Drought is one of the most complex natural hazards because of its slow onset and long-term impact; it has the potential to negatively affect many people. There are several advantages to using remote sensing to monitor drought, especially in developing countries with limited historical meteorological records and a low weather station density. In the present study, we assessed agricultural drought in the croplands of the BioBío Region in Chile. The vegetation condition index (VCI) allows identifying the temporal and spatial variations of vegetation conditions associated with stress because of rainfall deficit. The VCI was derived at a 250 m spatial resolution for the 2000–2015 period with the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13Q1 product. We evaluated VCI for cropland areas using the land cover MCD12Q1 version 5.1 product and compared it to the in situ Standardized Precipitation Index (SPI) for six-time scales (1–6 months) from 26 weather stations. Results showed that the 3-month SPI (SPI-3), calculated for the modified growing season (November–April) instead of the regular growing season (September–April), has the best Pearson correlation with VCI values with an overall correlation of 0.63 and between 0.40 and 0.78 for the administrative units. These results show a very short-term vegetation response to rainfall deficit in September, which is reflected in the vegetation in November, and also explains to a large degree the variation in vegetation stress. It is shown that for the last 16 years in the BioBío Region we could identify the 2007/2008, 2008/2009, and 2014/2015 seasons as the three most important drought events; this is reflected in both the overall regional and administrative unit analyses. These results concur with drought emergencies declared by the regional government. Future studies are needed to associate the remote sensing values observed at high resolution (250 m) with the measured crop yield to identify more detailed individual crop responses.

A study was carried out to parameterize the METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) model for estimating instantaneous values of albedo (shortwave albedo) (α_i), net radiation (R_ni) and soil heat flux (G_i), sensible (H_i) and latent heat (LE_i) over a drip-irrigated Merlot vineyard (location: 35°25′ LS; 71°32′ LW; 125 m.a.s. (l). The experiment was carried out in a plot of 4.25 ha, processing 15 Landsat images, which were acquired from 2006 to 2009. An automatic weather station was placed inside the experimental plot to measure α_i, R_ni and G_i. In the same tower an Eddy Covariance (EC) system was mounted to measure H_i and LE_i. Specific sub-models to estimate G_i, leaf area index (LAI) and aerodynamic roughness length for momentum transfer (z_om) were calibrated for the Merlot vineyard as an improvement to the original METRIC model. Results indicated that LAI, z_om and G_i were estimated using the calibrated functions with errors of 4%, 2% and 17%, while those were computed using the original functions with errors of 58%, 81%, and 5%, respectively. At the time of satellite overpass, comparisons between measured and estimated values indicated that METRIC overestimated α_i in 21% and R_ni in 11%. Also, METRIC using the calibrated functions overestimated H_i and LE_i with errors of 16% and 17%, respectively while it using the original functions overestimated H_i and LE_i with errors of 13% and 15%, respectively. Finally, LE_i was estimated with root mean square error (RMSE) between 43 and 60 W∙m⁻² and mean absolute error (MAE) between 35 and 48 W∙m⁻² for both calibrated and original functions, respectively. These results suggested that biases observed for instantaneous pixel-by-pixel values of R_ni, G_i and other intermediate components of the algorithm were presumably absorbed into the computation of sensible heat flux as a result of the internal self-calibration of METRIC.

As precipitation is a fundamental component of the global hydrological cycle that governs water resource distribution, the understanding of its temporal and spatial behavior is of great interest, and exact estimates of it are crucial in multiple lines of research. Meteorological data provide input for hydroclimatic models and predictions, which generally lack complete series. Many studies have addressed techniques to fill gaps in precipitation series at annual and monthly scales, but few have provided results at a daily scale due to the complexity of orographic characteristics and in some cases the non-linearity of precipitation. The objective of this study was to assess different methods of filling gaps in daily precipitation data using regression model (RM) and machine learning (ML) techniques. RM included linear regression (LRM) and multiple regression (MRM) algorithms, while ML included multiple regression algorithms (ML-MRM), K-nearest neighbors (ML-KNN), gradient boosting trees (ML-GBT), and random forest (ML-RF). This study covered the Malas, Omas, and Cañete River (MOC) watersheds, which are located on the Pacific Slope of central Peru, and a nineteen-year period of records (2001–2019). To assess model performance, different statistical metrics were applied. The results showed that the optimized machine learning (OML) models presented the least variability in estimation errors and the best approximation of the actual data from the study zone. In addition, this investigation shows that ML interprets and analyzes non-linear relationships between rain gauges at a daily scale and can be used as an efficient method of filling gaps in daily precipitation series.

The quantification of evaporation is very important for water resource management, determination of water availability and environmental modeling. Evaporation (E) is a key component of the hydrological cycle, which becomes more relevant under a changing climate scenario, where increases in temperature and E are projected on a regional scale. This study evaluated the hourly applicability of 21 methods to estimate E in a mountain lake. Models were grouped as combination, solar radiation-temperature and mass transfer methods. Estimated E by all models were compared to three measurement campaigns with an eddy covariance system in Lake Laja during the month of January (22 to 28), March (17 to 23) and November (09 to 15) of 2016. The average evaporated water during the measurement sessions were 3.40, 3.38 and 1.89 mm d⁻¹, respectively. Best model performance was obtained with models whose main E principles are heat flow and water vapor flow. The best performance in this group was the Penman model using a calibrated wind function with a determination coefficient (R²) of 0.91, Nash–Sutcliffe coefficient (NS) of 0.9, and index of agreement (W) of 0.98. Comparing daytime and nighttime hours, it was found that the daytime E in the lake is best explained by the product of the vapor pressure deficit and wind speed, while the nighttime E was explained by the flow of heat in the water. The results highlight the importance of the analysis of diurnal dynamics of water flux and energy stored in water to better understand the E in water bodies.