Precipitation Products Research Articles

Assessment and Application of Multi-Source Precipitation Products in Cold Regions Based on the Improved SWAT Model

In hydrological modeling, the accuracy of precipitation data and the reflection of the model’s physical mechanisms are crucial for accurately describing hydrological processes. Identifying reliable data sources and exploring reasonable hydrological evolution mechanisms for hydrology and water resources research in high-altitude mountainous regions with sparse stations and limited data constitute a significant challenge and focus in the field of hydrology. This study focuses on the Yarkant River Basin in Xinjiang, which originates from glaciers and contains a substantial amount of meltwater runoff. A dynamic glacier melt module considering the synergistic effects of multiple meteorological factors was developed and integrated into the original Soil and Water Assessment Tool (SWAT) model. Four precipitation datasets (ERA5-land, MSWEP, CMA V2.0, and CHM-PRE) were selected to train the model, including remote sensing precipitation products and station-interpolated precipitation data. The applicability of the improved SWAT model and precipitation datasets in the source region of the Yarkant River was evaluated and analyzed using statistical indicators, hydrological characteristic values, and watershed runoff simulation effectiveness. The optimal dataset was further used to analyze glacier evolution characteristics in the basin. The results revealed the following: (1) The improved model fills the gap in glacier runoff simulation with respect to the original SWAT model, with the simulation results more closely aligning with the actual runoff variation patterns in the study area, better describing the meltwater runoff process. (2) CMA V2.0 precipitation data has the best applicability in the study area. This is specifically reflected in the rationality of the spatial and temporal distribution patterns of the inverted precipitation, the accuracy observed in capturing precipitation events and actual precipitation characteristics, the goodness of fit in driving hydrological models, and the observed precision in reflecting the composition of watershed runoff, all of which are superior to those pertaining to other precipitation products. (3) The glacier melt calculated using the improved SWAT model informed by CMA V2.0 shows that during the study period, the basin formed a pattern with a positive–negative glacier balance demarcation at 36.5° N, featuring melting at higher latitudes and accumulation at lower latitudes. The results of this study are of significant importance for hydrometeorological applications and hydrological and water resources research in this region.

An innovative process for clean ammonium-free vanadium precipitation and one-step preparation of vanadium dioxide based on hydrothermal enhancement of organic alcohols

As a transition metal oxide, VO2 has excellent optical and electrical properties and is widely used in many fields. The production of VO2 by the reduction of NH4VO3 and V2O5 will inevitably produce ammonia–nitrogen wastewater and NH3 emission during the production of NH4VO3 and V2O5, which will cause serious environmental pollution and will increase economic costs. The large-scale and low-cost synthesis of VO2 still faces great challenges. In this paper, VO2(B) was successfully prepared by a one-step hydrothermal method using NaVO3 as the vanadium source and CH3CH2OH as the reducing agent, and the optimal conditions for vanadium precipitation are as follows: vanadium concentration = 30 g/L, CH3CH2OH dosage = 20 % (percentage of solution volume), initial pH = 1, reaction temperature = 220 °C, reaction time = 12 h. The vanadium precipitation efficiency under the optimal conditions could reach 98.93 %, and the purity of VO2(B) could reach 98.59 %. The precipitation products were characterized by XRD, TG, FTIR, XPS, and SEM-EDS. The mechanism of VO2(B) preparation by ethanol reduction was proposed. This process is characterized by green, clean, and high efficiency. At the same time, the vanadium precipitation effect and vanadium precipitation products of various alcohols were studied to provide technical and theoretical support for the preparation of VO2(B).

Rain event detection and magnitude estimation during Indian summer monsoon: Comprehensive assessment of gridded precipitation datasets across hydroclimatically diverse regions

Accurate precipitation estimates are quintessential for hydrologic modeling and climate studies. Different gridded precipitation products are available in any region, and selecting the best one is essential for hydroclimatic modeling and analysis. In the current study, observation- (APHRODITE), reanalysis- (IMDAA, ERA5-Land, PGF), satellite-based (IMERG, CHIRPS, PERSIANN-CDR), and hybrid (MSWEP) gridded precipitation products with different spatial and temporal resolutions are evaluated using several continuous, categorical, graphical, and interval-based performance measures towards detecting Indian Summer Monsoon Rainfall (ISMR) events and estimating their magnitudes for the subcontinent of India, considering IMD gauge-based gridded as reference product. We confine our analysis to the monsoon season (i.e., June to September), the principal rainy season in the Indian sub-continent. The dearth of data and limited rain gauge-based observations from non-uniform sparse monitoring networks across India necessitated grid-to-grid comparative evaluations instead of point-to-grid assessments. We propose a new ranking framework to determine the suitability of precipitation datasets for twenty-seven hydroclimatic regions comprising homogeneous rainfall zones, Köppen-Geiger climate zones, and major river basins. Results from the comprehensive evaluation suggest that (1) APHRODITE, MSWEP, and ERA5-Land best approximate precipitation event occurrences across India, (2) MSWEP and ERA5-Land are most suitable (highest rank) alternatives at the regional level, while APHRODITE is found to be next suitable dataset owing to its persistent dry bias, (3) performances of CHIRPS and IMERG have reasonably lower rank score across India, (4) close agreement of examined datasets is noted over semi-arid and sub-humid regions (e.g., peninsular and central India), whereas ERA5-Land, IMDAA, and APHRODITE fail to detect and reproduce the intensity of the events along the west coast and northeastern India, (5) PGF and PERSIANN-CDR are the least situated datasets. Moreover, the present study provides a unique and innovative perspective to characterise the precipitation over a vast topographic, ecologic, and climatic gradient region like India.

How Do Satellite Precipitation Products Affect Water Quality Simulations? A Comparative Analysis of Rainfall Datasets for River Flow and Riverine Nitrate Load in an Agricultural Watershed

Excessive nitrate loading from agricultural runoff leads to substantial environmental and economic harm, and although hydrological models are used to mitigate these effects, the influence of various satellite precipitation products (SPPs) on nitrate load simulations is often overlooked. This study addresses this research gap by evaluating the impacts of using different satellite precipitation products—ERA5, IMERG, and gridMET—on flow and nitrate load simulations with the Soil and Water Assessment Tool Plus (SWAT+), using the Tar-Pamlico watershed as a case study. Although agricultural activities are higher in the summer, this study found the lowest nitrate load during this season due to reduced runoff. In contrast, the nitrate load was higher in the winter because of increased runoff, highlighting the dominance of water flow in driving riverine nitrate load. This study found that although IMERG predicts the highest annual average flow (120 m3/s in Pamlico Sound), it unexpectedly results in the lowest annual average nitrate load (1750 metric tons/year). In contrast, gridMET estimates significantly higher annual average nitrate loads (3850 metric tons/year). This discrepancy underscores the crucial impact of rainfall datasets on nitrate transport predictions and highlights how the choice of dataset can significantly influence nitrate load simulations.

Comparing Satellite, Reanalysis, Fused and Gridded (In Situ) Precipitation Products Over Türkiye

ABSTRACTPrecipitation is the fundamental source for various research areas, including hydrology, climatology, geomorphology, and ecology, serving essential roles in modelling, distribution, and process analysis. However, the accuracy and precision of spatially distributed precipitation estimates is a critical issue, particularly for daily scale and topographically complex areas. Although many datasets have been developed based on different algorithms and sources are developed for this purpose, determining which of these datasets best reflects actual conditions is quite challenging. This study, hence, aims to compare the 25 global distributed precipitation estimates (gridded, satellite, model, and fused) concerning 221 ground‐based observations based on the ranking of 18 continuous (evaluation statistics), eight categorical (precipitation indices), and two seasonality metric (high and low precipitation). Upon examining the results, gridded and model precipitation data including APHRODITE (Asian Precipitation—Highly‐Resolved Observational Data Integration Towards Evaluation), CPC (Global Unified Gauge‐Based Analysis of Daily Precipitation), ERA5‐Land (ECMWF Reanalysis 5th Generation for Lands), and CFSR (Climate Forecast System Reanalysis) occupy the top four positions in continuous metrics. In contrast, satellite data such as PERSIANN‐PDIR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), CMORPH (Climate Prediction Center morphing method), IMERG (The Integrated Multi‐Satellite Retrievals for GPM), and TRMM‐TMPA (Tropical Rainfall Measuring Mission/Multi‐satellite Precipitation Analysis) dominate in the top four positions in categorical metrics. For seasonality of high and low precipitation, fused, gridded, and reanalyses products such as CPC, MSWEP (Multi‐Source Weighted‐Ensemble Precipitation, version 2), HydroGFD (Hydrological Global Forcing Data), CFSR rank among top four. Based on the first five rankings of all metrics, fused (multiple sourced) and gridded datasets accurately reflect the actual situations compared to other precipitation products. Reanalysis (model) and satellite‐based follow this rank, respectively. The results clearly indicate that fused precipitation derived products from multiple sources offer better accuracy and precision in representing the spatial distribution of precipitation on a daily scale.

Performance Assessment of Satellite-Based Precipitation Products in the 2023 Summer Extreme Precipitation Events over North China

In the summer of 2023, North China experienced a rare extreme precipitation storm due to Typhoons Doksuri and Khanun, leading to significant secondary disasters and highlighting the urgent need for accurate rainfall forecasting. Satellite-based quantitative precipitation estimation (QPE) products like Integrated Multi-Satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) from the Global Precipitation Measurement (GPM) Mission have great potential for enhancing forecasts, necessitating a quantitative evaluation before deployment. This study uses a dense rain gauge as a benchmark to assess the accuracy and capability of the latest version 7B IMERG and version 8 GSMaP satellite-based QPE products for the 2023 summer extreme precipitation in North China. These satellite-based QPE products include four satellite-only products, namely IMERG early run (IMERG_ER) and IMERG late run (IMERG_LR), GSMaP near-real-time (GSMaP_NRT), and GSMaP microwave-infrared reanalyzed (GSMaP_MVK), along with two gauge-corrected products, namely IMERG final run (IMERG_FR) and GSMaP gauge adjusted (GSMaP_Gauge). The results show that (1) GSMaP_MVK, IMERG_LR, and IMERG_FR effectively capture the space distribution of the extreme rainfall, with relatively high correlation coefficients (CCs) of approximately 0.77, 0.75, and 0.79. The IMERG_ER, GSMaP_NRT, and GSMaP_Gauge products exhibit a less accurate spatial pattern capture (CCs about 0.66, 0.73, and 0.67, respectively). Each of the six QPE products tends to underestimate rainfall (RBs < 0%). (2) The IMERG products surpass the corresponding GSMaP products in serial rainfall measurement. IMERG_LR demonstrates superior performance with the lowest root-mean-square error (RMSE) (about 0.38 mm), the highest CC (0.97), and less underestimation (RB about −6.37%). (3) The IMERG products at rainfall rates ≥ 30 mm/h, GSMaP_NRT and GSMaP_MVK products at rainfall rates ≥ 55 mm/h, and GSMaP_Gauge products at ≥ 40 mm/h showed marked limitations in event detection, with a near-zero probability of detection (POD) and a nearly 100% false alarm ratio (FAR). In this extreme precipitation event, caution is needed when using the IMERG and GSMaP products.

Characteristics of climate resource utilization and economic benefits in new planting pattern for fresh faba bean and fresh maize in Yangtze River Delta

ABSTRACT In this research, we aimed to construct a novel planting pattern for faba bean and maize with high-efficiency utilization of climate resources for application in production. We incorporated relay intercropping in two triple-crop planting patterns, namely faba bean/maize-maize and faba bean/maize-soybean. A field experiment was conducted to compare the annual yield, utilization efficiency of climatic resources and economic benefits among these two triple-crop planting patterns and the conventional faba bean – maize double-cropping system at Nantong, Jiangsu, China, from 2016 to 2020. Compared with the conventional double-cropping system, the faba bean/maize-maize triple-crop planting pattern resulted in significantly increased annual yield by 7,392 kg hm−2 to 10,519 kg hm−2, and increased annual biomass by 5,342 kg hm−2 to 7,172 kg hm−2.The triple-crop planting patterns increased the annual economic benefit by 12,195 RMB hm−2 and 15,680 RMB hm−2, respectively. The faba bean/maize-maize triple-crop planting pattern increased the annual radiation production efficiency by 0.18 g·MJ−1 to 0.31 g·MJ−1, the annual temperature production efficiency by 2.70 kg·hm−2·°C−1 to 3.78 kg·hm−2·°C−1, and the annual precipitation production efficiency by 4.55 kg·mm−1·hm−2 to 7.20 kg·mm−1·hm−2. Considering the yields, resource-use efficiency, and economic benefits, the triple-cropping planting patterns show promise as multi-crop systems to achieve high yields and high resource-use efficiency in the Yangtze River Delta region. These results demonstrate the use of a new relay intercropping planting pattern for use in crop production and represent a theoretical contribution to the development of efficient agricultural production.

Evaluation of satellite-derived precipitation datasets: a case study in the Ten Tributaries region of the Yellow River Basin

ABSTRACT Satellite-derived precipitation datasets are essential components of hydrological simulations, particularly in data-scarce regions of western China. However, a comprehensive assessment of their accuracy and reliability is required. Here, the accuracy of two high-resolution satellite-derived precipitation datasets, Integrated Multi-satellite Retrievals for GPM – Final (IMERG-F) and Gauge-Adjusted Global Satellite Mapping of Precipitation (GSMaP-Gauge), was evaluated across the Ten Tributaries region of the Yellow River Basin in western China using four quantitative metrics and three categorical scoring indicators. This evaluation sought to ascertain the retrieval accuracy of these products on both the daily scale and hourly scale of heavy precipitation events, and investigated their inversion error characteristics across various spatiotemporal scales. Both datasets effectively captured the spatiotemporal patterns of annual average precipitation within the study area. Notably, the daily-scale accuracy of these satellite-derived precipitation products surpassed their hourly and half-hourly counterparts. Both GPM-IMERG and GSMaP-Gauge adeptly reproduced most precipitation events in the Ten Tributaries region, with peak detection performance observed in the central and southern zones, providing a reliable data source for drought monitoring and hydrological modeling. Overall, compared with GPM-IMERG, GSMaP-Gauge displayed superior inversion accuracy across diverse spatiotemporal scales.

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Climate change and human activities alter the global freshwater cycle, causing nonstationary processes as its distribution shifting over time, yet a comprehensive understanding of these changes remains elusive. Here, we develop a remote sensing-informed terrestrial reanalysis and assess the nonstationarity of and interconnections among global water cycle components from 2003 to 2020. We highlight 20 hotspot regions where terrestrial water storage exhibits strong nonstationarity, impacting 35% of the global population and 45% of the area covered by irrigated agriculture. Emerging long-term trends dominate the most often (48.2%), followed by seasonal shifts (32.8%) and changes in extremes (19%). Notably, in mid-latitudes, this encompasses 34% of Asia and 27% of North America. The patterns of nonstationarity and their dominant types differ across other water cycle components, including precipitation, evapotranspiration, runoff, and gross primary production. These differences also manifest uniquely across hotspot regions, illustrating the intricate ways in which each component responds to climate change and human water management. Our findings emphasize the importance of considering nonstationarity when assessing water cycle information toward the development of strategies for sustainable water resource usage, enhancing resilience to extreme events, and effectively addressing other challenges associated with climate change.

Response of nano-pores and heterogeneity of tar-rich coal to microwave pyrolysis

ABSTRACT In-situ pyrolysis of tar-rich coal can alleviate the external dependence of oil and gas in China, and achieve efficient and safe exploitation of tar-rich coal. In fact, the nano-pores and heterogeneity of tar-rich coal undergo significant changes during pyrolysis, but there is currently limited research, which seriously restricts the efficient utilization of tar-rich coal. In this study, the pore and heterogeneity characteristics of tar-rich coal were studied by microwave pyrolysis experiment, gas adsorption results and multifractal theory, and the pyrolysis evolution mechanism of pores of tar-rich coal was proposed. The results show that microwave pyrolysis significantly promoted the development of nano-pores in tar-rich coal. The nano-pore size distribution of tar-rich coal after pyrolysis showed obvious multifractal characteristics, and the influence of sparse area on nano-pore size distribution was more obvious, while the nano-pore distribution heterogeneity increases initially and subsequently drops with temperature. Moreover, the development of nano-pores in tar-rich coal occurred in three stages. Stage I, the total pore volume changed little, increasing only from 0.06851 to 0.09463 ml/g, and thermal cracking is the main reason for pore development. Stage II, numerous pores were produced as a result of the pyrolysis products’ generation, and total pore volume grew rapidly increased to 0.331 ml/g. Stage III, liquid hydrocarbons and precipitated volatile products blocked nano-pores, so that the total PV began to decrease. The total pore volume was only 0.19967 ml/g at 1000°C. This study provides guidance for the investigation of pore evolution during pyrolysis and enriches the theory of in-situ pyrolysis of tar-rich coal.

Critical Importance of Tree and Non‐Tree Vegetation for African Precipitation

AbstractVegetation is a major contributor of terrestrial evaporation and influences subsequent precipitation over land. Studies suggest that forests are crucial for moisture recycling, although the specific contribution of different vegetation to precipitation remains unclear. Using a moisture recycling approach, we investigate the contribution of transpiration from trees and non‐tree vegetation to precipitation over Africa. We use precipitation source regions from simulated atmospheric moisture trajectories, constrained by observation‐based evaporation and precipitation products, and fractional vegetation cover data. Our findings show that trees provide a higher flux to precipitation (∼777 mm year−1) than non‐tree vegetation (∼342 mm year−1). However, considering the smaller spatial extent of trees compared to non‐tree vegetation, precipitation in most watersheds effectively depends more on the latter. Overall, non‐tree vegetation appears equally important as trees in terms of volumetric contributions to precipitation, and deserves attention in further research, considering ongoing land use changes that affect the continental water cycle.

The performance of a high‐resolution satellite‐derived precipitation product over the topographically complex landscape of Eswatini

The performance of a high‐resolution satellite‐derived precipitation product over the topographically complex landscape of Eswatini

Precipitation Retrieval from FY-3G/MWRI-RM Based on SMOTE-LGBM

Using the FY-3G/MWRI-RM observations, this paper proposes a precipitation retrieval method that combines the Synthetic Minority Over-sampling Technique with Light Gradient Boosting Machine (SMOTE-LGBM) and analyzes the impact of MWRI-RM channel settings on precipitation retrieval. The SMOTE-LGBM-based model consists of two LGBM models for precipitation identification and estimation, respectively. The SMOTE method is used to address the imbalance between precipitation and non-precipitation samples. Using the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product as a reference, we validate the retrieved precipitation by the SMOTE-LGBM-based model with an independent testing dataset. The critical success indexes are 0.483 and 0.526, and the Pearson correlation coefficients are 0.611 and 0.645 for the ocean and land regions, respectively. The spatial distributions of the retrieved and IMERG accumulated precipitation in the testing dataset are similar. In addition, we visualize and analyze the cases of Meiyu and two typhoons. The results indicate that the SMOTE-LGBM-based model effectively represents the spatial distribution characteristics of precipitation and achieves high agreement with IMERG precipitation products. Overall, the SMOTE-LGBM-based model successfully retrieves precipitation from MWRI-RM and provides accurate precipitation products for FY-3G/MWRI-RM for the first time.

ER-MACG: An Extreme Precipitation Forecasting Model Integrating Self-Attention Based on FY4A Satellite Data

Extreme precipitation events often present significant risks to human life and property, making their accurate prediction an essential focus of current research. Recent studies have primarily concentrated on exploring the formation mechanisms of extreme precipitation. Existing prediction methods do not adequately account for the combined terrain and atmospheric effects, resulting in shortcomings in extreme precipitation forecasting accuracy. Additionally, the satellite data resolution used in prior studies fails to precisely capture nuanced details of abrupt changes in extreme precipitation. To address these shortcomings, this study introduces an innovative approach for accurately predicting extreme precipitation: the multimodal attention ConvLSTM-GAN for extreme rainfall nowcasting (ER-MACG). This model employs high-resolution Fengyun-4A(FY4A) satellite precipitation products, as well as terrain and atmospheric datasets as inputs. The ER-MACG model enhances the ConvLSTM-GAN framework by optimizing the generator structure with an attention module to improve the focus on critical areas and time steps. This model can alleviate the problem of information loss in the spatial–temporal convolutional long short-term memory network (ConvLSTM) and, compared with the standard ConvLSTM-GAN model, can better handle the detailed changes in time and space in extreme precipitation events to achieve more refined predictions. The main findings include the following: (a) The ER-MACG model demonstrated significantly greater predictive accuracy and overall performance than other existing approaches. (b) The exclusive consideration of DEM and LPW data did not significantly enhance the ability to predict extreme precipitation events in Zhejiang Province. (c) The ER-MACG model significantly improved in identifying and predicting extreme precipitation events of different intensity levels.

Surface water dynamics of Africa: Analysing continental trends and identifying drivers for major lakes and reservoirs

ABSTRACT Africa has the most dynamic demographic development worldwide. Current projections predict a population of > 3 billion people by the end of the century. Sub-Saharan Africa alone will likely see a 40% increase in population between 2020 and 2050. Although it is well known that large parts of Africa are in a constant state of water stress, its surface water resources remain understudied. This study analyses long-term trends of surface water in Africa. It identifies causal impacts on major lakes and reservoirs for the timeframe 2003–2020, as well as dynamic and causal similarities between the various lakes. For this, a set of daily time series based on Earth observation is employed. Global WaterPack data is used for a daily uninterrupted time series of the continent’s surface water area. Additionally, an array of relevant independent variables, namely precipitation, total evapotranspiration, groundwater, soil moisture, and gross primary productivity (GPP) in different land use areas is analysed. For causal identification, the Peter and Clark Momentary Conditional Independence algorithm is used. Findings show that > 42% of African countries and > 34% of African ecoregions experience shrinking surface water area. Over 80% of investigated surface water bodies are driven by the surface water in their upstream subbasins and GPP in agriculturally used areas. About 85% of investigated lakes are significantly driven by agricultural usage, often in the form of water abstraction, as referenced regional studies confirm. Our analysis demonstrates the feasibility of conducting causal analyses of surface water dynamics using Earth observation data. Dynamically similar lakes are often impacted by the same drivers, forming regional lake clusters. Considering the causes identified may greatly help adapt strategies for sustainable development. A causality analysis to identify drivers of surface water dynamics has, to our knowledge, never been performed before on this scale and at such high temporal resolution.

Accuracy evaluation and comparison of GSMaP series for retrieving precipitation on the eastern edge of the Qinghai-Tibet Plateau

Study RegionMin River Basin on the eastern Qinghai-Tibet Plateau. Study FocusPrecipitation is critical for hydrological processes, making accurate data essential for water management and flood forecasting. Satellite precipitation products offer valuable high-resolution spatiotemporal information, with the GSMaP series being widely used. However, comprehensive evaluations of different versions are limited. This study assesses the accuracy of Gauge and MVK products (versions 06, 07, and 08) across spatial and temporal scales and evaluates their performance in detecting precipitation events of varying intensities. New Hydrological Insights for the Study Region(1) GSMaP versions 06 and 07 exhibit higher detection rates for precipitation events, with POD values exceeding 0.8, while version 08 has a lower false alarm rate, with FAR values below 0.15. (2) GSMaP products are more successful in capturing precipitation events during the rainy season than the dry season. (3) With increasing elevation, the Gauge product consistently maintains a high hit rate and reduced false alarm rate, whereas the MVK product's hit rates improve. (4) For different rainfall intensities, GSMaP products more accurately detect moderate and heavy rain events, with the Gauge product outperforming the MVK product in terms of accuracy. These insights enhance the understanding of GSMaP product performance on eastern edge of the Qinghai-Tibet Plateau, aiding in improved water management practices.

Intercomparison of reanalysis and satellite precipitation products in endorheic and exorheic basins on the Tibetan Plateau

Study Region: Endorheic and exorheic basins of the Tibetan Plateau (TP). Study Focus: Reanalysis and satellite precipitation products provide alternatives for regions of sparse ground precipitation observation, but pose a tough task to select a suitable one for the TP. This study conducts a multi-scale evaluation of six reanalysis and satellite precipitation products in endorheic and exorheic basins using water balance and extended triple collocation (ETC) methods. The reanalysis precipitation products include ECMWF Re-Analysis version 5 (ERA5-Land), China Meteorological Forcing Dataset (CMFD), Global Land Data Assimilation Systems (GLDAS), and High-resolution Precipitation dataset for the Third Pole region (TPHiPr). The satellite precipitation data include Global Precipitation Measurements (GPM) and Tropical Rainfall Measuring Mission (TRMM) products. New Hydrological Insights for the Region: The precipitation products vary in accuracy from basin to basin, with better performance in exorheic than endorheic basins. Reanalysis-based ERA5-land, TPHiPr, and CMFD perform well in most basins at annual scale, among which TPHiPr performs best at daily scale. At regional scale, GPM performs well in endorheic region, and ERA5-land in exorheic region. While all the products increase significantly in accuracy from basin to regional scale in endorheic region, ERA5-land shows best performance at annual and multi-year scales in the entire region. Our findings provide valuable supports for precipitation product selection in the Tibetan endorheic and exorheic basins.

The Non-Monotonic Response of Cumulus Congestus to the Concentration of Cloud Condensation Nuclei

This study uses idealized simulations to investigate the impact of cloud condensation nuclei (CCN) on a cumulus congestus. Thirteen cases with the initial CCN_C, which is the CCN concentration at 1% supersaturation with respect to water, from 10 to 10,000 cm−3 are simulated. The analysis focuses on the liquid phase due to the negligible ice phase in this study. A non-monotonic response of cloud properties and precipitation to CCN concentration is observed. When CCN_C is increased from 10 to 50 cm−3, the enhanced condensation due to the more numerous droplets invigorates the cumulus congestus. The delayed precipitation formation due to the smaller droplets also facilitates the cloud development. The two processes together lead to a higher liquid water path (LWP), higher cloud top, and heavier precipitation. The cumulus congestus has the highest cloud top, the strongest updraft, and the most accumulated precipitation and at CCN_C = 50 cm−3. When CCN_C is increased from 50 to 500 cm−3, the condensation near the cloud base is further enhanced and the precipitation is further delayed, both of which lead to more liquid water remaining in the cloud, and thus an even higher LWP and heavier precipitation rate in the later stage. However, the significantly enhanced evaporation near the cloud top limits the vertical development of the cumulus congestus, leading to a lower cloud top. When CCN_C is further increased to be higher than 1000 cm−3, the cumulus congestus is strongly suppressed, and no precipitation forms. The ratio of the precipitation production rate to vertical cloud water flux in the updraft is not a constant, as is generally assumed in cumulus parameterization schemes, but decreases significantly with increasing CCN concentration. It is also found that the CCN effect on the cumulus congestus relies on which parameters are used to describe the cloud strength. In this study, as CCN_C increases, the LWP and the maximum precipitation rate peak at CCN_C = 500 cm−3, while the cloud top height, maximum updraft, and accumulated precipitation amount peak at CCN_C = 50 cm−3.

Correction: Dynamical downscaling improves upon gridded precipitation products in the Sierra Nevada, California

Correction: Dynamical downscaling improves upon gridded precipitation products in the Sierra Nevada, California

Non-breeding conditions induce carry-over effects on survival of migratory birds

Identifying the processes that limit populations is a foundational objective of ecology and an urgent need forconservation. For migratory animals, researchers must study individuals throughout their annual cycles to determine how environmental conditions limit demographic rates within each period of the annual cycleand also between periods through carry-over effects and seasonal interactions.1,2,3,4,5,6 Our poor understanding of the rates and causes of avian migration mortality7 hinders the identification of limiting factors and the reversal of widespread avian population declines.8,9 Here, we implement new methods to estimate apparent survival (hereafter survival) during migration directly from automated telemetry data10 in Kirtland's Warblers (Setophaga kirtlandii) and indirectly from mark-recapture data in Black-throated Blue Warblers (S.caerulescens). Previous experimental and observational studies of our focal species and other migratory songbirds have shown strong effects of Caribbean precipitation and habitat quality on food availability,11,12,13,14 body condition,12,13,14,15,16,17,18,19 migration timing,11,12,15,16,20,21,22,23 natal dispersal,24,25 range dynamics,26 reproductive success,20,22,27 and annual survival.18,19,20,23,28,29,30,31 Building on this research, we test the hypotheses that environmental conditions during the non-breeding period affect subsequent survival during spring migration and breeding. We found that reduced precipitation and environmental productivity in the non-breeding period strongly influenced survival in both species, primarily by reducing survival during spring migration. Our results indicate that climate-driven environmental conditions can carry over to affect survival in subsequent periods and thus likely play an important role in year-round population dynamics. These lethal carry-over effects may be widespread and are likely magnified by intensifying climate change.