Satellite-based Precipitation Datasets Research Articles

Hydrological Modeling in the Chaohu Lake Basin of China—Driven by Open-Access Gridded Meteorological and Remote Sensing Precipitation Products

This study assessed the performance of two well-known gridded meteorological datasets, CFSR (Climate Forecast System Reanalysis) and CMADS (China Meteorological Assimilation Driving Datasets), and three satellite-based precipitation datasets, TRMM (Tropical Rainfall Measuring Mission), CMORPH (Climate Prediction Center morphing technique), and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), in driving the SWAT (Soil and Water Assessment Tool) model for streamflow simulation in the Fengle watershed in the middle–lower Yangtze Plain, China. Eighteen model scenarios were generated by forcing the SWAT model with different combinations of three meteorological datasets and six precipitation datasets. Our results showed that (1) the three satellite-based precipitation datasets (i.e., TRMM, CMORPH, and CHIRPS) generally provided more accurate precipitation estimates than CFSR and CMADS. CFSR and CMADS agreed fairly well with the gauged measurements in maximum temperature, minimum temperature, and relative humidity, but large discrepancies existed for the solar radiation and wind speed. (2) The impact of precipitation data on simulated streamflow was much larger than that of other meteorological variables. Satisfactory simulations were achieved using the CMORPH precipitation data for daily streamflow simulation and the TRMM and CHIRPS precipitation data for monthly streamflow simulation. This suggests that different precipitation datasets can be used for optimal simulations at different temporal scales.

Estimation of Flood Discharge in Ungauged Basin Using GPM-IMERG Satellite-Based Precipitation Dataset in a Moroccan Arid Zone

Estimation of Flood Discharge in Ungauged Basin Using GPM-IMERG Satellite-Based Precipitation Dataset in a Moroccan Arid Zone

Investigating Rainfall Patterns in the Hubei Province, China and Northern Italy During the Covid-19 Lockdowns

In the spring of 2020, many countries enacted strict lockdowns to contain the spread of the 2019 coronavirus disease (COVID-19), resulting in a sharp observed decrease in regional atmospheric pollutant concentrations, such as NOx and aerosols in early 2020. Atmospheric composition can influence cloud properties and might have a significant effect on the initiation of precipitation. This study investigated changes in precipitation patterns during COVID-19 lockdowns and compared them to patterns observed during the previous 19 years (2001 through 2019) across two regions of interest, the Hubei province in China and Northern Italy using a satellite-based precipitation dataset. Results indicated that overall rainfall averages were higher in the spring of 2020 with respect to their corresponding climatological means, with higher standard deviations especially in the more urbanized regions like Wuhan, China and Milan, Italy. Precipitation rates observed during the Spring of 2020 tend to fall outside of the climatological 25–75th percentile bounds. Similarly, the number of rainy pixels was in several cases in Spring 2020 higher than the climatological 75th percentile and sometimes even higher than the 95th one. These anomalies may be due to natural variations and may not be caused directly by the reduction in atmospheric pollutant concentrations. Nevertheless, our analysis proved that precipitation patterns during the lockdowns were on the extreme tails of the precipitation climatological distributions for both regions of interest. Lastly, decorrelation lags and distances in Northern Italy remained similar to their corresponding climatological values, whereas in the Hubei province some differences were observed, with the Spring 2020 spatial correlation variogram almost overlapping the climatological 5th percentile and with a decorrelation distance shorter than the climatological value.

Evaluation of satellite-based and reanalysis precipitation datasets by hydrologic simulation in the Chenab river basin

Abstract Several satellite-based and reanalysis products with a high spatial and temporal resolution have become available in recent decades, making it worthwhile to study the performance of multiple precipitation forcing data on hydrological modeling. This study aims to examine the veracity of five precipitation products employing a semi-distributed hydrological model, i.e., the Soil and Water Assessment Tool (SWAT) to simulate streamflow over the Chenab River Basin (CRB). The performance indices such as coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE) and percentage bias (PBIAS) were used to compare observed and simulated streamflow at daily and monthly scales during calibration (2015–2018) and validation (2019–2020). The hydrologic performance of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA) 5-Land (ERA5) was very good at daily (calibration R2=0.83, NSE=0.81, PBIAS=−6%; validation R2=0.75, NSE=0.74, PBIAS=−9.6%) and monthly ( calibration R2=0.94, NSE=0.94, PBIAS=−3.3%; validation R2=0.91, NSE=0.89, PBIAS=−3.2%) scales. This study suggests that the ERA5 precipitation product was the most reliable of the five precipitation products, while the CHIRPS performance was the worst. These findings contribute to highlighting the performance of five precipitation products and reference in the selection of precipitation data as input data to the SWAT model in similar regions.

Spatial Pattern of Precipitation in GPM-Era Satellite Products against Rain Gauge Measurements over Nepal

Precipitation in a mountainous region is highly variable due to the complex terrain. Satellite-based precipitation estimates are potential alternatives to gauge measurements in these regions, as these typical measurements are not available or are scarce in high elevation areas. However, the accuracy of these gridded precipitation datasets need to be addressed before further usage. In this study, an evaluation of the spatial precipitation pattern in satellite-based precipitation products is provided, including satellite-only (Integrated Multi satellite Retrievals for GPM IMERG-UCORR and Global Satellite Mapping of Precipitation (GSMaP-MVK) and gauge calibrated (IMERG-CORR and GSMaP-Gauge) products, with a spatial resolution of 0.1°, which is compared to 387-gauge measurements in Nepal from April 2014 to December 2016. The major results are as follows: (1) The gauge calibrated version 5 IMERG-CORR and version 6 GSMaP-Gauge are relatively better than the satellite-only datasets, although they all underestimate the observed precipitation. (2) The daily gauge calibrated GSMaP-Gauge performs fairly well in low and mid-elevation areas, whereas the monthly gauge calibrated IMERG-C performs better in high-elevation areas. (3) For the daily time scale, IMERG-CORR shows a better ability to detect the true precipitation (higher Probability of Detection (POD)) and (lowest False Alarm Ratio (FAR)) events among all datasets. However, all four satellite-based precipitation datasets accurately detect (Critical Success Index (CSI) >40%) precipitation and no-precipitation events. The results of this work provide the systematic quantification of IMERG and GSMaP of satellite precipitation products over Nepal using station observations and delivers a helpful statistical basis for the selection of these datasets for future scientific research.

Precipitation Data Retrieval and Quality Assurance from Different Data Sources for the Namoi Catchment in Australia

Within the Horizon 2020 Project WaterSENSE a modular approach was developed to provide different stakeholders with the required precipitation information. An operational high-quality rainfall grid was set up for the Namoi catchment in Australia based on rain gauge adjusted radar data. Data availability and processing considerations make it necessary to explore alternative precipitation approaches. The gauge adjusted radar data will serve as a benchmark for the alternative precipitation data. The two well established satellite-based precipitation datasets IMERG and GSMaP will be analyzed with the temporal and spatial requirements of the applications envisioned in WaterSENSE in mind. While first results appear promising, these datasets will need further refinements to meet the criteria of WaterSENSE, especially with respect to the spatial resolution. Inferring information from soil moisture-derived from EO observations to increase the spatial detail of the existing satellite-based datasets is a promising approach that will be investigated along with other alternatives.

The Development of a Combined Satellite-Based Precipitation Dataset across Bolivia from 2000 to 2015

This study proposes the use of satellite-based precipitation (SBP) products in combination with local rain gauges in Bolivia. Using this approach, the country was divided into three major hydrographic basins: the Altiplano, La Plata, and Amazon. The selected SBP products were Global Satellite Mapping of Precipitation (GSMaP) and Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). The correlation coefficients of SBP were found to be from 0.94 to 0.98 at monthly temporal scale. The applied methodology iterates correction factors, taking advantage of surface measurements from the national rain gauge network; five iterations showed stability in the convergence. Once the improved SBP product was obtained, validation was performed by reducing ten percent the number of rain gauges randomly. After applying the correction factors, the combined products improved their correlation coefficient values by up to 0.99. The validation of the methodology showed that with a combination of products using 90% of the rain gauges, correlation coefficients ranged from 0.98 to 0.99. Among the three basins, the Amazon basin presented the poorest results; this fact may be related to low rain gauge density compared to the other two basins. The validation approach shows that the methodology has an acceptable performance. The database generated in this study, now open to the public, is ready to be used for different hydrological applications such as precipitation time-series analysis, water balance, and water assessment at the sub-basin scale within Bolivia.

Satellite-Based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia

Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: 1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. 2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. 3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. 4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. 5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone.

Evaluation of Multi-Satellite Precipitation Datasets and Their Error Propagation in Hydrological Modeling in a Monsoon-Prone Region

This study comprehensively evaluates eight satellite-based precipitation datasets in streamflow simulations on a monsoon-climate watershed in China. Two mutually independent datasets—one dense-gauge and one gauge-interpolated dataset—are used as references because commonly used gauge-interpolated datasets may be biased and unable to reflect the real performance of satellite-based precipitation due to sparse networks. The dense-gauge dataset includes a substantial number of gauges, which can better represent the spatial variability of precipitation. Eight satellite-based precipitation datasets include two raw satellite datasets, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Prediction Center MORPHing raw satellite dataset (CMORPH RAW); four satellite-gauge datasets, Tropical Rainfall Measuring Mission 3B42 (TRMM), PERSIANN Climate Data Record (PERSIANN CDR), CMORPH bias-corrected (CMORPH CRT), and gauge blended datasets (CMORPH BLD); and two satellite-reanalysis-gauge datasets, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS). The uncertainty related to hydrologic model physics is investigated using two different hydrological models. A set of statistical indices is utilized to comprehensively evaluate the precipitation datasets from different perspectives, including detection, systematic, random errors, and precision for simulating extreme precipitation. Results show that CMORPH BLD and MSWEP generally perform better than other datasets. In terms of hydrological simulations, all satellite-based datasets show significant dampening effects for the random error during the transformation process from precipitation to runoff; however, these effects cannot hold for the systematic error. Even though different hydrological models indeed introduce uncertainties to the simulated hydrological processes, the relative hydrological performance of the satellite-based datasets is consistent in both models. Namely, CMORPH BLD performs the best, which is followed by MSWEP, CMORPH CRT, and TRMM. PERSIANN CDR and CHIRPS perform moderately well, and two raw satellite datasets are not recommended as proxies of gauged observations for their worse performances.

Validation of CHIRPS satellite-based precipitation dataset over Pakistan

This study evaluates high resolution Climate Hazard Groups Infrared Precipitation with Stations (CHIRPS) with Surface Precipitation Gauge (SPG) of recent 38 years (1981–2018) through statistical metrics including Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Correlation Coefficient (CC) on daily, monthly, annual and seasonal timescales over the entire Pakistan. Trends of both the datasets were also assessed using non-parametric Mann-Kendall (MK) and Modified MK (MMK) tests. CHIRPS (SPG) captured maximum precipitation in the range of 8.5–9.0 (8.0–8.5) mm/day in northeastern parts during the Monsoon season. CHIRPS slightly underestimated SPG in the range of −0.80 to −0.17 mm/day on area averaged precipitation of 46 stations for the period 1981–2018. Maximum ME, MAE, RMSE were observed as 2.0 mm/day, 1.8 mm/day and 1.8 mm/day respectively on annual timescale. Strong linear relationship (CC > 0.70) was noted between CHIRPS and SPG on monthly, Winter, Monsoon and Post-Monsoon season timescales. CHIRPS (SPG) showed increasing trends in 43 (26) stations using MK test and almost similar trend behavior was detected using MMK test on monthly timescale. CHIRPS showed increasing trends for all the six administrative regions for the period 1981–2018 using MK test and same trend behavior was observed using MMK test during the Monsoon season. Overall, good agreement between MK and MMK trend tests were observed with trivial variations in most of the administrative regions. Hence, it is concluded that CHIRPS can be a substitute of SPG in low and medium elevated and data scarce regions.

Evaluating the hydrological utility of satellite-based rainfall products using neural network models over the Ghare Ghieh River basin, Iran

Abstract In recent years, gridded precipitation data derived from satellite rainfall products have become critical data sources for hydrological applications, especially in ungauged basins where rain gauges are sparse or nonexistent. Also, in streamflow simulations, since the existing rainfall–runoff modelling methods require exogenous input with some assumptions, neural networks can be an efficient solution. In this paper, to simulate daily streamflow on the Ghare Ghieh River basin in northwestern Iran, the Levenberg–Marquardt Neural Network (LMNN) and the Particle Swarm Optimization Neural Network (PSONN) models are proposed. These models are trained and tested with different input patterns from ground-based data for water years of 1988–2008. Then, three satellite-based precipitation datasets, including TRMM-3B42V7, TRMM-3B42RT, and PERSIANN with 0.25° × 0.25° resolutions from 2003 to 2008, are used as inputs for the best-trained models which were selected in the testing step. These products are evaluated before and after calibration in streamflow simulation, and the Geographical Difference Analysis method is used to calibrate them. The results showed that the PSONN model performed better than the LMNN model. Also, in both models, before calibration of satellite precipitation products, TRMM-3B42 showed better performance in streamflow simulation, and after calibration, TRMM-3B42RT performed much better.

Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information

Reliable near real-time precipitation estimates are essential for monitoring and managing of natural disasters such as floods. Quality of inputs and capability of the retrieval algorithm are two important aspects for developing satellite-based precipitation datasets. Most retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. However, their sole reliance on IR information limits their capability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. Moreover, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. This study investigates the effectiveness of adding geographical information (i.e. latitude and longitude) to IR information and the application of a U-Net-based convolutional neural network for improving the accuracy of retrieval algorithms. This research suggests that applying an appropriate CNN architecture on geographical and IR information provides an opportunity to improve the satellite-based precipitation products.

Evaluating intensity-duration-frequency (IDF) curves of satellite-based precipitation datasets in Peninsular Malaysia

In recent years the use of remotely sensed precipitation products in hydrological studies has become increasingly common. The capability of the products in producing rainfall intensity-duration-frequency (IDF) relationships, however, has not been examined in any great detail. The performance of four remote-sensing-based gridded rainfall data processing algorithms (GSMaP_NRT, GSMaP_GC, PERSIANN and TRMM_3B42V7) was evaluated to determine the ability to generate reliable IDF curves. The work was undertaken in Peninsular Malaysia. The best-fitted probability distribution functions (PDFs) of rainfall totals for different durations were used to generate the IDF curves. The accuracy of the gridded IDF curves was evaluated by comparing observed versus estimated IDF curves at 80 locations. The results revealed that a generalized extreme value (GEV) distribution had the best fit to the rainfall intensity for different durations at 62% of the stations, and this was then used to develop the IDF curves. A comparison of these remote sensing derived IDF curves with the observed IDF data revealed that the GSMaP_GC product performed best. In general, the satellite-based precipitation products tended to underestimate the IDF curves. The GSMaP_GC IDF curves were found to be the least biased (8%–27%) compared to the TRMM_3B42V7 IDF curves (65%–67%). The biases in rainfall intensity of different return periods for GSMaP_GC for all grid points were estimated. These results can be used in designing hydraulic structures where gauged data are unavailable.

Evaluation of GPM-Era Satellite Precipitation Products on the Southern Slopes of the Central Himalayas Against Rain Gauge Data

The Global Precipitation Measurement (GPM) mission provides high-resolution precipitation estimates globally. However, their accuracy needs to be accessed for algorithm enhancement and hydro-meteorological applications. This study applies data from 388 gauges in Nepal to evaluate the spatial-temporal patterns presented in recently-developed GPM-Era satellite-based precipitation (SBP) products, i.e., the Integrated Multi-satellite Retrievals for GPM (IMERG), satellite-only (IMERG-UC), the gauge-calibrated IMERG (IMERG-C), the Global Satellite Mapping of Precipitation (GSMaP), satellite-only (GSMaP-MVK), and the gauge-calibrated GSMaP (GSMaP-Gauge). The main results are as follows: (1) GSMaP-Gauge datasets is more reasonable to represent the observed spatial distribution of precipitation, followed by IMERG-UC, GSMaP-MVK, and IMERG-C. (2) The gauge-calibrated datasets are more consistent (in terms of relative root mean square error (RRMSE) and correlation coefficient (R)) than the satellite-only datasets in representing the seasonal dynamic range of precipitation. However, all four datasets can reproduce the seasonal cycle of precipitation, which is predominately governed by the monsoon system. (3) Although all four SBP products underestimate the monsoonal precipitation, the gauge-calibrated IMERG-C yields smaller mean bias than GSMaP-Gauge, while GSMaP-Gauge shows the smaller RRMSE and higher R-value; indicating IMERG-C is more reliable to estimate precipitation amount than GSMaP-Gauge, whereas GSMaP-Gauge presents more reasonable spatial distribution than IMERG-C. Only IMERG-C moderately reproduces the evident elevation-dependent pattern of precipitation revealed by gauge observations, i.e., gradually increasing with elevation up to 2000 m and then decreasing; while GSMaP-Gauge performs much better in representing the gauge observed spatial pattern than others. (4) The GSMaP-Gauge calibrated based on the daily gauge analysis is more consistent with detecting gauge observed precipitation events among the four datasets. The high-intensity related precipitation extremes (95th percentile) are more intense in regions with an elevation below 2500 m; all four SBP datasets have low accuracy (<30%) and mostly underestimated (by >40%) the frequency of extreme events at most of the stations across the country. This work represents the quantification of the new-generation SBP products on the southern slopes of the central Himalayas in Nepal.

An updated moving window algorithm for hourly-scale satellite precipitation downscaling: A case study in the Southeast Coast of China

Accurate gridded precipitation products with both finer tempo-spatial resolutions are critical for various scientific communities (e.g., hydrology, meteorology, climatology, and agriculture). Downscaling on coarse satellite-based rainfall estimates is an optimal approach to obtain such datasets. The Integrated Multi-satellitE Retrivals for Global Precipitation Measurement (GPM) (IMERG) data provides the “best” satellite-based precipitation estimates at half-hourly/0.1° scales, while its spatial resolution is still coarse for certain hydrometeorology research. To acquire hourly downscaled precipitation estimates based on IMERG, there are two great challenges: (1) limited rainfall-related environmental variables (0.01°×0.01°, hourly) used to downscale the IMERG data; and (2) far few rainfall pixels used for regressing traditional relationships between precipitation and environmental variables. In this case, most traditional or commonly-used regression/empirical models and the state-of-art machine learning algorithms are not suitable to cater these requirements. Therefore, we proposed a new strategy to obtain hourly downscaled precipitation estimates based on IMERG called Geographically Moving Window Weight Disaggregation Analysis (GMWWDA). Additionally, we explored multiple cloud properties, including cloud effective radius (CER), cloud top height (CTH), cloud top temperature (CTT) and cloud optical thickness (COT), as covariates to downscale IMERG using GMWWDA method, and concluded as follows: (1) the downscaled results (0.01° × 0.01°, hourly) based on the above mentioned cloud properties outperformed the original IMERG data; (2) the downscaled results based on CER performed better than those based on CTH, CTT and COT, respectively; (3) the accuracy of satellite-based precipitation products pose significant effects on those of the downscaled results. This study provides a great potential solution for generating satellite-based precipitation dataset with finer spatio-temporal resolutions.

Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China

Abstract The wide application of satellite-based and reanalysis-based precipitation data has greatly promoted hydrometeorological research in areas where precipitation observations are scarce. However, the suitability of such precipitation products needs to be carefully evaluated before applications in certain basins because their inherited errors vary with different climate zones, seasonal cycles and land surface conditions; in addition, precipitation products have not been evaluated in the Xihe River basin, China. In this paper, two representative satellite-based precipitation products (Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR)) and two reanalysis-based precipitation products (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and National Centers for Environmental Prediction - Climate Forecast System Reanalysis (CFSR)) were selected for evaluation and corrected against gauge-observed data (OBS). Furthermore, the performances of precipitation products in hydrological simulations were also assessed using the SWAT model calibrated with OBS forcing and not with individual precipitation products. The results show that satellite-based precipitation has a higher quality than reanalysis-based precipitation. The CFSR and CDR overestimate precipitation (the overestimation of CDR precipitation is mainly concentrated in the precipitation intensity range of 1 mm/d to 5 mm/d), while the TRMM and CMADS underestimate precipitation in the Xihe River basin. The TRMM precipitation performs best during the wet season, while the CDR precipitation performed best during the dry season. After bias correction, the quality of TRMM precipitation improves significantly. The Nash-Sutcliffe coefficient (NS) (the percent bias (|PBIAS|)) increases (decreases) by 0.61 (77.27%) and 0.7 (39.15%) under the two different correction scenarios during 2009–2015. Overall, the above original precipitation products cannot be used as supplements to the OBS in the Xihe River basin unless they are corrected by the OBS.

Merging ground and satellite-based precipitation data sets for improved hydrological simulations in the Xijiang River basin of China

Watershed management, disaster warning, and hydrological modeling require accurate spatiotemporal precipitation data sets. This paper presents a comprehensive assessment of a gauge-satellite-based precipitation product that merges the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) satellite precipitation product (SPP) and ground precipitation data at 134 rain gauges in the Xijiang River basin, South China. Two regression-based schemes, principal component regression (PCR) and multiple linear regression (MLR), were used to combine the gauge-based precipitation data and PERSIANN-CDR SPP and were compared at daily and annual scales. Furthermore, a hydrological model Variable Infiltration Capacity was used to calculate streamflow and to evaluate the impact of four different precipitation interpolation methods on the results of the hydrological model at the daily scale. The result shows that the PCR method performs better than MLR and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. On the whole, the combined scheme consistently exhibits good performance and thus serves as a suitable tool for producing high-resolution gauge-and satellite-based precipitation datasets.

The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data

The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access to the three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), and PERSIANN-Climate Data Record (CDR). These datasets have the potential for widespread use by various researchers, professionals including engineers, city planners, and so forth, as well as the community at large. Researchers at CHRS created the CHRS Data Portal with an emphasis on simplicity and the intention of fostering synergistic relationships with scientists and experts from around the world. The following paper presents an outline of the hosted datasets and features available on the CHRS Data Portal, an examination of the necessity of easily accessible public data, a comprehensive overview of the PERSIANN algorithms and datasets, and a walk-through of the procedure to access and obtain the data.

Evaluation of Five Satellite-Based Precipitation Products in Two Gauge-Scarce Basins on the Tibetan Plateau

The sparse rain gauge networks over the Tibetan Plateau (TP) cause challenges for hydrological studies and applications. Satellite-based precipitation datasets have the potential to overcome the issues of data scarcity caused by sparse rain gauges. However, large uncertainties usually exist in these precipitation datasets, particularly in complex orographic areas, such as the TP. The accuracy of these precipitation products needs to be evaluated before being practically applied. In this study, five (quasi-)global satellite precipitation products were evaluated in two gauge-sparse river basins on the TP during the period 1998–2012; the evaluated products are CHIRPS, CMORPH, PERSIANN-CDR, TMPA 3B42, and MSWEP. The five precipitation products were first intercompared with each other to identify their consistency in depicting the spatial–temporal distribution of precipitation. Then, the accuracy of these products was validated against precipitation observations from 21 rain gauges using a point-to-pixel method. We also investigated the streamflow simulation capacity of these products via a distributed hydrological model. The results indicated that these precipitation products have similar spatial patterns but significantly different precipitation estimates. A point-to-pixel validation indicated that all products cannot efficiently reproduce the daily precipitation observations, with the median Kling–Gupta efficiency (KGE) in the range of 0.10–0.26. Among the five products, MSWEP has the best consistency with the gauge observations (with a median KGE = 0.26), which is thus recommended as the preferred choice for applications among the five satellite precipitation products. However, as model forcing data, all the precipitation products showed a comparable capacity of streamflow simulations and were all able to accurately reproduce the observed streamflow records. The values of the KGE obtained from these precipitation products exceed 0.83 in the upper Yangtze River (UYA) basin and 0.84 in the upper Yellow River (UYE) basin. Thus, evaluation of precipitation products only focusing on the accuracy of streamflow simulations is less meaningful, which will mask the differences between these products. A further attribution analysis indicated that the influences of the different precipitation inputs on the streamflow simulations were largely offset by the parameter calibration, leading to significantly different evaporation and water storage estimates. Therefore, an efficient hydrological evaluation for precipitation products should focus on both streamflow simulations and the simulations of other hydrological variables, such as evaporation and soil moisture.

Evaluation and hydrological application of satellite-based precipitation datasets in driving hydrological models over the Huifa river basin in Northeast China

Satellite-based precipitation estimates with high spatial and temporal resolution and large areal coverage have provided hydrologists a potential alternative source for hydrological applications since the last few years, especially for ungauged regions. This study evaluates five satellite-based precipitation datasets, namely, Fengyun, TRMM 3B42, TRMM 3B42RT, CMORPH_BLD and CMORPH_RAW, against gauge observations for streamflow simulation with a distributed hydrological model (SWAT) over the Huifa river basin, Northeast China. Results show that, by comparing the statistical indices (MA, M5P, STDE, ME, BIAS and CC) and inter-annual precipitation, it is demonstrated that Fengyun TRMM 3B42 and CMORPH_BLD show better agreement with gauge precipitation data than CMORPH_RAW and TRMM 3B42RT. When the SWAT model for each dataset calibrated and validated individually, satisfactory model performances (defined as: NS > 0.5) are achieved at daily scale for Fengyun, TRMM 3B42 and gauge-driven model, and very good performances (defined as: NS > 0.75) are achieved at monthly scale for Fengyun and gauge-driven model, respectively. The CMORPH_BLD forced daily simulations also yield higher values of NS and R2 than CMORPH_RAW and TRMM 3B42RT at daily and monthly step. From the uncertainty results, variations of P-factor values and frequency distribution curves of NS suggest that the simulation uncertainty increase when operating the Fengyun, 3B42RT, CMORPH_BLD and CMORPH_RAW-driven model with best fitted parameters for rain gauge SWAT model. The results also indicate that the influence of parameter uncertainty on model simulation results may be greater than the effect of input data accuracy. It is noted that uncertainty analysis is necessary to evaluate the hydrological applications of satellite-based precipitation datasets.