Evaluation of five gridded precipitation products for estimating precipitation and drought over Yobe, Nigeria

Water Science & Technology Water Supply, Год журнала: 2024, Номер 24(6), С. 2039 - 2054

Опубликована: Май 21, 2024

ABSTRACT Ground observations are often considered as the most reliable and precise source of precipitation data. However, long-term data from ground lacking in many parts world. Gridded products (GPPs) therefore have emerged crucial alternatives to observations, but it is essential assess their capability accurately replicate patterns. This study aims evaluate performance five GPPs, NASA POWER, TerraClimate, Climate Hazards Group Infrared Precipitation with Data (CHIRPS), GPCC, Research Unit (CRU), capturing drought patterns 1981 2021 Yobe, Nigeria. The results indicate that GPCC had good at both monthly annual scales, high correlation coefficients low error values. tends underestimate amounts certain areas. Other also exhibit satisfactory moderate correlations observations. Drought analysis indicates outperforms other standardised index-6 calculations, while POWER demonstrates inconsistencies particularly during early 1980s mid-2000s. In conclusion, preferable GPP for Yobe State

Язык: Английский

---

Comparison of the performance of estimated precipitation data via remote sensing in the Midwest Region of Brazil

Theoretical and Applied Climatology, Год журнала: 2023, Номер 153(3-4), С. 1105 - 1116

Опубликована: Июнь 13, 2023

Язык: Английский

---

Multiscale phenology of seasonally dry tropical forests in an aridity gradient

Frontiers in Environmental Science, Год журнала: 2023, Номер 11

Опубликована: Дек. 18, 2023

The leaf phenology of seasonally dry tropical forests (SDTFs) is highly seasonal, marked by synchronized flushing new leaves triggered the first rains wet season. Such phenological transitions may not be accurately detected remote sensing vegetation indices and derived transition dates (TDs) due to coarse spatial temporal resolutions satellite data. aim this study was compared TDs from PhenoCams (RS) used calculated select best thresholds for RS time series calculate TDs. For purpose, we assembled cameras in seven sites along an aridity gradient Brazilian Caatinga, a region dominated SDTFs. leafing patterns were registered during one three growing seasons 2017 2020. We drew interest (ROI) images normalized green chromatic coordinate index. camera data with NDVI (2000–2019) near-infrared (NIR) red bands MODIS product Using calibrated PhenoCam reduced mean absolute error 5 days SOS 34 EOS, common land surface studies. On average, season length (LOS) did differ significantly among types, but driest showed highest interannual variation. This pattern applied (SOS) fall (EOS) as well. found positive relationship between accumulated precipitation LOS maximum minimum temperatures productivity (peak NDVI). Our results demonstrated that (A) fine resolution phenocamera improved definitions landscape phenology; (b) long-term greening responded variability rainfall, adjusting their timing green-up green-down, (C) amount although determinant season, related estimates productivity.

Язык: Английский

---

Impact of precipitation extremes on energy production across the São Francisco river basin, Brazil

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Март 18, 2024

The Brazilian electrical system (BES) relies heavily on hydrothermal energy, specifically hydroelectric power plants (HPPs), which are highly dependent rainfall patterns. São Francisco River Basin (SFRB) is a critical component of the BES, playing key role in electricity generation. However, climate extremes have increasingly impacted energy production recent decades, posing challenges for HPP management. This study, explores relationship between extreme precipitation events SFRB and two crucial variables: Stored Energy (STE) Affluent Natural (ANE). We analyze spatial distribution trends 11 indices investigate seasonality, trends, correlations these variables indices. Our findings reveal downward both ANE STE. Additionally, we identify seasonal pattern influenced by rates at various time scales. results indicate that it possible to estimate STE efficiently employing three machine learning (ML) algorithms (Random Forest, Artificial Neural Networks k-Nearest Neighbors) using data. These offer valuable insights strategic planning management aiding decision-making development security.

Язык: Английский

---

Analysis of Land Use Change and Hydrogeological Parameters in the Andean Semiarid Region of Ecuador

Water, Год журнала: 2024, Номер 16(6), С. 892 - 892

Опубликована: Март 20, 2024

Access to freshwater in developing regions remains a significant concern, particularly arid and semiarid areas with limited annual precipitation. Groundwater, vital resource these regions, faces dual threats—climate change unsustainable exploitation. This study analyzes changes land use, vegetation cover, hydrogeological parameters Catacocha parish, situated the southern Ecuadorian Andes region. The methodology incorporates integration of data from Paltas Municipality, Ministerio del Ambiente, Agua y Transición Ecológica—MAATE—and Instituto Geográfico Militar—IGM. Utilizing GIS tools, analysis is combined comparative assessment discharge spanning 2000 2022. MAATE IGM play an instrumental role evaluating alterations cover across years. also examines characteristic curves wells their coefficient storage. Additionally, it assesses facilitating infiltration explores potential relationship precipitation patterns area. In prioritizing management natural essential, either through conservation projects or reforestation plans throughout year. Moreover, population emigration has revitalized reserving specific for conservation. transformation observed supplying parish its 2022 serves as demonstration this change. Discharge remain essential monitoring variations well ensuring consistent daily supply potable water.

Язык: Английский

---

Impact of precipitation extremes on energy production across the São Francisco river basin, Brazil

Theoretical and Applied Climatology, Год журнала: 2024, Номер 155(7), С. 6899 - 6916

Опубликована: Июнь 8, 2024

Язык: Английский

---

Extreme Rainfall Analysis in Pernambuco, Northeast Brazil, Using a High‐Resolution Gridded Dataset

International Journal of Climatology, Год журнала: 2024, Номер 44(16), С. 5693 - 5710

Опубликована: Ноя. 12, 2024

ABSTRACT This paper presents a detailed spatio‐temporal analysis of the rainfall in state Pernambuco, Northeast Brazil. It is based on climate indices for extreme precipitation recommended by Expert Team Climate Change Detection, Monitoring and Indices. To accomplish this, daily 1data (1961–2019) were extracted from 809 high‐resolution grid points (0.1° × 0.1°) using Brazilian Daily Weather Gridded Data (BR‐DWGD). The significance magnitude index trends assessed modified Mann–Kendall Sen's slope tests. study also examined whether there existed significant difference among three regions (Sertão, Agreste Zona da Mata) within state. findings revealed notable negative PRCPTOT, R10mm, R20mm, Rx1day, Rx5day CWD across all exhibiting gradient coast to state's interior. Reduction values up 15 mm year −1 0.7 day 0.2 0.01 0.03 Rx5day, 0.4 observed. Furthermore, an alarming pattern was noted CDD, displaying higher concentration positive state, with estimated increases 1.4 . Conversely, balance trends—both negative—was observed entire R95p R99p, majority proving non‐significant. SDII exhibited frequency showing trend, particularly Sertão Mata regions, where differences absent. However, remaining showcased regional differences, decreasing east west except CDD. will assist decision makers, providing long‐term information essential preventing natural disasters supporting socioeconomic environmental policies

Язык: Английский

---

Effects of land use and land cover change on soil erosion in the Caatinga biome

Environment Development and Sustainability, Год журнала: 2024, Номер unknown

Опубликована: Дек. 26, 2024

Язык: Английский

---

Evaluation of national and international gridded meteorological products for rainfall-runoff modelling in Northern Italy

Journal of Hydrology Regional Studies, Год журнала: 2024, Номер 56, С. 102031 - 102031

Опубликована: Ноя. 4, 2024

Язык: Английский

---

Impact of precipitation extremes on energy production across the São Francisco river basin

Опубликована: Июль 21, 2023

The Brazilian electrical system is predominantly hydrothermal, with hydroelectric power plants (HPP’s) dependent on rainfall variability. São Francisco river basin plays a fundamental role in the country's electricity production HPP’s Northeast and Southeast regions. However, climate extremes events have affected energy production. To manage use of to avoid shortage during dry periods activation thermal plants, challenge as it increases costs may result water wastage rainy periods. main variables influencing operational decisions are Stored Energy (STE) Affluent Natural (ANE), used calculate Marginal Cost Operation (MCO) Settlement Price Differences (SPD). current study investigates relationships between these precipitation basin. Spatial distribution trends 11 indices analyzed. seasonality, trends, correlation extreme also investigated. Three machine learning algorithms (Random Forest, Artificial Neural Networks, k-Nearest Neighbors) were applied regression models estimate (ANE, STE, MCO, SPD). Correlations show impact changes ANE STE availability MCO SPD, Southeast/Midwest subsystems. showed downward while SPD experienced an upward trend. Furthermore, seasonal behavior throughout year was demonstrated for ANE, influenced by rates different time scales. Trends indicate reduction total (PRCTOT) number wet days (CWD), well increase (CDD). Results based algorithm that reasonable efficiently using data. These findings significant implications planning management sector, contributing strategic decision-making formulation public policies ensure security. Keywords: Energy. Prediction.

Язык: Английский

---