Historical records are an important source of information on extreme and rare floods and fundamental to establish a reliable flood return frequency. The use of long historical records for flood frequency analysis brings in the question of flood stationarity, since climatic and land-use conditions can affect the relevance of past flooding as a predictor of future flooding. In this paper, a detailed 400 yr flood record from the Tagus River in Aranjuez (central Spain) was analysed under stationary and non-stationary flood frequency approaches, to assess their contribution within hazard studies. Historical flood records in Aranjuez were obtained from documents (Proceedings of the City Council, diaries, chronicles, memoirs, etc.), epigraphic marks, and indirect historical sources and reports. The water levels associated with different floods (derived from descriptions or epigraphic marks) were computed into discharge values using a one-dimensional hydraulic model. Secular variations in flood magnitude and frequency, found to respond to climate and environmental drivers, showed a good correlation between high values of historical flood discharges and a negative mode of the North Atlantic Oscillation (NAO) index. Over the systematic gauge record (1913–2008), an abrupt change on flood magnitude was produced in 1957 due to constructions of three major reservoirs in the Tagus headwaters (Bolarque, Entrepeñas and Buendia) controlling 80% of the watershed surface draining to Aranjuez. Two different models were used for the flood frequency analysis: (a) a stationary model estimating statistical distributions incorporating imprecise and categorical data based on maximum likelihood estimators, and (b) a time-varying model based on "generalized additive models for location, scale and shape" (GAMLSS) modelling, which incorporates external covariates related to climate variability (NAO index) and catchment hydrology factors (in this paper a reservoir index; RI). Flood frequency analysis using documentary data (plus gauged records) improved the estimates of the probabilities of rare floods (return intervals of 100 yr and higher). Under non-stationary modelling flood occurrence associated with an exceedance probability of 0.01 (i.e. return period of 100 yr) has changed over the last 500 yr due to decadal and multi-decadal variability of the NAO. Yet, frequency analysis under stationary models was successful in providing an average discharge around which value flood quantiles estimated by non-stationary models fluctuate through time.

Satellite-based precipitation products and reanalysis precipitation products have the potential to overcome the lack of information in regions where there are no or insufficient rain gauges to achieve any hydrological study. The Google Earth Engine (GEE) data analysis platform has products in its repository with global coverage that offers different geospatial information capable of measuring the amount of precipitation. However, it is necessary to evaluate the reliability of the products. There are precipitation information biases in Mexico due to the scarce presence of gauging stations, failed operations, access difficulty, and data capture errors. This study evaluates the reliability of satellite and reanalysis precipitation products hosted in the GEE repository against rain gauge observation from 2001 to 2017 using data from 4,658 stations over Mexico. The evaluation was carried out using statistical indicators comparing the behavior across different topographic, climatic, and temporal conditions. The results exhibit that the performance of the products hosted in GEE seems to depend on elevation conditions for other climatic regions in Mexico. The results show that all products can capture the general precipitation patterns at annual, seasonal, and monthly scales; however, the accuracy of the product is clearly lower at a daily scale. All products are highly biased on low precipitation events.

Resilience is an indicator of the ability of systems to withstand disruption within acceptable degradation parameters and also their recovery time. It is essential for public policies to understand how the population reacts to a particular risk. In this paper we have performed a study that quantitatively measures perceptions of flooding and resilience to flooding in the city of Colima-Villa de Alvarez, Mexico 2018–2019. A resilience index has been applied to ten zones of the city. In our research we assessed risk perception through a city-wide survey with questions based on a Likert scale. An analysis was performed on public knowledge of the existing security protocols for floods and evaluated the public perception of the availability of critical services, such as fresh water, electricity, food, drainage, communications and public transport during a flash flood events. This research has identified populated low resilience zones that can be considered as priorities for resource and effort to mitigate floods and their impacts. The novel resilience index developed in this work can also be applied to other type of risk that humans face and used as a basis for discussions about urban resilience.

Frequency analysis has been the most widely used tool worldwide to dimension water-related infrastructures and evaluate flood risks. The concept of stationarity has been a common and practical hypothesis in hydrology for many years. However, in recent decades due to climate change pressure and changes in land use, it has been related to the presence of time-series trends that in hydrology indicate non-stationary effects. In this sense, the need to comprehensively address non-stationary frequency analysis has been identified. This study proposes to incorporate the non-stationary flood frequency analysis into the dimensioning process of road structures with the following objectives: i) evaluate the effect of land use on peak flow in a simulated period of 129 years, ii) evaluate covariates related to land use, and iii) evaluate covariates related to climate change. To this end, road drainage simulation exercises were carried out in three sections of the Ibagué-Cajamarca road located in Colombia. Likewise, the Generalized Additive Models for Location, Scale and Shape was implemented for the non-stationary analysis, and covariates related to climate variability were included, such as El Niño-Southern Oscillation indices (ONI12, ONI3.4, MEI, and SOI), and the Pacific Decadal Oscillation (PDO) index, as well as some related to the evolution of land use such as hydraulic conductivity, soil water storage in the root zone, and infiltration capacity represented in the curve number. The results indicate that the non-stationary analysis improves the prediction of maximum flows, and it is possible to obtain road drainage dimensioning that adjusts to climate and land-use variations.

Tropical cyclone (TC) landfall is often accompanied by storm surges, strong winds, and heavy rain that cause destructive flash floods, especially in mountainous regions. However, there is limited understanding of the contribution of TCs to major flood events, especially in Western Mexico. In this study, we assess the contribution of TCs to the annual rainfall, extreme rainfall and stream flow in the mountainous region of Colima, one of the most TC‐exposed areas in Western Mexico. The top 1% of daily rainfall and stream flow, annual maximum rainfall and the highest 20 stream flow events from 1970 to 2015 are examined for their association to TCs. Results indicate that the relative contribution of TCs to the average annual rainfall can exceed 25% in the coastal area of Colima. Over 25–35% of heavy daily rainfall (top 1% rainfall) recorded in the coastal rain gauges is found to be associated with TCs. In terms of high flow, approximately 20–24% of the top 1% flow events and 28–35% (~7 events) of the top 20 flow events are driven by TCs. The heaviest precipitation and high flow events occur typically in the late TC season (September and October). Our results provide insights on the role of TCs in inducing rainfall and stream flow relevant for water and flood risk management.

There is great uncertainty about the future effects of climate change on the global economic, social, environmental, and water sectors. This paper focuses on watershed vulnerabilities to climate change by coupling a distributed hydrological model with artificial neural networks and spatially distributed indicators for the use of a predictive model of such vulnerability. The analyses are complemented by a Monte Carlo evaluation of the uncertainty associated with the projections of the global circulation models, including how such uncertainty impacts the vulnerability forecast. To test the proposal, the paper uses current and future vulnerabilities of the Turbio River watershed, located in the semi-arid zone of Guanajuato (Mexico). The results show that nearly 50% of the watershed currently has medium and high vulnerabilities, and only the natural areas in the watershed show low vulnerabilities. In the future, an increase from medium to high vulnerability is expected to occur in urban and agricultural areas of the basin, with an associated uncertainty of ±15 mm in the projected precipitation.