Rainfall and streamflow extreme events in the São Francisco hydrographic region

Abstract

Extreme rainfall and streamflow events are becoming increasingly more relevant to discussions concerning potential climate changes on planet earth. In most cases, impacts related to the occurrence of such phenomena can be amplified due to the lack of planning and structure. In this context, the main objective of the study is to estimate the period and level of return of intense rainfall events in homogeneous subregions of the São Francisco hydrographic region, as well as to define which of these subregions are most vulnerable to the recurrence of these episodes. We used rainfall data from 105 rain gauges and streamflow data from 103 stream gauges comprising the years from 1988 to 2017. Through cluster analysis, four homogeneous rainfall subregions were defined and validated by the silhouette index, capable of identifying misallocated stations in the clustering process. A generalized Pareto distribution from the extreme value theory was used to calculate the period and level of return of intense rainfall events in each subregion. We then related the retrieved estimates with information about the drainage network, streamflow, land use and occupation, and socioeconomic and demographic factors in the watershed region. Results indicated the existence of subregions vulnerable to hydrological and geological processes intensified due to human activity and the recurrence of hydrometeorological phenomena. The scenarios found for the recurrence of intense rainfall events indicated the severity to which some subregions may be subjected, moreover, they reinforce the need for the development of public policies for the prevention and minimization of impacts.

Assessment of dry and heavy rainfall days and their projected changes over Northeast Brazil in Coupled Model Intercomparison Project Phase 6 models

“…For this purpose, the absolute threshold below 1 mm was used for dry days (DD) and the 99th percentile for heavy rainfall days (HRD). We restricted our analyses for these two conditions because such information is the most important to stakeholders and water resources managers, especially due to the water vulnerability and poor adaptive capacity for extreme rainfall events in NEB (Darela‐Filho et al ., 2016; Gondim et al ., 2018; Oliveira et al ., 2020). After percentile calculation and subsequent identification of days without precipitation (<1 mm) and with heavy precipitation (>99th percentile), they were divided by seasons: summer (December, January, and February—DJF), autumn (March, April, and May—MAM), winter (June, July, and August—JJA), and spring (September, October, and November—SON).…”

Direct Sampling for Spatially Variable Extreme Event Generation in Resampling‐Based Stochastic Weather Generators

“…The use of extreme rainfall generation can simulate the effects of heavy precipitation and assist in developing effective mitigation strategies. In hydrological modeling, for instance, extreme rainfall generation can be used to predict the potential impacts of extreme precipitation events on streamflow (Oliveira et al., 2021), water quality (Exum et al., 2018), and ecosystem health (Wang et al., 2018). It can also be used to improve the design of water resources systems, such as reservoirs and irrigation systems, by simulating the effects of extreme rainfall on the systems' performance (Samuels et al., 2009; Woldemichael et al., 2012).…”

Analysis of hydrological extremes in the Guaíba hydrographic region: an application of extreme values theory

“…As for the differences between stations, Rupa and Mujumdar (2017), using GEV, also observed a significant variation in spatial return levels of extreme precipitation over the Bangalore city. Oliveira et al (2021), analyzing streamflow and rainfall data in the San Francisco hydrographic region through the Extreme Value Theory, identified scenarios of recurrence of intense rainfall events, but used the Pareto generalized distribution.…”