High resolution monthly precipitation isotope estimates across Australia from machine learning

River damming and drought affect water cycle dynamics in an ephemeral river based on stable isotopes: The Dagu River of North China

Continuously measuring the efficiency of wastewater treatment plants is crucial to progress in sanitation management. Regulations for decentralized wastewater treatment plants (WWTP) can include rudimentary specifications for sporadic sampling, unencouraging continuous monitoring, and missing crucial domestic wastewater (DW) variability, especially in low- and middle-income countries. However, few studies have focused on modeling and understanding spatiotemporal DW variability. We developed and calibrated an agent-based model (ABM) to understand spatial and temporal DW variability, its role in estimated WWTP efficiency, and provide recommendations to improve sampling regulations. We simulated DW variability at various spatial and temporal resolutions in Santa Ana Atzcapotzaltongo, Mexico, focusing on chemical oxygen demand (COD) and total suspended solids (TSS). The model results show that DW variability increases at higher spatiotemporal resolutions. Without a proper understanding of DW variability, treatment efficiency can be overestimated or underestimated by as much as 25% from sporadic sampling. Sensor measurements at 6-min intervals over 3 hours are recommended to overcome uncertainty resulting from temporal variability during heavy drinking water demand in the morning. Reporting of sewage catchment areas, population sizes, and sampling times and intervals is recommended to compare WWTP efficiencies to overcome uncertainty resulting from spatiotemporal variability. The proposed model is a useful tool for understanding DW variability. It can be used to estimate the impact of spatiotemporal variability when measuring WWTP efficiencies, support improvements to sampling regulations for decentralized sanitation, and alternatively for designing and operating WWTPs.

Regional water cycle health evaluation and obstacle factor analysis: A case study of the Yellow River basin in Henan Province, China

Orbitally driven evolution of Asian monsoon and stable water isotope ratios during the Holocene: Isotope-enabled climate model simulations and proxy data comparisons

Stable isotope composition of δ2H and δ18O was investigated in the water resources of the Shwan sub-Basin northeast of Iraq. The study objects conceived the possible factors that affect the stable isotopes’ composition in precipitation additionally to achieve information concerning recharge processes and estimate the groundwater recharge sources. In this study, four precipitation samples were collected at the study area for the 2020–2021 hydrological year. Thirty-two groundwater samples and one surface water sample from Lesser Zab River (LZR) were collected during the same period for two sampling seasons. The results of observed meteorological data show a very small amount of precipitation for the sampling year. This year is considered a dry hydrological year with total annual precipitation of 100.62 mm compared with the previous 40 hydrological years with total annual precipitation of 325.43 mm. The isotopic composition in precipitation was highly varied as it primarily depends on environmental conditions. The depleted values are recognized with the increasing precipitation amount, whereas the enriched values were the most affected by evaporation. Back trajectory analysis revealed that stable isotopes in precipitation are primarily influenced by air masses and moisture sources. The sources of the trajectory that came from the Mediterranean Sea, Arabian Gulf and the Red Sea would lead to variations in the values of precipitation stable isotope. Stable isotope values in groundwater showed that the samples for both periods are located between the East Mediterranean water line (EMWL) and global meteoric water line (GMWL) close to the local meteoric water line (LMWL) indicating that the groundwater recharge is mainly through precipitation. Groundwater recharges by an indirect recharge mechanism from the LZR, based on stable isotope similarity between depleted stable isotopes in groundwater and river water. The estimated groundwater recharge based on weighted oxygen isotopes is about 9.2% of annual rainfall infers that the recharge during the sampling year was very low. The low recharge value experiences dry weather conditions from low precipitation amounts besides increasing evaporation during the current study.

Spatio-temporal variability in stable water isotopes of Brahmaputra river system and its implications.

The relative importance of spatial and temporal variability in shaping the distribution of soil microbial communities at a large spatial scale remains poorly understood. Here, we explored the relative importance of space versus time when predicting the distribution of soil bacterial and fungal communities across North China Plain in two contrasting seasons (summer versus winter). Although we found that microbial alpha (number of phylotypes) and beta (changes in community composition) diversities differed significantly between summer and winter, space rather than season explained more of the spatiotemporal variation of soil microbial alpha and beta diversities. Environmental covariates explained some of microbial spatiotemporal variation observed, with fast-changing environmental covariates-climate variables, soil moisture, and available nutrient-likely being the main factors that drove the seasonal variation found in bacterial and fungal beta diversities. Using random forest modeling, we further identified a group of microbial exact sequence variants (ESVs) as indicators of summer and winter seasons and for which relative abundance was associated with fast-changing environmental variables (e.g., soil moisture and dissolved organic nitrogen). Together, our empirical field study's results suggest soil microbial seasonal variation could arise from the changes of fast-changing environmental variables, thus providing integral support to the large emerging body of snapshot studies related to microbial biogeography.IMPORTANCE Both space and time are key factors that regulate microbial community, but microbial temporal variation is often ignored at a large spatial scale. In this study, we compared spatial and seasonal effects on bacterial and fungal diversity variation across an 878-km transect and found direct evidence that space is far more important than season in regulating the soil microbial community. Partitioning the effect of season, space and environmental variables on microbial community, we further found that fast-changing environmental factors contributed to microbial temporal variation.

<div>Atmospheric spatial and temporal variability are closely related with the former being relatively well observed compared to the latter. The former is also regularly assessed in the validation of numerical weather prediction models while the latter is more difficult to estimate. Likewise, thermodynamical fields and circulation are closely coupled calling for an approach that considers them simultaneously.  </div> <div>In this contribution, spatio-temporal variability spectra of the four major reanalysis datasets are discussed and applied for the validation of a climate model prototype.  A relationship between deficiencies in simulated variability and model biases is derived. The underlying method includes dynamical regime decomposition thereby providing a better understanding of the role of tropical variability in global circulation. </div> <div>Results of numerical simulations are validated by a 20th century reanalysis. A climate model was forced either with the prescribed SST or with a slab ocean model that updates SST in each forecast step.  Scale-dependent validation shows that missing temporal variance in the model relative to verifying reanalysis increases as the spatial scale reduces that appears associated with an increasing lack of spatial variance at smaller scales. Similar to variability, bias is strongly scale dependent; the larger the scale, the greater the bias. Biases present in the SST-forced simulation increase in the simulation using the slab ocean. The comparison of biases computed as a systematic difference between the model and reanalysis and between the SST-forced model and slab-ocean model (a perfect-model scenario) suggests that improving the atmospheric model increases the variance in the model on synoptic and subsynoptic scales but large biases associated with a poor SST remain at planetary scales.</div> <p> </p>

ABSTRACTThe Earth’s water cycle involves energy exchange and mass movement in the hydrosphere and thus sustains the dynamic balance of global hydrologic cycle. All water cycle variables on the Earth are closely interconnected with each other through the process of energy and water circulation. Observing, understanding and predicting the storage, movement, and quality of water remains a grand challenge for contemporary water science and technology, especially for researches across different spatio-temporal scales. The remote sensing observing platform has a unique advantage in acquiring complex water information and has already greatly improved observing, understanding, and predicting ability of the water cycle. Methods of obtaining comprehensive water cycle data are also expanded by new remote sensing techniques, and the vast amount of data has become increasingly available and thus accelerated a new Era: the Remote Sensing Big Data Study of Global Water Cycle. The element inversion, time and space reconstruction, and scale conversion are three key scientific issues for remote sensing water cycle in such Era. Moreover, it also presents a huge opportunity of capitalizing the combinations of Remote Sensing and Big Data to advance and improve the global hydrology and water security research and development, and uncork the new bottlenecks.

Hydrochemical and isotopic characteristics on the southern and northern slopes of the Himalayas: spatio-temporal controls and source apportionment

The South American Summer Monsoon (SASM) is a prominent feature of summertime climate over South America and has been identified in a number of paleoclimatic records from across the continent, including records based on stable isotopes. The relationship between the stable isotopic composition of precipitation and interannual variations in monsoon strength, however, has received little attention so far. Here we investigate how variations in the intensity of the SASM influence δ18O in precipitation based on both observational data and Atmospheric General Circulation Model (AGCM) simulations. An index of vertical wind shear over the SASM entrance (low level) and exit (upper level) region over the western equatorial Atlantic is used to define interannual variations in summer monsoon strength. This index is closely correlated with variations in deep convection over tropical and subtropical South America during the mature stage of the SASM. Observational data from the International Atomic Energy Agency-Global Network of Isotopes in Precipitation (IAEA-GNIP) and from tropical ice cores show a significant negative association between δ18O and SASM strength over the Amazon basin, SE South America and the central Andes. The more depleted stable isotopic values during intense monsoon seasons are consistent with the so-called ’‘amount effect‘’, often observed in tropical regions. In many locations, however, our results indicate that the moisture transport history and the degree of rainout upstream may be more important factors explaining interannual variations in δ18O. In many locations the stable isotopic composition is closely related to El Niño-Southern Oscillation (ENSO), even though the moisture source is located over the tropical Atlantic and precipitation is the result of the southward expansion and intensification of the SASM during austral summer. ENSO induces significant atmospheric circulation anomalies over tropical South America, which affect both SASM precipitation and δ18O variability. Therefore many regions show a weakened relationship between SASM and δ18O, once the SASM signal is decomposed into its ENSO-, and non-ENSO-related variance.

Aim: While it is well recognised that climate can vary spatially so that grapevine phenology significantly varies within a winegrowing region, the magnitude of temporal variability in climate determining factors to engender a significant difference in phenology over time can only currently be described in general terms. This study’s aim was to quantify and compare the temporal variability, and its interaction with spatial variability, in grapevine phenology and three derived viticulture climate indexes for three viticulture regions in eastern Australia with varying topographies, latitudes and continentalities.Methods and Results: Maximum and minimum temperature data were spatially interpolated to produce fine scale topoclimate maps at 30 m resolution for every day of a 20 year period from 1998 to 2018 for the three viticulture regions. Grapevine phenology modelling using a heat accumulation methodology was then applied to estimate growing season temperature, cool night index and post-harvest growing degree days (GDD) for every 30 m map pixel of the three regions in each of the 20 growing seasons.Conclusions: Summary statistics that quantify the spatial and year-to-year temporal variability and the interaction of spatial and temporal variability (i.e. spatiotemporal variability) demonstrated significant differences in grapevine phenology between each of three regions. The key conclusion was that within-region temporal variability in growing season temperature, cool night index and post-harvest GDD exceeded within-region spatial variability.Significance and impact of the study: This is the first study to quantify temporal variability in modelled grapevine phenology, compare this with the level of spatial variability and also consider the interaction of spatial and temporal variability in grapevine phenology within viticultural regions. This study opens many potential lines of further investigation into the effect of temporal variability on wine production and the cultural terroir factors that develop within a wine region as a result, particularly in the context of forecast future increases in inter-annual temperature variability.

Lakes are major emitters of methane (CH4); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003-2020) and in situ measured data (2011-2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH4 emissions and found that the random forest (RF) model achieved the best fitting accuracy (R2 = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH4 emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH4 emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003-2020) daily time series of CH4 emissions. This study demonstrates the great potential of satellites to map lake CH4 emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.

In response to the global imperative of reducing greenhouse gas emissions, the optimisation of renewable energy systems under regionally favourable conditions has become increasingly essential. Solar irradiance forecasting plays a pivotal role in enhancing energy planning, grid reliability, and long-term sustainability. However, in the context of Turkey, existing studies on solar radiation forecasting often rely on traditional statistical approaches and are limited to single-site analyses, with insufficient attention to regional diversity and deep learning-based modelling. To address this gap, the present study focuses on Turkey’s Mediterranean region, characterised by high solar potential and diverse climatic conditions and strategically relevant to national clean energy targets. Historical data from 2020 to 2023 were used to forecast solar irradiance patterns up to 2026. Five representative locations—Adana, Isparta, Fethiye, Ulukışla, and Yüreğir—were selected to capture spatial and temporal variability across inland, coastal, and high-altitude zones. Advanced deep learning models, including artificial neural networks (ANN), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), were developed and evaluated using standard performance metrics. Among these, BiLSTM achieved the highest accuracy, with a correlation coefficient of R = 0.95, RMSE = 0.22, and MAPE = 5.4% in Fethiye, followed by strong performance in Yüreğir (R = 0.90, RMSE = 0.12, MAPE = 7.2%). These results demonstrate BiLSTM’s superior capacity to model temporal dependencies and regional variability in solar radiation. The findings contribute to the development of location-specific forecasting frameworks and offer valuable insights for renewable energy planning and grid integration in solar-rich environments.

Size at maturity is incorporated into stock assessments of walleye pollockTheragra chalcogramma. For eastern Bering Sea walleye pollock, however, this important biological variable has not been examined since 1976 and possible interannual and geographic variations have never been evaluated. Maturity condition and fish length data were collected from 10,197 walleye pollock aboard factory trawlers during winter 2002 and 2003. We also analyzed a smaller, previously unanalyzed data set on maturity collected annually by National Marine Fisheries Service scientists during eight echo integration‐trawl surveys over 1989‐2002. Length at 50% maturity (L50) was estimated by year and area by logistic regression using maximum likelihood methods. Spatial (within subareas) and temporal (interannual) variability were found. For instance, fish matured at the smallest lengths north of the Pribilof Islands and in the years 1989, 1991, and 1995 and at the largest lengths in the southeastern Bering Sea and in the years 2001 and 2002. We found evidence that the variability in size at maturity was related to biological mechanisms. Length at 50% maturity appeared to be directly related to growth as measured by length at age. Moreover, an inverse relationship between walleye pollock biomass andL50suggests that growth is density dependent. This spatiotemporal variability in size at maturity has implications for estimates of spawning stock biomass and should be incorporated into the annual stock assessments for setting annual catch specifications.