Precipitation Analysis Research Articles

Characteristics of Runoff Components in the Mingyong Glacier Basin, Meili Snow Mountains

As an important hydrological ecosystem component, the glacier basin has great significance for climate and environment, and it is also linked to regional water sustainability. In this paper, the sampling and isotope analysis of glacial ice, ice-melt water, river water (river midstream and river downstream), groundwater (spring), and precipitation were carried out in a hydrological year of the Mingyong Glacier basin, which is located at the Meili Snow Mountains, Southeastern Tibetan Plateau. At the same time, the hydrograph separation of the recharge sources of the lower mountain pass is studied. The results show that the range of δD, δ18O, and d-excess (deuterium excess) in natural water bodies are significantly different, and the precipitation is the most obvious. The high values of δD and δ18O in the water samples all appeared in spring and summer, and the low values appeared in autumn and winter, while glacial ice showed opposite trends. Meanwhile, the local meteoric water line (LMWL) of the Mingyong Glacier basin is δD = 8.04δ18O + 13.06. The End-Member Mixing Analysis (EMMA) was adopted to determine the sources proportion of river water (river downstream) according to the δD, δ18O, and d-excess ratio relationships. The results showed that the proportion of ice-melt water, groundwater, and precipitation in the ablation period was 80.6%, 17.2%, and 2.2% as well as 19.2%, 73.1%, and 7.7% in the accumulation period, respectively. Ice-melt water has a higher conversion recharge rate to groundwater and indirectly recharges river water, especially in nonmonsoon seasons. In other words, the main recharge source of river water in the lower reaches of the Mingyong Glacier basin during the ablation period is ice-melt water. In the accumulation period, the main recharge source of river water in the lower reaches of the Mingyong Glacier basin is groundwater, while nearly half of the recharge of groundwater comes from ice-melt water. Therefore, regardless of the ablation period or the accumulation period, ice-melt water is sustainable and important to this region.

Climate-Driven Dynamics of Runoff in the Dayekou Basin: A Comprehensive Analysis of Temperature, Precipitation, and Anthropogenic Influences over a 25-Year Period

Understanding runoff dynamics is vital for effective water management in climate-affected areas. This study focuses on the Dayekou basin in China’s Qilian Mountains, known for their high climate variability. Using 25 years of data (1994–2018) on river runoff, precipitation, and temperature, statistical methods were applied to explore the annual variations and climate change impacts on these parameters. Results reveal a significant variability in the river runoff (132.27 to 225.03 mm), precipitation (340.19 to 433.29 mm), and average temperature (1.38 to 2.08 °C) over the period. Decadal rising rates average 17 mm for runoff, 17 mm for precipitation, and 0.25 °C for temperature, with the peak precipitation and runoff occurring in 1998–2000, 2008, and 2016. The annual runoff distribution also exhibited a unimodal pattern, peaking at 39.68 mm in July. The cumulative runoff during low periods constituted only 13.84% of the annual total, concentrated in the second half of the year, particularly during the June-October flood season. The correlation analysis underscored a strong relationship between river runoff and precipitation (correlation coefficient > 0.80), while the temperature correlation was weaker (correlation coefficient < 0.80). This 25-year analysis provides valuable insights into runoff variation, elucidating the interconnected effects of temperature and precipitation in the Dayekou basin, with substantial implications for sustainable development amid climate challenges.

Extreme value analysis of annual precipitation in districts of Maharashtra, India

Extreme value analysis of annual precipitation in districts of Maharashtra, India

An Airborne Multifrequency Microwave Analysis of Precipitation within Two Winter Cyclones

Abstract NASA’s Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign gathered data using “satellite-simulating” (albeit with higher-resolution data than satellites currently provide) and in situ aircraft to study snowstorms, with an emphasis on banding. This study used three IMPACTS microwave instruments—two passive and one active—chosen for their sensitivity to precipitation microphysics. The 10–37-GHz passive frequencies were well suited for detecting light precipitation and differentiating rain intensities over water. The 85–183-GHz frequencies were more sensitive to cloud ice, with higher cloud tops manifesting as lower brightness temperatures, but this did not necessarily correspond well to near-surface precipitation. Over land, retrieving precipitation information from radiometer data is more difficult, requiring increased reliance on radar to assess storm structure. A dual-frequency ratio (DFR) derived from the radar’s Ku- and Ka-band frequencies provided greater insight into storm microphysics than reflectivity alone. Areas likely to contain mixed-phase precipitation (often the melting layer/bright band) generally had the highest DFR, and high-altitude regions likely to contain ice usually had the lowest DFR. The DFR of rain columns increased toward the ground, and snowbands appeared as high-DFR anomalies. Significance Statement Winter precipitation was studied using three airborne microwave sensors. Two were passive radiometers covering a broad range of frequencies, while the other was a two-frequency radar. The radiometers did a good job of characterizing the horizontal structure of winter storms when they were over water, but struggled to provide detailed information about winter storms when they were over land. The radar was able to provide vertically resolved details of storm structure over land or water, but only provided information at nadir, so horizontal structure was less well described. The combined use of all three instruments compensated for individual deficiencies, and was very effective at characterizing overall winter storm structure.

Optimizing austenite and carbide content in medium carbon silicon-rich steel: A stepped partitioning strategy and analysis of strengthening and ductility enhancement mechanisms

To achieve the objective of enhancing the yield strength (YS) of quenching and partitioning (Q&P) steel without compromising tensile strength and ductility, a stepped partitioning (SP) strategy was proposed for Q&P treatment on medium carbon silicon-rich steel. The optimized partitioning strategy involves a two-stage partitioning, the first stage is conducted at temperatures above the martensite start (Ms) temperature, followed by the second stage carried out at a lower temperature for an extended period, involving isothermal partitioning. Experimental results demonstrate that the SP strategy yields a significant quantity of small carbides, with an average size of 59.02 nm, along with 15.71 vol% of retained austenite (RA) and a carbon concentration of 1.01 wt% in RA. Furthermore, the SP-treated specimen exhibits a remarkable balance of strength and ductility, showcasing YS, ultimate tensile strength (UTS), and total elongation (TE) values reaching 1732 MPa, 2263 MPa, and 14.9%, respectively. The microstructure evolution during tensile deformation demonstrates that RA in short-duration conventional partitioning specimens underwent premature martensitic transformation during tensile deformation due to early deformation, whereas RA in long time conventional partitioning specimens was overstabilized due to highest C concentration, which resulted in poor enhancement of strengthening and ductility by TRIP effect. In contrast, the SP strategy achieved a reasonable stability of RA, resulting in a simultaneous increase of tensile strength and ductility. Simultaneously, an analysis of precipitation strengthening indicates that the abundance of fine carbides in martensite contributes to an increase in YS due to precipitation strengthening.

Spatiotemporal assessment of rainfall variability and its characteristics over Rapti River Basin, India

Abstract The determination of rainfall characteristics of a basin is obligatory for mitigation planning and adaptation. This study presents the spatiotemporal analysis of gridded precipitation to understand the variability and seasonality over the Rapti Basin. This basin is affected by frequent flooding; hence it becomes necessary to understand the variability of rainfall. To carry out this analysis, daily precipitation data is extracted for 22 grids for 30 years (1990–2020) of high resolution. Extreme rainfall indices are computed as per the threshold decided by the Indian Meteorological Department (IMD). To determine the trend of the rainfall, Mann Kendall (MK) test, Modified MK test and Innovative Trend Analysis was used and magnitude of trend was determined using slope. Trends of daily rainfall, annual, and seasonal rainfall were determined. Annual rainfall has shown significant decreasing trend over the basin. Monsoon season has a noticeable tendency, but other seasons have only seen small shifts. From all the rainfall indices, PRCPTOT and SDII showed a decreasing trend with Sen Slope of −2.56 and 3.00 respectively. While examining the variance in precipitation across the basin, this study will be helpful.

A tick saliva serpin, IxsS17 inhibits host innate immune system proteases and enhances host colonization by Lyme disease agent.

Lyme disease (LD) caused by Borrelia burgdorferi is among the most important human vector borne diseases for which there is no effective prevention method. Identification of tick saliva transmission factors of the LD agent is needed before the highly advocated tick antigen-based vaccine could be developed. We previously reported the highly conserved Ixodes scapularis (Ixs) tick saliva serpin (S) 17 (IxsS17) was highly secreted by B. burgdorferi infected nymphs. Here, we show that IxsS17 promote tick feeding and enhances B. burgdorferi colonization of the host. We show that IxsS17 is not part of a redundant system, and its functional domain reactive center loop (RCL) is 100% conserved in all tick species. Yeast expressed recombinant (r) IxsS17 inhibits effector proteases of inflammation, blood clotting, and complement innate immune systems. Interestingly, differential precipitation analysis revealed novel functional insights that IxsS17 interacts with both effector proteases and regulatory protease inhibitors. For instance, rIxsS17 interacted with blood clotting proteases, fXII, fX, fXII, plasmin, and plasma kallikrein alongside blood clotting regulatory serpins (antithrombin III and heparin cofactor II). Similarly, rIxsS17 interacted with both complement system serine proteases, C1s, C2, and factor I and the regulatory serpin, plasma protease C1 inhibitor. Consistently, we validated that rIxsS17 dose dependently blocked deposition of the complement membrane attack complex via the lectin complement pathway and protected complement sensitive B. burgdorferi from complement-mediated killing. Likewise, co-inoculating C3H/HeN mice with rIxsS17 and B. burgdorferi significantly enhanced colonization of mouse heart and skin organs in a reverse dose dependent manner. Taken together, our data suggests an important role for IxsS17 in tick feeding and B. burgdorferi colonization of the host.

Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the “dynamic clustered Bayesian averaging (DCBA)” approach with those utilizing the “regional principal component analysis (RPCA)” under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan’s diverse climatic zones—glacial, humid, arid, and hyper-arid—employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil’s U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil’s U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil’s U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs.

Trend analysis and forecast of annual precipitation and temperature series in the Eastern Mediterranean region

The present study aims to examine the current trend of annual precipitation and temperature series referred to the Eastern Mediterranean basin on a national basis, including the annual variability of sea surface temperature anomalies and the connection effect with the North Atlantic Oscillation and the Mediterranean Oscillation indices (NAOI and MOI, respectively). The period under consideration is mainly the last 32 years, from 1990 to 2021. Additionally, the prediction of monthly and yearly temperature and precipitation series, based on the autoregressive integrated moving average model on a national base, for the next four or eight years, is another interesting feature of the present study. Results indicate rising trends in the annual precipitation during 1990-2021, mostly non-significant and significant warming trends in the annual temperature, including sea surface temperature. The NAOI is highly correlated mostly with annual temperature, whereas the MOI does not affect the variation of annual precipitation and temperature. The results of the present study are in general agreement with the results of available studies in the literature, and they could be of high interest to national authorities and environmental unions/organizations, to help decision-makers face climate change.

GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications

Abstract. Ensemble geophysical datasets are foundational for research to understand the Earth system in an uncertainty-aware context and to drive applications that require quantification of uncertainties, such as probabilistic hydro-meteorological estimation or prediction. Yet ensemble estimation is more challenging than single-value spatial interpolation, and open-access routines and tools are limited in this area, hindering the generation and application of ensemble geophysical datasets. A notable exception in the last decade has been the Gridded Meteorological Ensemble Tool (GMET), which is implemented in FORTRAN and has typically been configured for ensemble estimation of precipitation, mean air temperature, and daily temperature range, based on station observations. GMET has been used to generate a variety of local, regional, national, and global meteorological datasets, which in turn have driven multiple retrospective and real-time hydrological applications. Motivated by an interest in expanding GMET flexibility, application scope, and range of methods, we have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP) that offers GMET functionality along with additional methodological and usability improvements, including variable independence and flexibility, an efficient alternative cross-validation strategy, internal parallelization, and the availability of the scikit-learn machine learning library for both local and global regression. This paper describes GPEP and illustrates some of its capabilities using several demonstration experiments, including the estimation of precipitation, temperature, and snow water equivalent ensemble analyses on various scales.

Analysis of spatio-temporal precipitation and temperature variability and trend over Sudd-Wetland, Republic of South Sudan

The Sudd Wetland, which supports millions of pastoralists and farmers, as well as the internationally renowned Ramsar Wetland, are under pressure from population growth and climate change extremes such as flooding and drought. Using the Climate Hazards Group Infrareds Precipitation with Station (CHIRPS) and Climate Research Unit (CRU-TS4.06) datasets, this study investigates the spatiotemporal trend and variability of precipitation and temperature. As a result, descriptive statistics were used to describe the spatiotemporal behavior of precipitation and temperature. The Rotated Empirical Orthogonal Function (REOF) was used to detect the spatiotemporal variability of precipitation. The Pearson correlation method was used to investigate the relationship between Sea Surface Temperature (SST) and precipitation. The results revealed that Sudd-wetland receives 910 mm mean spatial annual precipitation and experienced 36 °C mean annual temperature with high spatio-temporal variations. It receives the highest precipitation in summer season followed by autumn and spring. The precipitation amount decreases towards the north in all seasons. In both the summer and winter seasons, observed rainfall variability corresponds substantially to ENSO variability, whereas the spring and autumn seasons are more connected with Atlantic and Indian Ocean Sea surface temperature gradients. The Sudd wetland has been affected by prolonged drought and frequent flood events alternatively. The prolonged drought linking with the shrinking of the wetland are potential threats to the farmers and pastoralists to adapt the changing climate. Hence, it is imperative to systematically analyze the wetland ecosystem from different dimensions and adjust the livelihood settings of the people in to less climate sensitive activities. Therefore, we suggest in depth drought and flood risk evaluation and implementation of realistic adaptation strategy to sustain the ecological and economic benefit of the wetland.

Microstructural Analysis of Grain Morphology and Precipitations on Molybdenum Cladding Layers Fabricated by the Direct Energy Deposition Process

Microstructural Analysis of Grain Morphology and Precipitations on Molybdenum Cladding Layers Fabricated by the Direct Energy Deposition Process

Analysis of Precipitation and Drought in the Main Southeastern Iberian River Headwaters (1952–2021)

This study evaluated the long-term changes in precipitation patterns and drought conditions in one of the key recharge areas of the hydrological system of southern and southeastern Spain, namely, the Sierra de Cazorla y Segura, which contains the headwater sectors of the catchment basins of two important rivers, namely, the Guadalquivir and the Segura. The research covered a period of 70 years (1952–2021) and undertook an exhaustive analysis of data from 348 pluviometric stations. The most relevant results are as follows: (1) most areas experienced a decrease in the precipitation volume and number of rainy days during the study period; (2) summer and winter showed the most significant decreases; (3) weak and moderate precipitation (<40 mm/d) showed significant decreases in both volume and frequency, while heavy precipitation (≥40 mm/d) showed the opposite behavior; (4) the durations of dry periods increased, while the durations of wet periods decreased in most areas; and (5) the SPEI showed an increase under drought conditions. This research underscores the need for water resource management and resilience strategies with interdisciplinary relevance in the face of changing hydrological patterns.

Some Geospatial Insights on Orange Grove Site Selection in a Portion of the Northern Citrus Belt of Mexico

This study aimed to delineate the most suitable areas for sustainable citrus production by integrating multi-criteria decision analysis, time-series remote sensing, and principal component analysis in a portion of the northern citrus belt of Mexico, particularly in the Rioverde Valley. Fourteen specific factors were grouped into four main factors, i.e., topography, soil, climate, and proximity to water sources, to carry out a multi-criteria decision analysis for classifying production areas according to suitability levels. To explore the effect of precipitation on land suitability for citrus production, we analyzed the historical record of annual precipitation estimated by processing 20-year NDVI daily data. The multi-criteria model was run for every precipitation year. The final map of land suitability was obtained by using the first component after principal component analysis on annual land suitability maps. The results indicate that approximately 30% of the study area is suitable for growing orange groves, with specific areas designated as suitable based on both mean annual precipitation (MAP) and principal component analysis (PCA) criteria, resulting in 84,415.7 ha and 95,485.5 ha of suitable land, respectively. The study highlighted the importance of remotely sensed data-based time-series precipitation in predicting potential land suitability for growing orange groves in semiarid lands. Our results may support decision-making processes for the effective land management of orange groves in the Mexico’s Rioverde region.

Inferring the impacts of climate extreme in the Kabul River Basin

The increasing temperature and variability in precipitation, in terms of both frequency and intensity, are affecting different sectors in the Himalayan region. This study aims to quantify the future scenario and related extremes in the Kabul River Basin (KRB) of the western Himalaya using high-resolution climate datasets. We selected four representative General Circulation Model (GCM) runs from Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios, based on future projections, climatic extremes and their abilities to represent the historical climate cycle (1981–2010) of KRB. The seasonal analysis of precipitation shows decreasing pattern during the winter and pre-monsoon seasons and annual mean temperature will increase consistently by 3 to 5 °C in RCP4.5 and 8.5 scenarios. Ten indices were selected to study climatic extremes pertaining to the health, agriculture and water resources sectors. The extremes, like consecutive summer days, warm days and heatwaves, will increase, whereas the frost days, cold nights, cold waves and extreme precipitation days will decrease towards the end of this century. Besides, the extremes are not homogenous in time and space. Based on the results of this study, there is a need for prompt climate actions in order to increase the adaptive capacity against these extreme changes and to build resilient livelihoods in the KRB.

Analysis of temperature and precipitation series of Hirfanli Dam Basin by Mann Kendall, Spearman’s Rho and Innovative Trend Analysis

In this study long-term trend analysis of precipitation and temperature series are determined in the Hirfanli dam basin of Turkey. Data is obtained from the Turkish State Meteorological Service for the period of 1968 to 2017 for Gemerek, Kayseri, Kirsehir, Nevsehir, Sivas and Zara. Mann-Kendall, Spearman's Rho and Innovative Trend Analysis are used for trend analysis with 95% confidence levels. According to the results of the temperature series upward trend were determined. The results of all methods are similar but increasing significant trends were determined by Mann Kendall and Spearman's Rho except Zara. According to the precipitation series results, with decreasing trends in Gemerek, Kirsehir, Nevsehir and Zara, increasing trends were determined in Kayseri and Sivas. The results of Mann Kendall and Spearman's Rho methods show parallelism with each other. Contrary to other methods, Innovative Trend Analysis determined a decreasing trend in Kayseri. As a result of the analysis, the trends in the precipitation series are not significant at the 95% confidence level. In addition to statistical analyzes, evaluations were made in terms of integrated disaster management for drought disaster in the basin with arid climate characteristics.

Long‐Term Alpine Precipitation Reconstruction (LAPrec): A Gridded Monthly Data Set Dating Back to 1871

AbstractSpatial climate data sets that extend back in time over many decades are an important resource for climate monitoring. The long‐term consistency of such data sets is, however, compromised by changes in the measurement systems over time. In this paper, we introduce a data set of monthly precipitation on a 5‐km grid over the European Alps that extends back to the late 19th century. In deriving the “long‐term Alpine precipitation reconstruction” (LAPrec), special care is taken of variations in the station network, in order for the data set to satisfy high standards in long‐term consistency. LAPrec builds on a reconstruction method that integrates the available information in two portions: The first is a set of high‐quality homogenized station series, taken from the HISTALP data archive, covering the entire period almost continuously. The second is a high‐resolution gridded precipitation analysis, taken from the “Alpine Precipitation Grid Data Set,” constructed from thousands of rain‐gauges but covering a few decades only. We demonstrate how the reconstruction approach successfully introduces mesoscale structures that are not resolved by the available long‐term station series, more plausibly so than a predecessor data set using conventional interpolation. We also illustrate that LAPrec reveals long‐term precipitation trends that are spatially more consistent and more detailed than the trends in popular climate monitoring data sets. Over the period 1871–2017 a statistically significant increase is found in winter over the northern parts of the Alps (1%–2% per 10 years). LAPrec is available in two versions (back until 1871 and 1901 respectively) from the Copernicus climate data store.

Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing

High-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management.

Recent Precipitation Trends in Six Districts of the Ken River Basin of Bundelkhand Region, India

The future development and sustainable management of water resources will be greatly influenced by the trend analysis of precipitation. This paper focuses on the analysis of rainfall trends in the Bundelkhand region, particularly in six districts covering the Ken basin. Mann-Kendall test was employed to identify potential trends, and the Sen’s slope estimator was utilized to quantify the magnitude of change during 1990 to 2020. Given that a significant portion of rainfall occurs during the southwest monsoon season, the study included both seasonal (monsoon) and annual analyses. The findings indicated a decreasing trend in precipitation for both the monsoon season and the annual scale, although many districts exhibited insignificant trends. The outcomes of this study offer valuable insights for agriculture, related sectors, and contribute to the food and energy security considerations for the six districts in the Bundelkhand region and regions with similar physiography.

How Accurately Are Climatological Characteristics and Surface Water and Energy Balances Represented for the Colombian Amazon Basin?

Context: The Colombian Amazon basin (CAB) is characterized by having many areas with few or no in situ observations. To help fix this shortcoming, there are different gridded datasets that represent meteorological variable estimates from in situ, satellite-based, merged, and model-based products. However, before using them, it is necessary to evaluate their performance.Methods: We analyzed the capacity of five grid datasets to represent the annual cycle, the annual means, and the interquartile range of temperature and the variables that intervene in the surface water and energy balances of the CAB. The five datasets were a regional climate model (REMO) and four reanalyses (ERA40, ERA-Interim, NCEP/NCAR, and NCEP-v2). For the temperature and precipitation analyses, the Delaware, GPCP, CPC, CMAP, and CMAP-v2 datasets were also taken into account. Results: The average annual temperature cycle in the CAB has an unimodal behavior, with higher values between October and March. Only Delaware and ERA-Interim describe this behavior and exhibit values close to those of the observations. The average annual cycle of precipitation is also unimodal, with higher values between March and July. Delaware, GPCP, CPC, and ERA-Interim describe this behavior, although ERA-Interim overestimates precipitation. Additionally, the four reanalyses, especially ERA-Interim, show an unexpected trend of increase in mean annual precipitation. As for the water balance, the precipitation and runoff maps of ERA-Interim and REMO show higher values in the foothills and east of 73 degrees W, a pattern that shows better agreement with the observations than those of the other datasets. ERA-Interim, REMO, and ERA-40 meet the closure condition of both the long-term water and the energy budgets. However, REMO and ERA-Interim overestimated precipitation, ERA-40 underestimated it, and ERA-Interim overestimated solar radiation. This implies that some of the other variables for the water and energy balances are also poorly estimated. Conclusions: The five gridded datasets that allow studying surface water and energy balances are not adequate, so better models and reanalyses are required.