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MINISTRY OF EDUCATION AND TRAINING MINISTRY OF AGRICULTURE AND RURAL DEVELOPMENT THUYLOI UNIVERSITY NGUYEN THAI HA ESTABLISHING THE EARLY WARNING MODEL OF METEOROLOGICAL DROUGHT IN WATER RESOURCES EXPLOITATION AND MANAGEMENT FOR THE CENTRAL COASTAL REGION Specialization: Water Resources Engineering Code No: 58 02 12 SUMMARY OF DOCTORAL DISSERTATION HANOI, 2019 This dissertation was completed at Thuyloi University Supervisor 1: Assoc Prof Dr Nguyen Dang Tinh Supervisor 2: Prof Dr Nguyen Van Tinh Reviewer 1: Reviewer 2: Reviewer 3: The dissertation will be defended at the Council of dissertation evaluation, at: Thuyloi University 175 Tay Son str., Dong Da, Hanoi At hour date month year The dissertation can be read at: - National Library - Thuyloi University’s Library INTRODUCTION Urgency of the thesis Drought is a natural phenomenon It occurs due to rain shortage, high evaporation rate, and overexploitation of water resources Drought can happen in every part of the world and every type of climate zone, and it is quite common in Vietnam, especially the Central Coastal Region (CCR) The severity of a drought period not only depends on the duration, intensity and space, but also the water usage for crops and living activities Currently in Vietnam there have been many researches on drought However, those researches still have some limits such as using an unsystematic method of research or no wide application in practice In order to help managers, policy planners and the local people be proactive in exploiting and managing water resources, researches on drought are both important and necessary For that reason, the project “Establishing the early warning model of meteorological drought in Water Resources Exploitation and Management for the Central Coastal Region” was proposed for research Research objective Assess drought development, build an early warning model of meteorological drought for the purpose of exploitation and management of water resources in the Central Coastal region Research subjects and scope Research subjects: the meteorology, characteristic, intensity, trend and distribution of drought Scope of research: Central Coastal region Research content of the thesis Analyze and assess drought development in the researched area; analyze drought development in the researched area when ENSO is occurring; correlatively assess drought of the researched area with Sea Surface Temperature (SST) and Southern Oscillation Index (SOI) to choose input variables for the drought forecasting model; Build a structure for the meteorological drought forecasting model using statistical method (Adaptive Neuro-Fuzzy Inference System, ANFIS) and select a suitable model for the researched area; Build an early warning model of meteorological drought for the researched area using drought index forecasting map Research method Statistical analysis method; inheritance method; Non-Contiguous Drought Area (NCDA) method; correlation analysis method; meta-analysis method; and mathematical model method (ANFIS model) Scientific and practical significance This thesis will contribute to the scientific basis in using and selecting drought index, building drought maps using the NCDA method and drought forecasting method The result of the thesis can be applied to build an early warning model of meteorological drought for the researched area as well as others By doing so it helps managers, policy planners and the local people be proactive in exploiting and managing water resources Thesis structure The content of this thesis includes foreword, conclusion and chapters: Chapter 1: Summary of current drought researches and forecast Chapter 2: Scientific basis and meteorological drought forecasting method for the Central Coastal region, Chapter 3: Building an early warning model of meteorological drought for the Central Coastal region CHAPTER SUMMARY OF CURRENT DROUGHT RESEARCHES AND FORECAST 1.1 Definition of drought 1.1.1 Definition and classification of drought Drought is a natural phenomenon It can occur for one or many reasons, such as rain shortage, high evaporation rate and overexploitation of water resources Drought appears in every part of the world and every type of climate, with significant variation in characteristics from one region to another According to World Meteorological Organization (WMO) Drought is classified into type: (1) meteorological drought, (2) Agricultural drought, (3) Hydrological drought and (4) Socioeconomic drought 1.1.2 Drought indices Drought index is usually a unique number representing the general drought condition at the time of measurement Drought index most suitable for the researched area and research purpose is chosen Below are some drought indices commonly used around the world:  Meteorological drought indices: Standardized Precipitation Index (SPI); Sazonov Index (SaI); Standardized Precipitation-Evapotranspiration Index (SPEI) Of the above meteorological drought indices, Vietnam mostly uses SPI and SaI, whereas SPEI has only been proposed on 2010 and has yet to be widely used  Agricultural drought indices: Root-Weighted Soil Moisture Index (RSMI), Soil Moisture Anomaly Percentage Index (SMAPI); Palmer Drought Severity Index (PDSI); Standardized Soil Moisture Index (SSI); Soil Moisture Index (SMI) Of the above agricultural drought indices, PDSI is most widely used both globally and in Vietnam  Hydrological drought indices: Surface Water Supply Index (SWSI)  Socioeconomic drought indices: Social Water Scarcity Index (SWSI) 1.1.3 Characteristics of drought Drought periods usually differentiate from each other based on three characteristics: intensity, duration and outspread 1.2 Drought situation and researches around the world 1.2.1 Drought situation around the world In recent decades, drought has been occurring around the world, causing significant damages to the economy and negatively affecting people’s lives and the ecosystem Every year approximately 21 million hectares of land lose economic productivity due to drought In the last quarter of the century, the number of people facing risks from drought on dry lands has increased by 80% More than 1/3 of the world’s land has been dried up, yet 17.7% of the world population still lives on them The damages caused by drought around the world are detrimental to both people and properties 1.2.2 Drought researches around the world There have been many drought researches around the world Due to the complexity of this phenomenon, however, there has yet to be a common method used for drought researches In detecting, identifying, monitoring and warning drought, researchers usually use drought indices as their main tool Research results show that no index has a clear advantage over others in all situations Therefore, choosing which drought index to apply depends on the specific conditions of each area as well as its already existing monitoring data Currently the task of forecasting and warning are being executed using two main method: (1) directly forecasting drought index using traditional statistical forecasting model This method is based on the correlation between drought index and large scale circulation factors as well as characteristics of ENSO, etc Many researches have proved that the characteristics of ENSO are important factors in detecting drought and can be used for forecasting; (2) the second method is forecasting based on the forecasting results of climate model and hydrological model This method directly related to the ability to forecast climate and hydrologic conditions which provides physical features of drought, such as rain, temperature, flow and soil moisture level 1.3 Drought situation and researches in Vietnam 1.3.1 Drought situation in Vietnam Drought development in Vietnam is becoming increasingly complex From 1985-2016, Vietnam suffered multiple droughts such as: severe drought in the Central region and Southern Delta in 1992; Harsh drought in multiple provinces of Central Vietnam during the winter-spring crops of 1994-1995; widespread drought in 2002 and 2004-2005, especially in Northern Central, Southern Central and Highland regions; water shortage, weak flow and historically low water level of river systems throughout the country in 2009-2010 causing water shortage for agriculture production; the severe widespread drought during the winter-spring crops of 1997-1998 caused, when only counting agricultural properties, 5000 billion Vietnam dong in damages; and in 2014-2016, the El Nino phenomenon caused drought and salinization Drought has threaten both agricultural production and human livelihood, with approximately 5.572 billion Vietnamese dong total damage 1.3.2 Drought researches in Vietnam Drought researches in Vietnam in recent years mainly focused on issues: (1) General research on drought and its effects on livelihood, economy and society; (2) Solution, prevention and damage mitigation against drought In 2001, Nguyen Duc Hau performed a research on detecting and applying drought index to assess the influences of ENSO on drought and creating drought forecasting equations for climate zones in Vietnam In 2007, Nguyen Van Thang managed to assess the drought level, selected suitable drought indices and created a forecasting and early warning technology for climate zones in Vietnam using hydro – meteorological data and remote sensing materials to serve socioeconomic development In 2015, Nguyen Van Thang managed to create a suitable a drought criteria set for monitoring and warning; built technologies and meta-statistical meteorological drought forecasting procedures for the entire country using SPI; built technologies and procedures for applying warning products of different models around the world in drought warning in Vietnam with up to month in advance For specific areas, Nguyen Trong Hieu (2000) and Nguyen Van Cu (2001) determined the drought criteria, assessed the effects of drought, figured out causes of desertification and suggested ways to prevent and combat drought and desertification in different provinces: Quang Ngai, Binh Dinh, Ninh Thuan and Binh Thuan Dao Xuan Hoc (2004) used the Sezonov index to survey and assess drought in the CCR area In 2005, Nguyen Quang Kim researched the drought situation at the time and established a scientific basis for drought forecasting procedure for South Central and Central Highlands regions; programmed drought index calculation software and meteorological drought forecasting software using SPI In 2008, Tran Thuc assessed the drought level as well as water shortage level of provinces in South Central and Central Highlands regions In 2014, Nguyen Luong Bang used SPI and SPEI in researching the influences of ENSO on meteorological drought development in Khanh Hoa river basin The result of that research showed that SPEI is more fitting than SPI for assessing drought development in river basins Most researches in Vietnam used statistical method for their drought forecast Nguyen Quang Kim (2005) used the multivariate linear regression model to forecast drought using SPI for South Central and Central Highlands regions, forecasting factors used are SOI, SST and terrain altitude at 500mb Nguyen Van Thang (2015) also used the multivariate linear regression model for SPI forecast for climate zones of Vietnam Nguyen Luong Bang (2015) used ANFIS model to forecast drought through SPI and SPEI for Khanh Hoa province with sea surface temperature (SST) as inputting variable Nguyen Van Thang (2015) managed to build technologies and procedures for applying warning products of different models around the world in drought warning in Vietnam with up to month in advance, using SPI 1.4 Overview of researched area The CCR includes 14 provinces from Thanh Hoa to Binh Thuan The researched area usually suffers from drought due to influences of El Nino, especially from Khanh Hoa to Binh Thuan 1.5 Conclusion of chapter Through overall research and analysis of drought forecasting and warning models around the world as well as in Vietnam, this thesis has chosen the research contents and methods as shown in the chart below: Figure 1.3 Research content and methods chart CHAPTER SCIENETIFIC BASIS AND METEOROLOGICAL DROUGHT FORECASTING METHOD FOR THE CENTRAL COASTAL REGION 2.1 Current drought situation of the researched area According to statistical data provided by the Department of Water Resources in 35 years from 1980 to 2014, the CCR has suffered many droughts, damaging hundreds of thousands hectares of land and causing severe water shortage for millions of people The drought land/farm land ratio is at its highest in 1993, followed by 2010, 2005, 1998, 1985 and 1988 In reality, however, 1988 suffered the harshest drought with 180,836 hectares of drought land and 51,130 hectares of land completely losing its crops The most intense drought period of the researched area was the summer-autumn crops from the end of June to the beginning of September, the frequency of drought happening in large scale is once every 5-9 years in the CCR Figure 2.3 Drought land/farm land ratio of CCR 2.2 ENSO introduction and necessary data to collect 2.2.1 ENSO introduction ENSO is the interaction between the atmosphere and ocean in the Pacific Ocean (PO) that results in changing climate El Nino is the name of the abnormal warming in STT in east-central Equatorial Pacific In contrast to El Nino, the phenomenon when the SST in east-central Equatorial Pacific gets abnormally cold is called La Nina The Southern Oscillation (SO) is what causes the air exchange between the Eastern and Western hemisphere SO is determined using the differences in sea surface air pressure between Tahiti (17.5S; 149.6W) Southeast of Pacific and Darwin (12.4S; 130.9E) Northwest of Australia To monitor ENSO activities, SSTA of Equatorial Pacific is used An El Nino cycle is a continuous period, with average SSTA of Nino3.4 (5oN-5S, 120oW170oW) equal or higher than 0.5oC, while a La Nina cycle is a continuous period with average SSTA in month of Nino3.4 equal or smaller than -0.5oC From 1985 to 2014, there have been instances of El Nino and instances of La Nina The change in SST in the Pacific Ocean creates ENSO activities, which leads to the anomaly of air masses as well as monsoon activities and equator-tropical disturbance changing the weather of West PO, including the researched area 2.2.2 Necessary data to collect Precipitation, temperature: Collected from 27 weather stations in CCR Data used in the research were from January-1985 to December-2014 Sea Surface Temperature Anomaly (SSTA): SSTA of Nino3.4 was taken from http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_ v5.php from January-1985 to December-2014 The Southern Oscillation Index (SOI): SOI data was taken from https://www.ncdc.noaa.gov/teleconnections/enso/indicators/soi/ from January1985 to December-2014 2.3 Meteorological drought forecasting method for the researched area Content and method of meteorological drought assessment, forecasting and warning for the researched area are demonstrated in the chart below: Figure 2.8 Meteorological drought content and forecasting method demonstration chart Input variables are SSTA and SOI data with high correlation coefficient with SPI and SPEI, which also combine with SPI and SPEI in different time stages previously Details about type of variables and input variables of the forecasting model will be demonstrated in Figure 2.4 below Forecasting method The ANFIS model, proposed by J.S.R Jang and his partners on 1997, was used to build forecasting models for SPI and SPEI with different forecasting factors to find a suitable forecasting model for the researched area ANFIS is based on an open interface system and is trained by an algorithm originated from neural network theory and has the following structure: Figure 2.11 Structure of ANFIS model Output value of SPI or SPEI is calculated as follows: w1 f1  w2 f  w1  w2 w ( x , y , z ) f  x , y , z   w ( x, y , z ) f  x , y , z   w1 ( z, y, z ) w2 x, y, z  SPI or SPEI = f  x, y, z   (2-41) With the mixed mathematical algorithm of ANFIS model, the input data is divided into sets: network training process data and testing process data Construxtion of meteorological drought forecasting models for the researched area  Structures of forecasting models Structures of forecasting models (variables types, number of input variables) are shown in detail in Figure 2.13 Network training data is from 1985-2009 Testing data is from 2010-2014 11 Figure 2.13 Structure of forecasting models  Assessment of forecasting quality The assessment of forecasting quality of the models during the network training process and testing process is done using coefficients: RSR (RMSEobservations standard deviation ratio); CORR (Correlation Coefficient); and E (Efficiency), also called Nash Index A forecasting model has good quality when its CORR and E values are closer to 1.0 and RSR value is closer to 0.0 In addition, RSR and E values must meet the criteria of WMO’s assessment standard as shown in this table: Table 2.4 Criteria for forecasting quality assessment Ranking Very good Good Pass Fail RSR ≤ RSR ≤ 0.5 0.5 < RSR ≤ 0.6 0.6 < RSR ≤ 0.7 RSR > 0.7 12 E 0.75 < E ≤ 0.65 < E ≤ 0.75 0.50 < E ≤ 0.65 E ≤ 0.5 The meteorological drought forecasting models for the researched area are programmed on Matlab software The block diagram of the meteorological drought forecasting program is shown in the following Figure: Figure 2.14 Block diagram of forecasting program 13 2.4 Conclusion of chapter (1) The CCR is one of the regions with frequent droughts Drought is most intense during the summer-autumn crops from the end of June to the beginning of September In this research, the author chose two meteorological drought indices: SPI and SPEI to assess, forecast and warn drought for the researched area (2) Drought development assessment method for the researched area during the El Nino period and method of analyzing the correlation between SSTA & SOI and SPI & SPEI were used to assess the influences of ENSO on drought development in the CCR; (3) Meteorological forecasting method is the ANFIS model with output variables being drought indices (SPI & SPEI) from previous time period; (4) Five forecasting models with different variables and input variables were created The forecasting results of these models were compared and assessed using coefficients, which are RSR, CCRR and E CHAPTER BUILDING AN EARLY WARNING MODEL OF METEOROLOGICAL DROUGHT FOR THE CENTRAL COASTAL REGION 3.1 Drought development of the researched area based on space and duration 3.1.1 Analysis of the testing result of the precipitation and temperature interpolation The IDW interpolation method is reliable enough to interpolate the precipitation and temperature to serve drought index calculation of the researched area 3.1.2 Drought development of the researched area based on duration The drought development of the researched area based on duration using SPI and SPEI are shown in the following Figures: Figure 3.5 Drought development of the CCR according to SPI1 and SPEI1 14 Figure 3.6 Drought development of the CCR according to SPI3 and SPEI3 The drought development of CCR using the aforementioned indices show 1988, 1993, 1998, 2005, and 2010 suffering from long drought period, matching with drought periods in reality SPEI, however, show the drought development of the researched area more accurately compared to SPI, and SPEI3 is more accurate than SPEI1 3.1.3 Drought development of researched area based on space The Drought area ratio according to SPI, SPEI and actual agricultural production of the researched area are shown in the following Figure: Figure 3.11 Drought area ratio according to SPI3, SPEI3 and actual agricultural production Drought area ratio according to SPEI is higher than SPI in most years, with only one year where it’s the reverse The drought area ratio according to SPEI3 is the highest, followed by SPI3, SPEI1 and finally SPI1 The drought frequency of indices are shown in the following Figure: 15 Figure 3.12 Drought frequency (%) of different indices based on space 3.2 Influences of ENSO on the drought development of researched area 3.2.1 Drought development of researched area during ENSO periods The drought development of the researched area according to SPI and SPEI during ENSO periods are shown in the following Figures: Figure 3.27 SPI1 and SPEI1 values during ENSO periods Figure 3.28 SPI3 and SPEI-3 values during ENSO periods During El Nino periods, drought always happened in researched area, but the start-time of drought periods is later than El Nino periods Drought level reflected 16 by SPEI3 of the researched area during El Nino was higher compared to other indices 3.2.2 Assessment of the correlation between SSTA, SOI and SPI, SPEI The average correlation coefficient results between SSTA, SOI and SPI, SPEI in all grids of the researched area are shown in table 3.4 The correlation between SSTA and SPI, SPEI is at its highest when the SSTA data is months ahead of SPI and SPEI (3 months latency), whereas the correlation between SOI and SPI, SPEI is at its highest when SOI data is months ahead of SPI and SPEI (2 months latency) Table 3.4 Correlation coefficient between SSTA, SOI and SPI, SPEI Correlation coefficient between SSTA1 and SPI1, SPEI1 Latency (*) (*) (*) (*) 10 (month) SPI1 -0.14 -0.157 -0.167 -0.161 -0.146 -0.119 -0.108 -0.094 -0.089 -0.073 SPEI1 -0.14 -0.157 -0.167 -0.161 -0.146 -0.119 -0.108 -0.094 -0.089 -0.073 Correlation coefficient between SSTA3 với SPI3, SPEI3 Latency (*) (*) (*) (*) 10 (month) SPI3 -0.256 -0.269 -0.274 -0.266 -0.244 -0.212 -0.183 -0.156 -0.134 -0.118 SPEI3 -0.256 -0.269 -0.274 -0.266 -0.244 -0.212 -0.183 -0.156 -0.134 -0.118 Correlation coefficient between SOI1 and SPI1, SPEI1 Latency (*) (*) (*) (*) 10 (month) SPI1 0.162 0.169 0.143 0.144 0.138 0.088 0.085 0.063 0.058 0.045 SPEI1 0.162 0.169 0.143 0.144 0.138 0.088 0.085 0.063 0.058 0.045 Correlation coefficient between SOI3 and SPI3, SPEI3 Latency (*) (*) (*) (*) 10 (month) SPI3 0.278 0.303 0.292 0.266 0.238 0.213 0.17 0.124 0.096 0.079 SPEI3 0.278 0.303 0.292 0.266 0.238 0.213 0.17 0.124 0.096 0.079 11 12 -0.062 -0.064 -0.062 -0.064 11 12 -0.109 -0.102 -0.109 -0.102 11 12 0.042 0.037 0.042 0.037 11 12 0.071 0.06 0.071 0.06 Note: * Correlation has reliability rate higher than 90% 3.3 Construction of an early warning model of meteorological drought for the researched area 3.3.1 Assessment of the results of drought forecasting model for researched area The statistical results (CORR, E and RSR) of SPI and SPEI forecasting models are shown in the table below: 17 Table 3.5 Assessment of drought index forecasting result of models SPI1 Network training process Model CORR E RSR M1 0.33 0.11 0.94 M2 0.38 0.15 0.92 M3 0.56 0.31 0.83 M4 0.82 0.67 0.58 M5 1.00 1.00 0.06 SPI3 Network training process Model CORR E RSR M1 0.53 0.28 0.85 M2 0.58 0.34 0.81 M3 0.76 0.57 0.65 M4 0.90 0.81 0.44 M5 1.00 0.99 0.08 SPEI1 Network testing Testing process Testing process process Model CORR E RSR CORR E RSR CORR E RSR 0.26 0.06 0.97 M1 0.38 0.14 0.92 0.45 0.13 0.93 0.29 0.05 0.97 M2 0.46 0.22 0.89 0.35 0.09 0.96 0.36 0.12 0.94 M3 0.63 0.40 0.77 0.40 0.14 0.93 0.46 0.21 0.89 M4 0.83 0.68 0.56 0.48 0.18 0.91 0.16 -0.01 1.01 M5 1.00 1.00 0.04 0.55 0.21 0.89 SPEI3 Network training Testing process Testing process process Model CORR E RSR CORR E RSR CORR E RSR 0.52 0.24 0.87 M1 0.60 0.36 0.80 0.77 0.32 0.82 0.60 0.28 0.85 M2 0.64 0.41 0.77 0.78 0.36 0.80 0.42 0.12 0.94 M3 0.76 0.57 0.65 0.78 0.46 0.73 0.63 0.38 0.79 M4 0.91 0.82 0.42 0.82 0.68 0.56 0.60 0.34 0.81 M5 1.00 0.99 0.07 0.74 0.53 0.69 The SPEI forecasting model M4 has the best result, meeting WMO standard (Table 2.4) The comparison result between forecasted and actually calculated SPEI3 of the network training process and testing process using the M4 model for the researched area are shown as follows: Figure 3.32 Comparing SPEI3 forecasting results using M5 for the entire area 3.3.2 Selection of an index for meteorological drought early warning for the researched area According to the drought development assessment results based on space and duration using SPI and SPEI in section 3.1, SPEI’s reflection of drought development is more suitable for the actual drought situation of the researched area than other indices In addition, the research result in section 3.3.1 shows that 18 drought forecasting using SPEI3 also gave the best results Therefore, in this thesis, the author chose SPEI3 as the index for meteorological drought forecasting Table 3.6 Drought levels and meteorological drought early warning levels using SPEI3 SPEI3 ≥ - 0.49 Climate condition No drought -0.50 ÷ - 0.99 Slight drought -1.0 ÷ -1.49 Moderate drought -1.5 ÷ -1.99 Severe drought ≥ -2.0 Extreme drought Warning level Level 1: Rain shortage warning Make sure to conserve water if SPEI3 at different time of the month and/or recent months are also at this level Level 2: Moderate drought warning Make sure to conserve water and prepare prevention works Be extra careful if SPEI3 at different time of the month and/or recent moths are also at this level Level 3: Severe drought warning Water conservation and usage limit solutions are required, especially if SPEI3 at different time of the month and/or recent months are also at this level Check prevention procedures If water supplies (reservoir, surface runoff, ground water) get low, stopping water supply for least important households is allowed Level 4: Extreme drought warning Depending on the condition of surface water supply, stop water supply for least important households and/or apply water usage limit regulation May need to start relief campaigns 3.3.3 Selection of a drought forecasting model for the researched area In this research, the author propose forecasting models for the next months of monitoring period in the researched area using types of forecasting (1) forecasting the first months (January and February of 2015) and (2) forecasting the next months (March to June of 2015) The parameters for the aforementioned forecast are shown in the following table: Table 3.7 Forecasting models’ parameters for the next months Model Input parameter Short drought SOI3 (10/2014), SOI3 (11/2014), SSTA3 (10/2014), M4 SSTA3 (11/2014), SPEI3 (12/2014) SOI3 (11/2014), SOI3 (12/2014), SSTA3 (11/2014), M4 SSTA3 (12/2014), SPEI3 (1/2015) Long drought 19 Output SPEI3 (1/2015) SPEI3 (2/2015) M4 M4 M4 M4 SOI3 (12/2014), SOI3 (1/2015), SSTA3 (12/2014), SSTA3 (1/2015), SPEI3 (2/2015) SOI3 (1/2015), SOI3 (2/2015), SSTA3 (1/2015), SSTA3 (2/2015), SPEI3 (3/2015) SOI3 (2/2015), SOI3 (3/2015), SSTA3 (2/2015), SSTA3 (3/2015), SPEI3 (4/2015) SOI3 (3/2015), SOI3 (4/2015), SSTA3 (3/2015), SSTA3 (4/2015), SPEI3 (5/2015) SPEI3 (3/2015) SPEI3 (4/2015) SPEI3 (5/2015) SPEI3 (6/2015) 3.3.4 Construction of meteorological drought early warning models for the researched area From the results and content in the above sections of the research, the author propose the following early warning model of meteorological drought for the researched area: Figure 3.42 Early warning model of meteorological drought for the researched area This early warning model of meteorological drought includes main blocks: (1) Monitoring and collecting data; (2) Calculating and handling data; and (3) Meta20 analysis of monitoring data and forecasting results to create maps and assessment board on drought situation and drought development forecast 3.3.5 Maps and data on meteorological drought early warning for the researched area Warning maps for the first months (January and February of 2015) January/2015 January/2015 Figure 3.43 Forecasting and warning maps according to SPEI3 for January and February of 2015 Warning maps for the next months (March to June of 2015) March/2015 April/2015 March/2015 April/2015 Figure 3.44 Forecasting and warning maps according to SPEI3 for March, April, May and June of 2015 21 3.4 Conclusion of chapter (1) SPEI’s reflection of drought development was more suitable for the actual drought situation of the researched area than SPI Durations, scales, intensities and frequencies of drought in the CCR according to SPEI were also higher then SPI (2) When El Nino was happening, drought would appear in the researched area and lasted for many months, however its start-times were usually later than El Nino’s start-times SSTA and SOI chains with big correlation to SPI and SPEI chains would be chosen as input variables for meteorological drought forecasting models (3) SPEI3 forecasting result using M4 had the highest quality and met the criteria of forecasting quality assessment standard Drought index chosen for drought level assessment and early warning model of meteorological drought was SPEI3 months short drought forecasting model and following months long drought forecasting model were chosen (4) An early warning model of meteorological drought for the researched area was built with main blocks: monitoring and collecting data; calculating and handling data; and Meta-analysis of monitoring data and forecasting results At the same time meteorological drought early warning maps for the researched area using SPEI3 from January to June of 2015 was created CONCLUSION AND RECOMMENDATION Achieved results of the thesis Drought can occur on every parts of the world and in all climate zone, including CCR In order to have affective solutions for exploitation and management of water resources, the meteorological drought early warning task is essential Therefore, this thesis has solved and achieved the following results: (1) From 1985-2014, the CCR suffered many droughts, of them were extreme droughts Drought occurred during all crops and caused damages to millions hectares of land The most intense period was the summer-spring crop from the 22 beginning of June to the end of September, and occurred in large scale once every to years (2) Drought developments according to SPI and SPEI show that 1988, 1993, 1998, 2005 and 2010 all suffered drought that lasted for many months, matching with the actual drought periods which lasted for a long time during all crops However, SPEI’s reflection of drought development was more suitable for the actual drought situation of the researched area than other indices (3) The influences level of El Nino to drought development of the researched area was quite high Whenever El Nino occurred a drought would appeared in the researched area and lasted for many months However its start-times were usually later than El Nino’s start-times (4) When the SST of Nino3.4 increased (positive SSTA) and SOI in negative, they both affected the drought development of the researched area, especially when the SST of El Nino3.4 increased (SSTA>= 0.5oC, leading to El Nino), there were significant chances a drought would occurred in the researched area but usually 2-3 months later (5) The SPEI3 forecast result with SSTA3 and SOI3 chains 2-3 month earlier and SPEI3 month earlier (M4 model) as input variables gave the most reliable result, meeting the criteria of forecasting quality assessment standard (6) Meteorological drought early warning maps according to SPEI3 from January to June of 2015 were built using short drought forecasting model (1-2 months) and long drought forecasting model (3-6 months) They were created with ANFIS network with M4 structure as demonstrated in Table 3.6 (7) An early warning model of meteorological drought for the researched area was built with main blocks: monitoring and collecting data; calculating and handling data; and Meta-analysis of monitoring data and forecasting results This model is demonstrated in detail in Figure 3.42 Summarizing the new conclusions of the Dissertation (1) Determining the relationship among ENSO, Sea Surface Temperature Anomalies (SSTA), and the Southern Oscillation Index (SOI) with meteorological drought events in the Central Coast Region (through SPI and 23 SPEI indicators), Vietnam And, establishing the drought risk map for the research area (2) Establishing the early warning model of meteorological drought for Central Coast Region using Adaptive Neuro-Fuzzy Inference System (ANFIS) Limitation of the thesis This thesis only assessed the drought development of the researched area using meteorological drought indices When building drought maps to assess drought development based on space, it did not considered terrain during interpolation Moreover, the thesis only used ANFIS model for meteorological drought forecasting in the researched area and had yet to compare it to other forecasting methods Research recommendations after the thesis To increase the reliability as well as being able to apply this forecasting and warning method more widely, the following researches should be: (1) Comparing the reliability of ANFIS method and other statistical methods as well as forecasting methods from models around the world (2) Building a system to operate, provide and update the result of this research on a website 24 LIST OF PAPERS PUBLISHED Nguyen Thai Ha, Nguyen Đang Tinh, Nguyen Luong Bang “Developing spatial temporal metheorological drought maps for Vietnam Central Coastal Region using non-contigous drought analysis (NCDA)” Science and Technology Journal of Agriculture & Rural Development, no 3+4, pp.199-204, 2018 Nguyen Thai Ha, Nguyen Đang Tinh, Nguyen Luong Bang “Impact of El Nino events on spatial - temporal drought conditions at Vietnam Central Coastal Region” Science and Technology Journal of Agriculture & Rural Development, no 9, pp.55-61, 2018 25 ... Quang Ngai, Binh Dinh, Ninh Thuan and Binh Thuan Dao Xuan Hoc (2004) used the Sezonov index to survey and assess drought in the CCR area In 2005, Nguyen Quang Kim researched the drought situation... in the Pacific Ocean (PO) that results in changing climate El Nino is the name of the abnormal warming in STT in east-central Equatorial Pacific In contrast to El Nino, the phenomenon when the... Luong Bang used SPI and SPEI in researching the influences of ENSO on meteorological drought development in Khanh Hoa river basin The result of that research showed that SPEI is more fitting than
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