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首页> 外文期刊>Science of the total environment >Tracking seasonal and monthly drought with GRACE-based terrestrial water storage assessments over major river basins in South India
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Tracking seasonal and monthly drought with GRACE-based terrestrial water storage assessments over major river basins in South India

机译:跟踪季节性和每月干旱与恩典南印度主要河流盆地的基于栅栏河流储存评估

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摘要

Drought is a complex natural hazard that affects ecosystems and society in several ways and it is important to quantify drought at the river basin scale. Assessment of drought requires both hydrological observations and simulation models as the data are generally scarce. Therefore, we use remote sensing products to help understand drought conditions in four basins in South India. This study analysed the correlation among five drought indices for four seasons: gravity recovery and climate experiment - drought severity index (GRACE-DSI), standardized precipitation index (SPI), self-calibrated palmer drought severity index (sc_PDSI), standardized precipitation-evapotranspiration index (SPEI), and combined climatologic deviation index (CCDI) with GRACE terrestrial water storage anomalies (TWSA) using the Pearson correlation coefficient (r) from 2002 to 2016 over the Goda-vari, Krishna, Pennar, and Cauvery river basins. Basin scale drought events are evaluated using CCDI, GRACEDSI, sc_PDSI, SPI12, and SPEI12 at seasonal and monthly time scale. Characteristics of drought event analysis are calculated for CCDI monthly. The results showed that GRACE TWS is highly correlated with GRACE-DSI, CCDI, and sc_PDSI. Seasonally, high spatial correlations between CCDI and GRACE-DSI with GRACE TWS are evident for all the river basins. Additionally, correlation is found to exist between sc_PDSI and GRACE TWS as soil moisture content is an operating variable between them. The 12-month SPI and SPEI correlated better with GRACE TWS than the 3 and 6-month periods. Among the four basins, droughts in the Krishna Basin lasted 29 months, longer than in the rest of the basins between 2003 and 2005. Overall, CCDI and GRACE-DSI indices are found to be effective for examining and evaluating the drought conditions at the basin scale.
机译:干旱是一种复杂的自然危害,以多种方式影响生态系统和社会,重要的是在河流阵线上量化干旱。当数据通常稀缺时,干旱评估需要水文观测和模拟模型。因此,我们使用遥感产品来帮助了解南印度的四个盆地中的干旱状况。这项研究分析了四季五次干旱指数之间的相关性:重力恢复和气候实验 - 干旱严重性指数(Grace-DSI),标准化降水指数(SPI),自我校准的Palmer干旱严重性指数(SC_PDSI),标准化降水蒸发在Goda-Vari,Krishna,Pennar和Cauvery River盆地上,使用Pearson相关系数(R)与Genta-Vari,Krishna,Pennar和Cauvery River河流域的Pearson相关系数(r)与恩典陆地水储存异常(TWSA)的结合高潮偏差指数(CCDI)。使用CCDI,GRACEDSI,SC_PDSI,SPI12和SPEI12在季节性和每月时间尺度下进行评估盆地规模干旱事件。干旱事件分析的特征是为CCDI每月计算的。结果表明,Grace TWS与Grace-DSI,CCDI和SC_PDSI高度相关。季节性地,CCDI与Grace-DSI之间的高空间相关性与Grace TWS的所有河流盆都很明显。另外,在SC_PDSI和Grace TW之间存在相关性,因为土壤湿度含量是它们之间的操作变量。 12个月的SPI和SPEI与优于3和6个月的时间更好地相关。在四个盆地中,克里希纳盆地的干旱持续了29个月,比2003年至2005年之间的其他盆地更长。总体而言,发现CCDI和GRACE-DSI指数有效地检查和评估盆地的干旱条件规模。

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