Gridded Rainfall Products Research Articles

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.

Rainfall characteristics over the Congo Air Boundary Region in southern Africa: A comparison of station and gridded rainfall products

Strong meridional rainfall gradients exist between the tropics and subtropics in southwestern Africa, bounded to the north by the moist Congo basin and to the south by the Kalahari Desert. This domain received relatively little scientific attention compared to the rest of southern Africa. In this study, the limited available station data are assessed against six gridded rainfall products (CHIRPS, PERSIANN-CDR, ERA5, GPCC and CPC) for various rainfall characteristics. The nearest neighbour approach was used to match the closest rainfall dataset pixel to each station location, with the assumption that each rain gauge represents observation of various pixels of products, irrespective of product resolution. Results reveal that ERA5, CHIRPS and PERSIANN-CDR tend to represent the monthly rainfall totals and number of rainy days well for most stations although magnitudes and monthly peaks differ. CPC and GPCC tend to perform poorly for magnitudes of rainfall, rainy days and monthly cycles especially for Angolan stations. These products also fail to adequately capture spatial distributions of rainfall, with poor representation of the strong gradients found in the region.Correlations between various gridded rainfall products mostly show good agreement in rainfall totals and rainy days. For early summer (October–November-December) moderate rainy days, ERA5 and CHIRPS products tend to have more days than the stations while CPC and GPCC products perform poorly over Angola and in the south. ERA5 generally overestimates rainfall in mountainous regions, while other products tend to underestimate it. Based on the Simple Daily Intensity Index, it was found that for most of the gridded rainfall products tend to overestimate rainfall during rainy days in the northern and wetter part of the domain. Furthermore, for heavy rainfall, CPC and GPCC tend to compare fairly well with stations in the southern and eastern parts of the domain but poorly with those in the western parts. PERSIANN-CDR tends to underestimate heavy rainy days for most stations in early and late summer (January–February-March-April). However, CHIRPS compares well at several stations, while ERA5 performs well for stations located in the south. This study helps provide useful guidance in choosing suitable rainfall gridded datasets for assessing long term rainfall cycles, daily rainfall characteristics as well as extremes over southwestern Africa.

Evaluation and inter-comparison of twenty-three gridded rainfall products representing a typical urban monsoon climate in India

Accurate and reliable estimation of rainfall is crucial for scientific research and various applications. However, the observed rainfall data is often limited. With the advancements in technology, many global gridded rainfall products are now available, but their accuracy levels vary across the world. In this study, we comprehensively analyzed the reliability and effectiveness of 23 publicly available global rainfall datasets against the observed rainfall for Patna, representing a typical urban monsoon climate in India. Thirteen continuous and ten categorical statistical metrics were applied at daily, weekly, monthly, and annual intervals over 16 years (2000–2015). The results indicate that the reliability of all derived rainfall datasets varied on different temporal scales and reference datasets used. Overall, in continuous metrics, MERRA2 and MSWEP consistently outperformed in all the temporal scales whereas in categorical metrics for analyzing the rainfall detection ability, AIMERG, followed by MERRA2 demonstrated superior performance among others. Furthermore, IMD GRID, GSMAP, PCCS, AIMERG, and IMERG performed well in estimating different rainfall intensities. MERRA2 and MSWEP, which have not been widely considered for evaluation in a monsoon climate were found to be outstanding performers consistently. Therefore, we suggest broadening the selection of global rainfall products in the evaluation to fully utilize the potentiality of all available options. Furthermore, our approach offers a reliable framework to comprehensively assess the performance of different gridded rainfall products and assist in the selection of the best rainfall product for a particular region and purpose.

More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India

Abstract. The rapidly expanding Himalayan road network connects rural mountainous regions. However, the fragility of the landscape and poor road construction practices lead to frequent mass movements alongside roads. In this study, we investigate fully or partially road-blocking landslides along National Highway (NH-7) in Uttarakhand, India, between Rishikesh and Joshimath. Based on an inventory of >300 landslides along the ∼250 km long corridor following exceptionally high rainfall during September and October 2022, we identify the main controls on the spatial occurrence of mass-movement events. Our analysis and modeling approach conceptualizes landslides as a network-attached spatial point pattern. We evaluate different gridded rainfall products and infer the controls on landslide occurrence using Bayesian analysis of an inhomogeneous Poisson process model. Our results reveal that slope, rainfall amounts, lithology and road widening are the main controls on landslide occurrence. The individual effects of aggregated lithozones are consistent with previous assessments of landslide susceptibilities of rock types in the Himalayas. Our model spatially predicts landslide occurrences and can be adapted to other rainfall scenarios, thus having potential applications for efficiently allocating efforts for road maintenance. To this end, our results highlight the vulnerability of the Himalayan road network to landslides. Climate change and increasing exposure along this pilgrimage route will likely exacerbate landslide risk along the NH-7 in the future.

Evaluation of Gridded Rainfall Products in Three West African Basins

In recent years, accessing rainfall data from ground observation networks maintained by national meteorological services in West Africa has become increasingly challenging. This is primarily due to high acquisition costs and the often sparse distribution of rainfall gauges across the region, which limits their use in hydrological studies and related research. At the same time, the rising availability of precipitation products derived from satellite/earth observations, reanalysis datasets, and in situ measurements presents exciting prospects for hydrological applications. Nonetheless, these datasets constitute indirect measurements, necessitating rigorous validation against ground-based rainfall data. This study comprehensively assesses twenty-three gridded rainfall products, including sixteen from satellites, six from reanalysis data, and one from in situ measurements, across the Senegal, Gambia, and Casamance River basins. Performance evaluation is conducted across distinct climatic zones, both pre- and post-resampling against observed rainfall data gathered from forty-nine rainfall stations over a six-year period (2003–2008). Evaluation criteria include the Kling–Gupta Efficiency (KGE) and Percentage of Bias (PBIAS) metrics, assessed at daily, monthly, and seasonal time steps. The results reveal distinct performance levels among the evaluated rainfall products. RFE, ARC2, and CPC notably yield the highest KGE scores at the daily time step, while GPCP, CHIRP, CHIRPS, RFE, MSWEP, ARC2, CPC, TAMSAT, and CMORPHCRT demonstrate superior performance at the monthly time step. During the rainy season, these products generally exhibit robustness. However, rainfall estimates derived from reanalysis datasets (ERA5, EWEMBI, MERRA2, PGF, WFDEICRU, and WFDEIGPCC) perform poorly in the studied basins. Based on the PBIAS metric, most products tend to underestimate precipitation values, while only PERSIANN and PERSIANNCCS lead to significant overestimations. Spatially, optimal performance of the products is observed in the Casamance basin and the Sudanian and Sahelian climatic zones within the Gambia and Senegal basins. Conversely, in the Guinean zone of the Gambia and Senegal Rivers, the rainfall products displayed the poorest performance.

Multiple-point geostatistics-based spatial downscaling of heavy rainfall fields

High-resolution gridded rainfall products at sub-daily and 100 km scales are required for hydrological applications in mountainous and urban catchments. As most catchments are ungauged, gridded rainfall data are often obtained through remote sensing. However, their spatial resolution is often too coarse (at 101 km) and requires to be downscaled to a finer resolution. The challenge is not only to downscale the rainfall intensity to a finer scale by considering areal reduction factors, but also the spatial structure of the storm, as both elements are equally important to the assessment of the surface hydrological response. As a result of the lack of training data, the latter is difficult to obtain. Further development of the stochastic multiple-point geostatistics (MPS) framework is presented to downscale long-term satellite-derived gridded rainfall series using only a few years of high-resolution rainfall observations. We demonstrate how the MPS framework can be used to downscale the satellite-derived CMORPH rainfall from 8 to 1 km resolution for 1998–2019, taking the city of Beijing as a case study, with a specific focus on extreme rainfall events. The high-resolution multisource-merged CMPAS dataset (1 km, hourly), available for 2015–2020, is used as the source of the training images. We show that the downscaling framework preserves the observed mean areal rainfall (with a bias of 2 %), reproduces the spatial coefficient of variance (with a similar bias), and also retains extreme rainfall at the 99th percentile (with a bias of 6 %). Furthermore, it adequately reproduces the rainfall spatial structure, preserving the variograms of the rainfall fields. Similarities were also observed comparing the 2- to 30-year return period maps of the downscaled rainfall extreme with ground observations, with half of the stations (10 out of 19) agreeing on the location and intensity of the extreme rainfall for all return periods. The results indicate that our framework downscales rainfall intensities and preserves the spatial structure well, especially for heavy rainfall, even if limited data is available. The proposed approach can be applied to other rainfall datasets and regions.

Evaluation of CMIP6 models in reproducing observed rainfall over Ethiopia

Abstract Ethiopia is highly susceptible to the effects of climate change and variability. This study evaluated the performances of 37 CMIP6 models against a gridded rainfall product of Ethiopia known as Enhancing National Climate Services (ENACTS) in simulating the observed rainfall from 1981 to 2014. Taylor Skill Score was used for ranking the performance of individual models for mean monthly, June–September, and February–May seasonal rainfall. Comprehensive rating metrics (RM) were used to derive the overall ranks of the models. Results show that the performances of the models were not consistent in reproducing rainfall distributions at different statistical metrics and timeframes. More than 20 models simulated the largest dry bias on high topographic and rainfall-receiving areas of the country during the June–September season. The RM-based overall ranks of CMIP6 models showed that GFDL-CM4 is the best-performing model followed by GFDL-ESM4, NorESM2-MM, and CESM2 in simulating rainfall over Ethiopia. The ensemble of these four Global Climate Models showed the best performance in representing the spatiotemporal patterns of the observed rainfall relative to the ensembles of all models. Generally, this study highlighted the existence of dry bias in climate model projections for Ethiopia, which requires bias adjustment of the models, for impact assessment.

A Novel Framework for Correcting Satellite-Based Precipitation Products for Watersheds with Discontinuous Observed Data, Case Study in Mekong River Basin

Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of precipitation data is on the distinctive geographical features of each location, they remain at a considerable distance from station-based data. This paper examines the effectiveness of a convolutional autoencoder (CAE) architecture in pixel-by-pixel bias correction of SP products for the Mekong River Basin (MRB). Two satellite-based products (TRMM and PERSIANN-CDR) and a gauge-based product (APHRODITE) are gridded rainfall products mined in this experiment. According to the estimated statistical criteria, the CAE model was effective in reducing the gap between SP products and benchmark data both in terms of spatial and temporal correlations. The two corrected SP products (CAE_TRMM and CAE_CDR) performed competitively, with CAE TRMM appearing to have a slight advantage over CAE CDR, however, the difference was minor. This study’s findings proved the effectiveness of deep learning-based models (here CAE) for bias correction of SP products. We believe that this technique will be a feasible alternative for delivering an up-to-current and reliable dataset for MRB studies, given that the sole available gauge-based dataset for this area has been out of date for a long time.

Derivation of New Daily Rainfall Values from TAO 1-Min Rain Gauge Data

Abstract Daily rainfall accumulation estimates have been derived from 1-min volume data collected via self-syphon rain gauges deployed in the Tropical Atmosphere–Ocean (TAO) array of oceanographic buoys. The underlying high-resolution volume data were obtained directly from the Global Tropical Moored Buoy Array (GTMBA) Project Office of NOAA/Pacific Marine Environmental Laboratory. The derived accumulations have been incorporated into the Pacific Rainfall (PACRAIN) database as estimated daily values to augment existing sea level oceanic rainfall records gathered using traditional rain gauges. They have also been included in the PACRAIN historical, monthly gridded rainfall product. The methodology presented, which employs differencing of least squares–regressed sensor levels about 0000 UTC and rain gauge syphon events, is shown to offer improved error characteristics over the methodology used to compute previously published GTMBA rain rates. In particular, the PACRAIN method yields larger coefficients of determination and smaller standard errors than the duplicated GTMBA method when applied to synthetic rainfall data with noise magnitude and decorrelation times encompassing those observed in the real 1-min data. These results are shown to be consistent with mathematical expectations. Sources of instrument and catchment errors, as well as evaporation, are discussed in the context of their potential effects on accumulation estimates for periods of a day or longer. Significance Statement In this paper, we describe the derivation of daily rainfall amounts from raw rain gauge data obtained from buoy-mounted rain gauges. These new accumulation estimates expand the store of rainfall estimates from locations approximating the open-ocean conditions of the tropical Pacific Ocean. The derivation technique we describe exhibits better performance than the method used to generate previously published estimates using the same dataset.

Testing the mHM-MPR Reliability for Parameter Transferability across Locations in North–Central Nigeria

Hydrologic modeling in Nigeria is plagued by non-existent or paucity of hydro-metrological/morphological records, which has detrimental impacts on sustainable water resource management and agricultural production. Nowadays, freely accessible remotely sensed products are used as inputs in hydrologic modeling, especially in regions with deficient observed records. Therefore, it is appropriate to utilize the fine-resolution spatial coverage offered by these products in a parameter regionalization method that supports sub-grid variability. This study assessed the transferability of optimized model parameters from a gauged to an ungauged basin using the mesoscale Hydrologic Model (mHM)—Multiscale Parameter Regionalization (MPR) technique. The ability of the fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis product (ERA5), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Global Precipitation Climatology Centre (GPCC), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) gridded rainfall products to simulate observed discharge in three basins was first assessed. Thereafter, the CHIRPS rainfall product was used in three multi-basin mHM setups. Optimized model parameters were then transferred to independent basins, and the reproduction of observed discharges was assessed. Kling–Gupta Efficiency (KGE) scores showed improvements when mHM runs were performed using optimized parameters in comparison to using default parameters for discharge simulations. Optimized mHM runs performed reasonably (KGE > 0.4) for all basins and rainfall products. However, only one basin showed a satisfactory KGE value (KGE = 0.54) when optimized parameters were transferred to an ungauged basin. This study underscores the utility of the mHM-MPR tool for parameter transferability during discharge simulation in data-scarce regions.

Developing a high-resolution gridded rainfall product for Bangladesh during 1901–2018

A high-resolution (1 km × 1 km) monthly gridded rainfall data product during 1901–2018, named Bangladesh Gridded Rainfall (BDGR), was developed in this study. In-situ rainfall observations retrieved from a number of sources, including national organizations and undigitized data from the colonial era, were used. Leave-one-out cross-validation was used to assess product’s ability to capture spatial and temporal variability. The results revealed spatial variability of the percentage bias (PBIAS) in the range of −2 to 2%, normalized root mean square error (NRMSE) <20%, and correlation coefficient (R2) >0.88 at most of the locations. The temporal variability in mean PBIAS for 1901–2018 was in the range of −4.5 to 4.3%, NRMSE between 9 and 19% and R2 in the range of 0.87 to 0.95. The BDGR also showed its capability in replicating temporal patterns and trends of observed rainfall with greater accuracy. The product can provide reliable insights regarding various hydrometeorological issues, including historical floods, droughts, and groundwater recharge for a well-recognized global climate hotspot, Bangladesh.

Evaluation of the Analysis of Record for Calibration (AORC) Rainfall across Louisiana

The use of a long-term and high-quality precipitation dataset is crucial for hydrologic modeling and flood risk management. This study evaluates the Analysis of Period of Record for Calibration (AORC) dataset, a newly released product with high temporal and spatial resolutions. Our study region is centered on Louisiana because of the major flooding it has been experiencing. We compare the AORC hourly precipitation to other widely used gridded rainfall products and rain-gauge observations. To evaluate the performance of rainfall products according to different weather conditions causing severe flooding, we stratify the analyses depending on whether precipitation is associated with a tropical cyclone (TC) or not. Compared to observations, our results show that the AORC has the highest correlation coefficients (i.e., values above 0.75) with respect to observations among all rainfall products for both TC and non-TC periods. When the skill metric is decomposed into the potential skill and biases, the AORC clearly shows the highest potential skill with relatively small biases for the whole period. In addition, the AORC performs better for the TC period compared to the non-TC ones. Our results suggest that AORC precipitation shows good potential to be viable for hydrologic modeling and simulations of TC and non-TC events.

Hydrological evaluation of satellite and reanalysis-based rainfall estimates over the Upper Tekeze Basin, Ethiopia

Abstract Spatial rainfall data is an essential input to parametrically distributed hydrological models and is a main contributor to hydrological model uncertainty. Two important issues should be addressed before the use of satellite and reanalysis rainfall product at the basin level: (1) How useful are these rainfall estimates as forcing data for regional hydrological modelling? and (2) Which product should be selected at high-flow and low-flow conditions? This paper presents the hydrological performance of satellite and reanalysis rainfall products (CHIRPSv8 and EWEMBI) at three hydrological stations in the Upper Tekeze River Basin (UTB), northern Ethiopia. Results showed that the daily rainfall data from both CHIRPSv8 and EWEMBI are close to the rain gauge data, with relative errors 2.12 and 3.85%, respectively. The monthly streamflow simulated by the Soil and Water Assessment Tool (SWAT) model driven by CHIRPSv8 and EWEMBI had a Kling-Gupta Efficiency of 0.6–0.79 and 0.58–0.64, respectively. The hydrological performance during high-flow seasons is superior to low-flow seasons for both CHIRPSv8 and EWEMBI. In particular, CHIRPSv8 showed a relatively better hydrological performance than EWEMBI. This study provides insights on the usefulness of gridded rainfall products for hydrological modelling and conditions under which they can be used over the UTB and other similar basins.

Uncertainty in Drought Identification Due to Data Choices, and the Value of Triangulation

Droughts are complex and gradually evolving conditions of extreme water deficits which can compromise livelihoods and ecological integrity, especially in fragile arid and semi-arid regions that depend on rainfed farming, such as Kitui West in south-eastern Kenya. Against the background of low ground-station density, 10 gridded rainfall products and four gridded temperature products were used to generate an ensemble of 40 calculations of the Standardized Precipitation Evapotranspiration Index (SPEI) to assess uncertainties in the onset, duration, and magnitude of past droughts. These uncertainties were driven more by variations between the rainfall products than variations between the temperature products. Remaining ambiguities in drought occurrence could be resolved by complementing the quantitative analysis with ground-based information from key informants engaged in disaster relief, effectively formulating an ensemble approach to SPEI-based drought identification to aid decision making. The reported trend towards drier conditions in Eastern Africa was confirmed for Kitui West by the majority of data products, whereby the rainfall effect on those increasingly dry conditions was subtler than just annual and seasonal declines and greater annual variation of rainfall, which requires further investigation. Nevertheless, the effects of increasing droughts are already felt on the ground and warrant decisive action.

Statistical Comparison of IMERG Precipitation Products with Optical Rain Gauge Observations over Kototabang, Indonesia

Satellite-based precipitation estimates play a crucial role in many hydrological and numerical weather models, especially to overcome the scarcity of rain gauge data. Globally gridded rainfall product from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) has been used in a wide range of hydrological applications. However, the IMERG is inherently prone to errors and biases. This study evaluated the performance of the IMERG-Final run (IMERG-F) product to estimate rainfall in a mountainous area of Sumatra. Validation was carried out using optical rain gauge (ORG) data for 15 years (2002-2016), at Kototabang, West Sumatra, Indonesia. In general, IMERG-F overestimated rainfall in all time scales. The longer the time scale was, the better the performance of IMERG-F we obtained. This feature was indicated by all quantities of continuous and categorical statistical matrices used. The performance of IMERG-F was lower than in other areas of the Maritime Continent, except for the probability of detection (POD) value. IMERG-F could detect rain very well, including for daily and hourly data, but the false alarm rate (FAR) was also relatively high. Such high FAR value may indicate a significant small-scale spatial rainfall variability in mountainous area of Sumatra.

The Connection between Extreme Precipitation Variability over Monsoon Asia and Large-Scale Circulation Patterns

Spatial and temporal variability in precipitation has been dramatically changed due to climate variability and climate change over the global domain. Increasing in extreme precipitation events are pronounced in various regions, including monsoon Asia (MA) in recent decades. The present study evaluated precipitation variability in light of intensity, duration, and frequency with several extreme precipitation climate change indices developed by the Expert Team on Climate Change Detection Indices (ETCCDI) over the MA region. This study uses an improved version (APHRO_V1901) of the Asian Precipitation Highly Resolved Observation Data Integration Towards Evaluation of extreme events (APHRODITE-2) gridded rainfall product. Results showed that the spatial variability of the extreme precipitation climate change indices is reflected in the annual mean rainfall distribution in MA. Maximum one-day precipitation (R × 1) and precipitation contributed from extremes (R95) depict a peak in decadal mean rainfall values over topography regions. A significant positive trend in R × 1 (with a slope of 0.3 mm/yr) and precipitation greater than the 95th percentile (R95: with a slope of 0.5 mm/yr) are predominantly observed in decadal trends in regional average extreme precipitation climate change indices over MA. Maritime continental countries exhibit an inclined trend in R10, whereas central Asian arid regions show a decreasing tendency in continuous dry days (CDD). The positive trend in R95 is observed over central India, the monsoon region in China, countries that reside over the equator and some parts of Japan, and the Philippines. When comparing the influence of surface temperature (T) and total column water vapor (TCW) on precipitation climate change indices, TCW seems to be a crucial attributor to climate change indices meridional variability. The mutual correlation analysis depicts that precipitation contributed from extremes (R95) strongly correlates in terms of temporal variability with all extreme precipitation indices. Among various global circulation patterns, the prevalent conditions of sea surface temperature (SST) over the equatorial Pacific Ocean have a significant influence on decadal variability in extreme precipitation climate change indices. R10 and R95 possess a relatively significant correlation (0.86 and 0.91) with the Southern Oscillation Index. The maximum number of consecutive dry days (CDD) shows an increasing trend with a positive phase of the North Atlantic Oscillation Index.

Datasets and approaches for the estimation of rainfall erosivity over Italy: A comprehensive comparison study and a new method

The paper considers a methodology to assess rainfall erosivity in Italy for the recent decades (1981–2010), building on datasets and materials freely available, such as those included within the Climate Data Store (CDS) of the Copernicus Climate Change Service (C3S). Twenty-one referenced empirical models to assess rainfall erosivity (R-factor) based on coarse rainfall data are tested and compared; then, a custom model is calibrated, with the support of seasonal rainfall pattern clustering by means of the Self-Organizing Map. Moreover, a large database of sub-hourly rainfall observations, covering the period 2002–2011, is collected and used for validation. Model performances are analysed at the point-scale of the rain gauges and at the spatial scale of different relevant gridded rainfall products: the fifth generation of ECMWF ReAnalysis (ERA5, ERA5-Land), the gridded European observational dataset (E-OBS), all included in the CDS, and SCIA-ISPRA (the Italian standard rainfall gridded dataset).New Hydrological Insights from the RegionThe proposed methodology provides four alternatives of spatially distributed rainfall erosivity datasets covering Italy with diverse levels of reliability. Analysis of results shows that the best performance is achieved by the combined use of the custom model and the SCIA-ISPRA dataset, followed by ERA5-Land, with the main source of error lying in the use of an empirical model instead of the rigorous model for R-factor estimation, and secondly in the use of gridded rainfall data instead of point-scale rainfall observations.

Evaluation of fine resolution gridded rainfall datasets over a dense network of rain gauges in Niger

This study evaluates the reliability of gridded rainfall products with fine spatial resolution using 29 gauges of a dense observation network in Niger. Four gridded datasets were considered, viz. the African Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), Climate Prediction Center morphing technique (CMORPH) and Tropical Applications of Meteorology using Satellite data and ground-based observations (TAMSAT). The datasets were quantitatively compared to the gauge rainfall at different timescales and their rainfall detection skills were assessed using four validation metrics and three contingency indices, respectively. Furthermore, their potentials for trend analysis was assessed using a modified Mann-Kendall test; and finally, their capability of capturing damaging rainfalls was evaluated using data of five historical damaging rainfall amounts on a daily basis. The CMORPH product showed a good performance in detecting rainfall events and exhibited the best linear relationship with the gauge rainfall; however, it displayed high errors and relative bias (RB > 100%)—showing its inaccuracy in rainfall quantification. At daily, dekadal and monthly timescale, CHIRPS showed the lowest errors, followed by the ARC2 and TAMSAT, whereas ARC2 exhibited the lowest RB—between −12% and 22%. All of the four datasets were unable to unveil the significant trends found in ground observations and were inaccurate in sensing the depths of damaging rainfall amounts. The overall results revealed that the 3-hourly CMORPH dataset is a good sub-daily rainfall product—whereas daily, dekadal and monthly CHIRPS and ARC2 rainfalls have good potentials for hydro-climatological applications in Niger.

Impact of sampling of GPM orbital data on streamflow simulations

Global precipitation estimation depends on constellations of mainly passive microwave sensors along with satellite radars and constellations of geosynchronous earth orbit infrared sensors. With passive microwave constellations, a persisting challenge is the limited sampling, which impacts applications such as hydrological modeling. This study presents a framework to examine the impact of the Global Precipitation Measurement (GPM) orbital sampling on streamflow simulations. Orbital data from the GPM Dual Precipitation Radar (DPR) 2A-Ku is used within the Variable Infiltration Capacity (VIC) model to derive ensembles of runoff simulations. Ensembles of synthetic precipitation fields are generated at different sampling intervals which are representative of GPM orbital data products with the Satellite Rainfall Error Model (SREM-2D) over the Hirakud catchment in the Indian subcontinent over a period of two years (2015–2016). The NOAA-NCEP Climate Prediction Center (CPC) morphing technique (CMORPH) satellite-based gridded rainfall product is considered as the reference rainfall for our study. Sampling uncertainty in orbital precipitation data is shown to amplify in streamflow simulations. The statistical analysis indicates that increasing sampling rates tend to generate more realistic land surface fluxes. The results from our study indicate that streamflow simulations from VIC model using precipitation forcing at 6-hourly integration outperforms the simulations as compared to 3-hourly and 12-hourly forcing integration times. This is exemplified by the statistical indices, with mean relative error of 0.17 for simulations with 3-hourly forcing, 0.12 for simulation with 6-hourly forcing, and 0.57 for 12-hourly forcing. The relative Root-Mean-Square-Error (RMSE) for 3-hourly, 6-hourly, 12-hourly for 2015 are 0.31, 0.25, 1.43 and for 2016 are 0.25, 0.15, 1.06, respectively. It can be noticed that both mean relative error and relative RMSE show a similar pattern for both years. A generic framework is therefore proposed to study the manifestation of satellite constellation sampling uncertainty in hydrologic simulations, which are crucial for future missions such as the CubeSat-based Temporal Experiment for Storms and Tropical Systems, and the Time-Resolved Observations of precipitation structure and storm intensity with a constellation of smallsats.

Evaluation of remotely sensed rainfall products over Central Africa

AbstractAn intercomparison of seven gridded rainfall products incorporating satellite data (ARC, CHIRPS, CMORPH, PERSIANN, TAPEER, TARCAT, TMPA) is carried out over Central Africa, by evaluating them against three observed datasets: (a) the WaTFor database, consisting of 293 (monthly records) and 154 (daily records) rain‐gauge stations collected from global datasets, national meteorological services and monitoring projects, (b) the WorldClim v2 gridded database, and (c) a set of stations expanded from the FAOCLIM network, these two latter sets describing climate normals. All products fairly well reproduce the mean rainfall regimes and the spatial patterns of mean annual rainfall, although with some discrepancies in the east–west gradient. A systematic positive bias is found in the CMORPH product. Despite its lower spatial resolution, TAPEER shows reasonable skills. When considering daily rainfall amounts, TMPA shows best skills, followed by CMORPH, but over the central part of the Democratic Republic of the Congo, TARCAT is amongst the best products. Skills ranking is however different at the interannual time‐scale, with CHIRPS and TMPA performing best, though PERSIANN has comparable skills when only fully independent stations are used as reference. A preliminary study of Southern Hemisphere dry season variability, from the example of Kinshasa, shows that it is a difficult variable to capture with satellite‐based rainfall products. Users should still be careful when using any product in the most data‐sparse regions, especially for trend assessment.