Low radiation is one of the most important factors which limits potential yield in potato. Under humid conditions, the dominance of diffuse radiation not only imposes challenges for radiation use efficiency in crops but also limits the water status surveillance through non-invasive methods like infrared radiometry. This study was carried out in the humid desert of the Peruvian central coast with the aim to relate maximum stomatal conductance (g_{s_max}, an important water status indicator) with leaf and air temperature (dT) and crop water stress index (CWSI). In a potted trial, g_{s_max} vs. dT were compared along the day in well-irrigated (field capacity) and water restricted (half field capacity) plants. In an additional field experiment, CWSI was validated by testing two irrigation timing treatments with pre-established g_{s_max} threshold (0.15 [T1] and 0.50 [T2] mol H₂O m⁻² s⁻¹) against a control (frequently irrigated). An acute stomatal closure sensitivity was detected which drove a g_{s_max} fall (g_s↓) near the solar noon. The intense stomatal closure caused a dT rise which showed positive higher values (> 2 °C) after g_s↓. The significant yield reduction of T1 in relation to the control (− 38.2 ± 10.7%) highlighted that g_{s_max} values > 0.15 must be used to warrant a high potato yield. These findings support the use of CWSI values ≤ 0.3–0.4 as thresholds for an appropriate irrigation in potatoes with assessments taken at around 15:00 hours, time in which plants have accumulated enough radiation allowing an appropriate detection of thermal emission under humid conditions.

Revista dirigida a especialistas y académicos interesados en la investigación y la búsqueda del conocimiento y soluciones de problemas relacionados con el agua.

Effects of climate change have led to a reduction in precipitation and an increase in temperature across several areas of the world. This has resulted in a sharp decline of glaciers and an increase in surface runoff in watersheds due to snowmelt. This situation requires a better understanding to improve the management of water resources in settled areas downstream of glaciers. In this study, the snowmelt runoff model (SRM) was applied in combination with snow-covered area information (SCA), precipitation, and temperature climatic data to model snowmelt runoff in the Santa River sub-basin (Peru). The procedure consisted of calibrating and validating the SRM model for 2005–2009 using the SRTM digital elevation model (DEM), observed temperature, precipitation and SAC data. Then, the SRM was applied to project future runoff in the sub-basin under the climate change scenarios RCP 4.5 and RCP 8.5. SRM patterns show consistent results; runoff decreases in the summer months and increases the rest of the year. The runoff projection under climate change scenarios shows a substantial increase from January to May, reporting the highest increases in March and April, and the lowest records from June to August. The SRM demonstrated consistent projections for the simulation of historical flows in tropical Andean glaciers.