نوع مقاله : مقاله پژوهشی
نویسندگان
1 استادیار دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی، دانشگاه تهران
2 کارشناسی ارشد، دانشکده مهندسی نقشه برداری و اطلاعات مکانی، پردیس دانشکده های فنی، دانشگاه تهران
چکیده
در سال‌های اخیر کشور ایران با خشکسالی‌های متعددی مواجه بوده است، لذا برآورد دقیق میزان خشکسالی به منظور پیش‌بینی و مدیریت بهینه منابع طبیعی امری اجتناب‌ناپذیر است. بدین منظور روش‌های مرسوم برآورد خشکسالی که مبتنی بر مشاهدات ایستگاه‌های هواشناسی هستند، به‌کار گرفته می‌شوند. یکی از مشکلات اصلی این روش‌ها در نظر نگرفتن تغییر شرایط اقلیمی و آب و هوایی در مناطق بزرگ است و معمولاً این روش‌ها در مناطق محلی جواب مناسبی به‌دست می‌دهند. به‌منظور بهبود دقت برآورد میزان خشکسالی در سطوح وسیع، استفاده تلفیقی از داده‌های حاصل از تصاویر ماهواره‌ای و ایستگاه‌های زمینی ضرورت خواهد داشت. در سال‌های اخیر استفاده از روش‌های ثقل‌سنجی ماهواره‌ای و تصاویر ماهواره‌ای به‌عنوان ابزاری مفید برای پایش مکانی و زمانی خشکسالی در مناطق وسیع، مورد توجه محققین قرار گرفته است. هدف از این مطالعه استفاده از داده‌های ماهواره بازیابی گرانش و آب و هوا (GRACE) و محصول شاخص پوشش گیاهی سنجنده MODIS و مشاهدات زمینی ایستگاه‌های سینوپتیک برای ارزیابی خشکسالی در بازه زمانی ۲۰۰۳ تا ۲۰۱۶ در کل کشور ایران است. بدین منظور در مقاله حاضر شاخصی کارا با عنوان شاخص خشکسالی داده‌های ادغام‌شده (MDI) مبتنی بر تصاویر شاخص پوشش گیاهی یکنواخت شده (NDVI) حاصل از سنجنده مادیس، داده‌های محتوی آب زمین (TWS) استخراج شده از ماهواره GRACE و داده‌های بارندگی استخراج شده از ایستگاه‌های سینوپتیک ارائه شده است. نتایج به‌دست آمده همبستگی ۷۵٪ با شاخص استاندارد جهانی شدت میزان خشکسالی پالمر (PDSI) را نشان می‌دهد. نتایج شاخص MDI و PDSI روند خشکسالی در سال‌های ۲۰۰۸ تا ۲۰۱۵ در ایران را به‌خوبی نشان می‌دهند.
کلیدواژه‌ها
عنوان مقاله [English]
Combining satellite-based gravity data and information received from ground stations to provide an efficient index for drought monitoring in Iran
نویسندگان [English]
- Saeed Farzaneh 1
- Reza Shahhoseini 1
- Iman Kordpour 2
1 Assistant professor, School of surveying and geospatial engineering, College of engineering, University of Tehran, Tehran, Iran
2 MSc, School of surveying and geospatial engineering, College of engineering, University of Tehran, Tehran, Iran
چکیده [English]
Introduction
Drought is considered to be one of the most widespread natural disasters, ranking second in terms of damages. Due to the complex relationship between hydrological cycle parameters and atmospheric observations, predicting or modeling drought lacks the necessary precision. One of the most significant problems in drought monitoring is lack of proper spatial coverage for the collected data (due to unavailibility of field data in some regions) and also lack of a suitable time scale (observations and thus drought estimation is not always possible). Since satellite observations do not face challenges like lack of spatial scale which is quite common in field observations, remote sensing satellites can provide a better estimate of droughts. However, satellite observations alone are not capable of accurately estimating the occurrence of droughts. Therefore, a combination of field and satellite observations has been used recentely to reach a better estimate of hydrological problems.
Materials & Methods
Temporal and spatial complexity of droughts have made a new global index combining ground-based and satellite-based observations quite necessary. Given the kind of data used in MDI index, we cannot expect it to be global. However, its performance is still acceptable in similar environments and climates, and thus it has been used in the United States (Texas). Datasets selected for the present study have different temporal and spatial scales and thus, a common scale must be found before calculating the index. Data received from GRACE satellite and MODIS sensor were downloaded monthly, but precipitation data were collected on a daily basis. Thus, aritmatic mean of precipitation data was calculated to reach a monthly avarage. Regarding the spatial scale, one-degree precipitation data were received from GRACE and MODIS while precipitation data extracted from synoptic stations had a point-based nature. Therefore, Inverse Distance Weighting (IDW) method was used to produce a one-degree network. Three types of observations were used in the present study including data received from synoptic stations of Iran meteorological organization, GRACE mission satellite-based gravity data and MODIS remote sensing satellite-based data. These were selected to identify droughts over a 14-year time series.
Results & Discussion
The present study has calculated MDI drought index on a one-degree spatial scale and monthly temporal scale for 168 months using Precipitation, NDVI, and TWS data. Severe droughts in northwestern and central areas of Iran from 2004 to 2014 have led to a shortage of water in reservoirs. In addition to drought, too much water harvesting in northwestern Iran has resulted in a decrease in groundwater level and thus, increased water harvesting from rivers and canals leading to the Urmia Lake and reduced water level in this lake. The results of MDI drought index calculated for Iran over the period of 2000 to 2014 show a high correlation with the results of standardized precipitation-evapotranspiration drought index.
According to the type of data used to calculate MDI index, it is expected to have a strong correlation with PDSI index due to its sensitivity to precipitation, area temperature and soil moisture content. Since GRACE and MODIS satellite-based data, and data received from synoptic stations were used, a strong correlation with MDI is also expected. It should be noted that PDSI index is higher than MDI index in Iran, although both show the drought trends accurately. For example according to PDSI index, the worst drought of the last two decades in Iran has occurred in 2008, and MDI index shows the same year.
Conclusion
The present study has introduced a new drought index using a combination of precipitation data, GRACE_TWS and NDVI. These data were selected because of their high sensitivity to drought. GRACE_TWS observations monitor hydrological drought and include surface and subsurface water sources. NDVI observations are mostly used to identify photosynthetic activities of vegetation cover and are therefore very useful for detecting agricultural drought. Precipitation value shows the amount of surface water in the study area. Precipitation can have relatively rapid effects and is therefore useful for monitoring meteorological drought.
MDI index has identified several droughts in each region of the country in the period of 2003 to 2016. These identified droughts have generally covered the country over time. However, each drought has had a different impact on ecosystem. In Iran, the most severe droughts have occurred during 2008 to 2009 and 2011 to 2012. Since MDI correlates well with PDSI, both show a drought in these years. In order to develop the proposed algorithm, the effect of different zoning of the study area on MDI index can be studied.
کلیدواژه‌ها [English]
- Drought
- Iran
- Precipitation
- MDI
- GRACE
- MODIS
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