nitrate transport of the world system should be introduced to the city which have resulting from increase in fertilizer use (in relatively large population density. Japanese). Annual Journal of Hydraulic Engineering, JSCE, 49(1), 495–500. Yoshida, K. Azechi, I., Hariya, R., Tanaka, K., Noda, K., 5 REFERENCE Oki, K., Hongo, C., Honma, K. Maki, M. and H. Ao, T., Ishihira, H. and Takeuchi K. 1999: Study of Shirakawa, 2013; Future Water Availability in the distributed runoff simulation model based on the Asian Monsoon Region: A Case Study in Indonesia, block type Topmodel and Muskingum-Cunge Journal of Developments in Sustainable Agriculture, 8, 25-31.