Multiple Precipitation Products Research Articles

Performance evaluation of multi‐precipitation products fusion based on a novel dynamic K‐nearest neighbour Bayesian ensemble calibration framework

AbstractThe fusion of multiple precipitation products can effectively improve precipitation accuracy. In order to reduce the uncertainty of traditional precipitation members and improve the applicability of Bayesian model averaging (BMA), this study proposes a dynamic ensemble calibration framework with seasonality and real‐time capability, namely dynamic K‐nearest neighbour BMA (DKBMA), for the integration calibration and comparison of ERA5 reanalysis products and three satellite precipitation products (CMORPH, 3B42RT and 3B42V7). The application results of the DKBMA in the Yujiang River basin, Southern China, during the period of 2011–2016 show that (1) the DKBMA can overcome the problem of ERA5 error interference on the seasonal scale, reduce the systematic bias of ensemble precipitation members at coastal sites and demonstrate strong robustness at different altitudes. (2) Compared with the traditional BMA, the DKBMA has significantly improved accuracy, especially in capturing extreme precipitation events. The correlation coefficient has increased from 0.793 (traditional BMA) to 0.841 (DKBMA), the root‐mean‐square error has decreased from 35.61 (traditional BMA) to 30.95 (DKBMA), and the absolute value of relative bias has decreased from 62.80% (traditional BMA) to 49.94% (DKBMA). The proposed DKBMA in this study can provide a new solution for the fusion of multi‐source precipitation products in data‐scarce regions.

Evaluation of the RF-MEP Method for Merging Multiple Gridded Precipitation Products in the Chongqing City, China

Precipitation is a major component of the water cycle. Accurate and reliable estimation of precipitation is essential for various applications. Generally, there are three main types of precipitation products: satellite based, reanalysis, and ground measurements from rain gauge stations. Each type has its advantages and disadvantages. Recent efforts have been made to develop various merging methods to improve precipitation estimates by combining multiple precipitation products. This study evaluated for the first time the performance of the random forest-based merging procedure (RF-MEP) method in enhancing the accuracy of daily precipitation estimates in Chongqing city, China with a complex terrain and sparse observational data. The RF-MEP method was used to merge three widely used gridded precipitation products (CHIRPS, ERA5-Land, and GPM IMERG) with ground measurements from a limited number of rain gauge stations to produce the merged precipitation dataset. Eight stations (approximately 70% of the available stations) were used to train the RF-MEP approach, while four stations (30%) were used for independent testing. Various statistical metrics were employed to assess the performance of the merged precipitation dataset and the three existing precipitation products against the ground measurements. Our results demonstrated that the RF-MEP approach significantly enhances the accuracy of daily precipitation estimates, surpassing the performance of the individual precipitation products and two other merging methods (the simple linear regression model and the simple averaging). Among the three existing products, ERA5-Land exhibited the best performance in capturing daily precipitation, followed by GPM IMERG, while CHIRPS performed the worst. Regarding precipitation intensity, all three existing products and the RF-MEP merged dataset performed well in capturing light precipitation events with an intensity of less than 1 mm/day, which accounts for the majority (more than 70%) of occurrences. However, all datasets showed rather poor capability in capturing precipitation events beyond 1 mm/day, with the worst performance observed for extreme heavy precipitation events exceeding 50 mm/day. The RF-MEP approach significantly improves the detection ability for all precipitation intensities, except for the most extreme intensity (>50 mm/day), where only marginal improvement is observed. Analysis of the spatial pattern of precipitation estimates and the temporal bias of daily precipitation estimates further confirms the superior performance of the RF-MEP merged precipitation dataset over the three existing products.

Spatiotemporal analysis of meteorological drought across China based on the high-spatial-resolution multiscale SPI generated by machine learning

An accurate gridded Standardized Precipitation Index (SPI) at high spatial resolution is important for meteorological drought monitoring and assessment. However, the spatial estimation of the SPI solely derived from gridded precipitation products or interpolation of meteorological station-based data is subject to low accuracy and large uncertainty. Here, we developed a Gaussian process regression-based method by blending multiple precipitation products (GPR-BMP) with information from meteorological stations and topographical characteristics to improve the resolution and accuracy of spatial SPI. GPR-BMP was applied over China in this study, using five widely used precipitation products and meteorological station data, as well as elevation, slope, longitude and latitude. The gridded multiscale (i.e., 1-month, 3-month, 6-month, 9-month and 12-month) SPI datasets over China for 1984–2020 (GPR-BMP-SPI) were generated. Results showed that GPR-BMI-SPIs have higher accuracy than the SPIs derived from single precipitation products at all time scales. Additionally, GPR-BMP-SPIs can relatively accurately identify historical drought events. By analysing the drought spatiotemporal variation over China during 1984–2020, it was found that the annual drought frequency, severity and duration were relatively high in southeast China during 1984–2020. The annual trends of drought frequency, severity and duration showed a significant decrease on the Qinghai-Tibet Plateau. Winter drought in 1984–2020 exhibited the most distinctive features between the east and the west. Additionally, the drought area percentage showed an overall decreasing trend over the whole region of China on both annual and seasonal scales. It was also found that drought frequency contributed more to drought severity than drought duration. The GPR-BMP-SPI datasets generated in this study can serve as fundamental data support for future studies. The proposed model can be applied to other regions globally for drought monitoring and mitigation.

Disentangling error structures of precipitation datasets using decision trees

Characterizing error structures in precipitation products not only facilitates their proper applications for scientific and practical purposes but also helps improve their retrieval algorithms and processing methods. Despite the fact that multiple precipitation products have been assessed in the literature, factors that affect their error structures remain inadequately addressed. By interpreting 60 binary decision trees, this study disentangles the error characteristics of precipitation products in terms of their spatiotemporal patterns and geographical factors. Three independent precipitation products - two satellite-based and one reanalysis datasets: the Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) late run (IMERG-L), Soil Moisture to Rain-Advanced SCATterometer (SM2RAIN-ASCAT), and the Modern-Era Retrospective analysis for Research and Applications, Version 2 uncorrected precipitation output (MERRA2-UC), are evaluated across the contiguous United States from 2010 to 2019. The ground-based Stage IV precipitation dataset is used as the ground truth.Results indicate that the MERRA2-UC outperforms the IMERG-L and SM2RAIN-ASCAT with higher accuracy and more stable interannual patterns for the analysis period. Decision trees cross-assess three spatiotemporal factors and find that the underestimation of MERRA2-UC occurs in the east of the Rocky Mountains, and SM2RAIN-ASCAT underestimates precipitation over high latitudes, especially in winter. Additionally, the decision tree method ascribes system errors to nine different geographical characteristics, of which the distance to the coast, soil type, and DEM are the three dominant features. On the other hand, the land cover type, topography position index, and aspect are three relatively weak factors.

Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

A multi-sensor evaluation of precipitation uncertainty for landslide-triggering storm events.

Extreme precipitation can have profound consequences for communities, resulting in natural hazards such as rainfall‐triggered landslides that cause casualties and extensive property damage. A key challenge to understanding and predicting rainfall‐triggered landslides comes from observational uncertainties in the depth and intensity of precipitation preceding the event. Practitioners and researchers must select from a wide range of precipitation products, often with little guidance. Here we evaluate the degree of precipitation uncertainty across multiple precipitation products for a large set of landslide‐triggering storm events and investigate the impact of these uncertainties on predicted landslide probability using published intensity–duration thresholds. The average intensity, peak intensity, duration, and NOAA‐Atlas return periods are compared ahead of 177 reported landslides across the continental United States and Canada. Precipitation data are taken from four products that cover disparate measurement methods: near real‐time and post‐processed satellite (IMERG), radar (MRMS), and gauge‐based (NLDAS‐2). Landslide‐triggering precipitation was found to vary widely across precipitation products with the depth of individual storm events diverging by as much as 296 mm with an average range of 51 mm. Peak intensity measurements, which are typically influential in triggering landslides, were also highly variable with an average range of 7.8 mm/h and as much as 57 mm/h. The two products more reliant upon ground‐based observations (MRMS and NLDAS‐2) performed better at identifying landslides according to published intensity–duration storm thresholds, but all products exhibited hit ratios of greater than 0.56. A greater proportion of landslides were predicted when including only manually verified landslide locations. We recommend practitioners consider low‐latency products like MRMS for investigating landslides, given their near‐real time data availability and good performance in detecting landslides. Practitioners would be well‐served considering more than one product as a way to confirm intense storm signals and minimize the influence of noise and false alarms.

Modeling streamflow using multiple precipitation products in a topographically complex catchment

Modeling streamflow using multiple precipitation products in a topographically complex catchment

Applicability analysis of multiple precipitation products in the Qaidam Basin, Northwestern China.

In the Qaidam Basin, meteorological stations are sparsely distributed, and the observational precipitation data are therefore not comprehensive, which significantly hampers the accurate assessment and optimal allocation of regional water resources. This study aims to evaluate the accuracy of three precipitation products (MSWEP V2, GPM IMERG V6, and TRMM 3B43) against gauged-based precipitation data from nine meteorological stations and 12 hydrologic stations in the Qaidam Basin from 2001 to 2016. The results reveal the following: (1) At the annual and monthly scales, the MSWEP product has the highest accuracy, followed by the GPM product. However, the TRMM product only reveals a correlation at the monthly scale; (2) the MSWEP product performs better in the wet season than the dry season, and the TRMM product has an abnormally high value of precipitation in the wet season. Moreover, it was shown that the accuracy of the GPM product is superior to that of the TRMM product; however, it has a low detection capability in some mountainous areas; and (3) the average error of each precipitation product at the meteorological stations is smaller than that at the hydrological stations.

Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling

Precipitation is essential for modeling the hydrologic behavior of watersheds. There exist multiple precipitation products of different sources and precision. We evaluate the influence of different precipitation product on model parameters and streamflow predictive uncertainty using a soil water assessment tool (SWAT) model for a forest dominated catchment in India. We used IMD (gridded rainfall dataset), TRMM (satellite product), bias-corrected TRMM (corrected satellite product) and NCEP-CFSR (reanalysis dataset) over a period from 1998–2012 for simulating streamflow. The precipitation analysis using statistical measures revealed that the TRMM and CFSR data slightly overestimate rainfall compared to the ground-based IMD data. However, the TRMM estimates improved, applying a bias correction. The Nash–Sutcliffe (and R2) values for TRMM, TRMMbias and CFSR, are 0.58 (0.62), 0.62 (0.63) and 0.52 (0.54), respectively at model calibrated with IMD data (Scenario A). The models of each precipitation product (Scenario B) yielded Nash–Sutcliffe (and R2) values 0.71 (0.76), 0.74 (0.78) and 0.76 (0.77) for TRMM, TRMMbias and CFSR datasets, respectively. Thus, the hydrological model-based evaluation revealed that the model calibration with individual rainfall data as input showed increased accuracy in the streamflow simulation. IMD and TRMM forced models to perform better in capturing the streamflow simulations than the CFSR reanalysis-driven model. Overall, our results showed that TRMM data after proper correction could be a good alternative for ground observations for driving hydrological models.

A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products

Abstract. Ensemble estimates based on multiple datasets are frequently applied once many datasets are available for the same climatic variable. An uncertainty estimate based on the difference between the ensemble datasets is always provided along with the ensemble mean estimate to show to what extent the ensemble members are consistent with each other. However, one fundamental flaw of classic uncertainty estimates is that only the uncertainty in one dimension (either the temporal variability or the spatial heterogeneity) can be considered, whereas the variation along the other dimension is dismissed due to limitations in algorithms for classic uncertainty estimates, resulting in an incomplete assessment of the uncertainties. This study introduces a three-dimensional variance partitioning approach and proposes a new uncertainty estimation (Ue) that includes the data uncertainties in both spatiotemporal scales. The new approach avoids pre-averaging in either of the spatiotemporal dimensions and, as a result, the Ue estimate is around 20 % higher than the classic uncertainty metrics. The deviation of Ue from the classic metrics is apparent for regions with strong spatial heterogeneity and where the variations significantly differ in temporal and spatial scales. This shows that classic metrics underestimate the uncertainty through averaging, which means a loss of information in the variations across spatiotemporal scales. Decomposing the formula for Ue shows that Ue has integrated four different variations across the ensemble dataset members, while only two of the components are represented in the classic uncertainty estimates. This analysis of the decomposition explains the correlation as well as the differences between the newly proposed Ue and the two classic uncertainty metrics. The new approach is implemented and analysed with multiple precipitation products of different types (e.g. gauge-based products, merged products and GCMs) which contain different sources of uncertainties with different magnitudes. Ue of the gauge-based precipitation products is the smallest, while Ue of the other products is generally larger because other uncertainty sources are included and the constraints of the observations are not as strong as in gauge-based products. This new three-dimensional approach is flexible in its structure and particularly suitable for a comprehensive assessment of multiple datasets over large regions within any given period.

Evaluation of high-resolution satellite products for streamflow and water quality assessment in a Southeastern US watershed

Study regionSeneca River Basin South Carolina, USA. Study focusThis study focuses on evaluating different precipitation products on streamflow and water quality modeling. We used 13 years (2001–2014) of daily data from multiple precipitation products. The precipitation products are classified into three categories: (1) products based on observed precipitation data (rain gauges located within the study area and PRISM data), (2) five satellite-based precipitation products (3B42, 3B42RT, PERSIANN-CDR, PERSIANN, and GPCP-1DD); and (3) one Reanalysis product (CFSR). The Soil and Water Assessment Tool (SWAT) was calibrated and validated using these precipitation products against monthly observed streamflow, total nitrogen (TN) loads, and total phosphorus (TP) loads within the Seneca River Basin situated in northwest of South Carolina, USA. For each precipitation product, the best performing model parameters was determined using the Sequential Uncertainty Fitting Procedure (SUFP2). New hydrological insights for the regionResults showed that the spatio-temporal pattern of precipitation products varies and further influenced the streamflow and water quality simulations. Most of the precipitation products achieved at least satisfactory performance (R2 > 0.50, NS > 0.50, and PBIAS ≥ ±15) for streamflow and water quality. PRISM product showed the best overall performance, followed by 3B42 and PERSIANN-CDR, whereas CFSR and PERSIANN products comparatively performed poorly for streamflow and water quality simulations. The uncertainty in the precipitation products was reflected in the streamflow that further affected the water quality simulations (TN and TP loads). In addition, this study showed that the parameter uncertainty significantly varies among precipitation products, leading to a considerable uncertainty in simulation results, especially for TN and TP. Our results suggest that using multiple precipitation products can increase the level of confidence in hydrologic model simulation, particularly in water quality modeling.

Spatiotemporal Changes in China's Terrestrial Water Storage From GRACE Satellites and Its Possible Drivers

AbstractTerrestrial water storage (TWS) changes are driven by internal climate variability, external natural climate change, external human‐caused climate change, and human water management. Spatiotemporal patterns of TWS change and attribution can help understand the water cycle process and refine water management in a region. Here, the spatiotemporal changes in the TWS in China between 2003 and 2016 are analyzed based on three monthly mass concentration (mascon) data products from the Gravity Recovery and Climate Experiment (GRACE) satellites, and the possible drivers are investigated using multiple precipitation products, land surface models, and multisource remote sensing and socioeconomic data. Six major TWS change regions are detected, including negative trends in northwestern China, the southeastern Tibetan Plateau, and northern China and positive trends in western China, southern China, and northeastern China. Two global hydrological models, WaterGAP and PCR‐GLOBWB, substantially underestimate the TWS changes relative to GRACE. Four land surface models (CLM 2.0, VIC, MOSAIC, and NOAH 3.3) from the Global Land Data Assimilation System version 2.1 show large model uncertainties in simulating the TWS trend. A statistical attribution indicates that ice melting under human‐caused climate change is a driver of decreasing TWS in northwestern China and the southeastern Tibetan Plateau that cannot be ignored, while human water use is largely responsible for groundwater depletion in northern China. The increasing TWS in southern China and northeastern China is likely caused by precipitation increases, and the increasing TWS in western China is probably a result of precipitation increases and water supplementation from ice melting.

Comparing Multiple Precipitation Products against In-Situ Observations over Different Climate Regions of Pakistan

Various state-of-the-art gridded satellite precipitation products (GPPs) have been derived from remote sensing and reanalysis data and are widely used in hydrological studies. An assessment of these GPPs against in-situ observations is necessary to determine their respective strengths and uncertainties. GPPs developed from satellite observations as a primary source were compared to in-situ observations, namely the Climate Hazard group Infrared Precipitation with Stations (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). These products were compared to in-situ data from 51 stations, spanning 1998–2016, across Pakistan on daily, monthly, annual and interannual time scales. Spatiotemporal climatology was well captured by all products, with more precipitation in the north eastern parts during the monsoon months and vice-versa. Daily precipitation with amount larger than 10 mm showed significant (95%, Kolmogorov-Smirnov test) agreement with the in-situ data, especially TMPA, followed by CHIRPS and MSWEP. At monthly scales, there were significant correlations (R) between the GPPs and in-situ records, suggesting similar dynamics; however, statistical metrics suggested that the performance of these products varies from north towards south. Temporal agreement on an interannual scale was higher in the central and southern parts which followed precipitation seasonality. TMPA performed the best, followed in order by CHIRPS, MSWEP and PERSIANN-CDR.

Evaluation of Multi-Precipitation Products for Multi-Time Scales and Spatial Distribution During 2007-2015

Recently, several precipitation products are released with the improved algorithm to strengthen the performance of precipitation construction and monitoring. These data play a key role in a wide range of hydrological models, water resources modeling and environmental researches. Especially in developing countries like Vietnam, it is challenging to gather data for long-term time series at scales of daily and sub-daily due to the very coarse density of observation station. In order to overcome the problem of data scarcity, this study aims to evaluate the performance of newest multiple precipitation products including Tropical Rainfall Measuring Mission (TRMM 3B42 V7), Climate Prediction Center (CPC) MORPHing Version 1.0 (CMORPH_V1.0), European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis systems (ERA-Interim), Climate Research Unit Time series Version 4.0.1 (CRU TS 4.0.1) and Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources version 2 (APHRODITE) in comparison with measured precipitation for multiple time scales (daily, monthly, seasonal and annual), taking the VuGia-ThuBon (VG-TB) as a pilot basin where climate regime is complex. Seven continuous and four dichotomous statistics are applied to evaluate the precipitation estimates qualitatively at multiple time scales. In addition, specifically, evaluation of spatial distribution of multiple time scales is implemented. The results show lower precipitation estimates in areas of high elevation and higher precipitation estimates over the areas of plain and coastal in comparison with measured precipitation for all considered precipitation data. More importantly, ERA-Interim well captures rain events of heavy rain (50.0-100 mm/day). CMORHPH_V1.0 better reproduces the rain events with little overestimation of light rain (0.6-6 mm/day) than the others. For zero rain events (0-0.6 mm/day), TRMM 3B42 V7 gives the best performance. Furthermore, the cumulative distribution function of APHRODITE well matches the distribution of measured precipitation. All precipitation products completely fail to capture the rain events of extremely heavy rain. More importantly, a formula is proposed to scale and adjust the merged satellite precipitation at a sub-daily scale.

Evaluating Suitability of Multiple Precipitation Products for the Lancang River Basin

Global reanalysis precipitation products could provide valuable meteorological information for flow forecasting in poorly gauged areas, helping to overcome a long-standing challenge in the field. But not all data sources are suitable for all regions or perform the same way in hydrological modeling, so it is essential to test the suitability of precipitation products before applying them. In this study, five widely used global high-resolution precipitation products—Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), China Gauge-based Daily Precipitation Analysis developed by China Meteorological Administration (CMA) and Agricultural Model Intercomparison and Improvement Project based on the NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA)—were evaluated using statistical methods and a hydrological approach for their suitability for the Lancang River Basin. The results indicated that APHRODITE, CMA, AgMERRA and CHIRPS were more accurate precipitation indicators than NCEP-CFSR in terms of the multiyear average and seasonal spatial distribution pattern, all of the CHIRPS, AgMERRA and APHRODITE perform better than CMA and NCEP-CFSR at the small, medium and high precipitation intensities ranges in subbasin11 and sunbabsin46. All five products performed better in subbasin46 (a low-altitude region) than in subbasin11 (a high-altitude region) on the daily and monthly scales. In addition to NCEP-CFSR, the other four products all presented encouraging potential for streamflow simulation at daily (Yunjinghong) and monthly (Yunjinghong, Jiuzhou and Gajiu) scale. Hydrological simulations forced with APHRODITE were the best of the five for the Yunjinghong station in capturing daily and monthly measured streamflow. Except for NCEP-CFSR, all products were very good for hydrological simulations for the Gajiu and Jiuzhou stations.

Identifying precipitation uncertainty in crop modelling using Bayesian total error analysis

Precipitation is an important source of soil water, which is critical to crop growth, and is therefore an important input when modelling crop growth. Although advances are continually being made in predicting and recording precipitation, input uncertainty of precipitation data is likely to influence the robustness of parameter estimate and thus the predictive accuracy in soil water and crop modelling. In this study, we use the Bayesian total error analysis (BATEA) method for the water-oriented crop model AquaCrop to identify the input uncertainty from multiple precipitation products respectively, including gauge-corrected grid dataset CPC, remote sensing based TRMM and reanalysis based ERA-Interim. This methodology uses latent variables to correct the input data errors. Adopting a single-multiplier method for precipitation correction, we simulate maize growth in both field and regional levels in China for a range of different possible climatic scenarios. Meanwhile, we use the average of multiple products for model driving in comparison. The results show that the BATEA method can consistently reduce uncertainty for crop growth prediction among different precipitation products. In regional simulation, the improvements for the three products are 1%, 7.3% and 2.8% on average in drought scenarios. These results imply the BATEA approach can be of great assistance for crop modeling studies and agricultural assessments under future changing climates.

Unprecedented drought over tropical South America in 2016: significantly under-predicted by tropical SST

Tropical and sub-tropical South America are highly susceptible to extreme droughts. Recent events include two droughts (2005 and 2010) exceeding the 100-year return value in the Amazon and recurrent extreme droughts in the Nordeste region, with profound eco-hydrological and socioeconomic impacts. In 2015–2016, both regions were hit by another drought. Here, we show that the severity of the 2015–2016 drought ("2016 drought" hereafter) is unprecedented based on multiple precipitation products (since 1900), satellite-derived data on terrestrial water storage (since 2002) and two vegetation indices (since 2004). The ecohydrological consequences from the 2016 drought are more severe and extensive than the 2005 and 2010 droughts. Empirical relationships between rainfall and sea surface temperatures (SSTs) over the tropical Pacific and Atlantic are used to assess the role of tropical oceanic variability in the observed precipitation anomalies. Our results indicate that warmer-than-usual SSTs in the Tropical Pacific (including El Niño events) and Atlantic were the main drivers of extreme droughts in South America, but are unable to explain the severity of the 2016 observed rainfall deficits for a substantial portion of the Amazonia and Nordeste regions. This strongly suggests potential contribution of non-oceanic factors (e.g., land cover change and CO2-induced warming) to the 2016 drought.

Regional Structure of the Indian Summer Monsoon in Observations, Reanalysis, and Simulation

Abstract Regional variations in seasonal mean Indian summer monsoon rainfall and circulation for the period 1979–2009 are investigated using multiple data products. The focus is on four separate regions: the Western Ghats (WG), the Ganges basin (GB), the Bay of Bengal (BB), and Bangladesh–northeastern India (BD). Data reliability varies strongly by region, with particularly low correlations between different products for the BB and BD regions. Correlations between regions are generally not statistically significant, indicating rainfall varies independently in these four regions. The diagnosed associations between rainfall, circulation, and sea surface temperatures can be sensitive to the choice of rainfall product, and multiple precipitation products may need to be analyzed in this region to ensure that the results are robust. Enhanced precipitation in the BD region is associated with anomalous anticyclonic circulation at 850 mb and westerly anomalies along the foothills of the Tibetan Plateau, while precipitation in the other regions is associated with cyclonic flow and easterlies. These associations provide a dynamical explanation for previously reported weak, negative correlations between BD and the other regions. In addition to observed products, atmosphere-only simulations made using the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) during Project Athena are analyzed. While the simulations do not reproduce the observed interannual variations in rainfall, the fidelity of the simulated precipitation and circulation structure is comparable to or even outperforms the different state-of-the-art reanalysis products considered. Accuracy in representing interannual variability and regional structure thus appears to be independent.

Assessment of multiple precipitation products over major river basins of China

As the largest component of the global water cycle, precipitation plays a critical role in determining available water in the terrestrial ecosystems. Numerous precipitation products have been generated during the last decades, but they differ substantially in terms of their spatial and temporal variations. In this study, we compared seven precipitation products covering ten major river basins of China from 1980 to 2007 and found large differences among the products. Overall, the Global Precipitation Climatology Center (GPCC) product shows the highest correlation with rain gauge measurements and lowest estimation errors over the ten basins. The performances of other products also clearly differ over the ten basins, and the maximum estimates were 1.27 ± 0.21 times the minimum estimates in terms of interannual variability in precipitation. Based on the seven products, we generated a new precipitation dataset using the Bayesian model averaging method. Compared with all the individual products, the new dataset indicates the decreased root mean square error and increased coefficient of correlation. In the statistical analysis of the weight for each of the products, the GPCC product contributed the maximum weight among almost all river basins. Both the ERA-Interim and MERRA products contributed the minimum weights, being less than 10 %. Our results highlight the need to investigate and improve the performance of precipitation products when evaluating the regional water balance.

Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System Land Surface States

Abstract Precipitation is arguably the most important meteorological forcing variable in land surface modeling. Many types of precipitation datasets exist (with various pros and cons) and include those from atmospheric data assimilation systems, satellites, rain gauges, ground radar, and merged products. These datasets are being evaluated in order to choose the most suitable precipitation forcing for real-time and retrospective simulations of the Global Land Data Assimilation System (GLDAS). This paper first presents results of a comparison for the period from March 2002 to February 2003. Later, GLDAS simulations 14 months in duration are analyzed to diagnose impacts on GLDAS land surface states when using the Mosaic land surface model (LSM). A comparison of seasonal total precipitation for the continental United States (CONUS) illustrates that the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) has the closest agreement with a CPC rain gauge dataset for all seasons except winter. The European Centre for Medium-Range Weather Forecasts (ECMWF) model performs the best of the modeling systems. The satellite-only products [the Tropical Rainfall Measuring Mission (TRMM) Real-time Multi-satellite Precipitation Analysis and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] suffer from a few deficiencies—most notably an overestimation of summertime precipitation in the central United States (200–400 mm). CMAP is the most closely correlated with daily rain gauge data for the spring, fall, and winter seasons, while the satellite-only estimates perform best in summer. GLDAS land surface states are sensitive to different precipitation forcing where percent differences in volumetric soil water content (SWC) between simulations ranged from −75% to +100%. The percent differences in SWC are generally 25%–75% less than the percent precipitation differences, indicating that GLDAS and specifically the Mosaic LSM act to generally “damp” precipitation differences. Areas where the percent changes are equivalent to the percent precipitation changes, however, are evident. Soil temperature spread between GLDAS runs was considerable and ranged up to ±3.0 K with the largest impact in the western United States.