Satellite-based Precipitation Products Research Articles

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based Precipitation products for meteorological drought monitoring

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based Precipitation products for meteorological drought monitoring

Exploring the spatio-temporal variability of four satellite-based precipitation products (SPPs) in northern Morocco: a comparative study of complex climatic and topographic conditions

Exploring the spatio-temporal variability of four satellite-based precipitation products (SPPs) in northern Morocco: a comparative study of complex climatic and topographic conditions

Spatiotemporal evaluation of five satellite-based precipitation products under the arid environment of Saudi Arabia

In arid regions like Saudi Arabia, accurate precipitation data are crucial for water resource management and climate studies. However, satellite-based precipitation products (SPPs) can contain uncertainties, impacting their reliability. This study evaluated the accuracy of five high-resolution SPPs [IMERG-V06 variants, Tropical Rainfall Measuring Mission (TRMM)-3B42V7, and Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT)] over Saudi Arabia. We compared daily, monthly, and yearly precipitation estimates from SPPs with in situ rain gauge data (2010–2022) using both continuous and categorical metrics. The evaluation encompassed point-to-pixel comparisons, regional analysis, and national assessments. All SPPs effectively captured the spatiotemporal patterns of precipitation across the country. Notably, monthly estimates showed stronger agreement with rain gauge data than daily estimates, as indicated by higher correlation coefficients. IMERG products generally outperformed SM2RAIN-ASCAT and TRMM, with IMERG-LR exhibiting superior performance in estimating monthly precipitation. However, underestimation of light precipitation events (<2 mm/day) was observed across all SPPs. In addition, their ability to detect moderate and heavy precipitation events remained uncertain, requiring further investigation. While IMERG-FR showed reduced bias and root mean square error compared to IMERG-ER and IMERG-LR, its capability for precipitation event detection did not exhibit significant improvement. This study highlights the need for bias correction of IMERG-LR and IMERG-FR monthly estimates for improved application in hydrometeorological studies in Saudi Arabia. Our findings contribute valuable insights for both data users and SPP algorithm developers, aiming to enhance the accuracy and reliability of satellite-derived precipitation data in arid environments.

Regional Precipitation Regimes and Evaluation of National Precipitation Datasets against Satellite-Based Precipitation Estimates, Republic of Georgia

Abstract Analyzing water resources in areas with few hydrometeorological stations, such as those in post-Soviet countries, is difficult due to station closures after 1989. In Caucasus, evaluations often rely on outdated data from nearby rivers. We evaluated one national-level precipitation dataset, the Water Balance of Georgia (WBG) with two satellite-based precipitation products from 1981 to 2021, including the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data and CHIRPS blended with a dense rain gauge network (geoCHIRPS). We modeled mean annual precipitation from geoCHIRPS as a function of coastal distance and elevation. CHIRPS underestimated precipitation in the cold and wet seasons (R2 = 0.74, r = 0.86) and overestimated dry season precipitation, while geoCHIRPS performed well in all seasons (R2 = 0.86, r = 0.92). Distance from the coast was a more important predictor of precipitation than elevation in western Georgia, while precipitation correlated positively with elevation in the east. At four hydroelectric plants, the underperformance as a percentage of capacity (∼37%) corresponds with the percentage difference between differences in precipitation products (∼38%), suggesting that plants designed based on WBG may be systematically overdesigned, but further work is needed to determine the reasons for the underperformance of the plants and frequency. We conclude that 1) the existing WBG does not accurately reflect elevation–precipitation relationships near the coast, and 2) for accurate analysis of spatiotemporal precipitation variability and its impacts on hydropower generation and environmental and sustainable water resource management, it is essential to calibrate satellite-based precipitation estimates with additional rain gauge data.

A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d−1, RMSE = 2.86 mm d−1, and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB.

Efficiency of global precipitation datasets in tropical and subtropical catchments revealed by large sampling hydrological modelling

Satellite-based and reanalysis precipitation products are widely adopted as complementary information to in situ measurements for estimating river discharge using hydrological modelling. However, there is still a notable research gap in the literature associated with assessing the accuracy of satellite-based or reanalysis products in different tropical and sub-tropical catchments at large-sampling hydrological modelling with sensitivity analysis. We investigated the accuracy of precipitation, hydrological model performance and parameter sensitivity related to seven precipitation data sets based on satellite and reanalysis products, i.e., CHIRPS, TRMM, GLDAS, IMERG, MERRA-2, PERSIANN-CDR, and ERA5 over 714 contrasting tropical and subtropical catchments located in Brazil. We used the Génie Rural Journalier 4 (GR4J) hydrological model to simulate the hydrological processes of the different catchments with two approaches for the calibration: using measured ground-based precipitation data (approach I) or using each individual satellite/reanalysis precipitation products (approach II) to calibrate the models. The results showed that the precipitation products tend to overestimate precipitation, with the exception of ERA5 and MERRA-2. CHIRPS is the only product that produces unbiased precipitation estimates for most catchments. The model calibration using each precipitation product individually improved the hydrological model performance. CHIRPS, IMERG, and MERRA-2 showed good accuracy in terms of both, precipitation estimation and hydrological simulation performance in the calibration period. In the validation period, the best products in terms of KGE were CHIRPS, IMERG and TRMM (KGE > 0.64). The errors in precipitation products are better compensated via hydrological modelling in wet regions. The model parameter sensitivity varies according to precipitation input, climate, and catchment aridity. Overall, all seven products exhibited their worst hydrological performance in arid regions. This study helps to improve our understanding of the catchment response in tropical and subtropical regions while also providing key insights into the reliability of satellite/reanalysis rainfall products for streamflow simulation. This study is valuable in hydrometeorological applications, climate change assessment, water resources and disaster management, especially in regions with a relatively sparse density of precipitation stations.

Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China.

In hydrological studies, satellite and reanalysis precipitation products are increasingly being used to supplement gauge observation data. This study designed the composite simulation index (COSI), considering two factors: F1 (data accuracy assessment) and F2 (hydrological simulation performance), to compare the performance of the latest satellite-based and reanalysis-based precipitation products (IMERG, ERA5, ERA5-Land), the prior precipitation products (TRMM, CMADS), and the multi-source weighted-ensemble precipitation (MSWEP). The Soil and Water Assessment Tool (SWAT) model was then applied to compare and analyze the hydrological simulation performance of four preferred products using three data fusion methods including simple model averaging, variance-based weighted averaging, and the latest quantile-based Bayesian model averaging (QBMA). The results can be summarized as follows: (1) Reanalysis products are superior to satellite-based products in terms of F1. However, the satellite-based precipitation products exhibit less BIAS and relatively higher F2, while the MSWEP has relatively high performance on both F1 and F2. (2) Among reanalysis-based precipitation products, CMADS has the best COSI value of 0.53. Although ERA5-Land shows good performance for individual parameters, the comprehensive assessment reveals that ERA5 outperforms ERA5-Land in terms of both F1 and F2. (3) IMERG and TRMM exhibit similar spatiotemporal patterns and similar F1, but IMERG is superior in F2. (4) QBMA outperformed traditional methods in F2, improving the NS coefficient of SWAT model from 0.74 to 0.85. These findings provide a useful reference for analyzing the strengths and limitations of satellite-based and reanalysis precipitation products, and also provide valuable ideas for the combined application of multi-source precipitation products in hydrological studies.

Satellite-based precipitation error propagation in the hydrological modeling chain across China

Satellite-based precipitation products have great potential to promote hydrological modeling skill owing to the spatially homogeneous measurements they provide. However, satellite-based precipitation uncertainties and their hydrological impacts are poorly understood for different products and hydroclimate regions in China. This study comprehensively evaluates the error properties of ten satellite-based precipitation products and the precipitation error propagation characteristics in the hydrological modeling chain during the period 2001–2018. The results indicate that precipitation error tends to become smaller during the routing process for most satellite precipitation products; e.g., αRMAE, an error propagation ratio based on relative mean absolute error, decreased about 26% (from 0.58 to 0.43) when considering routing processes during hydrological simulation by using the PERSIANN-CDR satellite precipitation product. The linear slope of simulated streamflow against precipitation error greatly decreases (compared to that of simulated runoff) after flow routing of 366 catchments across China. This study also found that satellite-based precipitation product errors in hydrological simulations for drier and water-limited small catchments are more likely for amplify in the streamflow simulations. These findings are essential to improving understanding of the interactions between satellite-derived precipitation forcing and hydrological processes, and thus for improving the data accuracy of precipitation forcing for hydrological simulation in China.

Comparison of satellite-based and reanalysis precipitation products for hydrological modeling over a data-scarce region

Comparison of satellite-based and reanalysis precipitation products for hydrological modeling over a data-scarce region

Development of Intensity–Duration–Frequency (IDF) Curves over the United Arab Emirates (UAE) Using CHIRPS Satellite-Based Precipitation Products

The recent flooding events in the UAE have emphasized the need for a reassessment of flood frequencies to mitigate risks. The exponential urbanization and climatic changes in the UAE require a reform for developing and updating intensity–duration–frequency (IDF) curves. This study introduces a methodology to develop and update IDF curves for the UAE at a high spatial resolution using CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data. A bias correction was applied to the CHIRPS data, resulting in an improved capture of extreme events across the country. The Gumbel distribution was the most suitable theoretical distribution for the UAE, exhibiting a strong fit to the observed data. The study also revealed that the CHIRPS-derived IDF curves matched the shape of IDF curves generated using rain gauges. Due to orographic rainfall in the northeastern region, the IDF intensities were at their highest there, while the aridity of inland regions resulted in the lowest intensities. These findings enhance our understanding of rainfall patterns in the UAE and support effective water resource management and infrastructure planning. This study demonstrates the potential of the CHIRPS dataset for IDF curve development, emphasizes the importance of performing bias corrections, and recommends tailoring adjustments to the intended application.

Hydrological assessment of different satellite precipitation products in semi-arid basins in Morocco

In data-sparse regions and in developing countries such as Morocco, where flooding has serious socio-economic impacts, satellite-based precipitation products open new possibilities for monitoring and modelling water resources and floods. The objective of the study is to explore the possibility of using satellite precipitation products (SPPs) with hydrological models (CREST and MISDc) over 9 basins in Morocco. This work provides a hydrological assessment of three SPPs that have demonstrated good capabilities in reproducing precipitation over different basins in Morocco (GPM IMERG – PERSIANN CDR (PERCDR) and CHIRPS). The two hydrological models are coupled with a stochastic calibration method to provide the different ranges of uncertainties. In addition, we investigate the ability of SPPs on reproducing the November 2014 flood event that affected a large part of Morocco. The results indicated that, in calibration, both hydrological models provided similar performance to reproduce river discharge with observed precipitation or PERSIANN CDR. In validation, the combination of the MISDc model with PERSIANN CDR performed the best, notably allowing a good simulation of the flood hydrographs during the November 2014 event. Future analysis of relationships between SPPs, basin properties, and hydrological modelling technique will allow to find the appropriate combination for different application purposes.

Assessment of Bottom-Up Satellite Precipitation Products on River Streamflow Estimations in the Peruvian Pacific Drainage

In regions with limited precipitation information, like Peru, many studies rely on precipitation data derived from satellite products (SPP) and model reanalysis. These products provide near-real-time information and offer global spatial coverage, making them attractive for various applications. However, it is essential to consider their uncertainties when conducting hydrological simulations, especially in a key region like the Pacific drainage (Pd), where 56% of the Peruvian population resides (including the capital, Lima). This study, for the first time, assessed the performance of two bottom-up Satellite-based Precipitation Products (SPP), GPM + SM2RAIN and SM2RAIN-ASCAT, and one top-down approach SPP, ERA5-Land, for runoff simulation in the Pacific drainage of Peru. Hydrological modeling was conducted on 30 basins distributed across the Pd, which were grouped into 5 regions (I–V, ordered from south to north). The results showed that SM2RAIN-ASCAT performed well in regions I-III-IV, ERA5-Land in region II, and GPM + SM2RAIN in region V. The hydrological model GR4J was tested, and better efficiency criteria were obtained with SM2RAIN-ASCAT and GPM + SM2RAIN when comparing the simulated versus observed streamflows. The hydrological modeling using SM2RAIN-ASCAT and GPM + SM2RAIN demonstrated satisfactory efficiency metrics (KGE > 0.75; NSE > 0.65). Additionally, ten hydrological signatures were quantified to assess the variability of the simulated streamflows in each basin, with metrics such as Mean Flow (Q mean), 5th Quantile Flow (Q5), and 95th Quantile Flow (Q95) showing an overall better performance. Finally, the results of this study demonstrate the reliability of using bottom-up satellite products in Pd basins.

Performance and uncertainties of five popular satellite-based precipitation products in drought monitoring for different climate regions

Satellite-based precipitation products (SPPs) can provide crucial precipitation estimations for large-scale, near-real-time meteorological drought monitoring. However, the performances and uncertainties of SPPs in capturing drought information may vary greatly by product and climate region and are still poorly understood. In this study, we comprehensively evaluated the accuracy of five popular SPPs (MSWEP, TMPA 3B42, PERSIANN-CDR, CHIRPS, and COMRPH) in characterizing meteorological drought in mainland China. (1) Under global warming conditions, the standardized precipitation evapotranspiration index (SPEI) identified a significant decrease in drought associated with higher potential evapotranspiration (PET), which indicated the essential role of PET in determining drought evolution over different climate regions. (2) Among all the SPPs investigated, MSWEP, TMPA 3B42, and PERSIANN perform best in capturing the spatiotemporal pattern of the SPEI trend in the arid Tibetan Plateau regions, transition regions, and humid regions, respectively. In terms of drought category and drought characteristics, MSWEP and TMPA 3B42 perform very well for humid regions, and CHIRPS is better for cold and arid regions, with the poorest performance being found in CMORPH. However, no product performs well for all climate regions for drought monitoring. (3) The optimized parameters (OPs) are the dominant factor for drought uncertainties, followed by the interaction effect and SPPs, with great regional dependence. The OPs contributed the most to drought uncertainties over the relatively arid area of northern China, the interaction of OPs and SPPs did so in the humid area of China, and SPPs were found to contribute the most in the central Tibetan Plateau and some parts of humid regions. Our findings provide guidelines on the choice and application of existing SPP datasets for drought monitoring in different climate zones, as well as some clues to further improve the products over areas where they perform poorly.

On the reliability of 12 high-resolution precipitation products for process-based hydrological modeling in China

Numerous satellite- and gauge-based precipitation products (SBPs/GBPs) have emerged in recent decades, providing unprecedented opportunities for process-based hydrological modeling. However, their reliability in high-resolution hydrological modeling at continental scales, especially for variables other than streamflow, remains to be investigated. Factors influencing their hydrological performance (e.g., catchment sizes, precipitation products’ spatial resolution and raw observation information) are also under debate. Here, we provide a comprehensive analysis of the hydrological reliability of 12 state-of-the-art precipitation datasets over China using a process-based land surface model at 0.125° resolution. Simulations driven by different precipitation products are evaluated across ∼ 200 basins with different catchment sizes. Results show that all simulations well reproduce the streamflow, soil moisture, evapotranspiration, and snow-depth at monthly timescale. None of the precipitation products provides best results for all variables, but the gauge-based and infrared-microwave-merged satellite products are superior to the infrared satellite products. The gauge-based regional precipitation product based on the 0.25° China precipitation dataset and the 6-km China Land Data Assimilation System (CN051_CLDAS) provides the best performance generally, followed by two global precipitation products from the Global Precipitation Climatology Centre (GPCC V2022) and the Global Precipitation Measurement Integrated Multi-satellite Retrievals (IMERGE-F V6). However, the advantages of CLDAS_CN051 and GPCC V2022 against satellite-based precipitation products are scale-dependent and tend to disappear over large-scale catchments (>106 km2). The spatial resolutions of precipitation products show no relationship with their hydrological performance, but the number of stations used to generate/bias-correct the precipitation is positively correlated with the performance of streamflow and soil moisture simulations. Differences among the climatology of terrestrial water-cycle components caused by using different precipitation products are notable and tend to increase over the dry and cold regions. Snow depth is the variable most sensitive to precipitation datasets, followed by runoff, ET, and soil moisture. This study suggests that using more station observations is more important than increasing spatial resolutions to enhance the applications of precipitation products in high-resolution hydrological modeling.

Evaluation of IMERG precipitation product over various temporal scales in a semi-arid region of southern Iran

In the recent decades, poor spatial coverage of rain gauges and weather radars, mostly in less developed countries, has caused satellite-based precipitation products to gain more popularity. However, they are considered less precise compared to traditional rain gauges and weather radars. Therefore, their thorough evaluations are essential before implementing these products in practice. In this study, IMERG satellite-based precipitation product was evaluated in the Fars province, Iran, using daily rain gauges as reference data to find the relationships between the uncertainties in the product and various factors such as elevation, temperature, and rainfall intensity by employing continuous and categorical statistical metrics. The findings revealed that: a) The IMERG product overestimates light rainfall and underestimates heavy rainfall, while showing the best performance in the range 40–80 mm/day. b) The accuracy of the product tends to vary in different months of the year and it is less biased in ones with milder temperatures. c) Higher correlation in mid-elevated areas (up to 0.96), positive bias in low-elevated areas (up to 2.5) and negative bias in high-elevated areas (down to −0.4) were observed. d) A considerable improvement in the IMERG estimates was observed for longer time scales for almost all validation indices. Overall, IMERG products show potential for hydrological and atmospheric decision-making, but further improvements in retrieval algorithms and developing local correction models, considering the trends found in this study, are needed for increased reliability.

Comprehensive Evaluation of Global Precipitation Products and Their Accuracy in Drought Detection in Mainland China

Abstract Thorough evaluations of satellite precipitation products are necessary for accurately detecting meteorological drought. A comprehensive assessment of 15 state-of-the-art precipitation products (i.e., IMERG_cal, IMERG_uncal, GSMaP-G, CPC-Global, TRMM3B42, CMORPH-CRT, PERSIANN-CDR, PERSIANN, PERSIANN-CCS, SM2RAIN, CHIRPS, ERA5, ERA-Interim, MERRA-2, and GLDAS) is herein conducted for the period 2010–19 giving special attention to their performance in detecting meteorological drought over mainland China at 0.25° spatial resolution. The cited precipitation products are compared against China’s gridded gauge-based Daily Precipitation Analysis (CGDPA) product, derived from 2400 meteorological stations, and their quality is assessed at daily, seasonal, and annual precipitation time scales. Meteorological droughts in the datasets are determined by calculating the standardized precipitation evapotranspiration index (SPEI). The performance of the precipitation products for drought detection with respect to the SPEI is assessed at three time scales (1, 3, and 12 months). The results show that the GSMaP-G outperforms other satellite-based datasets in drought detection and precipitation estimation. The MERRA-2 and the ERA5 are on average closer to the CGDPA reference data than other reanalysis products for precipitation estimation and drought detection. These products capture well the spatial and temporal pattern of the SPEI in southern and eastern China having a probability of detection (POD) above 0.6 and a correlation coefficient (CC) above 0.65. CPC-Global, IMERG, and the ERA5 reanalysis product are ideal candidates for application in western China, especially in the Qinghai–Tibetan Plateau and the Xinjiang Province. Generally, the accuracy of precipitation products for drought detection is improved with longer time scales of the SPEI (i.e., SPEI-12). This study contributes to drought-hazard detection and hydrometeorological applications of satellite precipitation products. Significance Statement The purpose of this study is to comprehensively evaluate the quality of 15 global satellite-based, gauge-based, and reanalysis precipitation products for meteorological drought detection at 1-, 3-, and 12-month time scales. This work systematically evaluates these products’ capacity to capture precipitation occurrence and intensity in different seasons. This is followed by a comparison of the precipitation products’ performance in drought detection. This work’s findings and evaluation results will improve the ability of those who develop precipitation products in identifying error sources and further improving retrieval algorithms. This paper’s results will serve as a valuable reference for end users seeking to better understand the application of precipitation products to drought detection.

Do satellite-based products suffice for rainfall observations over data-sparse complex terrains? Evidence from the North-Western Himalayas

Remotely sensed observations are crucial for conducting meteorological studies, particularly over complex mountainous terrains such as, the North-Western Himalayas (NWH), which are characterized by a very sparse and uneven distribution of ground observatories. These complex terrains are subject to huge inter-basin climatic and topographic heterogeneity, which often induces large inconsistencies in the form of varying rainfall estimations and observations among the satellite-based gridded precipitation products (GPPs). This underpins the need for a detailed sub-regional validation of GPPs and attribution of the observed discrepancies over such complex terrains. This study provides a comprehensive validation analysis of four major high-resolution GPPs (TMPA-3B42V7, CHIRPS-V2.0, CMORPH-CDR, and PERSIANN-CDR) against the in-situ gridded observations provided by IMD, across the six sub-basins of NWH. Utilizing various descriptive, statistical, and categorical metrics, we evaluated the daily, seasonal, and annual rainfall climatology for each dataset and also extracted their longitudinal and latitudinal error profiles and detection capabilities as a function of elevation and local relief. High inconsistencies were observed among the datasets, with each dataset performing differently over different sub-basins. All the datasets underperformed IMD over higher elevations, while TMPA-3B42V7 performed better even over the Upper Indus sub-basin. CHIRPS-V2.0 performed well over Jhelum, and Chenab sub-basins, whereas the CDR version of CMORPH, performed well over the flatter terrains of Ravi, Beas, and Satluj sub-basins, particularly during the summer monsoons. The IR-based PERSIANN-CDR, on the other hand, failed to capture the daily rainfall estimates and underperformed IMD throughout NWH. The findings further highlight low detection capability of datasets at higher rainfall thresholds, indicating underestimation of extreme rainfall. With respect to the relationship between GPPs performance and topographic components, the results showed that the occurrence frequency of rain detection in GPPs decreases, while the occurrence frequency of over-detection increases with increase in elevation and local relief. Although, the performance of GPPs co-varies with both topographic components, local relief governed by the multi-step topographic gradients comprising valleys, orographic fronts, and orographic interiors was observed to have a more profound effect on error profiles. Lastly, the study identifies and discusses the probable regional (e.g., rainfall structure, orographic influence, biophysical, data scarcity, spatial resolution), and product-specific (e.g., source of generation, remote sensing instrumentation, satellite algorithms, data assimilation techniques) factors attributing to the inconsistencies observed among the datasets. The findings from the present research offer insights to the algorithm developers for improving product accuracy and hydro-meteorologist for utilizing appropriate GPP over respective sub-basin.

Spatiotemporal Evaluation of Satellite-Based Precipitation Products in the Colorado River Basin

Abstract Gridded precipitation products from satellite-based systems provide continuous and seamless data that can overcome the limitations of ground-based precipitation data. Remote sensing (RS) products can provide efficient precipitation data in the desert rangelands and the Rocky Mountains of the western United States, where ground-based rain gauges are sparse. In this study, we evaluated the quality of precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) in the Upper Colorado River basin (UCRB) for the period 2000–20. The reliability of daily precipitation data from these products was tested against ground-based observations from the National Oceanic and Atmospheric Administration (NOAA) using two continuous and four categorical statistical evaluation metrics. We investigated the effects of topographical conditions on the quality of precipitation estimates. Results show that all three products have 3–4 mm day−1 differences in daily precipitation rates compared to ground observations. In addition, the difference in monthly precipitation rates was more prominent in the wet season (November–April) than in the dry season (May–October). The margin of errors varied with the type of RS system and by location. A categorical evaluation suggests a moderate ability to detect precipitation occurrence with 50%–60% detection ability. The reliability of precipitation estimates is mainly limited by elevation and different ecoregions and climate features.

Implementation of HydroBID Model with Satellite-Based Precipitation Products in Guadalquivir Basin, Bolivia

The use of distributed precipitation data in hydrological models is critically important to simulate processes at a micro-basin scale. However, aerial precipitation at a high resolution is required to run these models. This study aimed to set up the HydroBID tool in the Guadalquivir River basin using satellite-based precipitation products. The employed products included GSMaP gauge version 6, interpolated rain gauges using Kriging, the combined GS product for Bolivia, and the proposed combined product for the Guadalquivir basin. The GS Guadalquivir was generated by combining the satellite-based product GSMaP gauge version 6 with the local rain gauge network. The main difference with GS Bolivia is the improvement of the resolution from 5 km to 250 m. An iteration scheme using 230 micro-basins was employed, reaching a correlation of 0.98 compared to the control dataset. By using the hydrological model with the precipitation products, the daily river discharge was obtained, showing a high correlation of 0.99 and efficiency of 0.96 in relation to observed data between 2000 and 2016 at Obrajes station. Simulated flows with Kriging and GS Guadalquivir products presented similarly high correlations compared to the observed flows. In the case of GSMaP and GS Bolivia, these products showed general underestimations of the simulated flows, reaching correlations between 0.28 and 0.91, respectively. Moreover, annual volumes were analyzed, where the overestimation of GSMaP, Kriging, and GS Guadalquivir showed similar characteristics concerning the distribution of specific river discharges and volumes. Therefore, HydroBID appeared to be a feasible tool with enough adaptability to use distributed precipitation and simulate flows at a micro-basin scale. Therefore, we recommend applying this scheme to other basins to carry out analysis of events, water balance, and floods and similar studies.

Machine learning algorithms for merging satellite-based precipitation products and their application on meteorological drought monitoring over Kenya

Machine learning algorithms for merging satellite-based precipitation products and their application on meteorological drought monitoring over Kenya