No file available
To read the file of this research,
you can request a copy directly from the authors.
Evaluating the accuracy of eight satellite and reanalysis daily precipitation estimates over Angola: the advantages of targeted regional evaluations to support effective water management strategies
Preprints and early-stage research may not have been peer reviewed yet.
ResearchGate has not been able to resolve any citations for this publication.
We introduce Version 2 of our widely used 1-km Köppen-Geiger climate classification maps for historical and future climate conditions. The historical maps (encompassing 1901–1930, 1931–1960, 1961–1990, and 1991–2020) are based on high-resolution, observation-based climatologies, while the future maps (encompassing 2041–2070 and 2071–2099) are based on downscaled and bias-corrected climate projections for seven shared socio-economic pathways (SSPs). We evaluated 67 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and kept a subset of 42 with the most plausible CO2-induced warming rates. We estimate that from 1901–1930 to 1991–2020, approximately 5% of the global land surface (excluding Antarctica) transitioned to a different major Köppen-Geiger class. Furthermore, we project that from 1991–2020 to 2071–2099, 5% of the land surface will transition to a different major class under the low-emissions SSP1-2.6 scenario, 8% under the middle-of-the-road SSP2-4.5 scenario, and 13% under the high-emissions SSP5-8.5 scenario. The Köppen-Geiger maps, along with associated confidence estimates, underlying monthly air temperature and precipitation data, and sensitivity metrics for the CMIP6 models, can be accessed at www.gloh2o.org/koppen.
Increasingly, satellite-derived rainfall data is used for climate research and action in Africa. In this study, we use six years of rain gauge data from 596 stations operated by the Trans-African Hydro-Meteorological Observatory (TAHMO) to validate three gauge-calibrated satellite rainfall products – CHIRPS, TAMSAT and GSMaP_wGauge – and one satellite-only rainfall product – GSMaP. Validations are stratified to evaluate performance across the continent and in East Africa, Southern Africa, and West Africa at daily, pentadal, and monthly timescales. For daily mean rainfall over Africa, CHIRPS has the highest bias at 15.5 % (0.5 mm) whereas GSMaP_wGauge has the lowest bias at 0.02 mm (0.7 %). We find higher daily rainfall event detection scores in the GSMaP products than in CHIRPS or TAMSAT. Generally, for every two rainfall events predicted by CHIRPS and TAMSAT, the GSMaP products predict three or more events. The highest mean monthly biases are produced by CHIRPS in East Africa (29 %; 26.3 mm wet bias), TAMSAT in Southern Africa (13 %; 10.4 mm dry bias) and GSMaP in West Africa (23 %; 19.6 mm wet bias). Considerable biases in seasonal rainfall are observed in all sub-regions for every satellite product. There is an increase of 0.6–1.3 mm in satellite rainfall RMSE for a 1 km increase in elevation revealing the influence of elevation on rainfall estimation by satellite models. Overall, satellite-derived rainfall products have notable errors, while GSMaP products produce comparable or better results at multiple timescales relative to CHIRPS and TAMSAT.
In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales.
Reanalysis products are often taken as an alternative solution to observational weather and climate data due to availability and accessibility problems, particularly in data-sparse regions such as Africa. Proper evaluation of their strengths and weaknesses, however, should not be overlooked. The aim of this study was to evaluate the performance of ERA5 reanalysis and to document the progress made compared to ERA-interim for the fields of near-surface temperature and precipitation over Africa. Results show that in ERA5 the climatological biases in temperature and precipitation are clearly reduced and the representation of inter-annual variability is improved over most of Africa. However, both reanalysis products performed less well in terms of capturing the observed long-term trends, despite a slightly better performance of ERA5 over ERA-interim. Further regional analysis over East Africa shows that the representation of the annual cycle of precipitation is substantially improved in ERA5 by reducing the wet bias during the rainy season. The spatial distribution of precipitation during extreme years is also better represented in ERA5. While ERA5 has improved much in comparison to its predecessor, there is still demand for improved products with even higher resolution and accuracy to satisfy impact-based studies, such as in agriculture and water resources.
We present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr−1, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.
Accurate representation of the real spatio-temporal variability of catchment rainfall inputs is currently severely limited. Moreover, spatially interpolated catchment precipitation is subject to large uncertainties, particularly in developing countries and regions which are difficult to access. Recently, satellite-based rainfall estimates (SREs) provide an unprecedented opportunity for a wide range of hydrological applications, from water resources modelling to monitoring of extreme events such as droughts and floods.
This study attempts to exhaustively evaluate – for the first time – the suitability of seven state-of-the-art SRE products (TMPA 3B42v7, CHIRPSv2, CMORPH, PERSIANN-CDR, PERSIAN-CCS-Adj, MSWEPv1.1, and PGFv3) over the complex topography and diverse climatic gradients of Chile. Different temporal scales (daily, monthly, seasonal, annual) are used in a point-to-pixel comparison between precipitation time series measured at 366 stations (from sea level to 4600 m a.s.l. in the Andean Plateau) and the corresponding grid cell of each SRE (rescaled to a 0.25∘ grid if necessary). The modified Kling–Gupta efficiency was used to identify possible sources of systematic errors in each SRE. In addition, five categorical indices (PC, POD, FAR, ETS, fBIAS) were used to assess the ability of each SRE to correctly identify different precipitation intensities.
Results revealed that most SRE products performed better for the humid South (36.4–43.7∘ S) and Central Chile (32.18–36.4∘ S), in particular at low- and mid-elevation zones (0–1000 m a.s.l.) compared to the arid northern regions and the Far South. Seasonally, all products performed best during the wet seasons (autumn and winter; MAM–JJA) compared to summer (DJF) and spring (SON). In addition, all SREs were able to correctly identify the occurrence of no-rain events, but they presented a low skill in classifying precipitation intensities during rainy days. Overall, PGFv3 exhibited the best performance everywhere and for all timescales, which can be clearly attributed to its bias-correction procedure using 217 stations from Chile. Good results were also obtained by the research products CHIRPSv2, TMPA 3B42v7 and MSWEPv1.1, while CMORPH, PERSIANN-CDR, and the real-time PERSIANN-CCS-Adj were less skillful in representing observed rainfall. While PGFv3 (currently available up to 2010) might be used in Chile for historical analyses and calibration of hydrological models, the high spatial resolution, low latency and long data records of CHIRPS and TMPA 3B42v7 (in transition to IMERG) show promising potential to be used in meteorological studies and water resource assessments. We finally conclude that despite improvements of most SRE products, a site-specific assessment is still needed before any use in catchment-scale hydrological studies.
The performance of seven operational high-resolution satellite-based rainfall products – Africa Rainfall Estimate Climatology (ARC 2.0), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Estimation (RFE 2.0), Tropical Applications of Meteorology using SATellite (TAMSAT), African Rainfall Climatology and Time-series (TARCAT), and Tropical Rainfall Measuring Mission (TRMM) daily and monthly estimates – was investigated for Burkina Faso. These were compared to ground data for 2001–2014 on a point-to-pixel basis at daily to annual time steps. Continuous statistics was used to assess their performance in estimating and reproducing rainfall amounts, and categorical statistics to evaluate rain detection capabilities. The north–south gradient of rainfall was captured by all products, which generally detected heavy rainfall events, but showed low correlation for rainfall amounts. At daily scale they performed poorly. As the time step increased, the performance improved. All (except TARCAT) provided excellent scores for Bias and Nash–Sutcliffe Efficiency coefficients, and overestimated rainfall amounts at the annual scale. RFE performed the best, whereas TARCAT was the weakest. Choice of product depends on the specific application: ARC, RFE, and TARCAT for drought monitoring, and PERSIANN, CHIRPS, and TRMM daily for flood monitoring in Burkina Faso. © 2016 Africa Rice Centre. Published by Informa UK Limited, trading as Taylor & Francis Group.
The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demon-strates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all eleva-tions. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding re-gional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.
African societies are dependent on rainfall for agricultural and other water-dependent activities, yet rainfall is extremely variable in both space and time and reoccurring water shocks, such as drought, can have considerable social and economic impacts. To help improve our knowledge of the rainfall climate, we have constructed a 30-year (1983–2012), temporally consistent rainfall dataset for Africa known as TARCAT (TAMSAT African Rainfall Climatology And Time-series) using archived Meteosat thermal infra-red (TIR) imagery, calibrated against rain gauge records collated from numerous African agencies. TARCAT has been produced at 10-day (dekad) scale at a spatial resolution of 0.0375°. An intercomparison of TARCAT from 1983 to 2010 with six long-term precipitation datasets indicates that TARCAT replicates the spatial and seasonal rainfall patterns and interannual variability well, with correlation coefficients of 0.85 and 0.70 with the Climate Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) gridded-gauge analyses respectively in the interannual variability of the Africa-wide mean monthly rainfall. The design of the algorithm for drought monitoring leads to TARCAT underestimating the Africa-wide mean annual rainfall on average by −0.37 mm day−1 (21%) compared to other datasets. As the TARCAT rainfall estimates are historically calibrated across large climatically homogeneous regions, the data can provide users with robust estimates of climate related risk, even in regions where gauge records are inconsistent in time.
The Shuttle Radar Topography Mission (SRTM), that flew in February 2000, is a cooperative project between NASA and the National Imagery and Mapping Agency. SRTM employed a single-pass radar interferometer to produce a digital elevation model of the Earth's land surface between about 60 degrees norht and south latitude. Data processing will take about two years, although preliminary products are already available.
In situ ground observation measurement of precipitation is difficult in sparsely populated areas with trying access conditions, as is the case in many countries in Africa. The use of remote sensors installed in satellites can be very useful in overcoming this challenge, enabling the improvement of the spatial variability description of this variable and the extension of data series.A number of standard products offering precipitation estimates on a regular basis is now available and may be used for water planning and management purposes. The present study examines the performance of four of these products in Angola, namely the Tropical Rainfall Measuring Mission (TRMM) 3B43 (version 6), Global Precipitation Climatology Project (GPCP) Combined Precipitation Data Set (version 2.2), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Centre (CPC) Morphing Technique (CMORPH), by comparing annual and monthly precipitation estimates with ground observation measurements. The data set of precipitation ground observation measurements was collected by the authors from different sources in Angola and Portugal, and is the result of an intense effort to gather hydrological records from Angola. It is believed to be one of the most complete data sets of monthly precipitation data from Angola.The four remote-sensing products are able to describe the main features of the spatial and temporal variability of annual and monthly precipitation in Angola. The results also show that the estimates from the TRMM are more accurate than the estimates offered by the other products, a conclusion which is in line with previous studies and which may be explained by the fact that this is the first product to incorporate measurements from precipitation radar. The estimation bias of TRMM is also more consistent which means that the results presented in the present study can be used in an operational environment to reduce the precipitation estimation error.
This paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1 degrees spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System(GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983-2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems.
A system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of this system is an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information. The model was initially calibrated over the Japanese Islands using remotely sensed infrared data collected by the Geostationary Meteorological Satellite (GMS) and ground-based data collected by the Automated Meteorological Data Acquisition System (AMeDAS). The model was then validated for both the Japanese Islands (using GMS and AMeDAS data) and the Florida peninsula (using GOES-8 and NEXRAD data). An adaptive procedure is used to recursively update the network parameters when ground-based data are available. This feature dramatically improves the estimation performance in response to the diverse precipitation characteristics of different geographical regions and time of year. The model can also be successfully updated using only spatially and/or temporally limited observation data such as ground-based rainfall measurements. Another important feature is a procedure that provides insights into the functional relationships between the input variables and output rainfall rate.
Consider a population in which sexual selection and natural selection may or may not be taking place. Assume only that the deviations from the mean in the case of any organ of any generation follow exactly or closely the normal law of frequency, then the following expressions may be shown to give the law of inheritance of the population.
Evaluating Satellite Rainfall Products for Southern Africa
- S Adebayo
- J Ayoade
- T Fadeyibi
Adebayo, S., Ayoade, J., Fadeyibi, T., 2018. Evaluating Satellite Rainfall Products for Southern Africa. Climate Research, 75(1),
Validation of satellite rainfall
- S Ageet
- A H Fink
- M Maranan
- J E Diem
- J Hartter
- A L Ssali
- P Ayabagabo
Ageet, S., Fink, A.H., Maranan, M., Diem, J.E., Hartter, J., Ssali, A.L., Ayabagabo, P., 2022. Validation of satellite rainfall
Datasets and code utilized in the development of the following manuscript: Evaluating the 589 accuracy of eight satellite and reanalysis daily precipitation estimates over Angola: The advantages of targeted regional 590 evaluations to support effective water management strategies
- M Almeida
- P Coelho
Almeida, M., Coelho, P., 2024. Datasets and code utilized in the development of the following manuscript: Evaluating the
589
accuracy of eight satellite and reanalysis daily precipitation estimates over Angola: The advantages of targeted regional
590
evaluations to support effective water management strategies [Data set].
Performance evaluation of satellite-and model-based precipitation
- M Amjad
- M T Yilmaz
- I Yucel
- K K Yilmaz
Amjad M, Yilmaz, M.T., Yucel, I., Yilmaz, K.K., 2020. Performance evaluation of satellite-and model-based precipitation
products over varying climate and complex topography
products over varying climate and complex topography. J Hydrol 584:124707. https://doi.org/10.1016/j.jhydrol.2020.124707
Quality Issues in Rainfall Data from Angolan Stations
- C Chauke
- N Nhamo
- L Ndlovu
Chauke, C., Nhamo, N., Ndlovu, L., 2019. Quality Issues in Rainfall Data from Angolan Stations. African Journal of Agricultural
Plano Nacional da Água. Tomo 5. Avaliação das disponibilidades hídricas superficiais
- Coba
Coba, 2014. Plano Nacional da Água. Tomo 5. Avaliação das disponibilidades hídricas superficiais. Républica de Angola,
602
Agricultural Planning and Rainfall Patterns in Angola: A Satellite Data Approach. 604 Agricultural and Forest Meteorology
- F Cunha
- P Silva
- H Pereira
Cunha, F., Silva, P., Pereira, H., 2021. Agricultural Planning and Rainfall Patterns in Angola: A Satellite Data Approach.
604
Agricultural and Forest Meteorology, 308, 108524.
Precipitation patterns and climate zones in Angola: An overview
- M Cunha
- C Costa
- P Pereira
Cunha, M., Costa, C., Pereira, P., 2018. "Precipitation patterns and climate zones in Angola: An overview." International Journal
606
of Climatology, 38(3), 1047-1060.
Challenges in Rain Gauge Data Collection in Rural Angola
- T Dinku
Dinku, T., 2019. Challenges in Rain Gauge Data Collection in Rural Angola. International Journal of Remote Sensing, 40(7),
Drought Analysis in Angola Using Remote Sensing Data
- S Fernandes
- C Furtado
- J Ribeiro
Fernandes, S., Furtado, C., Ribeiro, J., 2019. Drought Analysis in Angola Using Remote Sensing Data. Hydrology Research,
Performance of Satellite Rainfall Estimates in Angola: A Case Study. Remote
- D Ferreira
- A Rodrigues
- P Costa
Ferreira, D., Rodrigues, A., Costa, P., 2019. Performance of Satellite Rainfall Estimates in Angola: A Case Study. Remote
The Impact of Climate Variability on Droughts in Angola
- A Giannini
- D Gochis
- G Poveda
Giannini, A., Gochis, D., Poveda, G., 2018. The Impact of Climate Variability on Droughts in Angola. Climate Dynamics, 50(3),
Towards improved calibration of hydrological models: Combining the strengths 628 of global optimization and Bayesian analysis
- H V Gupta
- S Sorooshian
- P O Yapo
Gupta, H. V., Sorooshian, S., Yapo, P. O., 2009. Towards improved calibration of hydrological models: Combining the strengths
628
of global optimization and Bayesian analysis. Hydrological Processes, 23(4), 348-363.
Robustness of the precipitation-runoff relation: An evaluation of the 630 precipitation-runoff model and parameter estimation techniques
- H H Hsu
- S Sorooshian
- H V Gupta
Hsu, H. H., Sorooshian, S., Gupta, H. V., 1997. Robustness of the precipitation-runoff relation: An evaluation of the
630
precipitation-runoff model and parameter estimation techniques. Journal of Hydrology, 203(1-4), 115-136.
Algorithm Theoretical Basis Document (ATBD) Version 06, Global Precipitation Measurement (GPM) National 636 Aeronautics and Space Administration
- J Wolff
- D B Xie
J., Wolff, D.B., Xie, P., 2019. Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission
635
(IMERG). Algorithm Theoretical Basis Document (ATBD) Version 06, Global Precipitation Measurement (GPM) National
636
Aeronautics and Space Administration (NASA) Prepared.
Evaluation of precipitation reanalysis products for regional hydrological 641 modelling in the Yellow River Basin
- C Jiang
- E J R Parteli
- Q Xia
- Y Shao
Jiang, C., Parteli, E.J.R., Xia, Q., Shao, Y. 2024, Evaluation of precipitation reanalysis products for regional hydrological
641
modelling in the Yellow River Basin. Theor Appl Climatol 155, 2605-2626. https://doi.org/10.1007/s00704-023-04758-w
642
Comparison of different efficiency criteria for hydrological model assessment
- P Krause
- D P Boyle
- F Base
Krause, P., Boyle, D. P., Base, F., 2009. "Comparison of different efficiency criteria for hydrological model assessment."
643
Advances in Geosciences, 11, 31-42.
National strategy plan for rehabilitation of the hydrometric network in Angola
- E P Lars
Lars, E. P., 2004. National strategy plan for rehabilitation of the hydrometric network in Angola. Report No. 8, Norwegian
Accuracy of satellite and reanalysis rainfall estimates over Africa: A multi-scale assessment of eight This preprint research paper has not been peer reviewed
- Y Munzimi
Munzimi, Y., 2023. Accuracy of satellite and reanalysis rainfall estimates over Africa: A multi-scale assessment of eight
This preprint research paper has not been peer reviewed. Electronic copy available at: https://ssrn.com/abstract=4934289
for continental applications. Journal of Hydrology: Regional Studies 49:101514. https://doi. org/ 10.1016/j. ejrh. 2023.
655
101514
The Role of Rainfall Estimation in Agricultural Decision-Making in Angola
- M P Mendes
- V Ribeiro
- S Lima
Mendes, M. P., Ribeiro, V., Lima, S., 2022. The Role of Rainfall Estimation in Agricultural Decision-Making in Angola. Journal
Water Resource Management in Angola: An Overview
- M R Mojena
- U Nair
- Van Den
- J Berg
Mojena, M. R., Nair, U., van den Berg, J., 2018. Water Resource Management in Angola: An Overview. Journal of Hydrology
Performance Evaluation of CHIRPS and IMERG Over Southern Africa
- C Munyanduri
- J Nhan
- T Makara
Munyanduri, C., Nhan, J., Makara, T., 2020. Performance Evaluation of CHIRPS and IMERG Over Southern Africa. Journal
ERA5-Land: A State-Of-The-Art Global Reanalysis Dataset for Land Applications
- J Muñoz-Sabater
- Coauthors
Muñoz-Sabater, J., Coauthors, 2021. ERA5-Land: A State-Of-The-Art Global Reanalysis Dataset for Land Applications. Earth
666
Assessing Flood Risks in Angola Using Satellite Data. Remote Sensing of 674 Environment
- C R Santos
- R C Moreira
- J Pereira
Santos, C. R., Moreira, R. C., Pereira, J., 2021. Assessing Flood Risks in Angola Using Satellite Data. Remote Sensing of
674
Environment, 255, 112172.
The Use of Satellite Rainfall Data for Flood Forecasting in Southern Africa
- K Selemogo
- T Chikowore
- A Moreira
Selemogo, K., Chikowore, T., Moreira, A., 2022. The Use of Satellite Rainfall Data for Flood Forecasting in Southern Africa.
676
International Journal of Climatology, 42(8), 3792-3806.
Decline in Operational Rain Gauge Stations in Angola: A
- L Silva
- C Moreira
- R Almeida
Silva, L., Moreira, C., Almeida, R., 2021. Decline in Operational Rain Gauge Stations in Angola: A Critical Review.
678
Environmental Monitoring and Assessment, 193, 45.
Climate variability and change in Angola
- M Tchamba
- J Santos
- M Almeida
Tchamba, M., Santos, J., Almeida, M., 2016. "Climate variability and change in Angola." African Journal of Environmental
680
Science and Technology, 10(12), 419-430.
Flood Monitoring in Angola: A Review
- A A Teodoro
- F M Silva
- J Almeida
Teodoro, A. A., Silva, F. M., Almeida, J., 2020. Flood Monitoring in Angola: A Review. African Journal of Environmental
682
Science and Technology, 14(6), 245-258.
Validation of satellite-based precipitation 684 products over sparsely Gauged African River basins
- V Thiemig
- R Rojas
- M Zambrano-Bigiarini
- V Levizzani
- A De Roo
Thiemig, V., Rojas, R., Zambrano-Bigiarini, M., Levizzani, V., De Roo, A., 2012. Validation of satellite-based precipitation
684
products over sparsely Gauged African River basins. J. Hydrometeorol. 13, 1760-1783. https://doi.org/10.1175/JHM-D-12-
Annual Report on the Status of the Global Observing System. World Meteorological Organization. Retrieved from 687 WMO website
- Wmo
WMO, 2020. Annual Report on the Status of the Global Observing System. World Meteorological Organization. Retrieved from
687
WMO website.
Satellite Rainfall Data Accuracy in Angola: A Review
- F Zanotti
- T Pereira
- R Silva
Zanotti, F., Pereira, T., Silva, R., 2021. Satellite Rainfall Data Accuracy in Angola: A Review. International Journal of
697
Climatology, 41(15), 6635-6650.
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011