ABSTRACT:

The distribution of groundwater recharge can be estimated using several methods, but generally, the most appropriate method depends on both physical and study objectives. In this case, a large area in Southern Portugal has been subject to a strong intensification of agricultural practices. This was due to the construction of the Alqueva dam, the largest dam and artificial lake (250 km2) in Western Europe, which has caused a significant land-use change process in recent years. One of the most critical observed effects is the soil tillage process, which causes a great impact on runoff and recharge patterns and consequent soil erosion. For the assessment of groundwater recharge changes in this area, this study intended to make a high-resolution estimation based on drone imagery analysis. Accordingly, two high-resolution digital elevation models (DEM) with 7 cm resolution were created using photogrammetric processing of the images collected by drone. In order to have a representative land-use change, these images were collected before and after soil tillage. The image acquisition process was based on a pre-programmed drone (Unmanned Aerial Vehicle –UAV) with the following flight specifications: 90% frontal overlap and 70% lateral. As the research intended to have a high-accuracy DEM of the soil surface, two perpendicular flights were made. After the photogrammetric processing, a high-resolution output of topography was obtained prior to and after soil tillage. To assess the groundwater recharge changes, the WetSpass-M model was used for the two time frames. This model has the ability to simulate spatially distributed recharge, surface runoff, and evapotranspiration for averaged conditions and adaptive scales or resolutions. Depending on several inputs such as land cover, soil texture, hydrometeorological parameters and topography, the latter stands in this case as the main change between the two time frames. Thus, the groundwater recharge changes were estimated for an area of 55 ha, from which 18.7 ha were subjected to soil tillage. The remaining 36.3 ha were used as validation areas. In conclusion, the usage of drone imagery can be considered a fundamental tool for providing important hints on the impacts of land use and topography changes on the recharge, as well as constituting a methodology framework to support water balance assessments and groundwater resources management through the production of high-resolution spatially distributed recharge estimates.

ABSTRACT:

The distribution of groundwater recharge can be estimated using several methods, but generally, the most appropriate method depends on both physical and study objectives. In this case, a large area in Southern Portugal has been subject to a strong intensification of agricultural practices. This was due to the construction of the Alqueva dam, the largest dam and artificial lake (250 km2) in Western Europe, which has caused a significant land-use change process in recent years. One of the most critical observed effects is the soil tillage process, which causes a great impact on runoff and recharge patterns and consequent soil erosion. For the assessment of groundwater recharge changes in this area, this study intended to make a high-resolution estimation based on drone imagery analysis. Accordingly, two high-resolution digital elevation models (DEM) with 7 cm resolution were created using photogrammetric processing of the images collected by drone. In order to have a representative land-use change, these images were collected before and after soil tillage. The image acquisition process was based on a pre-programmed drone (Unmanned Aerial Vehicle –UAV) with the following flight specifications: 90% frontal overlap and 70% lateral. As the research intended to have a high-accuracy DEM of the soil surface, two perpendicular flights were made. After the photogrammetric processing, a high-resolution output of topography was obtained prior to and after soil tillage. To assess the groundwater recharge changes, the WetSpass-M model was used for the two time frames. This model has the ability to simulate spatially distributed recharge, surface runoff, and evapotranspiration for averaged conditions and adaptive scales or resolutions. Depending on several inputs such as land cover, soil texture, hydrometeorological parameters and topography, the latter stands in this case as the main change between the two time frames. Thus, the groundwater recharge changes were estimated for an area of 55 ha, from which 18.7 ha were subjected to soil tillage. The remaining 36.3 ha were used as validation areas. In conclusion, the usage of drone imagery can be considered a fundamental tool for providing important hints on the impacts of land use and topography changes on the recharge, as well as constituting a methodology framework to support water balance assessments and groundwater resources management through the production of high-resolution spatially distributed recharge estimates.

ABSTRACT:

Recently, the satellite images have been used in remote sensing allowing observations with high temporal and spatial distribution. The use of water indices has proved to be an effective methodology in the monitoring of surface water resources. However, precise or automatic methodologies using satellite imagery to determine reservoir volumes are lacking. To fulfil that gap, this methodology proposes 3 stages: use Google Earth Engine (GEE) to select images; automatically calculate flooded surface areas applying water indices; determine the volume stored in reservoirs over those years based on the relation between the flooded area and the stored volume. The method was applied in four reservoirs and contemplate Landsat 4 and 5 ETM and Landsat 8 OLI. For the calculation of the flooded area the NDWI Indexes (McFeeters, 1996; Gao, 1996), and the MNDWI index (Xu, 2006) were applied and tested. The estimation of stored volume of water was made based on the area indices and a cross-check between real stored volume and calculated volume was made. Finally, an analysis on the selection of the best fit water indices was made. The results of every case studies herein displayed showed a quantifiable proficiency and reliability for quite a varied natural conditions. As a conclusion, this methodology could be seen as a tool for water resources management in developing countries, and not only, to measure automatically trends of stored volumes and its relation with the precipitation, and could eventually be extended to other types of surface water bodies, as lakes and coastal lagoons.

ABSTRACT:

Recently, the satellite images have been used in remote sensing allowing observations with high temporal and spatial distribution. The use of water indices has proved to be an effective methodology in the monitoring of surface water resources. However, precise or automatic methodologies using satellite imagery to determine reservoir volumes are lacking. To fulfil that gap, this methodology proposes 3 stages: use Google Earth Engine (GEE) to select images; automatically calculate flooded surface areas applying water indices; determine the volume stored in reservoirs over those years based on the relation between the flooded area and the stored volume. The method was applied in four reservoirs and contemplate Landsat 4 and 5 ETM and Landsat 8 OLI. For the calculation of the flooded area the NDWI Indexes (McFeeters, 1996; Gao, 1996), and the MNDWI index (Xu, 2006) were applied and tested. The estimation of stored volume of water was made based on the area indices and a cross-check between real stored volume and calculated volume was made. Finally, an analysis on the selection of the best fit water indices was made. The results of every case studies herein displayed showed a quantifiable proficiency and reliability for quite a varied natural conditions. As a conclusion, this methodology could be seen as a tool for water resources management in developing countries, and not only, to measure automatically trends of stored volumes and its relation with the precipitation, and could eventually be extended to other types of surface water bodies, as lakes and coastal lagoons.