Spatial Variability Of Rainfall Research Articles

Variations in Present and Future Hourly Extreme Rainfall: Insights from High-Resolution Data and Novel Temporal Disaggregation Model

Extreme rainfall-induced events adversely affect agriculture, infrastructure, and socioeconomic development in a region. Therefore, a comprehensive understanding of their occurrences and past and future variability in the context of global warming is imperative, especially at the fine temporal (sub-daily) and spatial (local to regional) scales for better contextualizing inferences from a policymaking perspective. This study provides a detailed analysis of global warming’s impacts on extreme rainfall in Jiangsu Province, utilizing the latest high-resolution ERA5-Land reanalysis data and the latest climate models. A novel temporal disaggregation model was developed to predict future hourly extreme rainfall. The results show that the bias-corrected model reduced the overestimation of extremes by as much as ~7.4% for the location parameter and accurately reproduced the spatial variability of rainfall. Projections from eight climate models indicate a future increase in rainfall intensity by an average of over 7%. Moreover, the projections indicate two contrasting trends for different event durations: short-duration events (e.g., 1 h) show a 7.1% increase at the 5-year return period and a more pronounced 8.9% increase at the 50-year return period. Conversely, long-duration events (e.g., 24 h) experience an 8.4% increase at the 5-year return period and a smaller 6.0% increase at the 50-year return period. This suggests that rarer, short-duration events are expected to increase more than less rare ones, while rarer, long-duration events show a smaller increase than their less rare counterparts. Addressing spatial heterogeneity in extreme rainfall patterns provides actionable insights for climate adaptation and mitigation, supporting initiatives like the ‘Jiangsu Province Climate Change Adaptation Action Plan’. This study underscores the need for policy-driven, community-led climate actions to mitigate flood risks and enhance resilience in a region vulnerable to flooding amidst global warming and increasing human interventions.

Spatial assessment of the reproducibility of Indian summer monsoon rainfall regimes in multiple gridded rainfall products

The Indian summer monsoon (ISM) is a complex and multiscale interacting climate system, which is responsible for major contribution in India’s annual rainfall. Understanding the spatial and temporal variability of ISM rainfall is critical for managing water resources, which directly impact and regulate the functioning of India’s socio-economic conditions and subsequently, sustenance of over a billion people. This study evaluates the suitability of various gridded precipitation data products with different spatiotemporal resolutions, essential requirement for hydrologic modeling, disaster mitigation, irrigation allocation and agricultural application. Hence, we evaluate the performance of seven gridded datasets in generating time-matched characteristic event occurrences and their respective magnitudes using gauge-based Indian Meteorological Department (IMD) gridded data as reference. We observe that reanalysis datasets underperform compared to satellite and hybrid products in identifying both normal and extreme precipitation events. We develop a performance measure, called ‘rank score’ that considers deviations from IMD data in magnitude, statistical moments, and rain event detectability for a robust assessment and identifying best-suited dataset. Results indicate that APHRODITE, MSWEP, and CHIRPS (in descending order) are the most suitable data products across India. Additionally, region-specific evaluations provide valuable insights into the applicability of these datasets in different climatic and homogeneous rainfall zones.

A stochastic precipitating quasi-geostrophic model

Efficient and effective modeling of complex systems, incorporating cloud physics and precipitation, is essential for accurate climate modeling and forecasting. However, simulating these systems is computationally demanding since microphysics has crucial contributions to the dynamics of moisture and precipitation. In this paper, appropriate stochastic models are developed for the phase-transition dynamics of water, focusing on the precipitating quasi-geostrophic (PQG) model as a prototype. By treating the moisture, phase transitions, and latent heat release as integral components of the system, the PQG model constitutes a set of partial differential equations (PDEs) that involve Heaviside nonlinearities due to phase changes of water. Despite systematically characterizing the precipitation physics, expensive iterative algorithms are needed to find a PDE inversion at each numerical integration time step. As a crucial step toward building an effective stochastic model, a computationally efficient Markov jump process is designed to randomly simulate transitions between saturated and unsaturated states that avoids using the expensive iterative solver. The transition rates, which are deterministic, are derived from the physical fields, guaranteeing physical and statistical consistency with nature. Furthermore, to maintain the consistent spatial pattern of precipitation, the stochastic model incorporates an adaptive parameterization that automatically adjusts the transitions based on spatial information. Numerical tests show the stochastic model retains critical properties of the original PQG system while significantly reducing computational demands. It accurately captures observed precipitation patterns, including the spatial distribution and temporal variability of rainfall, alongside reproducing essential dynamic features such as potential vorticity fields and zonal mean flows.

ANÁLISE ESPACIAL E TEMPORAL DE EVENTOS DE SECAS NA REGIÃO HIDROGRÁFICA ALTO RIO PARANÁ DE 1990 A 2020

The Paraná River Hydrographic Region is of great socioeconomic relevance for Brazil. Therefore, due to the history of droughts in the region, the aims of the current study were to assess rainfall spatial and temporal variability, as well as to analyze drought features based on using the Normalized Percentage Index (NPI) to contribute to water management processes in Upper Paraná River (UPR). The study area encompasses 14 Planning and Management Units, distributed in Goiás, Mato Grosso do Sul, Minas Gerais and São Paulo states. Climate data were obtained from the National Hydrometeorological Network database. A geostatistical analysis of rainfall was carried out, based on the annual series of 408 stations, and subsequently the calculation of NPI for classifying the droughts severity. All periods analyzed (1990-2020) showed spatial dependence (moderate to high), allowing the elaboration of droughts maps. The dry and rainy periods and areas did not indicate direct influence of El Niño-Southern Oscillation (ENSO). Mato Grosso do Sul and São Paulo units were the main contributors to the identified droughts. The data showed that 2010-2020 was the driest decade of the period, with a greater contribution from the 2019/2020 period, where droughts exceeded 60% of the area for two consecutive years, an event never identified in the last 3 decades. The NPI curves referring to “initial drought” and “mild” allowed verifying years of water crisis when their summed percentages reached at least 50% of the region. The methodology used is versatile, allowing its use in any watershed. Keywords: Rains. Water management. NPI. Kriging.

Geostatistical Simulation of Daily Rainfall Fields—Performance Assessment for Extremes in West Africa

Abstract The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.

Urbanization and Suspended Sediment Transport Dynamics: A Comparative Study of Watersheds with Varying Degree of Urbanization Using Concentration-Discharge Hysteresis.

Suspended sediment is a critical water quality parameter and an indicator of geomorphic processes, but suspended sediment dynamics in urban streams may not conform to the first-flush model widely used for other pollutants. We analyzed discharge and turbidity data for 367 events from three urban watersheds (impervious cover 16-45%) in Cleveland, Ohio (USA). Less intensely urbanized watersheds exhibit higher turbidity compared to that of the most highly urbanized watershed. Proportionally, more counterclockwise hysteresis is observed in the two less urbanized watersheds, and more clockwise hysteresis occurs in the highly urbanized watershed. However, hysteresis patterns are driven by different mechanisms in each watershed, and geomorphic analysis was critical to identifying the underlying mechanisms. In the least urbanized watershed, spatial rainfall variability controls sediment hysteresis. In the intermediate watershed, the erosion of upstream weathered shale banks during dry periods plays a significant role in the sediment supply and shaping hysteresis. In the most urbanized watershed, high eroding banks in downstream reaches lead to more frequent clockwise hysteresis. Overall, we suggest that as the impervious surfaces increase, the availability of instream sediments (bed and banks) plays an increased role in suspended sediment dynamics, and geomorphology remains essential for guiding management decisions.

Rainfall Trend Analysis in Mysuru and Bengaluru Districts of Karnataka, India

Climate change is one of the most pressing challenges facing humanity today, with temperature, precipitation, and runoff being key indicators closely linked to its impact. Understanding the spatial variability and temporal trends of rainfall is crucial for effective water resource management and agriculture. This study focuses on analyzing rainfall data from the Mysuru and Bengaluru districts, located in the Southern and Eastern dry zones of Karnataka, respectively. These regions play a pivotal role in the state's agricultural development and overall economic growth. We utilized daily precipitation records from two meteorological stations, spanning the period from 1983 to 2018, to analyze rainfall trends on monthly, seasonal, and annual scales. To assess these trends, we applied both parametric and non-parametric statistical methods, including the simple regression method, Mann-Kendall test, and Sen’s slope estimator. The Mann-Kendall test was used to detect significant trends in the precipitation time series, while Sen’s slope estimator quantified the magnitude of these trends. Bengaluru had higher average annual rainfall (928.81 mm) compared to Mysuru (681.65 mm), with excess rainfall in 2005 and a positive Pre-Monsoon trend (+3.543 mm/year), while Mysuru experienced maximum rainfall deficit in 2016 and a negative Monsoon trend (-3.238 mm/year). Neither district showed a significant trend in annual rainfall. The findings of this trend analysis are essential for informed water resource planning and management in the region.

Analysing Spatial Variability of Monsoon Rainfall over West Africa

Climate variability and changes have become a topic of increasing importance in recent decades due to extreme climate events such as floods and droughts, which profoundly impact socioecological systems across West Africa during the monsoon months. This study analyses and updates rainfall variability and spatial trends during these crucial months using monthly gridded rainfall data from 1950 to 2022. We applied the ordinary least square regression test, Buishand's, Standard normal homogeneity and Pettitt's tests for trend and change point detection analyses at a 5% significant level. The spatial variability and trends in rainfall were examined across three time periods: 1950-2022, 1963-1992, and 1993-2022. Change-point detection analysis revealed that there had been significant changes in the monsoon months' rainfall variability within the study period except in August. The detection of both positive and negative trends, representing trends toward wetter and drier conditions respectively, provides valuable insights for understanding and managing the impacts of climate variability and change for the most vulnerable areas.

Spatial variability and moisture tracks of Indian monsoon rainfall and extremes

Spatial variability and moisture tracks of Indian monsoon rainfall and extremes

Spatial variability of rainfall: deciphering flood characteristics and model precision

ABSTRACT This study investigates the intricate role of spatial variability in rainfall (SVR) concerning flood characteristics and its impact on refining flood prediction models. A spatial variability index was used to classify rainfall events into two categories: spatially homogeneous (Class A) and heterogeneous (Class B). The analysis of historical flood events suggests that the SVR influences flood peaks. This research introduces a novel approach to assess SVR’s role in calibrating hydrological models, subsequently improving model selection. By separately calibrating Class A and B events within both lumped and distributed models, the models yield superior results compared to the conventional approach. For the catchments considered, the lumped models demonstrated heightened performance for Class A events, while the distributed models outperformed in Class B events. This study underscores not only the influence of SVR on flood dynamics but also the efficacy of event-based classification in refining hydrological models for superior flood prediction accuracy.

Spatial and Temporal Trends in Precipitation and Temperature and their Influence on Groundwater in Ballia

A study examined the impact of long-term pre- and post-monsoon precipitation and temperature on groundwater level fluctuations in 17 blocks of Ballia District. The study used 100 years of daily precipitation data (1917-2016) and 62 years of daily temperature data (1951-2012) from the Indian Meteorological Department. Seasonal analysis was performed using pre-monsoon, monsoon, post-monsoon, and winter data. The Mann-Kendall method and Sen's slope were applied. The spatial variability of rainfall and temperature was mapped using the IDW technique in ArcGIS. Significant annual precipitation decreases were found in Murlichapra and Sear blocks. Maniar and Pandah blocks showed precipitation increases. An increasing pre-monsoon trend was observed in several blocks, while post-monsoon trends varied. Groundwater levels fluctuated across Ballia, with a notable decrease of 2.1 m in Bairia Block from 2006 to 2016.

Spatiotemporal variability of rainfall and drought characterization in Kaduna, Nigeria

Rainfall variability and drought events can affect food security globally. This study aimed to assess the spatial and temporal trends and variability of rainfall in Kaduna State, Nigeria. Gridded rainfall data were obtained from the Climate Research Unit (CRU_TS 4.07) (1952–2022) for Birnin Gwari, Kaduna, Kafanchan and Zaria towns. The rainfall data were subjected to the Anderson‒Darling normality test and found to be normally distributed. The data were analysed using the coefficient of variation (CV), rainfall anomaly index (RAI) and precipitation concentration index (PCI). The trend was assessed using the Mann‒Kendall trend test, and finally, meteorological drought was computed using the standardized precipitation index (SPI). The results revealed that all four locations had low to moderate variability, with CVs < 15%. The rainfall anomaly index revealed that 1983 was the driest year for Birnin Gwari, Kaduna and Kafanchan, with RAI values of − 4.863, − 4.474 and − 4.316, respectively, while in Zaria, the driest year was 1974, with a RAI value of − 4.065. The PCI showed that there was a high concentration of rainfall in Birnin Gwari, Kaduna, and Kafanchan. However, Zaria experienced 41 years of high concentrations and 30 years of very high concentrations of precipitation. The Mann‒Kendall trend test revealed that all four locations experienced a significant downward trend in rainfall within the study period. The Z statistics for Birnin Gwari, Kaduna, Kafanchan and Zaria were − 1.112, − 0.357, − 0.556 and − 0.764, respectively, all indicating decreasing rainfall amounts. Finally, the SPI Birnin Gwari and Kaduna experienced 18 months of severe drought, with maximum intensities of − 2.671 and − 3.111, respectively. Kafanchan experienced 25 months of drought with a maximum intensity of − 2.643, while Zaria experienced 30 months of drought with a maximum intensity of − 3.069. These severe droughts occurred from the 1970s to the 1990s. The study concluded that rainfall concentrations were mostly high and very high, and severe, catastrophic drought events occurred in the 1970s and 1980s.

Spatial Variation of Rainfall Between Nineveh and Basra Governorates due to Terrain Elevation Using Digital Elevation Model–Geographic Information System

Spatial Variation of Rainfall Between Nineveh and Basra Governorates due to Terrain Elevation Using Digital Elevation Model–Geographic Information System

GIS-based spatio-temporal analysis of rainfall trends under climate change in different agro-ecological zones of Wolaita zone, south Ethiopia

Understanding the spatiotemporal dynamics of climatic conditions within a region is paramount for informed rural planning and decision-making processes, particularly in light of the prevailing challenges posed by climate change and variability. This study undertook an assessment of the spatial and temporal patterns of rainfall trends across various agro-ecological zones (AEZs) within Wolaita, utilizing data collected from ten strategically positioned rain gauge stations. The detection of trends and their magnitudes was facilitated through the application of the Mann–Kendall (MKs) test in conjunction with Sen's slope estimator. Spatial variability and temporal trends of rainfall were further analyzed utilizing ArcGIS10.8 environment and XLSTAT with R programming tools. The outcomes derived from ordinary kriging analyses unveiled notable disparities in the coefficient of variability (CV) for mean annual rainfall across distinct AEZs. Specifically, observations indicated that lowland regions exhibit relatively warmer climates and lower precipitation levels compared to their highland counterparts. Within the lowland AEZs, the majority of stations showcased statistically non-significant positive trends (p > 0.05) in annual rainfall, whereas approximately two-thirds of midland AEZ stations depicted statistically non-significant negative trends. Conversely, over half of the stations situated within highland AEZs displayed statistically non-significant positive trends in annual rainfall. During the rainy season, highland AEZs experienced higher precipitation levels, while the south-central midland areas received a moderate amount of rainfall. In contrast, the northeast and southeast lowland AEZs consistently received diminished rainfall across all seasons compared to other regions. This study underscores the necessity for the climate resilient development and implementation of spatiotemporally informed interventions through implementing region-specific adaptation strategies, such as water conservation measures and crop diversification, to mitigate the potential impact of changing rainfall patterns on agricultural productivity in Wolaita.

Unravelling the impact of microclimatic changes on coastal aquifer of multiple land use through integrated techniques: A comparative study using two decadal data

Regional climatic changes reflect the microclimatic variations and these affect the water resources of a region. Urbanization and population growth significantly influence microclimates, impacting groundwater resources and Land Surface Temperature (LST). The climatic variables (rainfall and temperature) interplay with geospatial parameters such as Land Use Land Cover (LULC), LST influence the quality and quantity of groundwater. Hence, this current approach employs geospatial techniques to explore the interrelationship between these variables by studying the long-term variation for a period of 20 years (1997–2018) to assess the microclimate variation and its impact on groundwater resources of a coastal aquifers with complex lithology. Results reveal a notable increase in minimum and maximum temperatures, with an average annual rise of 0.5°C predominantly along the western part. Spatial rainfall variation for the study period was higher in the northern region influenced by urban heat islands. The change in LULC reflected a 12% increase in agricultural areas and a 1.7% expansion in habitation, accompanied by a decline in surface water bodies in 2018. Maximum increase in electrical conductivity(EC) is observed along the southern coastal region, indicating rise in ionic concentration due contamination from sea water, urban and agricultural sources, witnessed by the isotopic signatures. Comparison of the depth to groundwater between the different study periods indicate a maximum depletion of 49m in northeastern and western regions due to extraction for domestic water supply, agriculture and mining. Further, a maximum rise of 14m in the regions adjacent to the reservoirs was observed, during 2018. The current study reveals the relationship between microclimate and groundwater (quality and the quantity) is predominantly observed in the northern and western parts of the study area. Our findings suggest that managing groundwater extraction and improving land use practices are essential for sustainable development, primarily through policy and governance.

Climate change shocks and crop production: The foodgrain bowl of India as an example

Global warming is causing climate change (CC) characterized by increased frequency of heatwaves, droughts, erratic rains, hailstorms, cloudbursts, floods, landslides etc. The CC has already adversely affected ecosystems. In spite of efforts to mitigate greenhouse gas (GHG) emissions, which lead to warming, the global temperature during 2011–20 was 1.1°C above that during pre-industrial era. The projections are that warming will continue to increase and adverse effects will intensify particularly in developing countries like India. In India a number of studies have recorded wide spatial variability in rainfall, though, many reported a general overall negative trend since mid-20th century. Further, varying pattern of rainfall has been recorded in three agroclimatic regions of Punjab state, the granary of India. Unseasonal rains followed by spiked temperature during winter (rabi) season of 2021–22 reduced wheat (Triticum aestivum L.) yield in Punjab by 651 kg/ha and by 301 kg/ha in Haryana compared to 2020–21. Further, the grain was of lower quality. During rainy (kharif) season of 2022, Southern Rice Black-streaked Dwarf Virus (SRBSDV), appeared for the first time in Punjab and Haryana. Some farmers ploughed the affected fields. Adverse weather during rabi 2022–23 also, reduced the wheat yield (143–150 kg/ha) in these states. At the national level, erratic weather during rabi 2021–22 and kharif 2022 caused losses of about 3 mt of grain of each of wheat and rice (Oryza sativa L.). The projected increased adverse effects due to intensified CC include food insecurity. Thus, there is an emergent need to accelerate implementation of adaptation and mitigation strategies in agriculture. The adaptation options include cultivar improvement, conservation agriculture altering growing seasons, crop diversification and sustainable soil, and water resource management. In the process of adaptive management of crop production, adjusting sowing dates and breeding cultivars having varying duration in consonance with CC has been one of the central aspects. Shifting sowing dates to find appropriate crop cultivation season is a low-cost measure. However, cultivar development is time and resource consuming. Novel biotechnological tools enable fast cultivar development with precision, and facilitate mobilization of genes from wild-weedy relatives, which are rich in genes conferring resistance/tolerance to biotic and biotic stresses, required to combat CC challenge. In view of CC stress on water resources, improving water-use efficiency (WUE) has gained importance. Sensor-based micro-irrigation/fertigation has great potential to enhance water and fertilizer-use efficiency. Similarly, the application of other smart technologies like nanotechnology, sensor-based pesticide application, bio-fertilizers and bio-pesticides, need to be mobilised. In view of agro-ecological diversity in India, right-sized regionspecific technology packages have to be developed implying that crop research will expand exponentially. This needs strengthening of human resources and institutional infrastructure, expanding and linking basic and applied researches, and fortifying inter-disciplinary/inter-institutional collaborations to develop and diffuse technology innovations. Enabling factors include enhanced funding and international cooperation. All-out efforts are needed to have more climate-resilient agriculture.

Spatio-Temporal Trend Analysis of Annual and Seasonal Rainfall in Ho Chi Minh City

Spatio-temporal variations and trends in climatic variables play an essential role in the adaptation and mitigation of climate change policy. This study analyzed spatial and temporal variability of annual and seasonal rainfall in Ho Chi Minh City based on non-parametric statistical trend tests and spatial interpolation techniques. In particular, Sen’s Slope Estimator and Inverse Distance Weighting (IDW) methods were applied to construct the spatial distribution of annual and seasonal rainfall over the city. The outcomes show that annual and seasonal rainfall experienced significant increasing trends. Generally, these findings draw out an overall picture of rainfall variability in terms of space and time, which makes it possible for local decision-makers to promote more promising action plans in order to respond to natural disasters in the context of climate change.

Spatiotemporal variability of the rainy season in the Yucatan Peninsula

AbstractThe rainfall regime is a critical factor in the Yucatan Peninsula, as the spatial and multiannual variability of rainfall is a major concern, particularly for crops. Variability in the rainy season was examined considering the onset and demise of the annual rainy season, the total rain volume, the rainfall season duration and the intense precipitation events recorded in meteorological stations (1978–2020). We analysed individual time series and calculated the long‐term trend. Additionally, we explored the relationship between each summer rainfall characteristic and several oceanographic indices using multivariate techniques. We also developed a Trans‐Isthmic Index from the relationship between the El Niño–Southern Oscillation and the Atlantic Multidecadal Oscillation. This index allows for determining the effect of the overall influence of the ocean on climate. The timeseries analysis revealed a high interannual variability and long‐term positive trends concerning the duration of the rainy season with earlier onset and later demise, and the total rainfall volume and also a positive trend for the occurrence of heavy precipitation suggesting a shift in intra‐annual patterns. Spatially, the analysis revealed clusters of stations with a similar variation, probably related to the AMO, NIÑO3.4 or TII indices. The spatial pattern was confirmed by analysing CHIRPS gridded precipitation data. Our results show that wetter conditions are associated with lower temperatures in the equatorial Pacific and warmer conditions in the Atlantic.

Utilizing the HEC-HMS Model for Hydrological Modelling of the Rambiara Watershed: Rainfall and Runoff Estimation

Abstract: Hydrological modelling and flood prediction are essential components of the Rambiara watershed's water resource management. To improve disaster preparedness and reduce the danger of flooding in the area, the effectiveness of the HEC HMS model in flood forecasting must be assessed. In this study, various techniques for estimating rainfall are examined, and a customized hydrological model tailored to the hydrological complexities of the Rambiara watershed is developed. The goal of this project is to provide practical insights for better flood risk reduction and water resource management through the integration of cutting-edge modelling tools and rigorous analysis. This work advances our knowledge of watershed dynamics and provides guidance for sustainable water resource planning projects in the Rambiara watershed and beyond by thoroughly examining precipitation patterns and hydrological processes. In addition, the research takes into account a number of factors related to flood behaviour, such as determining places that are vulnerable to flooding, analysing the size of floods, and estimating how frequently they occur. By taking a holistic approach, it is possible to gain a thorough understanding of the dynamics of flooding in the Rambiara watershed, which paves the way for the development of efficient flood risk management techniques. This study looks into the temporal and spatial variability of rainfall in the Rambiara watershed in addition to research on floods. Through the comparison of several methods for estimating rainfall, including deterministic and geostatistical approaches, the study offers valuable insights into the precision and dependability of rainfall data, which are essential for hydrological modelling and flood forecasting. The hydrological processes of the Rambiara watershed can be more accurately represented by creating a bespoke hydrological model with the HEC HMS program. Informed decision-making and proactive flood risk reduction strategies are made possible by the study's model calibration and validation, which guarantee the model's dependability in simulating runoff and forecasting flood events. Overall, this study advances our knowledge of flood dynamics in the Rambiara watershed and advances hydrological modelling tools. The goal of this study is to promote sustainability and resilience in the face of hydrological hazards by offering practical insights for managing water resources and reducing the danger of flooding.

Variability of temperature, rainfall and reference evaporation of catolé do Rocha-PB municipality, semi-arid region of Brazil

The state of Paraíba, specifically in the alto sertão of Paraíba, is characterized by the spatial and temporal variability of rainfall, of temperature and evapotranspiration, coming from the semi-arid climate that is inserted in the region, and yet, the performance of different meteorological systems. The objective was to quantify the rainfall, temperature and reference evaporation (mm/ month and mm/year) comprised in the experimental period between 2016 to 2020, in the municipality of Catolé do Rocha-PB, in order to determine the history of the rainy and dry season, and thus, minimize agricultural production losses due to environmental factors. The daily rainfall, temperature and reference evaporation data were obtained by means of a rain gauge and the indirect method of the class A tank, respectively. The rainfall (mm year-1) was analyzed by the nonparametric Friedman test, the Conover test and reference evaporation (mm year-1) applied the parametric test of the ANAVA and the test of Tukey HSD. Both parametric and non-parametric tests were analyzed by Real Statistics. Temperature data was compiled from the website (www.inmet.gov.br) in order to control, statically collecting and decision-making in the process of building scientific knowledge regarding meteorological data (climate - Temperature). Were also analyzed pearson's correlation matrices and elementary principal components analysis for the rainfall variables, Eto and Temperature in the studied experimental period. Were also studied the principal component analysis between the years 2016 to 2020 and its significance by the t test. The average reference evaporation between 2016 and 2020 was 2.1 times greater than rainfall. Mean rainfall and reference evaporation in Catolé do Rocha-PB between 2016 and 2020 were 827 mm ano-1 and 1704 mm year-1 and are compatible with historical averages of 800 mm year-1 and 1700 mm year-1, respectively. Inglesa.