پديد آورندگان :
چراغعلي‌زاده، مجيد دانشگاه تهران , نازي قمشلو، آرزو دانشگاه تهران - پرديس كشاورزي و منابع طبيعي - گروه مهندسي آبياري و آباداني،كرج , بذرافشان، جواد دانشگاه تهران - پرديس كشاورزي و منابع طبيعي - گروه مهندسي آبياري و آباداني،كرج
كليدواژه :
بارش , جريان رودخانه , شاخص‌هاي تركيبي , روش چند متغيره , تبخير و تعرق
چكيده فارسي :
در مطالعه حاضر، پايش يكپارچه وضعيت خشك‌سالي هواشناسي (بر مبناي متغير‌هاي دما و بارش) و خشك سالي آب شناسي (بر مبناي جريان رودخانه) در حوضه كسيليان مازندران مورد توجه قرار گرفت. هدف اصلي تحقيق حاضر، ارائه يك شاخص خشك سالي تركيبي با استفاده از روش چند متغيره تحليل مؤلفه اصلي (PCA) در حوضه مورد بررسي است. براي پايش خشك سالي هواشناسي از شاخص‌هاي بارش استاندارد (SPI) و شاخص بارش- تبخير و تعرق پتانسيل استاندارد (SPEI) و براي پايش خشك‌سالي آبشناسي از شاخص خشك سالي جريان رودخانه (SDI) استفاده شد. داده هاي مورد نياز اين مطالعه از ايستگاه هاي هواشناسي و آبشناسي مستقر در حوضه كسيليان براي يك دوره آماري 43 سال آبي (50-1349 تا 92-1391) گردآوري شد. پس از انجام كنترل‌هاي مقدماتي روي كيفيت داده ها، شاخص‌هاي خشك سالي هواشناسي و آب شناسي در چهار پنجره زماني 3، 6، 9 و 12 از ابتداي سال آبي محاسبه شد. در مرحله بعد، دو شاخص تركيبي براي ارزيابي خشك سالي‌هاي هوا-آب شناسي، يكي SPI-SDI و ديگري SPEI-SDI با استفاده از روش PCA ساخته شد. شاخص تركيبي كه فرم استاندارد شده نخستين مؤلفه اصلي شاخص‌هاي مورد استفاده در تركيب است، به‌طور جداگانه براي ايستگاه هاي آب شناسي ولكبن و شيرگاه واقع در بالادست و پايين‌دست حوضه محاسبه گرديد. نتايج نشان داد كه در شناسايي سال هاي خشك، در بالا دست حوضه، تركيب SPEI-SDI به دليل ساختار همبستگي قوي‌تر و توجيه درصد تغييرپذيري بيشتر توسط اولين مؤلفه اصلي آنها (75/5 تا 87/9 درصد) موفقيت بيشتري نسبت به تركيب SPI-SDI دارد. اين در حالي است كه بين دو تركيب در پايش خشك سالي‌ها در پايين دست تفاوت چنداني وجود ندارد. همچنين، در دوره‌هاي خشك ممتد، شاخص تركيبي يك ماه زودتر از شاخص‌هاي منفرد وضعيت خشك سالي را اعلام مي‌كند.
چكيده لاتين :
Drought is a temporary status of water deficit with respect to its long term average condition. Combined Drought Indices (CDIs) are new tools to evaluate general status of drought in a region. In this study, we focus on the integrated monitoring of meteorological droughts (based on temperature and precipitation data) and hydrological droughts (only based on streamflow data) in the Kasilian's basin. The main goal of the investigation is to present a combined drought index called Hydro–Meteorological Drought Index (HMDI) using Principal Component Analysis (PCA) in the basin. PCA is a multivariate technique to reduce dimensionality of data in a number of principal components. The Standardized Precipitation Index (SPI) and the Standardized Precipitation–Evapotranspiration Index (SPEI) were applied to monitor meteorological droughts and the Streamflow Drought Index (SDI) for monitoring hydrological droughts. The data were gathered from the meteorological and hydrometric stations located in Kasilian's basin for the period 1349–50 to 1391–92 as the water year. The station Derzikola (in the upstream) was selected for meteorological analysis and two stations Valikbon and Shirgah were employed to analyze hydrologic drought conditions in the upstream and downstream of the basin, respectively. The preliminary controls on the quality of available data were accomplished using some statistical tests for randomness, normality, adequacy of record length, outliers and temporal trend. Employing 49 probability distributions showed that Wakeby is the best fit distribution for precipitation and streamflow data and General Extreme Value for the difference series of precipitation minus evapotranspiration. The meteorological (SPI and SPEI) and hydrological (SDI) drought indices were calculated at four time windows including 3, 6, 9 and 12 months (each of which starts from the month Octobr). In the next stage, for calculation of hydro–meteorological droughts, using PCA technique, two combined drought indices including SPI–SDI and SPEI–SDI were built. The combined indices, which are the standardized form of the first principal component (PC1), was individually calculated at upstream (for hydrometric station of Valikbon) and downstream (for hydrometric station of Shirgah) of the basin. PC1s were able to explain 74.3–87.9% of variabilities in data. The PC1 of the combination SPEI–SDI explained more variability than the SPI–SDI, both in upstream and in downstream of the basin. This may be related to the high correlation of SPEI and SDI series. The results showed that, for identification of dry years, SPEI–SDI is more successful than SPI–SDI at the upstream station. Therefore, combination of two indices with high correlation made satisfactory results in detecting overall status of droughts in the basin of interest. On the other hand, both combined drought indices have no differences in monitoring droughts at the downstream station. Also, during continuing dry periods, combined indices indicated drought status one month earlier in comparison with single indices. Accordance of the classified series of SPI and SPEI with combined drought indices was higher at larger time scales than smaller ones. This may be due to smoother series of single drought indices at larger time scales as well as high correlation level between indices employed in constructing HMDI.
