Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record Research Articles

Assessing the influence of El Niño on the California precipitation regime during the satellite precipitation era

AbstractA strong El Niño condition is likely to bring heavy precipitation over California. However, the 2015/2016 El‐Niño generally considered similar and comparable to another extreme event—the 1997/1998 El Niño—failed to bring above‐average precipitation over California. The muted influence of the 2015–2016 El‐Niño on California rainfall has renewed interest in the relationship between El Niño and California precipitation variability. This study assesses seasonal and regional aspects of the precipitation regimes in California for the past two decades using primarily satellite‐based precipitation products. Two satellite‐based precipitation datasets—Integrated Multi‐satellitE Retrievals for GPM (IMERG) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR)—along with the ground‐based measurement—Global Precipitation Climatology Centre (GPCC) dataset as a reference—are analysed to understand the seasonal and regional influence of El‐Niño on precipitation regimes during the 2001–2019 period using Multivariate El Niño Southern Oscillation Index (MEI). The results show that El Niño's impact on precipitation rate varies in different seasons and regions. Specifically, we observe a statistically significant strong negative correlation (−0.72 to −0.66) between MEI and precipitation rate over southern California during the El Niño months. Both satellite precipitation products and ground‐based precipitation measurements indicate a similar El Niño–precipitation relationship over California during the study period. These results contradict the pre 2000 ‘classic’ story that El Niño was positively correlated with higher California precipitation.

Impacts of precipitation uncertainty on hydrological ensemble simulations over the Ganjiang River basin

Study regionThe Ganjiang River basin (113°−117°E, 24°−30°N; 83,374 km2) is a large watershed with complex topography in the Poyang Lake basin in Jiangxi province, China. Study focusThis study evaluates three quantitative precipitation estimates (QPEs) over the Ganjiang River basin, namely the China Gauged-Based Daily Precipitation Analysis (CGDPA) data, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) data, and the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The study also investigates the impacts of precipitation uncertainty on hydrological ensemble streamflow simulations using the three QPEs as precipitation inputs of the Variable Infiltration Capacity (VIC) hydrological model. New hydrological insights for the regionAPHRODITE underestimates precipitation compared to CGDPA, while PERSIANN-CDR shows greater spatiotemporal variability. The ensemble mean streamflow demonstrates greater improvement compared to the results obtained from a single parameter set. Among the three QPEs, the simulations forced by CGDPA show the best deterministic and probabilistic verification scores, followed by APHRODITE. PERSIANN-CDR tends to underestimate evaporation and leads to the lowest score of ensemble streamflow simulations, but shows advantages in simulating extremely low streamflow. The study highlights that high-density gauge-based QPEs remain the most accurate source of precipitation inputs for reliable hydrological simulations, while satellite-gauge merged QPEs can provide valuable inputs for hydrological simulations over the basins where meteorological stations are scarce.

Performance of the PERSIANN Family of Products over the Mekong River Basin and Their Application for the Analysis of Trends in Extreme Precipitation Indices

Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System) and PDIR-Now (PERSIANN-Dynamic Infrared Rain Rate near-real-time)—were compared to each other and evaluated against IMERG Final (Integrated Multi-satellite Retrievals for Global Precipitation Measurement—Final Run) from 2015 to 2020 over the Mekong River Basin and Delta (MRB) using a spatial resolution of 0.1∘ by 0.1∘ and at a daily scale. PERSIANN-CDR (PERSIANN-Climate Data Record) was also included in the evaluation but was not compared against the real-time products. In this evaluation, PDIR-Now exhibited a superior performance to that of PERSIANN-CCS, and the performance of PERSIANN-CDR was deemed satisfactory. The second part of the study entailed performing a Mann–Kendall trend test of extreme precipitation indices using 38 years of PERSIANN-CDR data over the MRB. This annual trend analysis showed that extreme precipitation over the 95th and 99th percentiles has decreased over the Upper Mekong River Basin, and the consecutive number of wet days has increased over the Lower Mekong River Basin.

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

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

Climatology of the global summer monsoon rainy seasons: Revisited from a high-resolution satellite climate data record

Reliable information on the monsoon rainfall at finer spatiotemporal scales has always been crucial for applications in hydrometeorology, water resource management, disaster prevention, and assessment of numerical model outputs. Here, the spatial-temporal characteristics of the summer monsoon rainy seasons worldwide are revisited using a global, high-resolution, bias-corrected satellite rainfall climate data record. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides 0.25° rainfall estimates for the latitude band 60°S–60°N from 1983 to the near present. The global monsoon regimes are defined based on the annual rainfall range and the summer rainfall ratio. The onset, peak, and withdrawal of the rainy season are then determined using a concise yet pertinent rainfall parameter with universal objective criteria.Overall, six major summer monsoon regimes of the globe, the Asian Monsoon, Indonesian–Australian monsoon, North African monsoon, South African monsoon, North American and South American monsoon, are all reasonably represented by PERSIANN-CDR. The progression patterns of climatological onset, peak, and withdrawal phases of these monsoons are consistent with those reported by previous global and regional monsoon studies. Furthermore, compared with other coarser resolution satellite rainfall estimates such as the Climate Prediction Center Merged Analysis of Precipitation (CMAP), the present result is clearly in better agreement with that obtained from ground-based observations since it allows the determination of more detailed information associated with local-scale factors such as the sharp land-sea contrast, local surface heating, and topography effects. However, some “spots”, particularly over complex topography lacking ground truth validations, are revealed to have rainy season characteristics that differ substantially from those over the surrounding areas and have not been fully described by published literature. These distinct features need to be confirmed by further regional monsoon studies.

Hydrological response to climate change and human activities in the Three-River Source Region

Abstract. The Three-River Source Region (TRSR), which is known as “China's Water Tower” and affects the water resources security of 700 million people living downstream, has experienced significant hydrological changes in the past few decades. In this work, we used an extended variable infiltration capacity (VIC) land surface hydrologic model (VIC-Glacier) coupled with the degree-day factor algorithm to simulate the runoff change in the TRSR during 1984–2018. VIC-Glacier performed well in the TRSR, with Nash–Sutcliffe efficiency (NSE) above 0.68, but it was sensitive to the quality of the limited ground-based precipitation. This was especially marked in the source region of the Yangtze River: when we used Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR), which has better spatial details, instead of ground-based precipitation, the NSE of Tuotuohe station increased from 0.31 to 0.86. Using the well-established VIC-Glacier model, we studied the contribution of each runoff component (rainfall, snowmelt, and glacier runoff) to the total runoff and the causes of changes in runoff. The results indicate that rainfall runoff contributed over 80 % of the total runoff, while snowmelt runoff and glacier runoff both contributed less than 10 % in 1984–2018. Climate change was the main reason for the increase in runoff in the TRSR after 2004, accounting for 75 %–89 %, except in the catchment monitored by Xialaxiu station. Among climate change factors, precipitation had the greatest impact on runoff. Finally, through a series of hypothetical climate change scenario experiments, we found that a future simultaneous increase in precipitation and temperature would increase the total runoff, rainfall runoff, and glacier runoff. The snowmelt runoff might remain unchanged because the increased precipitation, even with seasonal fluctuations, was basically completely compensated for by the decreased solid-to-liquid precipitation ratio. These findings improve our understanding of hydrological processes and provide insights for policy-makers on how to optimally allocate water resources and manage the TRSR in response to global climate change.

Applicability comparison of various precipitation products of long-term hydrological simulations and their impact on parameter sensitivity

Precipitation is an important component of water circulation and an essential input for various hydrological models. A high quality, high spatial resolution, and long-term precipitation dataset would benefit hydrological investigations, particularly for regions having insufficient precipitation records. The upper Huaihe River Basin (UHRB) was selected as the research location in this study, and the accuracies of three precipitation products (PPs: a high-resolution daily gridded precipitation dataset for China (HRLT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Global (CPC) precipitation dataset) were assessed at multiple spatio-temporal scales comparing with the gauge precipitation (GP) for 2000–2019. Subsequently, the applicability of the three PPs on streamflow (Q) and sediment yield (SY) simulations, as well as the impact on parameter sensitivity, were compared using the Soil and Water Assessment Tool (SWAT) model. The results showed that the accuracy of the three PPs were ranked as CPC > HRLT > PERSIANN-CDR on the watershed average scale, HRLT would underestimate the extreme precipitation; and PERSIANN-CDR would overestimate the annual precipitation. On the grid-to-point scale, PERSIANN-CDR was found to be the most stable with high accuracy, followed by CPC and HRLT on all temporal scales. The ability of these PPs to detect rainfall events was ranked as CPC > HRLT > PERSIANN-CDR. The sensitivity of the Q parameters changed with the variation in the precipitation input. The sensitive parameters for GP were distributed on average for almost all processes, while the sensitive parameters for PPs mainly controlled the groundwater and evapotranspiration processes. Among all the PPs, the performance of CPC in the Q and SY simulations was found to be the best, followed by HRLT and PERSIANN-CDR, and all the PPs could simulate SY better than Q in spatial distribution. HRLT has the potential to be used in long-term hydrological simulations in ungauged or small watersheds based on its high spatial resolution compared to other products.

A COMPARISON OF SIMULATED RUNOFF BASED ON GROUND RAIN GAUGES AND PERSIANN-CDR SATELLITE PRECIPITATION RECORDS USING SWAT MODEL

Abstract. Easy access to valid climatic data has always played a fundamental role in progressing hydrological studies. That is why numerous satellite-based precipitation products (SPPs) have been generated in the contemporary era. Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is considered one of the most popular climatic databases which started to produce daily rainfall data with 0.25° × 0.25° temporal and spatial resolutions in 1983. The aim of this research is to evaluate how well PERSIANN-CDR has performed in a rainfall-runoff modeling application over the period of 1994 to 2015. In this regard, using Soil & Water Assessment Tool (SWAT), two rainfall-runoff models based on Ground-based Rain Gauge stations (GRGs) and PERSIANN-CDR precipitation records were developed for the Chelgerd sub-basin, which is the main branch of the Zayandeh-Roud Basin in central Iran, in order to analyze how accurate the simulated runoff by PERSIANN-CDR database is. Comparing the developed SWAT model calibration results using the satellite database precipitation (NS = 0.78, P-Factor = 0.52, and R-Factor = 0.41) to calibration results of the developed model based on GRGs (NS = 0.81, P-Factor = 0.54, and R-Factor = 0.42) showed that although PERSIANN-CDR precipitation magnitudes were typically less than GRGs records, accuracy indicators of simulated runoffs to Ghale-Shahrokh were almost the same.

Assessment of Long-Term Rainfall Variability and Trends Using Observed and Satellite Data in Central Punjab, Pakistan

This study explores the spatio-temporal distribution and trends on monthly, seasonal, and annual scales of rainfall in the central Punjab districts of Punjab province in Pakistan by using observation and satellite data products. The daily observed data was acquired from the Pakistan Metrological Department (PMD) between 1983 and 2020, along with one reanalysis, namely the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and one satellite-based daily Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks climate data record (PERSIANN-CDR) using the Google Earth Engine (GEE) web-based API platform to investigate the spatio-temporal fluctuations and inter-annual variability of rainfall in the study domain. Several statistical indices were employed to check the data similarity between observed and remotely sensed data products and applied to each district. Moreover, non-parametric techniques, i.e., Mann–Kendall (MK) and Sen’s slope estimator were applied to measure the long-term spatio-temporal trends. Remotely sensed data products reveal 422.50 mm (CHIRPS) and 571.08 mm (PERSIANN-CDR) mean annual rainfall in central Punjab. Maximum mean rainfall was witnessed during the monsoon season (70.5%), followed by pre-monsoon (15.2%) and winter (10.2%). Monthly exploration divulges that maximum mean rainfall was noticed in July (26.5%), and the minimum was in November (0.84%). The district-wise rainfall estimation shows maximum rainfall in Sialkot (931.4 mm) and minimum in Pakpattan (289.2 mm). Phase-wise analysis of annual, seasonal, and monthly trends demonstrated a sharp decreasing trend in Phase-1, averaging 3.4 mm/decade and an increasing tendency in Phase-2, averaging 9.1 mm/decade. Maximum seasonal rainfall decreased in phase-1 and increased Phase-2 during monsoon season, averaging 2.1 and 4.7 mm/decade, whereas monthly investigation showed similar phase-wise tendencies in July (1.1 mm/decade) and August (2.3 mm/decade). In addition, as district-wise analyses of annual, seasonal, and monthly trends in the last four decades reveal, the maximum declined trend was in Sialkot (18.5 mm/decade), whereas other districts witnessed an overall increasing trend throughout the years. Out of them, Gujrat district experienced the maximum increasing trend in annual terns (50.81 mm/decade), and Faisalabad (25.45 mm/decade) witnessed this during the monsoon season. The uneven variability and trends have had a crucial imprint on the local environment, mainly in the primary activities.

Reliability of satellite-based precipitation products in capturing extreme precipitation indices over Iran

The goal of this study is to assess the performance of four widely-used satellite precipitation products in capturing extreme precipitation indices across Iran over the period 2001–2018; these products include GPM IMERG (Integrated Multi-Satellite Retrievals for Global Precipitation Measurement), TRMM 3B42 (Tropical Rainfall Measuring Mission), CHIRPS (Climate Hazards Center InfraRed Precipitation with Station data), and PERSIANN-CDR (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record). For this aim, a national gridded precipitation dataset was developed using a dense network of rain gauges as a reference dataset. The results suggest that the IMERG product outperforms the other three precipitation products in capturing extreme precipitation indices both temporally and spatially. TRMM 3B42 data show promising results in identifying many extreme indices, while the CHIRPS and PERSIANN-CDR products show less performance in accurately generating many of the extreme precipitation indices.

Evaluating the Applicability of PERSIANN-CDR Products in Drought Monitoring: A Case Study of Long-Term Droughts over Huaihe River Basin, China

In this study, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) was evaluated for the assessment of long-term drought monitoring in Huaihe River Basin using daily gauge observation data for the period from 1983 to 2017. The evaluation results show that the PERSIANN-CDR algorithm has a good detection ability for small precipitation events over the whole basin, but a poor ability for extreme precipitation events (>50 mm/day). Daily PERSIANN-CDR estimates perform relatively better in areas with abundant precipitation, while the monthly and yearly PERSIANN-CDR estimates are highly consistent with gauge observations both in magnitude and space. The Standardized Precipitation Index (SPI) at various time scales (3, 6, and 12 months) was calculated based on PERSIANN-CDR and gauge observation, respectively. Grid-based values of statistics derived from those SPI values demonstrate that PERSIANN-CDR has a good ability to capture drought events of each time scale across the basin. However, caution should be applied when using PERSIANN-CDR estimates for basin-scale drought trend analysis. Furthermore, three drought events with long duration and large extent were selected to test the applicability of PERSIANN-CDR in drought monitoring. The results show that it has a good ability to capture when and where droughts occur and how far they spread. Due to the overestimation of small precipitation events, PERSIANN-CDR tends to overestimate the number of extreme droughts and their extents. This needs to be considered in future algorithm improvement.

Examinations on global changes in the total and spatial extent of tropical cyclone precipitation relating to rapid intensification

Moderate tropical cyclone precipitation (TCP) is of great significance to regional water resource supply, while extreme TCP could bring significant adverse impacts to ecosystems and society, especially when tropical cyclones intensify rapidly, leaving no time to take prevention actions. Whether rapid intensification (RI) of tropical cyclones (TCs) affect TCP in both land and ocean remains unknown. Here we classified TCs which have undergone increases in the maximum sustained wind speed (MSW) by at least 30 knots within 24-h into RI category. We analyzed TCP totals provided by daily precipitation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and spatial extent from 1983 to 2019 in the four categories based on regions (land and ocean) and RI-experiencing characteristics (with- and without-RI). TCP totals and spatial extent was identified by the restricted moving neighborhood method and semi-variogram framework. The results show that TCP totals on the ocean are larger than those on the land, since RI-experiencing TCP are higher than TCP without RI-experiencing, although RI processes tend to increase TCP totals in the extremely high percentiles more significantly on land than ocean. The effects of RI processes on global TCP spatial extent are not statistically significant, and there are no definite relations between MSW and TCP spatial extent. The four regions of the Northeast Pacific Ocean (EP), South Pacific Ocean (SP), Northwest Pacific Ocean (WP), and North Atlantic Ocean (NA) show increases in regional mean and extreme TCP totals. The highest increase in the extreme TCP totals (0.37 mm day−1 year−1) over the NA region occurs in the RI_ocean category, which is 2.6 times the average positive enhancement trend across all basins. The increasing rate of the extreme TCP totals over the WP region is higher in track points with RI-experiencing than without RI-experiencing. The category of RI_land over the regions of NA, EP and SP shows a significant increase in the regional mean TCP spatial extent.

Statistical Evaluation and Trend Analysis of ANN Based Satellite Products (PERSIANN) for the Kelani River Basin, Sri Lanka

Satellite-based precipitation products, (SbPPs) have piqued the interest of a number of researchers as a reliable replacement for observed rainfall data which often have limited time spans and missing days. The SbPPs possess certain uncertainties, thus, they cannot be directly used without comparing against observed rainfall data prior to use. The Kelani river basin is Sri Lanka’s fourth longest river and the main source of water for almost 5 million people. Therefore, this research study aims to identify the potential of using SbPPs as a different method to measure rain besides using a rain gauge. Furthermore, the aim of the work is to examine the trends in precipitation products in the Kelani river basin. Three SbPPs, precipitation estimation using remotely sensed information using artificial neural networks (PERSIANN), PERSIANN-cloud classification system (CCS), and PERSIANN-climate data record (CDR) and ground observed rain gauge daily rainfall data at nine locations were used for the analysis. Four continuous evaluation indices, namely, root mean square error (RMSE), (percent bias) PBias, correlation coefficient (CC), and Nash‒Sutcliffe efficiency (NSE) were used to determine the accuracy by comparing against observed rainfall data. Four categorical indices including probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and proportional constant (PC) were used to evaluate the rainfall detection capability of SbPPs. Mann‒Kendall test and Sen’s slope estimator were used to identifying whether a trend was present while the magnitudes of these were calculated by Sen’s slope. PERSIANN-CDR performed well by showing better performance in both POD and CSI. When compared to observed rainfall data, the PERSIANN product had the lowest RMSE value, while all products indicated underestimations. The CC and NSE of all three products with observed rainfall data were also low. Mixed results were obtained for the trend analysis as well. The overall results showed that all three products are not a better choice for the chosen study area.

Improving the accuracy of satellite and reanalysis precipitation data by their ensemble usage

This study evaluated the outputs of five precipitation (PCP) datasets. These models are ECMWF reanalysis 5th generation (ERA5), precipitation estimation from remotely sensed information using artificial neural networks-climate data record (PERSIANN-CDR), Asian precipitation-highly resolved observational data integration toward evaluation (APHRODITE), The national centers for environmental prediction climate forecast system reanalysis (NCEP CFSR) and climatic research unit (CRU). The PCP outputs of these models were compared with data of nine synoptic stations in the Khuzestan province. The results indicated a better match between the APHRODITE outputs and the PCP data at most stations (R2 > 0.85, root-mean-square error (RMSE) < 17.049 mm and − 4.25 < Bias < 2.633 mm). However, CRU model has the highest critical success index (more than 0.711) and the lowest false alarm ratio (less than 0.2) and ERA5 has the highest probability of detection (more than 0.967) at most stations. Then, PCP outputs of five reanalysis (ERA5), interpolated (APHRODITE, NCEP CFSR and CRU) and satellite (PERSIANN-CDR) PCP datasets were combined to reduce the PCP estimation error. The multivariate adaptive regression splines models were employed for this purpose. The results show that the RMSE of all the stations, except Ahvaz station, decreased and the BIAS decreased too. Given the results, using ensemble data methods is a suitable way for reducing the error and increasing the accuracy of these models.

Evaluation of multi-satellite precipitation products in estimating precipitation extremes over mainland China at annual, seasonal and monthly scales

The wide and consistent global coverage of satellite-based quantitative precipitation estimates (QPEs) has shown great potential for monitoring precipitation (PR) at large spatial scales. Evaluation of QPEs in estimating PR extremes is vital to forecasting hydrologic extremes. Here, we present a systematic evaluation of four commonly used QPEs: (1) the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), (2) Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42 V7), (3) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and (4) Multi-Source Weighted-Ensemble Precipitation (MSWEP), in their abilities to detect PR extremes in mainland China at annual-seasonal-monthly scales. Four Standard Extreme Precipitation Indices (SEPIs) are chosen as the assessment metrics, including the maximum 1-day PR (Rx1day), the simple PR intensity index (SDII), the count of days with PR ≥ 20 mm (R20mm), and the consecutive dry days (CDD). Results indicate that PERSIANN-CDR performs best for all SEPIs, followed by CHIRPS and TRMM 3B42 V7. All QPEs (except MSWEP) perform better (worse) in capturing CDD (SDII) than other SEPIs at all three timescales. However, large differences among the performance of QPEs in estimated seasonal SEPIs are found - CHIRPS (PERSIANN-CDR) outperforms the other QPEs in spring (summer and autumn), while PERSIANN-CDR and CHIRPS outperform the other QPEs in winter. All QPEs perform better in detecting extreme PR occurrence in summer than in other seasons, and spatially most of them perform better in humid southeastern China than in arid northwestern China. CHIRPS and TRMM 3B42 V7 overestimate Rx1day, R20mm and SDII in most of China, while MSWEP notably underestimates CDD in most of China and overestimates Rx1day, R20mm and SDII in western China.

Error Characteristic Analysis of Satellite-Based Precipitation Products over Mainland China

Satellite-based precipitation products (SPPs) provide valuable precipitation information for various applications. Their performance, however, varies significantly from region to region due to various data sources and production processes. This paper aims to evaluate four selected SPPs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Gauge-adjusted Global Satellite Mapping of Precipitation (GSMAP-gauge), and Global Precipitation Measurement (GPM)) over mainland China from 2016 to 2019. Both conventional statistical indicators (e.g., correlation coefficients (CC), root mean square error (RMSE), mean absolute error (MAE), relative bias (RB), and Nash–Sutcliffe efficiency (NSE)) and categorical indicators (probability of detection (POD), probability of true detection (POTD), false-alarm rate (FAR), and critical success index (CSI)) are used for quantitative analysis. The results show that: (1) GSMAP-gauge and GPM perform best in reproducing the spatial distribution pattern of precipitation over mainland China, whereas SPPs generally underestimate summer precipitation with a high frequency of no-rain cases. (2) MSWEP has the best capability for recording precipitation events, although some parts of northern China exhibit abnormal overestimations for winter precipitation. (3) All SPPs, especially the PERSIANN-CDR, significantly underestimate the precipitation in the mountainous areas of southwestern China. (4) The GSMAP-gauge and GPM outperformed the other two of the four SPPs, in terms of the probability density function of daily precipitation for cases (PDFc) and the probability density function of daily precipitation for volume (PDFv). Generally, PERSIANN-CDR shows the poorest performance when compared to the other three products. The product’s algorithm for estimating heavy precipitation and mountainous precipitation needs further improvement.

The Application of PERSIANN Family Datasets for Hydrological Modeling

This study investigates the application of precipitation estimation from remote sensing information using artificial neural networks (PERSIANN) for hydrological modeling over the Russian River catchment in California in the United States as a case study. We evaluate two new PERSIANN products including the PERSIANN-Cloud Classification System–Climate Data Record (CCS–CDR), a climatology dataset, and PERSIANN–Dynamic Infrared Rain Rate (PDIR), a near-real-time precipitation dataset. We also include older PERSIANN products, PERSIANN-Climate Data Record (CDR) and PERSIANN-Cloud Classification System (CCS) as the benchmarks. First, we evaluate these PERSIANN datasets against observations from the Climate Prediction Center (CPC) dataset as a reference. The results showed that CCS–CDR has the least bias among all PERSIANN family datasets. Comparing the two near-real-time datasets, PDIR performs significantly more accurately than CCS. In simulating streamflow using the nontransformed calibration process, EKGE values (Kling–Gupta efficiency) for CCS–CDR (CDR) during the calibration and validation periods were 0.42 (0.34) and 0.45 (0.24), respectively. In the second calibration process, PDIR was considerably better than CCS (EKGE for calibration and validation periods ~ 0.83, 0.82 for PDIR vs. 0.12 and 0.14 for CCS). The results demonstrate the capability of the two newly developed datasets (CCS–CDR and PDIR) of accurately estimating precipitation as well as hydrological simulations.

Probabilistic drought forecasting using copula and satellite rainfall based PERSIANN‐CDR and MSWEP datasets

AbstractIn situ rainfall data plays a vital role in drought assessment. However, adequate in situ data are not available in many parts of the world, and they do not provide the proper spatial coverage for drought assessment. With the advacements in satellite rainfall estimates (SREs), it is possible to monitor droughts in ungauged basins. However, the applications of SREs for drought forecasting are not widely explored due to the inherent uncertainties associated with these products.In this study, we evaluated two long‐term SREs for drought forecasting in the Zayandehrood basin, a critical region in the central plateau of Iran. The performance of two SREs, including Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks‐Climate Data Record (PERSIANN‐CDR), and Multi‐Source Weighted‐Ensemble Precipitation (MSWEP) are compared with observations during 1983–2015. The overall results indicate that utilizing MSWEP data in the forecasting model can slightly overestimate the probability of spring drought based on winter drought (with the highest error of 8.5%). In comparison, the PERSIANN‐CDR underestimated the probabilities (with the lowest error being −44%). The performance of copula models and SREs can vary based on the thresholds for drought severity. For example, the performance of MSWEP datasets for predicting moderate to severe droughts during the Spring season is closer to the predicted values by gauge datasets. It is concluded that the MSWEP may be considered more reliable in drought forecasting than the PERSIANN‐CDR. Our results highlight the potential application of copula‐based forecasting models for seasonal drought forecasting using SREs datasets. Such models can be implemented for global‐scale drought predictions, especially in ungagged basins.

Evaluating the performance of eight global gridded precipitation datasets across Iran

Precipitation is one of the crucial variables in hydrological and water resources studies. Runoff, soil moisture, groundwater recharge, and other hydrological variables change with the amount of precipitation in a region. Thus, accurate precipitation estimation is a major issue in the field of water resources and hydrological modeling. Most hydrological studies and water management decisions in Iran are established using in situ observation of precipitation. However, due to limited spatial distribution and lack of long-term high-quality data in many stations, this information usually does not fully meet the information requirements of a given study. In this study, the accuracies of eight global gridded precipitation datasets including Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU), Climate Prediction Center (CPC), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), PERSIANN-Climate Data Record (PERSIANN-CDR), Global Precipitation Climatology Project (GPCP), Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2), and ECMWF Reanalysis version 5 (ERA5) were evaluated to identify the strengths and weaknesses of each dataset in different regions and main catchments of Iran. The precipitation of 96 synoptic stations during the years 1987–2016 was used as the basis for the evaluations. The evaluations were made at monthly, seasonal, and annual scales using grid-based evaluation and areal average evaluation at the catchments scale. In addition to the amount of precipitation, the datasets were evaluated using three statistical performance indices (i.e., correlation coefficient, RMSE, and relative bias) and other metrics such as probability distribution similarity with observation data (using Anderson-darling test), empirical cumulative distribution function (ECDF), and Taylor diagrams. The results indicated that dataset performance varied by catchment. Except for central parts, GPCC was the best dataset in 44% of the evaluated grids in terms of time series pattern recognition. It showed correlation coefficients of 0.71, 0.80, and 0.85, in monthly, seasonal, and annual scales, respectively. In central areas, MERRA2 estimated the average annual precipitation in this catchment with + 2 mm difference, and CRU had a correlation coefficient greater than 0.90. Moreover, CHIRPS was the best dataset in terms of relative bias in the south and southwestern catchments, so that the relative bias was negligible in these catchments in most months. This study provides rankings and recommendations for selecting an appropriate alternative precipitation dataset, which, in turn, provides the knowledge required in monitoring systems and hydrological and water resources modeling that need high-resolution input data.

Assessment of Three Long-Term Satellite-Based Precipitation Estimates against Ground Observations for Drought Characterization in Northwestern China

Long-term satellite-based precipitation estimates (LSPE) play a significant role in climatological studies like drought monitoring. In this study, three popular LSPEs (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP)) were evaluated on a monthly scale using ground-based stations for capturing drought event characteristics over northwestern China from 1983 to 2013. To reflect dry or wet evolution, the Standardized Precipitation Index (SPI) was adopted, and the Run theory was used to identify drought events and their characteristics. The conventional statistical indices (relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE)), as well as categorical indices (probability of detection (POD), false alarm ratio (FAR), and missing ratio (MISS)) are used to evaluate the capability of LSPEs in estimating precipitation and drought characteristics. We found that: (1) three LSPEs showed generally satisfactory performance in estimating precipitation and characterizing drought events. Although LSPEs have acceptable performance in identifying drought events with POD greater than 60%, they still have a high false alarm ratio (&gt;27%) and a high missing ratio (&gt;33%); (2) three LSPEs tended to overestimate drought severity, mainly because of an overestimation of drought duration; (3) the ability of CHIRPS to replicate the temporal evolution of precipitation and SPI values is limited; (4) in severe drought events, PERSIANN-CDR tends to overestimate precipitation, and drought severity, as well as drought area; (5) among the three LSPEs, MSWEP outperformed the other two in identifying drought events (POD &gt; 66%) and characterizing drought features. Finally, we recommend MSWEP for drought monitoring studies due to its high accuracy in estimating drought characteristics over northwestern China. In drought monitoring applications, the overestimation of PERSIANN-CDR for drought peak value and area, as well as CHIRPS’s inferiority in capturing drought temporal evolution, must be considered.