A methodology to estimate surface water quality using remote sensing is presented based on Landsat satellite imagery and in situ measurements taken every six months at four separate sampling locations in a tropical reservoir from 2015 to 2019. The remote sensing methodology uses the Box–Cox transformation model to normalize data on three water quality parameters: total organic carbon (TOC), total dissolved solids (TDS), and chlorophyll a (Chl-a). After the Box–Cox transformation, a mathematical model was generated for every parameter using multiple linear regression to correlate normalized data and spectral reflectance from Landsat 8 imagery. Then, significant testing was conducted to discard spectral bands that did not show a statistically significant response (α = 0.05) from the different water quality models. The r² values achieved for TOC, TDS, and Chl-a water quality models after the band discrimination process were found 0.926, 0.875, and 0.810, respectively, achieving a fair fitting to real water quality data measurements. Finally, a comparison between estimated and measured water quality values not previously used for model development was carried out to validate these models. In this validation process, a good fit of 98% and 93% was obtained for TDS and TOC, respectively, whereas an acceptable fit of 81% was obtained for Chl-a. This study proposes an interesting alternative for ordered and standardized steps applied to generate mathematical models for the estimation of TOC, TDS, and Chl-a based on water quality parameters measured in the field and using satellite images.

River basin delineation can be inappropriate to determine surface water availability in a country, even if it is established by its water authority. This is because the effect of agricultural and urban infrastructure in runoff direction is ignored, and the anthropogenic changes in hydrography and topography features distort the runoff. This situation is really important because water rights are granted based on volumes that are not physically accessible. The existence of this problem is demonstrated through a case of study: the Culiacan River Basin in Mexico. To overcome such a situation, this study poses criteria to revise official river basin configurations and to delineate new river basins based on digital elevation models, vector files of agricultural infrastructure, and extensive field verification. Significant differences were noticed in surface water availability calculated under distinct river basin delineations.

Traditional photogrammetry techniques require the use of Ground Control Points (GCPs) to accurately georeference aerial images captured by unmanned aerial vehicles (UAVs). However, the process of collecting GCPs can be time-consuming, labor-intensive, and costly. Real-time kinematic (RTK) georeferencing systems eliminate the need for GCPs without deteriorating the accuracy of photogrammetric products. In this study, a statistical comparison of four RTK georeferencing systems (continuously operating reference station (CORS)-RTK, CORS-RTK + post-processed kinematic (PPK), RTK + dynamic RTK 2 (DRTK2), and RTK + DRTK2 + GCP) is presented. The aerial photo was acquired using a Dà-Jiāng Innovation Phantom 4 RTK. The digital photogrammetric processing was performed in Agisoft Metashape Professional software. A pair of global navigation satellite systems (GNSSs) receiving antennas model CHC x900 were used for the establishment of check points (CPs). The accuracy of photogrammetric products was based on a comparison between the modeled and CP coordinates. The four methods showed acceptable planimetric accuracies, with a root mean square error (RMSE) _X,Y ranging from 0.0164 to 0.0529 m, making the RTK-CORS + PPK method the most accurate (RMSE _X,Y = 0.0164 m). RTK-CORS + PPK, RTK-DRTK2, and RTK-DRTK2 + GCP methods showed high altimetric accuracies, with RMSE_Z values ranging from 0.0201 to 0.0334 m. In general, RTK methods showed a high planimetric and altimetric accuracy, similar to the accuracy of the photogrammetric products obtained using a large number of GCPs.

The development of instruments for the administration of water use is a pending issue in both developing and developed countries. UNESCO has published guidance on determining water availability in Latin American and Caribbean countries. We applied this method to the Culiacán River basin, the most significant basin in Mexico for agricultural productivity. We find that surface water availability has been overestimated due to the inclusion of non-physical terms in the water balance equation, miscalculation of natural runoff and the omission of ecological water flow. Thus, unsustainable surface water use is allowed based on a miscalculation of physical water availability.

Urban development decreases infiltration, increases the runoff velocity, and reduces the concentration times. This situation increases the flood risk in urban watersheds, which represent a management challenge for urban communities and authorities. To increase the resilience of communities due to modifications of the hydrological cycle produced by climate change and urban development, a methodology is proposed to delineate flood-prone areas in urban basins. This methodology is implemented in an urban subbasin of Culiacan, Mexico, and is based on stream order. A high-resolution digital elevation model was used, which was validated independently through a photogrammetric flight with an unmanned aerial vehicle and ground control points obtained with GNSS (global navigation satellite systems) receivers. Morphometric parameters related to geometry, shape, relief, and drainage network aspects of the subbasin were determined and analyzed. Then, flood-prone area zonation was carried out based on stream-order classification and flow direction. Fieldwork was also carried out for the inspection of the sewage network conditions. This methodology simplifies the identification of the flood-prone areas in urban subbasins without carrying out complex hydraulic calculations.