Meteorological Stations Research Articles

A novel error decomposition and fusion framework for daily precipitation estimation based on near-real-time satellite precipitation product and gauge observations

Near-real-time satellite precipitation products (SPPs) possess inherent application prospects in the hydro-meteorological field due to their convenient acquisition and accessibility. Integrating gauge-based measurements with near-real-time SPPs is an effective approach for achieving precise spatial precipitation estimates at daily scale. This study proposed a newly developed error decomposition and fusion framework, named EDGWR, which integrates error decomposition and geographically weighted regression (GWR). The final merged product, denoted as IMERG-EDGWR, was obtained from the near-real-time Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-E) product. IMERG-EDGWR was compared with the raw IMERG-E, near-real-time IMERG-L, post-real-time IMERG-F, global multi-source merged precipitation data (MSWEP), interpolated results (IDW and OK), and direct application of GWR outcomes (IMERG-GWR), utilizing the daily ground measurements from 12 meteorological stations located in the Yellow River source region (YRSR). The evaluation results from 2014 to 2018 revealed that IMERG-EDGWR exhibited significant enhancements over the raw IMERG-E, surpassed the research-level IMERG-F, and generally outperformed IDW, OK, MSWEP, and IMERG-GWR. Notably, IMERG-EDGWR enhances the detection of heavy precipitation events, refining estimates of both magnitude and frequency for precipitation over 25 mm. During the winter season, IMERG-EDGWR produced the most accurate precipitation estimates with notable improvement of precipitation detection capabilities. An experiment reducing input data by excluding gauge observations from the GPCC dataset tested the robustness of the EDGWR algorithm, confirming its superiority even with diminished input data. The merging framework proposed in this study constitutes an efficacious and implementable solution to enhance the accuracy of near-real-time SPPs and is expected to be implemented in different regions in future research.

Methodology for the assessment of poor-data water resources.

Surface hydrologic modeling becomes a problem when insufficient spatial and temporal information is available. It is common to have useful modeling periods of less than 15 years. The purpose of this work is to develop a methodology that allows the selection of meteorological and hydrometric stations that are suitable for modeling when information is scarce in the area. Based on the scarcity of data, a series of statistical tests are proposed to eliminate stations according to a decision-making process. Although the number of stations decreases drastically, the information used is reliable and of adequate quality, ensuring less uncertainty in the surface simulation models. Individual basin modeling can be carried out considering the poor data. The transfer of parameters can be applied through the nesting of basins to have information distributed over an extensive area. Therefore, temporally and spatially extended modeling can be achieved with information that preserves statistical parameters over time. If data management and validation is performed, the modeled watersheds are well represented; if this is not done, only 26% to 50% of the runoff is represented.

Drivers of seasonal rainfall variability over the Angolan and Namibian plateaus

AbstractSouthern Africa has been strongly affected by ongoing climate change in recent decades. Rainfall variability is modulated by regional patterns of moisture advection and convergence in the lower troposphere. Using reanalysis, ground and satellite‐based rainfall products and observations from 10 weather stations, we perform a synoptic and climatological analysis focussing on atmospheric circulation, moisture transport and their relationship with rainfall anomalies over the Angolan and Namibian Plateau region. Results clearly show that a stronger (weaker) Zambezi low level jet (LLJ) magnitudes are associated with above (below) normal rainfalls over the main Angolan and Namibian plateaus. With lower confidence, a stronger Limpopo LLJ may also lead to enhanced rainfall over Namibia and southeast Angola. The Zambezi LLJ moisture fluxes are moderately controlled by Mozambique Chanel Trough and Angola Low intensities, while the Limpopo LLJ intensities have very low influence from the Mozambique Chanel Trough and Angola Low, respectively. The Angola Low in its tropical phase is associated with deeper moisture convergence and stronger vertical velocities, leading to higher amounts of precipitable water within the air column, thus enhancing precipitation over the region. It is shown that the major moisture source of rainfall, which is advected to via the Zambezi LLJ, is the Indian Ocean. Meanwhile, the Atlantic Ocean plays a minor role. Given the current lack of observations and projected climate change, further research and investments are urgently needed in the region, for example, regarding the expansion of the surface data network.

Evaluation and refinement of ERA5-land 2 m atmospheric temperature in GNSS precipitable water vapor

Since the land 2 m air temperature from European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5-land) is vulnerable to interference by regional climate characteristics and local topography, how to objectively and accurately evaluate its accuracy and then establish a refined model is the key to improve its multifaceted and deep-level applications. This study proposed a systematic and complete refinement method for ERA5-land air temperature based on the densely distributed weather stations in Shandong Province in eastern China, including the compensation of elevation matching bias (EMB) caused by ASTER GDEM and the overall refinement using the remove-and-restore model (RRM). Taking the hottest July and the coldest December in this area, surface air temperature, atmospheric weighted mean temperature (Tm) and GNSS precipitable water vapor (PWV) were used to verify the feasibility of this method. The results show that: (1) Whether the temperature is high in July or low in December, the accuracy can be improved by about 42.2 % when the refinement method was used. Especially for high altitude areas (the summit of Mount Tai, 1553 m above sea level), the accuracy can be improved by 74.5 %. (2) In contrast to only the temperature lapse rate (TLR) correction, the average root mean square (RMS) can be reduced by 7.4 % and 60.4 % in July and 7.0 % and 59.5 % in December for the met stations less than 150 m in altitude and the peak station of Mount Tai, respectively, when the new method was used. (3) Compared with the Tm and PWV computed by interpolated temperature without any correction, the accuracy of Tm can be improved by 31.2 % and 34.1 % in July and December for weather station in the altitude of 100 ∼ 350 m after the TLR correction, EMB compensation and RRM refinement successively. Meanwhile, the accuracy of PWV can be improved by 47.8 % in July and 42.0 % in December, respectively, and especially for PWV high-value moments. Besides, after correction by RRM, both Tm and PWV in low altitude areas achieve at least 20 % accuracy improvement. In summary, the systematic and complete refinement method proposed in this study are important to significantly improve the ERA5-land 2 m temperature availability and GNSS PWV accuracy, and the compensation of EMB is mainly effective for high altitude areas.

New secondary decomposition ensemble support vector regression for monthly drought index forecasting

Drought prediction on a monthly timescale is conducive to the timely identification of drought characteristics and provides a reference for drought mitigation. Current research is generally based on drought indices on timescales of three months or more for drought prediction, whereas the presence of highly nonlinear and drastic changes in the characteristics of drought indices leads to suboptimal drought prediction for drought index sequences on monthly timescales. In this study, a secondary decomposition model based on extreme-point symmetric mode decomposition (ESMD), variational mode decomposition (VMD), and support vector regression (SVR) was investigated to predict a monthly-scale drought index. To evaluate the predictive ability of the model, it was applied to 100 meteorological stations in the Pearl River Basin of China and compared and analyzed using four comparative models. The results showed that the model proposed in this study has better performance index values, and the prediction accuracies of all stations are improved compared with the four comparative models, with the most significant improvement in the Nash–Sutcliffe efficiency (NSE) index value, followed by the root mean square error (RMSE) and mean absolute error (MAE) index values, and the smallest improvement in the Wilmot consistency index (WIA) index value. The monthly-scale drought quadratic decomposition (ESMD-VMD) combined prediction model established in this study provides a novel method for drought prediction.

Hybrid wind-solar energy potential modeling using ERA5 and solar irradiation data in google Earth Engine

Renewable energies play a significant role in mitigating the climate change impacts. Wind and solar energy are projected to supply larger amount to the total renewable energy capacity of many nations. Long-term wind and solar data availability and analysis is vital for sustainable renewable energy farming. In this research, we analyze wind speed u- and v-components at 10 m height of the ERA5 daily reanalysis aggregates at a mesh of 31 Km during a long period (2010–2020) over the Red Sea and the Gulf of Suez coasts of Egypt. Google Earth Engine (GEE) code is developed and used for the analysis. The prime objective is to derive estimates on the spatio-temporal variability of speed, direction, and the wind energy estimates in the area. Further, the solar energy potential has been evaluated using reference data of the PVOUT – Photovoltaic power potential (kWh/m2/day) data, at nearly 1 km resolution, which is normalized at the evaluated sites, and then used to rank sites with their solar energy potential. Evaluation of estimates is carried out at selected thirteen sites distributed along the coasts to better understand the hybrid wind-solar energy potential. Frequency analysis is carried out to the wind speed spatial estimates to demarcate highest frequency wind events potential for wind farming. Reference in-situ wind speed observations available at daily and monthly time scales for Hurghada weather station is used for investigating the reanalysis data performance. The Hurghada site had the highest average wind speed (6.7 m s−1), frequency of events (75 %), and mean daily (60.4 kWh) and annual (22.02 MWh) wind energy. The windiest time intervals were the September month, 2017 year, and the summer season. The north had wind energy directions of 130°, while the south had 110°. Trend analysis showed declining trends for northern locations and rising yearly wind speed estimates for Berenice (0.005) and Halayeb (0.002). El-Tor location had the highest solar energy output, with a daily mean of 5.52 kWh/m2 and an annual mean of almost 2015 kWh/m2. The Hurghada, Jemsa, G. Zeit, and El-Tor sites achieved the highest rankings in terms of hybrid wind-solar energy potential. The results can be very valuable for optimal location of hybrid solar-wind energy facilities and provide viable and promising solutions for sustainably meeting Egypt's growing clean energy demands.

Precipitation and cloud Parameters's characteristics of stratus in summer in Ningxia, eastern of northwest China

Stratus is the main artificial seeding target for weather modification in Ningxia. Meteorological satellites can detect macro and microcosmic physical parameters of large-scale stratus. Researching precipitation and cloud parameter characteristics of stratus can provide scientific and technological support for understanding the development and evolution of stratus to identify seeding conditions and effects. Eight typical stratus precipitation events of the summers of 2016–2021 of Ningxia were selected, using the nine point average method, that matched characteristic cloud parameters retrieved by FY-2G satellite with precipitation classification data of 25 national meteorological observation stations in Ningxia. These stratus precipitation characteristics were correlated with cloud parameters. The results indicate that precipitation decreased rapidly from the southeast to the northwest of Ningxia. In the synoptic background of precipitation, the cloud top height, super-cooled layer thickness, cloud optical thickness, cloud particle effective radius, and cloud liquid water content were all larger than those in the synoptic background of no precipitation. The spatial distribution feature manifests as gradually increased from southeast to northwest. The cloud top temperature and cloud blackbody temperature were smaller than those in the synoptic background of no precipitation. The cloud top temperature was near the cloud “seeding temperature window”, and the depth of the super-cooled layer was thick. This was in line with the “Seeder-Feeder” structure characteristic of cold stratus.

Asian rust of soybean favorability Mato Grosso-Brazil

This study presents the map that indicates favorable environmental conditions for the development of Asian soybean rust in Mato Grosso, for a given period. It’s about a plant's disease caused by the fungus Phakopsora pachyrhizi, which has soybean as its main host. By affecting the plantation, anticipates the ripening of the grains, which can considerably reduce your productivity. The map is the reproduction, for Mato Grosso, of the image developed by Alves et al. (2011) for the state of Minas Gerais, generated from a fuzzy logic-based rule system. The input variables for this system are temperature and leaf wetness duration, while the output is the area below the disease progression curve. To supply it, data were collected from automatic meteorological stations located in the state and neighboring regions. Records were made available hourly for January 2019. In possession of these results, for interpolation were tested the inverse distance weighting and kriging models. The method that showed the best results was the first, with an exponent equal to two.

Utilization of Big Data Analytics in Understanding Climate Change Patterns: Latest Trends and Findings on Climate Patterns in Indonesia

Climate change has become a global concern. In Indonesia, a deep understanding of climate change patterns is increasingly important in planning effective mitigation and adaptation measures. In this study, we explore the utilization of big data analytics to understand climate change patterns and identify trends findings related to climate patterns in Indonesia based on variables such as temperature, humidity, rainfall, and wind speed. Data used were collected from satellite observations, weather stations from 2010 to 2020. Purpose this study aims to explore the utilization of big data analytics in understanding climate change patterns in Indonesia, with a focus on identifying trends and recent findings in climate patterns. Methodology–We applied big data analytics techniques such as descriptive statistical analysis, spatial regression modeling, and cluster analysis to identify climate change patterns and recent trends in Indonesia. Findings–Through big data analysis, we successfully identified significant climate change patterns in Indonesia. Practical implications the findings of this study can provide a better understanding of climate change dynamics in Indonesia, serving as a basis for decision-making in natural resource management, disaster risk mitigation, and climate change adaptation strategies at both regional and national levels and can serve as a reference for researchers.

Climatic characteristics of heat wave events (1959–2023) in Jiangxi Province, China

This study investigated the temporal and spatial patterns of heat wave (HW) events in Jiangxi, China. Our analysis is based on the observation data, including daily maximum temperature and daily average air relative humidity from 81 meteorological stations from 1959 to 2023. We also examined the impact of HW events on summer electricity consumption by taking the capital city of Jiangxi, Nanchang, as a case study. The results reveal an overall increase in the frequency of HW events over the past 65 years, with a pronounced increase since 1997, particularly in central and southern Jiangxi. The onset of HW events has generally occurred earlier, while their cessation has been delayed across most areas of Jiangxi Province, resulting in extended HW periods. Economic growth in regions like the Jitai Basin, the Ganfu Plain, and the hilly areas of northeastern Jiangxi has been accompanied by more intense and prolonged HW events. In Nanchang City, the summer electricity load increased by approximately 9% on holidays compared to working days and by about 20% on high-temperature days compared to normal conditions. These insights highlight the need for adaptive strategies to mitigate the impacts of increasing HW occurrences.

Surviving the cold: Assessing long-term outcomes among Korean CKD patients exposed to low perceived temperature during winter

Perceived temperature (PT), which encompasses meteorological factors such as wind speed, cloud cover, and humidity, reflects the actual effect of temperature on the human body. However, limited data exist on the health implications of prolonged exposure to low temperatures during winter in individuals with chronic kidney disease (CKD). We investigated the association between winter PT and long-term outcomes among CKD patients. A total of 32,870 CKD patients from three tertiary hospitals in Seoul were enrolled in this retrospective study (2001–2018). PT was calculated using Staiger's equation, integrating temperature data from 29 automated weather stations across Seoul, along with dew point temperature, wind velocity, and cloud cover data. Kriging interpolation was utilized to estimate PT values at the patients' locations. Overall mortality and major adverse cardiovascular events (MACEs) were assessed using a time-varying Cox proportional hazards model. Additionally, the Cox regression model evaluated PT corresponding to temperature thresholds for cold surge watches or warnings. Over a median follow-up of 6.14 ± 3.96 years, 6147 deaths (18.7%) were recorded. We found that as the average or minimum PT and Ta decreased by 1 °C, the risk of overall mortality significantly increased. In multivariable analyses, the hazard ratio (HR) for the average PT was 1.049 (95% confidence interval [CI] 1.028–1.071), and that for the minimum PT was 1.038 (CI 1.027–1.052). Furthermore, a cold surge warning at a PT of −25.63 °C indicated an HR of 1.837 (CI 1.764–1.914) and a C-index of 0.792. The increased risk of mortality was more pronounced in patients with low or middle socioeconomic statuses. For MACEs, lower average and minimum PT and Ta were associated with an increased risk, following a similar trend to overall mortality, although not all results reached statistical significance. These findings emphasize the importance of targeted public health policies to mitigate risks among vulnerable CKD patients.

Cucumber Downy Mildew Disease Prediction Using a CNN-LSTM Approach

It is of great significance to develop early prediction technology for controlling downy mildew and promoting cucumber production. In this study, a cucumber downy mildew prediction method was proposed by fusing quantitative disease information and environmental data. Firstly, the number of cucumber downy mildew spores during the experiment was collected by a portable spore catcher, and the proportion of cucumber downy mildew leaf area to all cucumber leaf area was recorded, which was used as the incidence degree of cucumber plants. The environmental data in the greenhouse were monitored and recorded by the weather station in the greenhouse. Environmental data outside the greenhouse were monitored and recorded by a weather station in front of the greenhouse. Then, the influencing factors of cucumber downy mildew were analyzed based on the Pearson correlation coefficient method. The influencing factors of the cucumber downy mildew early warning model in greenhouse were identified. Finally, the CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) algorithm was used to establish the cucumber downy mildew incidence prediction model. The results showed that the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and determination coefficient (R2) of the CNN-LSTM network model were 0.069, 0.0098, 0.0991, and 0.9127, respectively. The maximum error between the predicted value and the true value for all test sets was 16.9398%. The minimum error between the predicted value and the true value for all test sets was 0.3413%. The average error between the predicted and true values for all test sets was 6.6478%. The Bland–Altman method was used to analyze the predicted and true values of the test set, and 95.65% of the test set data numbers were within the 95% consistency interval. This work can serve as a foundation for the creation of early prediction models of greenhouse crop airborne diseases.

Assessing Drought Vulnerability in the Brazilian Atlantic Forest Using High-Frequency Data

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation and maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from using imputation methods due to potential bias. Drought quantification involved the computation of the aridity index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann–Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting that geographical location and elevation do not strongly influence observed dryness trends.

Active Wildland Fires in Central Chile and Local Winds (Puelche)

Central Chile (CC, latitudes 32–40°S) experienced very active fires in 2017 and 2023. These fires burned large areas and killed many people. These unprecedented fires for CC presented a need for more defined fire weather conditions on the synoptic scale. In this paper, fire weather conditions were analyzed using various satellite-derived fire data (hotspots, HSs), wind streamlines, distribution maps of wind flow and temperature, and various synoptic-scale weather maps. Results showed that local winds, known as Puelche, blew on the peak fire days (26 January 2017 and 3 February 2023). The number of HSs on these days was 2676 and 2746, respectively, about 90 times the average (30). The occurrence of Puelche winds was confirmed by streamlines from high-pressure systems offshore of Argentina to the study area in CC. The formation of strong winds and high-temperature areas associated with Puelche winds were identified on the Earth survey satellite maps. Strong winds of about 38 km h−1 and high temperatures above 32 °C with low relative humidity below 33% were actually observed at the weather station near the fire-prone areas. Lastly, some indications for Puelche winds outbreaks are summarized. This paper’s results will be used to prevent future active fire occurrences in the CC.

Factors determining the timing of ice appearance on the Kama Reservoir

The analysis and assessment of the correlations of the ice dates vice the zero-isotherm dates, dates of water temperatures transition through 2 and 3 °C were carried out. The initial data were long-term series of observations of air temperature at weather stations, water temperature and ice dates at the gauge stations of Roshydromet for the whole period of the reservoir existence (1956–2021). As the characteristic under study, the dates of ice formation in autumn were used. Main results: verification of previously obtained correlations on independent data (1996–2021) showed that the accuracy of issued forecasts (P) was decreased by 20% compared with the accuracy on dependent data (1956–1995); an analysis of the whole period applicability (1956–2021) for constructing predictive correlations with an assessment of their justification showed that there was no noticeable refinement. The accuracy of the forecasts based on the dates of water temperatures transition through 3 °C has not changed, while forecasts based on the zero-isotherm dates and the dates of water temperatures transition through 2 °C, has noticeably decreased at all stations; the use of data only for the period of modern climate changes (1996–2021) led to the following: correlations of the ice dates vice the zero-isotherm dates and water temperature through 2 °C did not pass the criteria S and P. This period is characterized by a decrease in the frequency of small forecast errors ( 4–5 days) and an increase in large errors ( 6–11 days) by 2–3 times; a forecast technique is proposed based on the separation of the studied correlations for years with different meteorological conditions in autumn (years with stable cooling and with heat returns). The criterion for using this or that dependence in a particular year is the weather forecast. Forecasts issued according to such dependencies have a higher accuracy.

A comparative assessment of five precipitation products in the Saharan desert of Morocco: Sakia El Hamra basin case study.

This study evaluates the performance of five satellite precipitation products (GPM IMERG, TRMM 3B42, ERA5, PERSIANN, and CHIRPS) compared to monthly observations from two weather stations (Laayoune and Essmara) over 2001-2017 using statistical metrics including correlation coefficient (CC) and mean square error (MSE). The results reveal notable differences between products. On a monthly timescale, GPM IMERG shows the best overall accuracy with a MSE of 16.8mm/month. However, TRMM 3B42 exhibits higher temporal correlations with a CC around 0.83. The analysis provides insights into product capabilities and limitations for hydrological monitoring in data-sparse regions. Key findings include the superior performance of latest generation datasets like GPM alongside biases requiring localized calibration. The study delivers an assessment framework to guide integration of multiple satellite estimates for enhanced precipitation quantification and hydroclimatic modeling in water-stressed environments.

Correlated spatiotemporal downscaling of Euro‐CORDEX climatic data for infrastructure resilience assessment

AbstractA methodology is presented for downscaling the Euro‐CORDEX climatic projections in order to derive spatially and temporally correlated weather fields that can be used for risk and resilience assessment of large‐scale asset portfolios or interconnected infrastructure. The temporal resolution of the Euro‐CORDEX data is downscaled to a 10 min basis by employing a modified analogue‐type approach that utilizes the k‐NN algorithm along with measurements from weather stations. The aim is to generate composite “Frankenstein” days comprising 144 jigsaw pieces of observed 10 min timeseries that are scaled and/or shifted, and matched together to form a continuous daily record. These point‐estimates, valid only at the locations of the weather stations, are expanded spatially by employing high‐fidelity weather intensity measure fields that provide variable yet synchronous patterns of weather parameters at all locations of interest. As a case study, the Euro‐CORDEX projections for wind, temperature, and precipitation are downscaled for the Metsovo‐Panagia segment of Egnatia Odos highway in Greece, by employing high‐fidelity Computational Fluid Dynamic simulations that account for the topography of the site to simulate turbulent wind flows. These are combined with measurements of two local weather stations to generate the Frankenstein timeseries and corresponding weather fields that can be used for estimating operability, recovery and direct/indirect loss statistics on an event‐by‐event basis for an ensemble of interconnected highway assets.

Risk Assessment of Compound Dry–Hot Events for Maize in Liaoning Province

Extreme climates can result in marked damage to crop yields and threaten regional and global food security. Maize is a major grain crop in Liaoning Province which is severely affected by dry and hot weather events. This study was based on the maize yield and daily meteorological data from various meteorological stations in Liaoning Province from 2000 to 2020. We calculated the standardized dry and hot index and constructed a method for estimating the maize yield reduction risk under compound dry–hot events (CDHE) in Liaoning Province by combining the coefficient of variation in maize yield reduction, yield loss risk index, and the frequency of CDHE during yield reduction. The results showed that the high-risk area for the occurrence of CDHE in maize was Chaoyang City, located in the western part of Liaoning Province. Cities in the low-risk area accounted for approximately 64.3% of the total number of cities in Liaoning Province, mainly in the central and northern parts of Liaoning Province. This study emphasizes the impact of CDHE on agricultural production and provides an index for assessing the risk of CDHE on maize production.

Modeling habitat suitability of Dorema ammoniacum D Don. in the rangelands of central Iran

The purpose of this study was to evaluate the predictive accuracy of habitat suitability models, identifying the potential distribution range of Dorema ammoniacum, and its habitat requirements in the rangelands of Yazd province, central Iran. Bafgh, Mehriz and Nadoushan, were three habitats that were identified, and sampling was conducted in each habitat using a random-systematic method. A set of 10 plots were established (at equal distances) along 350 m long 18 transects. Soil samples (two depths: 0–30 and 30–60 cm from 36 profiles) were collected and measured in the laboratory. Elevation, slope, and aspect maps were derived, and climate information was collected from nearby meteorological stations. The habitat prediction of the species was modeled using Logistic Regression (LR), Maximum Entropy (MaxEnt), and Artificial Neural Network (ANN). The Kappa coefficient and the area under the curve (AUC) were calculated to assess the accuracy of the forecasted maps. The LR model for habitat prediction of the studied species in Mehriz (K = 0.67) and Nadoushan (K = 0.56) habitats were identified as good. The MaxEnt model predicted the habitat distribution for the selected species in Bafgh and Mehriz habitats as excellent (K = 0.89, AUC = 0.76, K = 0.89, AUC = 0.98), and in the Nadoushan habitat as very good (K = 0.78, AUC = 0.85). However, the ANN model predicted Bafgh and Nadoushan habitats as excellent and Mehriz habitat as very good (K = 0.87, K = 0.90, and K = 0.63, respectively). In general, in order to protect species D. ammoniacum, the development of its habitats in other areas of Yazd province and the habitats under study in conservation programs should be given priority.

Snow Depth Estimation and Spatial and Temporal Variation Analysis in Tuha Region Based on Multi-Source Data

In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a−1 over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones.