Regional Weather Stations Research Articles

Joint Short-term Power Forecasting of Hydro-Wind-Photovoltaic Considering Spatiotemporal Delay of Weather Processes

Regional integrated energy systems (RIES) represented by hydro, wind, and solar energy are crucial avenues for future clean energy development, fully leveraging their complementarity can facilitate the orderly supply of renewable energy, significantly enhance the level of new energy consumption, and make outstanding contributions to achieving carbon neutrality goals. However, run-of-river hydropower (RHP), wind power (WP), and photovoltaic (PV) are affected by the chaotic characteristics of the weather system, with significant random fluctuations, and their output characteristics have significant heterogeneity, which brings a big challenge for the complementary coordinated scheduling. The high-accuracy power prediction of RHP, WP, and PV in the region is one of the key means for solving the above problems. To meet this challenge, by analyzing the spatio-temporal correlation properties between weather processes and station output, a novel joint prediction framework for short-term power forecasting (STPF) of regional hydro-wind-PV clusters is proposed. Firstly, to solve the problem of significant heterogeneity of water, wind, and solar, a Differentiated Spatio-Temporal Mixture Network (DSTMN) is proposed, which differentially encodes the weather and power information at multiple locations, enabling the extraction of common features while preserving their specificities, which addresses the difficulty of representing the inherent correlation patterns between wide-area meteorological information and heterogeneous energy sources. Secondly, to further explore the spatio-temporal correlation between meteorological information and power information, to improve the forecasting performance of the model, a spatio-temporal-lagged correlation (STLC) that can quantify their spatio-temporal delays is proposed. By analyzing the correlation characteristics between regional grid numerical weather prediction (NWP) information and station output at different spatial and temporal scales, the optimal spatial location and delay time of NWP inputs under the current scenario are determined for each station. Based on this, the best NWP scene probabilistic transfer model is established based on the Rank Bayesian Ensemble (RBE) method. By fitting the conditional probability distribution of the current best NWP spatiotemporal location and the 2nd day’s best NWP spatiotemporal location, providing a reliable input for the STPF model. Finally, a case study with 164 hydro-wind-solar stations in China demonstrates the effectiveness of the proposed method. Specifically, we achieved an accuracy improvement of 0.46% — 11.03% on the 2nd day of forecasting compared to benchmark methods.

Deep learning estimation of northern hemisphere soil freeze-thaw dynamics using satellite multi-frequency microwave brightness temperature observations.

Satellite microwave sensors are well suited for monitoring landscape freeze-thaw (FT) transitions owing to the strong brightness temperature (TB) or backscatter response to changes in liquid water abundance between predominantly frozen and thawed conditions. The FT retrieval is also a sensitive climate indicator with strong biophysical importance. However, retrieval algorithms can have difficulty distinguishing the FT status of soils from that of overlying features such as snow and vegetation, while variable land conditions can also degrade performance. Here, we applied a deep learning model using a multilayer convolutional neural network driven by AMSR2 and SMAP TB records, and trained on surface (~0-5 cm depth) soil temperature FT observations. Soil FT states were classified for the local morning (6 a.m.) and evening (6 p.m.) conditions corresponding to SMAP descending and ascending orbital overpasses, mapped to a 9 km polar grid spanning a five-year (2016-2020) record and Northern Hemisphere domain. Continuous variable estimates of the probability of frozen or thawed conditions were derived using a model cost function optimized against FT observational training data. Model results derived using combined multi-frequency (1.4, 18.7, 36.5 GHz) TBs produced the highest soil FT accuracy over other models derived using only single sensor or single frequency TB inputs. Moreover, SMAP L-band (1.4 GHz) TBs provided enhanced soil FT information and performance gain over model results derived using only AMSR2 TB inputs. The resulting soil FT classification showed favorable and consistent performance against soil FT observations from ERA5 reanalysis (mean percent accuracy, MPA: 92.7%) and in situ weather stations (MPA: 91.0%). The soil FT accuracy was generally consistent between morning and afternoon predictions and across different land covers and seasons. The model also showed better FT accuracy than ERA5 against regional weather station measurements (91.0% vs. 86.1% MPA). However, model confidence was lower in complex terrain where FT spatial heterogeneity was likely beneath the effective model grain size. Our results provide a high level of precision in mapping soil FT dynamics to improve understanding of complex seasonal transitions and their influence on ecological processes and climate feedbacks, with the potential to inform Earth system model predictions.

Climatic Effects on Position and Dynamics of Upper Open Forest Boundary in Altay and Western Sayan in the Last 60 Years

The upper treeline ecotone is a global and typically climate-dependent phenomenon. Its elevation is usually coupled with the thermal limitations of tree growth. The air temperature rise connected with global warming is assumed as the main cause of treeline upslope shifts in the last century. It has been found that the treeline elevation also correlates with the distance from the coastline and the aridity or continentality of the climate or the mass elevation effect. However, previous and contemporary publications have not explained how the upper treeline position directly couples with climate parameters. Often, this has been restricted by a lack of climate measurements and spatial data. In our study, we obtained data from 339 regional weather stations for 1964–1974 and interpolated them to Altay and Western Sayan using regional DEMs and a specially developed regression model. Moreover, we semiautomatically identified the elevational position of the upper open forest boundary (OFB) (crown closure > 10%) on the slopes of 30 mountains in Altay and Western Sayan in 1960 and 2020. We took into account the slope aspect and edaphic constraints. The obtained data allowed us to undertake a regression analysis of the dependence of the OFB elevation on climatic parameters. As a result, we found that, in the 1960s, at OFB elevations rising from the outer to the inner parts of the study area to approximately 500–700 m, the summer air temperature and precipitation linearly decreased, but the summer sunshine duration increased. In the multiple regression analysis, including the climatic parameters as independent variables and the OFB elevation as a dependent variable, significant relations were found only for the combination of air temperature and sunshine duration. We assume that the OFB elevation is determined not only by the air temperature but also by the direct solar irradiation level, changing with latitude and cloudiness. We also found that the ratio between the OFB elevation on the northern and southern slopes varied with respect to latitude. The spatial analysis of OFB shifts in 1960–2020 revealed significant differences in its value in the central (80–90 m) and outer parts of the study area (110–130 m). We suppose that the OFB advance over the past 60 years has local specificity associated with the peculiarities of the climatic changes (summer temperature rise, precipitation decrease, and sunshine duration increase) in different parts of Altay and Western Sayan. Our results highlight the need to clearly determine climatic parameters when forecasting woody vegetation reactions to future climate changes.

Impact of Climate Change and Heat Stress on Milk Production in Korean Holstein Cows: A Large-Scale Data Analysis.

This study investigated the effects of heat stress on milk production in Korean Holstein cows using large-scale data. Heat stress was assessed using the temperature-humidity index (THI). Weather records (2016 to 2020) were collected from 70 regional weather stations using an installed automated surface observing system (ASOS). A dataset of 2,094,436 milk production records from 215,276 Holstein cows obtained from the Dairy Cattle Genetic Improvement Center was analyzed. Stepwise selection was used to select the input variables, including the daily maximum THI (THI_max). Least-squares means were calculated for milk yield, fat and protein corrected milk (FPCM), fat and protein yield, fat-to-protein ratio, solids not fat, and lactation persistency. Segmented linear regression analysis determined the break points (BPs) of the THI_max. Over the five years, heat stress exposure increased, particularly from May to September. This study identified BPs around THI_max of 80-82 for milk yield and FPCM. Similar patterns for other milk traits were observed, which significantly decreased beyond their respective BPs. These findings indicate that THI variations adversely affect milk yield and composition in dairy cows, highlighting the importance of appropriate feeding management strategies to ensure the optimal productivity of Holstein cows under varying climatic conditions.

Cause analysis of the extreme hourly precipitation and its relationship with the urban heat island intensity in Shenyang, China

Based on the hourly temperature and precipitation data from China national meteorological stations and regional automatic weather stations in Shenyang, the relationship between extreme hourly precipitation (ExHP) and urban heat island Intensity (UHII) is analyzed. Results show that the UHII is higher at night and in the early morning. The ExHP events mostly occur at night in summer when the UHII is relatively high. The spatial distribution of UHII in Shenyang is consistent with the economic development and the transportation density. Denser population and transportation, and high-rise buildings in the urban center contribute to higher UHII. There are three types of ExHP, namely the abrupt-type ExHP, the growing-type ExHP and the continuous-type ExHP. The overall variation characteristics of the three types of ExHP are relatively consistent. Their UHII values are positive and relatively stable in 6–12 h before the start of ExHP. The UHII begins to increase dramatically about 6 h before the ExHP, but decreases obviously and turn negative after the precipitation begins. Before the abrupt ExHP, the UHII is relatively high and can rapidly return to positive after the ending of ExHP. The UHII of the abrupt-type ExHP is remarkably larger than that of the growing-type and continuous-type ExHP. The UHII before and after the abrupt-type ExHP differs greatly. Before the abrupt ExHP, the UHII is high in the center and low at both ends, and the high-value areas of UHII are mainly located in the urban area. After the abrupt-type EXHP, the UHII drops and turn negative in the whole area. The UHII is obviously increasing with urbanization. The diurnal variation of UHII is enormous, which is higher at night than during the daytime. The increasing UHII can cause abnormal air pressure in cities and villages. The air in the lower atmospheric layer of the city can be heated and expanded, hence resulting in lower local air pressure. Then, the lower air pressure can promote the convergence and upward movement of air, hence facilitating the establishment of UHII circulation. This phenomenon is particularly distinct at night, which is conducive to the occurrence of ExHP events.

Evaluation and Analysis on the Temperature Prediction Model for Bailing Mushroom in Jizhou, Tianjin

Based on the air temperature, wind speed, humidity, air pressure, etc., of the regional automatic weather station in Chutouling Town, Jizhou from April 2019 to November 2020, and the air temperature of the microclimate observation station receiving data every 10 min in a bailing mushroom greenhouse, this paper analyzed and evaluated a BP (back propagation) neural network and stepwise regression method to establish a prediction model for the temperature in the Bailing mushroom greenhouse for different seasons. The results showed that: (1) The air temperature, wind speed, humidity and air pressure outside the shed were the main factors for building the temperature prediction model for the inside temperature, and the air temperature was the most important factor affecting the temperature inside the shed. After introducing humidity, wind speed and air pressure, the accuracy of the model was significantly improved. (2) The temperature prediction model based on the BP neural network method, for every 10 min interval in the greenhouse, for the Bailing mushroom in different seasons, was more accurate than the stepwise regression model. The simulation results of the two models had the highest accuracy in summer, followed by autumn. (3) The root means square error of the BP neural network and stepwise regression model for inside the greenhouse, simulating the daily temperature variations for different seasons, was 1.25, 1.10, 1.08, 1.31 °C and 1.29, 1.19, 1.11, 1.37 °C, respectively. The BP neural network method performed better for predicting the daily temperature variations in seasons. (4) The specifying data of high temperature (24 July 2020) and strong cold wave (31 December 2019) were selected to test the two model methods; the results showed that the simulation of the BP neural network model was better than the stepwise regression model.

A statistical analysis of the distribution of convective windstorms and their radar characteristics in Hubei Province, China. Part 2

A statistical analysis of the distribution of convective windstorms and their radar characteristics in Hubei Province, China. Part 2

Influence of Urbanization on Spatio-Temporal Characteristics of Extreme Hourly Precipitation in Shenyang

Understanding changes in extreme hourly precipitation is critical to urban planners for building more sustainable and resilient cities. In this study, we use satellite nighttime light data, urban land area data, population, and economic data to objectively classify urban and rural stations. Based on the hourly precipitation data from national meteorological stations in 1974–2020 and from regional automatic weather stations in 2005–2020 in Shenyang (China), the spatio-temporal distribution characteristics of the thresholds, maximums, intensities, and frequencies of extreme hourly precipitation (ExHP) in urban and rural areas are analyzed and compared. The results show that the large-value centers of ExHP thresholds, maximums, and intensities are mainly concentrated in urban areas. Both the frequency and intensity of ExHP at urban stations are obviously larger than those at rural stations, and the peaks mainly appear at night for stations of both two types. From 1974 to 2020, the average frequency and intensity of ExHP at urban stations both show increasing trends, with the increasing rate being much higher than those at rural stations. In terms of temporal variation, precipitation events of the abrupt type are the most frequent, accounting for 48.6% of the total, followed by the growing type (42.7%) and continuous type (8.7%). ExHP events of the abrupt type are mostly concentrated in Kangping County and Faku County of Northern Shenyang, but rarely occur in Xinmin City. ExHP events of the growing type are mainly found in Xinmin City and the municipal district of Shenyang. For urban stations, the ExHP frequency decreases in the early stage of urbanization, while increasing evidently during the rapid urbanization stage. However, the situation is just the opposite for rural stations. This indicates that the rapid urbanization in Shenyang has a certain impact on the increase in ExHP.

Projected changes of groundwater levels in northeastern Romania according to climate scenarios for 2020–2100

Study regionA plain region covering 8000 km2 in extreme north-eastern Romania in which two groundwater levels are identified, near the surface (1.5 m) and in-depth (4.5 m) respectively. Study focusThe statistical relationship between climate conditions and underground water resources was assessed by means of correlation analysis and autoregressive neural networks (ANNs). Based on this, the study explores the quantitative changes in groundwater levels towards the end of this century according to most relevant climate scenarios regarding the evolution of air temperature and precipitation amount. New hydrological insights for the regionThe results of the ANNs model show that air temperature together with precipitation amount explain between 50% and 70% of the groundwater levels variability for 1983–2017 interval. For local conditions instead, analysed at the level of main regional weather stations, the explained variability by the climate conditions decreases between 40% and 60%. This overall good statistical performance enables us to assimilate into the model the climate scenarios data for the region. The groundwater projections indicate that the decline in groundwater levels is expected to be general in both climate scenarios analysed. Thus, the impact of climate change on groundwater supply is expected to be very high over the study region, adding to the onset effect of diminishing water quality, raising a great risk on water availability for natural and socio-economic environment.

Spatiotemporal climate variability in the Andes of northern Peru: Evaluation of gridded datasets to describe cloud forest microclimate and local rainfall

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

Climate Warming-Induced Changes in Plant Phenology in the Most Important Agricultural Region of Romania

Changes in plant phenology are a direct indicator of climate change and can produce important consequences for agricultural and ecological systems. This study analyzes changes in plant phenology in the 1961–2010 period (for both the entire interval and in three successive multi-decades: 1961–1990, 1971–2000 and 1981–2010) in southern and southeastern Romania, the country’s most important agricultural region. The analysis is based on mean monthly air temperature values collected from 24 regional weather stations, which were used for extracting the length (number of days) of phenophases (growing season onset, budding–leafing, flowering, fruiting, maturing, dissemination of seeds, start of leaf loss, end of leaf loss) and of the overall climatic growing season (CGS, which includes all phenophases), by means of the histophenogram method. Using a number of reliable statistical tools (Mann–Kendall test, Sen’s slope estimator and the regression method) for exploring annual trends and net (total) changes in the length of the phenological periods, as well as for detecting the climate—growing season statistical relationships, our results revealed complex phenology changes and a strong response in phenological dynamics to climate warming. Essentially, a lengthening of all phenophases (maximal in the maturing period, in terms of statistical significance and magnitude of trends—on average 0.48 days/yr/24 days net change in the 1961–2010 period, or even 0.94 days/yr/28 days net change in the 1971–2000 sub-period) was noticed, except for the fruiting and dissemination phenophases, which were dominated by negative trends in the number of days, but partially statistically significant (at a confidence level threshold of at least 90%). The CGS exhibited overall increasing trends, with an average of 0.21 days/yr/11 days net change in the 1961–2010 interval, and even of 0.90 days/yr/27 days net change in the 1981–2010 sub-period. Moreover, based on the slope values obtained upon application of a linear regression to mean temperature and CGS, we discovered that a 1 °C increase in climate warming accounted for a remarkable lengthening of the CGS, on average of 14 days between 1961 and 2010, and of 16 days between 1981 and 2010. Our results can help improve the adaptation of agroecological systems to future climate change.

МЕТОДИКА РАСЧЕТА ПРОСТРАНСТВЕННОГО РАСПРЕДЕЛЕНИЯ ВЫСОТЫ СНЕЖНОГО ПОКРОВА В УСЛОВИЯХ СЛОЖНОГО РЕЛЬЕФА ПРИ НЕДОСТАТОЧНОЙ МЕТЕОРОЛОГИЧЕСКОЙ ИНФОРМАЦИИ (НА ПРИМЕРЕ АНЮЙСКОГО ХРЕБТА)

In modern conditions of intensive economic development of the Eastern Siberia mountain regions, the spatial variability of the snow cover characteristics in complex terrain needs to be assessed. Such assessment is difficult due to the small number of weather stations in mountain regions. Indirect methods for calculating the snow cover characteristics for the areas with complex terrain have not been developed yet. A method is proposed for calculating the snow depth in mountain terrain, which is based on using microclimatic methods for obtaining detailed climatic information. The quantification of changes in application-specific characteristics of the snow cover depending on altitude in mountain terrain of the Anyuy Range is carried out.

Identifying the need for locally-observed wet bulb globe temperature across outdoor athletic venues for current and future climates in a desert environment

Exertional heat illness and stroke are serious concerns across youth and college sports programs. While some teams and governing bodies have adopted the wet bulb globe temperature (WBGT), few practitioners use measurements on the field of play; rather, they often rely on regionally modeled or estimated WBGT. However, urban development-induced heat and projected climate change increase exposure to heat. We examined WBGT levels between various athletic surfaces and regional weather stations under current and projected climates and in hot-humid and hot-dry weather regimes in the southwest U.S. in Tempe, Arizona. On-site sun-exposed WBGT data across five days (07:00–19:00 local time) in June (dry) and August (humid) were collected over five athletic surfaces: rubber, artificial turf, clay, grass, and asphalt. Weather station data were used to estimate regional WBGT (via the Liljegren model) and compared to on-site, observed WBGT. Finally, projected changes to WBGT were modeled under mid-century and late-century conditions. On-field WBGT observations were, on average, significantly higher than WBGT estimated from regional weather stations by 2.4 °C–2.5 °C, with mean on-field WBGT across both months of 28.5 ± 2.76 °C (versus 25.8 ± 3.21 °C regionally). However, between-athletic surface WBGT differences were largely insignificant. Significantly higher mean WBGTs occurred in August (30.1 ± 2.35 °C) versus June (26.9 ± 2.19 °C) across all venues; August conditions reached ‘limit activity’ or ‘cancellation’ thresholds for 6–8 h and 2–4 h of the day, respectively, for all sports venues. Climate projections show increased WBGTs across measurement locations, dependent on projection and period, with average August WBGT under the highest representative concentration pathway causing all-day activity cancellations. Practitioners are encouraged to use WBGT devices within the vicinity of the fields of play, yet should not rely on regional weather station estimations without corrections used. Heat concerns are expected to increase in the future, underlining the need for athlete monitoring, local cooling design strategies, and heat adaptation for safety.

Review of automatized meteorological stations use for agricultural purposes

The article deals with the questions of application and functioning of automated weather stations in agriculture. Digitalization of agriculture can significantly increase the efficiency of production and reduce the cost of manufacturing products by obtaining and accumulating information about the ongoing technological processes and making appropriate management decisions. A huge role is given to the possibility of obtaining operational data on the level of soil moisture reserves, the prevailing meteorological conditions, etc. in real time. The use of automated meteorological stations makes it possible to obtain data that can be used in the management of operations, requiring control and monitoring. This paper discusses the application and operation of automated meteorological stations in agriculture, and provides an analysis of the operation of the Davis Vantage Pro 2, Sokol-M and Meteobot® Pro weather stations in Krasnogvardeisky, Belogorsky and Saky regions. The analysis of weather station configurations, sensor installation methods, measurement accuracy, and more is made. The measured data was evaluated with the data, obtained from the weather stations of the WMO network. The prospects of further use of automated weather stations in agricultural monitoring tasks are considered.

Optimization of the irrigation rate of agricultural crops based on the use of energy resources

The results of methodological studies on the calculation of the total water consumption and irrigation rate of agricultural crops, based on the analysis of the use of energy resources, the radiation balance of the arid zone of the Republic of Kazakhstan are presented. It is proposed to determine the actual total water consumption and the size of the irrigation norm of agricultural crops through the radiation coefficient of the area (KRd), which is determined by the functional dependence with the total lack of air humidity for the growing season (?d) and the radiation indicator of a specific area (R). The dimensions of irrigation norms obtained as a result of calculations are on average 838.9 m3/ha, which is 76.40-83.90 m3/ha higher than the experimental data calculated according to the formula of A. N. Kostyakov. The dimensions of the irrigation norm calculated at the Kordai, Taraz, and Tyuken weather stations are lower than the experimental data. The established fact gives reason to believe that for many years insufficient justification for the appointment of irrigation standards, and, consequently, the saving of irrigation water contributed to the degradation of irrigated massifs. The calculations of the total water consumption according to the indications of 6 regional weather stations were carried out. The calculation of the total water consumption indicator by the absolute mark does not differ from the calculated one by the method of S. M. Alpatiev by more than 10%. The same difference is shown by the calculation of the total water consumption indicator for the radiation indicator. The calculations of the radiation coefficient of the area show that there are energy opportunities to optimize not only irrigation norms and irrigation regime, but also the entire farming system of the arid zone of the Republic of Kazakhstan, which are primarily due to the imperfection of the operated irrigation systems, low quality of irrigation and other reasons.

Analysis of the Causes of a Hail Weather in the Western Tarim Basin in July 2018

Using the conventional data, sounding data, NCEP/NCAR 1 ° × 1 ° reanalysis data and new generation weather radar data of regional automatic weather stations, the paper analyzes the occurrence and development mechanism of a hail weather process in the western Tarim Basin on July 2, 2018. The results show that: (1) The main influence system of the hail weather was caused by the continuous splitting of short waves in the southern section of the lower Siberian trough crossing the Pamirs; the hail weather occurrence time section was mainly in the area where warm and low pressure and cold air meet, and the ground convergence was the trigger mechanism for this hail weather. (2) The easterly winds appearing in the eastern to central regions of the southern Xinjiang Basin played an air cushion role, and the emergence of ground heat and low pressure enhanced the convergence of the ground near the ground. (3) There was obvious wind speed convergence in the ground layer of the area from Kashi Prefecture to Tumxuk in Xinjiang prior to the genration of the convective cell storm, which was favorable to the occurrence of local hail weather. (4) The radar reflectivity factor strong center corresponds to the large value region of the reflectivity factor gradient, which has a good indication for the identification and early warning of the hail cloud, and the intensity of the echo top was presented as banded distribution and small range, large intensity gradient and fast moving speed. There were suspended echoes and weak echo regions on the reflectivity factor profile (RCS) map, with typical super cell characteristics.

An Integrated Modeling System for the Evaluation of Water Resources in Coastal Agricultural Watersheds: Application in Almyros Basin, Thessaly, Greece

This study presents an integrated modeling system for the evaluation of the quantity and quality of water resources of coastal agricultural watersheds. The modeling system consists of coupled and interrelated models, including (i) a surface hydrology model (UTHBAL), (ii) a groundwater hydrology model (MODFLOW), (iii) a crop growth/nitrate leaching model (REPIC, an R-ArcGIS-based EPIC model), (iv) a groundwater contaminant transport model (MT3DMS), and (v) a groundwater seawater intrusion model (SEAWAT). The efficacy of the modeling system to simulate the quantity and quality of water resources has been applied to the Almyros basin in Thessaly, Greece. It is a coastal agricultural basin with irrigated and intensified agriculture facing serious groundwater problems, such as groundwater depletion, nitrate pollution, and seawater intrusion. Irrigation demands were estimated for the main crops cultivated in the area, based on precipitation and temperature from regional weather stations. The models have been calibrated and validated against time-series of observed crop yields, groundwater table observations, and observed concentrations of nitrates and chlorides. The results indicate that the modeling system simulates the water resources quantity and quality with increased accuracy. The proposed modeling system could be used as a tool for the simulation of water resources management and climate change scenarios.

Spatio-temporal distribution of the rainstorm in the east side of the Helan Mountain and the possible causes of its variability

Compared with other arid areas, the convective rainstorm occurs more easily in the east side of the Helan Mountain, which is characterized by the short duration, large intensity, high precipitation efficiency and significant local distribution. In this paper, the 5-min and hourly precipitation at 512 regional automatic weather stations and other high-resolution observation data are used to analyze the spatio-temporal distribution and evolution characteristics of the rainstorm in the east side of the Helan Mountain during 2006 and 2019. Then, based on the daily precipitation data at 12 national observation stations, the rainstorm characteristics in this region during 1951 and 2005 are analyzed. On this basis, the rainstorm characteristics in the two periods are compared and studied. Finally, the possible causes of the distribution characteristics of rainstorm are initially discussed. The results show that the rainstorm with large magnitude and high intensity is mainly located in the mountainous region, and it mainly occurs in July with a high frequency period from afternoon to the first half of the night. Compared with 1951–2005, in 2006–2019 the rainstorm frequency, intensity and extremity are all increased, and the nocturnal and topographic features are more significant. The spatio-temporal distribution of rainstorm in the east side of the Helan Mountain is closely related to the nighttime enhancement of the low-level southeasterly jet and the interaction between the low-level jet and the mountainous topography. In the windward slope of the Helan Mountain, the interaction between the low-level jet and the mountainous topography could trigger or enhance the meso- and small-scale rainstorm systems, which would favor the occurrence of rainstorm. Overall, the conclusions in this study can deep the understanding of the rainstorms in the Helan Mountain, which could provide a reference for the monitoring and warning of rainstorms in this region.

Sensitivity of reference evapotranspiration to weather variables across seven regions of Turkey

AbstractDaily weather data from 1984 to 2018 for 14 weather stations representing seven regions of Turkey were used to estimate reference evapotranspiration (ETref). We performed sensitivity analyses of input weather variables (air temperature, wind speed, and solar radiation) to determine the relative effects of measurement errors in weather data on the accuracy of calculated grass (ETos) and alfalfa (Medicago sativa L., ETrs) reference ET. In addition, changes in estimated irrigation water requirement for maize (Zea mays L.), a major crop in all regions of Turkey, due to errors in major climatic variables were also determined across seven regions of Turkey. Comparison of the sensitivity coefficients indicated that ETref was most sensitive to wind speed (sensitivities from −2.46 to 5.56 mm d–1), followed by air temperature (sensitivities from −2.13 to 3.1 mm d–1) across Turkey. However, responses of ETos and ETrs to changes in weather variables varied across the regions. For examples, the Marmara region had the largest impact of errors in air temperature but the smallest impact of errors in wind speed on the calculated ETref. Likewise, the Southeastern and Eastern Anatolia regions had the greatest impact of errors in wind speed but the smallest impact of errors in air temperature on the calculated ETref. The errors in weather variables led to substantial over‐ or underestimation of irrigation water requirement for maize. In some cases, irrigation water requirement was overestimated by more than double. These results suggest that different sensor accuracies are needed for weather stations in different regions of Turkey to accurately estimate ETref and irrigation water requirement.

Monitoring Spatial and Temporal Differences in Andean Snow Depth Derived From Satellite Tri-Stereo Photogrammetry

Quantifying the high elevation winter snowpack in mountain environments is crucial for lowland water supply, though it is notoriously difficult to accurately estimate due to a lack of observations and/or uncertainty in the distribution of meteorological variables in space and time. We compare high spatial resolution (3 m), satellite-derived snow depth maps for two drought years (2017 and 2019) in a high mountain catchment of the central Chilean Andes, applying a recently updated methodology for spaceborne photogrammetry. Regional weather station observations revealed an 80% reduction in precipitation for 2019 (the second driest winter since 1950) relative to 2017, though only a 10% reduction in total snow-covered area is seen in the satellite imagery. We threshold surface height changes based upon uncertainty of stable (snow-free) terrain differences for topographic characteristics of the catchment (slope, aspect, roughness etc). For a conservative analysis of change, outside of the topographically-derived confidence intervals, we calculate a mean 0.48 ± 0.28 m reduction of snow depth and a 39 ± 15% reduction in snow volume for 2019, relative to 2017 (for 23% of the total catchment area). Our findings therefore quantify, for the first time in the Andes, the relationship of high-resolution mountain snow depth observations with low elevation precipitation records and characterise its inter-annual variability over high elevation, complex terrain. The practical use of such detailed snow depth information at high elevations is of great value to lowland communities and our findings highlight the clear need to relate the high spatial (Pléiades) and temporal (in-situ) scales within the available datasets in order to improve estimates of region-wide snow volumes.