Gridded Precipitation Products Research Articles

Spatio-temporal assessment of Gridded precipitation products across topographic and climatic gradients in Colombia

Gridded precipitation products (GPPs) represent an alternative to and a complement to gauge-based observations of precipitation, improving the representation of spatio-temporal precipitation variability in regions with scarce gauge networks. However, GPPs' accuracy varies among products, temporal resolution, and regions, indicating the need for particular regional performance assessments. In this study, we evaluate the performance of eight state-of-the-art GPPs (CHIRPSv2, CMORPHv1, ERA5, ERA5-Land, IMERG, MSWEPv2.8, PERSIANN-CCS, and PERSIANN-CDR) over the steep topographic and climatic gradients of Colombia, in northwestern South America. We use a point-to-pixel comparison between ground-based observations from 1334 gauges with the corresponding grid cell of each GPP at 3-day, monthly, seasonal (DJF, MAM, JJA, SON), and annual scales over 2005–2014. Five categorical indices are used to assess the ability of each GPP to capture different 3-day precipitation intensity events correctly. The Kling-Gupta efficiency and its components are used to assess the GPPs' performance over Colombia and its five natural regions. Our results show that, at the 3-day scale, GPPs can correctly identify the occurrence of no-precipitation events (< 1 mm/day), but their capacity to classify precipitation events (≥ 1 mm/day) is low. The best performance is observed at the monthly scale, particularly at low elevation zones (0–1000 m.a.s.l), compared to 3-day and annual scales. Seasonally, GPPs tend to perform better during the dry season compared to the wet season in all regions. Overall, CHIRPSv2 outperforms other GPPs throughout the country and its five natural regions for all elevation zones and temporal scales, followed closely by MSWEPv2.8 and IMERG. PERSIANN-CCS and ERA5 products had the lowest performance, mainly over the driest and higher-altitude regions, respectively. This study provides insights into the performance of GPPs in complex climatic and topographic settings and, importantly, suggests the most suitable products for climate, hydrological, or ecological studies in a climatic, hydrological, and ecologically-strategic region.

Improving snow water equivalent simulations in an alpine basin by blending precipitation gauge and snow pillow measurements

AbstractMountain snowpack is a critical freshwater reservoir that sustains ecosystems and provides water for hydropower, agriculture, and urban uses. Accurate basin‐scale snowpack estimates are essential for ecosystem and water resources management. Our ability to predict snowpack evolution is hampered by the low density of precipitation gauges in high mountain regions, as most precipitation gauges are installed in easily accessible areas, which are often below the snowline. These measurements are typically spatially interpolated to fill data gaps in high mountain areas, resulting in a range of gridded products with considerable differences. On the other hand, natural resources agencies rely on a few strategically placed snow pillows and snow courses to infer basin‐scale snow water equivalent (SWE). In the Kings River Basin, California, there is an opportunity to combine precipitation and snow pillow measurements to improve basin‐scale snowpack estimates. In this study, we blend precipitation gauge observations with snowpack measurements to force a spatially distributed, process‐based snowpack evolution model in order to improve basin‐scale precipitation and snowpack predictions. We test the blended precipitation (Blended scenario) against other gridded precipitation products, which are derived from precipitation gauges only (Gauge scenario) and the PRISM precipitation product (PRISM scenario). Our results show that the Blended scenario had improved snowpack predictions (NSE = 0.85) over the Gauge and PRISM scenarios (NSE = 0.37 and 0.81) when compared to observed SWE, which can be attributed to better representation of precipitation at high elevations. The Blended scenario was also the only scenario to produce sufficient surface water input (SWI) to account for the combined estimates of full natural flow and evapotranspiration in the basin. We also examine the model sensitivity to warming and discuss the impact of warming on SWE and SWI and implications for water management. The results underscore the need for improved snow observation networks at high elevations that can be used to improve the representation of precipitation patterns, which is critical for the hydrologic modelling of mountainous basins.

Evaluation of different gridded precipitation products for drought monitoring: a case study of Central India

Evaluation of different gridded precipitation products for drought monitoring: a case study of Central India

Performance of Seven Gridded Precipitation Products over Arid Central Asia and Subregions

The evaluation of gridded precipitation products is important for the region where meteorological stations are scarce. To find out the applicable gridded precipitation products in arid Central Asia (ACA) for better follow-up research, this paper evaluated the accuracy of five long-term gridded precipitation products (GPCC, CRU, MERRA-2, ERA5-Land, and PREC/L) and two short-term products (PERSIANN-CDR and GPM IMERG) on different time scales for the whole ACA and two subregions, Central Asia (CA) and Xinjiang of China (XJ). Seven evaluation indices were used to evaluate the consistency between the seven gridded precipitation products and observations at 328 meteorological stations for 40 years from 1980 to 2019. The main findings were as follows: (1) Each product can correctly reflect the trend of decreasing annual precipitation in CA and increasing annual precipitation in XJ, except for ERA5-Land. (2) GPCC captured extreme events by 75.9% for heavy rainfall and 67.9% for drought events, and GPM IMERG outperformed PERSIANN-CDR with a capture probability of 61% for heavy rainfall and 50% for drought events. (3) Annually, except for GPCC and CRU without significant deviations (BIAS &lt; 2%), ERA5-Land, GPM IMERG, and PERSIANN-CDR generally overestimated precipitation (20% &lt; BIAS &lt; 60%). MERRA-2 and PREC/L underestimated precipitation, with approximately −5% for PREC/L and −20% for MERRA-2. (4) Seasonally, GPCC outperformed the other four long-term products in all seasons with the lowest BIAS (&lt;0.93%), and GPM IMERG (BIAS &lt; 30.88%) outperformed PERSIANN-CDR. (5) Monthly, the areas with large deviations (BIAS &gt; 60%) for the seven products were near the Tianshan Mountains; comparatively, they performed better in CA than in XJ, with BIAS approximately 20% for CA and 40% for XJ. Despite regional differences, GPCC performed the best across the five long-term products overall, followed by CRU, MERRA-2, PREC/L, and ERA5-Land. For the two short-term products, GPM IMERG outperformed PERSIANN-CDR.

Evaluation of gridded precipitation products in the selected sub-basins of Lower Mekong River Basin

Hydrological and meteorological studies demand accurate, continuous, long-term, reliable, and uniformly distributed precipitation data. Considering low density rain gauges with incomplete data in developing nations, a plethora of gridded precipitation products (GPPs) have made their place as an alternative to rain gauge records. However, GPPs house inherent errors depending on the type of data, gauge density, gridding algorithm, etc. Hence, it is crucial to evaluate them prior to their application. This study evaluated monthly products of eight GPPs over 17 years (1998-2014) – Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation data (APHRODITE), Climate Prediction Center (CPC), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Southeast Asian Observed dataset (SA-OBS), Climate Prediction Center Morphing Technique (CMORPH), The Tropical Rainfall Measuring Mission (TRMM)-daily products, Climate Research Unit (CRU), and Global Precipitation Climatology Center (GPCC). An entropy-based weight calculation for each statistical index and compromise programming was employed to rank the GPPs in the selected sub-basins (Nam Ngum River Basin, NRB, and Vietnam Mekong Delta, VMD) of the Lower Mekong Region (LMR) for mean and six extreme precipitation indices. The correlation coefficient (r), root mean square error (RMSE), skilled score (SS), and bias were the continuous statistical indices and probability of detection (POD), false alarm ratio (FAR) and critical success index (CSI) were the categorical indices used in this study. In terms of capturing mean monthly precipitation, GPCC outweighed all other products for both the studied basins. However, APHRODITE ranked first for daily precipitation products based on compromise programming algorithm for NRB. APHRODITE consistently recorded r between 0.85 and 0.95, RMSE between 50 and 100 mm/month, and SS between 0.72 and 0.90 for the 5 observed stations. Similarly, in case of VMD, TRMM ranked first for the daily precipitation products with r between 0.8 and 0.95, RMSE between 50 and 70 mm/month, and SS between 0.56 and 0.9 when evaluated with 11 observed stations. The APHRODITE for NRB and TRMM for VMD can be used as alternate to gauge data for hydrological and meteorological studies.

Recent Increase of Spring Precipitation over the Three-River Headwaters Region—Water Budget Analysis Based on Global Reanalysis (ERA5) and ET-Tagging Extended Regional Climate Modeling

Abstract Precipitation change is critical for the Three-River Headwaters (TRH) region, which serves downstream communities in East Asia. The spring (March–May) precipitation over the TRH region shows an increasing trend from 1979 to 2018, as revealed by a Chinese gridded precipitation product (CN05.1). However, the physical processes responsible for this precipitation change are still unclear. This study investigated the characteristics of spring precipitation and the water budget over the TRH region using the ERA5 global reanalysis and the Weather Research and Forecast (WRF) Model. The WRF version employed in this study includes online calculations of the atmospheric water budget and an evapotranspiration (ET) tagging procedure to trace evapotranspired water in the atmosphere. Both ERA5 and WRF reproduce the spring precipitation increase. Moreover, WRFD02 (with a 3-km domain) reduces the wet bias by around 60% and 77% compared to WRFD01 (9 km) and ERA5 (30 km). Both ERA5 and WRF demonstrate that the increase of spring precipitation is dominated by moisture convergence, especially the atmospheric water fluxes from the southern boundary. The enhanced moisture inflow is sustained by enhanced mass flux while the enhanced moisture outflow is sustained by increased moisture. The ET-tagging results further demonstrate the weakened precipitation recycling process because of the significant increase of precipitation produced by external moisture. Compared to ERA5, the reduced wet bias with WRF is attributed to a better spatial resolution of orographic barrier effects, which reduce the southerly water fluxes. The results highlight the potential of regional climate downscaling to better represent the atmospheric water budget in complex terrain.

Dynamic Modelling of Water and Wind Erosion in Australia over the Past Two Decades

Soil erosion caused by water and wind is a complicated natural process that has been accelerated by human activity. It results in increasing areas of land degradation, which further threaten the productive potential of landscapes. Consistent and continuous erosion monitoring will help identify the location, magnitude, and trends of soil erosion. This information can then be used to evaluate the impact of land management practices and inform programs that aim to improve soil conditions. In this study, we applied the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) to simulate water and wind erosion dynamics. With the emerging earth observation big data, we estimated the monthly and annual water erosion (with a resolution of 90 m) and wind erosion (at 1 km) from 2001 to 2020. We evaluated the performance of three gridded precipitation products (SILO, GPM, and TRMM) for monthly rainfall erosivity estimation using ground-based rainfall. For model validation, water erosion products were compared with existing products and wind erosion results were verified with observations. The datasets we developed are particularly useful for identifying finer-scale erosion dynamics, where more sustainable land management practices should be encouraged.

Quantifying and reducing the uncertainty in multi-source precipitation products using Bayesian total error analysis: A case study in the Danjiangkou Reservoir region in China

Quantifying and reducing the errors and uncertainties in high-resolution gridded precipitation products are essential for hydrological modelling to generate realistic parameter estimates and accurate model predictions. Formal Bayesian inference provides a powerful tool to quantify rainfall uncertainty and other uncertainty factors in hydrological modelling. An efficient way to reduce rainfall uncertainty in hydrological modelling is to merge precipitation estimates from multiple sources prior to their real-world applications. However, it remains a challenging task to optimally determine the weightings of different precipitation estimates based on the predictive ability of a hydrological model, especially from a probabilistic perspective. This study addresses this issue by evaluating and merging six gridded precipitation products using formal Bayesian inference schemes, including the Markov Chain Monte Carlo (MCMC) approach and Bayesian total error analysis (BATEA). MCMC describes the total predictive uncertainty using residual error models. In contrast, BATEA utilizes the storm depth multiplier approach to separately account for rainfall uncertainty in probabilistic uncertainty quantification. A novel signature of model predictability that combines the reliability and precision of probabilistic model predictions is developed to quantify the predictive ability of a hydrological model from precipitation estimates. Using the proposed predictability signature, we examined the benefit of optimally merging multi-source precipitation estimates in improving the accuracy of hydrologic simulations by applying the method to the Danjiangkou Reservoir region (DRR) between 1998 and 2007. The analysis demonstrates that (1) BATEA, compared to MCMC, can provide more realistic estimates of model parameters and tighter predictive intervals of daily streamflow when using rain gauge observations as well as gridded precipitation products; and (2) the proposed predictability signature is efficient for optimally merging precipitation measurements as it can produce tighter uncertainty bands and better streamflow simulations than individual precipitation estimates. This study provides insights into improved quantification and reduction of uncertainty in multi-source precipitation products for hydrological modelling.

Comprehensive evaluation of satellite-derived precipitation products considering spatial distribution difference of daily precipitation over eastern China

Study regionEight main basins at eastern China. Study focusThis study proposes a comprehensive framework using Shannon’s entropy-weighting method for assessing satellite precipitation data from spatial and temporal perspectives. First, three new spatial evaluation metrics, the false monitoring score (FMS), spatial coverage approximation (SCA), and precipitation spatial difference index (PSDI), were introduced based on spatial distribution differences of daily precipitation. Then, the error and performance of the five satellite precipitation datasets over eastern China were analyzed. New hydrological insights for the regionThe spatial metrics, SCA, PSDI, and fraction skill score (FSS), provide an effective quantitative evaluation of the daily spatial distribution of precipitation. CMADS and MSWEP appear to be superior at reproducing the spatial distribution of precipitation. Except for CHIRPS, all other four datasets have a tendency to underestimate precipitation measurements in the study area. According to the evaluation, the latest version of MSWEP may be a suitable alternative for use in water and climate research due to its relatively high accuracy in eastern China. The spatial error analysis method utilized in this study can be transferred to evaluating other gridded precipitation products. Meanwhile, the spatial error indices are also acceptable as optimization targets for future satellite precipitation data production.

Cross-Examining Precipitation Products by Rain Gauge, Remote Sensing, and WRF Simulations over a South American Region across the Pacific Coast and Andes

Precipitation estimate is important for earth science studies and applications, and it is one of the most difficult meteorological quantities to estimate accurately. For regions such as Peru, reliable gridded precipitation products are lacking due to complex terrains and large portions of remote lands that limit the accuracy of satellite precipitation estimation and in situ measurement density. This study evaluates and cross-examines two high-resolution satellite-based precipitation products, a global rain-gauge interpolated precipitation product, and a Weather Research and Forecast (WRF) model that simulated precipitation for a ten-year period from 2010 to 2019 in the Peruvian Andes region across the Pacific coast, Andes, and in the Amazon. The precipitation estimates examined in this study are the Integrated Multi-SatellitE Retrievals for GPM (IMERG), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Center product (GPCC), and a 3 km grid spacing WRF-based regional climate model (RCM) simulation. The evaluation and cross-examination were performed at sub-daily (6 h), daily, and monthly time scales, and at various spatial resolutions. The results show that the WRF simulation performs as well as, if not better than, GPM IMERG in the low precipitation and dry regions but becomes inaccurate in wet regions. GPM IMERG is more suitable for higher precipitation and wet regions, and MSWEP shows a systematic overestimation over the study area. It is therefore important to choose the most suitable precipitation product based on research needs and climate condition of the study for the challenging Peruvian Andes region.

Long-Term Performance Evaluation of the Latest Multi-Source Weighted-Ensemble Precipitation (MSWEP) over the Highlands of Indo-Pak (1981–2009)

The paucity of in-situ records, particularly in the glaciated mountainous region, is an obstacle in cryosphere ecology and environmental studies. Generally, available gauge station data is fragmented and covers valleys; thus, the use of gridded precipitation products (GPPs) is crucial in such complex terrains. However, these GPPs suffer from systematic biases and uncertainties owing to parameterization deficiencies. Therefore, the main goal of this research is to systematically evaluate the long-term performance and differences of the newly launched MSWEP in comparison to APHRO, CHIRPS, ERA-5, and PGMFD over the transboundary region of Indo-Pak (1981–2009) at spatial (whole to sub-basins) and temporal (daily to seasonal) scales. Findings reveal (1) overall, five GPPs produced well annual spatial precipitation variability with high magnitudes in the northwestern and low in the northeastern region. (2) The estimations from GPPs also divulged better correlation with in-situ observations (MSWEP = 0.86, APHRO = 0.76, ERA-5 = 0.81, CHIRPS = 0.57 and PGMFD = 0.68) at daily span. Better performance was observed during the monsoon compared to winter and pre-monsoon seasons. (3) Lately, estimates from MSWEP are more reliable for all the seasons, especially in the winter season, with the highest CC (0.90) and lowest relative bias (3.03%). (4) All GPPs (excluding ERA-5) overestimated light precipitation (0–1 mm/day) and underestimated moderate to heavy precipitation, in contrast to the ERA-5 that tended to underestimate the light but overestimate moderate (1–20 mm/day) and heavy precipitation (&gt;20 mm/day) events. The CHIRPS was less accurate in detecting most of the precipitation events. The MSWEP product captured all precipitation intensities more accurately than other GPPs. The current research indicates considerable implications for product improvement and data users for choosing better alternative precipitation data at a local scale.

Evaluation of the accuracy of seven gridded satellite precipitation products over the Godavari River basin, India

Evaluation of the accuracy of seven gridded satellite precipitation products over the Godavari River basin, India

Evaluation of Three Gridded Precipitation Products in Characterizing Extreme Precipitation over the Hengduan Mountains Region in China

Extreme precipitation events can lead to severe mountain hazards, and they have therefore received widespread attention. The study of extreme precipitation can be hindered by the insufficient number and uneven distribution of rain gauge stations, especially in mountainous areas with complex terrain. In this study, the daily precipitation data of three gridded precipitation products (Integrated Multi-satellite Retrievals for GPM, IMERG; Multi-Source Weighted-Ensemble Precipitation, MSWEP; and Tropical Rainfall Measuring Mission, TRMM) were compared with rain gauge observations at 62 ground stations from 2001 to 2016 over the Hengduan Mountain region in China. Deviations between the gridded and ground precipitation datasets were compared using four daily heavy rainfall sequences. Various extreme precipitation indices were used to evaluate the performance of selected precipitation products. The results show that IMERG and TRMM are better than MSWEP in characterizing extreme precipitation. The accuracy of these three products in detecting heavy precipitation varied with altitude gradient. All products provided more accurate estimates of heavy precipitation in higher-altitude areas than in lower-altitude areas. Notably, they are more applicable for heavy precipitation detection in subalpine or alpine regions, and there are still uncertainties in capturing the accurate characterization of extreme precipitation at low (&lt;1000 m) altitudes in the Hengduan Mountain region. These precipitation products should be used with caution in future applications when analyzing extreme precipitation at low elevations.

Development and Evaluation of Ensemble Consensus Precipitation Estimates over High Mountain Asia

Abstract Precipitation estimates are highly uncertain in complex regions such as High Mountain Asia (HMA), where ground measurements are very difficult to obtain and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA’s strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets [the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and the ECMWF reanalysis ERA5] with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatiotemporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends.

Assessing Gridded Precipitation and Air Temperature Products in the Ayakkum Lake, Central Asia

We evaluated the performance of gridded precipitation and air temperature datasets near the Ayakkum Lake at the southern margin of Xinjiang, arid central Asia. Statistical measures were applied to assess these climate products on a monthly basis from 2013 to 2018. For monthly precipitation amount, the European Centre for Medium-Range Weather Forecasts Reanalysis 5 shows a good performance among the five products based on most statistical measures, and the China Meteorological Forcing Dataset can also be used as an alternative, especially for estimating the long-term annual mean. For monthly air temperature, WorldClim historical weather data are recommended because of the low mean absolute error, root mean square error and distance between indices of simulation and observation. Better spatial and temporal coverages of in-situ observations are still needed to produce an optimal correction scheme for the mountainous regions of arid central Asia.

Reliability of Gridded Precipitation Products for Water Management Studies: The Case of the Ankavia River Basin in Madagascar

Hydrological modeling for water management in large watersheds requires accurate spatially-distributed rainfall time series. In case of low coverage density of ground-based measurements, gridded precipitation products (GPPs) from merged satellite-/gauge-/model-based rainfall products constitute an attractive alternative. The quality of which must, nevertheless, be verified. The objective of this study was to evaluate, at different time scales, the reliability of 6 GPPs against a 2-year record from a network of 14 rainfall gauges located in the Ankavia catchment (Madagascar). The GPPs considered in this study are the African Rainfall Estimate Climatology (ARC2), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), the European Centre Medium-Range Weather Forecasts ECMWF Reanalysis on global land surface (ERA5-Land), the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 Final (IMERG), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS), and the African Rainfall Estimation (RFEv2) products. The results suggest that IMERG (R2 = 0.63, slope of linear regression a = 0.96, root mean square error RMSE = 12 mm/day, mean absolute error MAE = 5.5 mm/day) outperforms other GPPs at the daily scale, followed by RFEv2 (R2 = 0.41, a = 0.94, RMSE = 15 mm/day, MAE = 6 mm/day) and ARC2 (R2 = 0.30, a = 0.88, RMSE = 16 mm/day, MAE = 6.7 mm/day). All GPPs, with the exception of the ERA5, overestimate the ‘no rain’ class (0–0.2 mm/day). ARC2, IMERG, PERSIANN, and RFEv2 all underestimate rainfall occurrence in the 0.2–150 mm/day rainfall range, whilst CHIRPS and ERA5 overestimate it. Only CHIRPS and PERSIANN could estimate extreme rainfall (&gt;150 mm/day) satisfactorily. According to the Critical Success Index (CSI) categorical statistical measure, IMERG performs quite well in detecting rain events in the range of 2–100 mm/day, whereas PERSIANN outperforms IMERG for rain events larger than 150 mm/day. Because it performs best at daily scale, only IMERG was evaluated for time scales other than daily. At the yearly and monthly time scales, the performance is good with R2 = 0.97 and 0.87, respectively. At the event time scale, the probability distribution function PDF of rain gauge values and IMERG data show good agreement. However, at an hourly time scale, the correlation between ground-based measurements and IMERG data becomes poor (R2 = 0.20). Overall, the IMERG product can be regarded as the most reliable gridded precipitation source at monthly, daily, and event time scales for hydrological applications in the study area, but the poor agreement at hourly time scale and the inability to detect extreme rainfall &gt;100 mm/day may, nevertheless, restrict its use.

Comprehensive evaluation of mainstream gridded precipitation datasets in the cold season across the Tibetan Plateau

Study regionThe Tibetan Plateau (TP). Study focusThis study evaluated the accuracy of six mainstream gridded precipitation products, including the Asian precipitation dataset by calibrating the GPM-era IMERG (AIMERG), Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), China Meteorological Forcing Dataset (CMFD), ECMWF Re-Analysis version 5 (ERA5-Land), Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR), in the cold season across the TP. New hydrological insights for the regionThe observations and these six precipitation products all revealed high precipitation occurs in the eastern and southeastern TP, and low precipitation occurs in the Qaidam Basin. But in the western regions with sparse gauge networks, the spatial patterns revealed by these six products were different. The precipitation occurrences in the cold season detected by CMFD had the highest accuracy, followed by AIMERG and IMERG, while CHIRPS had the worst accuracy. CMFD, IMERG and AIMERG outperformed CHIRPS, ERA5-Land and PERSIANN-CCS-CDR against observations at the annual and monthly scale, ERA5-Land and PERSIANN-CCS-CDR overestimated while AIMERG, CHIRPS, CMFD and IMERG underestimated cold season precipitation. CMFD had the highest accuracy when using the total adjusted observations as a reference at monthly scale, while IMERG had the highest accuracy when only using the adjusted precipitation by Geonor gauge as a reference.

Evaluation of Gridded Precipitation Data for Hydrologic Modeling in North-Central Texas

Over the past few decades, several high-resolution gridded precipitation products have been developed using multiple data sources and techniques, including measured precipitation, numerical modeling, and remote sensing. Each has its own sets of uncertainties and limitations. Therefore, evaluating these datasets is critical in assessing their applicability in various climatic regions. We used ten precipitation datasets, including measured (in situ), gauge-based, and satellite-based products, to assess their relevance for hydrologic modeling at the Bosque River Basin in North-Central Texas. Evaluated datasets include: (1) in situ station data from the Global Historical Climate Network (GHCN); (2) gauge-based dataset Daymet and the Parameter-elevation Regression on Independent Slope Model (PRISM); (3) satellite-based dataset Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Early and Late, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and PERSIANN-CCS (Cloud Classification System); (4) satellite-based gauge-corrected dataset IMERG-Final, PERSIANN-CDR (Climate Data Record), and CHIRPS (Climate Hazards Group Infrared Precipitation with Station data). Daily precipitation data (2000–2019) were used in the Soil and Water Assessment Tool (SWAT) for hydrologic simulations. Each precipitation dataset was used with measured monthly United States Geological Survey (USGS) streamflow data at three locations in the watershed for model calibration and validation. The SUFI-2 (Sequential Uncertainty Fitting) method on the SWAT-CUP (Calibration and Uncertainty Program) was used to quantify and compare the uncertainty in streamflow simulations from all precipitation datasets. The study has also analyzed the uncertainties in SWAT model parameter values under different gridded precipitation datasets. The results showed similar or better model calibration/validation statistics from gauge-based (Daymet and PRISM) and satellite-based gauge-corrected products (CHIRPS) compared with the GHCN data. However, satellite-based precipitation products such as PERSIANN-CCS and PERSIANN-CDR unveil comparatively inferior to capture in situ precipitation and simulate streamflow. The results showed that gauge-based datasets had comparable and even superior performances in some metrics compared with the GHCN data.

Performance Evaluation of Six Gridded Precipitation Products throughout Iran Using Ground Observations over the Last Two Decades (2000–2020)

Precipitation, as an important component of the Earth’s water cycle, plays a determinant role in various socio-economic practices. Consequently, having access to high-quality and reliable precipitation datasets is highly demanded. Although Gridded Precipitation Products (GPPs) have been widely employed in different applications, the lack of quantitative assessment of GPPs is a critical concern that should be addressed. This is because the inherent errors in GPPs would propagate into any models in which precipitation values are incorporated, introducing uncertainties into the final results. This paper aims to quantify the capability of six well-known GPPs (TMPA, CHIRPS, PERSIANN, GSMaP, IMERG, and ERA5) at multiple time scales (daily, monthly, and yearly) using in situ observations (over 1.7 million) throughout Iran over the past two decades (2000–2020). Both continuous and categorical metrics were implemented for precipitation intensity and occurrence assessment based on the point-to-pixel comparison approach. Although all metrics did not support the superior performance of any specific GPP, taking all investigations into account, the findings suggested the better performance of the Global Satellite Mapping of Precipitation (GSMaP) in estimating daily precipitation (CC = 0.599, RMSE = 3.48 mm/day, and CSI = 0.454). Based on the obtained continuous metrics, all the GPPs had better performances in dry months, while this did not hold for the categorical metrics. The validation at the station level was also carried out to present the spatial characteristics of errors throughout Iran, indicating higher overestimation/underestimation in regions with higher precipitation rates. The validation analysis over the last two decades illustrated that the GPPs had stable performances, and no improvement was seen, except for the GSMaP, in which its bias error was significantly reduced. The comparisons on monthly and yearly time scales suggested the higher accuracy of monthly and yearly averaged precipitation values than accumulated values. Our study provides valuable guidance to the selection and application of GPPs in Iran and also offers beneficial feedback for further improving these products.

Evaluation of Long-Term and High-Resolution Gridded Precipitation and Temperature Products in the Qilian Mountains, Qinghai–Tibet Plateau

Long-term and high-resolution gridded products of precipitation and temperature data are highly important to study the changes in climate and environment under global warming. Considering the uncertainties of these products in mountainous areas, it is necessary to evaluate the data reliability. This study evaluates the performances of the CMFD (China Meteorological Forcing Dataset) and ERA5-Land in simulating precipitation and temperature in the Qilian Mountains over the period of 1980–2018. We use the observation data of 28 basic meteorological stations in the Qilian Mountains to compare with the reanalysis products. Error metrics (the correlation coefficient (CC), the root mean square error (RMSE), the mean absolute error (MAE), and the relative bias (BIAS)) are used to quantify the monthly differences in existence between the observed data and reanalysis data. Our findings indicate that both CMFD and ERA5-Land could well reproduce the spatial distribution of mean monthly precipitation and temperature in the region. A good correlation is found between CMFD and OBS under different amounts of monthly precipitation conditions. The monthly average temperatures of CMFD and ERA5-Land reveal a high correlation with the observed results. Moreover, the CC values of CMFD and ERA5-Land precipitation products are the highest in autumn and the lowest in winter, and the CC values of both CMFD and ERA5-Land temperature products are higher in spring and autumn. However, we find that both reanalysis products underestimate the temperature to varying degrees, and the amount of precipitation is overestimated by ERA5-Land. The results of the evaluation show that the errors in precipitation yielded by CMFD as a whole are distinctly fewer than those yielded by ERA5-Land, while the errors in air temperature yielded by both ERA5-Land and CMFD are nearly identical to each other. Overall, ERA5-Land is more suitable than CMFD for studying the trends of temperature changes in the Qilian Mountains. As for simulation of precipitation, CMFD performs better in the central and eastern parts of the Qilian Mountains, whereas ERA5-Land performs better in the western part of the Qilian Mountains.