Browsing by Autor "Jorge Molina‐Carpio"

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A cold wave of winter 2021 in central South America: characteristics and impacts
(Springer Science+Business Media, 2023) José A. Marengo; Jhan Carlo Espinoza; L. Bettolli; Ana Paula Martins do Amaral Cunha; Jorge Molina‐Carpio; María de los Milagros Skansi; Kris Correa; Andrea M. Ramos; Roberto Salinas; Juan Pablo Sierra
A principal component analysis approach to assess CHIRPS precipitation dataset for the study of climate variability of the La Plata Basin, Southern South America
(Springer Science+Business Media, 2020) Wilmar L. Cerón; Jorge Molina‐Carpio; Irma Ayes Rivera; Rita V. Andreoli; Mary Toshie Kayano; Teresita Canchala
Absolute and relative height-pixel accuracy of SRTM-GL1 over the South American Andean Plateau
(Elsevier BV, 2016) Frédéric Satgé; Matheus Denezine; Ramiro Pillco Zolá; F. Timouk; Sébastien Pinel; Jorge Molina‐Carpio; Jérémie Garnier; F. Seyler; Marie‐Paule Bonnet
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.
Assessment of satellite rainfall products over the Andean plateau
(Elsevier BV, 2015) Frédéric Satgé; Marie‐Paule Bonnet; Marielle Gosset; Jorge Molina‐Carpio; Wilson Hernan Yuque Lima; Ramiro Pillco Zolá; F. Timouk; Jérémie Garnier
Clima y variabilidad espacial de la ceja de monte y andino húmedo
(2019) Jorge Molina‐Carpio; Daniel Espinoza; Enrique Coritza; Franklin Salcedo; Cristian Farfán; Leonardo Mamani; Javier Mendoza
Climate and spatial variability of the humid upper Andes
(2019) Jorge Molina‐Carpio; Daniel Espinoza; Enrique Coritza; Franklin Salcedo; Cristian Farfán; Leonardo Mamani; Javier Mendoza
Comparative Analysis of Climate Change Impacts on Meteorological, Hydrological, and Agricultural Droughts in the Lake Titicaca Basin
(Multidisciplinary Digital Publishing Institute, 2021) Ricardo Zubieta; Jorge Molina‐Carpio; Wilber Laqui; Juan Sulca; Mercy Ilbay-Yupa
The impact of climate change on droughts in the Lake Titicaca, Desaguadero River, and Lake Poopo basins (TDPS system) within the Altiplano region was evaluated by comparing projected 2034–2064 and observed 1984–2014 hydroclimate time series. The study used bias-corrected monthly climate projections from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), under the Representative Concentration Pathway 8.5 (RCP8.5) emission scenarios. Meteorological, agricultural, and hydrological droughts were analyzed from the standardized precipitation, standardized soil moisture, and standardized runoff indices, respectively, the latter two estimated from a hydrological model. Under scenarios of mean temperature increases up to 3 °C and spatially diverse precipitation changes, our results indicate that meteorological, agricultural, and hydrological droughts will become more intense, frequent, and prolonged in most of the TDPS. A significant increase in the frequency of short-term agricultural and hydrological droughts (duration of 1–2 months) is also projected. The expected decline in annual rainfall and the larger evapotranspiration increase in the southern TDPS combine to yield larger projected rises in the frequency and intensity of agricultural and hydrological droughts in this region.
Complementarity of Sentinel-1 and Sentinel-2 Data for Soil Salinity Monitoring to Support Sustainable Agriculture Practices in the Central Bolivian Altiplano
(Multidisciplinary Digital Publishing Institute, 2024) J. W. Sirpa-Poma; Frédéric Satgé; Ramiro Pillco Zolá; Eléonore Resongles; María Eufemia Pérez-Flores; M. G. Flores Colque; Jorge Molina‐Carpio; Oswaldo Eduardo Ramos Ramos; Marie‐Paule Bonnet
Soil salinization will affect 50% of global cropland areas by 2050 and represents a major threat to agricultural production and food sovereignty. As soil salinity monitoring is costly and time consuming, many regions of the world undertake very limited soil salinity observation (in space and time), preventing the accurate assessment of soil salinity hazards. In this context, this study assesses the relative performance of Sentinel-1 radar and Sentinel-2 optical images, and the combination of the two, for monitoring changes in soil salinity at high spatial and temporal resolution, which is essential to evaluate the mitigation measures required for the sustainable adaptation of agriculture practices. For this purpose, an improved learning database made of 863 soil electrical conductivity (i.e., soil salinity) observations is considered for the training/validation step of a Random Forest (RF) model. The RF model is successively trained with (1) only Sentinel-1, (2) only Sentinel-2 and (3) both Sentinel-1 and -2 features using the Genetic Algorithm (GA) to reduce multi-collinearity in the independent variables. Using k-fold cross validation (3-fold), overall accuracy (OA) values of 0.83, 0.88 and 0.95 are obtained when considering only Sentinel-2, only Sentinel-1 and both Sentinel-1 and -2 features as independent variables. Therefore, these results highlight the clear complementarity of radar (i.e., Sentinel-1) and optical (i.e., Sentinel-2) images to improve soil salinity mapping, with OA increases of approximately 10% and 7% when compared to Sentinel-2 and Sentinel-1 alone. Finally, pre-sowing soil salinity maps over a five-year period (2019–2023) are presented to highlight the benefit of the proposed procedure to support the sustainable management of agricultural lands in the context of soil salinization on a regional scale.
Consistency of satellite precipitation estimates in space and over timecompared with gauge observations and snow-hydrological modellingin the Lake Titicaca region
(2018) Frédéric Satgé; Denis Ruelland; Marie‐Paule Bonnet; Jorge Molina‐Carpio; Ramiro Pillco Zolá
Abstract. This paper proposes a protocol to assess the space-time consistency of satellite precipitation estimates (SPEs) according to various indicators including: (i) direct comparison of SPEs with 72 precipitation gauges; (ii) sensitivity of streamflow modelling to SPEs at the outlet of four basins; and (iii) the sensitivity of distributed snow models to SPEs using a MODIS snow product as reference in an unmonitored mountainous area. The protocol was applied successively to four different time windows (2000–2004, 2004–2008, 2008–2012 and 2000–2012) to account for the space-time variability of the SPEs and to a large dataset composed of 12 SPEs (CMORPH-RAW, CMORPH-CRT, CMORPH-BLD, CHIRP, CHIRPS, GSMaP, MSWEP, PERSIANN, PERSIANN-CDR, TMPA-RT, TMPA-Adj and SM2Rain), an unprecedented comparison. The aim of using different space-time scales and indicators was to evaluate whether the efficiency of SPEs varies with the method of assessment, time window and location. Results revealed very high discrepancies between SPEs compared to precipitation gauge observations. Some SPEs (CMORPH‒RAW, CMORPH‒CRT, GSMaP, PERSIANN, TMPA‒RT and SM2Rain) are unable to estimate regional precipitation whereas the others (CHIRP, CHIRPS, CMORPH‒BLD, MSWEP, PERSIANN‒CDR and TMPA‒Adj) produce a realistic representation despite recurrent spatial limitation over regions with contrasted emissivity, temperature and orography. In nine out of ten of the cases studied, streamflow was more realistically simulated by the hydrological model tested when SPEs were used as forcing precipitation data rather than precipitation derived from the available precipitation gauge networks. Interestingly, the potential of SPEs to reproduce the observed streamflow varied significantly depending on the basin and period considered and did not systematically corroborate SPE potential compared with gauge precipitation observations. SPE’s ability to reproduce the duration of MODIS-based snow cover also showed variable consistency over time with poorer simulations in comparison to those simulated from available precipitation gauges. Using snow cover simulations as indicator led to a different efficiency ranking of the SPEs that the ones obtained when using observed gauge precipitation and streamflow. SPEs thus present space-time errors that may not be detected when short time windows and/or scarce gauge networks and/or single indicators are used, underlining how important it is to carefully consider their space-time consistency before using them for hydro-climatic studies. Moreover SPE efficiency ranked differently depending on the assessment indicators used, suggesting that SPE efficiency should be assessed using indicators related to their final use. Among all the SPEs assessed, MSWEP showed the highest space-time accuracy and consistency in reproducing gauge precipitation estimates, streamflow and snow cover duration. After some adjustment over Lake Titicaca, MSWEP should thus be preferred for the regional hydro-meteorological survey.
Consistency of satellite-based precipitation products in space and over time compared with gauge observations and snow- hydrological modelling in the Lake Titicaca region
(Copernicus Publications, 2019) Frédéric Satgé; Denis Ruelland; Marie‐Paule Bonnet; Jorge Molina‐Carpio; Ramiro Pillco Zolá
Abstract. This paper proposes a protocol to assess the space–time consistency of 12 satellite-based precipitation products (SPPs) according to various indicators, including (i) direct comparison of SPPs with 72 precipitation gauges; (ii) sensitivity of streamflow modelling to SPPs at the outlet of four basins; and (iii) the sensitivity of distributed snow models to SPPs using a MODIS snow product as reference in an unmonitored mountainous area. The protocol was applied successively to four different time windows (2000–2004, 2004–2008, 2008–2012 and 2000–2012) to account for the space–time variability of the SPPs and to a large dataset composed of 12 SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, CMORPH–BLD v.1, CHIRP v.2, CHIRPS v.2, GSMaP v.6, MSWEP v.2.1, PERSIANN, PERSIANN–CDR, TMPA–RT v.7, TMPA–Adj v.7 and SM2Rain–CCI v.2), an unprecedented comparison. The aim of using different space scales and timescales and indicators was to evaluate whether the efficiency of SPPs varies with the method of assessment, time window and location. Results revealed very high discrepancies between SPPs. Compared to precipitation gauge observations, some SPPs (CMORPH–RAW v.1, CMORPH–CRT v.1, GSMaP v.6, PERSIANN, and TMPA–RT v.7) are unable to estimate regional precipitation, whereas the others (CHIRP v.2, CHIRPS v.2, CMORPH–BLD v.1, MSWEP v.2.1, PERSIANN–CDR, and TMPA–Adj v.7) produce a realistic representation despite recurrent spatial limitation over regions with contrasted emissivity, temperature and orography. In 9 out of 10 of the cases studied, streamflow was more realistically simulated when SPPs were used as forcing precipitation data rather than precipitation derived from the available precipitation gauge networks, whereas the SPP's ability to reproduce the duration of MODIS-based snow cover resulted in poorer simulations than simulation using available precipitation gauges. Interestingly, the potential of the SPPs varied significantly when they were used to reproduce gauge precipitation estimates, streamflow observations or snow cover duration and depending on the time window considered. SPPs thus produce space–time errors that cannot be assessed when a single indicator and/or time window is used, underlining the importance of carefully considering their space–time consistency before using them for hydro-climatic studies. Among all the SPPs assessed, MSWEP v.2.1 showed the highest space–time accuracy and consistency in reproducing gauge precipitation estimates, streamflow and snow cover duration.
Decline of Fine Suspended Sediments in the Madeira River Basin (2003–2017)
(Multidisciplinary Digital Publishing Institute, 2019) Irma Ayes Rivera; Elisa Armijos Cardenas; Raúl Espinoza-Villar; Jhan Carlo Espinoza; Jorge Molina‐Carpio; José Max Ayala; Omar Gutierrez‐Cori; Jean-Michel Martínez; Naziano Filizola
The Madeira River is the second largest Amazon tributary, contributing up to 50% of the Amazon River’s sediment load. The Madeira has significant hydropower potential, which has started to be used by the Madeira Hydroelectric Complex (MHC), with two large dams along the middle stretch of the river. In this study, fine suspended sediment concentration (FSC) data were assessed downstream of the MHC at the Porto Velho gauging station and at the outlet of each tributary (Beni and Mamoré Rivers, upstream from the MHC), from 2003 to 2017. When comparing the pre-MHC (2003–2008) and post-MHC (2015–2017) periods, a 36% decrease in FSC was observed in the Beni River during the peak months of sediment load (December–March). At Porto Velho, a reduction of 30% was found, which responds to the Upper Madeira Basin and hydroelectric regulation. Concerning water discharge, no significant change occurred, indicating that a lower peak FSC cannot be explained by changes in the peak discharge months. However, lower FSCs are associated with a downward break in the overall time series registered at the outlet of the major sediment supplier—the Beni River—during 2010.
Deforestation Impacts on Amazon-Andes Hydroclimatic Connectivity
(2021) Juan Pablo Sierra; Clémentine Junquas; Jhan Carlo Espinoza; Hans Segura; Thomas Condom; Marcos Andrade; Jorge Molina‐Carpio; Laura Ticona; Valeria Mardóñez; Luis Blacutt
<title>Abstract</title> Amazonian deforestation has accelerated during the last decade, threatening an ecosystem where almost one third of the regional rainfall is transpired by the local rainforest. Due to the precipitation recycling, the southwestern Amazon, including the Amazon-Andes transition region, is particularly sensitive to forest loss. This study evaluates the impacts of Amazonian deforestation in the hydro-climatic connectivity between the Amazon and the eastern tropical Andes during the austral summer (December-January-February) in terms of hydrological and energetic balances. Using 10-year high-resolution simulations (2001–2011) with the Weather Research and Forecasting Model, we analyze control and deforestation scenario simulations. Regionally, deforestation leads to a reduction in the surface net radiation, evaporation, moisture convergence and precipitation (~ 20%) over the entire Amazon basin. In addition, during this season, deforestation increases the atmospheric subsidence over the southern Amazon and weakens the regional Hadley cell. Atmospheric stability increases over the western Amazon and the tropical Andes inhibiting convection in these areas. Consequently, major deforestation impacts are observed over the hydro-climate of the Amazon-Andes transition region. At local scale, nighttime precipitation decreases in Bolivian valleys (~ 20–30%) due to a strong reduction in the humidity transport from the Amazon plains toward Andes linked to the South American low-level jet. Over these valleys, a weakening of the daytime upslope winds is caused by local deforestation, which reduces the turbulent fluxes at lowlands. These alterations in rainfall and atmospheric circulation could impact the rich Andean ecosystems and its tropical glaciers.
Deforestation impacts on Amazon-Andes hydroclimatic connectivity
(2021) Juan Pablo Sierra; Jhan Carlo Espinoza; Clémentine Junquas; Jan Polcher‬; Miguel Saavedra; Jorge Molina‐Carpio; Marcos Andrade; Thomas Condom; Laura Ticona
&lt;p&gt;The Amazon rainforest is a key component of the climate system and one of the main planetary evapotranspiration sources. Over the entire Amazon basin, strong land-atmosphere feedbacks cause almost one third of the regional rainfall to be transpired by the local rainforest. Maximum precipitation recycling ratio takes place on the southwestern edge of the Amazon basin (a.k.a. Amazon-Andes transition region), an area recognized as the rainiest and biologically richest of the whole watershed. Here, high precipitation rates lead to large values of runoff per unit area providing most of the sediment load to Amazon rivers. As a consequence, the transition region can potentially be very sensitive to Amazonian forest loss. In fact, recent acceleration in deforestation rates has been reported over tropical South America. These sustained land-cover changes can alter the regional water and energy balances, as well as the regional circulation and rainfall patterns. In this sense, the use of regional climate models can help to understand the possible impacts of deforestation on the Amazon-Andes zone.&lt;/p&gt;&lt;p&gt;This work aims to assess the projected Amazonian deforestation effects on the moisture transport and rainfall behavior over tropical South America and the Amazon-Andes transition region. We perform 10-year austral summer simulations with the Weather Research and Forecasting model (WRF) using 3 one-way nested domains. Our finest domain is located over the south-western part of the basin, comprising two instrumented Andean Valleys (Zongo and Coroico river Valleys). Convective permitting high horizontal resolution (1km) is used over this domain. The outcomes presented here enhance the understanding of biosphere-atmosphere coupling and its deforestation induced disturbances.&lt;/p&gt;
Deforestation impacts on Amazon-Andes hydroclimatic connectivity
(Springer Science+Business Media, 2021) Juan Pablo Sierra; Clémentine Junquas; Jhan Carlo Espinoza; Hans Segura; Thomas Condom; Marcos Andrade; Jorge Molina‐Carpio; Laura Ticona; Valeria Mardóñez; Luis Blacutt
Development of Hourly Resolution Air Temperature Across Titicaca Lake on Auxiliary ERA5 Variables and Machine Learning-Based Gap-Filling
(Multidisciplinary Digital Publishing Institute, 2025) J. W. Sirpa-Poma; Juan Marcos Calle; Elvis Uscamayta-Ferrano; Jorge Molina‐Carpio; Frédéric Satgé; Osmar Cuentas Toledo; Ricardo Duran; Paula Pacheco Mollinedo; Rizuana Iqbal Hussain; Ramiro Pillco Zolá
This article presents an innovative procedure that combines advanced quality control (QC) methods with machine learning (ML) techniques to produce reliable, continuous, high-resolution meteorological data. The approach was applied to hourly air temperature records from six automatic weather stations located around Lake Titicaca in the Altiplano region of South America. The raw dataset contained time gaps, inconsistencies, and outliers. To address these, the QC stage employed Interquartile Range, Biweight, and Local Outlier Factor (LOF) statistics, resulting in a clean dataset. Two gap-filling methods were implemented: a spatial approach using time series from nearby stations and a temporal approach based on each station's time series and selected variables from the ERA5-Land reanalysis. Several ML models were also employed in this process: Random Forest (RF), Support Vector Machine (SVM), Stacking (STACK), and AdaBoost (ADA). Model performance was evaluated on a validation subset (30% of station data). The RF model achieved the best results, with R<sup>2</sup> values up to 0.9 and Root Mean Square Error (RMSE) below 1.5 °C. The spatial approach performed best when stations were strongly correlated, while the temporal approach was more suitable for locations with low inter-station correlation and high local variability. Overall, the procedure substantially improved data reliability and completeness, and it can be extended to other meteorological variables.
Downscaling Daily Satellite-Based Precipitation Estimates Using MODIS Cloud Optical and Microphysical Properties in Machine-Learning Models
(Multidisciplinary Digital Publishing Institute, 2023) Sergio Callaú Medrano; Frédéric Satgé; Jorge Molina‐Carpio; Ramiro Pillco Zolá; Marie‐Paule Bonnet
This study proposes a method for downscaling the spatial resolution of daily satellite-based precipitation estimates (SPEs) from 10 km to 1 km. The method deliberates a set of variables that have close relationships with daily precipitation events in a Random Forest (RF) regression model. The considered variables include cloud optical thickness (COT), cloud effective radius (CER) an cloud water path (CWP), derived from MODIS, along with maximum and minimum temperature (Tx, Tn), derived from CHIRTS. Additionally, topographic features derived from ALOS-DEM are also investigated to improve the downscaling procedure. The approach consists of two main steps: firstly, the RF model training at the native 10 km spatial resolution of the studied SPEs (i.e., IMERG) using rain gauge observations as targets; secondly, the application of the trained RF model at a 1 km spatial resolution to downscale IMERG from 10 km to 1 km over a one-year period. To assess the reliability of the method, the RF model outcomes were compared with the rain gauge records not considered in the RF model training. Before the downscaling process, the CC, MAE and RMSE metrics were 0.32, 1.16 mm and 6.60 mm, respectively, and improved to 0.48, 0.99 mm and 4.68 mm after the downscaling process. This corresponds to improvements of 50%, 15% and 29%, respectively. Therefore, the method not only improves the spatial resolution of IMERG, but also its accuracy.
Evaluating the Sensitivity of Hydrological Models to Remotely Sensed Precipitation in a Transboundary Basin
(2025) Paula Pacheco Mollinedo; Frédéric Satgé; Renaud Hostache; Marie‐Paule Bonnet; Jorge Molina‐Carpio; Ramiro Pillco Zolá; Edson Ramírez; Daniel Espinoza
Accurate precipitation data is vital for hydrological modelling, particularly in transboundary basins with scarce hydro-climatic stations. This study evaluates the performance of 20 gridded precipitation products (GPPs), derived from remotely sensed data and reanalyses, in the transboundary Lake Titicaca basin. The methodology integrates two approaches: first, a spatial and temporal accuracy assessment of the GPPs, and second, their application as input data in hydrological models.For spatial accuracy, annual precipitation maps were generated for each GPP, preserving their native resolution, and compared with gauge-based maps. Temporal accuracy was assessed using Taylor diagrams. To evaluate the impact of GPPs on hydrological modelling, streamflow simulations were performed using the GR4J (lumped) and MGB-IPH (semi-distributed) models for three sub-basins, with model performance assessed through Kling-Gupta Efficiency (KGE).Results indicate that CHIRPS, IMERG, and MSWEP excel in spatial and temporal accuracy, capturing the north-to-south precipitation gradient shaped by Andean topography. Streamflow simulations showed that GPPs often outperform gauge-based precipitation in basins with uneven station distribution. In GR4J, MSWEP and CHIRPS yielded the highest KGE values across all sub-basins, while in MGB-IPH, SM2Rain_CCI and IMERG-FR performed best. Notably, the higher KGE scores observed for the GR4J model can be attributed to its lumped structure, which compensates for GPP over/under estimations and spatial distribution inconsistencies.This comprehensive evaluation demonstrates the potential of remotely sensed precipitation products to address data scarcity in transboundary basins. By improving streamflow simulations, these products support informed water resource management, climate adaptation, and transboundary collaboration.
Extreme droughts in the Amazon Basin during cyclic ENSO events coupled with Indian Ocean Dipole modes and Tropical North Atlantic warming
(Elsevier BV, 2025) Leonardo Mamani; Rita V. Andreoli; Itamara Parente de Souza; Wallace Cevalho; Djanir Sales de Moraes; Mary Toshie Kayano; Rodrigo Augusto Ferreira De Souza; Jorge Molina‐Carpio; Wilmar L. Cerón; Tabata L. B. de Macêdo
Hydroclimate of the Andes Part I: Main Climatic Features
(Frontiers Media, 2020) Jhan Carlo Espinoza; René Garreaud; Germán Poveda; Paola A. Arias; Jorge Molina‐Carpio; Mariano Masiokas; Maximiliano Viale; Lucía Scaff
The Andes is the longest cordillera in the world and extends from northern South America (11°N) to the southern tip of the continent (53°S). The Andes runs through seven countries and is characterized by a wide variety of ecosystems strongly related to the contrasting climate over its eastern and western sides and along its latitudinal extension. In fact, the tropical Andes is the most biodiverse region on Earth. Currently, this region faces the highest potential impact of climate change, which could affect food security and water supplies for about 90 million people. From a scientific and societal view, the Andes present specific challenges because of its unique landscape and the fragile equilibrium between the growing population and its environment. In this manuscript, we provide an updated review of the most relevant scientific literature regarding the hydroclimate of the Andes. This review paper is presented in two parts. Part I is dedicated to summarize the scientific knowledge about the main climatic features of the Andes, with emphasis on mean large-scale atmospheric circulation, the Andes-Amazon hydroclimate interconnections, and the regular cycles of precipitation, including the most characteristic diurnal and annual cycles of precipitation. Part II, which is also included in the research topic “Connecting Mountain Hydroclimate Through the American Cordilleras”, focuses on hydroclimate variability of the Andes at a sub-continental scale.