Gonzalo Cortés

A Landsat-Era Sierra Nevada Snow Reanalysis (1985–2015)

AbstractA newly developed state-of-the-art snow water equivalent (SWE) reanalysis dataset over the Sierra Nevada (United States) based on the assimilation of remotely sensed fractional snow-covered area data over the Landsat 5–8 record (1985–2015) is presented. The method (fully Bayesian), resolution (daily and 90 m), temporal extent (31 years), and accuracy provide a unique dataset for investigating snow processes. The verified dataset (based on a comparison with over 9000 station years of in situ data) exhibited mean and root-mean-square errors less than 3 and 13 cm, respectively, and correlation greater than 0.95 compared with in situ SWE observations. The reanalysis dataset was used to characterize the peak SWE climatology to provide a basic accounting of the stored snowpack water in the Sierra Nevada over the last 31 years. The pixel-wise peak SWE volume over the domain was found to be 20.0 km3 on average with a range of 4.0–40.6 km3. The ongoing drought in California contains the two lowest snowpack...

A Particle Batch Smoother Approach to Snow Water Equivalent Estimation

AbstractThis paper presents a newly proposed data assimilation method for historical snow water equivalent SWE estimation using remotely sensed fractional snow-covered area fSCA. The newly proposed approach consists of a particle batch smoother (PBS), which is compared to a previously applied Kalman-based ensemble batch smoother (EnBS) approach. The methods were applied over the 27-yr Landsat 5 record at snow pillow and snow course in situ verification sites in the American River basin in the Sierra Nevada (United States). This basin is more densely vegetated and thus more challenging for SWE estimation than the previous applications of the EnBS. Both data assimilation methods provided significant improvement over the prior (modeling only) estimates, with both able to significantly reduce prior SWE biases. The prior RMSE values at the snow pillow and snow course sites were reduced by 68%–82% and 60%–68%, respectively, when applying the data assimilation methods. This result is encouraging for a basin like...

Characterizing the extreme 2015 snowpack deficit in the Sierra Nevada (USA) and the implications for drought recovery

Analysis of the Sierra Nevada (USA) snowpack using a new spatially distributed snow reanalysis data set, in combination with longer term in situ data, indicates that water year 2015 was a truly extreme (dry) year. The range-wide peak snow volume was characterized by a return period of over 600 years (95% confidence interval between 100 and 4400 years) having a strong elevational gradient with a return period at lower elevations over an order of magnitude larger than those at higher elevations. The 2015 conditions, occurring on top of three previous drought years, led to an accumulated (multiyear) snowpack deficit of ~ −22 km3, the highest over the 65 years analyzed. Early estimates based on 1 April snow course data indicate that the snowpack drought deficit will not be overcome in 2016, despite historically strong El Nino conditions. Results based on a probabilistic Monte Carlo simulation show that recovery from the snowpack drought will likely take about 4 years.

Examining spatial and temporal variability in snow water equivalent using a 27 year reanalysis: Kern River watershed, Sierra Nevada

This paper used a data assimilation framework to estimate spatially and temporally continuous snow water equivalent (SWE) from a 27 year reanalysis (from water year 1985 to 2011) of the Landsat-5 record for the Kern River watershed in the Sierra Nevada, California. The data assimilation approach explicitly treats sources of uncertainty from model parameters, meteorological inputs, and observations. The method is comprised of two main components: (1) a coupled land surface model (LSM) and snow depletion curve (SDC) model, which is used to generate an ensemble of predictions of SWE and fractional snow cover area (FSCA) for a given set of prior (uncertain) inputs, and (2) a retrospective reanalysis step, which updates estimation variables to be consistent with the observed fractional snow cover time series. The final posterior SWE estimate is generated from the LSM-SDC using the posterior estimation variables consistently with all postulated sources of uncertainty in the model, inputs, and observations. A reasonable agreement was found between the SWE reanalysis and in situ SWE observations and streamflow data. The data set was studied to evaluate factors controlling SWE spatial and temporal variability. Elevation was found to be the primary control on spatial patterns of peak-SWE and day-of-peak. The easting coordinate had additional explanatory power, which is hypothesized to be related to rain shadow effects due to the prevailing storm track directions. The spatial patterns were found to be interannually inconsistent. However, drier years and lower elevations were found more variable than wetter years and higher elevations, respectively. Only a very small percentage of the Kern River watershed had a significant trend in peak-SWE and day-of-peak. Trends deemed to be significant were found to be positive (peak-SWE is increasing and day-of-peak occurs later) at higher elevations, but negative (peak-SWE is decreasing and day-of-peak occurs earlier) at lower elevations. The reanalysis approach proved to be useful in terms of identifying subwatershed variability and trends, and could be extended to larger regions and areas where in situ data are sparse or unavailable.

A robust multimodel framework for ensemble seasonal hydroclimatic forecasts

We provide a framework for careful analysis of the different methodological choices we make when constructing multimodel ensemble seasonal forecasts of hydroclimatic variables. Specifically, we focus on three common modeling decisions: (i) number of models, (ii) multimodel combination approach, and (iii) lead time for prediction. The analysis scheme includes a multimodel ensemble forecasting algorithm based on nonparametric regression, a set of alternatives for the options previously pointed, and a selection of probabilistic verification methods for ensemble forecast evaluation. The usefulness of this framework is tested through an example application aimed to generate spring/summer streamflow forecasts at multiple locations in Central Chile. Results demonstrate the high impact that subjectivity in decision-making may have on the quality of ensemble seasonal hydroclimatic forecasts. In particular, we note that the probabilistic verification criteria may lead to different choices regarding the number of models or the multimodel combination method. We also illustrate how this objective analysis scheme may lead to results that are extremely relevant for the case study presented here, such as skillful seasonal streamflow predictions for very dry conditions.

Downscaling Climate Changes for Santiago: What Effects can be Expected?

This chapter describes the methodology used to analyse climate scenarios and their impact on hydro-meteorological variables in the Metropolitan Region of Santiago de Chile (MRS) and the results thereof. Using a downscaling methodology for future IPCC A2 and B1 scenarios (and B2 for stream flow), temperature, precipitation and secondary variable trends are estimated for the 2045–2065 time frame. The findings suggest that Santiago will be a drier and hotter city in the near future and have a high number of days with extreme temperatures. Lower precipitation rates are expected to lead to decreasing magnitudes in the stream flow of the two main rivers, Maipo and Mapocho, particularly in the summer months. Based on the data presented below, expected climate change impacts are analysed and adaptation needs identified for the MRS.

Impacts of El Niño and La Niña on interannual snow accumulation in the Andes: Results from a high‐resolution 31 year reanalysis

We present new insights on extratropical Andean snow climatology (27°S to 37°S) based on the results from a 31-year high-resolution reanalysis. The snow water equivalent (SWE) estimates were generated by integrating observed snow depletion data from Landsat together with a snow model forced by the Modern-era Retrospective Analysis for Research and Applications (MERRA). The spatial resolution (180 m), geographic extent (175000 km2) and temporal span (1984-2015) constitute an unprecedented dataset for the region. SWE reaches annual peak volumes between 13 and 66 km3, with a climatological average of 27.7 km3. A positive correlation between SWE and the Oceanic Nino Index (R2 = 0.35) exists for the region, with a strengthening of the signal from North to South, peaking at 34°S. Although the correlation between El Nino and positive SWE anomalies is significant, La Nina was not found to drive negative anomalies beyond what is observed during non-La Nina years.