Land use and land cover (LULC) scenarios in rural catchment hydrology are crucial to describe the effects of future water dynamics. However, there is a lack of understanding of the effectiveness of including static land covers at the subbasin level to provide inter-annual stability in changing the different water balance components. We developed a step-by-step mapping protocol to extend and enrich the hydrological assessment of future LULC scenarios defined through participatory stakeholder involvement. This novelty included specific allocation of static and dynamic LULC change among the scenarios and then compared the change of water dynamics to the current situation. For this, we quantified the LULC impact on the components of the water balance from three contrasting participatory scenarios implemented with the SWAT model in a rural basin in central Spain. The Land-sharing scenario (LSH) had the highest percentage of permanent grassland and shrubs and no increase of irrigated land compared to baseline. The land-sparing scenario (LSP) intensified agricultural land use close to urban areas, and the land balance scenario (LBA) was intermediate. The LSH increased the aquifer recharge by +1.7% and streamflow by +1.5%, while evapotranspiration and soil water storage decreased by -0.2%. In contrast, the LBA decreased in the riverine flux of -0.5%, an aquifer recharge of -0.6%, a soil water storage of -3.5%, and an evapotranspiration rate of +0.3%. Thus, LSH revealed that the allocation of permanent land cover such as grassland could buffer water dynamics, suggesting that dedicated planning and allocation of permanently vegetated LULC will favour land and water conservation.

Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid.

Deforestation by human activities is a common issue in Amazonian countries. This occurs at different spatial and temporal scales causing primary forest loss and land fragmentation issues. During the deforestation process as the forest loses connectivity, the deforested patches create new intricate connections, which in turn create complex networks. In this study, we analyzed the local connected fractal dimension (LCFD) of the deforestation process in the Sumaco Biosphere Reserve (SBR) with two segmentation methods, —CA-wavelet and K-means—to categorize the complexity of deforested patches’ connections and then relate these with the spatial processes. The results showed an agreement with both methods, in which LCFD values below 1 corresponded to isolated patches with simple shapes and those above 1 signified more complex and connected patches. From CA-wavelet a threshold of 1.57 was detected allowing us to identify and discern low and high land transformation, while the threshold for K-means was 1.61. Both values represent the region from which deforestation performs local aggressive expansion networks. The thresholds were used to map the LCFD in which all spatial processes were visually detected. However, the threshold of 1.6 ± 0.03 was more effective in discerning high land transformation. such as shrinkage and attrition, in the deforestation process in the SBR.