Pablo A. Mendoza

University of Chile

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James McPhee

University of Chile

Thomas E. Shaw

Swiss Federal Institute for Forest, Snow and Landscape Research

Martyn Clark

University of Calgary

K. N. Musselman

Institute of Arctic and Alpine Research

Jesús Revuelto

Instituto Pirenaico de Ecología

Naoki Mizukami

NSF National Center for Atmospheric Research

ÃA

Ãlvaro Ayala

Centro de Recursos Educativos Avanzados

Ximena Vargas

University of Chile

Simon Gascoin

Centre d'Études Spatiales de la Biosphère

Juan Ignacio López‐Moreno

Instituto Pirenaico de Ecología

Cooperative Institutions

University of Chile

NSF National Center for Atmospheric Research

University of Washington

Research Applications (United States)

Center for Climate and Resilience Research

Centre National de la Recherche Scientifique

University of Colorado Boulder

University of Saskatchewan

Research Applications Laboratory

University of Colorado System

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Improving medium-range ensemble streamflow forecasts through statistical post-processing

HAL (Le Centre pour la Communication Scientifique Directe) (2017)

Pablo A. Mendoza Andrew W. Wood E. Clark Bart Nijssen Martyn Clark

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Citations (3)

Snow depth fractal behavior at the Izas experimental catchment from 2011 to 2017

Zenodo (CERN European Organization for Nuclear Research) (2019)

Pablo A. Mendoza K. N. Musselman J. S. Deems Jesús Revuelto Juan Ignacio López‐Moreno

All the files included here contain the data produced for the manuscript "Seasonal and annual variability of snow depth fractal behavior in a sub-alpine catchment", submitted for possible publication in Water Resources Research. The raw data used to generate the figures for the manuscript can be found below: Revuelto, J., Azorin-Molina, C., Alonso-González, E., Sanmiguel- Vallelado, A., Navarro-Serrano, F., Rico, I., and López- Moreno, J. I.: Observations of snowpack distribution and meteorological variables at the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017 [Data set], Zenodo, https://doi.org/10.5281/zenodo.848277, 2017. The following data files and scripts were used to produce results for the Izas experimental catchment: Variogram_YYYYMMDD_SD.rsav: Omnidirectional variogram for snow depth map obtained from Lidar scan acquired on YYYY-MM-DD. Variogram_YYYYMMDD_SD_directional.rsav: Directional variogram for snow depth map obtained from Lidar scan acquired on YYYY-MM-DD. Variogram_DEM_Izas1m.rsav: Omnidirectional variogram for bare earth topography. Variogram_DEM_Izas1m_directional.rsav: Directional variogram for bare earth topography (needs to be loaded in R, as any other .rsav file). Variogram_Omnidirectional.R: Script used to generate omnidirectional variograms, save data and plot results. Variogram_Directional.R: Script used to generate directional variograms, save data and plot results.

10.5281/zenodo.2552750

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Lidar-derived snow depth maps along the Chilean Extratropical Andes, winter 2018

Zenodo (CERN European Organization for Nuclear Research) (2020)

Pablo A. Mendoza Thomas E. Shaw James McPhee K. N. Musselman Jesús Revuelto

All the files included here contain the data produced for the manuscript "Spatial distribution and scaling properties of lidar-derived snow depth in the extratropical Andes", submitted for possible publication in Water Resources Research. Lidar measurements for snow-covered conditions were conducted on September 4th, August 9th, and October 25th 2018 in Tascadero, Las Bayas and Valle Hermoso, respectively. Each data acquisition was conducted using a Riegl VZ6000 long range scanner on dates with and without snow, using an angular resolution of 0.01°. Lidar-derived maps are contained in the following files: SiteName_SD_TPI.txt (SiteName: Tascadero, Las Bayas, VH East or VH West). Where the file structure is as follows: X,Y,Z,SD,SLP,NOR,TPI04,TPI07,TPI15,TPI20,TPI30,TPI40,TPI50 where Z is bare earth elevation (m a.s.l.), SD is snow depth, SLP is slope (°), NOR is northness (°) whereby 180 = north facing and 0 = south facing, TPIXX is the Topographic Position Index (TPI) computed for a search distance of XX m, whereby positive differences are convex landforms, and negative is concave. The magnitude of TPI indicates the scale of the relative concavity/convexity. Variogram results are contained in files .rsav with the following nomenclature: SiteName_X_TypeOfVariogram.rsav Where X can be SD (snow depth) or Z (bare earth topography), and the type of variogram can be omnidirectional or directional.

Extratropical cyclone

10.5281/zenodo.3964393

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Scaling behavior of lidar-derived snow depth across the semi-arid Chilean Andes

Pablo A. Mendoza Thomas E. Shaw Fabiola Pinto Miguel Lagos Jesús Revuelto

<p>The seasonal melt of mountain snow-cover provides a vital source of freshwater for downstream systems, sustaining multiple productive uses, population needs, and unique ecosystems. In the semi-arid Andes Cordillera, the snowpack acts as a natural water reservoir, releasing spring snowmelt runoff that accounts for more than 60 % of the total annual streamflow. Hence, understanding and characterizing the spatial variability of snow over this large domain is critical for accurate hydrological predictions. We examine the probability density functions and the geostatistical structure of snow depth through variogram analysis, using terrestrial lidar scans acquired during two seasons (2018 and 2019). First, we compare the spatial patterns of snow depth near maximum accumulation at three experimental sites: (i) the Tascadero catchment (-31.26&#176;N, 3270-3790 m a.s.l.), (ii) the Las Bayas experimental catchment (-33.31&#176;N, 3218-4022 m a.s.l.); and (iii) the Valle Hermoso catchment (-36.91&#176;N, 1449-2563 m a.s.l.). Second, we analyze the inter- and intra-annual variability of snow depth patterns in the Las Bayas catchment, where seven scans were acquired during seasons 2018 and 2019.&#160;</p><p>The comparison across sites reveals snow depth fractal behavior until a first omnidirectional scale break in the range 15-22 m for unvegetated areas, and a short-range fractal dimension spanning 2.5-2.65. In the woodland of Valle Hermoso, a much shorter (5 m) scale break and a larger short-range fractal dimension (2.73) are found. Secondary scale ranges and breaks spanning 62-125 m are found in all sites but Tascadero, where snow depth follows a bimodal distribution across the domain. In the Las Bayas domain, inter-annual consistency is found in snow scaling patterns, with two distinct regions separated by a short scale break ~6 m early in the winter, increasing to larger break lengths (15-18 m) in July and August. These results help to inform about the appropriate spatial configuration for snowpack modeling across the Andes. Efforts to better understand the modulation of topography (slope and wind exposure) and vegetation on snow depth distribution patterns, as well as impacts of dominant wind directions on anisotropies in fractal parameters, are ongoing.</p>

Snowpack

Snowmelt

Variogram

10.5194/egusphere-egu2020-11313

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A multi‐objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments

Hydrological Processes (2021)

Danny Saavedra Pablo A. Mendoza Nans Addor Harold Llauca Ximena Vargas

Abstract The assessment of climate change impacts on water resources and flood risk is typically underpinned by hydrological models calibrated and selected based on observed streamflow records. Yet, changes in climate are rarely accounted for when selecting hydrological models, which compromises their ability to robustly represent future changes in catchment hydrology. In this paper, we test a simple framework for selecting an ensemble of calibrated hydrological model structures in catchments where contrasting climatic conditions have been observed. We start by considering 78 model structures produced with the FUSE modular modelling framework and rely on a Pareto scheme to select model structures maximizing model efficiency in both wet and dry periods. The application of this approach in three case study basins in Peru enables the identification of structures with good robustness, but also good performance according to hydrological signatures not used for model selection. We also highlight that some model structures that perform well according to traditional efficiency metrics have low performance in contrasting climates or suspicious internal states and fluxes. Importantly, the model selection approach followed here helps to reduce the spread in precipitation elasticities and temperature sensitivities, providing a clearer picture of future hydrological changes. Overall, this work demonstrates the potential of using contrasting climatic conditions in a multi‐objective framework to produce robust and credible simulations, and to constrain structural uncertainties in hydrological projections.

Robustness

Hydrological modelling

10.1002/hyp.14446

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Citations (12)

Snow depth fractal behavior at the Izas experimental catchment from 2011 to 2017

Zenodo (CERN European Organization for Nuclear Research) (2020)

Pablo A. Mendoza K. N. Musselman Jesús Revuelto J. S. Deems Juan Ignacio López‐Moreno

All the files included here contain the data produced for the manuscript "Inter-annual and seasonal variability of snow depth scaling behavior in a sub-alpine catchment", submitted for possible publication in Water Resources Research. The raw data used to generate the figures for the manuscript can be found below: Revuelto, J., Azorin-Molina, C., Alonso-González, E., Sanmiguel- Vallelado, A., Navarro-Serrano, F., Rico, I., and López- Moreno, J. I.: Observations of snowpack distribution and meteorological variables at the Izas Experimental Catchment (Spanish Pyrenees) from 2011 to 2017 [Data set], Zenodo, https://doi.org/10.5281/zenodo.848277, 2017. The following data files and scripts were used to produce results for the Izas experimental catchment: Variogram_YYYYMMDD_SD_ext.rsav: Omnidirectional variogram for snow depth map obtained from Lidar scan acquired on YYYY-MM-DD. Variogram_YYYYMMDD_SD_directional_ext.rsav: Directional variogram for snow depth map obtained from Lidar scan acquired on YYYY-MM-DD. Variogram_DEM_Izas1m_ext.rsav: Omnidirectional variogram for bare earth topography. Variogram_DEM_Izas1m_directional_ext.rsav: Directional variogram for bare earth topography (needs to be loaded in R, as any other .rsav file). VarAnalysis_Omnidirectional_Izas.R: Script used to analyze omnidirectional variograms and plot results. VarAnalysis_Directional_Izas.R: Script used to analyze directional variograms and plot results. Variogram_Omnidirectional_Izas.R: Script used to generate directional variograms and save data. Variogram_Directional_Izas.R: Script used to generate directional variograms and save data.

10.5281/zenodo.2552749

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Effects of different regional climate model resolution and forcing scales on projected hydrologic changes

Journal of Hydrology (2016)

Pablo A. Mendoza Naoki Mizukami Kyoko Ikeda Martyn Clark E. D. Gutmann

Forcing (mathematics)

Water balance

Hydrological modelling

10.1016/j.jhydrol.2016.08.010

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Citations (49)

Representing Subgrid Precipitation Variability in Snowpack and Hydrological Modeling: Is Adding Complexity Worth it?

Francisco Givovich Pablo A. Mendoza Nicolás Vasquéz Octavio Murillo Eduardo Muñoz-Castro

Snowpack

Hydrological modelling

10.2139/ssrn.5100353

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An intercomparison of approaches for improving operational seasonal streamflow forecasts

Hydrology and earth system sciences (2017)

Pablo A. Mendoza Andrew W. Wood E. Clark Eric Rothwell Martyn Clark

Abstract. For much of the last century, forecasting centers around the world have offered seasonal streamflow predictions to support water management. Recent work suggests that the two major avenues to advance seasonal predictability are improvements in the estimation of initial hydrologic conditions (IHCs) and the incorporation of climate information. This study investigates the marginal benefits of a variety of methods using IHCs and/or climate information, focusing on seasonal water supply forecasts (WSFs) in five case study watersheds located in the US Pacific Northwest region. We specify two benchmark methods that mimic standard operational approaches – statistical regression against IHCs and model-based ensemble streamflow prediction (ESP) – and then systematically intercompare WSFs across a range of lead times. Additional methods include (i) statistical techniques using climate information either from standard indices or from climate reanalysis variables and (ii) several hybrid/hierarchical approaches harnessing both land surface and climate predictability. In basins where atmospheric teleconnection signals are strong, and when watershed predictability is low, climate information alone provides considerable improvements. For those basins showing weak teleconnections, custom predictors from reanalysis fields were more effective in forecast skill than standard climate indices. ESP predictions tended to have high correlation skill but greater bias compared to other methods, and climate predictors failed to substantially improve these deficiencies within a trace weighting framework. Lower complexity techniques were competitive with more complex methods, and the hierarchical expert regression approach introduced here (hierarchical ensemble streamflow prediction – HESP) provided a robust alternative for skillful and reliable water supply forecasts at all initialization times. Three key findings from this effort are (1) objective approaches supporting methodologically consistent hindcasts open the door to a broad range of beneficial forecasting strategies; (2) the use of climate predictors can add to the seasonal forecast skill available from IHCs; and (3) sample size limitations must be handled rigorously to avoid over-trained forecast solutions. Overall, the results suggest that despite a rich, long heritage of operational use, there remain a number of compelling opportunities to improve the skill and value of seasonal streamflow predictions.

Predictability

Teleconnection

Initialization

Flood forecasting

10.5194/hess-21-3915-2017

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Citations (92)

The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

Hydrology and earth system sciences (2018)

Camila Álvarez-Garretón Pablo A. Mendoza Juan Pablo Boisier Nans Addor Mauricio Galleguillos

Abstract. We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation (0 to 6993 m a.s.l.) ranges, and it relies on multiple data sources (including ground data, remote-sensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basin-averaged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydro-climatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r>0.91) and lower correlation (r<0.76) in hyper-arid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. CAMELS-CL can be visualised from http://camels.cr2.cl and downloaded from https://doi.pangaea.de/10.1594/PANGAEA.894885.

Sample (material)

10.5194/hess-22-5817-2018

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Citations (404)