Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India.
Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India.
Sci Total Environ. 2022 Feb 10;807(Pt 3):151083. doi: 10.1016/j.scitotenv.2021.151083. Epub 2021 Oct 30.
Drought is one of the significant natural disasters that incurs billion dollars of economic damage every year. Among all, agricultural drought needs critical attention for drought monitoring due to its direct effect on crop yield and management of irrigation water. Most of the previous studies focused on regionalizing drought using k-means, hierarchal, fuzzy, and entropy-based clustering techniques. However, these techniques are not suitable where the clusters are not separated distinctively, and the number of clusters cannot be estimated automatically. In this study, we have developed agricultural drought hotspot maps using Soil moisture deficit index (SMDI) and the regional severity (S), duration (D), and frequency (F) curves using complex network algorithm for the future warming climate (2041-2070) of the Mahanadi River basin (MRB) in India. We have used a modified dynamic Budyko (DB) hydrological model to simulate daily soil moisture at a spatial scale of 0.25° × 0.25° using input from four GCMs for the RCP 4.5 scenario. The modified DB model was calibrated and validated for the study area. The model proved to be capable of simulating the soil moisture dynamics over the basin and also effectively captured the historical droughts occurred in the basin. The drought hotspot maps of the basin suggest that the northern, south-eastern, and central parts of the basins are going to experience more number of droughts. The results suggest that for most of the clusters, the regional S-D-F curve can be utilized to understand the future drought characteristics at site-specific as well as regional scale, as the confidence band is found to be very narrow. Overall, our study provides a framework to develop regional S-D-F curve.
干旱是每年造成数十亿美元经济损失的重大自然灾害之一。在所有自然灾害中，由于农业干旱直接影响作物产量和灌溉水管理，因此需要对其进行重点监测。大多数先前的研究都集中在使用 K-均值、层次、模糊和基于熵的聚类技术对干旱进行区域化。然而，这些技术在聚类不明显且无法自动估计聚类数量的情况下并不适用。在这项研究中，我们使用土壤湿度亏缺指数（SMDI）和复杂网络算法的区域严重度（S）、持续时间（D）和频率（F）曲线，针对印度马哈纳迪河流域（MRB）未来变暖气候（2041-2070 年）开发了农业干旱热点图。我们使用了改进的动态布地科（DB）水文模型，该模型使用四个 GCM 的输入以 0.25°×0.25°的空间分辨率模拟日土壤湿度，用于 RCP4.5 情景。对改进的 DB 模型进行了校准和验证。该模型证明能够模拟流域的土壤湿度动态，并且还有效地捕捉了流域中发生的历史干旱。流域的干旱热点图表明，流域的北部、东南部和中部地区将经历更多的干旱。结果表明，对于大多数聚类，区域 S-D-F 曲线可用于了解特定地点和区域尺度的未来干旱特征，因为置信带被发现非常狭窄。总的来说，我们的研究为开发区域 S-D-F 曲线提供了一个框架。
