Simulation of flow processes in hyper-regulated Mediterranean watersheds is critical when examining general water demand and established ecological flows of River Basin Management Plans. Weather dynamics in the Mediterranean zone in recent decades have been characterised by a natural variation of drought cycles. In addition, exacerbated climate change proves that water fluxes must be estimated with more exhaustive models. The aim of this study is to assess the water balance of the Cega-Eresma-Adaja (CEA) watershed, including a detailed assessment of land uses and management practices to quantify agricultural water demand for the time period 2004-2014. We used the Soil and Water Assessment Tool (SWAT), given that it is a widespread tool that involves complex processes of the water cycle on a basin scale, providing information on water dynamics related to land use as a fundamental characteristic for water balance calculation. The Nash-Sutcliffe coefficient efficiency value, the main index of calibration and validation performance, was 0.86 for the Eresma-Adaja River and 0.67 for the Cega River. This presents a good result considering the large-scale watershed studied. Analysing dry hydrological years, we found that the estimation of ecological flows for sub-arid zones needs to consider the shallow aquifer-river relationship. During spring-summer periods, with very low flow, monitoring the shallow aquifer levels ensures a good ecological status. The study reveals that aspects such as crop rotation, soil management and their associated measures in Mediterranean basins are key factors for water resource management during drought periods. These results are expected to serve stakeholders and river basin authorities in conducting better-integrated water management practices in the watershed.

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In the three last decades, continental Ecuador biosphere reserves (CEBRs) have undergone important land cover transformations because of human‐managed systems such as agriculture, livestock, forestry, and urbanization. Thus, the native landscape structure has been altered, exhibiting a mosaic of patches with varying sizes and shapes. The resulting landscape could shape some patterns over time, reflecting the complex interactions between human activities and biotic‐abiotic factors, such as soil, climate, and water availability. Quantifying the landscape patterns and their dynamics is essential for the monitoring and assessment of the ecological consequences of land‐cover change in time. Based on the soil maps and raster land‐cover maps from 1990 to 2016 of two CEBRs, the spatial deforestation patterns were quantified through lacunarity and multifractal analysis. To conduct these analyses, the deforested areas were identified and overlapped to predominant soil types. The results showed that the deforestation process pattern and soil types interact through complex relations due to specific site characteristics. Thereby, both soil and land cover interacted in fractal structures. The patch area distribution of deforestation patterns linked to soil types reveals a power‐law relationship in which, as the area of the deforested patches increased, their frequency decreased. Also, this relation presents self‐similarity and multifractal behaviour over a wide range of scales from 46–2,674 pixels, with a pixel size of 30 m × 30 m. However, each CEBR showed varying degrees of heterogeneity and complexity, which were reflected through lacunarity and multifractal parameters. The dynamics of deforestation patterns linked to soil types can be spatially dependent on the initial patchiness state, the distance to the adjacent patches, and the suitability of the soil for agrarian activities. Altogether, these were the spatial underlying factors of the deforestation evolution in the two CEBRs. Highlights Deforestation patterns show spatial self‐similarity Deforestation patterns are linked to soil types Landscape ecology, lacunarity, and multifractal analyses are useful to understand deforestation patterns Initial patch location, distance to the nearest neighbour and soil suitability boost the deforestation expansion

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.

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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.