Browsing by Autor "Fara Pascale Rakotomandrindra"

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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.
Groundwater monitoring and modelling, a crucial challenge in a semi-arid and poorly documented region affected by a high poverty rate (southern Madagascar)
(2025) Romane Berthelin; Fara Pascale Rakotomandrindra; rojin alimohammad nejad; Camille Ollivier; Ony Nantenaina Andriamiandrisoa; Matthieu Texier; Tsitola Benahy Ramananjato; Ludovic Oudin; Frédéric Satgé; Albert Olioso
Groundwater plays a key role in providing access to drinking water, especially in semi-arid regions where surface water is scarce or absent for much of the year. In the semi-arid region of southern Madagascar, approximately 2,000,000 people face one of the highest poverty rates in the world, making them particularly vulnerable to climatic hazards. As a result, describing and predicting groundwater dynamics is essential to understand and anticipate drought-related humanitarian crises. How to estimate groundwater recharge in a such poorly documented area?Our work consisted of comparing two complementary approaches for estimating groundwater recharge. First, the Groundwater Resource Observatory for Southwestern Madagascar was established in 2014 in difficult logistical settings to monitor piezometric level from 16 boreholes located in various hydrogeological systems. This observatory provides long-term piezometric time series at an hourly time step, which were used to calculate recharge following the Water Table Fluctuation (WTF) Method.Second, a spatial hydrology approach was developed to estimate potential recharge using precipitation and evapotranspiration global products based on remote sensing data. The two approaches were compared, revealing the potential and limits of both. Based on these results, we compare our findings with health outcomes, offering new avenues for research.