首页> 外文期刊>Hydrology and Earth System Sciences >Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods
【24h】

Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

机译:巴基斯坦上部梧桐盆地的季节性流流量预测:对方法的评估

获取原文
获取原文并翻译 | 示例
           

摘要

Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April-September) streamflow forecasts for inflow to Pakistan's two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM-ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM for this location. Cross validation of the streamflow forecasts shows that the BJP approach using two predictors (March flow and an El Nino Southern Oscillation, ENSO, climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April-September), early Kharif (April-June) and late Kharif (July-September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requir
机译:Hational Managers在全球许多地区用于季节性水分分配前景,用于灌溉者,水库运营,环境流量管理,水市场和干旱反应策略的季节性管理人员使用及时和熟练的季节性流出预测。在澳大利亚,澳大利亚气象局部署了贝叶斯联合概率(BJP)统计方法,以来自2010年以来在全国各地提供季节性流流量预测。在这里,我们评估了使用先行条件和气候指数作为预测因素的BJP方法,以生产Kharif赛季(4月至9月)流出预测,为巴基斯坦两个最大的梧桐盆地(UIB)供水水坝,塔尔巴拉(在印度)和Mangla(在Jhelum上)。对于Mangla,我们将这些BJP预测从雪花径流模型(SRM)和(ii)使用具有SRM-ESP预测手段的混合方法和(ii)使用SRM-ESP预测装置的混合方法作为额外的预测器。对于Tarbela来说,我们只使用前一种和气候预测器评估BJP预测,因为我们没有获得此位置的SRM。 Streamflow预测的交叉验证表明,使用两个预测器(3月流量和EL Nino Southern振荡,enso,气候指数)的BJP方法提供了熟悉的概率预测,这些预测可靠地对Mangla和Tarbela的不确定性传播。对于Mangla来说,SRM方法导致预测出现一些偏差,并且在不确定性传播中不可靠,而混合方法不会导致更好的预测技能。 Kharif(4月至9月)的技能水平,kharif早期(六月)和kharif(7月至9月)BJP预测在两个地点之间变化。 Mangla预测显示出汗肝早期汗肝和中等技能的高技能,而kharif和晚期的适度技巧,而对Tarbela的预测也显示出所有Kharif和晚期Kharif的中等技能,但早期的Kharif技能低。 BJP方法很简单,输入数据要求小

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号