Using hourly records from 51 rain gauges, spanning between 22 and 28 yr, the authors study the diurnal cycle of precipitation over the tropical Andes of Colombia. Analyses are developed for the seasonal march of the diurnal cycle and its interannual variability during the two phases of El Niño–Southern Oscillation (ENSO). Also, the diurnal cycle is analyzed at intra-annual time scales, associated with the westerly and easterly phases of the Madden–Julian oscillation, as well as higher-frequency variability (<10 days), mainly associated with tropical easterly wave activity during ENSO contrasting years. Five major general patterns are identified: (i) precipitation exhibits clear-cut diurnal (24 h) and semidiurnal (12 h) cycles; (ii) the minimum of daily precipitation is found during the morning hours (0900–1100 LST) regardless of season or location; (iii) a predominant afternoon peak is found over northeastern and western Colombia; (iv) over the western flank of the central Andes, precipitation maxima occur either near midnight, or during the afternoon, or both; and (v) a maximum of precipitation prevails near midnight amongst stations located on the eastern flank of the central Cordillera. The timing of diurnal maxima is highly variable in space for a fixed time, although a few coherent regions are found in small groups of rain gauges within the Cauca and Magdalena River valleys. Overall, the identified strong seasonal variability in the timing of rainfall maxima appears to exhibit no relationship with elevation on the Andes. The effects of both phases of ENSO are highly consistent spatially, as the amplitude of hourly and daily precipitation diminishes (increases) during El Niño (La Niña), but the phase remains unaltered for the entire dataset. We also found a generalized increase (decrease) in hourly and daily rainfall rates during the westerly (easterly) phase of the Madden–Julian oscillation, and a diminished (increased) high-frequency activity in July–October and February–April during El Niño (La Niña) years, associated, among others, with lower (higher) tropical easterly wave (4–6 day) activity over the Caribbean.

Bathymetric surveying to gather information about depths and underwater terrain is increasingly important to the sciences of hydrology and geomorphology. Submerged terrain change detection, water level, and reservoir storage monitoring demand extensive bathymetric data. Despite often being scarce or unavailable, this information is fundamental to hydrodynamic modeling for imposing boundary conditions and building computational domains. In this manuscript, a novel, low-cost, rapid, and accurate method is developed to measure submerged topography, as an alternative to conventional approaches that require significant economic investments and human power. The method integrates two types of Unmanned Aerial Systems (UAS) sampling techniques. The first couples a small UAS (sUAS) to an echosounder attached to a miniaturized boat for surveying submerged topography in deeper water within the range of accuracy. The second uses Structure from Motion (SfM) photogrammetry to cover shallower water areas no detected by the echosounder where the bed is visible from the sUAS. The refraction of light passing through air–water interface is considered for improving the bathymetric results. A zonal adaptive sampling algorithm is developed and applied to the echosounder data to densify measurements where the standard deviation of clustered points is high. This method is tested at a small reservoir in the U.S. southern plains. Ground Control Points (GCPs) and checkpoints surveyed with a total station are used for properly georeferencing of the SfM photogrammetry and assessment of the UAS imagery accuracy. An independent validation procedure providing a number of skill and error metrics is conducted using ground-truth data collected with a leveling rod at co-located reservoir points. Assessment of the results shows a strong correlation between the echosounder, SfM measurements and the field observations. The final product is a hybrid bathymetric survey resulting from the merging of SfM photogrammetry and echosoundings within an adaptive sampling framework.

This paper reviews the current state of high‐resolution remotely sensed soil moisture (SM) and evapotranspiration (ET) products and modeling, and the coupling relationship between SM and ET. SM downscaling approaches for satellite passive microwave products leverage advances in artificial intelligence and high‐resolution remote sensing using visible, near‐infrared, thermal‐infrared, and synthetic aperture radar sensors. Remotely sensed ET continues to advance in spatiotemporal resolutions from MODIS to ECOSTRESS to Hydrosat and beyond. These advances enable a new understanding of bio‐geo‐physical controls and coupled feedback mechanisms between SM and ET reflecting the land cover and land use at field scale (3–30 m, daily). Still, the state‐of‐the‐science products have their challenges and limitations, which we detail across data, retrieval algorithms, and applications. We describe the roles of these data in advancing 10 application areas: drought assessment, food security, precision agriculture, soil salinization, wildfire modeling, dust monitoring, flood forecasting, urban water, energy, and ecosystem management, ecohydrology, and biodiversity conservation. We discuss that future scientific advancement should focus on developing open‐access, high‐resolution (3–30 m), sub‐daily SM and ET products, enabling the evaluation of hydrological processes at finer scales and revolutionizing the societal applications in data‐limited regions of the world, especially the Global South for socio‐economic development.

To achieve water resource sustainability in the water-limited southwestern US, it is critical to understand the potential effects of proposed forest thinning on the hydrology of semi-arid basins, where disturbances to headwater catchments can cause significant changes in the local water balance components and basinwise streamflows. In Arizona, the Four Forest Restoration Initiative (4FRI) is being developed with the goal of restoring 2.4 million acres of ponderosa pine along the Mogollon Rim. Using the physically based, spatially distributed triangulated irregular network (TIN)-based Real-time Integrated Basin Simulator (tRIBS) model, we examine the potential impacts of the 4FRI on the hydrology of Tonto Creek, a basin in the Verde–Tonto–Salt (VTS) system, which provides much of the water supply for the Phoenix metropolitan area. Long-term (20-year) simulations indicate that forest removal can trigger significant shifts in the spatiotemporal patterns of various hydrological components, causing increases in net radiation, surface temperature, wind speed, soil evaporation, groundwater recharge and runoff, at the expense of reductions in interception and shading, transpiration, vadose zone moisture and snow water equivalent, with south-facing slopes being more susceptible to enhanced atmospheric losses. The net effect will likely be increases in mean and maximum streamflow, particularly during El Niño events and the winter months, and chiefly for those scenarios in which soil hydraulic conductivity has been significantly reduced due to thinning operations. In this particular climate, forest thinning can lead to net loss of surface water storage by vegetation and snowpack, increasing the vulnerability of ecosystems and populations to larger and more frequent hydrologic extreme conditions on these semi-arid systems.

Flood forecasting in mountain basins remains a challenge given the difficulty in accurately predicting rainfall and in representing hydrologic processes in complex terrain. This study identifies flood predictability patterns in mountain areas using quantitative precipitation forecasts for two summer events from radar nowcasting and a distributed hydrologic model. The authors focus on 11 mountain watersheds in the Colorado Front Range for two warm-season convective periods in 2004 and 2006. The effects of rainfall distribution, forecast lead time, and basin area on flood forecasting skill are quantified by means of regional verification of precipitation fields and analyses of the integrated and distributed basin responses. The authors postulate that rainfall and watershed characteristics are responsible for patterns that determine flood predictability at different catchment scales. Coupled simulations reveal that the largest decrease in precipitation forecast skill occurs between 15- and 45-min lead times that coincide with rapid development and movements of convective systems. Consistent with this, flood forecasting skill decreases with nowcasting lead time, but the functional relation depends on the interactions between watershed properties and rainfall characteristics. Across the majority of the basins, flood forecasting skill is reduced noticeably for nowcasting lead times greater than 30 min. The authors identified that intermediate basin areas [~(2–20) km2] exhibit the largest flood forecast errors with the largest differences across nowcasting ensemble members. The typical size of summer convective storms is found to coincide well with these maximum errors, while basin properties dictate the shape of the scale dependency of flood predictability for different lead times.