The last ten years have shown that Climate Change (CC) is a major global issue to attend to. The integration of its effects into coastal impact assessments and adaptation plans has gained great attention and interest, focused on avoiding or minimizing human lives and asset losses. Future scenarios of mean sea level rises and wave energy increase rates have then been computed, but downscaling still remains necessary to assess the possible local effects in small areas. In this context, the effects of CC on the wave climate in the Gulf of California (GC), Mexico, have received little attention, and no previous studies have tackled the long-term trend of wave climate at a regional scale. In this paper, the long-term trends of the wave height, wave period and wave energy in the GC were thus investigated, using the fifth-generation climate reanalysis dataset (ERA5). The long-term shoreline evolution was also examined from historical Landsat images, so as to identify erosional hotspots where intervention can be prioritized. The results indicate that both the mean and extreme wave regimes in the GC are getting more energetic and that two-thirds of the coast is suffering chronic erosion. A discrepancy between the trends of the wave period and wave height in some regions of the Gulf was also found. Finally, the importance of natural processes, human activity and CC in the shoreline change is highlighted, while addressing the need for future permanent field observations and studies in the GC.

In coastal regions, the combined effects of natural processes, human activity, and climate change have caused shoreline changes that may increase in the future. The assessment of these changes is essential for forecasting their future position for proper management. In this context, shoreline changes in the Gulf of California (GC), Mexico, have received little attention and no previous studies have addressed future forecasting. In this study, the researchers assessed the historical shoreline changes to forecast the long-term shoreline positions. To address this, shoreline data were obtained from Landsat satellite images for the years 1981, 1993, 2004, 2010, and 2020. The Net Shoreline Movement (NSM), Linear Regression Rate (LRR), End Point Rate (EPR), and Weighted Linear Regression (WLR) geo-spatial techniques were applied to estimate the shoreline change rate by using a Digital Shoreline Analysis System (DSAS) in the GIS environment. A Kalman filter model was used to forecast the position of the shoreline for the years 2030 and 2050. The results show that approximately 72% of the GC shoreline is undergoing steady erosion, and this trend is continuing in the future. This study has provided valuable and comprehensive baseline information on the state of the shoreline in the GC that can guide coastal engineers, coastal managers, and policymakers in Mexico to manage the risk. It also provides both long-term and large-scale continuous datasets that are essential for future studies focused on improving the shoreline forecast models.

Arid and semiarid regions are geographic units that cover approximately 43% of the earth’s surface worldwide, and conditions of extreme drought and reduced vegetation cover predominate in these regions. In Mexico, arid and semiarid ecosystems cover more than half of the territory, with desertification, mainly caused by anthropogenic activities and climatic events, as the main problem in these regions. The present research aimed to assess, identify, and classify arid and semiarid zones by employing a methodology based on multicriteria evaluation analysis (MCA) using the weighted linear combination (WLC) technique and geographic information systems (GIS) in the hydrological administrative regions (HARs) of the North Pacific, Northwest, and Baja California Peninsula, located in Northwest Mexico. Data related to aridity, desertification, degradation, and drought were investigated, and the main factors involved in the aridity process, such as surface temperature, soil humidity, precipitation, slopes, orientations, the normalized difference vegetation index (NDVI), and evapotranspiration, were obtained. For the standardization of factors, a fuzzy inference system was used. The weight of each factor was then determined with the analytical hierarchy process (AHP). To delimit arid regions, the classification of arid zones proposed by the United Nations Environment Program (UNEP) was used, and the result was an aridity suitability map. To validate the results, the sensitivity analysis method was applied. Quantitative and geospatial aridity indicators were obtained at the administrative hydrological level and by state. The main results indicated that semiarid and dry subhumid zones predominated, representing 40% and 43% of the surface of the study area, respectively, while arid regions represented 17%, and humid regions represented less than 1%. In addition, of the states for which 100% of the surface lay in the study area, it was observed that Baja California and Baja California Sur had the largest arid and semiarid zones, while subhumid regions predominated in Sonora and Sinaloa.

This study aims to understand the role of key drivers of historical changes in the shoreline and its implications with the erosion risk in the northern coastal strip of the state of Sinaloa, located on the east coast of the Gulf of California. Digital maps from different years (1981, 1991, 2004, and 2018) are analyzed using geographic information system software (DSAS and CERA) to examine: (a) the movement and rate of the shoreline change; and (b) the potential vulnerability consequences, and erosion risk. The obtained results indicate that between 1981 and 2018: (a) anthropogenic actions (dams and breakwaters) were the main drivers of both the shoreline changes and the environmental damage underlying the erosion risk that has occurred in recent decades; (b) the coastline of the study area has been eroding with an average EPR of -3.1 m per year, which has led to an average NSM of -112.9 m; and (c) the risk of erosion remained moderate, although the vulnerability increased from a moderate to a high level and potential consequences from a very low to a moderate level. Besides, the results of this study provide a basis for future analyses focused on predicting shoreline changes and coastal risk.

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