On the Utility of Ensemble Rainfall Forecasts Over River Basins in India

IF 1.9 4区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS
Anumeha Dube, Raghavendra Ashrit
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Abstract

Rivers form a lifeline for the agriculture based economy in India, but recent heavy rainfall events have caused major floods in the rivers resulting in loss of life and property. In order to accurately forecast the stream flow from the rivers firstly, an accurate forecast of rainfall over the river basins (RB) is required. Until recently, for operational flood forecasting in India, rainfall forecasts from deterministic models were used. Deterministic models often result in incorrect forecasts as they do not contain the uncertainty information. Ensemble prediction systems (EPS) sample this uncertainty and can add value to the deterministic forecasts. This study seeks to address the question ‘whether the ensemble rainfall forecasts over RBs in India are ready for hydrological applications?’ In order to answer this and generate more confidence in using probabilistic rainfall forecasts from an EPS for hydrological purposes the accuracy of the forecasts has to be established. For this purpose, we have carried out an in-depth verification of the probabilistic rainfall forecasts obtained from the NCMRWF EPS (NEPS) over 8 major RBs of India during the southwest monsoon (SWM) seasons of 2018 to 2021. The basin averaged rainfall forecasts from NEPS and observations from the Integrated Multi-satellitE Retrievals for GPM (IMERG) are used in this study. It was seen from the study that the model possesses good skill in predicting low to moderate rainfall over Himalayan Rivers like Ganga and peninsular rivers like Tapi, Narmada, Cauvery, and Krishna. This is seen in terms of a low Brier Score (BS), high Brier Skill Score (BSS) and low Continuous Ranked Probability Score (CRPS), as well as lower RMSE in the ensemble mean. The skill of the model is further confirmed by comparing the RMSE in the mean with the spread in the members. The best match between the RMSE in ensemble mean and spread is seen for Ganga RB. The Relative Economic Value (REV) determines the economic value of forecasts and it shows that over Ganga, Mahanadi, and Narmada the rainfall forecasts show the maximum economic value. However, the model shows relatively poorer skill in predicting rainfall over the Brahmaputra RB located in northeastern India. From this study it can be concluded that NEPS model has reasonably good skill in predicting rainfall over RBs in northern and peninsular parts of India and it would be beneficial to use these forecasts for forecasting floods.

Abstract Image

论印度河流流域降雨量集合预测的实用性
河流是印度以农业为基础的经济的生命线,但最近的强降雨事件导致河流发生大洪水,造成生命和财产损失。为了准确预报河流的流量,首先需要对流域的降雨量进行准确预报。直到最近,印度的洪水预报还使用了确定性模型的降雨预报。确定性模型往往导致不正确的预测,因为它们不包含不确定性信息。集合预测系统(EPS)对这种不确定性进行采样,可以为确定性预测增加价值。这项研究试图解决这样一个问题:“印度RBs的整体降雨预报是否准备好用于水文应用?”“为了回答这个问题,并在使用EPS的概率降雨预报用于水文目的时产生更大的信心,必须建立预报的准确性。为此,我们对nmrwf EPS (NEPS)在2018 - 2021年西南季风(SWM)季节对印度8个主要RBs的概率降雨预报进行了深入验证。本研究利用NEPS的流域平均降水预报和IMERG (Integrated Multi-satellitE retrieval for GPM)的观测数据。从研究中可以看出,该模型在预测恒河等喜马拉雅河流和塔皮河、纳尔马达河、高韦里河和克里希纳河等半岛河流的中低降雨方面具有良好的技能。这可以从低Brier分数(BS),高Brier技能分数(BSS)和低连续排名概率分数(CRPS)以及较低的RMSE中看出。通过比较均值的RMSE与成员的差值,进一步验证了模型的有效性。在Ganga RB中,总体平均值的RMSE与差值之间的匹配最好。相对经济价值(REV)决定了预报的经济价值,它表明在恒河、马哈纳迪和纳尔马达的降雨预报显示了最大的经济价值。然而,该模型在预测位于印度东北部的雅鲁藏布江RB的降雨方面显示出相对较差的技能。通过本研究可以得出结论,NEPS模型在预测印度北部和半岛部分地区RBs的降雨方面具有相当好的技能,并且有利于利用这些预测来预测洪水。
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来源期刊
pure and applied geophysics
pure and applied geophysics 地学-地球化学与地球物理
CiteScore
4.20
自引率
5.00%
发文量
240
审稿时长
9.8 months
期刊介绍: pure and applied geophysics (pageoph), a continuation of the journal "Geofisica pura e applicata", publishes original scientific contributions in the fields of solid Earth, atmospheric and oceanic sciences. Regular and special issues feature thought-provoking reports on active areas of current research and state-of-the-art surveys. Long running journal, founded in 1939 as Geofisica pura e applicata Publishes peer-reviewed original scientific contributions and state-of-the-art surveys in solid earth and atmospheric sciences Features thought-provoking reports on active areas of current research and is a major source for publications on tsunami research Coverage extends to research topics in oceanic sciences See Instructions for Authors on the right hand side.
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