01 Mar 2023
 | 01 Mar 2023
Status: a revised version of this preprint is currently under review for the journal HESS.

Seasonal prediction of end-of-dry season watershed behavior in a highly interconnected alluvial watershed, northern California

Claire Marie Kouba and Thomas Harter

Abstract. In undammed watersheds in Mediterranean climates, the timing and abruptness of the transition from the dry season to the wet season have major implications for aquatic ecosystems. Of particular concern in many coastal areas is whether this transition can provide sufficient flows at the right time to allow passage for spawning anadromous fish, which is determined by dry season baseflow rates and the timing of the onset of the rainy season. In (semi-) ephemeral watershed systems, these functional flows also dictate the timing of full reconnection of the stream system. In this study, we propose methods to predict, approximately five months in advance, two key hydrologic metrics in the undammed rural Scott River watershed (HUC8 18010208) in northern California. Both metrics are intended to quantify the transition from the dry to the wet season, to characterize the severity of a dry year and support seasonal adaptive management. The first metric is the minimum 30-day dry season baseflow volume, Vmin, 30 days, which occurs at the end of the dry season (September–October) in this Mediterranean climate. The second metric is the cumulative precipitation, starting Sept. 1st, necessary to bring the watershed to a "full" or "spilling" condition (i.e. initiate the onset of wet season storm- or baseflows) after the end of the dry season, referred to here as Pspill. As potential predictors of these two values, we assess maximum snowpack, cumulative precipitation, the timing of the snowpack and precipitation, spring groundwater levels, spring river flows, reference ET, and a subset of these metrics from the previous water year. We find that, though many of these predictors are correlated with the two metrics of interest, of the predictors considered here, the best prediction for both metrics is a linear combination of the maximum snowpack water content and total October–April precipitation. These two linear models could reproduce historic values of Vmin, 30 days and Pspill  with an average model error (RMSE) of 1.4 Mm3 / 30 days (19.4 cfs) and 20.7 mm (0.8 inches), respectively. Although these predictive indices could be used by governance entities to support local water management, careful consideration of baseline conditions used as a basis for prediction is necessary.

Claire Marie Kouba and Thomas Harter

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-41', Anonymous Referee #1, 29 Mar 2023
    • AC1: 'Reply on RC1', Claire Kouba, 24 Jul 2023
  • RC2: 'Comment on hess-2023-41', Rob van Kirk, 04 Apr 2023
    • AC2: 'Reply on RC2', Claire Kouba, 24 Jul 2023

Claire Marie Kouba and Thomas Harter

Data sets

Github Repository - Kouba 2023 End of Dry Season Manuscript Claire Kouba

Claire Marie Kouba and Thomas Harter


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Short summary
In some watersheds, the severity of the dry season has a large impact on aquatic ecosystems. In this study, we design a way to predict, about 5 months in advance, how severe the dry season will be in a rural watershed in northern California. This early warning can support seasonal adaptive management. To predict these two values, we assess data about snow, rain, groundwater, and river flows. We find that maximum snowpack and total wet season rainfall best predict dry season severity.