SCVWD Runoff and System Operation Analysis

[Authors]

Introduction

Human-caused climate disruption is already affecting hydrologic systems and will produce increasing impacts through the 21st century (Melillo et al., 2014). In California these impacts include increasing intensity and duration of droughts (Diffenbaugh et al., 2015; Mann & Gleick, 2015) and increasing floods due to changing precipitation patterns and snowmelt dynamics (Das et al., 2013; Pierce et al., 2013). For managed water systems, these changes can cause complex interactions (Anderson et al., 2008; Hanak & Lund, 2012), which can require modifications to operating decisions or produce changes in the reliability of water deliveries or the level of flood protection.

To assess the risks posed by climate change to a managed water system, a typical approach uses projected meteorology (precipitation, temperature) to drive a hydrology model, which produces new inflows to the managed system (e.g., Brekke et al., 2004; Hamlet & Lettenmaier, 1999; Vicuna et al., 2010). While hydrology models can perform quite well by many metrics, even small biases can be enough to confound the use of the generated flows by a water resources system model. For this reason, alternative approaches are often employed that make use of existing data sets of inflows, perturbing them to reflect system inflows in a changed climate scenario (Snover et al., 2003; Vicuna et al., 2007). Bias correcting land surface runoff or routed streamflows has been successfully used in different hydrologic forecasting environments, as well as in climate change studies (Vano et al., 2010; Ye et al., 2014; Yuan & Wood, 2012).

In this study we examine potential impacts of changed monthly inflow sequences under a climate change scenario for the Santa Clara Valley Water District (SCVWD). We produce sets of perturbed inflows for the SCVWD reservoir model, where the modified flows are representative of approximately 2040, a planning horizon applicable to local water resources planning activities. We compare two methods of perturbing the inflows: a simple delta method, where each monthly inflow is perturbed by a fixed factor, and a more sophisticated hybrid delta approach that uses a continuous function of perturbation factors based on flow quantiles. We then assess the impacts of changes in inflows on operating decisions of the system and the reliability of water supply with changed conditions.

Methods

The focus region of this study is shown in Figure 1, which illustrates the catchments contributing flow to the 9 major reservoirs operated by the SCVWD. Because of the relatively coarse scale of the climate and hydrology projections, these two regions are represented as two points as indicated: West (W) and East (E). Climate projections for the region were extracted from the online archive described in detail elsewhere (Maurer et al., 2014; Reclamation, 2013). These projections are based on precipitation and temperature output from global climate models (or general circulation models, GCMs) simulations conducted as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5, Taylor et al., 2012), which formed the basis for the climate projections used in the Fifth Assessment of the

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Intergovernmental Panel on Climate Change (IPCC, 2013). Since the climate model output is at a native spatial scale of 100 km or more, it must be downscaled to provide regionally-applicable information. The precipitation and temperature data obtained for this study was downscaled to 1/8° latitude/longitude resolution (~ 12 km) using the BCSD method, originally developed by Wood et al. (2004; 2002). The BCSD method has been widely applied across the western U.S. (e.g., Barnett et al., 2008; Das et al., 2013; Ficklin et al., 2012) and around the globe (Girvetz et al., 2009) in studies of the hydrological impact of climate change.

As part of a past effort (Reclamation, 2011), the downscaled precipitation and temperature projections for the period 1950-2099 were used to drive the Variable Infiltration Capacity (VIC, Liang et al., 1994) hydrology model, producing runoff estimates across the conterminous U.S. For this study, we used precipitation, temperature, and runoff produced by the 10 GCMs listed in Table 1. For the data based on each GCM run, we used a historical simulation and the future projection associated with the emission pathway RCP8.5 (for which the total added radiative forcing at year 2100 is 8.5 W/m2), which represents increasing greenhouse gas emissions essentially as a business-as-usual scenario and also most closely traces observed emissions thus far in the 21st century (Fuss et al., 2014).

[describe the delta method here]

A second method used to perturb the design SCVWD inflow sequences was the Hybrid Delta method (Tohver et al., 2014), as applied directly to streamflow for bias correction (Snover et al., 2003). This method as used in this study applies a common quantile mapping procedure (Gudmundsson et al., 2012; Panofsky & Brier, 1968) to monthly streamflows. For each representative point (labeled E and W in Figure 1) sequences of runoff were obtained from the archive described above, and these were adjusted as described below.

Each runoff sequence was bias-corrected using quantile mapping, using the overlapping 1950-1999 period as the base climatology. This procedure involves separating the data (for 1950-1999) by month, and for each month assembling all inflow values for both the historic sequence (the SCVWD data) and the VIC-produced simulated runoff into cumulative distribution functions (CDFs), producing 12 CDFs for the VIC simulated flows and 12 for observed inflows. Then for each month in the VIC simulation (using the entire 1950-2099 period), the quantile is determined using the CDF for VIC simulations, and a new value is drawn for the same quantile from the observation-based CDF. This produces a new 1950-2099 runoff sequence that statistically matches the observations for the 1950-1999 period (though, importantly, sequencing is not preserved) but evolves into the future as simulated by each GCM. These are referred to here as ‘bias-corrected’ inflows

Finally, an inverse quantile mapping is applied to ‘perturb’ the historic SCVWD record of reservoir inflows. This uses a similar method to that above, but develops new sets of CDFs, both based on the bias-corrected inflows. The first is for 1950-1999, representing recent historical values, and the second based on 2025-2054, representing a changed climatology applicable to the 2040 planning horizon. The historical SCVWD inflows (for 1922-2015) are then adjusted with a quantile mapping from the historic to the future CDFs. This results in a sequence of inflows that has the same temporal pattern as the

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historical sequence, including extended dry and wet periods, but including projected changes as indicated by the downscaled GCM precipitation and temperature.

[describe SWAT modeling]

[describe methods of analyzing output, statistical tests, …]

Results and Discussion

Conclusions

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Table 1 - GCMs included in this study.

GCM No. Model Name Modeling Group1 gfdl-esm2g NOAA Geophysical Fluid Dynamics Laboratory2 canesm2 Canadian Centre for Climate Modelling and Analysis3 cnrm-cm5 Centre National de Recherches Météorologiques / Centre Européen

de Recherche et Formation Avancée en Calcul Scientifique4 csiro-mk3-6-0 Commonwealth Scientific and Industrial Research Organization in

collaboration with Queensland Climate Change Centre of Excellence5 inmcm4 Institute for Numerical Mathematics6 miroc5 Atmosphere and Ocean Research Institute (The University of Tokyo),

National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology

7 mpi-esm-lr Max-Planck-Institut für Meteorologie (Max Planck Institute for Meteorology)

8 mri-cgcm3 Meteorological Research Institute9 ccsm4 National Center for Atmospheric Research10 noresm1-m Norwegian Climate Centre

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Figure 1 - Region of Study: cross-hatching indicates areas draining into SCVWD reservoirs; points labeled W and E are the two representative locations used for each side of the valley.

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