IDENTIFICATION OF REGIONAL EVAPORATION ZONES IS VERY IMPORTANT BECAUSE INFORMATION ON THE CLASSIFICATION OF EVAPORATION PATTERNS IN AN AREA CAN BE USED FOR VARIOUS PURPOSES, ONE OF WHICH IS KNOWING THE AMOUNT OF WATER IN THE RESERVOIR THAT IS LOST DUE TO EVAPORATION. IN THIS STUDY, THE EVAPORATION ZONE IN INDONESIA WAS CREATED USING CLUSTER ANALYSIS. THE EVAPORATION DATA FROM 144 BMKG OBSERVATION STATIONS FOR 6 YEARS FROM 2014 TO 2019 ARE USED AFTER THE NORMAL MONTHLY CALCULATION IS THEN STANDARDIZED WITH MEAN = 0 AND STANDARD DEVIATION = 1 TO ENSURE THAT ALL VARIABLES ARE GIVEN THE SAME WEIGHT SO THAT IT BECOMES THE 144 X 12 MATRIX USED IN THE ANALYSIS.  HIERARCHICAL CLUSTER ANALYSIS WAS CHOSEN TO REGIONALIZE EVAPORATION. FIVE DIFFERENT TECHNIQUES ARE APPLIED TO INITIALLY DETERMINE THE MOST SUITABLE METHOD FOR THE AREA. CLUSTER STABILITY IS ALSO TESTED. IT WAS DECIDED THAT WARD'S METHOD IS THE MOST LIKELY TO PRODUCE ACCEPTABLE RESULTS IN THIS EVAPORATION VARIABLE. 3 DIFFERENT CLASSIFICATIONS OF EVAPORATION ZONES ARE FOUND IN INDONESIA. EVAPORATION ZONE 1 WITH THE CHARACTERISTICS OF HAVING A FAIRLY HIGH ANNUAL CYCLE VARIATION AND THE PEAK OF THE MONTHLY AVERAGE EVAPORATION OCCURRED IN SEPTEMBER REACHING 160 MM/MONTH IN 45 OBSERVATION STATIONS SPREAD OVER PARTS OF SUMATRA, KALIMANTAN, JAVA, NORTHERN SULAWESI, MALUKU ISLANDS, AND PAPUA. EVAPORATION ZONE 2 WITH THE CHARACTERISTIC OF HAVING A LOW MONTHLY CYCLE VARIATION AND THE PEAK OF THE AVERAGE MONTHLY EVAPORATION OCCURS IN JULY, AUGUST, AND SEPTEMBER WITH A MONTHLY AVERAGE BELOW 100 MM/MONTH IN 64 OBSERVATION STATIONS SPREAD ACROSS PARTS OF SUMATRA, KALIMANTAN, JAVA, NORTHERN SULAWESI, MALUKU, AND PAPUA ISLANDS. EVAPORATION ZONE 3 WITH THE CHARACTERISTIC OF HAVING A LOW MONTHLY CYCLE VARIATION AND TWO PEAKS OF AVERAGE MONTHLY EVAPORATION OCCURRED IN MARCH AND DECEMBER WITH A MONTHLY AVERAGE OF AROUND 100 MM/MONTH LOCATED IN 34 DOMINANT OBSERVATION STATIONS SPREAD THROUGHOUT THE ARCHIPELAGO. SOUTHEAST, PARTS OF SUMATRA, JAVA, SOUTHERN KALIMANTAN, AND SULAWESI.

 

RION SUAIB SALMAN¹, AYUFITRIYA²

INDONESIA AGENCY FOR METEOROLOGY CLIMATOLOGY AND GEOPHYSICS, PATTIMURA METEOROLOGICAL STATION OF AMBON, MALUKU, INDONESIA^{1 2}

 

EMAIL: IONIUQ@GMAIL.COM

ABSTRACT. THE NUMBER OF PEOPLE WHO WANT TO GET THE PUBLIC WEATHER SERVICE INFORMATION IS INCREASING AND IT IS BECOMING A CHALLENGE FOR ALL BUREAU OF METEOROLOGY IN THE WORLD NOW. ALTHOUGH, THE ROUTINE PUBLIC WEATHER SERVICE INFORMATION IN THE LAST DECADE BRINGS THE GOOD WEATHER FORECAST INFORMATION FOR PEOPLE AND SERVICES TO PEOPLE WITH FAST, ACCURATE, WIDELY, AND EASY TO UNDERSTAND THAT INFORMATION AND THEY CAN FIND IN MANY SOURCES LIKE WEBSITE OR IN APPLICATION, BUT IT IS NOT GOOD ENOUGH IN THIS DECADE AND FOR THE FUTURE. PEOPLE NOT JUST NEED THE WEATHER FORECAST INFORMATION LIKE USUALLY AS TEXT IN PAPER, AND STANDARD INFOGRAPHIC, BUT MORE THAN THAT IT SHOULD BE ADDING SOME INFORMATION ABOUT WHAT IMPACT SHOULD BE AND HOW PEOPLE RESPONSE THAT IMPACT WITH THE OUTPUT IN STATIC GEOGRAPHIC INFORMATION SYSTEM (GIS) MAP IN STANDARD LAYOUT. THE MAIN REASON IS IF THE WEATHER IS GOING TO WORSE, PEOPLE KNOW WHAT THE STEP THEY CAN DO TO RESPONSE THAT AFTER RECEIVES THE INFORMATION. THAT IS MEAN A METEOROLOGIST NEEDS TO MAKE COOPERATION WITH MANY STAKEHOLDERS TO PRODUCE IMPACT MATRIX BASE ON EACH AREA RESPONSIBILITY. IN THIS PAPER WE ARE GOING TO ANALYSIS DAN MAKE THE IBF’S MAP BASE ON MULTI MODEL ENSEMBLE DATA AND NATIONAL DIGITAL FORECAST (NDF) DATA, THEN WE MAKE RANK AND SCORE WITH DISASTER SPATIAL DATA TO GET IMPACT AREA USING GIS SOFTWARE ARCMAP 10.8.1. WE ARE GOING TO USE BASIC AND ADVANCE METHOD IN ARCMAP 10.8.1 TO PRODUCE THE IMPACT AREA. BASIC METHOD IS LIKES ANALYSIS POINT DATA FROM NDF TO PRODUCE A SHAPEFILE AND ADVANCE ANALYSIS, WE ARE GOING TO USE THE ARCTOOLBOX IN ARCMAP 10.8.1 TO EXTRACT THE MAP FROM MULTI MODEL ENSEMBLE DATA IN RASTER FORMAT DATA TO PRODUCE A SHAPEFILE DATA, AFTER THAT WE ARE GOING TO COMBINE THE SHAPEFILES WITH DISASTER SPATIAL DATA. THEN THE FINAL STEP IS WE ARE GOING TO SETTING THE LAYOUT THAT EASY TO READ AND UNDERSTAND TO PEOPLE AND STAKEHOLDERS. OUTPUT IBF’S MAP IN PICTURE FORMAT AND PDF FILE FORMAT. USING THE ARCMAP 10.8.1 IS USEFUL AND EASY FOR A METEOROLOGIST WHO WANT TO CREATE IBF MAP IN EACH RESPONSIBILITY AREA. THE IBF ARE GOING TO SOLUTION WHAT THE ACTION PLAN FOR PEOPLE CAN DO AND WHAT THE STAKEHOLDERS NEED BECAUSE IN IBF’S MAP SHOWS THE SPECIFIC AREA WITH DETAIL WARNING AND IMPACT MATRIX.

 

THE CALCULATION OF THE SOLAR ENERGY POTENTIAL WAS CONDUCTED DURING 1990-2019 AROUND THE EQUATOR. THE RESEARCH SITES INCLUDED SAMBAS, MEMPAWAH, SINTANG, SUPADIO PONTIANAK, AND KETAPANG. IT IS NECESSARY TO CALCULATE THE POTENTIAL FOR SOLAR ENERGY TO INCREASE RENEWABLE ENERGY USE IN THE TROPICS. THEREFORE, THIS STUDY AIMED TO CALCULATE THE POTENTIAL FOR SOLAR ENERGY SURROUNDING THE EQUATOR. THE POTENTIAL FOR SOLAR ENERGY WAS CALCULATED EMPLOYING THE VALIDATED ANGSTROM-PRESCOTT EQUATION. THE ANGSTROM-PRESCOTT EQUATION'S VALIDATION RESULTS SURROUNDING THE EQUATOR, PARTICULARLY IN THE WEST KALIMANTAN AREA, WERE STATED GOOD WITH A AND B COEFFICIENT VALUES ??OF 0.28 AND 0.35, RESPECTIVELY. THE POTENTIAL FOR SOLAR ENERGY HAS ANNUAL AND MONTHLY VARIATIONS. THE POTENTIAL FOR SOLAR ENERGY IN WEST KALIMANTAN WAS IDENTIFIED AT 4.82 ± 0.84 KWH.M^-2.DAY^-1. FURTHERMORE, SOLAR ENERGY POTENTIAL IN AREAS WITH MONSOONAL RAINFALL PATTERNS WAS HIGHER THAN IN AREAS WITH EQUATORIAL RAINFALL PATTERNS. THE LOWEST POTENTIAL VALUE OF SOLAR ENERGY IN WEST KALIMANTAN OCCURRED IN DECEMBER IN THE LOW CATEGORY. MEANWHILE, THE HIGHEST VALUE OCCURRED IN MARCH AND AUGUST IN THE HIGH CATEGORY. THE POTENTIAL FOR SOLAR ENERGY CAN CHANGE BETWEEN DECADES. THERE WERE CHANGES IN THE CATEGORY OF SOLAR ENERGY BETWEEN PERIODS AT SEVERAL LOCATIONS, BUT THE ONLY SIGNIFICANT DIFFERENCES OCCURRED IN SAMBAS AND SUPADIO, PONTIANAK. LOCATION AFFECTED SOLAR POTENTIAL, WHERE KETAPANG IS IDENTIFIED TO HAVE THE HIGHEST POTENTIAL. THE POTENTIAL FOR SOLAR ENERGY COULD MEET THE ELECTRICITY NEEDS BY 97,160 TIMES, MAKING IT FEASIBLE TO BE DEVELOPED IN WEST KALIMANTAN.

KEYWORDS: SOLAR ENERGY, RENEWABLE ENERGY, SOLAR RADIATION DURATION, THE ANGSTROM-PRESCOTT EQUATION, THE EQUATOR

ABSTRACT: THE TOURISM INDUSTRY IS ONE OF THE LARGEST INDUSTRIES AND IS THE SERVICE SECTOR WITH THE FASTEST GROWTH RATE IN THE WORLD TODAY. THE INCREASE IN THE NUMBER OF DESTINATIONS AND INVESTMENT IN TOURISM DEVELOPMENT HAS TRANSFORMED TOURISM AS A KEY DRIVER OF A COUNTRY'S SOCIO-ECONOMIC PROGRESS THROUGH FOREIGN EXCHANGE EARNINGS, JOB CREATION AND BUSINESS OPPORTUNITIES, AND INFRASTRUCTURE DEVELOPMENT. APART FROM THAT, WEATHER AND CLIMATE HAVE A VITAL ROLE IN THE DEVELOPMENT SYSTEM OF THE TOURISM INDUSTRY. MOST TOURISTS WILL USE EASY-TO-UNDERSTAND CLIMATE INFORMATION TO DETERMINE TRAVEL TIMES. INDONESIA IS A REGION THAT HAS WIDE BEACHES AND ISLANDS SUCH AS THE PROVINCE OF EAST NUSA TENGGARA. TO FIND THE CLIMATE COMFORT VALUE OF AN AREA, A TOURISM CLIMATE INDEX (TCI) IS NEEDED. THE DATA USED ARE MONTHLY IN THE YEARS 1991-2015, NAMELY THE MAXIMUM AIR TEMPERATURE AND MINIMUM AIR HUMIDITY (DAYTIME COMFORT INDEX), AVERAGE AIR TEMPERATURE AND AVERAGE AIR HUMIDITY (DAILY COMFORT INDEX), RAINFALL, SUNSHINE DURATION AND AVERAGE SPEED. - AVERAGE WIND. THESE VARIABLES ARE CLASSIFIED INTO THE CLASS 'IDEAL' TO 'IMPOSSIBLE'. FURTHERMORE, EACH CITY WAS CLASSIFIED ACCORDING TO THE ANNUAL TCI DISTRIBUTION. THE RESULTS SHOWED THAT THE ROTE AND MAUMERE REGIONS WERE IN THE 'VERY GOOD' CATEGORY. HOWEVER, THIS AREA OCCURS IN SUMMER ONLY (JUNE AND JULY). MEANWHILE, SABU AND ROTE HAD THE HIGHEST NUMBER OF COMFORTABLE MONTHS, NAMELY 7 MONTHS. MEANWHILE, MAUMERE AND ELTARI WERE THE RESEARCH AREAS THAT HAD THE LEAST NUMBER OF COMFORTABLE MONTHS, NAMELY 5 MONTHS. SUMMER (JUNE, JULY, AUGUST) CAUSES THE TCI VALUE TO BE ≥ 70 (UP) WHILE THE RAINY SEASON (DECEMBER, JANUARY, FEBRUARY) HAS AN UNCOMFORTABLE EFFECT ON TCI ≥ 52 (DOWN).

 

KEYWORDS: TOURISM, CLIMATE, EAST NUSA TENGGARA.

NATIONAL EARTH MONITORING SYSTEM (SPBN) IS A NATURAL RESOURCE AND DISASTER INFORMATION SYSTEM DEVELOPED BY PUSFATJA LAPAN. DROUGHT MONITORING IS ONE OF THE SPBN DISASTER INFORMATION PRODUCTS, WHICH CONSISTS OF STANDARDIZED PRECIPITATION INDEX (SPI), VEGETATION GREENNESS LEVEL (TKV) AND MONTHLY ACCUMULATION OF RAINFALL INFORMATION. PRODUCT QUALITY IS IMPROVED TOWARDS DATA PROCESSING AUTOMATION SYSTEMS AS WELL AS PROVISION OF USER-ORIENTED INFORMATION PRODUCTS. THE DATA PROCESSING AUTOMATION PROCESS AND A BRIEF EXPLANATION OF THE RESULTING INFORMATION WILL BE EXPLAINED IN THIS PAPER. BY BLENDED TWO DATASET (TKV DATASET THAT CHARACTERIZED AGRICULTURAL DROUGHT AND MONTHLY RAINFALL DATASET THAT CHARACTERIZED METEOROLOGICAL DROUGHT) WE INTRODUCE A NEW DROUGHT INDEX USING SCORING METHOD. THE PURPOSE IS TO STATE METEOROLOGICAL AND AGRICULTURAL DROUGHT CONDITIONS IN A SINGLE INDEX. AS PRELIMINARY RESULT, A DROUGHT INDEX MODEL BASED ON BLENDED DATA HAS BEEN BUILT WHERE THE LEVEL OF DROUGHT IS DIVIDED INTO FIVE CLASSES, NAMELY CLOUD/WATER, SEVERE DRY, DRY AND NORMAL. INFORMATION PRODUCT DEVELOPMENT WHICH IS IN THE PROCESS OF FUTURE DEVELOPMENT WILL ALSO BE REVIEWED IN THIS PAPER.