Nearby Meteorological Station Research Articles

Calculation of Kinetic Fractionation of Open‐Water Evaporation Over an Upland Reservoir

AbstractBased on the fact that many water reservoirs built in headwater catchments receive no water input during dry season, we modified the Craig‐Gordon model and proposed an approach to calculate kinetic fractionation factors in open‐water evaporation taking into account isotopic compositions of precipitation and of lake water, lake volume, and land‐based meteorological data. The approach is centered around the determination of lake water isotope enrichment rate over time during dry periods. We then applied this approach for calculating kinetic fractionation factors, turbulence index, and open‐water evaporation for a small reservoir located in a sub‐tropical monsoonal climate. We found that the kinetic fractionation factors, εK,H and εK,O are, respectively 3.11‰ ± 0.41‰ and 2.50‰ ± 2.15‰, corresponding to the turbulence index of 0.43 ± 0.19. Evaporation rates in open‐water estimated by our approach is 1.55 times of the land‐based evaporation gauged in the nearby meteorological station. Wind speed, represented by turbulence index, is proved as an important climatic factor influencing kinetic fractionation. This study confirms that wind and surface area factors, which have been neglected in several isotope mass balance models, should be included into calculation process.

Analysis of Probability Distributions for Modelling Extreme Rainfall Events and Detecting Climate Change: Insights from Mathematical and Statistical Methods

Exploring the realm of extreme weather events is indispensable for various engineering disciplines and plays a pivotal role in understanding climate change phenomena. In this study, we examine the ability of 10 probability distribution functions—including exponential, normal, two- and three-parameter log-normal, gamma, Gumbel, log-Gumbel, Pearson type III, log-Pearson type III, and SQRT-ET max distributions—to assess annual maximum 24 h rainfall series obtained over a long period (1972–2022) from three nearby meteorological stations. Goodness-of-fit analyses including Kolmogorov–Smirnov and chi-square tests reveal the inadequacy of exponential and normal distributions in capturing the complexity of the data sets. Subsequent frequency analysis and multi-criteria assessment enable us to discern optimal functions for various scenarios, including hydraulic engineering and sediment yield estimation. Notably, the log-Gumbel and three-parameter log-normal distributions exhibit superior performance for high return periods, while the Gumbel and three-parameter log-normal distributions excel for lower return periods. However, caution is advised regarding the overuse of log-Gumbel, due to its high sensitivity. Moreover, as our study considers the application of mathematical and statistical methods for the detection of extreme events, it also provides insights into climate change indicators, highlighting trends in the probability distribution of annual maximum 24 h rainfall. As a novelty in the field of functional analysis, the log-Gumbel distribution with a finite sample size is utilised for the assessment of extreme events, for which no previous work seems to have been conducted. These findings offer critical perspectives on extreme rainfall modelling and the impacts of climate change, enabling informed decision making for sustainable development and resilience.

Monitoring climatic impacts on the moisture uptake of the first Swiss wildlife bridge made of wood

Wildlife bridges represent a major component of any sustainable strategy to counteract the negative consequences of cutting the natural habitat of wildlife into disconnected parts by motorways and rail. This is especially true for a small and densely populated country such as Switzerland with diverse wildlife scattered through its territory. Up to now all wildlife bridges in Switzerland have been made of concrete with steel reinforcement. The wildlife bridge under investigation here is the first one in Switzerland made of wood-based materials. The idea of building a wildlife bridge by using wood-based materials represents besides a challenging technological endeavor also an ecological progress regarding embodied energy. A further advantage which resulted after realizing the construction, was the short interruption time it needed for installation on a motorway in operation. The most urgent question with respect to the wood-based elements is their moisture uptake when subjected to weather conditions. The present paper reports on a long-term monitoring of this wooden wildlife bridge over a period of approximately 2 years. Different kind of sensors, data from a nearby meteorological station, data regarding hourly number of different kind of vehicles passing beneath the wildlife bridge as well as lab measurements have been used to enable a robust and reliable statement on wooden wildlife bridges subjected to Swiss flatland weather conditions.

Retroceso del glaciar del Carihuairazo y sus implicaciones en la comunidad de Cunucyacu

The retreat of glaciers is a reality throughout the Andes Mountain range, especially in low-altitude mountains. One of these cases is the loss of the remaining ice mass in Carihuairazo (Tungurahua, Ecuador), which in recent years has experienced a considerable retreat. This research aims to characterize the retreat of this glacier and its implications for the nearby community (Cunucyacu) through the application of a multi-source methodology, which includes the collection of glacier aerial photographs, data from nearby meteorological stations, the use of global climate reanalysis data, interviews with community members, and mountaineers who work and frequent the area. To characterize the glacier’s mass evolution, a hydroglaciological model was applied, using input data from meteorological series, and its parameters were calibrated with the photographic record of the glacier’s outline. The results show a glacier loss of 99% of its surface in 1956 (0.34 ) by 2021. The model successfully simulates the glacier area variation over 67 years, revealing a continuous decrease since 1978, with short periods of recovery and equilibrium, where temperature is the variable that best explains the glacier’s retreat. However, the model fails to consider the effect of external factors, such as the eruption of the Tungurahua volcano that could enhance the glacier retreat. The Carihuairazo glacier is in a situation of inevitable disappearance, highlighting the vulnerabilities of communities facing this phenomenon as a consequence of climate change.

Characterization of the Energy Balance of Wheat Grown under Irrigation in the Hot, Arid Environment of Sudan

An analysis of the crop microclimate is essential for assessing the climate’s appropriateness for cultivation. Here, the Bowen ratio (BR) was used to characterize the energy balance in an irrigated wheat field in a hot, arid environment in Sudan. The hourly BR was calculated using micrometeorological data, including net radiation (Rn) and soil heat flux (G), observed in the 2021–2022 and 2022–2023 growing seasons (December–March) and used to compute hourly daytime latent heat (LE) and sensible heat (H) fluxes during the days before and after irrigation. In both seasons, the observed significant evaporative cooling effect of irrigation led to a daily maximum temperature difference of 2.5–5.7 °C between the wheat field and a nearby meteorological station in a non-vegetated desert area. The energy balance calculation results showed that LE was dominant over H and G. Because BR tended to be negative, H was negative; thus, LE was larger than Rn because of sensible heat advection from the surrounding area. Further, a decrease in G after irrigation indicated an alteration in the soil’s thermal properties. These results provide insights into the micrometeorological response of irrigated wheat to a hot, arid environment and useful information for understanding soil–plant–atmosphere interactions in hot, dry environments.

Soil temperature regimes on the southern border of the zone of frozen bogs in Western Siberia

Numto Nature Park (Western Siberia) is one of the southernmost locations of frozen peatlands. In 2019–2022, soil temperatures were measured there using an automatic monitoring system. The measurements were carried out for Murshik Hemic Cryic Histosol on flat palsa peatlands and frost mounds. The temperature for Folic Albic Podzol was measured for reference. The average annual temperature of the soil surface was found to be positive in all study areas: + 0.8 °C on the frost mound; +1.3 °C on the flat palsa peatlands; and + 4.5 °C in Folic Albic Podzol. The low temperature on the frost mound is due to the low snow cover, so the soil surface cools down to the minimum in winter. As for flat palsa peatlands, peat remains frozen all year round, starting from a depth of 0.5 m. On the frost mound, at the same time, the depth of seasonal thawing is 2 m. In winter, the frost penetration on the mound doesn't reach the permafrost table, revealing its probable degradation in case of further climate warming. According to the soil thermal regime classification, the soil on the frost mound falls into the category of long-term seasonally frozen soils, while high palsa peatlands nearby Nadym Town belong to the permafrost type. Data from the nearby meteorological station show a trend of rising air temperature and rainfall. An analysis of the soil temperature regime and the course of exogenous processes demonstrate that Murshik Hemic Cryic Histosol on high palsa peatlands is unstable. Permafrost persists there due to the low thickness of the snow cover on the peaks, which facilitates winter cooling. If the snow-cover height increases, permafrost is likely to melt there.

An statistical model for the short-term albedo estimation applied to PV bifacial modules

Albedo estimation is an essential input parameter for bifacial PV modules. However, there was no clear agreement on which albedo values should be used and how they should be measured, either with satellite measurements or using onsite measurements which can be obtained from nearby meteorological stations. Since long-term measurements are not always available for new PV system locations, short-term albedo measurements are also used as input parameters in PV system performance estimation models. Short-term albedo presents high variability due to factors such as weather, seasonality or changes in the surrounding surface among others. In addition, apparently random albedo variations of 60% can be observed, even during consecutive days or within the same day. Therefore, this study presents a two-parameter exponential model that modelates the albedo data statistical distribution with an error of less than 5% in all cases and from which it is possible to determine the reliability of the obtained data. This model has been evaluated at a set of locations, with different surface types and climates, and assessed using onsite and satellite data. The impact of the methodology used to estimate the albedo data uncertainty and its reliability has also been studied as a function of several parameters such as the global horizontal irradiance and measurement time, allowing the process optimization and enhancing its reliability.

Harnessing deep learning to forecast local microclimate using global climate data

Microclimate is a complex non-linear phenomenon influenced by both global and local processes. Its understanding holds a pivotal role in the management of natural resources and the optimization of agricultural procedures. This phenomenon can be effectively monitored in local areas by employing models that integrate physical laws and data-driven algorithms relying on climate data and terrain conformation. Climate data can be acquired from nearby meteorological stations when available, but in their absence, global climate datasets describing 10 km-scale areas are often utilized. The present research introduces an innovative microclimate model that combines physical laws and deep learning to reproduce temperature and relative humidity variations at the meter-scale within a study area located in the Lombardian foothills. The model is exploited to perform a comparative study investigating whether employing the global climate dataset ERA5 as input reduces model’s accuracy in reproducing the microclimate variations compared to using data collected by the Lombardy Regional Environment Protection Agency (ARPA) from a nearby meteorological station. The comparative analysis shows that using local meteorological data as inputs provides more accurate results for microclimate modeling. However, in situations where local data is not available, the use of global climate data remains a viable and reliable approach.

A comprehensive investigation of the causes of drying and increasing saline dust in the Urmia Lake, northwest Iran, via ground and satellite observations, synoptic analysis and machine learning models

Nowadays, dried lakes have turned into important dust sources with serious environmental, climatic and socio-economic impacts. In this study, climatic, terrestrial and anthropogenic effects that caused desiccation of the Urmia Lake, NW Iran are investigated. The main objective is to assess the influence of climatic and regional factors against anthropogenic and local ones in the dryness of the lake during the last two decades. Annual temperature, precipitation and wind speed data measured at nearby meteorological stations, along with water discharge of eight main rivers flowing into the lake are analyzed. The results reveal that the main cause for turning Urmia into a dried lake bed was human interference, since climatic factors contributed positively but to a lesser degree, while terrestrial factors may also play a crucial role. Among anthropogenic interferences, the construction of a causeway had a weak effect in the lake's dryness, while construction of many river dams highly contributed to this disaster. Furthermore, four machine learning models showed that groundwater decline is also an important factor for lake's desiccation. Analysis of dusty days at stations in the Urmia Basin showed similar inter-annual and seasonal dust variability over the whole western Iran. This indicates that dust over the Urmia Basin is mainly affected by the regional dust activity, while the dried lake beds contribute to a lesser degree to the enhanced dust loading. The dominant wind flow is mostly southwesterly, carrying Urmia saline dust toward NE directions, affecting Tabriz city. Three main synoptic clusters, with dipole features consisted of high-pressure systems over Europe, Central Mediterranean and North Africa and low pressure over SW Asia, facilitated strong westerly winds from arid, desert areas of the Middle East toward the Urmia Basin during 118 dust storms in the region.

Diagnosing crop water status based on canopy temperature as a function of film mulching and deficit irrigation

Film mulching with deficit irrigation is considered an effective way to maximize water productivity and ensure a stable crop yield in the arid region of Northwest China. Deficit irrigation requires precise knowledge of crop response to drought conditions, whereas the canopy temperature and the related crop water stress index (CWSI) monitor crop water stress status. Although canopy temperature and CWSI are closely related to meteorological conditions, cultivation measure and crop growing conditions, it is unclear how film mulching influences the canopy temperature and CWSI. A two-year field experiment was conducted in a maize field with three irrigation levels and different film mulching conditions, i.e., non-mulching, plastic film mulching and biodegradable film mulching treatments, in the Shiyang River Basin of Northwest China. The study aimed to quantify the effects of film mulching on canopy temperature and CWSI, as well as to compare CWSI values obtained from the nearby meteorological station data (CWSI_M), and in situ (field) measurements (CWSI_I). The results showed that plastic film and biodegradable film significantly (p < 0.05) reduced the canopy temperature. The non-water-stressed baseline based on the air temperature and relative humidity of the nearby meteorological station was significantly (p < 0.05) different from those of the field measurement at both the vegetative and the reproductive stages. No significant differences in the non-water-stressed baselines (p > 0.05) were observed among different film mulching conditions. CWSI was calculated using the air temperature and relative humidity data from the aforementioned two sources. The results revealed showing that CWSI_I better correlated with the crop yields of various treatments when compared to CWSI_M. Irrigation scheduling based on CWSI_I could save 83 mm of irrigation amount. This underscore the preference of using CWSI_I (if available) as the primary choice for irrigation scheduling. These findings highlight the impact of meteorological and planting conditions on the canopy temperature, non-water-stressed baseline and CWSI, which may be valuable for diagnosing crop water status more accurately by regional water managers.

Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms.

In this study, machine learning models were implemented to predict the classification of coastal waters in the region of Eastern Macedonia and Thrace (EMT) concerning Escherichia coli (E. coli) concentration and weather variables in the framework of the Directive 2006/7/EC. Six sampling stations of EMT, located on beaches of the regional units of Kavala, Xanthi, Rhodopi, Evros, Thasos and Samothraki, were selected. All 1039 samples were collected from May to September within a 14-year follow-up period (2009-2021). The weather parameters were acquired from nearby meteorological stations. The samples were analysed according to the ISO 9308-1 for the detection and the enumeration of E. coli. The vast majority of the samples fall into category 1 (Excellent), which is a mark of the high quality of the coastal waters of EMT. The experimental results disclose, additionally, that two-class classifiers, namely Decision Forest, Decision Jungle and Boosted Decision Tree, achieved high Accuracy scores over 99%. In addition, comparing our performance metrics with those of other researchers, diversity is observed in using algorithms for water quality prediction, with algorithms such as Decision Tree, Artificial Neural Networks and Bayesian Belief Networks demonstrating satisfactory results. Machine learning approaches can provide critical information about the dynamic of E. coli contamination and, concurrently, consider the meteorological parameters for coastal waters classification.

Air Temperature Variations Due to Different Roofs and Their Impact on Energy Consumption and Emissions: Mexicali University Campus Case Study

Roof surfaces on which air conditioning equipment is installed cause significant air temperature increases around the condensers due to roof thermal properties, resulting in excess electrical energy consumption and greenhouse gas (GHG) emissions. An experiment to quantify such excesses during the summer was conducted at a university campus in Mexicali, Mexico. The air temperatures and surface temperatures for three types of roofs were recorded. Temperatures (condenser inlet air temperatures) were used as the input data to a priori estimate the electrical consumption of a 5-ton A/C equipment working over these different roofs. Temperatures recorded by a nearby meteorological station were used as a reference. The results indicate a differential of up to 4.81 °C, resulting in an excess of electricity consumption of 5.55 kWh and an additional 3.9 kg of CO2 emissions, representing an 8.2% energy surplus and differences of up to 2.1% in electricity consumption provoked by microclimate.

Local temperature near native vascular plants in the Argentine Islands–Kyiv Peninsula region, Antarctic Peninsula: annual variability and approximation using standard meteorological measurements

We describe the main features of LT variability that influence native vascular plants in the Antarctic and examine the relationship between the temperature regime at the micro-level and meteorological conditions at the macro-level. We used a period of over a year, during which 37 specialized mini-loggers recorded LT near vascular plants in the Argentine Islands–Kyiv Peninsula region of the Antarctic Peninsula. Rather than measuring standard air or soil temperature, these loggers detect the temperature near the ground, in the microhabitats that harbour vascular plants. On a daily scale, LT correlates with standard (2-m) air temperature, with the values higher at rock slopes than at rock terraces and ledges. A moderate correlation was found with wind and radiation parameters. Seasonality accounted for 75–93% of total LT variability, with better results on open rock terraces compared to protected areas and clefts. LT day-to-day variability during the cold season is mostly responsible for differences in R2 of the annual cycle. We estimated daily mean LT using regression dependencies from 2-m air temperature and wind speed measured at a nearby meteorological station. R2 for these statistical models varies from 0.46 to 0.68. However, they underestimate the observed LT. LT measured on rock slopes showed better modelling results with air temperature, whereas wind speed was a better predictor on rock ledges. This study contributes to our understanding of the micro-scale temperature regime that influences native vascular plants and provides a method for its rough approximation using standard meteorological parameters.

Temperature preference of sugar- or blood-fed Aedes japonicus mosquitoes under semi-natural conditions

Mosquito-borne diseases pose a major burden on humans and animals. Temperature strongly influences the physiology and life cycle of mosquitoes and also the pathogens they transmit. Thermoregulatory behaviour of mosquitoes has been addressed in a few laboratory studies. Here, we expand such studies by investigating the thermal preference when resting of Aedes japonicus, an invasive and putative vector species of many pathogens, in a semi-field setup during summers in a temperate climate. Blood-fed or sugar-fed Ae. japonicus females were released in the late afternoon in a large outdoor cage containing three resting boxes. The next morning, temperature treatments were applied to the boxes, creating a “cool” (over all experiments around 18 °C), and a “warm” (around 35 °C) microhabitat in addition to an untreated “ambient” (around 26 °C) one. The mosquitoes resting within the three boxes were counted five times, every 2 h between 9h and 17h. The highest proportions of mosquitoes (e.g. up to 21% of blood-fed ones) were found in the cool box while both blood-fed and sugar-fed mosquitoes avoided the warm box. The mean resting temperatures of Ae. japonicus were below the ambient temperatures measured by a nearby meteorological station, and this was more pronounced at higher outdoor temperatures and in blood-fed as compared to sugar-fed mosquitoes. Thus, over all experiments with blood-fed mosquitoes, the calculated average resting temperature was 4 °C below the outdoor temperature. As mosquitoes prefer cooler resting places than temperatures measured by weather stations in summer, models to predict mosquito-borne disease outbreaks need to account for the thermoregulatory behaviour of mosquitoes, especially in the wake of climate change.

A Methodology to Design a Wind Transfer Function: Application to the Valdevaqueros Dune (SW Spain)

In general, weather forecasting has been significantly developed at a large scale and, joined with statistical techniques, is used to predict at a local scale. However, there is no way to propagate winds between two nearby locations; this is a spatial transference, for example, for the waves. After studying coastal dunar systems affected by winds, we have proposed a way for the spatial propagation of wind for scales under 10 km. The proposed transference is based on local data, and it is developed in an easy and accurate way by different regression methods and the wind profile theory. The aim of this article is to establish a methodology for achieving a wind transfer function for local applications. For this purpose, we analyzed and compared data from a field experiment and from a nearby weather station. A combination of the wind profile and statistical downscaling technique formed the basis of this research, which leads to transfer equations for wind speeds and directions. To clarify the procedure, the proposed methodology was applied to the Valdevaqueros Coastal Dune in order to develop a transfer function using time series data from a nearby meteorological station located in Tarifa.

Radiative cooling scale method for correcting hourly surface air temperature error in numerical weather prediction models

AbstractGlobally, a variety of numerical weather prediction (NWP) models are employed by national agencies. However, the derived outputs from these models are generally inadequate for precise cultivation management due to bias error. This study uses observational data acquired over several months to develop an equation for correcting surface air temperature (SAT) error in NWP model outputs. With the proposed method, model outputs are converted into potential temperature to cancel the influence of differences between computational elevation and real elevation. The potential temperature errors in the NWP model outputs are corrected using a linear regression equation in which a radiative cooling scale (RCS) serves as the independent variable. The RCS is computed using air temperature observed at a nearby meteorological observation station, together with upper‐level pressure data from the NWP model. Versions of the correction models are developed for specific situations: day, night, and upper‐level meteorology, classified into six groups. In an application of the proposed method, the root mean square error (RMSE) of the corrected SAT output from a global spectral model operated by the Japan Meteorological Agency (JMA) was 1.2–1.6 K; the RMSE of the corrected SAT output from a nonhydrostatic model operated by the JMA was 1.2–1.5 K. The proposed method corrected the NWP model SAT output errors to a level of accuracy similar to that of data corrected using the Kalman filtering technique without the requirement for constant observation at the target site.

Climate Change Impacts and Adaptation in a Hill Farming System of the Himalayan Region: Climatic Trends, Farmers’ Perceptions and Practices

Farming communities in the hills and mountains of the Himalayan region are some of the most vulnerable to the changing climate, owing to their specific biophysical and socioeconomic conditions. Understanding the observed parameters of the changing climate and the farmers’ perceptions of it, together with their coping approaches, is an important asset to making farming communities resilient. Therefore, this study aimed to explore the observed change in climatic variables; understand farmers’ perceptions of the changing climate; and document their adaptation approaches in farming systems in the mid-hills of the central Himalayas. Data on the observed change in climatic variables were obtained from the nearby meteorological stations and gridded regional products, and farmers’ perceptions and their adaptation practices were collected from household surveys and from the interviews of key informants. The analysis of temperature data revealed that there has been a clear warming trend. Winter temperatures are increasing faster than summer and annual temperatures, indicating a narrowing temperature range. Results on precipitation did not show a clear trend but exhibited large inter-annual variability. The standardized precipitation index (SPI) showed an increased frequency of droughts in recent years. Farmers’ perceptions of the changing climate are coherent with the observed changes in climatic parameters. These changes may have a substantial impact on agriculture and the livelihood of the people in the study area. The farmers are adapting to climate change by altering their farming systems and practices. Location-specific adaptation approaches used by farmers are valuable assets for community resilience.

Regional tree-ring oxygen isotope deduced summer monsoon drought variability for Kumaun-Gharwal Himalaya

The precipitation in the Kumaun-Gharwal (Uttarakhand) Himalaya (KGH) is predominantly regulated by the Indian summer monsoon (ISM) and has been declining over the last century. However, because of limited historical data, it is difficult to place this recent decreasing trend in the context of pre-anthropogenic influences and understand a fuller range of hydroclimate scenarios for the region. Thus, we developed a 508-year regional tree-ring stable oxygen isotope (δ18OTR) record for multiple coniferous taxa (Abies spectabilis, Cedrus deodara and Picea smithiana) for the KGH, which spanned 1508–2015 CE. The δ18OTR record explained 35.8% of instrumental June–August Palmer Drought Severity Index (JJAPDSI) variance modelled from a nearby meteorological station. The JJAPDSI reconstruction shows regional coherency with both instrumental and proxy-based independent hydroclimatic records providing confidence that it is a reliable proxy to assess the long term hydroclimatic variability of the region. We identified decreasing strength of JJAPDSI reconstruction in KGH during recent decades indicating intensified drought. This is in the context of pre-anthropogenic influences and understand a fuller range of hydroclimate scenarios for the region. The δ18OTR also has coherency with Pacific and Indian Ocean sea surface temperature variability illustrating the broader teleconnections that influence the regional hydroclimate.

Irrigation of protected pepper crops according to growth stage using dynamic evapotranspiration estimates increases the water use efficiency

Current knowledge on crop irrigation in greenhouses is limited, and most irrigation measures are based on evapotranspiration (ET) estimated using external climatic conditions. Moreover, crop water requirement estimation by the Penman-Monteith (PM) model assumes complete coverage of the soil by the plants and usually does not account for partial cover, which changes during the growth of annual crops. The main goals of the present research were: (i) to develop and apply irrigation demand estimates for greenhouse pepper crops during their growth, based on internal meteorological conditions and real-time plant characteristics; and (ii) to examine the effect of that irrigation on yield. Experiments were conducted during two consecutive seasons in greenhouse pepper crops in the Jordan Valley region of eastern Israel. Four irrigation treatments were applied based on four different ET models: (i) PM model for outside reference evapotranspiration based on the FAO56 methodology, also denoted as the commercial treatment; (ii) PMrb model for pepper plants, which is a standard PM model in which a boundary layer resistance replaces the aerodynamic resistance; (iii) Shuttleworth-Wallace Layer (SWL) model which considers the soil and plants as separate interactive layers; (iv) Shuttleworth-Wallace Patch (SWP) model which considers the soil and plants as independent patches. The commercial treatment used external climatic conditions from a nearby meteorological station, whereas the other three treatments used internal meteorological conditions measured inside the greenhouse. Results showed a high correlation (R2 > 0.88) between leaf area index, vegetation cover fraction, and plant height during plant growth. Using the same meteorological conditions inside the greenhouse, the seasonal ET estimated by the PMrb model, which considers full vegetation cover, was larger by 21-28% than the SWL and SWP models, which consider the partial plant cover. Irrigation doses were deduced from ET multiplied by crop coefficients derived in a previous study for the same crop and structures. Using average data of the two seasons, we found that seasonal irrigation based on the two SW models was lower by 5-15% than that based on PMrb, and by 35-42% than the commercial irrigation. The water use efficiency of the commercial treatment was the lowest, while for the other treatments it was about 0.02 ton m-3 and higher by about 40% than the commercial. We conclude that irrigation of annual crops based on partitioning ET into evaporation and transpiration based on crop coverage and using internal climatic conditions allows water-savings without significant yield loss.

The Urmia playa as a source of airborne dust and ice-nucleating particles – Part 2: Unraveling the relationship between soil dust composition and ice nucleation activity

Abstract. Ice-nucleating particles (INPs) originating from deserts, semi-arid regions, and dried lakebeds may cause heterogeneous ice nucleation, impacting cloud properties. Recently, due to climate change and water scarcity, abandoned agricultural lands with little surficial crust and negligible vegetation cover have become an increasing source of atmospheric dust worldwide. Unlike deserts, these areas are rich in soluble salt and (bio-)organic compounds. Using soil samples from various sites of the Lake Urmia playa (LUP) in northwestern Iran and airborne dusts collected at nearby meteorological stations, we elucidate how minerals, soluble salts, and organic matter interact to determine the IN activity of saline soils and dust. X-ray powder diffraction shows that the mineralogical composition is dominated by K-feldspars (microcline), quartz, carbonates, and clay minerals. The samples were stripped stepwise of organic matter, carbonates, and soluble salts. After each removal step, the ice nucleation (IN) activity was quantified in terms of onset freezing temperatures (Thet) and heterogeneously frozen fractions (Fhet) by emulsion freezing experiments using differential scanning calorimetry (DSC). We examined the influence of soluble salts and pH on microcline and quartz in emulsion freezing experiments, comparing these with reference suspensions of microcline and quartz exposed to salt concentrations and pH levels characteristic of the LUP samples. These analyses, combined with correlations between Thet and Fhet, allow us to identify the components that contribute to or inhibit IN activity. The LUP dusts turn out to be very good INPs, with freezing onset temperatures around 248 K in immersion freezing experiments. Interestingly, their IN activity proves to be dominated by the relatively small share of (bio-)organic matter (1 %–5.3 %). After organic matter removal, the remaining IN activity (Thet≈244 K) can be traced back to the clay fraction, because Thet and Fhet correlate positively with the clay mineral content but negatively with quartz and microcline. We attribute the inability of quartz and microcline to act as INPs to the basic pH of the LUP samples as well as to the presence of soluble salts. After additionally removing soluble salts and carbonates, the IN activity of the samples increased again significantly (Thet≈249 K), and the negative correlation with quartz and microcline turned into a slightly positive one. Removing carbonates and salts from the natural samples leads to an increase in Thet and Fhet as well, indicating that their presence also suppresses the IN activity of the (bio-)organic INPs. Overall, this study demonstrates that mineral and organic INPs do not just add up to yield the IN activity of soil dust but that the freezing behavior is governed by inhibiting and promoting interactions between the components.