Abstract
Abstract. In many semi-arid regions, multisectoral demands often stress available water supplies. Such is the case in the Elqui River valley of northern Chile, which draws on a limited-capacity reservoir to allocate 25 000 water rights. Delayed infrastructure investment forces water managers to address demand-based allocation strategies, particularly in dry years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October–January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950–2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The methods applied here advance the understanding of the mechanisms and timing responsible for moisture transport to the Elqui Valley and provide a unique application of streamflow forecasting in the prediction of water right allocations.
Highlights
The sustainability of many water systems is challenged by current climate variability and may come under additional stress with changes in future climate and user demands
The first principal component of the Stat-Principal component regression (PCR) 1 September forecast is highly correlated with sea surface temperatures (SSTs) in the Niño 3.4 region (0.88), which confirms that streamflow, and precipitation in the Elqui Valley, is at least partially characterized by anomalous changes in SSTs
The focus of this research is to develop an understanding of the mechanisms contributing to austral summer streamflow in the Elqui Valley, investigate model skill at varied forecast leads, and produce forecast-based water right allocations to inform water resources management decision-making
Summary
The sustainability of many water systems is challenged by current climate variability and may come under additional stress with changes in future climate and user demands. A skillful streamflow forecast may allow more efficient water allocation and predictable trade-offs between flows for energy, irrigation, municipalities, environmental services, etc Such forecasts often provide the ability to prepare for anticipated conditions and not react to existing conditions, potentially reducing climate-related risks and offering opportunities (Helmuth et al, 2007). Water rights holders (users) have two decision points, May and September, to evaluate their allocation and weigh the need to supplement through market activity (trade or lease) This setting serves as an impetus for developing a framework to advance streamflow and water allocation forecasts at those decision points to better guide decision-making across the valley. Skillful streamflow forecasts coupled with reservoir allocation decision tools can improve allocation efficiency
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