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In recent years, the water level of the Dongting Lake (DTL) has been continuously low, and the wetland area and landscape pattern have changed significantly. Considering the obvious spatial heterogeneity of water regime changes in different waters of the DTL, this paper takes two core areas of the South Dongting Lake Nature Reserve (SDLNR) as study areas and analyzes the spatial distribution characteristics of the wetland blue–green landscape patterns by using remote sensing image data and hydrological and meteorological data. The multi-scale correlation between runoff, precipitation, temperature, and evapotranspiration in the SDLNR was studied via cross-wavelet transform analysis. The results show the following: (1) The change in the blue–green spatial patterns in different regions in different periods is inconsistent, and this inconsistency is related to the topography, climate, and human activities in each region; (2) there are seasonal fluctuations in precipitation, air temperature, and evapotranspiration in the SDLNR. Among them, the annual mean temperature shows a rising trend and passes the significance test with 95% confidence, while the annual mean precipitation and annual mean evapotranspiration show no significant change trend; and (3) our Pearson correlation analysis and cross-wavelet change results show that precipitation and temperature are strongly correlated with runoff, with a resonance period of 8–16 months, while the correlation between evapotranspiration and runoff is not significant. We recommend that policymakers establish an effective early warning system and make plans to store water through micro-terrain transformation in possible climate change treatments and strategies. [ABSTRACT FROM AUTHOR]

The study analyzed available data series of the Sava River's water temperature measured at the Zagreb gauging station. Official data from the Croatian Meteorological and Hydrological Service (DHMZ) in Zagreb were utilized. Over the 73 years from 1948 to 2020, there are only 53 years with complete measurement records. Despite this limiting fact, it was considered important to analyze the behavior of the Sava River's water temperatures in Zagreb over the past 70 years, during which a significant increase in air temperatures has been observed in the region, particularly in the city of Zagreb. Analyses were conducted on the characteristic (minimum, mean, and maximum) water temperatures over timescales of years, months, and days. The relationship between water temperatures (TW) and air temperatures (TA) measured at the Grič Observatory and the flows (Q) of the Sava River in Zagreb were investigated. A trend of rising water temperatures was observed throughout the entire period from 1948 to 2020, with the intensity significantly increasing in the recent period starting from 1988. The trend of rising air temperatures is the primary driver of the increase in the water temperatures of the Sava River in Zagreb. However, the impact of reduced water flow, especially during the warm season, should not be overlooked. This effect is amplified by the observed trend of decreasing minimum flows of the Sava during the warm season, from June to September. As a result, the most significant rise in water temperatures of the Sava River in Zagreb occurs during prolonged low-water summer periods, particularly in July. A strong trend has been observed in the increasing number of days per year with mean daily water temperatures of the Sava River exceeding 20 °C. This higher water temperature occurs increasingly earlier in the year, lasts longer, and ends later, often extending into September. [ABSTRACT FROM AUTHOR]

Water temperature is a critical variable for water quality control and management. The primary objective of this paper was to develop and compare simple methods to estimate hourly water temperatures in rivers. The wave function (WF) model, originally used to calculate hourly air temperature, was modified and applied to eight Alabama rivers. The results show significant improvement by using the modified WF model instead of direct linear and non-linear (polynomial and logistic) regression models with time lags (4-5 h). The average Nash-Sutcliffe coefficient (NS) used to evaluate model accuracy for the eight rivers improved from 0.71 for the linear model to 0.89 for the modified WF model with NS for most rivers exceeding 0.90. A lumped modified WF model was also developed by combining water temperature data for all eight rivers and can be applied for rivers in Alabama when no observed water temperatures are available to develop a site-specific WF model. The procedure to develop a modified WF model can be applied to other regions. [ABSTRACT FROM AUTHOR]

We investigated the changes of main climate factors and their relationships with the catches of offshore fisheries of the Republic of Korea over the past 30 years (1981 to 2010), using a trend analysis and Pearson's correlation analysis. This study focused on seven fish species that have been preferred in Korean cuisine for centuries. Not only the air temperature, but also the sea surface temperature (SST) on the coast of Korea has risen (p < 0.05) in the period. The rise in SST over 30 years is significantly correlated with the rise in temperature (p < 0.01), but not with precipitation. Over the past 30 years, catches of anchovies and squid have increased significantly, while Alaska/walleye pollock has become almost extinct (p < 0.01). The analysis of this study indicates that cold water fish species have decreased or disappeared in Korean fisheries and have been replaced by warm water fish species. Our findings suggest that the fish species caught in Korean offshore fisheries have changed due to climate change, especially global warming. These results also suggest that there may be a threat to the food security of Koreans, so it is necessary to take measures to protect this food resource. [ABSTRACT FROM AUTHOR]

We propose the methodology of building the process-driven models for medium-term forecasting of spring floods (including catastrophic ones) in the mountainous areas, the hydrological analysis of which is usually much more complicated in contrast to plains. Our methodology is based on system analytical modeling of complex hydrological processes in 34 river basins of the Altai-Sayan mountain country. Consideration of 13 types of landscapes as autonomous hydrological subsystems influencing rivers' runoff (1951–2020) allowed us to develop the universal predictive model for the most dangerous April monthly runoff (with ice motion), which is applicable to any river basin. The input factors of the model are the average monthly air temperature and monthly precipitation for the current autumn–winter period, as well as the data on the basin landscape structure and relief calculated by GIS tools. The established universal dependences of hydrological runoffs on meteorological factors are quite complex and formed under influence of solar radiation and physical–hydrological patterns of melting snow cover, moistening, freezing, and thawing of soils. The model shows the greatest sensitivity of April floods to the landscape composition of river basins (49% of common flood variance), then to autumn precipitation (9%), winter precipitation (3%), and finally, to winter air temperature (0.7%). When it is applied to individual river basins, the forecast quality is very good, with the Nesh–Sutcliffe coefficient NSE = 0.77. In terms of the accuracy of process-driven predictive hydrological models for the mountainous areas, the designed model demonstrates high-class performance. [ABSTRACT FROM AUTHOR]

Agriculturally used wet grassland sites (WGSs) with shallow water tables are considered to be sites with a special microclimate. Meteorological measurement series, examining the air temperature (Ta) and vapour pressure (VP) in three regions, reveal differences between WGSs and outside the lowland. The results show that the average annual Ta at all three WGSs is significantly lower than in their surrounding area (0.7 to 1.0 K). The differences are minimally larger in the summer half-year than in the winter half-year (1.0 vs. 0.7 K in the Spreewald region, 0.7 vs. 0.6 K in the Havelland region). The differences cannot only be explained by higher evapotranspiration (ET), but are mainly due to the ground heat balance of the sites with shallow water tables and organic soils. The VPs of the WGSs and the surrounding area only differ significantly from each other in the summer months and do not vary as clearly as the Ta. While the VP is higher in the Spreewald wetland than in the surrounding area (+0.05 kPa), it is lower in Paulinenaue than in that surrounding area (−0.04 kPa). The reason for this is different ET due to the different site conditions. [ABSTRACT FROM AUTHOR]

The air temperature trends measured at the central meteorological station Varaždin and the water temperature measured at the Botovo station on the Drava River were analyzed from 1 January 1969 to 31 December 2021. Analyses were performed for three different time scales: year, month, and day. Mann–Kendall testing statistically determined the significant trends over the analyzed period and found increasing air and water temperatures. From 1975 to 1989, three reservoirs of different volumes and water surface areas were built. The Botovo water measuring station is 11 km from the third largest reservoir and 28 km from the mouth. Applying the day-to-day (DTD) method, we determined that the variations in the daily air temperatures are significantly higher than the simultaneous variations in the daily water temperatures. Also, the rise in water temperatures at the downstream water measuring station Botovo was influenced by the construction of reservoirs. The commissioning of the second reservoir in 1982 caused a significant rise in water temperature at the Botovo station. Trends in water temperature increase during all months of the year were statistically significant, while air temperature trends were statistically significant during the warm parts of the year. [ABSTRACT FROM AUTHOR]

The objective of the paper consisted in determining the effect of macroscale types of NAO, AO, EA, EAWR, SCAND, and AMO atmospheric circulation on changes in water temperature in Polish rivers. The study has made use of a broad body of hydrometeorological materials covering daily water temperature values for 96 water gauge stations located on 53 rivers and air temperature values for 43 meteorological stations. Percentage shares of positive and negative coefficients of correlation of annual, seasonal, and monthly circulation type indices with air and river water temperature were determined, demonstrating the character of teleconnection. Determinations were made of water temperature deviations in positive and negative phases of the analyzed indices from average values from the years 1971–2015, and their statistical significance ascertained. Research has shown that relations between the temperature of river waters in Poland and macroscale circulation types are not strong, however they are noticeable, sometimes even statistically significant, and both temporally and spatially diverse. NAO, AO, EA, and AMO indices are characterized by a generally positive correlation with temperature, whereas SCAND and EWAR indices are characterized by a negative correlation. Research showed a varying impact of types of atmospheric circulation, with their effectiveness increasing in the winter season. The strongest impact on temperature was observed for the positive and negative NAO and AO phases, when deviations of water temperature from average values are correspondingly higher (up to 1.0 °C) and lower (by a maximum of 1.5 °C), and also for the positive and negative SCAND phases, when water temperature are correspondingly lower (by a maximum of 0.8 °C) and higher (by 1.2 °C) than average values. The strongest impact on water temperature in summer, mainly in July, was observed for AMO. The results point to the complexity of processes determining the thermal regime of rivers and to the possibility of additional factors—both regional and local—exerting an influence on their temporal and spatial variability. [ABSTRACT FROM AUTHOR]

Water temperature (WT) is a critical control for various physical and biochemical processes in riverine systems. Although the prediction of river water temperature has been the subject of extensive research, very few studies have examined the relative importance of elements affecting WT and how to accurately estimate WT under the effects of cascaded dams. In this study, a series of potential influencing variables, such as air temperature, dew temperature, river discharge, day of year, wind speed and precipitation, were used to forecast daily river water temperature downstream of cascaded dams. First, the permutation importance of the influencing variables was ranked in six different machine learning models, including decision tree (DT), random forest (RF), gradient boosting (GB), adaptive boosting (AB), support vector regression (SVR) and multilayer perceptron neural network (MLPNN) models. The results showed that day of year (DOY) plays the most important role in each model for the prediction of WT, followed by flow and temperature, which are two commonly important factors in unregulated rivers. Then, combinations of the three most important inputs were used to develop the most parsimonious model based on the six machine learning models, where their performance was compared according to statistical metrics. The results demonstrated that GB3 and RF3 gave the most accurate forecasts for the training dataset and the test dataset, respectively. Overall, the results showed that the machine learning model could be effectively applied to predict river water temperature under the regulation of cascaded dams. [ABSTRACT FROM AUTHOR]

Mongolia's Selenga sub-basin of the Lake Baikal basin is spatially extensive, with pronounced environmental gradients driven primarily by precipitation and air temperature on broad scales. Therefore, it is an ideal region to examine the dynamics of the climate and the hydrological system. This study investigated the annual precipitation, air temperature, and river discharge variability at five selected stations of the sub-basin by using Mann-Kendall (MK), Innovative trend analysis method (ITAM), and Sen's slope estimator test. The result showed that the trend of annual precipitation was slightly increasing in Ulaanbaatar (Z = 0.71), Erdenet (Z = 0.13), and Tsetserleg (Z = 0.26) stations. Whereas Murun (Z = 2.45) and Sukhbaatar (Z = 1.06) stations showed a significant increasing trend. And also, the trend of annual air temperature in Ulaanbaatar (Z = 5.88), Erdenet (Z = 3.87), Tsetserleg (Z = 4.38), Murun (Z = 4.77), and Sukhbaatar (Z = 2.85) was sharply increased. The average air temperature has significantly increased by 1.4 °C in the past 38 years. This is very high in the semi-arid zone of central Asia. The river discharge showed a significantly decreasing trend during the study period years. It has been apparent since 1995. The findings of this paper could help researchers to understand the annual variability of precipitation, air temperature, and river discharge over the study region and, therefore, become a foundation for further studies. [ABSTRACT FROM AUTHOR]

Measured evapotranspiration (LE) of screenhouse banana plantations was utilized to derive and compare two types of machine-learning models: artificial neural network (ANN) and multiple linear regression (MLR). The measurements were conducted by eddy-covariance systems and meteorological sensors in two similar screenhouse banana plantations during two consecutive seasons, 2016 and 2017. Most of the study focused on the season of 2017, which includes a more extended data set (141 days) than 2016 (52 days). The results show that in most cases, the ANN model was superior to MLR. When trained and validated over the whole data set of 2017, the ANN and MLR models provided R2 of 0.92 and 0.89, RMSE of 37.5 and 45.1 W m−2 and MAE of 21 and 27.2 W m−2, respectively. Models could be derived using a training dataset as short as one month and still provide reliable estimations. Depending on the chosen calendar month for training, R2 of the ANN model varied in the range 0.81–0.89, while for the MLR model, it ranged 0.73–0.88. When trained using a data set as short as one week, there was some deterioration in model performance; the corresponding ranges of R2 for the ANN and MLR models were 0.37–0.89 and 0.37–0.71, respectively. As expected for a screenhouse decoupled environment, solar radiation (Rg) was the variable that most influenced LE; using Rg as the sole input variable, the ANN model resulted in R2, RMSE and MAE of 0.88 and 47 W m−2 and 25.6 W m−2, respectively, values that are not much worse than using all input variables (solar radiation, air temperature, air relative humidity and wind speed). Using Rg alone as the input to the MLR model only slightly deteriorated R2 (=0.88); however, RMSE (=124 W m−2) and MAE (=75.7 W m−2) were significantly larger compared to a model based on all input variables. To examine model performance in different seasons, models were trained using the data set of 2017 and validated in 2016, and vice versa. Results showed that training on the data of 2017 and validation in 2016 provided superior results than the opposite, presumably since the 2017 measurement season was longer and weather conditions were more diverse than in the 2016 data set. It is concluded that the ANN and MLR models are reasonable options for estimating evapotranspiration in a banana screenhouse. [ABSTRACT FROM AUTHOR]

Gaps often occur in eddy covariance flux measurements, leading to data loss and necessitating accurate gap-filling. Furthermore, gaps in evapotranspiration (ET) measurements of annual field crops are particularly challenging to fill because crops undergo rapid change over a short season. In this study, an innovative deep learning (DL) gap-filling method was tested on a database comprising six datasets from different crops (cotton, tomato, and wheat). For various gap scenarios, the performance of the method was compared with the common gap-filling technique, marginal distribution sampling (MDS), which is based on lookup tables. Furthermore, a predictor importance analysis was performed to evaluate the importance of the different meteorological inputs in estimating ET. On the half-hourly time scale, the deep learning method showed a significant 13.5% decrease in nRMSE (normalized root mean square error) throughout all datasets and gap durations. A substantially smaller standard deviation of mean nRMSE, compared with marginal distribution sampling, was also observed. On the whole-gap time scale (half a day to six days), average nMBE (normalized mean bias error) was similar to that of MDS, whereas standard deviation was improved. Using only air temperature and relative humidity as input variables provided an RMSE that was significantly smaller than that resulting from the MDS method. These results suggest that the deep learning method developed here is reliable and more consistent than the standard gap-filling method and thereby demonstrates the potential of advanced deep learning techniques for improving dynamic time series modeling. [ABSTRACT FROM AUTHOR]

Ice phenomena are construed as the occurrence of ice in water irrespective of its structure, form, and duration. One of the most frequently discussed research problems is the possibility of using long-term ice phenology as an indicator of changes of the thermal conditions of ambient air. The study used correlation analysis and regression models in order to determine changes in the parameters studied over time. In order to compare the ice regime of the study reservoir and other lakes in the region, discriminant function analysis, principal components analysis (PCA), and canonical redundancy analysis (RDA) were applied. During the 52 winter seasons studied (1964–2015), there were weak but still statistically significant trends concerning the increase in air temperature in the region (by 0.3 °C per decade), the reduction in the number of days with ice cover (by 8.6 days per decade) and the decline in the maximum and average thicknesses of lake ice (by 2.0 cm and 1.2 cm per decade). The low average depth and volume capacity are reflected in the rapid freezing rate of the reservoir, and its location results in a longer duration of ice cover, greater ice thickness, and later dates of its melting. [ABSTRACT FROM AUTHOR]

Cyanobacteria blooms are a worldwide concern for water bodies and may be promoted by eutrophication and climate change. The prediction of cyanobacterial blooms and identification of the main triggering factors are of paramount importance for water management. In this study, we analyzed a comprehensive dataset including ten-years measurements collected at Lake Varese, an eutrophic lake in Northern Italy. Microscopic analysis of the water samples was performed to characterize the community distribution and dynamics along the years. We observed that cyanobacteria represented a significant fraction of the phytoplankton community, up to 60% as biovolume, and a shift in the phytoplankton community distribution towards cyanobacteria dominance onwards 2010 was detected. The relationships between cyanobacteria biovolume, nutrients, and environmental parameters were investigated through simple and multiple linear regressions. We found that 14-days average air temperature together with total phosphorus may only partly explain the cyanobacteria biovolume variance at Lake Varese. However, weather forecasts can be used to predict an algal outbreak two weeks in advance and, eventually, to adopt management actions. The prediction of cyanobacteria algal blooms remains challenging and more frequent samplings, combined with the microscopy analysis and the metagenomics technique, would allow a more conclusive analysis. [ABSTRACT FROM AUTHOR]

Climate change poses potential challenges to sensitive areas, such as high-elevation regions. The Yarlung Zangbo River (YLZR) basin is located in the southeast of the Qinghai-Tibetan Plateau. It contains large amounts of snow and numerous glaciers that are vulnerable to climate change. Based on daily observational data at 17 meteorological stations in and around the YLZR basin during 1957–2015, the variability of precipitation, air temperature, and streamflow were analyzed. The nonparametric Mann–Kendall test, Sen's slope estimate method, cross wavelet transform (XWT), and wavelet coherence (WTC) were used to identify the annual seasonal trends. the abrupt changes of precipitation and air temperature, and their associations with large-scale circulation. The results showed that the YLZR basin experienced an overall rapid warming and wetting during the study period, with an average warming rate of 0.33 °C/10 a and wetting rate of 4.25 mm/10a, respectively. Abrupt change points in precipitation and air temperature occurred around the 1970s and 1990s, respectively. The abrupt change points of three hydrological stations occurred around the late 1960s and the late 1990s, respectively. The precipitation, annual average temperature, and the streamflow of the three hydrological stations were negatively correlated with the Pacific decadal oscillation (PDO) and the multivariate El Niño-Southern Oscillation (ENSO) index (MEI), reaching a significant level of 0.05. [ABSTRACT FROM AUTHOR]

Rainfall interception is an important process of the water cycle that can have significant influence on surface runoff and groundwater storage. Since rainfall interception measurements are rare and time consuming, rainfall interception estimation can be made indirectly using different meteorological variables. Experimental data of rainfall interception for birch and pine trees was measured at an experimental plot located in an urban area of Ljubljana, Slovenia in this study. A copula model was applied to predict the rainfall interception using meteorological variables, namely air temperature and vapour pressure deficit data. The copula model performance was compared to some other models such as decision trees, multiple linear regressions, and exponential functions. Using random sampling, we found that the copula model where Khoudraji-Liebscher copula functions were used yielded slightly smaller root mean square error (RMSE) and mean absolute error (MAE) values than other tested methods (i.e., RMSE and MAE results for birch trees were 24.2% and 18.2%, respectively and RMSE and MAE results for pine trees were 25.0% and 19.6%, respectively). The results demonstrate that the copula-based proposed method and other tested models could be used for the prediction of rainfall interception at the considered plot and in the wider surroundings. Furthermore, these models could also be applied for the prediction of rainfall interception for these two tree species in other locations under similar vegetation and meteorological conditions. [ABSTRACT FROM AUTHOR]