Abstract
Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.
Highlights
Drought and floods are among the most important natural disasters globally in terms of socio-economic losses (Wilhite, 2000; Dilley et al, 2005)
We first discuss the variation of the kappa (κ) parameter defined by Mahanama et al (2011) and used by Shukla and Lettenmaier (2011) and illustrate the predictability of soil moisture (SM), snow water equivalent (SWE) and cumulative runoff (CR), respectively, in Sects. 3.2, 3.3 and 3.4
Where σw is the standard deviation of total initial moisture (SM + SWE) over the hindcast period (i.e., 1961–2007 in this case) and σP is the standard deviation of the total precipitation during the forecast period over the same hindcast period
Summary
Drought and floods are among the most important natural disasters globally in terms of socio-economic losses (Wilhite, 2000; Dilley et al, 2005). Since 2010, a record number of extreme drought and flood events have impacted many regions across the globe (Blunden et al, 2011; Blunden and Arndt, 2012) and caused enormous losses. Some recent studies have linked changes in the frequency and severity of natural hazards to climate change (Lau and Kim, 2012; Peterson et al, 2012; Trenberth and Fasullo, 2012) and projected a higher likelihood of occurrence of these kinds of extreme events in the future in many regions of the globe (Burke et al, 2006; Hirabayashi et al, 2008; Sheffield and Wood, 2008b; Kundzewicz et al, 2010; Dai, 2011). The stakes for the implementation of global hydrologic and drought prediction systems to provide outlooks for water
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