Browsing by Autor "Waldo Lavado‐Casimiro"

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Accuracy assessment of SRTM v4 and ASTER GDEM v2 over the Altiplano watershed using ICESat/GLAS data
(Taylor & Francis, 2015) Frédéric Satgé; Marie‐Paule Bonnet; F. Timouk; Stéphane Calmant; Ramiro Pillco Zolá; Jorge Molina‐Carpio; Waldo Lavado‐Casimiro; A. Arsen; Jean‐François Crétaux; Jérémie Garnier
The new Global Digital Elevation Model (GDEM v2) has been available since 17 October 2011. With a resolution of approximately 30 m, this model should provide more accurate information than the latest version of Shuttle Radar Topographic Mission (SRTM v4) with a resolution of 90 m outside of the USA. The accuracies of these two recently released digital elevation models (DEMs) were assessed over the Altiplano watershed in South America using ICESat/GLAS data (Ice, Cloud and Land Elevation Satellite/Geoscience Laser Altimeter System). On the global scale, GDEM v2 is more accurate than SRTM v4, which presents a negative bias of approximately 8.8 m. Strong correlations between the DEMs’ accuracies and mean slope values occurred. Regarding land cover, SRTM v4 could be more accurate or easier to correct on a smaller scale than GDEM v2. Finally, a merged and corrected DEM that considers all of these observations was built to provide more accurate information for this region. The new model featured lower absolute mean errors, standard deviations, and root mean square errors relative to SRTM v4 or GDEM v2.
Effects of undetected data quality issues on climatological analyses
(2017) Stefan Hunziker; Stefan Brönnimann; Juan Marcos Calle; Isabel Moreno; Marcos Andrade; Laura Ticona; Adrian Huerta; Waldo Lavado‐Casimiro
Abstract. Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are minimum and maximum temperature, and precipitation. About 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control method strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets, and hence may affect a high number of climatological studies. It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties.
Effects of undetected data quality issues on climatological analyses
(Copernicus Publications, 2018) Stefan Hunziker; Stefan Brönnimann; Juan Marcos Calle; Isabel Moreno; Marcos Andrade; Laura Ticona; Adrian Huerta; Waldo Lavado‐Casimiro
Abstract. Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are minimum and maximum temperature and precipitation. About 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control approach strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets and hence may affect a high number of climatological studies. It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties.
Long-term hydro-sediment dynamics of the Ucayali River (Amazon Basin) revealed through combined observations, remote sensing, and SWAT-Amazon modelling
(2025) William Santini; Alexandre Delort-Ylla; Jean-Michel Martínez; Waldo Lavado‐Casimiro; B. Camenen; Jérôme Le Coz; Joana Roussillon; Jorge Arévalo; Jorge Molina‐Carpio
Abstract. Since the early 1970s, the Amazon basin has experienced growing local and global changes, potentially reaching a climatic tipping point in the coming decades. However, due to cost constraints and limited access, conventional hydrological networks in the basin struggle to provide the spatial resolution and temporal extent required for accurate quantification of water and sediment budgets, which are essential for understanding biogeochemical cycles. Focusing on the Ucayali River, a major Amazonian foreland tributary, this study provides the first long-term hydro-sediment balances in this region at sub-basin scale, distinguishing fine sediments from sand loads (37 years for water and sands, 20 years for fine sediments). It is achieved by the integration of remote sensing and hydrological-hydraulic modelling using a modified SWAT model, SWAT-Amazon. A new hydraulic module for water routing was implemented in SWAT-Amazon to suit the Amazon diffusive flood wave, representing floodplains as reservoirs. Fine sediment loads were estimated using satellite-derived concentrations and simulated discharges, while suspended sand loads were simulated within SWAT-Amazon. Results indicate that the Andean Ucayali River exports 455 10⁶ t yr⁻¹ of suspended sediment (40 % sand). As the floodplain traps 36 % of the Andean sediments (65 % sand), mostly by tectonic subsidence, the Ucayali delivers 290 10⁶ t yr⁻¹ of total suspended sediment to the Amazon River, 26 % as sand. Floodplain recycling plays a crucial role as a secondary sediment source (22 % of the Ucayali load), with a water storage that peaks at 19.1 km³ in March (38 % of discharge). A previously undocumented sand sedimentation process is identified during the flooding period, capturing 14 % of the sand flux at peak discharge and thus decorrelating sediment transport from water discharge. No significant long-term trends in flood duration, discharge, or sediment fluxes were detected, suggesting contrasted evolution patterns of the precipitations in the basin due to its particular position in the Amazon Basin. This study emphasizes the need to rethink hydrological network management with robust and long-term conventional data at ‘super’ stations to support the calibration of remote sensing and modelling at ‘virtual’ stations. Extending this approach to other Amazonian basins could significantly enhance hydro-sediment and biogeochemical cycle research in large river systems. Additionally, it highlights the importance of regionally focused over large-scale assessments, which often carry high uncertainties and may mislead mitigation strategies.
The 2022-23 drought in the South American Altiplano: ENSO effects on moisture flux in the western Amazon during the pre-wet season
(Elsevier BV, 2024) Ricardo A. Gutiérrez; Jhan Carlo Espinoza; Waldo Lavado‐Casimiro; Clémentine Junquas; Jorge Molina‐Carpio; Thomas Condom; José A. Marengo