Estimating water fluxes in the critical zone using water stable isotope approaches in the Groundnut and Ferlo basins of Senegal

Sustainable water management in semi-arid agriculture practices requires quantitative knowledge of water fluxes within the soil-vegetation-atmosphere system. Therefore, we used stable-isotope approaches to evaluate evaporation (E_a), transpiration (T_a), and groundwater recharge (R) at sites in Senegal's Groundnut basin and Ferlo Valley pasture region during the pre-monsoon, monsoon, and post-monsoon seasons of 2021. The approaches were based upon (i) the isothermal evaporation model (for quantifying E_a); (ii) water and isotope mass balances (to partition E_a and T_a for groundnut and pasture); and (iii) the piston displacement method (for estimating R). E_a losses derived from the isothermal evaporation model corresponded primarily to Stage II evaporation, and ranged from 0.02 to 0.09 mm d⁻¹ in the Groundnut basin, versus 0.02–0.11 mm d⁻¹ in Ferlo. At the groundnut site, E_a rates ranged from 0.01 to 0.69 mm d⁻¹; T_a was in the range 0.55–2.29 mm d⁻¹; and the T_a/ET_a ratio was 74%–90%. At the pasture site, the ranges were 0.02–0.39 mm d⁻¹ for E_a; 0.9–1.69 mm d⁻¹ for T_a; and 62–90% for T_a/ET_a. The ET_a value derived for the groundnut site via the isotope approach was similar to those from eddy covariance measurements, and also to the results from the previous validated HYDRUS-1D model. However, the HYDRUS-1D model gave a lower T_a/ET_a ratio (23.2%). The computed groundwater recharge for the groundnut site amounted to less than 2% of the local annual precipitation. Recommendations are made regarding protocols for preventing changes to isotopic compositions of water in samples that are collected in remote arid regions, but must be analysed days later. The article ends with suggestions for studies to follow up on evidence that local aquifers are being recharged via preferential pathways.     

Improving the descendant relationship of cluster galaxies at z < 1

In this work, we study how to improve well-known techniques for detecting progenitors/descendants of galaxies, such as the NDpredict program, when applied to galaxies in clusters. The improvement of this particular method is based on the use of the red sequence of galaxies in those environments. Objects close to the red sequence in the color and magnitude diagram are more likely to belong to the cluster. This defines a probability scale which is then combined with the one generated by NDpredict. This procedure is optimized for the study of galaxies in clusters over different epochs. Our main result is that, for a sample composed of $120$ clusters, with masses greater than ${10}^{13.25}{M}_{\odot }$, selected from the IllustrisTNG simulations (namely, the TNG100 runs). In $99\%$ of the cases (i.e., $119$ systems), we obtain better performance with the red sequence method in comparison to the original NDpredict, and the average gain obtained is $28\%$ in the identification of descendants for this sample of cluster galaxies.