Rainfall Measurements Research Articles

Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin, China

Precipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature (Tm) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new Tm model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.

Machine learning prediction of landslide deformation behaviour using acoustic emission and rainfall measurements

Knowledge of landslide displacement trends is important to understand risks and establish early warning trigger thresholds so that action can be taken to protect people and critical infrastructure. However, the availability of direct continuous displacement measurements is often limited due to relatively high costs. This has driven research to establish models that quantify relationships between landslide displacements and other measured parameters such as pore water pressures, rainfall and more recently acoustic emission (AE), so that displacement can be predicted, and hence made available at a lower cost. This paper describes an investigation of established machine learning models to predict displacements using time series measurements of AE and rainfall. Data from a case study site has been used to train models using measured displacements and then test to assess prediction accuracy. The LASSO-ELM model was shown to perform best. It was able to predict displacements to a mean absolute percentage error < 2.5% up to 60 days after the end of the training period, which is better than similar reported studies. Training a LASSO-ELM model using continuous high resolution AE measurements combined with rainfall data has potential to provide predicted displacement trends once direct measurement of displacement is no-longer available.

Improved Gridded Precipitation Data Derived from Microwave Link Attenuation

The motivation for improving gridded precipitation data lies in weather now-casting and flood forecasting. Therefore, over the past decade, Commercial Microwave Link (CML) attenuation data have been used to determine rain rates between microwave antennas, and to produce more accurate countrywide precipitation grids. CML networks offer a unique advantage for precipitation measurements due to their high density. However, these data experience uncertainty from several sources as reported in earlier research. This current work determines the reliability of rainfall measurements for each link by comparing CML-derived rain rates to adjusted weather radar rainfall at the link location, over three months. Dynamic Time Warping (DTW) is applied to the pair of CML/radar time-series data in two study areas, Israel and Netherlands. Based on the DTW amplitude and temporal distance, unreliable links are identified and flagged, and interpolated gridded precipitation data are derived in each country after filtering out those unreliable links. Correlations between CML-derived grids and rain observations from an independent set of gauges, tested over several rain events in both study areas, are higher for the reliable subset of CML than the full set. For certain storm events, the Kendall rank correlation for the set of reliable CML is almost double that of the complete set, demonstrating that improved gridded precipitation data can be obtained by removing unreliable links.

Impacts of rainfall and soil conservation measures on soil, SOC, and TN losses on slopes in the black soil region, northeastern China

Water erosion significantly affects the productive black soils in Northeastern China. The aim of this study was to analyze the impacts of rainfall characteristics, slope, and soil conservation measures on soil, soil organic carbon (SOC), and total nitrogen (TN) losses and thus to help implement a rational soil loss control strategy. The present study was conducted using sampled soil, runoff, and soil loss data from the plots in the years 2014, 2015, and 2020 when the rainfall characteristics varied greatly. Studies found that soil, SOC, and TN losses on the 18 plots differed greatly, depending on soil conservation measures, slope and rainfall characteristics. Bare and up-downslope (UD) cultivated plots suffered the highest loss rates of soil, SOC, and TN. In 2014, annual loss rates of soil, SOC, and TN on the UD plots reached 5849, 88.55, and 6.84 t km−2 yr−1, respectively. In 2015, soil loss rate (SLR) on the UD plots approached 434.88 t km−2 yr−1 with SOC and TN loss rates of 5.83 and 0.44 t km−2 yr−1. In 2020, the mean SLR on the UD plots was 637.96 t km−2 yr−1 which was lower than that (i.e., 1385.61 t km−2 yr−1) on the bare plots. Annual loss rates of soil, SOC, and TN presented an increasing trend with increasing slope degree. The soil, SOC and TN control efficiencies of contour tillage and hedgerow planting on the 3° and 5° plots were around 90%. The mean SLR on the 53-m terraced plots was two times the tolerable value (i.e., 200 t km−2 yr−1). The size of terrace on steep slopes should be reconsidered, and slope length should be reduced. Bare and UD slopes must be implemented with rational soil conservation measures to control soil, TN, and SOC losses in the black soil region of northeastern China.

Assessing rainfall spatial variability in the Brazilian savanna region with TMPA rainfall dataset

Rainfall is a major component of the hydrological cycle. The lack of basic information on rainfall spatial variability is a major source of uncertainty in many fields of studies, e.g., meteorology, hydrology, and agriculture. Satellite rainfall measurements are becoming increasingly popular for their extensive database and reliable estimates. The objective of this study was to use the Tropical Rainfall Measuring Mission (TRMM) dataset along with rain gauges to characterize aspects of rainfall spatial variability and discuss the possible impacts from recent trends in the Brazilian savannah biome (Cerrado). Gauge stations were used to assess TRMM bias error and calibrate data for further analyses. Rainfall rates and their spatial variability showed a strong relationship to the transition zones between different biomes. Rainfall showed a decreasing trend for the eastern region of the Cerrado biome, a region characterized by a recent and significant expansion of crop areas. These trends agree with results from different studies which highlight the current drawdown of groundwater levels and reduced discharge, and possible lengthening of the dry season in the long run. As many conflicts have already been documented for this region, these decreasing trends are alarming for urgent and consistent hydroclimatic monitoring, and improved water resources planning and management. Positive trends for rainfall in the central Cerrado are likely a momentary recovery of recent-period drought.

A GIS Methodology to Determine the Critical Regions for Mitigating Eutrophication in Large Territories: The Case of Jalisco, Mexico

Inadequate management practices for solid waste and wastewater are some of the main causes of eutrophication globally, especially in regions where intensive livestock, agricultural, and industrial activities are coupled with inexistent or ineffective waste and wastewater treatment infrastructure. In this study, a methodological approach is presented to spatially assess the trophic state of large territories based on public water quality databases. The trophic state index (TSI) includes total nitrogen, total phosphorus, chlorophyll A, chemical oxygen demand, and Secchi disk depth values as water quality indicators. A geographical information system (GIS) was used to manage the spatiotemporal attributes of the water quality data, in addition to spatially displaying the results of TSI calculations. As a case study, this methodological approach was applied to determine the critical regions for mitigating eutrophication in the state of Jalisco, Mexico. Although a decreasing trend was observed for the TSI values over time for most subbasins (2012–2019), a tendency for extreme hypereutrophication was observed in some regions, such as the Guadalajara metropolitan area and the Altos region, which are of high economic relevance at the state level. A correlation analysis was performed between the TSI parameters and rainfall measurements for all subbasins under analysis, which suggested a tendency for nutrient wash-off during the rainy seasons for most subbasins; however, further research is needed to quantify the real impacts of rainfall by including other variables such as elevation and slope. The relationships between the water quality indicators and land cover were also explored. The GIS methodology proposed in this study can be used to spatially assess the trophic state of large regions over time, taking advantage of available water quality databases. This will enable the efficient development and implementation of public policies to assess and mitigate the eutrophication of water sources, as well as the efficient allocation of resources for critical regions. Further studies should focus on applying integrated approaches combining on-site monitoring data, remote sensing data, and machine learning algorithms to spatially evaluate the trophic state of territories.

Slope deformation, reservoir variation and meteorological data at the Khoko landslide, Enguri hydroelectric basin (Georgia), during 2016–2019

Abstract. The Greater Caucasus mountain belt is characterized by deep valleys, steep slopes and frequent seismic activity, the combination of which results in major landslide hazard. Along the eastern side of the Enguri water reservoir lies the active Khoko landslide, whose head scarp zone affects the important Jvari–Khaishi–Mestia road, one of the few connections with the interior of the Greater Caucasus. Here, we present a database of measurement time series taken over a period of 4 years (2016–2019) that enables us to compare slope deformation with meteorological factors and human-induced perturbations owing to variations in the water level of the reservoir. The monitoring system we used is composed of two digital extensometers, placed within two artificial trenches excavated across the landslide head scarp. The stations are also equipped with internal and near-ground surface thermometers. The dataset is integrated by daily measurements of rainfall and lake level. The monitoring system – the first installed in Georgia – was set up in the framework of a NATO-funded project, aimed at assessing different types of geohazards affecting the Enguri artificial reservoir and the related hydroelectrical plant. Our results indicate that the Khoko landslide displacements appear to be mainly controlled by variations in hydraulic load, in turn induced by lake level oscillations. Rainfall variations might also have contributed, though this is not always evident for all the studied period. The full databases are freely available online at the following DOI: https://doi.org/10.20366/unimib/unidata/SI384-2.0 (Tibaldi et al., 2020).

Application of TRMM in Deriving Intensity-Duration-Frequency Curve in Bandung Area

A rainfall Intensity-Duration-Frequency curve serves vital purposes in urban water management, typically generated using a long historical rainfall record. In most cases, such measurement is often sparse or poorly done. Hence it becomes a significant challenge to derive this curve. However, many studies have demonstrated the use of satellite rainfall measurement to cope with this data shortage. One of them is TRMM 3B42 Daily, which is employed in this study. This paper presents the application of TRMM to derive a better IDF curve in a data-scarce region for urban water management. It takes place in Bandung, where most of the upstream catchment area has limited rainfall records. The result shows that the TRMM-derived IDF curve demonstrates underestimated rainfall intensity compared to the ground measurement one. Consequently, the correction of daily data TRMM is necessary to improve the performance of TRMM-derived IDF. The correction of daily TRMM has statistically improved the performance of IDF. It is exhibited by the decreasing value of RMSE and RE. This analysis was culminated by producing an updated IDF curve using TRMM data from 1998-2019 for Bandung City. There is no significant trend that appears in rainfall intensity from 2016-2019.

Application of 3D Laser Image Scanning Technology and Cellular Automata Model in the Prediction of the Dynamic Process of Rill Erosion

Black soil areas are strongly affected by rill erosion due to the geomorphic characteristics of flood plains and heavy rainfall. To study the problem of rill erosion in black soil areas and achieve ecological restoration, based on the method of artificially simulated rainfall, the effects of rainfall intensity and slope on the characteristics of flow and sand production on the slope surface of black soil areas were studied, and the erosion pattern of the slope surface after rainfall was monitored by a 3D laser scanner to analyze the erosion of the soil on the slope surface. The slope erosion model was constructed on the basis of the cellular automata (CA) method, and the results of the model’s operation were compared with actual rainfall measurement results to deepen research on the slope erosion mechanism in black soil areas. By analyzing the slope erosion pattern after rainfall, it was found that the surface area and erosion volume of serious slope erosion areas increased with increases in slope gradient. Based on the physical model test results combined with the CA model to simulate flow and sand production on bare slopes under different rainfall intensities, comparison showed that the CA model can accurately simulate flow and sand production on a slope where the Ens coefficient of the flow production rate is between 0.70 and 0.97, thus theoretically verifying the reliability of the model, and on this basis, the erosion pattern of the slope after rainfall was predicted to explore the evolution and development law of erosion.

Simulation of Rainfall Data by The GPM Satellite (Case Study at Sriwijaya University, Indralaya)

The problem in obtaining rainfall data information is that the measurement results of rainfall from the measuring station are not recorded and areas are difficult to reach to measure rainfall directly. Lack of data availability and continuity of rainfall data is one of the obstacles in obtaining climatological information in South Sumatra. GPM satellites (Global Precipitation Measurement) need to be researched in order to overcome obstacles in obtaining rainfall measurement data in an area. This study aims to validate rainfall data from the GPM satellite and daily rainfall data from the measurement results of the Hellmen type automatic rain gauge located in the Hydraulics Laboratory of Sriwijaya University. Validation of rainfall data was carried out to determine the accuracy of the measurement results. Validation was carried out by the Root Mean Square Error Method and the Nash-Sutcliffe Method. The results of the validation showed a high correlation value for the average daily rainfall. The correlation coefficient value is 0,77, the RMSE value is 11,42 and the NSE value is 0,996. The results of the validation of these methods show a strong correlation between the measurement rainfall data in the field and the GPM satellite.

Wind Tunnel Validation of a Particle Tracking Model to Evaluate the Wind‐Induced Bias of Precipitation Measurements

AbstractA physical full‐scale experimental set‐up was designed and implemented in the wind tunnel to reproduce and capture the trajectories of falling water drops when approaching the collector of catching type precipitation gauges, reproducing rainfall measurements in windy conditions. The experiment allowed to collect, for the first time, a large data set of high‐resolution footages of the deviation of such trajectories, as induced by the bluff‐body aerodynamics of the outer gauge shape. By processing the collected images, a consistent quantitative interpretation of each drop pattern was possible, based on a detailed Computational Fluid Dynamics simulation of the airflow updraft and acceleration features above the collector of the gauge. Numerical airflow simulations were extensively validated in the wind tunnel, using local flow measurements and Particle Image Velocimetry. Capturing the deviation of the drop trajectories in the wind tunnel allowed a clear visualization of the physical reason for the wind‐induced undercatch of precipitation gauges, since drops were individually observed to fall outside instead of inside of the collector, contrary to what would be expected by extrapolating their undisturbed trajectory. The adopted Lagrangian Particle Tracking model and the formulation used for the drag coefficient were suitable to closely reproduce the observed drop trajectories when affected by the airflow deformation due to the bluff‐body aerodynamics of two investigated gauge geometries. The wind tunnel experiment provided the basis for the validation of the particle tracking model in terms of the difference between simulated and observed trajectories, after initial conditions were suitably set to represent the experimental setup.

Rainfall estimation using measurement report data from time-division long term evolution networks

Accurate rainfall measurement is essential for flood forecasting, effective water resources management, agriculture, etc. Many traditional rainfall monitoring methods, such as rainfall gauges, weather radars, and satellites, have been developed. Recently, a rainfall retrieval algorithm based on the radio connection between one telephone tower and another has seen widespread use across countries as it does not require any deployment procedures or costs. However, because underground fibre-optic cable networks have been widely introduced and deployed between telephone towers, usage of rainfall retrieval algorithms has reduced. Measurement report (MR) data from time-division long term evolution (TD-LTE) networks for providing instructions to the user terminal has the potential to estimate rainfall owing to the impact of rainfall-based attenuation on MR data. We present an analysis of MR data collected during the local flood season over 2 months (May and August 2019) in Huizhou city, southern China. Comparisons between estimated and observed rainfall exhibit promising results. Rainfall estimation from MR data is a potential alternative/complementary rainfall estimation method, as well as having use in runoff simulations, with acceptable accuracy (>0.8); this is particularly relevant in the context of disappearing microwave links between telephone towers in cellular communication networks. The rainfall estimation method proposed herein can also be implemented in rarely gauged or ungauged areas for flood simulation.

A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner

Abstract. Clouds and precipitation have vital roles in the global hydrological cycle and the radiation budget of the atmosphere–Earth system and are closely related to both the regional and the global climate. Changes in the status of the atmosphere inside clouds and precipitation systems are also important, but the use of multi-source datasets is hampered by their different spatial and temporal resolutions. We merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) with the multi-channel cloud-top radiance measured by the visible and infrared scanner (VIRS) and atmospheric parameters in the ERA5 reanalysis dataset. The merging of pixels between the precipitation parameters and multi-channel cloud-top radiance was shown to be reasonable. The 1B01-2A25 dataset of pixel-merged data (1B01-2A25-PMD) contains cloud parameters for each PR pixel. The 1B01-2A25 gridded dataset (1B01-2A25-GD) was merged spatially with the ERA5 reanalysis data. The statistical results indicate that gridding has no unacceptable influence on the parameters in 1B01-2A25-PMD. In one orbit, the difference in the mean value of the near-surface rain rate and the signals measured by the VIRS was no more than 0.87 and the standard deviation was no more than 2.38. The 1B01-2A25-GD and ERA5 datasets were spatiotemporally collocated to establish the merged 1B01-2A25 gridded dataset (M-1B01-2A25-GD). Three case studies of typical cloud and precipitation events were analyzed to illustrate the practical use of M-1B01-2A25-GD. This new merged gridded dataset can be used to study clouds and precipitation systems and provides a perfect opportunity for multi-source data analysis and model simulations. The data which were used in this paper are freely available at https://doi.org/10.5281/zenodo.4458868 (Sun and Fu, 2021).

Hydrological Processes in Eucalypt and Pine Forested Headwater Catchments within Mediterranean Region

Mediterranean headwater catchments have experienced major land-use changes in recent centuries, namely characterized by afforestation with fast-growing tree species (e.g., pine and eucalyptus). This paper investigates differences in the hydrological response of two forested catchments with distinct Pinus pinaster Aiton (PIN) and Eucalyptus globulus Labill. (EUC) covers over 6 hydrological years, from 2010 to 2016. The study was developed in paired catchments located in North–Central Portugal, under wet Mediterranean climate and schist bedrock. Based on rainfall and streamflow measurements, the hydrological responses were analyzed through the water-balance calculations, rainfall-runoff event analyses, and correlations between rainfall characteristics, evapotranspiration, soil moisture, overland flow, and land-cover parameters. The study period recorded a wide range of annual rainfall, between 984 and 2127 mm per years. The annual streamflow displayed greater inter-annual variation, from 208 to 1430 mm for PIN and from 217 to 1291 mm for EUC. The runoff coefficient was higher in the wettest years, with a maximum of 58% for PIN and 61% for EUC, and decreased substantially in the driest year, representing only 17% of the rainfall in PIN and 22% in EUC. Rainfall was predominant during the winter season (41%), followed by autumn (30%) and spring (24%). The streamflow displayed a seasonal behavior in both catchments, with higher runoff coefficient for EUC that for PIN. Half of the annual streamflow occurs during the winter, with the highest average runoff coefficient attained for EUC and PIN respectively being 68% and 64% of the rainfall amount. Annual evapotranspiration was relatively constant through the six years of study and showed higher average values in PIN (907 mm) than EUC (739 mm), highlighting the importance of forest type and showing that mature pine plantations lead to higher water consumption than eucalypt stands. Annual rainfall amount affects evapotranspiration rate (%), the percentage of ET increasing from 37% to 78% for PIN and from 34% to 73% for EUC between the wettest and the driest years, which raises concerns regarding the impact of climate changes on water availability in the mountain areas of the Mediterranean region.

Rainfall measurement from Commercial microwave links for urban hydrology in Africa: a simulation framework for sensitivity analysis.

Abstract Urban floods due to intense precipitation are a major problem in many tropical regions as in Africa. Rainfall measurement using microwave links from cellular communication networks has been proposed as a cost effective solution to monitor rainfall in these areas where the gauge network is scarce. The method consists in retrieving rainfall from the attenuation estimated along the commercial microwave links (CMLs) thanks to the power levels provided by an operator. In urban areas where the network is dense, rainfall can be estimated and mapped for hydrological prediction. Rainfall estimation from CMLs is subject to uncertainties. This paper analyzes the advantages and limitations of this rainfall data for a distributed hydrological model applied to an urban area. The case study is in West Africa in Ouagadougou where a hydrological model has been set up. The analysis is based on numerical simulations, using high resolution rain maps from a weather radar to emulate synthetic microwave links. Two sources of uncertainty in the rain estimation and on the simulated discharge are analyzed by simulations: i) the precision of the raw information provided by the operator and ii) the density and geometry of the network. A coarse precision (1 dB) in the signal provided by the operator can lead to substantial underestimation of rainfall and discharge, especially for links operating at low frequency (below 10 GHz) or short (less than 1 km). The density of the current mobile networks in urban areas is appropriate to analyze hydrological impact of tropical convective rainfall.

Rainfall Measurements during Summer 2020 with the Optical Precipitation Gage

Rainfall Measurements during Summer 2020 with the Optical Precipitation Gage

Assessment of Merged Satellite Precipitation Datasets in Monitoring Meteorological Drought over Pakistan

The current study evaluates the potential of merged satellite precipitation datasets (MSPDs) against rain gauges (RGs) and satellite precipitation datasets (SPDs) in monitoring meteorological drought over Pakistan during 2000–2015. MSPDs evaluated in the current study include Regional Weighted Average Least Square (RWALS), Weighted Average Least Square (WALS), Dynamic Clustered Bayesian model Averaging (DCBA), and Dynamic Bayesian Model Averaging (DBMA) algorithms, while the set of SPDs is Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG-V06), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and ERA-Interim (re-analyses dataset). Several standardized precipitation indices (SPIs), including SPI-1, SPI-3, and SPI-12, are used to evaluate the performances of RGs, SPDs, and MSPDs across Pakistan as well as on a regional scale. The Mann–Kendall (MK) test is used to assess the trend of meteorological drought across different climate regions of Pakistan using these SPI indices. Results revealed higher performance of MSPDs than SPDs when compared against RGs for SPI estimates. The seasonal evaluation of SPIs from RGs, MSPDs, and SPDs in a representative drought year (2008) revealed mildly to moderate wetness in monsoon season while mild to moderate drought in winter season across Pakistan. However, the drought severity ranges from mild to severe drought in different years across different climate regions. MAPD (mean absolute percentage difference) shows high accuracy (MAPD &lt;10%) for RWALS-MSPD, good accuracy (10% &lt; MAPD &lt;20%) for WALS-MSPD and DCBA-MSPD, while good to reasonable accuracy (20% &lt; MAPD &lt; 50%) for DCBA in different climate regions. Furthermore, MSPDs show a consistent drought trend as compared with RGs, while SPDs show poor performance. Overall, this study demonstrated significantly improved performance of MSPDs in monitoring the meteorological drought.

Prediction of rainfall-induced landslide movements in the presence of stabilizing piles

Among the different techniques usually employed to stabilize landslide bodies, the use of one or more rows of piles firmly embedded in the stable portion of the slope is commonly considered a reliable and effective measure. Nevertheless, a landslide may still be reactivated by an increase in the groundwater level due to rainfall. In this context, a novel method is proposed in the present study for a preliminary evaluation of the rainfall-induced movements of landslides in the presence of stabilizing piles. This method uses a water balance equation to evaluate the groundwater level changes due to rainfall, and the motion equation to relate these changes to landslide displacements. The piles contrast the landslide movements by a passive resistance force the value of which depends on the pile–soil failure mode and pile arrangement. The proposed method appears attractive from an engineering viewpoint, since it can be readily implemented in a common electronic sheet and requires few parameters as input data. Most of these parameters can be obtained from conventional geotechnical tests, whereas the remaining ones should be calibrated on the basis of available measurements of rainfall, groundwater level and soil displacement. After performing this parameter calibration, the method may be employed for predictive purposes to relate future landslide movements to expected rainfall scenarios. The predictive capability of the present approach is assessed by comparing the results with those observed in a published case study concerning a landslide body periodically reactivated by rainfall, in which a row of stabilizing piles was installed. • The paper deals with landslides reactivated by rainfall. • A method is proposed for predicting landslide movements in the presence of piles. • The method directly relates rain recordings to landslide movements. • The method is simple and can be readily implemented in a common electronic sheet. • One or more rows of piles are able to reduce the landslide mobility of about 70%.

Evaluation of GEOS Precipitation Flagging for SMAP Soil Moisture Retrieval Accuracy

The precipitation flag in the Soil Moisture Active Passive (SMAP) Level 2 passive soil moisture (L2SMP) retrieval product indicates the presence or absence of heavy precipitation at the time of the SMAP overpass. The flag is based on precipitation estimates from the Goddard Earth Observing System (GEOS) Forward Processing numerical weather prediction system. An error in flagging during an active or recent precipitation event can either (1) produce an overestimation of soil moisture due to short-term surface wetting of vegetation and/or surface ponding (if soil moisture retrieval was attempted in the presence of rain), or (2) produce an unnecessary non-retrieval of soil moisture and loss of data (if retrieval is flagged due to an erroneous indication of rain). Satellite precipitation estimates from the Integrated Multi-satellite Retrievals for GPM (IMERG) Version 06 Early Run (latency of ~4 hrs) precipitationCal product are used here to evaluate the GEOS-based precipitation flag in the L2SMP product for both the 6 PM ascending and 6 AM descending SMAP overpasses over the first five years of the mission (2015-2020). Consisting of blended precipitation measurements from the GPM (Global Precipitation Mission) satellite constellation, IMERG is treated as the “truth” when comparing to the GEOS model forecasts of precipitation used by SMAP. Key results include: i) IMERG measurements generally show higher spatial variability than the GEOS forecast precipitation, ii) the IMERG product has a higher frequency of light precipitation amounts, and iii) the effect of incorporating IMERG rainfall measurements in lieu of GEOS precipitation forecasts are minimal on the L2SMP retrieval accuracy (determined vs. in situ soil moisture measurements at core validation sites). Our results indicate that L2SMP retrievals continue to meet the mission’s accuracy requirement (standard deviation of the ubRMSE less than 0.04 m3/m3).

Spatial and temporal variabilities of rainstorms over China under climate change

Rainstorms are one of the extreme rainfall events that cause serious disasters, such as urban flooding and mountain torrents. Traditional studies have used rain gauge observations to analyze rainstorm events, but relevant information is usually missing in gauge-sparse areas. Satellite-derived precipitation datasets serve as excellent supplements or substitutes for the gauge observations. By developing a grid-based rainstorm-identification tool, we used the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) time series product to reveal the spatial and temporal variabilities of rainstorms over China during 1998–2017. Significant patterns of both increasing and decreasing rainstorm occurrences were detected, with no spatially uniform trend being observed across the whole country. There was an increase in the area being affected by rainstorms during the 20-year period, with rainstorm centers shifting along the southwest-northeast direction. Rainstorm occurrence was found to be correlated with local total precipitation. By comparing rainstorm occurrence with climate variables such as the El Nino-Southern Oscillation and Pacific Decadal Oscillation, we also found that climate change was likely to be the primary reason for rainstorm occurrence in China. This study complements previous studies that used gauge observations by providing a better understanding of the spatiotemporal dynamics of China’s rainstorms.