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The Colombian Andes (CA) are located over the northwestern corner of tropical South America (NW‐TropSA), where land and atmosphere interchange moisture and energy in a complex way owing to the orographic influence on the recycling of moisture over land. We aim to understand where and when water vapor evaporated from land turns into rainfall through moisture recycling, using the Water Accounting Model‐2layers (WAM‐2), an offline model to track atmospheric moisture forced with data from ERA‐5 at its native 0.25° resolution during 1980–2020. We define the spatiotemporal distribution of sources (high evaporation recycle ratios, ϵC) and receptors (high precipitation recycle ratios, ρC) of continental moisture at diverse timescales, including monthly, seasonal, annual and interannual (ENSO). Referring to the regional runs of WAM‐2 over NW‐TropSA (4°S–12°N/80°W–66°W), at elevations above 1,000 m.a.s.l., the CA has a mean annual ρC of 11% (ranging 6%–16%) and a mean annual ϵC of 35% (ranging 27%–40%). Moisture recycling in the CA exhibits a strong annual cycle over the region. The seasonal dynamic of moisture recycling shows two clear‐cut sources of moisture: the eastern foothills of the Eastern and Central ranges of the CA. Both foothills are also regions of high rainfall, although moisture recycling mechanisms differ. Sources of continental moisture grow spatially during September‐October‐November and March‐April‐May. The seasonal availability of moisture recycled coincides with regions where orography interacts with low level jets sourcing humidity. At interannual timescales, sources and receptors of continental moisture in the CA are modulated by the extreme phases of ENSO. Key Points: The orography of the Colombian Andes emerged as a critical factor shaping the interaction between land and atmospheric moisture fluxesFoothills and valleys of the Colombian Andes play a pivotal role in continental moisture recycling, in terms of convergence and divergenceENSO modulates both sources and receptors of continental moisture at interannual timescales [ABSTRACT FROM AUTHOR]

The extratropical west coast of South America has one of the largest frequencies of landfalling atmospheric rivers (ARs), with dozens of events per season that account for ~50% of the annual precipitation and can produce extreme rainfall events in south-central Chile. Most ARs form an acute angle with the Andes, but, in some cases, the moist stream impinges nearly perpendicular to the mountains, referred to as zonal atmospheric rivers (ZARs). Enhanced surface-based and upper-air measurements in Concepcion (36.8° S), as well as numerical simulations, were used to characterize a ZAR and a meridionally oriented AR in July 2022. They represent extremes of the broad distribution of winter storms in this region and exhibit key features that were found in a composite analysis based on larger samples of ZARs and tilted ARs. The latter is associated with an upper-level trough, broad-scale ascent, extratropical cyclone, and cold front reaching southern Chile. Instead, ZARs are associated with tropospheric-deep, strong zonal flow and a stationary front across the South Pacific, with ascent restricted upstream of the Andes. Consequently, ZARs have minimum precipitation offshore but a marked orographic precipitation enhancement and exhibit relatively warm temperatures, thus resulting in an augmented risk of hydrometeorological extreme events. [ABSTRACT FROM AUTHOR]

Extreme rainfall events frequently cause hazardous floods in many parts of the world. With growing human exposure to floods, studying conditions that trigger floods is imperative. Flash floods, in particular, require well-defined models for the timely warning of the population at risk. Intensity–duration–frequency (IDF) curves are a common way to characterize rainfall and flood events. Here, the copula method is employed to model the dependence between the intensity and duration of rainfall events flexibly and separately from their respective marginal distribution. Information about the localization of 93 flash floods in Jamaica was gathered and linked to remote-sensing rainfall data, and additional data on location-specific yearly maximum rainfall events were constructed. The estimated normal copula has Weibull and generalized extreme value (GEV) marginals for duration and intensity, respectively. Due to the two samples, it is possible to pin down above which line in the intensity duration space a rainfall event likely triggers a flash flood. The parametric IDF curve with an associated return period of 216 years is determined as the optimal threshold for flash flood event classification. This methodology delivers a flexible approach to generating rainfall IDF curves that can directly be used to assess flash flood risk. [ABSTRACT FROM AUTHOR]

El Niño–Southern Oscillation dynamics affect global weather patterns, with regionally diverse hydrological responses posing critical societal challenges. The lack of seasonally resolved hydrological proxy reconstructions beyond the observational era limits our understanding of boundary conditions that drive and/or adjust El Niño–Southern Oscillation variability. Detailed reconstructions of past El Niño–Southern Oscillation dynamics can help modelling efforts, highlight impacts on disparate ecosystems and link to extreme events that affect populations from the tropics to high latitudes. Here, mid‐Holocene El Niño–Southern Oscillation and hydrological changes are reconstructed in the south‐west Pacific using a stalagmite from Niue Island, which represents the period 6.4–5.4 ka BP. Stable oxygen and carbon isotope ratios, trace elements and greyscale data from a U/Th‐dated and layer counted stalagmite profile are combined to infer changes in local hydrology at sub‐annual to multi‐decadal timescales. Principal component analysis reveals seasonal‐scale hydrological changes expressed as variations in stalagmite growth patterns and geochemical characteristics. Higher levels of host rock‐derived elements (Sr/Ca and U/Ca) and higher δ18O and δ13C values are observed in dark, dense calcite laminae deposited during the dry season, whereas during the wet season, higher concentrations of soil‐derived elements (Zn/Ca and Mn/Ca) and lower δ18O and δ13C values are recorded in pale, porous calcite laminae. The multi‐proxy record from Niue shows seasonal cycles associated with hydrological changes controlled by the positioning and strength of the South Pacific Convergence Zone. Wavelet analysis of the greyscale record reveals that El Niño–Southern Oscillation was continuously active during the mid‐Holocene, with two weaker intervals at 6–5.9 and 5.6–5.5 ka BP. El Niño–Southern Oscillation especially affects dry season rainfall dynamics, with increased cyclone activity that reduces hydrological seasonality during El Niño years. [ABSTRACT FROM AUTHOR]

This study examines the changing rainfall patterns in the Vietnamese Mekong Delta (VMD) utilizing observational data spanning from 1978 to 2022. We employ the Mann–Kendall test, the sequential Mann–Kendall test, and innovative trend analysis to investigate trends in annual, wet, and dry season rainfall, as well as daily rainfall events. Our results show significant spatial variations. Ca Mau, a coastal province, consistently showed higher mean annual and seasonal rainfall compared to the further inland stations of Can Tho and Moc Hoa. Interestingly, Ca Mau experienced a notable decrease in annual rainfall. Conversely, Can Tho, showed an overall decrease in some months of the wet season and an increase in dry season rainfall. Furthermore, Moc Hoa showed an increase in the number of rainy days, especially during the dry season. Principal component analysis (PCA) further revealed strong correlations between annual rainfall and extreme weather events, particularly for Ca Mau, emphasizing the complex interplay of geographic and climatic factors within the region. Our findings offer insights for policymakers and planners, thus aiding the development of targeted interventions to manage water resources and prepare for changing climate conditions. [ABSTRACT FROM AUTHOR]

Subseasonal rainfall forecast skill is critical to support preparedness for hydrometeorological extremes. We assess how a process‐informed evaluation, which subsamples forecasting model members based on their ability to represent potential predictors of rainfall, can improve monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as test cases. We generate a constrained ensemble mean by subsampling 130 members from five dynamic forecasting models in the C3S multimodel ensemble based on their representation of both (a) zonal wind direction and (b) Pacific and Atlantic sea surface temperatures (SSTs), at the time of initialization. Our results show in multiple months and locations increased mean squared error skill by 0.4 and improved detection rates of rainfall extremes. This method is transferrable to other regions driven by slowly‐changing processes. Process‐informed subsampling is successful because it identifies members that fail to represent the entire rainfall distribution when wind/SST error increases. Plain Language Summary: Subseasonal rainfall forecasts provide alerts multiple weeks ahead. These forecasts present an opportunity to facilitate anticipatory actions yet are often unreliable to use when preparing for extreme weather. We develop a method to optimize rainfall forecasts by selecting individual members from a large ensemble of dynamic forecasting model outputs based on their ability to represent potential predictors of rainfall. We test our method on monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as key test cases. We select members from five contributing models of the C3S multimodel ensemble using regional predictors, including wind direction and sea surface temperatures (SSTs). Our results show improvements in the detection of low and high rainfall extremes. This method is transferrable to other regions driven by slowly‐changing processes like SSTs and is beneficial for operational forecasters who can leverage regional expertise of relevant rainfall‐generating processes to subsample better performing ensemble members for their regions. Key Points: Subsampling members using sea surface temperatures and zonal wind improves subseasonal ensemble rainfall forecasts in Central AmericaIn multiple months and locations mean squared error skill increases by 0.4 and extreme rainfall skill improves by 0.5 (Heidke skill)Process‐informed subsampling is useful because the models' representation of rainfall degrades as process error increases [ABSTRACT FROM AUTHOR]

The El Niño episode between September 2015 and February 2016 caused abundant rainfall in the state of Paraná in southern Brazil. In this study, we map the greatest magnitude of this climatic event and the respective geosystem responses in the landscape. For this purpose, 32 precipitation series were analyzed (1981–2016). The parameter most indicative of extreme rainfall events during the El Niño period was the six-month accumulated precipitation. The return period (RP) of this parameter was calculated and spatialized using kriging. The results show that a longitudinal strip of 450 km by 140 km had rainfall with RP > 30 years, reaching maximums greater than 150 years. Surveys performed in the field, with aerial images, official sources and media news show a great diversity of effects caused by rainfall, such as road blockages, interruption of water and power supply, and erosion in urban and rural areas. It is concluded that the stability of regional geosystems was disrupted because of extreme rainfall values, so that the northwest region of Paraná had less resilience compared to the north-central and Pioneer North regions, due to the differences between the lithologies and soils of these regions. [ABSTRACT FROM AUTHOR]

El Niño is generally associated with negative rainfall anomalies (below-average rainfall) in northern Northeast Brazil (NNEB). In 2019, however, the opposite rainfall pattern was observed during an El Niño episode. Here, we explore the mechanisms that overwhelmed typical El Niño-related conditions and resulted in positive rainfall anomalies (above-average rainfall) in NNEB. We focus on the austral autumn when El Niño is most prone to rainfall anomalies in the region. The analysis of several datasets, including weather station data, satellite data, reanalysis data, and modelled data derived from a dry linear baroclinic model, allowed us to identify that the austral autumn 2019 above-average rainfall in NNEB was likely associated with four combined factors; these are (1) the weak intensity of the 2019 El Niño; (2) the negative phase of the Atlantic Meridional Mode; (3) local and remote diabatic heating anomalies, especially over the western South Pacific and tropical South Atlantic, which resulted in anticyclonic and cyclonic circulations in the upper and lower troposphere, respectively, over the tropical South Atlantic; and (4) sub-seasonal atmospheric convection anomalies over the western South Pacific, which reinforced the low-frequency convection signal over that region. This latter factor suggests the influence of the Madden–Julian Oscillation on rainfall in NNEB during the first ten days of March 2019. We discuss these mechanisms in detail and provide evidence that, even during an El Niño event, above-average rainfall in NNEB in the austral autumn may occur, and its modulation is not limited to the influence of a single climate phenomenon. Our results may assist in the planning of several crucial activities, such as water resources management and agriculture. [ABSTRACT FROM AUTHOR]

The roles of rainfall with various intensities in hydraulic erosion processes are obviously different. In-depth knowledge about the spatiotemporal variation in extreme rainfall erosivity is critical for soil erosion risk assessment and formulation of response measures. In the period of 1961–2020, more than 390,000 erosive rainfall data from 212 meteorological stations were collected to explore the erosion characteristics of different intensities of rainfall erosivity across southern China. The asymmetric characteristic of extreme rainfall erosion was analyzed and expressed by an asymmetric change index (ACI) defined in this study. Correlation analysis was applied to study the impact of global extreme climate events on the ACI. The results showed that different patterns of precipitation had evident asymmetric characteristics in rainfall erosivity, and fewer high-intensity precipitation events contributed to much higher total rainfall erosivity, particularly for rainstorms (≥100 mm). The ACI of the rainfall erosivity exhibited evident spatial heterogeneity; insignificant increasing trends were found in the ACIs for heavy rainfall, torrential, and rainstorm erosivities over the past 60 years. The change rate of ACI varied greatly on both monthly and interdecadal scales, and the ACI of rainstorms showed the maximum linear increasing trend on the long-time scale. There were significantly high correlations between the ACI and erosive rainfall days and the ACI and erosive rainfall (p < 0.01), and the correlation coefficients were relatively higher from May to October, where the precipitation was concentrated. Additionally, the El Niño-Southern Oscillation and tropical cyclones clearly influenced the spatiotemporal distribution of the ACI and rainfall erosivity but were limited to specific periods and regions. These results could provide a reference for extreme soil erosion event monitoring and control in southern China. [ABSTRACT FROM AUTHOR]

Accurate rainfall fields are important for several hydrological and meteorological applications. In poorly instrumented basins, the lack of rain gauges heavily affects the spatial rainfall estimation, yet neither remote sensed nor climate model estimates are good enough for management applications. To tackle this problem, we investigate the impacts of combining both sources of information by varying the kernel bandwidth value of the double smoothing merging algorithm and analyzing the error of the rainfall fields. We explored the correlation between rain gauge density and bandwidth and compared the results against classical geostatistical interpolation methods based solely on in-situ measurements. Propagation of rainfall error into hydrological modelling is usual, and therefore we evaluated the influence of the bandwidth in streamflow simulations implementing two hydrological models. The hydrological evaluation considered the analysis of hydrological signatures rather than just performance metrics. We found that there is a clear correlation between kernel bandwidth and monitoring network density and that the bandwidth also affects hydrological performance. Simple bilinear downscaling did not produce a significant difference in meteorological or hydrological errors, and rain gauge network configuration also impacts the error of the field. We conclude that merging outperforms the results of classical interpolation methods, in some cases by 20% or 50%, suggesting the suitability of the method for being applied in data-scarce domains. Reliable rainfall fields are important for estimating water resource availability and other related applications. However, using just rain gauges can misrepresent the estimated spatial variability of the field. Around the world, the number of rainfall gauges has been decreasing, making this task more challenging. Satellite and reanalysis data can help to overcome these problems, but their direct use is also insufficient. Merging the two mentioned data sources (in-situ and reanalysis data) is the option the authors implemented in this work. The authors focused on evaluating the advantages and disadvantages of the merging by comparing both rainfall and streamflow observations with estimates simulated by a hydrological model. The authors chose the double smoothing algorithm because it is oriented to enhance rainfall estimates in areas with very sparse rain gauge data, yet we evaluate its behavior in two basins with different monitoring conditions. The authors conclude that the merging outperforms the results of classical interpolation methods, in some cases by 20% or 50%, suggesting the suitability of the method for being applied in data-scarce domains to improve hydrological modelling estimates and use their results in water resource management and planning, in climate classification, and in the study of droughts and floods, among others. [ABSTRACT FROM AUTHOR]

The occurrence of annual growth rings in tropical trees—the result of the seasonal activity of vascular cambium—has been explained by seasonal water deficit or flooding periods. However, little is known about the drivers of annual tree‐ring formation under tropical hyper‐humid conditions without clear seasonal dry periods or flooding (ever‐wet conditions). Shelford's law states that the deficit or the excess of environmental resources limits plant growth. Accordingly, we hypothesize that excess soil moisture, a slight seasonal reduction of precipitation and a reduction in light availability determine rhythmic growth in ever‐wet tropical forests.We first assessed the occurrence of rhythmic growth in 14 tree species from the Biogeographic Chocó Region (annual rainfall 7200 mm) using three methods: Radiocarbon (14C) dating (all studied species), tree‐ring synchronization (4 species that have replicates) and automatic dendrometers (two species). Then, we assessed the effect of environmental drivers (rainfall, short‐wave radiation, temperature and soil moisture) on tree growth based on tree ring and dendrometer observations.We present evidence of annual tree‐ring formation in all 14 studied tree species. Depending on the tree species, we observed positive and negative correlations between growth, water availability and light availability. These relationships suggest that both excess or deficit of environmental resources may explain the seasonal pattern of tree growth. Although we cannot differentiate between excess soil water and low light availability by high cloudiness, we suggest that cloudiness frequency could affect tree growth in these forests.Synthesis. We reveal the annual formation of growth rings in the unexplored wetter‐end tropical forests, where seasonal growth depends on either high soil moisture and hypoxia or light limitations by cloudiness and photosynthesis constraints. [ABSTRACT FROM AUTHOR]

Climatic drying is predicted for many tropical forests yet models remain poorly parameterized for these ecosystems, hampering predictions of forest‐climate interactions. We applied an integrated model–experiment approach, parameterizing an ecosystem model with tropical forest observational data and comparing model predictions to a field drying manipulation. We hypothesized that drying suppresses soil CO2 fluxes (i.e., respiration) in relatively dry tropical forests but increases CO2 fluxes in wetter tropical forests by alleviating anaerobiosis. We measured soil CO2 fluxes during wet‐dry cycles from 2015 to 2022 in four Panamanian forests that vary in rainfall and soil fertility. Measured soil CO2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in lower soil moisture optima for respiration than previously modeled. We then parameterized the model using field data and the new moisture‐respiration response functions. The updated model predicted increased soil CO2 fluxes with drying in wetter and fertile forests and suppressed fluxes in drier, infertile forests. In contrast to model predictions, a chronic throughfall exclusion experiment initially suppressed soil respiration across forests, with sustained suppression for four years in the wettest forest only (−28% ± 4% during the dry season). In the fertile forest, drying eventually elevated CO2 fluxes over this period (+75% ± 28% during the late wet season). The unexpected negative drying effect in the wettest, infertile forest could have resulted from reduced vertical flushing of nutrients into soils. Including hydro‐nutrient interactions in ecosystem models could improve predictions of tropical forest‐climate feedbacks. Plain Language Summary: Tropical forests are predicted to get drier with climate change over much of the globe. These forests hold some of the largest carbon stocks on Earth, so the effects of drying are significant to global climate change. We assessed effects of natural seasonal drying, and a rainfall reduction experiment, on soil CO2 fluxes (i.e., carbon losses via respiration) from four distinct tropical forests in Panama. We found that natural seasonal drying reduced soil CO2 fluxes. Using this data in an ecosystem model, we predicted that future drying will increase soil CO2 fluxes from more fertile and wetter tropical forests, where soils are often water logged, but will decrease soil CO2 fluxes from drier forests, where dry soils already suppress microbial and root activity. In contrast to these predictions, our rainfall reduction experiment showed that drying suppressed soil CO2 fluxes from the wettest forest, suggesting that not all of the important processes were represented in the model. We suggest that ecosystem models could be improved by including rainfall flushing of carbon and nutrients into soils from above, in addition to direct effects of soil moisture on respiration. Representing these processes could alter predictions of how tropical forests will respond to climatic drying. Key Points: Throughfall reduction initially suppressed soil CO2 fluxes across four tropical forests, but the effects diverged after 4 yearsOptimal soil moisture for soil respiration in these tropical forests was lower than previously parameterized in an ecosystem modelThe model mistakenly projected the highest CO2 fluxes with drying in the wettest forest, possibly missing soil hydro‐nutrient processes [ABSTRACT FROM AUTHOR]

Seasonal rainfall forecasts provide information several months ahead to support decision making. These forecasts may use dynamic, statistical, or hybrid approaches, but their comparative value is not well understood over Central America. This study conducts a regional evaluation of seasonal rainfall forecasts focusing on two of the leading dynamic climate ensembles: the Copernicus Climate Change Service seasonal forecasting system (C3S) and the North American Multimodel Ensemble (NMME). We compare the multimodel ensemble mean and individual model predictions of seasonal rainfall over key wet season periods in Central America to better understand their relative forecast skill at the seasonal scale. Three types of rainfall forecasts are compared: direct dynamic rainfall predictions from the C3S and NMME ensembles, a statistical approach using the lagged observed sea surface temperature (SST), and an indirect hybrid approach, driving a statistical model with dynamic ensemble SST predictions. Results show that C3S and NMME exhibit similar regional variability with strong performance in the northern Pacific part of Central America and weaker skill primarily in eastern Nicaragua. In the northern Pacific part of the region, the models have high skill across the wet season. Indirect forecasts can outperform the direct rainfall forecasts in specific cases where the direct forecasts have lower predictive power (e.g., eastern Nicaragua during the early wet season). The indirect skill generally reflects the strength of SST associations with rainfall. The indirect forecasts based on Tropical North Atlantic SSTs are best in the early wet season and the indirect forecasts based on Niño3.4 SSTs are best in the late wet season when each SST zone has a stronger association with rainfall. Statistical predictions are competitive with the indirect and direct forecasts in multiple cases, especially in the late wet season, demonstrating how a variety of forecasting approaches can enhance seasonal forecasting. [ABSTRACT FROM AUTHOR]

This work aims to generate and evaluate regional rainfall thresholds obtained from a combination of high-resolution gridded rainfall data, developed by the National Service of Meteorology and Hydrology of Peru, and information from observed shallow landslide events. The landslide data were associated with rainfall data, determining triggering and non-triggering rainfall events with rainfall properties from which rainfall thresholds are determined. The validation of the performance of the thresholds is carried out with events that occurred during 2020 and focuses on evaluating the operability of these thresholds in landslide warning systems in Peru. The thresholds are determined for 11 rainfall regions. The method of determining the thresholds is based on an empirical–statistical approach, and the predictive performance of the thresholds is evaluated with true skill statistics. The best predictive performance is the mean daily intensity–duration (Imean-D) threshold curve, followed by accumulated rainfall E. This work is the first estimation of regional thresholds on a country scale to better understand landslides in Peru, and the results obtained reveal the potential of using thresholds in the monitoring and forecasting of shallow landslides caused by intense rainfall and in supporting the actions of disaster risk management. [ABSTRACT FROM AUTHOR]

Observations of Argo profiles and TAO/TRITON array confirm the significant seasonality of the barrier layer (BL) and temperature inversion (TI) in the northeastern tropical Pacific (NETP). Statistical result of the occurrence based on the Argo profiles reveals a bimodal variability of the BL, with two peaks in July and October. This bimodal seasonality of BL is attributed to the out‐of‐phase variations of the eastern Pacific fresh and warm pools. The fresh and warm pools both expand westward from May to July, when the Inter‐Tropical Convergence Zone (ITCZ) becomes intense and broad. Heavy rainfall is the dominant contributor to the extension of the fresh and warm pools, leading to a high frequency of thick BL (40%). This frequent thick BL provides a precondition for its another development after August. The fresh pool is stable from August to November, while the warm pool contracts sharply. The cold tongue becomes active due to a prevailing trade wind and horizontal advection transports surface cold water to the northeastern warm pool. This cold advection deepens the isothermal layer and contributes to a frequent TI (30%) and thick BL (46%). The results suggest that the ITCZ rainfall and northward cold advection from equator dominate the upper layer stratification of NETP in summer and autumn, respectively. Plain Language Summary: The marine barrier layer (BL) features strong salinity stratification at the base of a surface mixed layer insulating water exchange between the surface and subsurface. The formation mechanisms of the BL in the northeastern tropical Pacific are analyzed based on the observations of Argo floats and the TAO/TRITON array. The BLs show a bimodal seasonality with two peaks in July and October. The atmospheric forcing leads to extensions of the warm and fresh pools in the summer and causes the BL formation. Temperature inversions (TIs) occur frequently during the boreal autumn, with the surface temperature lower than the subsurface 1.82°C in October. The warm pool contracts sharply during autumn due to the development of the cold tongue, which causes frequent TIs and thickens the BLs. The out‐of‐phase changes in the warm and fresh pools result in bimodal seasonality and different formation mechanisms. Key Points: The bimodal barrier layer seasonality is due to the out‐of‐phase variabilities of the fresh and the warm poolsThe heavy rainfall and equatorial ocean dynamics dominate the upper layer stratification in summer and autumn, respectivelyThe cold transport by the cold tongue deepens the isothermal layer, contributing to the temperature inversion and thick barrier layer [ABSTRACT FROM AUTHOR]

Rainfall in the Moist Tropical Caribbean Region (MTCR) in Costa Rica occurs practically throughout the year, with the quarters June–August (JJA) and December–February (DJF) concentrating over 70% of annual rainfall. Contrarily, in March–April and September–October, it rains below 100 mm per month. This seasonal rainfall behavior makes the region ideal for producing bananas (Musa spp.) and pineapple (Ananas comosus) for export (10% and 8% of total exports in 2021, respectively). A national-scale study determined that agriculture in the MTCR is one of the most vulnerable sectors to climate changes. However, the climate in this region has been poorly studied so far. This research analyzed the spatial and temporal variability of annual, monthly, and seasonal (DJF, JJA, SO) rainfall in the MTCR and how they change in the study period based on quality-checked series of daily rainfall from 28 weather stations in two periods: 1985–2009 and 1997–2019. The results show that rainfall regimes in the region are variable in space and throughout the year, with peaks occurring close to the mountain range and minimum values close to the coast. Trends were statistically significant in the period 1985–2009 with a predominance of significant positive trends in DJF and significant negative trends in SO. No significant trends (positive or negative) were observed in the period 1997–2019. JJA rainfall has uneven regional distribution and presents a positive and significant trend in the mountain region. This paper contributes to filling the knowledge gap in rainfall seasonality, variability, and trends in a region where banana and pineapple commercial plantations are fundamental to the country's economy thus providing information to decision-making in the agri-food sector to reduce the negative impacts of changing rainfall regimes. [ABSTRACT FROM AUTHOR]

Extreme rainfall events (EREs) are very localized and intense events witnessing heavy rainfall resulting in flash floods and landslides in the Himalayan region. The trend of EREs in the state of Himachal Pradesh shows an increasing trend, so in this study, an attempt is being made for quantifying the climatological feature of these EREs in the state of Himachal Pradesh in the western Himalayan mountain region. Multi-source observed datasets for the long-term period, i.e., 1901 to 2020, are considered for the analysis. The climatology analysis indicates that northern and southern parts of Himachal Pradesh receive comparatively more rainfall, and the state witnessed a decreasing trend of the rainfall in the current decade. The classifications of these EREs in terms of monsoon and non-monsoon with a different threshold of daily rainfall are being analyzed, and it is found that almost 90% of extreme events are observed in the monsoon season. The zone centered around 32.5° N and 76.25° E seems to be the hotspot for the frequent EREs in the mountainous state. The spatial analysis of the ERE trend also indicated that almost 40% of the state witnesses the heavy rainfall throughout the years. This observational study will surely help in understanding the dynamics of these EREs in the higher altitude regions and can be used for hydro-meteorological disaster management and mitigation. [ABSTRACT FROM AUTHOR]

Intensity, duration and frequency (IDF) curves are necessary tools for the design and construction of hydraulic projects. However, the pluviographic records needed to determine the IDF curves do not exist or are scarce. This research presents the regionalization of the IDF curves for the department of Boyacá, Colombia, which is made up of 16 municipalities including the provincial capital, Tunja. For the regionalization, the adjustment parameters (u and α) of the IDF curve stations in the study area were used. In the case of regionalization by the parameters found for the construction of the IDF curves, estimation methods with ordinary moments means and maximum likelihood were used. The regionalization and interpolation of the data were performed with Arcgis software. The resulting isoline maps were made in the case of regionalization intensities, and each map is associated with a different return period and duration to construct the IDF curves in the studied area. In the case of the regionalization maps, the parameters associated with each individual parameter were performed last. The results show that the use of IDF curve data is more accurate and reduces errors in the design. With the methods proposed in this study, IDF curves can be constructed for any site of interest that does not have rainfall stations. [ABSTRACT FROM AUTHOR]

Accurate flood risk assessment requires a comprehensive understanding of flood sensitivity to regional drivers and climate factors. This paper presents the scaling of floods (duration, peak, volume) with geomorphologic characteristics of the basin (i.e., drainage area, slope, elevation) and precipitation patterns (rainfall accumulation, variability). Long‐term daily streamflow observations over the 20th and early 21st centuries from Hydro‐Climatic Data Network streamgages across the conterminous United States are used to create a flood event database based on their flood stage information. Antecedent daily rainfall accumulation and variability corresponding to these floods are computed using Global Historical Climatology Network daily data set. Two Bayesian scaling models are developed, and the spatial organization of scaling exponents is investigated. The baseline model quantifies the scaling of floods to geomorphologic characteristics. The dynamic model quantifies the scaling of floods to antecedent precipitation distribution which is further conditioned on geomorphologic characteristics. Results show that small and low‐elevation basins have a stronger response to antecedent rainfall distribution in amplifying flood peaks, while high‐elevation steeper basins have a lower response for flood duration and volume. The dynamic models demonstrate that there are significant variations in the flood scaling rates, with the largest rates up to 40% and 4.5% for flood duration, 64% and 44% for peak, and 98% and 40% for volume found across the Northeast, Coastal Southeast, and Northwest with intensifying rainfall accumulation and variability, respectively. This study advances flood predictions by better informing the flood attributes in the context of dynamical land‐atmosphere perturbations. Plain Language Summary: The relationships between floods, physical properties of river basins, and the spatio‐temporal distribution of precipitation define the regional flood scaling laws. Improved regional flood scaling models are required for updating flood risk scoring tools to help insurers and decision‐makers in the light of anthropogenic climate changes and natural variability of Earth systems. Two scaling models are developed here to examine how the physical properties of basins and precipitation patterns can inform flood attributes across the conterminous United States (US). Using long records of streamflow and precipitation observations, we identified different scaling relationships between the catchment factors, preceding precipitation, and the duration, peaks, and volume of floods. Larger basins located at higher elevations are found to have lower scaling rates with flood peaks when interacting with the preceding precipitation patterns. Overall, low‐elevation flat basins across the Eastern, Southern, and Northwestern US have a larger positive response to precipitation in amplifying flood attributes. These new scaling laws can help advance our understanding of controlling mechanisms for flash floods and mega‐floods, which is required for a dynamic flood risk assessment in a changing environment. Key Points: Two Bayesian scaling models are developed to understand the sensitivity of floods to geomorphologic characteristics and catchment rainfallLow‐elevation and flat basins with different sizes have greater scaling to rainfall in amplifying flood duration and volumesLow‐elevation and small basins with different steepness show larger sensitivity to rainfall distribution that enhances flood peaks [ABSTRACT FROM AUTHOR]

The present work analyses a time series of maximum intensities for sub-daily durations of 10 min, 20 min, up to 100 min, and their relationship with the maximum rainfall observations in twenty-four hours (P24), the total annual rainfall (PT), and the maximum, average, and minimum temperatures, using the records of the Rafael Núñez Airport station in the city of Cartagena de Indias, recorded from 1970 to 2015. The series of maximum intensities were obtained from the pluviographic records existing in the station. The analysis seeks to find evidence of climate change and climate variability. The series were tested for homogeneity, stationarity, trend, and periodicity. The degree of cross-correlation and temporal correlation between the different series were determined. Temperature series show homogeneity problems, while no correlation was found between the temperatures and the maximum sub-daily intensities, with the maximum rainfall observations in twenty-four hours and the total annual rainfall. The presence of marked periodicities was found in all the series, with a greater signal in the maximum. No significant trends were found in any of the series. Intensities and maximum rainfall observations in 24 h were found. In general, the series are stationary and do not show trends. Non-homogeneities in the series and the presence of periodicities can lead to an interpretation of non-stationarity and trend. [ABSTRACT FROM AUTHOR]

Convective processes are critical in the northernmost section of the Andes, where highly complex orography combines with tropical dynamics to produce intense precipitating systems. Data from satellite and ground radar have shown that Colombia's central Magdalena valley has a marked precipitation diurnal cycle, with high rainfall rates produced by deep convective events that intensify on the foothills of the surrounding valley. This study aims to contribute to the understanding of the orographic controls imposed on the convective activity observed in this important tropical valley. The atmospheric environments associated with convective events are assessed using WRF high-resolution simulations for a two-week period that includes intense rainfall events and marked dry spells. In addition, a simulation with modified orography is evaluated to analyze further the effect of topography in controlling the observed convective systems. The results suggest that northward and southward wind regimes channeled by the valley's topography and cross-valley flow result in a robust nocturnal convergence zone, which is associated with the strong diurnal cycle and the valley configuration. Furthermore, northerly low-level flow is an important source of moisture for convection in the valley. Additionally, results show midlevel tropospheric heating enhancement during days with more rainfall produced by convective systems in the western slope of the valley. The modified topography simulation suggests that circulation and precipitation in the valley are strongly dependent on the actual terrain features. [ABSTRACT FROM AUTHOR]

Rainfall mechanisms in the Central American Isthmus are controlled by complex physical interactions across spatial and temporal scales, which are reflected on the dynamics of atmospheric circulation patterns affecting the region. However, physical mechanisms and their relationships with thermodynamic distributions connected to overturning circulations remain elusive. Here, a set of six recurrent daily atmospheric patterns, or weather types (WT), is defined using a k‐means++ clustering algorithm on standardized fields of Convective Available Potential Energy (CAPE) and winds at 925, 850, and 200 hPa. The relationships between these weather types, their temporal characteristics, and anomalous distributions of moisture flux divergence, equivalent potential temperature (saturated and unsaturated), and observed rainfall are used to describe physical processes controlling the latter, for all seasons. Regional observed rainfall is analysed from a set of 174 automatic stations from all countries from Mexico to Panama. By modulating vertically integrated moisture fluxes, these weather types, and the different climate drivers linked to them, control the temporal and spatial rainfall characteristics in the region, especially over the Pacific side of the isthmus. During some stages of the regional rainy season, described by two weather types, thermal anomalies in convective quasi‐equilibrium characteristic of the upward branch of the Hadley cell force westerly flow over Central America, enhancing rainfall. While during other stages, the enhancement of the trades and the displacement of convection to the ITCZ area over the eastern tropical Pacific, characteristic of the midsummer drought, diminishes rainfall. This study sets the stage for a better understanding of the mechanistic relationship between these weather types and rainfall characteristics in general, like onset, demise, and duration of rainy seasons. Hence, these results can inform process‐based model diagnostics aiming at bias‐correcting climate predictions at multiple timescales. [ABSTRACT FROM AUTHOR]

Long‐term instrumental climate records are a major source of data for historical trends and extreme event analysis. Time series analysis of several rain gauges shows that the mid‐latitudes of Brazil experienced an increase in both annual and extreme rainfall over the last 60 years (1961–2020). The annual time series shows a significant upward trend, while changes in the frequency and intensity of heavy rainfall indicate a shortening of the recurrence interval for extreme precipitation events. In support of these results, the standardized precipitation index (SPI) also shows a positive trend for rainfall anomalies. Thus, the flood risk is higher at present than a few decades ago, while drought intensity is lower for both short and long monthly intervals used for the SPI calculation. The 60‐year instrumental rainfall data for southern Brazil shows that the amount of rainfall and changes in intensity could be higher than climate projections from models based on a broader spatial scale. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) and three-dimensional (3D) cloud-resolving model (CRM) results from the Tropical Rainfall Measuring Mission Kwajalein Experiment (KWAJEX) were applied to analyze the diurnal cycle of cloud development in the tropics. Cloud development is intimately associated with the growth of secondary circulation, which can be analyzed in the budget of perturbation kinetic energy (PKE). The ice and liquid water path (IWP+LWP) is a fundamental parameter for estimating clouds, with the analyzed results suggesting that (1) the ice and liquid water path (IWP+LWP) and PKE values attained in convective regions were higher during the nighttime than during the daytime and that the maxima of IWP+LWP and PKE occurred at midnight in the lower troposphere in the 3D model run, and that (2) the IWP+LWP and PKE values in stratiform regions were much higher in the afternoon than in the morning, while the maxima of IWP+LWP and PKE occurred in the afternoon in the middle troposphere in the 2D model run. Further analysis demonstrated that both the high IWP+LWP and PKE values in the lower troposphere at midnight were mainly associated with the warm–humid lower troposphere in convective regions. However, those in the middle troposphere in the afternoon were primarily linked to the dry–cold upper troposphere and moist–warm lower troposphere in stratiform regions. The results further revealed that (1) both IWP+LWP and PKE exhibited shorter time scales in the 2D model runs than in the 3D model runs and that (2) the maximum IWP+LWP values occurred in the afternoon in the 2D model runs and at midnight in the 3D model runs. [ABSTRACT FROM AUTHOR]

Climate hazards associated with compound events (CEs) have lately received increasing attention over South America (SA) due to their potential risks and amplification of impacts. This work addressed the evaluation of different temperature‐ and precipitation‐based CE in SA considering the CORDEX‐CORE ensemble of regional climate models (RCMs) and their driving earth system models (ESMs) in the reference period 1981–2010 and the late 21st century (2070–2099), for the Representative Concentration Pathways (RCPs) 2.6 and 8.5 scenarios. The assessment focused on model performance for the individual events—heatwaves (HWs), Extreme rainfall (ER) days, and dry‐spells (DSs)—and their compound occurrence in terms of climatological frequency and duration. The spatial patterns of individual events were adequately reproduced by the RCMs, evidencing general overestimations in extreme precipitation intensities. In terms of CE, the frequencies of coincident HWs and DSs (sequential DSs and ER) were remarkable over central‐eastern Brazil and southern SA (southeastern SA). The main features of CE were generally well‐simulated by the RCMs, although they presented regional differences such as an underestimation of the maximum frequencies of these two CE in northeastern Brazil and southeastern SA, respectively. The high‐resolution information was generally in line with the larger‐scale driving ESMs. The climate change signal analysis generally showed robust future increases in CE frequency and duration in different areas of SA, as for coincident HWs and DSs (sequential DSs and ER) over northern SA and southern Brazil (southeastern SA). This was mostly consistent among the RCMs ensemble and notably strengthened in the worst‐case scenario (RCP 8.5). Key Points: Coincident heatwaves and dry‐spells (DSs) and sequential DSs and extreme rainfall are remarkable compound events (CEs) over South AmericaRegional climate models can reproduce the frequency and duration of CEs, but with some regional differencesCEs are generally expected to be more frequent in the late 21st century, particularly in the Representative Concentration Pathway 8.5 scenario [ABSTRACT FROM AUTHOR]

In this research, we explored rainfall variability in the Sogamoso River Basin (SRB), its relationship with multiple scales of variability associated with El Niño–Southern Oscillation (ENSO), and the implications for rainfall prolongation during multiyear La Niña events. First, we examined time-frequency rainfall variations in the SRB based on the standardized precipitation index (SPI) from 1982 to 2019, using wavelet transform and principal component analysis (PCA). In addition, we applied wavelet analysis to investigate the links at different time scales between ENSO and the main mode of rainfall variability in the SRB. Finally, we explored the role that each scale of variability played in the prolongation and intensity of rainfall in the SRB during the 1998–2000 and 2010–2012 multiyear La Niña events. The results of the wavelet analyses revealed significant ENSO relationships affecting SRB rainfall at three different scales: quasi-biennial (2–3-years) between 1994 and 2002, as well as from 2008 to 2015; interannual (5–7 years) from 1995 to 2011; and quasi-decadal (9–12 years) from 1994 to 2012. This indicates that multiyear events are a consequence of the interaction of several scales of variability rather than a unique scale. During the 1998–2000 event, El Niño conditions were observed during the first half of 1998; subsequently, a cooling of the central and eastern tropical Pacific (western tropical Pacific) on the quasi-biennial (interannual) scale was observed during 1999; in 2000, only La Niña conditions were observed on the interannual scale. Therefore, during this event, the quasi-biennial (interannual) scale promoted wet conditions in the Caribbean, the Andes, and the Colombian Pacific from June–August (JJA) 1998 to JJA 1999 (during 1999–2000). During the 2010–2012 La Niña event, the interbasin sea surface temperature gradient between the tropical Pacific and tropical North Atlantic contributed to strengthening (weakening) of the Choco jet (Caribbean low-level jet) on the quasi-biennial scale during 2010, and the interannual scale prolonged its intensification (weakening) during 2011–2012, acting to extend the rainy periods over most of the Colombian territory. Variations on quasi-decadal scales were modulated by the Pacific decadal oscillation (PDO), resulting in a further intensification of the 2010–2012 La Niña event, which developed under conditions of the cold PDO (CPDO) phase, whereas the 1998–2000 La Niña occurred during the transition from warm (WPDO, 1977–1998) to cold (CPDO, 2001–2015) conditions. These results indicate that the interaction of quasi-biennial to quasi-decadal scales of variability could play a differential role in the configuration and prolongation of rainfall events in the SRB. [ABSTRACT FROM AUTHOR]

The Amazon region is of interest to the scientific community because it has the largest tropical rainforest in the world and has plentiful water and solar energy which are essential for climate regulation. In this context we analyzed the annual and seasonal rainfall of the Brazilian Legal Amazon Region over a period of 30 years (1986 to 2015) using data from 268 pluviometric stations from the ANA HIDROWEB database to provide information for the management and planning of the water resources. The spatial and temporal dynamics of the rainfall were analyzed using these data along with isohyet maps of the estimates of rainfall and rainfall volume; the trends and magnitudes of rainfall were analyzed by nonparametric Mann–Kendall and Sen's slope tests respectively. The results indicated a mean precipitation of 2070 mm/year. The Legal Amazon is a region with wide rainfall variability both spatially (the precipitation trend increases from the south to the north of the region) and temporally (both seasonally and interannual). The statistical tests indicated both positive and negative trends in and magnitudes of precipitation. In general an average decrease in precipitation of 5 mm per year was estimated in the Legal Amazon. In addition the rainfall behavior is not homogeneous and is influenced by atmospheric systems and phenomena especially the most intense ENSO events. Important changes are occurring in pluviometry and have the potential to cause hydrological and climatic risks. [ABSTRACT FROM AUTHOR]

During the last decades, the variability of the Caribbean precipitation has been characterized by dry trends over land. Aiming to gain insights into their drivers from an observational perspective, in this study such trends are diagnosed in relation to their concomitant low‐tropospheric circulation by applying linear regressions and Canonical Correlation Analysis to different datasets, but particularly using GPCP and ERA5 for the 1979–2019 period. During this period, two multidecadal modes of natural variability exhibited one‐signed trends: the Atlantic Multidecadal Oscillation (AMO) exhibited a positive trend, and the Pacific Decadal Oscillation (PDO) exhibited a negative trend. However, as shown by the results, the influence of these large‐scale trends cannot be directly associated with the dry precipitation trends: their influence would explain a pattern of anomalous low‐tropospheric westerlies that typically tends to cause episodes of above‐average rainfall. Also, the observed dry trends cannot be associated with two circulation features that cause dry episodes in the region: an intrusion of the North Atlantic subtropical high, and an intensified Caribbean Low‐Level Jet. On the other hand, the results suggest that the dry trends might have been intensified by the land–sea contrast trend that has characterized the effects of global warming on the low‐tropospheric pressure fields during the last decades; land–sea contrast would increase the vertical stability over land, suppressing convection. From these results, the influence of the AMO and the PDO is speculated to have been instrumental in preventing even drier trends. These insights suggest that dry trends in the region might be expected to worsen in the coming decades since the AMO and the PDO are expected to change their phase, and the land–sea contrast trend is expected to continue with a positive trend. [ABSTRACT FROM AUTHOR]

The identification of homogeneous rainfall regions facilitates the prioritization of watershed management measures. In the present study, it was applied the methodology of data clustering by the non‐hierarchical method of fuzzy C‐means, having as variables the geographic coordinates (latitude and longitude) and average annual precipitation, provided by the Global Precipitation Climatology Center (GPCC). The group formations were validated by Silhouette, PBM, Dunn, Davies Bouldin, and Xie Beni. The total annual precipitation values found for the regions are consistent with the precipitation volume recorded in the analysed region and found in the literature. The results presented satisfactory data from the application of the methodology, from which six homogeneous regions of well‐defined precipitation were formed, each with its own particularity, demonstrating the spatial–temporal variability of annual precipitation totals in the region. When analysing the climatological normals in relation to previous studies, a change in rainfall behaviour was noted, with changes occurring in the months with higher and lower average rainfall records in the area, mainly in homogeneous regions (HRs) 3 and 6. HRs 1 and 6 showed a unique behaviour, with their climatological normals different from the others. [ABSTRACT FROM AUTHOR]

Tropical montane cloud forest may be especially sensitive to climate change. However, our ability to understand effects of climate on montane biodiversity remains limited by the resolution of climate data. We compared 5 years of in situ weather data from cloud forests in northern Peru, regional weather stations, and gridded datasets to examine how climatologies reflect (a) forest microclimate buffering and (b) local rainfall in a sparse data region; we also examined spatiotemporal variability and regional trends. Across a 1,700–3,100 m gradient in which temperature did not covary with relative humidity (RH), in situ data showed interactions between climate and land‐use. Forest humidity buffered warming‐induced evaporative drying across elevations, and inside forest maximum vapour pressure deficit (VPDmax) did not change with elevation, whereas with a 22% reduction in RHmin at stations, VPDmax increased >10‐fold from high to low elevations. Cloud forest dried out on sunny days after 3 days without rain, especially during ENSO‐related drought concurrent with peak solar insolation. Climatologies were twice as precise for temperature as rainfall. Chelsa captured a 3.9°C reduction in maximum temperatures inside forest (MAE 1.6°C, R2 = 0.95) whereas WorldClim reflected drier lapse rates and higher Tmax outside forest. CHIRPS provided the best fit for monthly rainfall (MAE 23 mm, R2 = 48), capturing regional drought but underestimating rainfall >150 mm·month−1. Consistent with stations, CHIRPS showed strong support for regional increases in wet‐season rainfall. Reduced variability and more regular dry seasons were only detected by montane stations, especially south of 6°S, where rainfall seasonality shifted to earlier wet‐season peaks and reduced dry‐season rainfall as part of a transition from the Northern to Central Andes. Our results show that cloud forests may be partly buffered from warming but are likely to become extremely vulnerable under reduced humidity either through forest loss or drought. [ABSTRACT FROM AUTHOR]

The skill of the diverse-based precipitation products is investigated in comparison with the observed-derived HYBAM and with GoAmazon and TRMM-LBA field campaigns data. The performance of eight remote sensing-based datasets (CHIRPS, MSWEP, TRMM, CMORPH, IMERG, PERSIANN-CDR, PERSIANN-CCS-CDR, and PERSIANN-CCS) is evaluated from 1998 to 2009 considering different timescales (diurnal, intraseasonal, and seasonal) for the Amazon Basin (AB). To compare the rainfall products, we applied cluster analysis, the Seasonality Index, the Kling-Gupta Efficiency metric, categorical indices, spectral analysis, and composing technique. Overall, the databases poorly represent the precipitation in the northwest of the AB (NWAB), a region with a lack of in situ measurements. CHIRPS underestimates NWAB precipitation at the seasonal scale, while at the intraseasonal timescale most databases do not adequately represent the precipitation anomalies in the NWAB, except for MSWEP. The PERSIANN-CCS-CDR product overestimates precipitation and extreme rainfall, while CMORPH overestimates the number of no rain events. At the diurnal timescale, most databases overestimate nighttime precipitation and underestimate it in the afternoon. Although there is no single database more suitable for the AB at all timescales, CHIRPS, MSWEP, and PERSIANN-CDR are the most accurate databases at seasonal timescale, while at the intraseasonal scale MSWEP would be the most appropriate. The estimates that combine reanalysis data, surface measurements, and infrared and microwave satellite data through more sophisticated techniques, such as artificial neural networks, can improve the representation of the rainfall in the AB, especially at the diurnal timescale. [ABSTRACT FROM AUTHOR]

Understanding precipitation and its relation with atmospheric and oceanic conditions is vital in the face of climate change. This is crucial in the Tropical Andes (TA) because millions of people depend on water originated in the cordillera. Unfortunately, the paucity of meteorological monitoring that exists in mountainous regions is accentuated in the tropics. In this context, climate indices, remotely sensed, and gridded datasets, are useful tools to study climate and precipitation in the TA, and additional climate indices can be calculated from reanalysis datasets. The combination of this information with traditional indices has the potential to improve our understanding of precipitation. Our objective was to use the k‐means algorithm to regionalize precipitation in the TA (different regions have different climate), and then use the random forest algorithm to study the variables related to precipitation in each of these regions in seasonal timescales. Here, we show the suitability of the random forest algorithm to reveal climate processes and the high potential of the novel climate indices to improve the regressions. We found that convective available potential energy was the most important variable for precipitation in the northern TA, except for the Chocó, where v at 850 hPa was the most important one. Meanwhile, vertical integral of divergence of moisture flux was the most important one in the southern TA. Interestingly, in the DJF season when the South American low‐level jet (SALLJ) is more active, u and v at 850 hPa showed their lowest relative importance and the total column of water vapour showed its maximum, this could indicate that precipitation anomalies are controlled by atmospheric moisture availability rather than by the speed of the SALLJ during DJF. These results deepen our understanding of precipitation anomalies in the TA and the related oceanic and atmospheric conditions. The proposed methodology was proven to be suitable and it could be used in the future to test and formulate new hypotheses, and to forecast seasonal precipitation. [ABSTRACT FROM AUTHOR]

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

Knowledge about precipitation generation remains limited in the tropical Andes due to the lack of water stable isotope (WSI) data. Therefore, we investigated the key factors controlling the isotopic composition of precipitation in the P'aramo highlands of southern Ecuador using event-based (high frequency) WSI data collected between November 2017 and October 2018. Our results show that air masses reach the study site preferentially from the eastern flank of the Andes through the Amazon basin (73.2%), the Orinoco plains (11.2%), and the Mato Grosso Massif (2.7%), whereas only a small proportion stems from the Pacific Ocean (12.9%). A combination of local and regional factors influences the δ118O isotopic composition of precipitation. Regional atmospheric features (Atlantic moisture, evapotranspiration over the Amazon rainforest, continental rain-out, and altitudinal lapse rates) are what largely control the meteoric δ118O composition. Local precipitation, temperature, and the fraction of precipitation corresponding to moderate to heavy rainfalls are also key features influencing isotopic ratios, highlighting the importance of localized convective precipitation at the study site. Contrary to δ118O, d-excess values showed little temporal variation and could not be statistically linked to regional or local hydrometeorological features. The latter reveals that large amounts of recycled moisture from the Amazon basin contribute to local precipitation regardless of season and predominant trajectories from the east. Our findings will help to improve isotope-based climatic models and enhance paleoclimate reconstructions in the southern Ecuador highlands. [ABSTRACT FROM AUTHOR]

Climate model precipitation is the foremost input for hydrological models in climate change risk assessment. However, some aspects of precipitation (e.g., frequency, seasonality, and extremes) are usually not well represented by climate models, especially at the regional scale and in the tropics. In this study, we use a set of well‐established metrics to evaluate the marginal, temporal, and spatial aspects of CMIP5 and CMIP6 precipitation in Southern Brazil. This region is in the transition between tropical and subtropical climates with diverse rainfall generation mechanisms and complex topography. We compare the multi‐model‐ensemble mean (MME) and a constrained ensemble (CE) of CMIP5 and CMIP6 against a high‐resolution precipitation data grid. The constrained ensemble is obtained using a weighting approach that minimizes the difference between the simulated and observed cumulative distribution functions. We find that CMIP6 outperforms CMIP5 for most metrics, especially in the simulation of the seasonal cycle and the spatial distribution of precipitation. Simulated precipitation is more seasonal and more spatially dependent than the observations, with a dry bias characterized by lower precipitation amounts and higher consecutive dry days. Our analysis suggests that the models are not able to reproduce the transition between tropical and subtropical climates in this region as well as the passage of frontal systems. Future studies using CMIP6 should focus on those regional mechanisms of precipitation variability. [ABSTRACT FROM AUTHOR]

In this study, precipitation in Tropical South America in the 1931–2016 period is investigated by means of Principal Component Analysis and composite analysis of circulation fields. The associated dynamics are analyzed using the 20th century ERA-20C reanalysis. It is found that the main climatic processes related to precipitation anomalies in Tropical South America are: (1) the intensity and position of the South Atlantic Convergence Zone (SACZ); (2) El Niño Southern Oscillation (ENSO); (3) the meridional position of the Intertropical Convergence Zone (ITCZ), which is found to be related to Atlantic Sea Surface Temperature (SST) anomalies; and (4) anomalies in the strength of the South American Monsoon System, especially the South American Low-Level Jet (SALLJ). Interestingly, all of the analyzed anomalies are related to processes that operate from the Atlantic Ocean, except for ENSO. Results from the present study are in agreement with the state of the art literature about precipitation anomalies in the region. However, the added strength of the longer dataset and the larger study area improves the knowledge and gives new insights into how climate variability and the resulting dynamics are related to precipitation in Tropical South America. [ABSTRACT FROM AUTHOR]

Interaction network structure reflects the ecological mechanisms acting within biological communities, which are affected by environmental conditions. In tropical forests, higher precipitation usually increases fruit production, which may lead frugivores to increase specialization, resulting in more modular and less nested animal–plant networks. In these ecosystems, El Niño is a major driver of precipitation, but we still lack knowledge of how species interactions change under this influence. To understand bat–plant network structure during an extreme El Niño‐Southern Oscillation event, we determined the links between plantivorous bat species and the plants they consume by DNA barcoding seeds and pulp in bat faeces. These interactions were recorded in the dry forest and rainforest of Costa Rica, during the dry and the wet seasons of an extreme El Niño year. From these we constructed seasonal and whole‐year bat–plant networks and analysed their structures and dissimilarities. In general, networks had low nestedness, had high modularity, and were dominated by one large compartment which included most species and interactions. Contrary to our expectations, networks were less nested and more modular in drier conditions, both in the comparison between forest types and between seasons. We suggest that increased competition, when resources are scarce during drier seasons and habitats, lead to higher resource partitioning among bats and thus higher modularity. Moreover, we have found similar network structures between dry and rainforests during El Niño and non‐El Niño years. Finally, most interaction dissimilarity among networks occurred due to interaction rewiring among species, potentially driven by seasonal changes in resource availability. [ABSTRACT FROM AUTHOR]

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(1) Background: Desertification is one of the most important environmental impacts around the world. In the semiarid grassland of North of Chile, overgrazing has deep effects on arid lands and consequently on its economy and social development. It is necessary to conduct very detailed studies to determine how the climate, the botanical composition and the grazing system affects this process; (2) Methods: In this paper, we have determined the effect on arid grasslands of three goat managements: exclusions, continuous and deferred grazing on forage biomass, richness, Berger--Parker's dominance and Shannon's diversity. This study was developed in Las Cardas Range Station (CEALC) of the University of Chile in the Coquimbo region. The effect of annual and seasonal rainfall on biomass, diversity, richness and dominance parameters was determined; (3) Results: Allochthonous, endemic and native species showed significant changes both for seasonal and annual precipitation. In contrast, the grazing system only affected dominance and biomass of native and endemic species. Deferred grazing was the only management system that increased overall biomass productivity, especially on the best forage plant species. Exclusions showed a positive influence on more endangered species, which were the most vulnerable to goat overgrazing; (4) Conclusions: In consequence, we proposed a network of areas under deferred grazing combined with exclusions. This strategy can increase simultaneously forage productivity, grassland conservation and preservation of associated resources as hunting and wildlife tourism. Moreover, this strategy of range management will allow the sustainability of community of farmers in one of the poorest and most desertified areas in South America. [ABSTRACT FROM AUTHOR]

The Amazon forest enhances precipitation levels regionally as trees take up water from the soil and release it back into the atmosphere through transpiration. Therefore, land‐use changes in the Amazon affect precipitation patterns but to what extent remains unclear. Recent studies used hydrological and atmospheric models to estimate the contribution of tree transpiration to precipitation but assumed that precipitation decreases proportionally to the transpired portion of atmospheric moisture. Here, we relaxed this assumption by, first, relating observed hourly precipitation levels to atmospheric column water vapor in a relatively flat study area encompassing a large part of the Amazon. We found that the effect of column water vapor on hourly precipitation was strongly nonlinear, showing a steep increase in precipitation above a column water vapor content of around 60 mm. Next, we used published atmospheric trajectories of moisture from tree transpiration across the whole Amazon to estimate the transpiration component in column water vapor in our study area. Finally, we estimated precipitation reductions for column water vapor levels without this transpired moisture, given the nonlinear relationship we found. Although loss of tree transpiration from the Amazon causes a 13% drop in column water vapor, we found that it could result in a 55%–70% decrease in precipitation annually. Consequences of this nonlinearity might be twofold: although the effects of deforestation may be underestimated, it also implies that forest restoration may be more effective for precipitation enhancement than previously assumed. [ABSTRACT FROM AUTHOR]

Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products. [ABSTRACT FROM AUTHOR]

Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall on forest growth in a relatively longer time scale. We selected central India as the study site. It accommodates tropical natural vegetation of varied forest types such as moist and dry deciduous and evergreen and semi-evergreen forests that largely depend on the southwest monsoon. We used the MODIS derived NDVI and CHIRPS based rainfall datasets from 2001 to 2018 in order to analyze NDVI and rainfall trend by using Sen's slope and standard anomalies. The study observed a decreasing rainfall trend over 41% of the forests, while the rest of the forest area (59%) demonstrated an increase in rainfall. Furthermore, the study estimated drought conditions during 2002, 2004, 2009, 2014 and 2015 for 98.2%, 92.8%, 89.6%, 90.1% and 95.8% of the forest area, respectively; and surplus rainfall during 2003, 2005, 2007, 2011, 2013 and 2016 for 69.5%, 63.9%, 71.97%, 70.35%, 94.79% and 69.86% of the forest area, respectively. Hence, in the extreme dry year (2002), 93% of the forest area showed a negative anomaly, while in the extreme wet year (2013), 89% of forest cover demonstrated a positive anomaly in central India. The long-term vegetation trend analysis revealed that most of the forested area (>80%) has a greening trend in central India. When we considered annual mean NDVI, the greening and browning trends were observed over at 88.65% and 11.35% of the forested area at 250 m resolution and over 93.01% and 6.99% of the area at 5 km resolution. When we considered the peak-growth period mean NDVI, the greening and browning trends were as follows: 81.97% and 18.03% at 250 m and 88.90% and 11.10% at 5 km, respectively. The relative variability in rainfall and vegetation growth at five yearly epochs revealed that the first epoch (2001–2005) was the driest, while the third epoch (2011–2015) was the wettest, corresponding to the lowest vegetation vigour in the first epoch and the highest in the third epoch during the past two decades. The study reaffirms that rainfall is the key climate variable in the tropics regulating the growth of natural vegetation, and the central Indian forests are dominantly resilient to rainfall variation. [ABSTRACT FROM AUTHOR]

This paper examines the relative role of the Indian Ocean basin-wide (IOBW) mode and El Niño–Southern Oscillation (ENSO) in the atmospheric circulation and rainfall interannual variations over South America (SA) during southern summer of the 1951‒2016 period. The effects of the warm IOBW and El Niño (EN) events, and of the cold IOBW and La Niña (LN) events are examined using partial correlations. The ENSO and IOBW modes, through the associated large-scale and regional anomalous circulation patterns, induce contrasting effects on the rainfall in northeastern SA. The EN without the warm IOBW effect induces anomalously dry conditions over eastern Amazon and part of northeastern Brazil (NEB) through anomalous sinking motions of the EN-related anomalous Walker and Hadley cells and strong moisture divergence associated with a vigorous anticyclone over tropical South Atlantic (TSA) and SA. The warm IOBW without the EN effect induces anomalously wet conditions in NEB, which is marginally related to the anomalous Walker and Hadley cells but is modulated by an anticyclone over SA between the equator and 20° S, and a cyclone in the southwestern Atlantic between 20° S and 40° S. The results here might be relevant for climate monitoring and modeling studies. [ABSTRACT FROM AUTHOR]