评估季节性预测绩效,预测作物灌溉需求,以支持地中海地区的水管理决策

IF 5.9 1区 农林科学 Q1 AGRONOMY
Daniel Garcia , João Rolim , Maria do Rosário Cameira , Gilles Belaud , Nicolas R. Dalezios , George Karoutsos , João A. Santos , Paula Paredes
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引用次数: 0

摘要

由于气候变率和变化,地中海地区的灌溉水管理面临重大挑战。为了解决这一现实,越来越需要支持早期决策的工具。虽然季节性预报已显示出协助农业部门的潜力,但它们在灌溉管理方面的实际应用仍然有限。本研究评估了原始(非偏差校正)SWF在预测天气和气候需求条件、使用生长度数(GDD)方法估计作物周期持续时间和整个地中海地区作物灌溉用水需求方面的准确性和技能,重点关注季节性和位置因素如何影响其实用性。luceffit集体灌溉计划(葡萄牙)、克劳灌区(法国)和色萨利平原(希腊)被选为地中海地区的主要灌溉地点。选择硬粒小麦和玉米分别作为代表性秋冬作物和春夏作物。SWF数据在HubIS项目(PRIMA/0006/2019)中使用天气研究与预报系统生成。结果表明,SWF性能随预测变量、季节和位置而变化。总体而言,对气候需求(ETo)的预测精度高于降水,平均绝对误差(MAE)分别为17.0 ~ 170.3 mm和94.8 ~ 438.6 mm。预计更高的灌溉需求,特别是在SS,因为SWF倾向于高估ETo(高达20% %),低估季节性累积降水(高达90% %),低估温度(高达4.5ºC)和延长作物周期(高达60天)。小麦比玉米能更好地捕捉到灌溉需水量异常信号。SWF在几乎所有情况下都表现出低于长期(10年)天气观测平均水平的技巧,除了葡萄牙的AW ETo预测。然而,灌溉需水量的范围(小麦0-489 mm,玉米563-960 mm)表明,SWF具有早期灌溉用水管理的潜力,通过适当的预测数据修正可以更好地实现这一目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing seasonal forecast performance to predict crop irrigation requirements to support water management decision-making in the Mediterranean region
Irrigation water management in the Mediterranean region faces major challenges due to climate variability and changes. To address this reality, tools that support early decision-making are increasingly needed. While seasonal forecasts (SWF) have demonstrated potential to assist the agricultural sector, their practical application in irrigation management remains limited. This study evaluates the accuracy and skill of raw (non-bias-corrected) SWF in predicting weather and climatic demand conditions, crop cycle duration estimated using the growing degree day (GDD) approach and crop irrigation water requirements across the Mediterranean region, focusing on how seasonality and location factors may influence their usefulness. The Lucefecit Collective Irrigation Scheme (Portugal), the Crau irrigated area (France), and the Thessaly Plain (Greece) were selected as key irrigation locations in the Mediterranean region. Durum wheat and maize were selected as representative autumn-winter (AW) and spring-summer (SS) crops, respectively. SWF data were generated within the HubIS project (PRIMA/0006/2019) using the Weather Research and Forecasting system. The results showed that SWF performance varied by predicted variable, season, and location. Overall, the forecasts were more accurate in predicting climatic demand (ETo) than precipitation, with mean absolute errors (MAE) of 17.0–170.3 mm and 94.8–438.6 mm, respectively. Higher irrigation demands were predicted, especially in SS, as SWF tended to overestimate ETo (up to 20 %), underestimate seasonal accumulated precipitation (up to 90 %), underestimate temperature (up to 4.5 ºC) and extend crop cycles (up to 60 days). Irrigation requirements anomaly signals were better captured for wheat than for maize. SWF exhibited less skill than the long-term (10-years) weather observed average in almost all cases, except for AW ETo predictions in Portugal. Nevertheless, the range of irrigation requirements (for wheat 0–489 mm and for maize 563–960 mm) suggests SWF hold potential for early irrigation water management, which could be better achieved with appropriate forecast data corrections.
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来源期刊
Agricultural Water Management
Agricultural Water Management 农林科学-农艺学
CiteScore
12.10
自引率
14.90%
发文量
648
审稿时长
4.9 months
期刊介绍: Agricultural Water Management publishes papers of international significance relating to the science, economics, and policy of agricultural water management. In all cases, manuscripts must address implications and provide insight regarding agricultural water management.
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