Meteorological Datasets Research Articles

A high-resolution dataset for lower atmospheric process studies over the Tibetan Plateau from 1981 to 2020

ABSTRACT A high-resolution meteorological dataset (≤10 km) over the Tibetan Plateau (TP) is the foundation for investigating and predicting the weather and climate over Asia. The TP Subregional Dynamical Downscaling (TPSDD) dataset is a newly developed high-spatial-temporal resolution gridded dataset for land‒air exchange processes and lower atmospheric structure studies over the whole TP region, taking the climate characteristics of each TP subregion into consideration. The dataset spans from 1981 to 2020, covering the TP with a temporal resolution of 2 hr and spatial resolution of 10 km. Meteorological elements of the dataset include near-surface land–air exchange parameters, such as downward/upward longwave/shortwave radiation flux, sensible heat flux, latent heat flux, etc. In addition, the vertical distributions of 3-dimensional wind, temperature, humidity, and pressure from the surface to the lower stratosphere are also included. Independent evaluations were conducted to verify the performance of the TPSDD dataset by comparing TPSDD/reanalysis with surface and vertical observations through the calculation of statistical parameters. The results demonstrate the accuracy and superiority of this dataset against reanalysis data, which provides great potential for future climate change research.

Effectiveness of potato late blight (Phytophthora infestans) forecast by meteorological estimation in mountainous terrain based on CARAH rules

Potato late blight (PLB) caused by Phytophthora infestans (Mont.) de Bary, its incidence and development are highly dependent on meteorological conditions. To solve the problem of PLB in mountainous terrain under the condition of limited meteorological monitoring capability, the air temperature and humidity was estimated based on the basic meteorological datasets, the forecast effect of the onset period and infection cycle of PLB based on CARAH rules was evaluated. The average MAE, RMSE and CI of the estimated air temperature and observations were 1.17 °C, 1.52 °C and 0.95, respectively. The average MAE, RMSE and CI of the estimated relative humidity and observations were 8.0 %, 10.7 % and 0.53, respectively. The curve of the infection cycle of PLB at different locations were estimated from the basic meteorological datasets based on the CARAH rules, and the false alarm and missing ratios were 8.8 % and 4.6 % respectively. It may be delayed by 1 or 2 fungal generations compared to the observations, and then the protective fungicide should be adjusted to a systemic fungicide. The false alarm of the infection cycle of PLB may increase in dry air conditions, and the missing report may occur in humid condition.

Impacts of record-breaking compound heatwave and drought events in 2022 China on vegetation growth

In the summer of 2022, China experienced a series of record-breaking heatwaves and unprecedented low rainfall-induced droughts across much of China's regions. Using a reanalysis of the meteorological dataset, we confirmed that the 2022 concurrent heatwaves in China were more frequent and widespread than in the past (1979–2021). The heatwaves presented the greatest record for heatwave count, heatwave days, and cumulative intensity, especially in the Yangtze River basin, Tibetan Plateau, and Northern China. Moreover, heatwaves often occur with concurrent droughts. We found heatwaves and droughts reduced vegetation growth over half of the vegetated areas in China, represented by the negative detrended anomalies of the Normalized Difference Vegetation Index (NDVI; 66.89 %), Enhanced Vegetation Index (EVI; 58.68 %), and contiguous solar-induced chlorophyll fluorescence (CSIF; 56.47 %). More importantly, nearly half of the land surface of China (48.28 %) in 2022 summer experienced warmer and drier climate conditions than in the historical period (1979–2021). As a result, 72.9 % of vegetation stress anomalies (i.e., negative detrended anomalies of vegetation indices) in those areas can be attributed to warmer and drier climate (20.3 %), individual extreme dry climate (2.0 %), individual extreme warm climate (43.1 %), and compound extreme events combining the driest and warmest conditions (7.5 %), respectively. These findings implied that heatwaves were more critical in limiting vegetation growth than drought this summer. Using a large ensemble model (i.e., CanESM5), we estimated the probability of exceeding the 2022 summer extreme daily maximum temperature (Tmax) in China and its subregions in the future climate. Generally, heat extremes greater than the observed record (i.e., 2022) are found in China every decade, with models indicating a likelihood of 0.42 % to 85.69 % from 2023–2032 to 2083–2092 in the SSP585 future scenario. Our study provides a timely and essential evaluation of the impacts of compound heatwaves and droughts on plant growth.

Predicting ferry services with integrated meteorological data using machine learning

Ferry services that connect a huge number of islands and mainlands are vital transportation methods in several nations. However, a major disadvantage of ferry services is that they are crucially affected by weather conditions. Informing customers about regular ferry service operations is thus very important. With this in mind, the aim of this study was to predict whether ferry services can be provided in a timely manner through machine learning approaches with meteorological (6–48 h prior) and operation data sets. It was found that the random forest classifier achieved accuracy levels of 90.50% (6 h prior) and 88.78% (48 h prior) in predicting ferry services, which were greater than regulation-oriented determination. Both implications and limitations are presented based on the findings of this study.

Hierarchical Echo State Network With Sparse Learning: A Method for Multidimensional Chaotic Time Series Prediction.

Echo state network (ESN), a type of special recurrent neural network with a large-scale randomly fixed hidden layer (called a reservoir) and an adaptable linear output layer, has been widely employed in the field of time series analysis and modeling. However, when tackling the problem of multidimensional chaotic time series prediction, due to the randomly generated rules for input and reservoir weights, not only the representation of valuable variables is enriched but also redundant and irrelevant information is accumulated inevitably. To remove the redundant components, reduce the approximate collinearity among echo-state information, and improve the generalization and stability, a new method called hierarchical ESN with sparse learning (HESN-SL) is proposed. The HESN-SL mines and captures the latent evolution patterns hidden from the dynamic system by means of layer-by-layer processing in stacked reservoirs, and leverage monotone accelerated proximal gradient algorithm to train a sparse output layer with variable selection capability. Meanwhile, we further prove that the HESN-SL satisfies the echo state property, which guarantees the stability and convergence of the proposed model when applied to time series prediction. Experimental results on two synthetic chaotic systems and a real-world meteorological dataset illustrate the proposed HESN-SL outperforms both original ESN and existing hierarchical ESN-based models for multidimensional chaotic time series prediction.

IMDLIB: An open-source library for retrieval, processing and spatiotemporal exploratory assessments of gridded meteorological observation datasets over India

Addressing a pressing need for streamlined access to observed meteorological data in India, we present IMDLIB, an open-source Python library that operates on gridded datasets from the India Meteorological Department (IMD). IMDLIB simplifies the retrieval, management, and processing of IMD's rainfall, maximum and minimum temperature data at varying scales (point, regional, catchment, and country scales). IMDLIB stands out by directly interfacing with IMD's database through robust APIs, streamlining data acquisition. It offers transformation of IMD's binary data into common formats (e.g., NetCDF/GeoTIFF/CSV) and advanced analytics, including trend analysis, spatiotemporal data interpolation, calculation of meteorological indices and extreme characteristics, showcasing superior capabilities compared to traditional meteorological data processing tool available for the region. To underline its robustness, IMDLIB's application was illustrated in two distinct regions: Rajasthan State and the Godavari River Basin. Through IMDLIB, we envision enhanced data accessibility, leading to more insightful hydro-climatic research and analysis.

A global 5 km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth–Wallace model

Abstract. As the theoretical upper bound of evapotranspiration (ET) or water use by ecosystems, potential ET (PET) has always been widely used as a variable linking a variety of disciplines, such as climatology, ecology, hydrology, and agronomy. However, substantial uncertainties exist in the current PET methods (e.g., empiric models and single-layer models) and datasets because of unrealistic configurations of land surface and unreasonable parameterizations. Therefore, this study comprehensively considered interspecific differences in various vegetation-related parameters (e.g., plant stomatal resistance and CO2 effects on stomatal resistance) to calibrate and parametrize the Shuttleworth–Wallace (SW) model for forests, shrubland, grassland, and cropland. We derived the parameters using identified daily ET observations with no water stress (i.e., PET) at 96 eddy covariance (EC) sites across the globe. Model validations suggest that the calibrated model could be transferable from known observations to any location. Based on four popular meteorological datasets, relatively realistic canopy height, time-varying land use or land cover, and the leaf area index, we generated a global 5 km ensemble mean monthly PET dataset that includes two components of potential transpiration (PT) and soil evaporation (PE) for the 1982–2015 time period. Using this new dataset, the climatological characteristics of PET partitioning and the spatiotemporal changes in PET, PE, and PT were investigated. The global mean annual PET was 1198.96 mm with PT/PET of 41 % and PE/PET of 59 %, controlled moreover by PT and PE of over 41 % and 59 % of the globe, respectively. Globally, the annual PET and PT significantly (p<0.05) increase by 1.26 and 1.27 mm yr−1 over the last 34 years, followed by a slight decrease in the annual PE. Overall, the annual PET changes over 53 % of the globe could be attributed to PT, and the rest to PE. The new PET dataset may be used by academic communities and various agencies to conduct climatological analyses, hydrological modeling, drought studies, agricultural water management, and biodiversity conservation. The dataset is available at https://doi.org/10.11888/Terre.tpdc.300193 (Sun et al., 2023).

Forecasting groundwater levels using machine learning methods: The case of California’s Central Valley

Groundwater, the second largest stock of freshwater on the planet, is an important water source used for municipal water supply, irrigation, or industrial needs. For instance, California’s arid Central Valley relies on groundwater resources to produce a quarter of the United States’ food demand as farmers rely on this precious resource when surface water is scarce. Despite its importance, the nexus between groundwater dynamics and climate drivers remains difficult to quantify, model, and predict because of the lack of a comprehensive observation network. In this study, machine learning techniques were used to predict groundwater levels with a 3-month forecasting horizon for the Sacramento River Basin. For this, publicly available meteorological and hydrological datasets and in-situ well-level measurements were used. Time series, ensemble-based, and deep-learning models including transformers were all tested, with an ensemble-based, XGBoost model, producing the best mean standard deviation percent error (MSPE) of 32.23% and a root mean squared error (RMSE) of 1.05 m (m) when using a 3- month forecasting horizon and when tested using a monthly rolling window over the years 2017–2020. The model proved to be better at predicting into wet months than the dry summer months and was found to be better at extracting seasonality than explaining well-level residuals, with well-specific features, as opposed to exogenous meteorological features specific to the hydrological unit of the well, ranking as the most important features to the model. Though other forecasting horizons were tested, a 3-month look-ahead window resulted in the best balance of precision and accuracy, where smaller forecasting horizons resulted in smaller RMSE but larger MSPE scores and vice-versa for larger forecasting horizons.

Long-Term Assessment of Bioclimatic Conditions at Micro and Local Scales in the Cities of the Western Part of the Balkan Peninsula during the 21st Century

Thermal comfort assessments at local or micro-scales within urban areas can provide crucial insights for the urban adaptation strategies pertaining to climate-conscious urban planning and public health. However, the availability of long-term or mid-term daily or hourly meteorological data sets from urban environments remains a significant challenge even in the 21st century. Consequently, this study aimed to assess the thermal conditions in cities across the western part of the Balkan Peninsula, encompassing five countries (Slovenia, Croatia, Serbia, Bosnia and Herzegovina, and Montenegro), by utilizing the Physiological Equivalent Temperature (PET) index. Meteorological data sets, comprising air temperature, relative humidity, wind speed, and cloudiness, were collected from 32 national meteorological stations/measurement locations spanning the period from 2001 to 2020. The PET calculations were conducted based on meteorological data measured three times per day (7 a.m., 2 p.m., and 9 p.m.). Upon conducting a spatial analysis of the meteorological stations, it was observed that most of them (25 stations) were situated within built-up areas or urban suburbs, rendering them highly relevant for local or micro-scale climate and bioclimate assessments. The findings revealed that urban locations exhibited slightly higher PET heat stress levels, particularly during the summer season and at 2 p.m. Moreover, higher average PET values were observed in both urban and non-urban stations situated within a continental climate during warmer periods, such as summer. In contrast, during the colder seasons, namely winter and spring, higher PET values were prevalent in the Mediterranean region. Furthermore, the PET frequency analysis revealed a greater prevalence of extreme and severe heat stress levels in stations within continental climates, particularly those located in urban areas, as compared to stations in Mediterranean climates. In contrast, during the winter and spring seasons, monitoring stations in close proximity to the Adriatic Sea, characterized by a Mediterranean climate, exhibited significantly lower levels of cold stress compared to inland stations. Evidently, in addition to the climatic characteristics and surrounding terrain, the urban morphology significantly impacts the thermal conditions within cities.

Synchronized Structure and Teleconnection Patterns of Meteorological Drought Events over the Yangtze River Basin, China

Investigating the synchronized structure and teleconnection patterns of meteorological drought events (MDEs) contributes to elucidating drought’s evolution. In this study, the CN05.1 gridded meteorological dataset from 1961 to 2021 was utilized to calculate the 3-month standardized precipitation evapotranspiration index (SPEI-3) for each grid in the Yangtze River Basin (YRB). Based on these SPEI-3 series, the grid-based MDEs were then extracted. Subsequently, event synchronization and complex networks were employed to construct the MDE synchronized network over the YRB. This network was used to identify the MDEs’ topological structure, synchronized subregions, and representative grids. Finally, the MDE characteristics and MDE teleconnection patterns of individual subregions were investigated. The results of the MDE topological structure show that the northeastern portion of the YRB tends to experience widespread MDEs, while specific areas in the upper reaches are prone to localized MDEs. Synchronous MDEs mainly propagate along the central pathway and the eastern pathway, which display relatively low MDE spatial coherence. The YRB is partitioned into eight MDE synchronized subregions, each exhibiting distinct characteristics in terms of the frequency, duration, total severity, and peak of MDEs, as well as MDE temporal frequency distributions. Among all teleconnection factors, El Niño–Southern Oscillation (ENSO) exerts a strong influence on MDEs in all subregions, the Pacific Decadal Oscillation (PDO) shows a significant association with MDEs in all subregions except for Subregion 3 in the southeast, the North Atlantic Oscillation (NAO) displays a significant influence on MDEs in the southern subregions of the YRB, and the Arctic Oscillation (AO) has a more pronounced influence on MDEs in the northern subregions. This study provides valuable insights on drought’s evolution within the YRB and offers guidance to policymakers for advanced preventive measures.

Prediction of Wind Power with Machine Learning Models

Wind power is a vital power grid component, and wind power forecasting represents a challenging task. In this study, a series of multiobjective predictive models were created utilising a range of cutting-edge machine learning (ML) methodologies, namely, artificial neural networks (ANNs), recurrent neural networks (RNNs), convolutional neural networks, and long short-term memory (LSTM) networks. In this study, two independent data sets were combined and used to predict wind power. The first data set contained internal values such as wind speed (m/s), wind direction (°), theoretical power (kW), and active power (kW). The second data set was external values that contained the meteorological data set, which can affect the wind power forecast. The k-nearest neighbours (kNN) algorithm completed the missing data in the data set. The results showed that the LSTM, RNN, CNN, and ANN algorithms were powerful in forecasting wind power. Furthermore, the performance of these models was evaluated by incorporating statistical indicators of performance deviation to demonstrate the efficacy of the employed methodology effectively. Moreover, the performance of these models was evaluated by incorporating statistical indicators of performance deviation, including the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) metrics to effectively demonstrate the efficacy of the employed methodology. When the metrics are examined, it can be said that ANN, RNN, CNN, and LSTM methods effectively forecast wind power. However, it can be said that the LSTM model is more successful in estimating the wind power with an R2 value of 0.9574, MAE of 0.0209, MSE of 0.0038, and RMSE of 0.0614.

Analysis of wind regimes for energy estimation in Bamenda, of the North West Region of Cameroon, based on the Weibull distribution

The modelling and prediction of wind characteristics in a region is a primary requirement to the development of the corresponding wind energy system. This paper studies the wind energy potential for Bamenda in the North-West Region of Cameroon, with geographical coordinates: latitude 5.96°N, longitude 10.12°E and an elevation of 785 m. The analysis is based on data obtained from NASA surface meteorology and solar energy dataset for 11 years 1983 to 1993 through the RETScreen software tool provided by CANMET Canada. Through an analysis using the Weibull distribution function, the Weibull shape k, and scale c, parameters are determined using the least square graphical method to be 6.938 and 2.022 respectively. The mean wind speed, the variance, the standard deviation, the most probable /speed and the wind power density are also estimated characterising the wind regime of Bamenda. Comparing these results with the measured ones, it is shown that the Weibull distribution can be used with acceptable statistical accuracy for prediction of wind energy potential of Bamenda.

Substantial Reduction in Vegetation Photosynthesis Capacity during Compound Droughts in the Three-River Headwaters Region, China

Solar-induced chlorophyll fluorescence (SIF) is a reliable proxy for vegetative photosynthesis and is commonly used to characterize responses to drought. However, there is limited research regarding the use of multiple high-resolution SIF datasets to analyze reactions to atmospheric drought and soil drought, especially within mountain grassland ecosystems. In this study, we used three types of high-spatial-resolution SIF datasets (0.05°), coupled with meteorological and soil moisture datasets, to investigate the characteristics of atmospheric, soil, and compound drought types. We centered this investigation on the years spanning 2001–2020 in the Three-River Headwaters Region (TRHR). Our findings indicate that the TRHR experienced a combination of atmospheric drying and soil wetting due to increases in the standardized saturation vapor pressure deficit index (SVPDI) and standardized soil moisture index (SSMI). In the growing season, atmospheric drought was mainly distributed in the southern and eastern parts of the TRHR (reaching 1.7 months/year), while soil drought mainly occurred in the eastern parts of the TRHR (reaching 2 months/year). Compound drought tended to occur in the southern and eastern parts of the TRHR and trended upward during 2001–2020. All three SIF datasets consistently revealed robust photosynthetic activity in the southern and eastern parts of the TRHR, with SIF values generally exceeding 0.2 mW· m−2·nm−1·sr−1. Overall, the rise in SIF between 2001 and 2020 corresponds to enhanced greening of TRHR vegetation. Vegetation photosynthesis was found to be limited in July, attributable to a high vapor pressure deficit and low soil moisture. In the response of CSIF data to a drought event, compound drought (SVPDI ≥ 1 and SSMI ≤ −1) caused a decline of up to 14.52% in SIF across the source region of the Yellow River (eastern TRHR), while individual atmospheric drought and soil drought events caused decreases of only 5.06% and 8.88%, respectively. The additional effect of SIF produced by compound drought outweighed that of atmospheric drought as opposed to soil drought, suggesting that soil moisture predominantly governs vegetation growth in the TRHR. The reduction in vegetation photosynthesis capacity commonly occurring in July, characterized by a simultaneously high vapor pressure deficit and low soil moisture, was more pronounced in Yellow River’s source region as well. Compound drought conditions more significantly reduce SIF compared to singular drought events. Soil drought evidently played a greater role in vegetation growth stress than atmospheric drought in the TRHR via the additional effects of compound drought.

CoYangCZ: a new spatial interpolation method for nonstationary multivariate spatial processes

In multivariate spatial interpolation, the accuracy of a variable of interest can be improved using ancillary variables. Although geostatistical methods are widely used for multivariate spatial interpolation, these methods usually require second-order stationary assumption of spatial processes, which is difficult to satisfy in practice. We developed a new multivariate spatial interpolation method based on Yang-Chizhong filtering (CoYangCZ) to overcome this limitation. CoYangCZ does not solve the multivariate spatial interpolation problem from a purely statistical point of view but integrates geometry and statistics-based strategies. First, we used a weighted moving average method based on binomial coefficients (i.e. Yang-Chizhong filtering) to fit the spatial autocorrelation structure of each spatial variable from a geometric perspective. We then quantified the spatial autocorrelation of each spatial variable and the correlations between different spatial variables by analyzing the variances of different spatial variables. Finally, we obtain the best linear unbiased estimators at the unsampled locations. Experiments on air pollution and meteorological datasets show that CoYangCZ has a higher interpolation accuracy than cokriging, regression kriging, gradient plus-inverse distance squared, sequential Gaussian co-simulation, and the kriging convolutional network. CoYangCZ can adapt to second-order non-stationary spatial processes; therefore, it has a wider scope of application than purely statistical methods.

Analyzing temperature and precipitation extremes in China using multiple gridded datasets: A comparative evaluation

Gridded meteorological datasets offer valuable data sources for monitoring and studying climate extremes. Based on the 2291 CMA meteorological observations, this paper evaluates and compares seven high-resolution gridded datasets and a global climate extremes indices datasets, HadEX3. Temporal and spatial consistency are assessed at the location of CMA observations. High-resolution gridded products and HadEX3 can effectively monitor the inter-annual changes in the overall mean of extreme temperature. The temporal consistency of precipitation indices is lower due to uneven spatial distribution of precipitation. Each high-resolution gridded dataset shows good consistency in depicting the spatial distribution patterns of extreme climate. However, at locations of the observations, different indices have larger or smaller biases. These are mainly due to the spatial scale of gridded datasets and calculation and operation order of extreme indices. The study emphasizes the importance of in-situ observations in improving data accuracy. At the same time, it is found that the generation method of the dataset has an impact on the quality, and satellite and model simulation data as background fields can compensate for the lack of spatial coverage of int-situ observations. In the study of extreme climate, appropriate datasets should be selected according to different research purposes.

Assessing Vulnerability and Population Exposed to Drought in Various Climatic Regions of Northeastern Iran

Abstract The motivations of this research are the continuation and intensification of drought effects on various socioeconomic sectors and the observation of few studies and no coordinated efforts to provide a compatible framework for drought risk management in different economic sectors and population groups of the study region. Present research was carried out to assess the vulnerability and population exposed to drought in Khorasan Razavi Province. Meteorological datasets for the years 1950–2020; drought indices including self-calibrating Palmer (scPDSI), standardized precipitation (SPI), and standardized precipitation evapotranspiration (SPEI); population and livestock density indicators; agricultural lands; water stress; and socioeconomic and infrastructural factors have been used. Results indicate that dry and wet periods were estimated to be more intense by SPEI in all studied stations; also, a significant difference was observed between the results of the SPI and SPEI indices in determining the long dry and wet periods. The highest variation between the occurrence of dry and wet periods was estimated using SPEI, which could be related to seasonal fluctuations of temperature and computational evapotranspiration. Although no significant correlation was observed between used indices to identify the number of wet months, a significant positive correlation exists between the numbers of dry months estimated by those. Drought risk analysis demonstrated that the central and southern parts of the province are exposed to very severe drought while the northern and northeastern parts of the area are more inclined to severe drought. The highest class of drought exposure is observed in the southern, central, and eastern regions of the province, so they represent the high-risk category of drought.

Estimation of global land surface evapotranspiration and its trend using a surface energy balance constrained deep learning model

Estimating global land surface evapotranspiration (ET) is of great significance for assessing the impact of climate change on the global hydrological cycle and energy balance. In this study, we propose a surface energy balance constrained deep learning (DL-SEB) model for simulating global land surface evapotranspiration (ET). The accuracy of the DL-SEB model in estimating ET was tested using FLUXNET observations. The results suggested that the proposed DL-SEB model significantly enhanced the simulation capability of extreme ET events compared with the original deep learning model (without being coupled with the energy balance equation). The DL-SEB model was further applied to reconstruct global ET changes during 2000–2019 based on meteorological, soil, vegetation, and flux data sets. The annual average global land surface ET was 613 mm/yr during the period 2000–2019 (exclude Antarctica and deserts). The global land surface ET exhibited a significant upward trend with average increase rate of 1.16 mm/yr during the past two decades, which corresponds to approximately 3.8% increase above the mean global ET during 2000–2019. The positive trend of global land surface ET was driven by the combined effect of air temperature (Ta), soil moisture (SM), net radiation flux (Rn) and leaf area index (LAI). The natural climatic events such as El Niño events significantly altered short-term global ET variation, but did not changed the long-term increase trend of global ET. This study enhanced the understanding of the impact of climate change on the global land surface ET. The proposed DL-SEB model achieved a physics-based, smart and reliable ET simulation at global and regional scales.

New proglacial meteorology and river stage observations from Inglefield Land and Pituffik, NW Greenland

Abstract. Meltwater runoff from the Greenland ice sheet (GrIS) is an important contributor to global sea level rise, but substantial uncertainty exists in its measurement and prediction. Common approaches for estimating ice sheet runoff are in situ gauging of proglacial rivers draining the ice sheet and surface mass balance (SMB) modeling. To obtain hydrological and meteorological data sets suitable for both runoff stage characterization and, pending the establishment of stage–discharge curves, SMB model evaluation, we established an automated weather station (AWS) and a cluster of traditional and experimental river stage sensors on the Minturn River, the largest proglacial river draining Inglefield Land, NW Greenland. Secondary installations measuring river stage were installed in the Fox Canyon River and North River at Pituffik Space Base, NW Greenland. Proglacial runoff at these sites is dominated by supraglacial processes only, uniquely advantaging them for SMB studies. The three installations provide rare hydrological time series and an opportunity to evaluate experimental measurements of river stage from a harsh, little-studied polar region. The installed instruments include submerged vented and non-vented pressure transducers, a bubbler sensor, experimental bank-mounted laser rangefinders, and time-lapse cameras. The first 3 years of observations (2019 to 2021) from these stations indicate (a) a meltwater runoff season from late June to late August/early September that is roughly synchronous throughout the region; (b) the early onset (∼ 23 June to 8 July) of a strong diurnal runoff signal in 2019 and 2020, suggesting minimal meltwater storage in snow and/or firn; (c) 1 d lagged air temperature that displays the strongest correlation with river stage; (d) river stage that correlates more strongly with ablation zone albedo than with net radiation; and (e) the late-summer rain-on-ice events appear to trigger the region's sharpest and largest floods. The new gauging stations provide valuable in situ hydrological observations that are freely available through the PROMICE network (https://promice.org/weather-stations/, last access: 14 September 2023).

The Effects of Precipitation Event Characteristics and Afforestation on the Greening in Arid Grasslands, China

Global greening and its relationship with climate change remain the hot topics in recent years, and are of critical importance for understanding the interactions between the terrestrial ecosystem carbon cycle and the climate system. China, especially north China, has contributed a lot to global greening during the past few decades. As a water-limited ecosystem, human activities, not precipitation amount, were thought as the main contributor to the greening of north China. Considering the importance of precipitation event characteristics (PEC) in the altered precipitation regimes, we integrated long-term normalized difference vegetation index (NDVI) and meteorological datasets to reveal the role of precipitation regimes, especially PECs, on vegetation growth across temperate grasslands in north China. Accompanied with a significantly decreased growing season precipitation (GSP), NDVI increased significantly in the largest area of the temperate grasslands during 1982–2015, i.e., greening. We found that 28.44% of the area was explained by PECs, including more heavy or extreme precipitation events, alleviated extreme drought, and fewer light events, while only 0.92% of the area was associated with GSP. NDVI did not always increase over the 30 years and there was a decrease during 1996–2005. Taking afforestation projects in desertified lands into account, we found that precipitation, mainly PECs, explained more the increase and decline of NDVI during 1982–1995 and 1996–2005, respectively, while an equivalent explanatory power of precipitation and afforestation projects to the increase in NDVI after 2005. Our study indicates a possible higher productivity under future precipitation regime scenario (e.g., fewer but larger precipitation events) or intensive afforestation activity, implying more carbon sequestration or livestock production of temperate steppe in the future.

Quantification of Spatiotemporal Variability of Evapotranspiration (ET) and the Contribution of Influencing Factors for Different Land Cover Types in the Yunnan Province

Evapotranspiration (ET) is an important component of terrestrial ecosystems and is sensitive to climate and land-use change due to its obvious link to ecohydrological processes. Therefore, understanding the spatiotemporal variability of evapotranspiration and its drivers under different land cover types plays an important role in estimating the impact of environmental change on the regional water cycle. In this study, we first estimated the spatiotemporal variations of ET for different land cover types in the Yunnan Province from 2001 to 2020 using the MODIS-Terra ET product (MOD16A2.06) and meteorological datasets, and quantified the contribution of six factors: namely, temperature (TEMP), precipitation (PRCP), relative humidity (RH), wind speed (WDSP), soil moisture (SLME), NDVI, elevation, and slope, to the ET under different land cover types by using a ridge regression model. We then discussed the main reasons for the differences in ET in the Yunnan Province under different land cover types. The conclusions are as follows: during the study period, the annual mean ET ranged from 27 to 1183 mm, and there was a large spatial heterogeneity in its spatial distribution, with the smallest increasing trend of 2.1 mm/year in agricultural land and the largest increasing trend of 4.7 mm/year in grassland. Except for cropland, the sum of the relative contributions of the three influence factors, precipitation (PRCP), NDVI, and elevation, to all land cover types exceeded 40%, making them the most dominant factors influencing ET changes in the Yunnan Province. This study provides a comprehensive assessment of the impacts of climate, vegetation, topography, and soils on ET, and contributes to the development of appropriate water resource management policies for different subsurface types in the context of climate warming and revegetation programs.