Sub-daily Scales Research Articles

Evaluation of potential evapotranspiration models over fluxdata network cropland sites

Utilizing potential evapotranspiration (PET) for the estimation of actual evapotranspiration (AET) in cropland is highly valuable for determining agricultural water requirements and developing irrigation schedules. Discrepancies between anticipated and actual PET values may arise due to the limitations in previous reference standards employed for evaluation. A comprehensive evaluation of the accuracy of the PET models in cropland was conducted in this study. Non-water-stressed days with evaporation fraction (EF) exceeding the 95th percentile threshold were selected from cropland sites in the FLUXNET Tier 1 database as the basis for calibrating and validating 22 PET calculation models at the daily scale and 19 at the sub-daily scale. Results indicated that the Penman (1948), Penman (1963), and all radiation-based models showed strong performance, with R2, RMSE, and NSE of 0.82–0.89, 0.58–0.86 mm day−1, and 0.54–0.77 at the daily scale, and 0.73–0.81, 0.04–0.05 mm 0.5 h−1, and 0.62–0.72 at the sub-daily scale, respectively. Four recently developed radiation-based models have exhibited remarkable accuracy on a daily basis. The relative model errors (RMSE/MEAN) tended to decrease as net radiation (Rn), temperature (Ta), and vapor pressure deficit (VPD) increased at both daily and sub-daily time scales. The models displayed strong accuracy in estimating daily PET when Ta > 15 ℃ or 0.4 < VPD < 1.0, with RMSE/MEAN values ranging from 0.10 to 0.22 and R2 values ranging from 0.76 to 0.89. Higher accuracy in sub-daily scale PET estimates was achieved when Ta > 15 ℃ or 0.4 < VPD < 1.6, with RMSE/MEAN of 0.24–0.43 and R2 of 0.54–0.85, respectively. Additionally, the time lag between PET and Rn, Ta, and VPD increased as Rn, Ta, and VPD increased at the sub-daily scale, which may contribute to the reduced precision of sub-daily PET estimates compared to daily scale estimates. These findings suggest that PET models with calibrated parameters have the potential to serve as the basis for estimating cropland AET, and can provide direction for future model improvement.

MONTPEL: A multi-component Penman-Monteith energy balance model

Mechanistic modelling is gradually replacing empiricism in crop models, focusing on leaf-level physiological processes. This shift necessitates simulating crop surface temperature at infra-canopy sub-daily scales but many crop models still rely on empirical formulations for canopy temperature estimation, typically on a daily basis. We developed MONTPEL, a multi-component Penman-Monteith model that allows simulating the crop energy balance with flexible canopy representations (“BigLeaf” vs. “Layered”, “Lumped” vs. “Sunlit-Shaded”) and accounts for atmospheric stability conditions. We analyzed the model behavior, sensitivity and accuracy, using measurements from four wheat (Triticum aestivum L.) experiments conducted under varying pedoclimatic and water stress conditions. Measurements included hourly energy balance terms (total net radiation, soil heat flux, sensible and latent energy fluxes), hourly temperature of the canopy surface or of leaves at different depths inside the canopy, and sunlit and shaded leaf temperatures around solar noon at different dates. MONTPEL reproduced the measured energy balance terms with a root mean square error (RMSE) between 21 and 87 Wm-2 and a coefficient of determination (R²) exceeding 0.65. The model's accuracy in simulating canopy temperature, with RMSE ≤ 2.2 °C and R² ≥ 0.92, remained consistent regardless of measurement scale. Adjusting the aerodynamic resistance for atmospheric stability minimized simulated canopy temperature errors, notably in semi-arid conditions. Crop latent energy flux and temperature were most sensitive to the maximal stomatal conductance (gs,max) parameter. However, using a single gs,max value across the simulated experiments yielded satisfactory results, suggesting a weak sensitivity to the temporal and site-to-site variability of gs,max. Distinguishing sunlit from shaded canopy fractions systematically resulted in lower latent energy fluxes compared to “Lumped” canopy representation results. Analysis identified limitations in the multi-component approach, particularly an unrealistic uniform temperature shift across leaf layers when soil surface temperature changes.

Decorrelation rate and daily cycle in sub-daily time series of SAR coherence amplitude

A dataset of sub-daily C-band data, acquired with a ground-based synthetic aperture radar, has been used to study soil and vegetation dynamics during a complete growing season in a controlled agricultural test site. The data have been exploited to analyse the rate and sources of decorrelation in the scene, as well as the consequences of the observation conditions of a sub-daily satellite (with either low, medium or geosynchronous orbit): short revisit times, availability of multiple acquisitions during a single day, and shallow observations at some incidence angles. Repeat-pass coherence is found to be less affected by temporal decorrelation when the primary image is acquired during nighttime or the last hours predawn. Regarding the incidence angle, VV has increased sensitivity to certain phenological stages as the incidence angle increases. Additionally, a periodic oscillation on a sub-daily scale is observed when creating coherence time series with increasing temporal baseline. Factors which strongly contribute to these oscillations are the daily cycles of temperature, soil moisture and vegetation water dynamics.

Mean flow and eddy summer moisture transport over East Asia in reanalysis data and a regional climate simulation

Understanding the impact of atmospheric variability on climatological mean moisture transport is crucial because moisture transport determines continental water availability as well as convective organization and resulting precipitation. Here, we analyze the mean flow and eddy components of summer moisture transport in the downwind of the Tibetan Plateau (TP), a region that is characterized by interactions between monsoon systems, extratropical circulation, and mountainous weather systems. Using 40 years of ERA5 reanalysis data and a regional WRF simulation, we determine the absolute and relative contributions of mean flow and eddy moisture transport from multi-daily to sub-daily scales. We also link these components to large-scale circulation indices, precipitation, evaporation, and mesoscale convective systems (MCSs). The results show that the largest contributions of eddies to the climatological mean moisture transport are found in the immediate downwind region of the TP. Half of the total eddy transport downwind of the TP is due to multi-daily eddy transport and the other half is due to daily to sub-daily eddy transport. Regional precipitation anomalies are dominated by the mean flow component of southerly moisture influxes which in turn are positively correlated with different South Asian summer monsoon indices and negatively correlated with the West Northern Pacific monsoon index. The eddy transport from the south is positively correlated with a lower jet latitude but does not show any significant correlations with precipitation or MCS activity, likely due to the dominant role of the mean flow moisture transport. While the relative contributions of eddies to the climatological mean moisture transport are similar in ERA5 and WRF, the correlations between moisture transport components and large-scale circulation indices are generally weaker in WRF. This suggests that the dynamical downscaling does not significantly change the role of eddy moisture transport averaged for the region, but it resolves processes that decouple the moisture transport from its large-scale forcing.

Indian West Coast's Extreme Rainfall: Sub-daily scale variability

Unlike daily precipitation, the intensity variation of extreme rainfall events (EREs) on a sub-daily scale is governed by fast response dynamics through the variability of the vertical structure of thermodynamics. Understanding the causative mechanisms and controlling factors responsible for the rapid intensification of rainfall on the sub-daily scale over the West Coast (WC) of India is still inadequate. In this study, we investigated the sub-daily scale variability of extreme rainfall events and possible regulating factors over the WC region, India. Our results suggest that, on average, the rainfall event spell lasts for ∼4–6 days, having an intense rain rate distribution for a few hours (∼3–5 h). We observed that the nonlinear complex interaction among various regulating factors in a conducive environment responsively influences the extreme rain rate distribution on sub-daily scale over the WC region. Sub-daily scale rainfall durations of 1–3, 3–5, and 5–15 h are crucial factors pertinent to EREs. Offshore trough-driven large-scale moisture loaded winds along the west coast, when endured with strong updrafts, enhance the ice growth mechanism and lead to high surface rain intensity. The mid-troposphere moistening during the peak stage of the EREs amplifies the hydrometeor growth mechanisms as revealed by cloud specific liquid/ice water content distribution and cloud radar reflectivity profiles. A slopy and narrow radar reflectivity pattern above zero degrees isotherm suggests a contrast cloud vertical structure during the intense phase of the EREs. Though the intense nature of precipitation is caused by warm and mixed-phased clouds, the mixed-phase clouds result in precipitation events of a relatively longer duration.

Interpolation of environmental data using deep learning and model inference

The temporal resolution of environmental data sets plays a major role in the granularity of the information that can be derived from the data. In most cases, it is required that different data sets have a common temporal resolution to enable their consistent evaluations and applications in making informed decisions. This study leverages deep learning with long short-term memory (LSTM) neural networks and model inference to enhance the temporal resolution of climate datasets, specifically temperature, and precipitation, from daily to sub-daily scales. We trained our model to learn the relationship between daily and sub-daily data, subsequently applying this knowledge to increase the resolution of a separate dataset with a coarser (daily) temporal resolution. Our findings reveal a high degree of accuracy for temperature predictions, evidenced by a correlation of 0.99 and a mean absolute error of 0.21 °C, between the actual and predicted sub-daily values. In contrast, the approach was less effective for precipitation, achieving an explained variance of only 37%, compared to 98% for temperature. Further, besides the sub-daily interpolation of the climate data sets, we adapted our approach to increase the resolution of the Normalized difference vegetation index of Landsat (from 16 d to 5 d interval) using the LSTM model pre-trained from the Sentinel 2 Normalized difference vegetation index—that exists at a relatively higher temporal resolution. The explained variance between the predicted Landsat and Sentinel 2 data is 70% with a mean absolute error of 0.03. These results suggest that our method is particularly suitable for environmental datasets with less pronounced short-term variability, offering a promising tool for improving the resolution and utility of the data.

Sub-daily scale variations and trends in southwest monsoon rainfall over the West coasts of India and Myanmar

The West Coast of India (WCI) and the West Coast of Myanmar (WCM) receive a good amount of monsoon rainfall due to the orographic lifting and moisture transport from the neighbouring sea. Here, diurnal variation in rainfall amount, frequency, intensity, and contribution during the summer monsoon period was studied using TRMM 3 hourly precipitation data. The analysis was carried out over lowland, midland and highland regions of both the coasts. The subregions exhibit a similar diurnal pattern even though the intensity and frequency of rainfall vary from region to region. Generally, bimodal variations with late afternoon and early morning peaks are found over lowland and midland regions of both WCI and WCM in rainfall frequency, amount and contribution. However, highland regions exhibit unimodal variation with a single late afternoon peak. Over WCI, lowland regions exhibit primary peak during early morning, whereas lowlands of WCM exhibit primary peak during afternoon hours. Rainfall intensity exhibits a different pattern compared to other parameters. Rainfall intensity increases from south to north over both the coasts. Among the subregions, maximum rainfall intensity is found over midlands and minimum over highlands. In general, more rainfall is observed over subregions of WCM than subregions of WCI. Most of the lowland and midland regions, exhibit a significant increasing trend in rainfall frequency and contribution at morning hours and a significant decreasing trend at afternoon hours. Rainfall frequency exhibit different periodicities during different time indicating interannual heterogeneity in rainfall. The diurnal pattern of summer monsoon rainfall over the Indian and Myanmar West coasts shows remarkable latitudinal and topographical variations.

Disentangling Satellite Precipitation Estimate Errors of Heavy Rainfall at the Daily and Sub-Daily Scales in the Western Mediterranean

In the last decade, substantial improvements have been achieved in quantitative satellite precipitation estimates, which are essential for a wide range of applications. In this study, we evaluated the performance of Integrated Multi-satellitE Retrievals for GPM (IMERG V06B) at the sub-daily and daily scales. Ten years of half-hourly precipitation records aggregated at different sub-daily periods were evaluated over a region in the Western Mediterranean. The analysis at the half-hourly scale examined the contribution of passive microwave (PMW) and infrared (IR) sources in IMERG estimates, as well as the relationship between various microphysical cloud properties using Cloud Microphysics (CMIC–NWC SAF) data. The results show the following: (1) a marked tendency to underestimate precipitation compared to rain gauges which increases with rainfall intensity and temporal resolution, (2) a weaker negative bias for retrievals with PMW data, (3) an increased bias when filling PMW gaps by including IR information, and (4) an improved performance in the presence of precipitating ice clouds compared to warm and mixed-phase clouds. This work contributes to the understanding of the factors affecting satellite estimates of extreme precipitation. Their relationship with the microphysical characteristics of clouds generates added value for further downstream applications and users’ decision making.

Temporal and spatial variations in the sub-daily precipitation structure over the Qinghai–Tibet Plateau (QTP)

Precipitation is a vital component of the global atmospheric and hydrological cycles and influencing the distribution of water resources. Even subtle changes in precipitation can significantly impact ecosystems, energy cycles, agricultural production, and food security. Therefore, understanding the changes in the precipitation structure under climate change is essential. The Qinghai–Tibet Plateau (QTP) is a region sensitive to global climate change and profoundly impacts the atmospheric water cycle in Asia and even globally, rendering it a hot topic in climate change research in recent years. Few studies have examined on the sub-daily scale precipitation structure over the QTP. In this paper, the characteristics of sub-daily precipitation on the QTP were systematically investigated from multiple perspectives, including the concentration index, skewness (the third standardized moment of a distribution), and kurtosis (the fourth standardized moment of a distribution). The results indicated that the frequency of moderate-intensity nighttime precipitation on the QTP generally increased, and the analysis of both the concentration index and kurtosis (skewness) suggested that extreme precipitation was more frequent in the southwestern foothills of the QTP. Furthermore, potential high-risk areas for natural disasters were identified on the QTP, and found that the southeastern part of the plateau constituted a potential hotspot area for flood disasters. Given the complexity of climate change, a comprehensive analysis of the spatiotemporal characteristics of diurnal and nighttime precipitation changes on the QTP could help reveal the regularity of precipitation changes. This has significant implications for forecasting, warning, disaster preparedness, and mitigation efforts on the QTP.

Variações de vazão em escala sub-diária (hydropeaking) decorrentes da operação de reservatórios no Brasil

ABSTRACT International studies have focused on the hydrological impacts on an hourly or sub-daily scale that hydroelectric plants can cause through hydropeaking operations. However, this topic is still underexplored in Brazil, despite its large number of hydroelectric plants. Thus, to bring it to the Brazilian context, this study initially presents a literature review to characterize hydropeaking, its impacts and proposed mitigating measures, and research conducted in Brazil. Next, it was demonstrated that hydropeaking operations occur throughout the entire national territory, in hydroelectric plants of different sizes, that can cause changes increasing up to 450% of the base flow. Conflicts related to hydropeaking in Brazil are also brought up and, despite their occurrences and records of specific thresholds for their mitigation, this has not been addressed in environmental impact studies for licensing Small Hydropower Plants (SHPs) or legislation. Thus, the present study seeks to bring to light the importance of further research on hydropeaking in Brazil.

Thermal Comfort of The Palabuhanratu Tourism Area

The development of tourist areas is highly dependent on their facilities and attractions. The comfort level greatly affects nature-based tourism, which is based on outdoor activities. Palabuhanratu is popular with tourists because of its natural attractions, such as its beaches, water sports, and the new Ciletuh Geopark. The temperature and humidity of the air at the surface influence how comfortable travelers are in the area. These parameters are to be sensed directly by tourists participating in outdoor activities. The Temperature-Humidity Index (THI), based on the two climate parameters mentioned above, is used to show the comfort level. Spatiotemporal analysis based on GIS is used in this study, utilizing the ERA5 Reanalysis data for 1991–2020. The goal of this study is to find out how climate impacts comfort levels so that people can know when the best time is to visit. The THI values are highest in May, which means that this is the worst time of year to go to the area for outdoor activities. An examination of the diurnal pattern of THI is also carried out to demonstrate the sub-daily scale variation for further consideration.

Hydrometeorological Factors Affecting the Carbon Exchange of the Himalayan Pine-dominated Ecosystem

The net carbon uptake of the natural and dense Himalayan ecosystems is not well explored at a sub-daily scale due to limited data availability. Here, we present the first-ever analysis of the hydrometeorological controls of the sub-daily scale Net Ecosystem Exchange (NEE) in a Western Himalayan pine-dominated ecosystem (Pinus roxburghii). We used observed half-hourly flux tower data for 104 weeks from December 2014 to November 2016. We found that the sub-daily (6 h) scale variability of NEE contributes 20–40% to its within-week variability. We generated transfer entropy (TE)-based weekly process networks with a maximum memory of 6 h to understand sub-daily scale processes. Using weekly networks, we present consistent causal links on a seasonal scale. The hydrometeorological variables affecting the NEE and Ecosystem Respiration (RE) at a sub-daily scale are Net Solar Radiation (NSR), Relative Humidity (RH), Surface Air Temperature (TA), and Sensible Heat Flux (SH). These variables are consistently active in the networks with seasonally varying magnitudes of TE. We also found that for both monsoon and post-monsoon, NEE receives more incoming links from meteorological variables during dry weeks than wet weeks. Precipitation (P) did not send a direct causal link to NEE or RE within a sub-daily scale but strongly influenced the causal associations between hydrometeorological and carbon exchange variables. The network does not show any immediate (within 6 h) impact of P on NEE, which probably implies a delayed response of vegetation to P through soil moisture. At higher memory, P may act as an influential background variable.

Machine-learned actual evapotranspiration for an irrigated pecan orchard in Northwest Mexico

Accurate field-scale estimates of the actual evapotranspiration (ETact) based on readily available input data is indispensable to optimize irrigation in (semi-)arid regions. In this study, at an irrigated pecan orchard in Northwest Mexico we explored the potential for three different machine learning algorithms to improve upon the 30-min ETact estimates provided by the FAO Penman-Monteith method (FAO-PM) when trained and tested on multi-year eddy-covariance measurements. We found that all three machine learning algorithms performed well (R2>0.9), with the Random Forest (RF) giving the best overall performance. RF consistently outperformed FAO-PM on sub-daily scales in the growing season, showing its potential to further improve sub-daily ETact estimates. In addition, RF 1) shows a clear improvement (R2 increase up to 0.168) compared to a typical FAO-PM estimate based on the procedures described in FAO report 56, and 2) mostly performed similar as FAO-PM with a highly accurate locally fitted crop factor on the seasonal scale. The latter highlights its ability to infer the correct seasonal trend through input variables that reflect the underlying physical processes, giving potential for application at other similar fields in the region with limited local tuning. To assess whether incorporating existing (semi-)physical parameterizations could improve the performance, we additionally combined our machine learning models with FAO-PM both with and without locally fitted crop factor. Overall, we found this resulted in a modest improvement in performance in most cases (R2 increase up to 0.01). Notably, incorporating the locally calibrated crop factor and FAO-PM each reduced the errors made by the RF in the second half of the growing season. This implies that it is still useful to incorporate FAO-PM and a crop factor in the machine learning model, especially when the crop factor can be determined with high accuracy.

Understanding extreme precipitation scaling with temperature: insights from multi-spatiotemporal analysis in South Korea

With global warming, the intensification of extreme precipitation events is anticipated to follow an exponential growth pattern aligned with the Clausius–Clapeyron (CC) scaling rate (approximately 7% per degree Celsius). However, the regional-scale response of extreme precipitation shows significant variability, deviating from the expected CC rate. This deviation is likely caused by diverse weather patterns and local fluctuations in thermodynamic influences, resulting in differences across seasons and within the region of interest. In this study, we examine the spatial distribution of scaling relationships between extreme precipitation and temperature in South Korea, considering daily and sub-daily scales, both annually and seasonally. For a thorough analysis, we utilize multiple precipitation accumulation periods, temperatures, and different conditional quantiles. Our results reveal that, at the annual scale, most scaling patterns exhibit a peak-like structure, with significant variations in breakpoints observed across temperature variables and regions. However, the southern area presents a notable exception with a positive scaling pattern, particularly with the dew point temperature. At the seasonal scale, we observe more variability, with notable shifts occurring during the wet season across different temperatures and regions. Lastly, we explore the long-term historical changes in the peak value in extreme precipitation and find significant increases at high quantiles in the southern area of South Korea. It informs that the observed peak like pattern does not impose a potential upper limit for extreme precipitation. Overall, our findings emphasize the need for cautious interpretation of precipitation scaling within specific spatiotemporal contexts, which could provide a solid basis for better understanding future extreme precipitation events in a changing climate.

A probabilistic-deterministic approach for assessing climate change effects on infection risks downstream of sewage emissions from CSOs

The discharge of pathogens into urban recreational water bodies during combined sewer overflows (CSOs) pose a potential threat for public health which may increase in the future due to climate change. Improved methods are needed for predicting the impact of these effects on the microbiological urban river water quality and infection risks during recreational use. The aim of this study was to develop a novel probabilistic-deterministic modelling approach for this purpose building on physically plausible generated future rainfall time series. The approach consists of disaggregation and validation of daily precipitation time series from 21 regional climate models for a reference period (1971–2000, C20), a near-term future period (2021–2050, NTF) and a long-term future period (2071–2100, LTF) into sub-daily scale, and predicting the concentrations of enterococci and Giardia and Cryptosporidium, and infection risks during recreational use in the river downstream of the sewage emissions from CSOs. The approach was tested for an urban river catchment in Austria which is used for recreational activities (i.e. swimming, playing, wading, hand-to-mouth contact). According to a worst-case scenario (i.e. children bathing in the river), the 95th percentile infection risks for Giardia and Cryptosporidium range from 0.08 % in winter to 8 % per person and exposure event in summer for C20. The infection risk increase in the future is up to 0.8 log10 for individual scenarios. The results imply that measures to prevent CSOs may be needed to ensure sustainable water safety. The approach is promising for predicting the effect of climate change on urban water safety requirements and for supporting the selection of sustainable mitigation measures. Future studies should focus on reducing the uncertainty of the predictions at local scale.

Precipitation Extremes and Their Links with Regional and Local Temperatures: A Case Study over the Ottawa River Basin, Canada

In the context of global warming, the Clausius–Clapeyron (CC) relationship has been widely used as an indicator of the evolution of the precipitation regime, including daily and sub-daily extremes. This study aims to verify the existence of links between precipitation extremes and 2 m air temperature for the Ottawa River Basin (ORB, Canada) over the period 1981–2010, applying an exponential relationship between the 99th percentile of precipitation and temperature characteristics. Three simulations of the Canadian Regional Climate Model version 5 (CRCM5), at three different resolutions (0.44°, 0.22°, and 0.11°), one simulation using the recent CRCM version 6 (CRCM6) at “convection-permitting” resolution (2.5 km), and two reanalysis products (ERA5 and ERA5-Land) were used to investigate the CC scaling hypothesis that precipitation increases at the same rate as the atmospheric moisture-holding capacity (i.e., 6.8%/°C). In general, daily precipitation follows a lower rate of change than the CC scaling with median values between 2 and 4%/°C for the ORB and with a level of statistical significance of 5%, while hourly precipitation increases faster with temperature, between 4 and 7%/°C. In the latter case, rates of change greater than the CC scaling were even up to 10.2%/°C for the simulation at 0.11°. A hook shape is observed in summer for CRCM5 simulations, near the 20–25 °C temperature threshold, where the 99th percentile of precipitation decreases with temperature, especially at higher resolution with the CRCM6 data. Beyond the threshold of 20 °C, it appears that the atmospheric moisture-holding capacity is not the only determining factor for generating precipitation extremes. Other factors need to be considered, such as the moisture availability at the time of the precipitation event, and the presence of dynamical mechanisms that increase, for example, upward vertical motion. As mentioned in previous studies, the applicability of the CC scaling should not be generalised in the study of precipitation extremes. The time and spatial scales and season are also dependent factors that must be taken into account. In fact, the evolution of precipitation extremes and temperature relationships should be identified and evaluated with very high spatial resolution simulations, knowing that local temperature and regional physiographic features play a major role in the occurrence and intensity of precipitation extremes. As precipitation extremes have important effects on the occurrence of floods with potential deleterious damages, further research needs to explore the sensitivity of projections to resolution with various air temperature and humidity thresholds, especially at the sub-daily scale, as these precipitation types seem to increase faster with temperature than with daily-scale values. This will help to develop decision-making and adaptation strategies based on improved physical knowledge or approaches and not on a single assumption based on CC scaling.

Improving near-real-time satellite precipitation products through multistage modified schemes

Near-real-time (NRT) satellite precipitation products (SPPs) exhibit great application potential in global precipitation estimation for their convenient acquisition and access. A universal framework was developed to modify precipitation occurrence and intensity in stages to correct and integrate multiple NRT SPPs with open-access gridded observational data. Based on this framework, both machine learning- and statistical-based correction methods were utilized to construct multi-stage modified schemes and three schemes, namely the double machine learning (DML), statistical error correction (SEC) and hybrid (ML-SEC) schemes, were stepwise established. The improvements of NRT SPPs by these three schemes were comparatively analyzed based on a dense network of rain gauges located in the Yiluo River Basin, China, which does not depend on reference data for modification and has derived a reliable evaluation result at grid-point scale. The results indicated that the DML scheme attains the highest accuracy with a grid-averaged Kling-Gupta efficiency (KGE) value of 0.70 and a critical success index (CSI) value of 0.60, followed by the ML-SEC and SEC schemes. The KGE and CSI values of NRT SPPs have been improved by 22.20% ∼ 535.48% and 30.30% ∼ 100.24%, respectively, through the DML scheme. The DML-modified result also outperformed the mainstream post-processed SPPs with a 1.95% ∼ 222.25% and 9.22% ∼ 102.00% higher KGE and CSI values, respectively. The main error sources of the SPPs due to false and hit events were reduced by the multistage schemes, and the DML scheme achieved a balanced efficiency at both occurrence and intensity modification stages. Moreover, the DML scheme was more advantageous than spatial interpolation under the scenario with fewer gauges, particularly in estimating precipitation occurrence. The multistage modified schemes in this study, especially the DML scheme, exhibit potential in near-real time and sub-daily scale applications. Multistage schemes for NRT SPP modification are helpful for obtaining high-quality precipitation estimates.

How well does the IMERG satellite precipitation product capture the timing of precipitation events?

Precipitation occurs in the form of discrete “events” and the event characteristics (event duration, depth, peak intensity, start/end time) significantly influence the hydrologic response of a basin. Despite this importance, event-based performance of satellite precipitation products has still not been fully investigated to assess limitations in the retrieval algorithms, guide future improvements, and inform hydrologic applications. In this study, we evaluate the precipitation event performance of the GPM IMERG product using as reference the high-resolution ground gauge–radar dataset (GV-MRMS) in the Continental United States (CONUS) at the native IMERG resolution (0.1°×0.1°, 0.5 h), with a primary focus on the detectability and timing of events. Our results show that IMERG generally overestimates the event duration but underestimates the mean event precipitation intensity in the summer, while the opposite is true for winter. This discrepancy is mostly attributed to the under-representation of short-duration intense events in the summer and long-duration moderate events in the winter in IMERG. In terms of the detection of individual events, about 50% of the reference events are properly detected by IMERG, and conversely, 50% of IMERG events do not match a reference event. However, nearly 40% of the missed or false events result from temporal mismatching of less than 3 h between the retrieved and the reference event. The remaining 60% comes from IMERG not detecting an existing event or inventing a nonexistent event. When IMERG successfully detects an event, the average temporal overlap with the reference event is about 70% of its total duration, which mostly stems from the mistiming of IMERG-derived events. IMERG events tend to start, peak, and end earlier than GV-MRMS events, with national mean shifts of −26 min, −17 min, and −7min, respectively. For about only 20% of all situations the starting time of the event is correctly reproduced by IMERG, and the same applies to the peak time and end time. Our results provide guidance for applications of IMERG at sub-daily scales, as well as new insights for the improvement of satellite retrieval algorithms.

Estimation of future extreme rainfall in Barcelona (Spain) under monofractal hypothesis

AbstractClimate change effects on subdaily rainfall (from 5 min to a few hours) can hardly be measured in mid‐latitude climates due to the high natural variability of the precipitation patterns and their effects on local topography. The goal of this study was to obtain change projections of intensity–duration–frequency (IDF) curves, for up to 2‐h precipitation events, comparing two approaches that use the daily outputs of the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model projections: (a) direct scaling of the expected probable precipitation, from 2‐year to 500‐year return periods of daily rainfall and (b) a new semi‐stochastic approach, built by combining the physically forced outputs of climate models (on a daily scale) and stochastic simulation given by the probability distribution of a concentration index (n‐index) for individual rainfall events (on a subdaily scale). The approaches were applied to a set of 27 stations located around Barcelona, Spain, including a long reference series (with 5‐min rainfall records since 1927), representing the highly variable Mediterranean climate. The validation process showed a systematic error (bias) generally smaller than 10%, especially for rainfall extreme events with durations of less than 2 h. The concentration n‐index and IDF curves were projected by 10 downscaled CMIP5 climate models under 2 emission scenarios (RCP4.5 and RCP8.5), obtaining a consensual increase in both relative concentration and absolute intensities in Barcelona. Ensemble projection of rainfall concentration (n‐index) showed an increase up to 10% by 2071–2100 and about 20% (15%–30% range) for maximum intensities of 2‐year to 500‐year return periods. Results provide robustness in decision‐making regarding the design of stormwater management infrastructure at a local scale.

Evaluation of GPM-IMERG Precipitation Product at Multiple Spatial and Sub-Daily Temporal Scales over Mainland China

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) provides new-generation satellite precipitation datasets with high spatio-temporal resolution and accuracy, which is widely applied in hydrology and meteorology. However, most examinations of the IMERG were conducted on daily, monthly, and annual scales, and inadequate research focused on the sub-daily scale. Thus, this study set up four sub-daily scales (1 h, 3 h, 12 h, and 24 h at 0.1° spatial resolution) and four spatial scales (0.1°, 0.25°, 0.5°, and 1° at 1 h temporal resolution) to finely evaluate the performance of IMERG products in the summer seasons from 2014 to 2019 over mainland China. The precipitation amount (PA), frequency (PF), and intensity (PI) were adopted to assess the performance of the IMERG referenced by the ground-based precipitation product of the China Meteorological Administration (CMA). The results show that the IMERG can capture the spatial patterns of precipitation characters over mainland China, but the PA and PI are overestimated and the PF is underestimated, and the evaluation results are highly sensitive to the different temporal and spatial resolutions. Compared with fine spatio-temporal scales, the performance of the IMERG is significantly improved when scaled up to coarser scales. Moreover, the IMERG shows a better performance of PA and PI in larger regions and during longer periods. This study provided a reference for the application of IMERG products in different spatial and temporal scales.