Meteorological Datasets Research Articles

Multistep-Ahead Chaotic Time Series Prediction Based on Hierarchical Echo State Network With Augmented Random Features

Multistep-ahead chaotic time series prediction is a kind of highly nonlinear problem, which puts forward higher requirements both for the dynamical memory and nonlinearity of the model. Echo state network (ESN) is frequently employed in the realm of chaotic time series modeling and prediction, but the basic ESN has been proved to have an antagonistic trade-off between nonlinear transformation and memory capacity. To overcome this trade-off, a new architecture named hierarchical echo state network with augmented random features (HESN-ARF) is proposed. On the basis of traditional linear random projection, the proposed HESN-ARF further leverages nonlinear kernel transformation to construct augmented random features, which can enable the linear and nonlinear properties to be fully represented. Moreover, the HESN-ARF utilizes low-rank kernel approximation to further reduce the computational cost, preserving the advantage of efficient modeling as much as possible while ensuring the capacities of nonlinear transformation and dynamical memory simultaneously. The proposed HESN-ARF can mine and learn the latent evolution patterns hidden in the dynamic system layer by layer through the hierarchical strategy, and achieves excellent performance in multi-step-ahead chaotic time series prediction, as demonstrated by experimental findings on two synthetic chaotic systems and a real-world meteorological dataset.

DownScaleBench for developing and applying a deep learning based urban climate downscaling- first results for high-resolution urban precipitation climatology over Austin, Texas

Cities need climate information to develop resilient infrastructure and for adaptation decisions. The information desired is at the order of magnitudes finer scales relative to what is typically available from climate analysis and future projections. Urban downscaling refers to developing such climate information at the city (order of 1 – 10 km) and neighborhood (order of 0.1 – 1 km) resolutions from coarser climate products. Developing these higher resolution (finer grid spacing) data needed for assessments typically covering multiyear climatology of past data and future projections is complex and computationally expensive for traditional physics-based dynamical models. In this study, we develop and adopt a novel approach for urban downscaling by generating a general-purpose operator using deep learning. This ‘DownScaleBench’ tool can aid the process of downscaling to any location. The DownScaleBench has been generalized for both in situ (ground- based) and satellite or reanalysis gridded data. The algorithm employs an iterative super-resolution convolutional neural network (Iterative SRCNN) over the city. We apply this for the development of a high-resolution gridded precipitation product (300 m) from a relatively coarse (10 km) satellite-based product (JAXA GsMAP). The high-resolution gridded precipitation datasets is compared against insitu observations for past heavy rain events over Austin, Texas, and shows marked improvement relative to the coarser datasets relative to cubic interpolation as a baseline. The creation of this Downscaling Bench has implications for generating high-resolution gridded urban meteorological datasets and aiding the planning process for climate-ready cities.

Estimating monthly surface ozone using multi-source satellite products in China based on Deep Forest model

Surface ozone (O3), a well-recognized air pollutant, exists in the atmosphere, which has a detrimental effect on public health and the ecological environment. It is reported that surface O3 has seen a significant increase in many cities from 2019 to 2021 (COVID-19 pandemic). In this study, we applied an innovative machine learning model (Deep Forest) coupled with satellites, the Troposphere Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI), and meteorological datasets to estimate monthly surface O3 of 1 km spatial resolution across China during this pandemic period. Our model achieved an overall R2 of 0.974, 0.963, and root mean square error (RMSE) of 6.016 μg/m3, 7.214 μg/m3 on TROPOMI-based datasets and OMI-based datasets, respectively. Also, we found the higher ozone concentration regions were in Eastern China. Simultaneously, the surface O3 concentration was high in summer(average = 110.57 ± 15.01 μg/m3). And the ozone concentration in summer 2020 (average = 107.78 ± 13.90 μg/m3) declined unprecedently than in summer 2019 (average = 110.54 ± 16.58 μg/m3). Our results indicated that TROPOMI data could provide robust data support for surface ozone concentration estimation. Furthermore, this study could enhance our comprehension of the formation mechanisms of surface O3 in China and assist air environment management decision-making.

Performance of windshear/microburst detection algorithms using numerical weather prediction model data for selected tropical cyclone cases

AbstractThree windshear/microburst detection algorithms are used operationally at the Hong Kong International Airport. Their performance is studied in the present article by assuming the availability of the complete set of meteorological data from numerical weather prediction model output. Only selected tropical cyclone cases are considered. The performance is evaluated using pilot reports of windshear. It turns out that the glide path scan windshear detection algorithm has the best overall performance by considering the area under curve using the relative operating characteristic curves. This may be related to the better geometry in covering the glide paths of the airport for this particular algorithm.

Meteorological Driving Datasets for the U.S. Midwest and Great Lakes Region Incorporating Precipitation Gauge Undercatch Corrections

Abstract Precipitation (P) gauge undercatch (PUC) is an important source of error when using observed meteorological datasets for hydrologic modeling studies in regions with cold and windy winters. Preliminary simulations using the Variable Infiltration Capacity (VIC) hydrological model forced with different meteorological datasets showed significant underprediction of simulated streamflow throughout the domain. A new hybrid gridded meteorological dataset at 1/16° resolution based on observed station data was assembled over the U.S. Midwest and Great Lakes region from 1915 to 2021 at a daily time step. Correction of primary station data using existing techniques is generally difficult or infeasible in the United States due to missing station metadata and lack of local wind speed (WS) measurements. We developed and tested several different postprocessing adjustment techniques using regridded WS obtained from the NCEP–NCAR reanalysis. The most effective approach corrected rain or mixed P using WS alone, and P as snow using a regressed snow-to-P ratio from a group of wind-shielded reference stations (to account for different and generally unknown snow measurement techniques). The PUC-corrected gridded products were validated against high-quality shielded stations and corrected Global Historical Climatology Network stations with in situ WS, showing good overall agreement. Observed monthly streamflow at 40 river basins was also compared to hydrologic model simulations forced by datasets with and without PUC corrections. The best PUC-corrected dataset produced improvements in streamflow simulations in at least 80% of the streamflow locations for three validation metrics (r2, Nash–Sutcliff efficiency, bias in the mean), demonstrating its value for hydrometeorological studies in the greater Midwest region. Significance Statement Many applications in hydrology require in situ precipitation (P) measurements, which are known to have a systematic low bias due to the effects of wind, also known as precipitation undercatch (PUC). Addressing PUC is problematic in the United States due to limited access to detailed station metadata (SMD) and local wind speed (WS) measurements. In this paper we develop a set of procedures to create gridded precipitation datasets for the U.S. Midwest region that incorporate corrections for PUC without needing either (i) detailed SMD or (ii) local WS measurements. Among other tests, results in 40 test basins throughout the Midwest show substantial improvements in simulated streamflow in 32 out of 40 basins when PUC corrections are included in meteorological driving datasets.

Reconstructing Continuous Ice Sheet Elevation Changes in the Amundsen Sea Sector During 2003–2021 by Merging Envisat, ICESat, CryoSat‐2, and ICESat‐2 Multi‐Altimeter Observations

AbstractThe Amundsen Sea (AS) sector in West Antarctica accounts for a significant proportion of Earth's ice losses and is the largest contributor of Antarctica's mass loss. To evaluate its contribution to global sea‐level rise, we reconstruct the long‐term continuous surface elevation changes (CSEC) record of the AS sector by an improved least‐squares plane fitting method (ILSPFM), which merged the relative surface elevation change (SEC) series instead of height from Envisat, ICESat, CryoSat‐2, and ICESat‐2 missions during 2003–2021. The accuracy of CSEC is improved by 25.9% using ILSPFM. The average rate of CSEC in the AS sector was −24.25 ± 0.48 cm yr−1 during 2003–2021. The largest signals of SEC are found over Pine Island, Thwaites, and Pope Glaciers, with the largest decline of SEC over Pope Glacier with a total SEC of −82.44 ± 7.21 m and an annual change rate of −4.34 ± 0.38 m yr−1. The ridge between Pine Island and Thwaites Glaciers is found in the AS sector, indicating that the change of ice sheet is dynamic thinning and closely related to the topography and the distance from the grounding line. Compared with meteorological data sets, we find that the codirectional fluctuation in CSEC is delayed by 3 months with surface temperature, and the precipitation leading SEC series as the phase arrow points straight down from the cross wavelet transform. Our new record shows that the AS sector thinned rapidly from 2003 to 2021 but decelerated from 2019 to 2021, and it was clearly correlated to the surface temperature, precipitation, and local terrain.

Secular changes (1826–2021) of human thermal stress according to UTCI in Kraków (southern Poland)

AbstractThe set of meteorological data from the Jagiellonian University station in Kraków is one of the longest‐recorded in central Europe. Measurement records date back to 1826. Based on the average monthly values of air temperature, total cloudiness, water vapour pressure and wind speed at 1200 UTC, the UTCI values were calculated and the occurrence of cold stress (UTCI < −13°C) and heat stress (UTCI > 32°C) was estimated. The variability of the applied measures of thermal stress conditions was studied both annually and in individual seasons (MAM, JJA, SON, DJF). Statistically significant changes in the studied characteristics of the bioclimate were found. Average annual UTCI values increased at the rate of 0.27°C/10 years. In individual seasons, the trend of UTCI changes ranged from 0.10°C/10 years in summer to 0.43°C/10 years in winter. The number of days with cold stress decreased by 1.8 days/10 years and the number of days with heat stress significantly increased (0.5 days/10 years). Two subperiods with different trends of changes were determined. The first covers the years 1826–1940, and the second, 1941–2021. The former indicates a negative UTCI trend of −0.17°C/10 years, and the latter, an increasing trend of 1.09°C/10 years. The last eight decades have brought a decrease in cold days of 6.5/10 years and an increase in heat days of 2.6/10 years.

Toward a ground-based and long-term meteorological forcing dataset for South Korea

The Modern-Era Retrospective Analysis for Research and Application version 2 (MERRA-2) is a well-established reanalysis dataset and is widely used for driving global-scale hydrological models. However, owing to its relatively coarse spatial resolution (0.5°), the capability of MERRA-2 is repeatedly challenged in regional-scale studies, especially for smaller areas of interest. In addition, the availability of in situ observation data is a pressing issue for generating meteorological forcing. We developed a grid-based high spatial (0.125°) and temporal (hourly) resolution meteorological forcing dataset, which can evaluate hydrological processes in South Korea using state-of-the-art meteorological observations from 1980 to 2020. The forcing dataset was created by combining Automated Synoptic Observing System (ASOS) in situ measurement data from the Korean Meteorological Administration and MERRA-2 reanalysis datasets. Five meteorological variables were provided in the ASOS-MERRA2 (precipitation, air temperature, surface pressure, specific humidity, and wind speed). The study demonstrates that the region-based and high spatial resolution of ASOS-MERRA2 is superior to the existing MERRA-2 with improvements of all five weather variables, for example, from 5.6 to 2.8 mm root mean square error of precipitation. The ASOS-MERRA2 was more capable of reducing the biases and root mean squared error by improving the coefficient of determination compared with MERRA-2 for all five variables. The newly developed ASOS-MERRA2 provides an opportunity to drive land surface models to evaluate the hydroclimatic conditions in South Korea.

Complex systems modelling of UK winter wheat yield

Wheat is one of the most important global crops, understanding the drivers of wheat yield has significant societal benefits. Climate variables are particularly important in determining interannual variations in wheat yield, either as primary factors which directly influence the stages of wheat growth, or as secondary factors through their influence on pests, diseases and soil conditions. Here we present a new approach to model wheat yield; an empirical method based on nonlinear complex systems identification, known as NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs model). We deploy the NARMAX analytical approach for a specific site, Rothamsted, UK, where detailed meteorological variables are available, together with specific information on site conditions and crop growth stages. NARMAX yield forecasts are compared with those from the WOFOST crop model and nine state-of-the-art machine learning (ML) models; experimental results show that NARMAX outperforms all the compared methods in both prediction accuracy and model interpretability. We also develop regional wheat yield forecasts derived from a new gridded meteorological data product.The NARMAX approach produces skillful forecasts (r = 0.78) of Rothamsted wheat yield for a validation period, with small errors. The NARMAX regional forecasts, based on less specific information than WOFOST, also show a high degree of skill (r = 0.73). In addition, the predictor terms chosen for the model are identifiable and can help to give insight into potential key processes involved in the determination of wheat yield at a specific location. This approach can be extended in principle to other crop types and locations. It is straightforward and inexpensive to implement, using a limited number of meteorological predictor variables, which can be taken from site-based observations, or from gridded meteorological datasets. The method is a new tool to understand the environmental drivers of wheat yields on an annual basis.

Simulation of canopy urban heat island at a block scale based on local climate zones and urban weather generator: a case study of Beijing

ABSTRACT The current research on urban heat island (UHI) effect mostly focuses on the analysis of land use type changes and the surface UHI intensity. Few studies on the urban canopy heat island effect at a block scale of local climate zone (LCZ), although the canopy heat island effect is a key factor affecting human thermal comfort. Therefore, this study will combine the LCZ classification system and the urban weather generator (UWG) model to simulate and quantitatively analyse the urban canopy heat island effect in Beijing at the block scale. First, based on Sentinel-2 Multispectral remote sensing images, the residual neural network (ResNet) method was used to obtain the LCZ of Beijing, and the results of LCZs was validated based on the google earth engine (GEE). Then, according to the classification results of local climate zones, the input parameters of the UWG and their corresponding value ranges are calculated. Finally, the UWG model is used to simulate the canopy temperature in different local climate zones, and the urban canopy temperature is validated using the meteorological station dataset. We quantitatively analyse the temperature differences between different types of LCZs. The results shows that the canopy heat island effect in Beijing gradually weakened outward from the city centre. This is mainly due to the relatively dense distribution of compact local climate zones in the centre of Beijing, while the surrounding areas of Beijing have lower building density and better natural coverage. The canopy urban heat island intensity of built-up LCZs is significantly stronger than that of natural-covered LCZs. The heat island intensity of the compact LCZ is higher than that of the open LCZ with the same building height. However, for LCZs with comparable compactness, the heat island intensity of high-level LCZs is higher than that of low-level LCZs.

Evaluation of four meteorological reanalysis datasets for satellite-based PM2.5 retrieval over China

Meteorological reanalysis data is widely used for satellite-based retrieval of fine particulate matter (PM2.5); however, selecting appropriate data for specific regional applications remains a challenge. In China, numerous studies have used different meteorological reanalysis datasets for PM2.5 modeling, but few studies have provided their standards for data selection. In this study, we first evaluated the performance of four reanalysis datasets in China: the fifth-generation ECMWF Atmospheric Reanalysis (ERA5), the National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2), the GEOS-FP Atmospheric Data Assimilation System, and the Final Operational Model Global Tropospheric Analyses (FNL). The validation results show that ERA5 was the most accurate in terms of temperature, relative humidity, wind speed, and boundary layer height, whereas FNL had the highest uncertainty. The spatial accuracy of all four reanalysis datasets in the eastern region of China was notably higher than that in the western region (Qinghai-Tibet Plateau), owing to complex terrain and few ground-based observations for assimilation. We found that the use of ERA5 exhibited the best agreement with in situ measurements for PM2.5 retrieval, followed by MERRA2 and GEOS-FP, while FNL was the most inaccurate. Compared with FNL, ERA5 exhibited improved accuracy in retrieving PM2.5 during summer seasons by 12%, and reduced underestimation on heavy pollution days. Our results demonstrated that the ERA5 meteorological reanalysis is preferable for better PM2.5 retrieval in China. This study provides an actional guideline for selecting meteorological reanalysis data in subsequent satellite-based PM2.5 retrieval studies in China.

Estimation of Evaporation of Water from a Liquid Desiccant Solar Collector and Regenerator by Using Conservation of Mass and Energy Principles

Solar thermal energy-powered air conditioning technologies are receiving increased attention. Among the solar energy-driven cooling technologies, open type liquid desiccant air conditioning (AC) system is emerging as a promising technology, which has a solar driven desiccant solution regenerator. In this type of system, the evaporation of water and concentrating the desiccant or regenerator performance determines the cooling performance of the AC system, which necessitates its development and experimental performance testing under actual operating conditions. The setup is made of a black painted corrugated solar collector of area 0.8 m × 1.84 m covered with glass, and a liquid desiccant solution tank and distribution system over the absorber. Solar regeneration experiments on calcium chloride–water solution were carried out on the setup and a total of five sets of meteorological, collector and solution property data were collected through concentrating the desiccant from 32.9 initially to 51.3% in five days. The evaporation of water from the regenerator was analyzed using energy and desiccant mass conservation. For a typical day, the mass of water evaporated was estimated to be 3.10 and 3.16 kg over a day, as estimated by conservation of mass and energy principles from a 34.8 kg of calcium chloride solution with initial desiccant concentration of 43.6% stored in the tank.

Characterizing uncertainty in Community Land Model version 5 hydrological applications in the United States

Land surface models such as the Community Land Model Version 5 (CLM5) are essential tools for simulating the behavior of the terrestrial system. Despite the extensive application of CLM5, limited attention has been paid to the underlying uncertainties associated with its hydrological parameters and how these uncertainties affect water resource applications. To address this long-standing issue, we use five meteorological datasets to conduct a comprehensive hydrological parameter uncertainty characterization of CLM5 over the hydroclimatic gradients of the conterminous United States. Key datasets produced from the uncertainty characterization experiment include: a benchmark dataset of CLM5 default hydrological performance, parameter sensitivities for 28 hydrological metrics, and large-ensemble outputs for CLM5 hydrological predictions. The presented datasets will assist CLM5 calibration and support broad applications, such as evaluating drought and flood vulnerabilities. The datasets can be used to identify the hydroclimatological conditions under which parametric uncertainties demonstrate substantial effects on hydrological predictions and clarify where further investigations are needed to understand how hydrological prediction uncertainties interact with other Earth system processes.

Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0

Gross primary production (GPP) is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively, yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and simulations. This study evaluates the performance of the second version of the Beijing Climate Center Atmosphere−Vegetation Interaction Model (BCC_AVIM2.0) in simulating GPP on multiple spatial and temporal scales in the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments. Model simulations driven by two meteorological datasets were compared with two observation-based GPP products covering 1982–2008. Spatial patterns of annual GPP show a significant latitudinal gradient in each dataset, increasing from cold (tundra) and dry (desert) biomes to warm (temperate) and humid (tropical rainforest) biomes. BCC_AVIM2.0 overestimates GPP in most parts of the globe, especially in boreal forest regions and Southeast China, while underestimating GPP in subhumid regions in eastern South America and tropical Africa. The four datasets broadly agree on the GPP seasonal cycle, but BCC_AVIM2.0 predicts an earlier beginning of spring growth and a larger amplitude of seasonal variations than those in the observations. The observation-based datasets exhibit slight interannual variability (IAV) and weak GPP linear trends, while the BCC_AVIM2.0 simulations demonstrate relatively large year-to-year variability and significant trends in the low-latitudes and temperate monsoon regions in North America and East Asia. Regarding the possible relationships between annual means of GPP and climate factors, BCC_AVIM2.0 predicts more extensive regions of the globe where the IAV of annual GPP is dominated by precipitation, especially in mid-to-high latitudes of the Northern Hemisphere and tropical Africa, while the observed GPP in the above regions is temperature- or radiation-dominant. The positive GPP biases due to earlier spring growth in boreal forest regions and negative GPP biases in off-equator tropical areas in the BCC_AVIM2.0 simulations imply that cold stress on biomes in boreal mid-to-high latitudes should be strengthened to restrain plant growth, while drought stress in low-latitude regions might be eased to enhance plant production in the future version of BCC_AVIM.

Challenges in Hydrologic‐Land Surface Modeling of Permafrost Signatures—A Canadian Perspective

AbstractPermafrost thaw/degradation in the Northern Hemisphere due to global warming is projected to accelerate in coming decades. Assessment of this trend requires improved understanding of the evolution and dynamics of permafrost areas. Land surface models (LSMs) are well‐suited for this due to their physical basis and large‐scale applicability. However, LSM application is challenging because (a) LSMs demand extensive and accurate meteorological forcing data, which are not readily available for historic conditions and only available with significant biases for future climate, (b) LSMs possess a large number of model parameters, and (c) observations of thermal/hydraulic regimes to constrain those parameters are severely limited. This study addresses these challenges by applying the MESH‐CLASS modeling framework (Modélisation Environmenntale communautaire—Surface et Hydrology embedding the Canadian Land Surface Scheme) to three regions within the Mackenzie River Basin, Canada, under various meteorological forcing data sets, using the variogram analysis of response surfaces framework for sensitivity analysis and threshold‐based identifiability analysis. The study shows that the modeler may face complex trade‐offs when choosing a forcing data set; for current and future scenarios, forcing data require multi‐variate bias correction, and some data sets enable the representation of some aspects of permafrost dynamics, but are inadequate for others. The results identify the most influential model parameters and show that permafrost simulation is most sensitive to parameters controlling surface insulation and runoff generation. But the identifiability analysis reveals that many of the most influential parameters are unidentifiable. These conclusions can inform future efforts for data collection and model parameterization.

КОЛИЧЕСТВЕННАЯ ОЦЕНКА ПРОСТРАНСТВЕННО-ВРЕМЕННОЙ ИЗМЕНЧИВОСТИ ЭВАПОТРАНСПИРАЦИИ И ОПРЕДЕЛЯЮЩИХ ЕЕ КЛИМАТИЧЕСКИХ ФАКТОРОВ В БАНГЛАДЕШ В 1981-2018 ГГ

Reference evapotranspiration is a vital research item for hydrologic pattern systems in drought-flood risks of a country like Bangladesh. The present research examines spatiotemporal fluctuations in reference evapotranspiration and influencing factors of these fluctuations by applying the Mann-Kendall test with the Theil-Sen’s estimator, Inverse Distance Weighted interpolation method, differential equation and detrending method depending on diurnal meteorological data sets attained from 25 weather observatories between 1981 and 2018. By current research, the FAO 56 Penman-Monteith (PM) model is executed for determining seasonal and annual reference evapotranspiration (ETpmf), and differential equation and detrending method is implemented for assessing correlation, sensitivities, and benefaction of four climatological variables to ETpmf. The MK trend analysis has been performed jointly with the Theil-Sen’s estimator to discover movement of ETpmf and meteorological variables. ETpmf displayed a conspicuous descendant tendency ( p < 0.01) at post-monsoon (-0.87 mm/year), winter (-1.13 mm/year), and annual time scale (-3.31 mm/year) in whole Bangladesh, but at the same period, a non-significant descending tendency of ETpmf was observed in pre-monsoon (-1.47 mm/year) and monsoon (-0.07 mm/year) season in the study area. These changes of ETpmf could be explained by a notable rising tendency in average temperature (TA) and actual vapor pressure (VP) and noteworthy declining tendencies in wind speed (WS) and sunshine duration (SD). ETpmf had been maximum sensitive to TA, a bit less, to VP, SD, and WS in the annual and seasonal periods. TA, WS, and SD produced a positive effect, whereas the VP negatively affected seasonal and annual ETpmf. Despite the negative effect on ETpmf, VP was the second most sensitive to the annual and all seasonal ETpmf after TA. Crosswise the entire study area, WS had the greatest influence to ETpmf, after that TA and SD. At the same time, the positive contribution of VP to ETpmf could not counterbalance the negative contributions of WS, TA, and SD. While the consequence of VP on variations in ETpmf is minor, it has to be considered particularly in the cold period. The counter association among inclining TA and declining ETpmf, which is known as the “evaporation paradox,” happenned in Bangladesh. These research outcomes will be beneficial to develop forthcoming water scheduling and regulation of feasible water resources to lessen climate change effects on agronomic water prerequisites and regulate human induced actions.

Evaluating Enhanced Reservoir Evaporation Losses From CMIP6‐Based Future Projections in the Contiguous United States

AbstractEnhanced reservoir evaporation has become an emerging concern regarding water loss, especially when compounded with the ever‐increasing water demand. In this study, we evaluated the evaporation rates and losses for 678 major reservoirs (representing nearly 90% of total storage capacity) in the Contiguous United States over historical baseline (1980–2019), near‐term (2020–2039), and mid‐term (2040–2059) future periods. The evaporation rate was estimated using the Lake Evaporation Model (LEM), an advanced lake evaporation model that addresses both heat storage and fetch effects, driven by multi‐ensemble downscaled Coupled Model Intercomparison Projects 6 (CMIP6) projections under the SSP585 emission scenario. The results project that the evaporation loss may increase by 2.5 × 107 m3/yr through the research period (1980–2059). Among all regions, the Rio Grande is projected to have the largest increasing rate in the near‐term and mid‐term future, with values of 7.11% of 10.25%, respectively. At the seasonal scale, the most significant increase in the evaporation rate is projected during the fall. The evaporation is projected to increase faster than the streamflow over many of the regions in the southwestern US during the summer/fall, suggesting that the shortage of water will be further exacerbated. The climate models contribute the most to the variance, as compared to the other components related to the projection of evaporation losses (e.g., hydrological model, downscaling method, and historical meteorological data set). These findings demonstrate the need to consider accelerated water loss through open water evaporation in long‐term water resources planning across various spatiotemporal scales.

ЧУВСТВИТЕЛЬНОСТЬ РЕЗУЛЬТАТОВ МОДЕЛИРОВАНИЯ ЦИРКУЛЯЦИИ ОКЕАНА К ВЫБОРУ ИСТОЧНИКА ДАННЫХ ОБ АТМОСФЕРНЫХ ВОЗДЕЙСТВИЯХ И СЕТОЧНОГО РАЗРЕШЕНИЯ

A comparison of simulations of the global ocean circulation with the NEMO/SI3 model with two types of atmospheric forcing (DFS5.2 and CFSR/CDAS), as well as with two grid resolutions (1° and 0.25°) is presented. The objective of such comparison is to substantiate a choice of a source of data on atmospheric forcing and a grid resolution for use in an operational system for the ocean state diagnosing and forecasting. It is shown that all considered simulations reproduce main large-scale ocean characteristics. The CFSR/CDAS merged meteorological dataset, which provides better agreement with actual data, is quite suitable for specifying atmospheric forcing in retrospective and operational applications of the NEMO/SI3 model, including its use as a computational core in the data assimilation system within the framework of the analysis-forecast-analysis sequential assimilation scheme.

Effects of meteorological forcing uncertainty on high-resolution snow modeling and streamflow prediction in a mountainous karst watershed

In the mountainous Western U.S., a considerable portion of water supply originates as snowmelt passing through karst watersheds. Accurately simulating streamflow in snow-dominated, karst basins is important for water resources management. However, this has been challenging due to high spatiotemporal variability of meteorological and hydrogeological processes in these watersheds and scarcity of climate stations. To overcome these challenges, a physically based snow model is used to simulate snow processes at 100 m resolution, and the calculated snowmelt and potential evapotranspiration rates are fed into a deep learning model to simulate streamflow. The snow model was driven by meteorological variables from a regional scale Weather Research and Forecasting (WRF) model or from the North American Land Data Assimilation System (NLDAS-2). The two datasets were used both at the original resolution and downscaled to 100 m resolution based on orographic adjustments, leading to four sets of forcings. Snow model simulation results from the four sets of forcings showed large differences in simulated snow water equivalent (SWE) and snowmelt rate and timing. However, the differences were damped in simulated streamflow, as the deep learning model is partially immune to input bias and picked up different streamflow responses to snowmelt and rainfall when trained using snow model results. While the meteorological datasets considered yielded close streamflow simulation accuracy, averaging simulated streamflow from the four sets of forcings consistently achieved better performance, suggesting the value of including multiple meteorological datasets for modeling streamflow in mountainous watersheds.

Characteristics of NDVI Changes in the Altay Region from 1981 to 2018 and Their Relationship to Climatic Factors

Vegetation growth and its response to climatic factors have become one of the most pressing issues in ecological research. However, no consensus has yet been reached on how to resolve this problem in arid areas with a high-elevation gradient and complex underlying surface. Here, NOAA CDR AVHRR NDVI V5 for 1981–2018 and China’s regional surface meteorological faction-driven datasets were used. General linear regression, the Mann-Kendall test and sliding t-test, Pearson correlations, and the Akaike information criterion (AIC), on a grid-scale, were applied to analyze the annual normalized difference vegetation index (NDVI) and its relationship with temperature and precipitation in the Altay region. Results revealed that the temporal trend of NDVI for most grid cells was non-significant. However, mountains, coniferous forests, grasslands, and meadows in the high-elevation zone displayed a slow increasing trend in NDVI. Further, NDVI was positively correlated with the mean annual temperature and total annual precipitation, the latter playing a more significant role. Yet, for desert and shrub vegetation and coniferous forest, their NDVI had insignificant negative correlations with the mean annual temperature. Hence, both the trends and drivers of NDVI of high elevation are highly complex. This study’s findings provide a reference for research on vegetation responses to climate change in arid areas having a high-elevation gradients and complex underlying surfaces.