Browsing by Autor "Ramiro Pillco Zolá"

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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
CHIRTS Gridded Air Temperature Downscaling Integrating MODIS Land Surface Temperature Estimates in Machine-Learning Models
(Multidisciplinary Digital Publishing Institute, 2025) Elvis Uscamayta-Ferrano; Frédéric Satgé; Ramiro Pillco Zolá; Henrique Llacer Roig; Diego Tola-Aguilar; María Eufemia Pérez-Flores; Lautaro Bustillos; Fara Pascale Rakotomandrindra; Zo Rabefitia; Simon Carrière
Due to its sensitivity to topographic and land use land cover features, air temperature (maximum, minimum, and mean—Tx, Tn, and Tmean) is extremely variable in space and time. The sparse and unevenly distributed meteorological stations observed across remote regions cannot monitor such variability. Freely available, gridded temperature datasets (T-datasets) are positioned as an opportunity to overcome this issue. Still, their coarse spatial resolution (i.e., ≥5 km) does not allow for the observation of air temperature variations on a fine spatial scale. In this context, a set of variables that have a close relationship with daily air temperature (MODIS maximum, minimum, and mean Land Surface Temperature—LSTx, LSTn, and LSTmean; MODIS NDVI; SRTM topographic features—elevation, slope, and aspect) are integrated in three regression machine-learning models (Random Forest—RF, eXtreme Gradient Boosting—XGB, Multiple Linear Regression—MLR) to propose a T-dataset estimates (Tx, Tn, and Tmean) spatial resolution downscaling framework. The approach consists of two main steps: firstly, the machine-learning models are trained at the native 5 km spatial resolution of the studied T-dataset (i.e., CHIRTS); secondly, the application of the trained machine-learning models at a 1 km spatial resolution to downscale CHIRTS from 5 km to 1 km. The results show that the method not only improves the spatial resolution of the CHIRTS dataset, but also its accuracy, with higher improvements for Tn than for Tx and Tmean. Among the considered models, RF performs the best, with an R2, RMSE, and MAE improvement of 2.6% (0%), 47.1% (6.1%), and 55.3% (7%) for Tn (Tx). These results will support air temperature monitoring and related extreme events such as heat and cold waves, which are of prime importance in the actual climate change context.
Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds
(Multidisciplinary Digital Publishing Institute, 2017) Frédéric Satgé; Alvaro Xavier; Ramiro Pillco Zolá; Yawar Hussain; F. Timouk; Jérémie Garnier; Marie‐Paule Bonnet
The new IMERG and GSMaP-v6 satellite rainfall estimation (SRE) products from the Global Precipitation Monitoring (GPM) mission have been available since January 2015. With a finer grid box of 0.1°, these products should provide more detailed information than their latest widely-adapted (relatively coarser spatial scale, 0.25°) counterpart. Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) assessment is done by comparing their rainfall estimations with 247 rainfall gauges from 2014 to 2016 in Bolivia. The comparisons were done on annual, monthly and daily temporal scales over the three main national watersheds (Amazon, La Plata and TDPS), for both wet and dry seasons to assess the seasonal variability and according to different slope classes to assess the topographic influence on SREs. To observe the potential enhancement in rainfall estimates brought by these two recently released products, the widely-used TRMM Multi-satellite Precipitation Analysis (TMPA) product is also considered in the analysis. The performances of all the products increase during the wet season. Slightly less accurate than TMPA, IMERG can almost achieve its main objective, which is to ensure TMPA rainfall measurements, while enhancing the discretization of rainy and non-rainy days. It also provides the most accurate estimates among all products over the Altiplano arid region. GSMaP-v6 is the least accurate product over the region and tends to underestimate rainfall over the Amazon and La Plata regions. Over the Amazon and La Plata region, SRE potentiality is related to topographic features with the highest bias observed over high slope regions. Over the TDPS watershed, the high rainfall spatial variability with marked wet and arid regions is the main factor influencing SREs.
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.
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.
Estudio de la dinámica de recarga en zona no saturada, empleando la técnica de isótopos ambientales
(1999) Andrés Burgoa Mariaca; Ramiro Pillco Zolá
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.
Flow modelling in a high mountain valley equipped with hydropower plants: Rio Zongo Valley, Cordillera Real, Bolivia
(Wiley, 2004) Yvan Caballero; Pierre Chevallier; Robert Gallaire; Ramiro Pillco Zolá
Abstract Water management modelling of a hydroelectric system in a tropical high mountain context is presented. The study zone and the hydraulic network are described and the water management strategy analysed. Three different models are combined to describe the complexity of the specific hydrometeorological context: the spatial distribution of the climatic data over the river basin, the surface energy balance influence on the runoff production of a river basin and the surface flow transfer modelling through a hydraulic system. The atmospheric forcing spatial distribution is derived from the available climatic data records. The runoff production on the catchment's slopes is simulated using the land‐surface scheme ISBA. The system dynamics tool Vensim ® is used to simulate the hydraulic dynamics in the hydropower plants system. A short description of the three modelling methods is given, followed by the description of the coupled model construction. The simulation results of the ISBA land‐surface scheme on both a non‐glacial an a glacial sub‐basin during a 17 month period are presented. After pointing out the necessity of the water management model to simulate the river discharge at the outlet of the basin, the main reservoirs, simulated water level variations are shown. Copyright © 2004 John Wiley & Sons, Ltd.
High Spatial Resolution Soil Moisture Mapping over Agricultural Field Integrating SMAP, IMERG, and Sentinel-1 Data in Machine Learning Models
(Multidisciplinary Digital Publishing Institute, 2025) Diego Tola; Lautaro Bustillos; F. Arragan; René Chipana Rivera; Renaud Hostache; Eléonore Resongles; Raúl Espinoza-Villar; Ramiro Pillco Zolá; Elvis Uscamayta; María Eufemia Pérez-Flores
Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R2 increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R2 = 0.86, RMSE = 2.55%).
Mapping Evapotranspiration, Vegetation and Precipitation Trends in the Catchment of the Shrinking Lake Poopó
(Multidisciplinary Digital Publishing Institute, 2019) Juan Torres-Batlló; Belén Martí-Cardona; Ramiro Pillco Zolá
Lake Poopó is located in the Andean Mountain Range Plateau or Altiplano. A general decline in the lake water level has been observed in the last two decades, coinciding roughly with an intensification of agriculture exploitation, such as quinoa crops. Several factors have been linked with the shrinkage of the lake, including climate change, increased irrigation, mining extraction and population growth. Being an endorheic catchment, evapotranspiration (ET) losses are expected to be the main water output mechanism and previous studies demonstrated ET increases using Earth observation (EO) data. In this study, we seek to build upon these earlier findings by analyzing an ET time series dataset of higher spatial and temporal resolution, in conjunction with land cover and precipitation data. More specifically, we performed a spatio-temporal analysis, focusing on wet and dry periods, that showed that ET changes occur primarily in the wet period, while the dry period is approximately stationary. An analysis of vegetation trends performed using 500 MODIS vegetation index products (NDVI) also showed an overall increasing trend during the wet period. Analysis of NDVI and ET across land cover types showed that only croplands had experienced an increase in NDVI and ET losses, while natural covers showed either constant or decreasing NDVI trends together with increases in ET. The larger increase in vegetation and ET losses over agricultural regions, strongly suggests that cropping practices exacerbated water losses in these areas. This quantification provides essential information for the sustainable planning of water resources and land uses in the catchment. Finally, we examined the spatio-temporal trends of the precipitation using the newly available Climate Hazards Group Infrared Precipitation with Stations (CHIRPS-v2) product, which we validated with onsite rainfall measurements. When integrated over the entire catchment, precipitation and ET showed an average increasing trend of 5.2 mm yr−1 and 4.3 mm yr−1, respectively. This result suggests that, despite the increased ET losses, the catchment-wide water storage should have been offset by the higher precipitation. However, this result is only applicable to the catchment-wide water balance, and the location of water may have been altered (e.g., by river abstractions or by the creation of impoundments) to the detriment of the Lake Poopó downstream.
Mapping long-term evapotranspiration losses in the catchment of the shrinking Lake Poopó
(2019) Juan Torres-Batlló; Belén Martí-Cardona; Ramiro Pillco Zolá
Abstract. Lake Poopó is located in the Andean Mountain Range Plateau or Altiplano. A generalised decline in the lake water level has been observed since 2001, coinciding roughly with an intensification of agriculture exploitations such as quinoa crops. Several factors have been blamed for the lake recession, including climate change, increased farming, mining abstractions and population growth. Being an endorheic catchment, evapotranspiration (ET) losses are expected to be the main water output mechanism. This study used a time series of more than 1000 satellite data based products to map ET and vegetation index trends in the Poopó catchment between 2001 and 2014. The aim was to explore the links between ET, vegetation, land use and the lake recession. The years 2015 and 2016 were excluded of the analysis due to the strong impact of El Niño phenomenon over the study area, which could have masked long term temporal trends related to land use. We quantified the ET losses and vegetation indices for the main cover types in the Poopó catchment and their temporal trends in the study period. It became obvious that cultivated areas were the ones which had experienced the largest increase in water consumption, although they were not in all instances the land covers with the largest losses. This quantification provides essential information for the sustainable planning of water resources and land uses in the catchment. We also collected on-site and satellite precipitation data. When integrated over the entire catchment, the overall ET losses showed a sustained increasing trend at an average rate of 3.2 mm yr−1. Rainfall water inputs followed a similar trend, with a slightly higher increasing rate of 5.2 mm yr−1. Based on these results and from the point of view of the catchment water balance, the ET loss intensification derived from crop expansion has been compensated by the increase in precipitation. Consequently, this study found no clear link between the agriculture intensification and the Lake Poopó recession in the analysed period.
Modeling Lake Titicaca Daily and Monthly Evaporation
(2018) Ramiro Pillco Zolá; Lars Bengtsson; Ronny Berndtsson; Belén Martí-Cardona; Frédéric Satgé; F. Timouk; Marie‐Paule Bonnet; Luis Mollericon; Cesar Gamarra; José Pasapera
Abstract. Lake Titicaca is an important water ecosystem of South America. Due to uncertainties in estimating the evaporation losses from the lake, surface water storage calculations are uncertain. In this paper, we try to improve evaporation loss estimations by comparing different methods to calculate daily and monthly evaporation from Lake Titicaca. These were: water balance, heat balance, mass transfer method, and the Penman equation. The evaporation was computed at daily time step and compared with estimated evaporation using mean monthly meteorological observations. We found that the most reliable method of determining the annual lake evaporation is using the heat balance approach. To estimate the monthly lake evaporation using heat balance, the heat storage changes must be known in advance. Since convection from the surface layer is intense during nights resulting in a well-mixed top layer every morning, it is possible to determine the change of heat storage from the measured morning surface temperature. The mean annual lake evaporation was found to be 1700 mm. Monthly evaporation computed using daily data and monthly means resulted in minor differences.
Modelling Lake Titicaca's daily and monthly evaporation
(Copernicus Publications, 2019) Ramiro Pillco Zolá; Lars Bengtsson; Ronny Berndtsson; Belén Martí-Cardona; Frédéric Satgé; F. Timouk; Marie‐Paule Bonnet; Luis Mollericon; Cesar Gamarra; José Pasapera
Abstract. Lake Titicaca is a crucial water resource in the central part of the Andean mountain range, and it is one of the lakes most affected by climate warming. Since surface evaporation explains most of the lake's water losses, reliable estimates are paramount to the prediction of global warming impacts on Lake Titicaca and to the region's water resource planning and adaptation to climate change. Evaporation estimates were done in the past at monthly time steps and using the four methods as follows: water balance, heat balance, and the mass transfer and Penman's equations. The obtained annual evaporation values showed significant dispersion. This study used new, daily frequency hydro-meteorological measurements. Evaporation losses were calculated following the mentioned methods using both daily records and their monthly averages to assess the impact of higher temporal resolution data in the evaporation estimates. Changes in the lake heat storage needed for the heat balance method were estimated based on the morning water surface temperature, because convection during nights results in a well-mixed top layer every morning over a constant temperature depth. We found that the most reliable method for determining the annual lake evaporation was the heat balance approach, although the Penman equation allows for an easier implementation based on generally available meteorological parameters. The mean annual lake evaporation was found to be 1700 mm year−1. This value is considered an upper limit of the annual evaporation, since the main study period was abnormally warm. The obtained upper limit lowers by 200 mm year−1, the highest evaporation estimation obtained previously, thus reducing the uncertainty in the actual value. Regarding the evaporation estimates using daily and monthly averages, these resulted in minor differences for all methodologies.
Modern and long-term evaporation of central Andes surface waters suggests paleo archives underestimate Neogene elevations
(Elsevier BV, 2015) Richard P. Fiorella; Christopher J. Poulsen; Ramiro Pillco Zolá; M. Louise Jeffery; Todd A. Ehlers
Prediction of lake water-level fluctuations using adaptive neuro-fuzzy inference system hybridized with metaheuristic optimization algorithms
(Springer Nature, 2022) Quoc Bao Pham; Babak Mohammadi; Roozbeh Moazenzadeh; Salim Heddam; Ramiro Pillco Zolá; S. Adarsh; Vivek Gupta; Ismail Elkhrachy; Khaled Mohamed Khedher; Duong Tran Anh