High-resolution Precipitation Research Articles

Spatio-Temporal Trends in Precipitation, Temperature, and Extremes: A Study of Malawi and Zambia (1981–2021)

Analyzing long-term climate changes is a prerequisite for identifying hotspot areas and developing site-specific adaptation measures. The current study focuses on assessing changes in precipitation, maximum and minimum temperatures, and potential evapotranspiration in Zambia and Malawi from 1981 to 2021. High-resolution precipitation and temperature datasets are used, namely, Climate Hazards Group InfraRed Precipitation with Station data (0.05°) and Multi-Source Weather (0.1°). The Mann–Kendall trend test and Sen’s Slope methods are employed to assess the changes. The trend analysis shows a non-significant increase in annual precipitation in many parts of Zambia and Central Malawi. In Zambia and Malawi, the average annual and seasonal maximum and minimum temperatures show a statistically significant increasing trend (up to 0.6 °C/decade). The change in precipitation during the major rainy seasons (December–April) shows a non-significant increasing trend (up to 3 mm/year) in a large part of Zambia and Central Malawi. However, Malawi and Northern Zambia show a non-significant decreasing trend (up to −5 mm/year). The change in December–April precipitation significantly correlates with El Niño–Southern Oscillation (Indian Ocean Dipole) in Southern (Northern) Zambia and Malawi. To minimize the impact of the observed changes, it is imperative to develop adaptation measures to foster sustainability in the region.

Extreme precipitation detection ability of four high-resolution precipitation products dataset in Nepal

Given Nepal’s vulnerability to extreme precipitation (EP), it is imperative to conduct a comprehensive analysis to comprehend the historical trends of such events. However, acquiring precise precipitation data for EP remains challenging in mountainous countries like Nepal owing to the scarcity of densely gauged networks. This limitation impedes the dissemination of knowledge pertaining to EP variability events in Nepal. The current research on this topic is deficient for two main reasons: 1) there is a lack of studies leveraging recently released high-resolution precipitation products to identify their EP detection capabilities, which further hinders the usability of those products in data-scarce regions like Nepal, and 2) most studies have focused on the characterisation of EP events in Nepal rather than their spatial and temporal variability. To address these issues, this study evaluated the EP detection capabilities of four high-resolution precipitation product datasets (PPDs) across Nepal from 1985 to 2020. These datasets include the ERA5 Land reanalysis data, satellite-based precipitation data (PERSIANN_CCS_CDR and CHIRPS_V2.0) and a merged dataset (TPHiPr). We used various statistical and categorical indices to assess their ability to capture the spatial and temporal variability of EP events. The annual EP events were characterised by 11 indices divided into frequency and intensity categories. The TPHiPr merged dataset offered a robust depiction of monthly precipitation estimates, achieving the highest critical success index, accuracy, probability of detection and a low false alarm ratio for daily precipitation detection of 0.1 mm in Nepal. Conversely, the PERSIANN_CCS_CDR dataset exhibited poor performance. Most PPDs showed increasing trends in EP indices. However, the TPHiPr dataset showcased those trends with fewer errors and stronger correlations for many frequency (R10mm, R20mm and R25mm) and intensity (RX1day, RX5day, PRCPTOT and R99p) indices. The results indicate that TPHiPr outperformed other PPDs in accurately representing the spatial distribution of EP trends in Nepal from 1985 to 2020, particularly noting an exacerbation of EP events mostly in the eastern region of Nepal throughout the study period. While TPHiPr demonstrated superior performance in detecting various EP indices across Nepal, individual products like the ERA5 Land reanalysis dataset showed enhanced performance in the western region of Nepal. Conversely, PERSIANN_CCS_CDR and CHIRPS_V2.0 performed well in the eastern region compared to other PPDs.

Examining characteristics and sampling methods of phosphor dynamics in lowland catchments.

Despite over two decades since the EU Water Framework Directive have passed, achieving the desired water quality in German surface waters remains challenging, regardless of efforts to reduce phosphorus inputs and associated environmental impacts. This study aims at analyzing the characteristics governing the concentrations of four key water quality parameters (total phosphorus, orthophosphate, particulate phosphate, and suspended solids) in two lowland catchments: the 50 km2 catchment of the Kielstau, Germany, and its 7 km2 tributary, the Moorau, which are dominated by agricultural land use. To this end, different sampling methods, particularly high-resolution precipitation event-based sampling and daily mixed samples, are conducted and evaluated, and their effectiveness is compared. The identification of sources and characteristics that affect phosphorus and suspended sediment dynamics, both in general and specifically during heavy precipitation events, is one focus of the study. Over a 15-year period, increasing concentrations of these parameters were observed in daily mixed samples, exhibiting distinct seasonal patterns-higher in summer and lower in winter-consistent with lowland catchment behavior. Particularly during heavy precipitation events, the smaller catchment exhibits a more complex and less predictable response to chemical concentrations compared with the dilution effect observed in the larger catchment. The results underline the complexity of phosphorus dynamics in small catchments and emphasize the importance of event-based sampling for capturing short-term concentration peaks for all four parameters, particularly beneficial regarding measuring suspended solids. While daily mixed samples capture average phosphorus concentrations, event-based sampling is crucial for detecting short-term spikes, providing a more comprehensive understanding of phosphorus dynamics.

Bias-corrected high-resolution precipitation datasets assessment over a tropical mountainous region in Colombia: A case of study in Upper Cauca River Basin

Surface gauge measurements have been commonly employed to analyze the precipitation's high spatial and temporal variability. However, incomplete area coverage and deficiencies over most tropical and complex topography mean significant limitations of this measurement type. Satellite-derived datasets, combined with the integration of in-situ observations with satellite data, are an alternative to address these limitations by offering a more spatially homogeneous and temporally comprehensive coverage for scarce data areas of the globe. Nevertheless, applying a bias correction technique on the precipitation datasets is still necessary before they are used for research due to their considerable bias. Here, we analyze the performance of CHIRPS, WorldClim, and TerraClimate datasets compared to data from 30 rain gauge stations over the South-West of Colombia, specifically in the Upper Cauca River Basin-UCRB between 1981 and 2018. Additionally, we applied the Quantile Mapping correction to all gridded precipitation products, and subsequently, the corrected rainfall is compared to the observed data on the monthly, seasonal, and annual scale. Our results show that the CHIRPS dataset better captures the seasonal and monthly variability. CHIRPS presents the best performance during less rainy seasons and at low elevation zones (900–2000 m above sea level-m.a.s.l.), followed by TerraClimate. Utilizing the bias correction methodology, we generated a new, corrected, and more reliable monthly precipitation time series for each location from all gridded precipitation products. Additionally, we found that the correction of the CHIRPS dataset presented the best performance across all spatiotemporal scales in the UCRB. Therefore, this study provides an accurate precipitation database for a complex topographic tropical region with limited data availability.

A retrospective hydrological uncertainty analysis using precipitation estimation ensembles for a poorly gauged basin in High Mountain Asia

Abstract We study the impact of uncertain precipitation estimates on simulated streamflows for the poorly gauged Yarlung Tsangpo basin (YTB), High Mountain Asia (HMA). A process-based hydrological model at 0.5 km resolution is driven by an ensemble of precipitation estimation products (PEPs), including analyzed ground observations, high-resolution precipitation estimates, climate data records and reanalyses over the 2008-2015 control period. The model is then forced retrospectively from 1983 onward to obtain seamless discharge estimates till 2007, a period for which there is very sparse flow data coverage. Whereas temperature forcing is considered deterministic, precipitation is sampled from the predictive distribution, which is obtained through processing PEPs by means of a probabilisitc processor of uncertainty. The employed Bayesian processor combines the PEPs and outputs the predictive densities of daily precipitation depth accumulation as well as the probability of precipitation occurrence, from which random precipitation fields for probabilistic model forcing are sampled. The predictive density of precipitation is conditional on the precipitation estimation predictors that are bias-corrected and variance adjusted. For the selected HMA study site, discharges simulated from reanalysis and climate data records score lowest against observations at three flow gauging points, whereas high-resolution satellite estimates perform better, but are still outperformed by precipitation fields obtained from analyzed observed precipitation and merged products, which were corrected against ground observations. The applied methodology indicates how missing flows for poorly gauged sites can be retrieved and is further extendable to hydrological projections of climate.

Evaluation of high-resolution precipitation products (CMORPH-CRT, PERSIANN, and TRMM-3B42RT) and their performances as inputs to the hydrological model

Evaluation of high-resolution precipitation products (CMORPH-CRT, PERSIANN, and TRMM-3B42RT) and their performances as inputs to the hydrological model

Statistical downscaling of high-resolution precipitation in India using convolutional long short-term memory networks

Abstract Statistical downscaling of the General Circulation Model (GCM) simulations are widely used for accessing climate changes in the future at different spatiotemporal scales. This study proposes a novel Statistical Downscaling (SD) model established on the Convolutional Long Short-Term Memory (ConvLSTM) Network. The methodology is applied to obtain future projection of rainfall at 0.25° spatial resolution over the Indian sub-continental region. The traditional multisite downscaling models typically perform downscaling on a single homogeneous rainfall zone, predicting rainfall at only one grid point in a single model run. The proposed model captures spatiotemporal dependencies in multisite local rainfall and predicts rainfall for the entire zone in a single model run. The study proposes a Shared ConvLSTM model providing a single end-to-end supervised model for predicting the future precipitation for entire India. The model captures the regional variability in rainfall better than a region-wise trained model. The projected future rainfall for different scenarios of climate change reveals an overall increase in the rainfall mean and spatially non-uniform changes in future rainfall extremes over India. The results highlight the importance of conducting in-depth hydrologic studies for different river basins of the country for future water availability assessment and making water resource policies.

Assessing drought impacts on groundwater and agriculture in Iran using high-resolution precipitation and evapotranspiration products

Accurate identification of drought events, supported by reliable and consistent spatiotemporal data, is essential for the development of efficient management and mitigation plans. The objectives of this study are twofold. First, we evaluate a wide range of global precipitation and potential evapotranspiration (PET) products versus in-situ observations for the period of 2001–2016 to identify the best-performing ones for drought assessment. Second, we assess the drought impact on groundwater storage and agricultural yield using Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The impact of drought on groundwater and agricultural yield was examined by investigating the association between SPI and SPEI with the Gravity Recovery and Climate Experiment (GRACE) satellite data, total crop production (CP), and total wheat production (WP). The results showed that the most severe and prolonged droughts occurred primarily in northwest highlands, northeastern regions, and southern regions, with SPI values indicating more intense drought events than SPEI values. While the SPEI index was found to be more suitable for assessing long-term drought severity, the SPI index was more sensitive to short-term precipitation deficits. Groundwater storage changes were found to be highly associated with SPEI/SPI at longer time scales. Furthermore, the results indicate that the strongest association between drought and yield loss occurs at the 9-month time scale. However, SPI exhibits a higher association with crop yield compared to SPEI. These findings can contribute to the development of drought management and response strategies.

Evaluation of Multi-Source Precipitation Products in the Hinterland of the Tibetan Plateau

High-resolution precipitation products have been crucial for hydrology, meteorology, and environmental ecosystems over the Tibetan Plateau (TP). However, these products are usually subject to systematic errors, which may vary with time and topographic conditions. The study evaluated the suitability of four satellite-derived products (GPM IMERG, GSMaP, CMORPH, and PERSIANN-CDR) and four fusion precipitation products (ERA5-Land, CHIRPS, CMFD, and TPHiPr) by comparing with 22 rain gauges at a daily scale from 1 January 2014 to 31 December 2018 over the hinterland of the TP. The main findings are as follows: (1) TPHiPr and CMFD are better than the satellite-derived products, while the performance of CHIRPS is worse; (2) among the satellite-derived products, the quality of GPM IMERG is the highest on different time scales, and PERSIANN-CDR is better in the months of June to October, while GSMaP and CMORPH have poor performance; (3) the eight precipitation products have weaker detection capability for heavy precipitation events, and the quality of each product decreases with the increase in the precipitation threshold, while the rate of descent of fusion precipitation products is slower than that of satellite-derived products. This study demonstrates the performance of eight precipitation products over the hinterland of the TP, which is expected to provide valuable information for hydrometeorology applications.

Past, present and future rainfall erosivity in central Europe based on convection-permitting climate simulations

Abstract. Heavy rainfall is the main driver of soil erosion by water, which is a threat to soil and water resources across the globe. As a consequence of climate change, precipitation – especially extreme precipitation – is increasing in a warmer world, leading to an increase in rainfall erosivity. However, conventional global climate models struggle to represent extreme rain events and cannot provide precipitation data at the high spatiotemporal resolution that is needed for an accurate estimation of future rainfall erosivity. Convection-permitting simulations (CPSs), on the other hand, provide high-resolution precipitation data and a better representation of extreme rain events, but they are mostly limited to relatively small spatial extents and short time periods. Here, we present, for the first time, rainfall erosivity in a large modeling domain such as central Europe based on high-resolution CPS climate data generated with the regional climate model COSMO-CLM using the Representative Concentration Pathway 8.5 (RCP8.5) emission scenario. We calculated rainfall erosivity for the past (1971–2000), present (2001–2019), near future (2031–2060) and far future (2071–2100). Our results showed that future increases in rainfall erosivity in central Europe can be up to 84 % in the region's river basins. These increases are much higher than previously estimated based on regression with mean annual precipitation. We conclude that despite remaining limitations, CPSs have an enormous and currently unexploited potential for climate impact studies on soil erosion. Thus, the soil erosion modeling community should closely follow the recent and future advances in climate modeling to take advantage of new CPSs for climate impact studies.

Drought Atlas of India, 1901–2020

India has been considerably affected by droughts in the recent past. Despite the considerable impacts of droughts on agriculture and water resources, long-term datasets to examine droughts and their consequences at appropriate spatial and temporal scales have been lacking in India. Datasets that provide drought information are mostly available for a short period and at coarser resolutions, therefore, these do not comprehend the information regarding the major droughts that occurred in the distant past at administrative scales of decision-making. To fill this critical gap, we developed the high-resolution (0.05°) and long-term monthly precipitation and temperature datasets for the 1901–2021 period. We used long-term high-resolution precipitation and temperature to estimate droughts using standardized precipitation and evapotranspiration index (SPEI). As SPEI considers the role of air temperature in drought estimation, it can be used to examine meteorological, agricultural, and hydrological droughts. Using high-resolution SPEI, we developed drought atlas for India (1901–2020) that can provide comprehensive information on drought occurrence, impacts, and risks in India.

High-resolution Precipitation Prediction in Bangladesh via Ensemble Learning

High-resolution Precipitation Prediction in Bangladesh via Ensemble Learning

On the reliability of 12 high-resolution precipitation products for process-based hydrological modeling in China

Numerous satellite- and gauge-based precipitation products (SBPs/GBPs) have emerged in recent decades, providing unprecedented opportunities for process-based hydrological modeling. However, their reliability in high-resolution hydrological modeling at continental scales, especially for variables other than streamflow, remains to be investigated. Factors influencing their hydrological performance (e.g., catchment sizes, precipitation products’ spatial resolution and raw observation information) are also under debate. Here, we provide a comprehensive analysis of the hydrological reliability of 12 state-of-the-art precipitation datasets over China using a process-based land surface model at 0.125° resolution. Simulations driven by different precipitation products are evaluated across ∼ 200 basins with different catchment sizes. Results show that all simulations well reproduce the streamflow, soil moisture, evapotranspiration, and snow-depth at monthly timescale. None of the precipitation products provides best results for all variables, but the gauge-based and infrared-microwave-merged satellite products are superior to the infrared satellite products. The gauge-based regional precipitation product based on the 0.25° China precipitation dataset and the 6-km China Land Data Assimilation System (CN051_CLDAS) provides the best performance generally, followed by two global precipitation products from the Global Precipitation Climatology Centre (GPCC V2022) and the Global Precipitation Measurement Integrated Multi-satellite Retrievals (IMERGE-F V6). However, the advantages of CLDAS_CN051 and GPCC V2022 against satellite-based precipitation products are scale-dependent and tend to disappear over large-scale catchments (>106 km2). The spatial resolutions of precipitation products show no relationship with their hydrological performance, but the number of stations used to generate/bias-correct the precipitation is positively correlated with the performance of streamflow and soil moisture simulations. Differences among the climatology of terrestrial water-cycle components caused by using different precipitation products are notable and tend to increase over the dry and cold regions. Snow depth is the variable most sensitive to precipitation datasets, followed by runoff, ET, and soil moisture. This study suggests that using more station observations is more important than increasing spatial resolutions to enhance the applications of precipitation products in high-resolution hydrological modeling.

Rainfall Erosivity Mapping for Tibetan Plateau Using High-Resolution Temporal and Spatial Precipitation Datasets for the Third Pole

Low-density weather station and high topographic variance limited rainfall erosivity (RE) calculation for Tibetan Plateau (TP). The accuracy of RE prediction from three data sources (a High-resolution Precipitation dataset for the Third Pole (TPHiPr), IMERG Final Run (IMERG-F) and weather station daily precipitation data) were evaluated for the TP, and the variations were analyzed from 2001 to 2020. The results showed that TPHiPr can more accurately characterize spatial and temporal variations of the RE on the TP. TPHiPr can better represent the impact of topography on precipitation, effectively compensating the deficiencies in precipitation data from low-density stations. The R2 and NSE between the mean annual/monthly RE of TPHiPr and the station data were around 0.9. TPHiPr effectively revealed rain shadow areas on the northern slopes of the Himalayas and calculated RE more accurately in the broad-leaved evergreen forest zone on the southern flank of the Himalayas and the arid regions to the northwest. RE from 2001 to 2020 showed an overall increasing trend. However, TPHiPr produced underestimates in the southern valleys and the eastern Hengduan Mountains, while overestimates in the southeastern area at lower elevations. This research provided a new and more accurate RE data for the TP.

A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China

Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research.

Satellite sensed summer monsoon torrential rain events characteristics along the Himalayan regions of North India and their dynamics

In the past three decades, the total number of extreme rainfall events over northern India has increased, which majorly caused flooding and landslides over the Himalayan foothills and the north Indian region. Some extreme events are reported as torrential rainfall or cloudburst events; others are linked with large-scale weather systems. In this article, we identified extreme precipitation events over northern India during the summer monsoon season using satellite-sensed high-resolution precipitation. Historically noted cloudbursts were selected from these extreme events to study the mechanisms behind their development. The cloudburst events were classified into five primary classes, C1, C2, C3, C4, and C5, based on the large-scale weather systems in the lower, middle, and upper troposphere. A detailed understanding of each class of cloudburst has been carried out to know the precursors for their formation, the dynamical mechanism involved in their development, and the thermodynamic features responsible for causing extreme rainfall. It is found that cloudburst events of classes C1, C2, and C3 are seen during the active monsoon season. Classes C4 and C5 cloudburst events are linked with the monsoon hiatus/break period. In the active monsoon season, these cloudburst events are associated with lower atmosphere cyclonic circulation over the head Bay of Bengal in class C1, and cyclonic circulation centered over Central India in class C2, and East-West oriented low-pressure conveyor system over central India in class C3. Additionally, thermodynamic analysis of each class revealed that class C1, C2, and C3 cloudbursts were associated with the moist instability of the atmosphere during the cloudburst events, where orographic forcing created potential instability and made them intense and overwhelming.

Tracing isotope precipitation patterns across Mexico

Mexico encompasses a large spectrum of landscapes with topographic, geographic, and climatic factors interacting in a complex ecohydrological setting. For decades, isotope hydrogeological tools have been applied in Mexico using short-term or seasonal local meteoric water lines as valid input functions. Yet, a systematic evaluation of meteoric isotope characteristics is still lacking. Here we report on the spatial and temporal isotope variations of 21 precipitation monitoring stations across Mexico. Our database includes 608 monthly samples collected from 2018 to 2021 over four regions (between 5 and 2,365 m asl): the Pacific coast, the Gulf of Mexico/Caribbean Sea region, and the Central and Northern plateaus. Precipitation δ18O seasonality from the dry (winter) to the wet season (summer) was characterized by a notable W-shaped variability. Monthly precipitation amounts and δ18O compositions exhibited poor to strong linear regressions (Adj. r2<0.01 to 0.75), with inverse (positive) relationships over the northern monsoon-affected region. Low d-excess (5.1 to 9.7‰) corresponded with greater terrestrial moisture contributions (20.5%) over the arid northern regions. Moisture inputs from the Gulf of Mexico/Caribbean Sea and the Pacific Ocean were associated with near-equilibrium or greater d-excess values (8.8 to 14.3‰), respectively. The best-fit linear models for δ18O (Adj. r2 = 0.85) and δ2H (Adj. r2 = 0.88) were determined for topographic and geographical predictors, resulting in an updated high-resolution precipitation isoscape (100 m2 grid) for Mexico. Orographic barriers (-2.10‰ in δ18O/km) coupled with the interaction of tropical cyclones and cold fronts, the evolution of the North American Monsoon system, and the passage of easterly trade winds play a remarkable role in controlling the spatial isotope rainfall variability. Our findings provide a robust baseline for ecohydrological, climatic, forensic, archeological, and paleoclimate studies in North America.

The Impact of Extreme Precipitation Events and Their Variability on Climate Change Beliefs in the American Public

Abstract Although scientists agree that climate change is anthropogenic, differing interpretations of evidence in a highly polarized sociopolitical environment impact how individuals perceive climate change. While prior work suggests that individuals experience climate change through local conditions, there is a lack of consensus on how personal experience with extreme precipitation may alter public opinion on climate change. We combine high-resolution precipitation data at the zip-code level with nationally representative public opinion survey results (n = 4008) that examine beliefs in climate change and the perceived cause. Our findings support relationships between well-established value systems (i.e., partisanship, religion) and socioeconomic status with individual opinions of climate change, showing that these values are influential in opinion formation on climate issues. We also show that experiencing characteristics of atypical precipitation (e.g., more variability than normal, increasing or decreasing trends, or highly recurring extreme events) in a local area are associated with increased belief in anthropogenic climate change. This suggests that individuals in communities that experience greater atypical precipitation may be more accepting of messaging and policy strategies directly aimed at addressing climate change challenges. Thus, communication strategies that leverage individual perception of atypical precipitation at the local level may help tap into certain “experiential” processing methods, making climate change feel less distant. These strategies may help reduce polarization and motivate mitigation and adaptation actions. Significance Statement Public acceptance for anthropogenic climate change is hindered by how related issues are presented, diverse value systems, and information-processing biases. Personal experiences with extreme weather may act as a salient cue that impacts individuals’ perceptions of climate change. We couple a large, nationally representative public opinion dataset with station precipitation data at the zip-code level in the United States. Results are nuanced but suggest that anomalous and variable precipitation in a local area may be interpreted as evidence for anthropogenic climate change. So, relating atypical local precipitation conditions to climate change may help tap into individuals’ experiential processing, sidestep polarization, and tailor communications at the local level.

Quantifying the snowfall variations in the Third Pole region from 1980 to 2020

Snowfall is critical in the mountainous cryosphere cycle and is vulnerable to climate change. Due to data-scarcity and strong surface heterogeneity, its information is not sufficiently revealed across the Third Pole (TP) under warming. Accordingly, this study investigated the spatiotemporal variability of snowfall and its entropy information across the TP from 1980 to 2020 based on a new high-accuracy snowfall dataset (TPHiS), which was estimated from a long-term high-resolution precipitation dataset for the TP (TPHiPr) and High Asia Refined analysis version 2 reanalysis data (HARv2) using a threshold temperature method. The results indicated that the correlation coefficient, mean bias, and root-mean-square error of the daily TPHiS were 0.77, 0.03 mm/daily, and 0.18 mm/daily, respectively, compared against in-situ observation, illustrating that this dataset performs better than other snowfall datasets. The annual mean snowfall measured 259.38 mm across the TP and experienced a slight negative trend (−0.77 mm/decade, p > 0.05). A significantly positive trend in annual snowfall was observed in the intersection of the West Kunlun, Karakoram, and Western Inner Tibet, with elevations over 5500 m. In contrast, a significantly negative trend prevailed in the Pamir, Hengduan Shan, and West and Central Himalaya intersections, especially at elevations varied from 3000 to 4500 m. Compared with other seasons, a significantly negative trend in snowfall was observed in summer (−1.94 mm/decade, p < 0.05). Change in snowfall frequency entropy was more significant than that in snowfall amount entropy. A significantly positive trend in light snowfall frequency dominated the augmentation in snowfall frequency, causing a negative trend in snowfall frequency entropy, while a substantial reduction in snowfall frequency associated with the decline in light snowfall frequency induced a positive trend in snowfall frequency entropy. The outcomes provide a finer snowfall dataset for regional cryospheric research and might inform sustainable water resource management in alpines.

Evaluation of the GPM-IMERG V06 Final Run Products for Monthly/Annual Precipitation under the Complex Climatic and Topographic Conditions of China

Abstract The new-generation multisatellite precipitation algorithm, namely, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG), version 6, provides a high resolution and large spatial extent and can be used to offset the lack of surface observations. This study aimed to evaluate the precipitation detection capability of GPM-IMERG V06 Final Run products in different climatic and topographical regions of China for the 2014–20 period. This study showed that 1) GPM-IMERG could capture the spatial and temporal precipitation distributions in China. At the annual scale, GPM-IMERG performed well, with a correlation coefficient R &gt;0.95 and a relative bias ratio (RBias) between 15.38% and 23.46%. At the seasonal scale, GPM-IMERG performed best in summer. At the monthly scale, GPM-IMERG performed better during the wet season (April–September) (RBias = 7.41%) than during the dry season (RBias = 13.65%). 2) GPM-IMERG performed well in terms of precipitation estimation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. 3) The climate zone, followed by elevation, played a leading role in the GPM-IMERG accuracy in China, and the main sources of GPM-IMERG deviation in arid and semiarid regions were missed precipitation and false precipitation. However, the influences of missed precipitation and false precipitation gradually increased with increasing elevation. Despite the obvious differences between the GPM-IMERG and surface precipitation estimates, the study results highlight the potential of GPM-IMERG as a valuable resource for monitoring high-resolution precipitation information that is lacking in many parts of the world. Significance Statement The purpose of this study was to better understand the capability of GPM-IMERG for precipitation estimation and the causes of errors. GPM-IMERG performed well when estimating precipitation in Southwest China, Central China, East China, and South China, followed by Northeast China and North China, but it performed poorly in Northwest China and Tibet. Climate, followed by elevation, played a leading role in GPM-IMERG accuracy in China. Our results could provide a greater understanding of the accuracy of GPM-IMERG precipitation estimation in the different regions of China and can be applied to water resource management, afforestation (or reforestation) projects, and so on, in areas worldwide where meteorological stations are scarce.