Water Cycle Research Articles

Sensitivity and Uncertainty Analysis of the GeeSEBAL Model Using High-Resolution Remote-Sensing Data and Global Flux Site Data

Actual evapotranspiration (ETa) is an important component of the surface water cycle. The geeSEBAL model is increasingly being used to estimate ETa using high-resolution remote-sensing data (Landsat 4/5/7/8). However, due to surface heterogeneity, there is significant uncertainty. By optimizing the quantile values of the reverse-modelling automatic calibration algorithm (CIMEC) endpoint-component selection algorithm under extreme conditions through 212 global flux sites, we obtained the optimized quantile values of 11 vegetation types of cold- and hot-pixel endpoint components (Ts and NDVI). Based on the observation data of the global FLUXNET tower, the sensitivity of 20 parameters in the improved geeSEBAL model was determined through Sobol’s sensitivity analysis. Among them, the parameters dT and SAVI,hot were confirmed as the most sensitive parameters of the algorithm. Subsequently, we used the differential evolution Markov chain (DE-MC) method to analyse the uncertainty of the parameters in the geeSEBAL model used the posterior distribution of the parameters to modify the sensitive parameter values or ranges in the improved geeSEBAL model and to simulate the daily ETa. The results indicate that by analysing the end element components of the geeSEBAL model (Ts and NDVI), quantile numerical optimization and parameter optimization can be performed. Compared with the original algorithm, the improved geeSEBAL model has significantly improved simulation performance, as shown by higher R2 values, higher NSE values, smaller bias values, and lower RMSE values. The most suitable values of the predefined parameter Zoh were determined, and the reanalysis of meteorological data inputs (relative humidity (RH), temperature (T), wind speed (WS), and net radiation (Rn)) was also found to be an important source of uncertainty for the accurate estimation of ETa. This study indicates that optimizing the quantiles and key parameters of the model end component has certain potential for further improving the accuracy of the geeSEBAL model based on high-resolution remote-sensing data in estimating the ETa for various vegetation types.

Changes of blue and green water in arid inland dissipation area based on coupled surface water and groundwater model

Study regionThe Mainstream in the Tarim River Basin, Southern Xinjiang, China Study focusClimate change and human activities have significantly altered the water cycle, and water security evaluation and management are urgent. In arid and semi-arid areas, the assessment of blue and green water is particularly important. In this study, the MIKE SHE model is used to simulate the spatial and temporal changes of blue and green water resources in the mainstream of the Tarim River Basin (TRB) from 1990 to 2050 under land use and climate change conditions. The scarcity and vulnerability of blue-green water are introduced to evaluate the water security of the basin. The impacts of different land uses on blue-green water resources were also calculated according to the model zoning. New hydrological insights for the regionThe results indicate that from 1990 to 2050, blue water resources show a spatial pattern of gradual decrease from upstream to downstream. Compared to blue water, green water is dispersed more evenly over space. Future climatic scenarios will impact water security, as will changes in blue and green water security in terms of time and space. By comparing the influence of ecological water transport on the change of blue and green water before and after 2000, it was found that environmental water transport plays a certain role in improving the blue water scarcity in downstream of the TRB. The study is significant in maintaining regional water security and ecosystem stability.

Uncertainty in model estimates of global groundwater depth

Abstract Knowing the depth at which groundwater can be found below the land surface is critical for understanding its potential accessibility by ecosystems and society. Uncertainty in global scale water table depth (WTD) limits our ability to assess groundwater’s role in a water cycle altered by changing climate, land cover, and human water use. Global groundwater models offer a top–down pathway to gain this knowledge, but their uncertainty is currently poorly quantified. Here, we investigate four global groundwater models and reveal steady-state WTD disagreements of more than 100 m for one-third of the global land area. We find that model estimates of land areas with shallow groundwater at <10 m depth vary from 10% to 71% (mean of 23%). This uncertainty directly translates into subsequent assessments, as land areas with potential groundwater accessibility for forests, population, and areas equipped for irrigation, differ substantially depending on the chosen model. We explore reasons for these differences and find that contrary to observations, 3 out of 4 models show deeper water tables in humid than in arid climates and greatly overestimate how strongly topographic slope controls WTD. These results highlight substantial uncertainty associated with any global-scale groundwater analysis, which should be considered and ultimately reduced.

Scallop farming impacts on dissolved organic matter cycling in coastal waters: Regulation of the low molecular weight fraction

To elucidate the impacts of scallop farming on the biogeochemical characteristics of low molecular weight (LMW, <1 kDa) dissolved organic matter (DOM), samples collected from a bay scallop mariculture area (MA) and its surrounding areas were determined for absorption and fluorescence spectroscopy after microfiltration and centrifugal ultrafiltration. The values of absorption coefficient a350 showed a spatial variation trend of inshore area (IA) > MA > non-mariculture area (NMA) for both bulk (<0.7 μm) and LMW fractions. Four fluorescent components, namely two protein-like components (tryptophan-like C1 and tyrosine-like C2) and two humic-like components (microbial humic-like C3 and terrestrial humic-like C4), were identified. Scallop farming influenced DOM transformation by altering phytoplankton abundance and promoting microbial degradation. In July, the net contributions of phytoplankton to the spectroscopy parameters of LMW-DOM in the surface seawater were 11.0% for a350, 4.3% for C1, 0.8% for C2, 0.6% for C3 and 3.0% for C4, respectively; the corresponding values of bulk DOM in the surface seawater were 24.3% for a350, 20.1% for C1, 5.9% for C2, 2.0% for C3, 2.9% for C4, respectively. Compared with NMA, the contributions of microbial degradation to a350 in MA's surface seawater increased by 9.0% for LMW-DOM and 6.9% for bulk DOM in July; however, the effects on different fluorescent components varied. In August, compared with NMA, the contributions of microbial degradation to spectroscopy parameters in the bottom water of MA decreased by 35.7% for a350, 6.3% for C2, 1.3% for C3, and 4.4% for C4 for LMW-DOM fraction; for bulk DOM, the corresponding contribution decreased by 10.8% for C1. These variations indicate that protein-like substances from scallop aquaculture are easily degraded into LMW substances, while humic-like substances degradation diminishes over time.

Compressive strength of nano concrete materials under elevated temperatures using machine learning

In this study, four Artificial intelligence (AI) - based machine learning models were developed to estimate the Residual compressive strength (RCS) value of concrete supported with nano additives of Nanocarbon tubes (NCTs) and Nano alumina (NAl), after exposure to elevated temperatures ranging from 200 to 800 degrees. These models were developed via adapting meta- heuristic models including the Water cycle algorithm (WCA), Genetic algorithm (GA), and classical AI models of Artificial neural networks (ANNs), Fuzzy logic models (FLM), in addition to the statistical method of Multiple linear regression (MLR). 156 post heating experimental results available as a literature data (represents four input parameters of temperature change, heat exposure duration, nanomaterial type, and replacement proportion) are used to achieve the study’s objective. Results of the developed models demonstrated that ANN and FLM have strong potential in predicting RCS. However, it is often infeasible to generate practical equations that relate input and output variables from these models. Upon analysing the results of the WCA and GA, it was found that WCA yielded the most accurate predictions based on all performance indicators. Furthermore, RCS prediction equations with superior accuracy were derived utilizing the meta-heuristic AI models of WCA and GA, with Mean absolute errors (MAEs) of 3.09 kg/cm² and 3.53 kg/cm² for the training, 1.91 kg/cm² and 2.72 kg/cm² for the validation, and 1.91 kg/cm² and 2.72 kg/cm² for the testing data sets, respectively. Additionally, sensitivity analysis via neural networks weights and SHAP investigation were performed to reveals the impact and relationship of the input variables with the output variables. Both techniques reveal that temperature degree and time of exposure had the highest positive impact on RCS value, followed by NAl and NCTs, in order.

The Synergistic Effect of the Same Climatic Factors on Water Use Efficiency Varies between Daily and Monthly Scales

The coupled processes of ecosystem carbon and water cycles are usually evaluated using the water use efficiency (WUE), and improving WUE is crucial for maintaining the sustainability of ecosystems. However, it remains unclear whether the WUE in different ecosystem responds synchronously to the synergistic effect of the same climate factors at daily and monthly scales. Therefore, we employed a machine learning-driven factor analysis method and a geographic detector model, and we quantitatively evaluated the individual effects and the synergistic effect of climate factors on the daily mean water use efficiency (WUED) and monthly mean water use efficiency (WUEM) in different ecosystems in China. Our results showed that (1) among the 10 carbon flux monitoring sites in China, WUED and WUEM exhibited the highest positive correlations with the near-surface air humidity and the highest negative correlation with solar radiation. The correlation between WUEM and climate factors was generally greater than that between WUED and climate factors. (2) There were significant differences in the order of importance and degree of impact of the same climate factors on WUED and WUEM in the different ecosystems. Among the 10 carbon flux monitoring sites in China, the near-surface air humidity imposed the greatest influence on the WUED and WUEM changes, followed by the near-surface water vapor pressure. (3) There were significant differences in the synergistic effects of the same climate factors on WUED and WUEM in the different ecosystems. Among the 10 carbon flux monitoring sites in China, the WUED variability was most sensitive to the synergistic effect of solar radiation and photosynthetically active radiation, while the WUEM variability was most sensitive to the synergistic effect of the near-surface air humidity and soil moisture. The research results indicated that synchronous responses of the WUE in very few ecosystems to the same climate factors and their synergistic effect occurred at daily and monthly scales. This finding enhances the understanding of sustainable water resource use and the impact of climate change on water use efficiency, providing crucial insights for improving climate-adaptive ecosystem management and sustainable water resource utilization across different ecosystems.

Research on a cloud model intelligent computing platform for water resource management

ABSTRACT As the demand for water management information systems continues to increase, addressing issues such as poor generalizability, low reusability, and difficulties in updating and maintaining water resource planning cloud model service platforms becomes crucial. To achieve goals like business-oriented functionality, high availability, and reliability, this study proposes constructing a cloud model service platform for basin water resource planning based on cloud computing technology and business workflows. This study couples water cycle models with multi-objective optimization models for water resource allocation, using digital topological water networks to achieve dynamic regional water resource allocation. The cloud service platform adopts a business-oriented modeling method based on B/S development architecture. This paper takes the Weihe River Basin as an example to simulate and analyze the evolution of the water cycle pattern and optimize the annual water resources allocation plan. Results show that: (1) the water cycle model of the cloud model service platform can better describe the runoff change process in the verification period; (2) through the cloud platform service model, the water shortage rate of the Weihe River Basin in 2025 is 7.95%. The research findings provide technical references and insights for intelligent water management and refined allocation of water resources in the Weihe River Basin.

Fit-for-Purpose Water in the Baltic Sea Region

Climate change is visible everywhere, including in Baltic Sea Region (BSR). One of the symptoms is water scarcity, occurring relatively locally but persisting over longer and longer periods. The European and Global Drought Observatories (https://drought.emergency.copernicus.eu) present increasing BSR areas where soil moisture is alarmingly low. Water shortages cause technical and organizational problems in water utilities, agriculture, energy, industry, etc. Hence, competition for water between industry, agriculture, and municipal services has emerged. Sustainable water resource management requires solutions that rationalize and optimize the use of available resources with particular care for the environment. One of the methods of remedying local problems is closing local water and nutrient cycles by recovering water from urban wastewater and using rainwater

Extensive iron–water exchange at Earth’s core–mantle boundary can explain seismic anomalies

Seismological observations indicate the presence of chemical heterogeneities at the lowermost mantle, just above the core–mantle boundary (CMB), sparking debate over their origins. A plausible explanation for the enigmatic seismic wave velocities observed in ultra-low-velocity zones (ULVZs) is the process of iron enrichment from the core to the silicate mantle. However, traditional models based on diffusion of atoms and penetration of molten iron fail to account for the significant iron enrichment observed in ULVZs. Here, we show that the chemical reaction between silicate bridgmanite and iron under hydrous conditions leads to profound iron enrichment within silicate, a process not seen in anhydrous conditions. Our findings suggest that the interaction between the core and mantle facilitates deep iron enrichment over a few kilometres at the bottom of the mantle when water is present. We propose that the seismic signatures observed in ULVZs indicate whole mantle convection, accompanied by deep water cycles from the crust to the core through Earth’s history.

Quantifying the Contributions of Vegetation Dynamics and Climate Factors to the Enhancement of Vegetation Productivity in Northern China (2001–2020)

Vegetation dynamics are critical to the terrestrial carbon and water cycle, with China recognized as one of the largest contributors to global greening due to significant variations in forest coverage. However, distinguishing the effects of vegetation changes from those of climate factors on vegetation productivity remains challenging. This study conducted a comprehensive analysis of vegetation productivity in Northwest China over the past two decades, focusing on the spatiotemporal patterns and drivers of gross primary production (GPP) within ecological restoration areas. Using trend analysis and ridge regression models, we assessed the relative contributions of climate factors and vegetation coverage changes to GPP dynamics. The results revealed a significant increase in both the GPP and vegetation coverage in Northern China from 2001 to 2020, with GPP rising by 6.7 g C m−2 yr−1 and forest coverage increasing by 0.08% per year. A strong positive correlation (r = 0.9) was observed between vegetation coverage changes and GPP. The increase in GPP was driven by both climate factors and changes in forest coverage, with climate factors contributing 61.0% and vegetation coverage changes contributing 39.0%. Among the climate factors, radiation, temperature, and precipitation contributed 15.4%, 6.4%, and 39.2%, respectively. The study highlights the critical role of ecological restoration efforts, particular in regions like the Less Plateau and Inner Mongolian Plateau, in enhancing vegetation productivity. These findings provide valuable insights for addressing desertification and inform strategies for ecological restoration and sustainable development in Northern China.

Analysis of Extreme Daily Rainfall in Cotonou, Benin (West Africa)

Global warming has a global impact on the water cycle and in particular on precipitation. This work is devoted to the analysis of the variability of extreme rainfall in the economic capital of Benin. For this purpose, the 58-year daily rainfall data collected at the Meteo Benin synoptic station in Cotonou between 1960 and 2018 are used. The annual daily maxima was established for the analysis. The generalized distribution of extreme values ​​(GEV) is used to estimate the quantiles according to the defined return periods. The daily heavy rainfall had a normal character with return periods of less than six years in Cotonou, between 1960 and 2018. There was a marginal increase in frequency of extreme rainfall since the beginning of 21st century. Extreme rainfall events may lead to floods as seen in 2010. Therefore, pro-active short- and long-term measures are the need of the hour to cope up against rainfall variability under the global warming scenario.

Single-Atom Doped Fullerene (MN4-C54) as Bifunctional Catalysts for the Oxygen Reduction and Oxygen Evolution Reactions.

Development of high-performance oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) catalysts is crucial to realizing the electrolytic water cycle. C60 is an ideal substrate material for single atom catalysts (SACs) due to its unique electron-withdrawing properties and spherical structure. In this work, we screened for a novel single-atom catalyst based on C60, which anchored transition metal atoms in the C60 molecule by coordination with N atoms. Through first-principles calculations, we evaluated the stability and activity of MN4-C54 (M = Fe, Co, Ni, Cu, Rh, Ru, Pd, Ag, Pt, Ir, Au). The results indicate that CuN4-C54, which is based only on earth-abundant elements, exhibited low overpotentials of 0.46 and 0.47 V for the OER and ORR, respectively, and was considered a promising bifunctional catalyst, showing better performance than the noble-metal ones. In addition, according to the linear relationship of intermediates, we established volcano plots to describe the activity trends of the OER and ORR on MN4-C54. Finally, d-band center and crystal orbital Hamiltonian populations methods were used to explain the catalytic origin. Suitable d-band centers lead to moderate adsorption strength, further leading to good catalytic performances.

The Properties of Microorganisms and Plants in Soils after Amelioration

Soil, as a vital foundation of the Earth’s biosphere, supports plant growth, carbon storage, water cycling, and nutrient supply, making it a crucial resource for ensuring global food production and ecosystem health [...]

Soil health – a perspective

Governments and organizations are expressing growing concerns about soil health, driven largely by uncertainties of food security with an increasing human population and unpredictable effects of climate change. Although considerable literature and debate exist, there is discord around the question, what is a healthy soil? This is not surprising, given the complex roles the soil provides, from the range of food, fiber and medical products, hosting a biodiverse community, and supporting the water and nutrient cycles. While a consensus seems to suggest that a soil in good health should be able to provide goods and services in perpetuity, this does not define soil health, rather its provisioning functions. To explore the question, ‘what is healthy?’, we propose an analogy comparing indicators of human and soil health. For example, to identify the cause of a symptom, we compare the diagnostic pH in both humans and soil, demonstrating the similarities between the way human and soil health concerns are addressed. Additionally, we consider the context that necessitates health and use a set of holistic predictors to link human and soil health further. In humans, genetics express many traits and can predispose one to certain illnesses or diseases, in the same way, parent material, soil texture, and length of time exposed to weathering can inform a soil’s capability and predisposition for certain habitats or uses. In both cases, science informs the state of health and appropriate management solutions. We posit the null hypothesis “the concept of human health cannot be applied to soil”.

Spatial–Temporal Variations in Water Use Efficiency and Its Influencing Factors in the Li River Basin, China

As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm−1·m−2, and it kept decreasing at a rate of 0.0072 gC· mm−1·m−2·a−1 in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland &gt; cropland &gt; forest &gt; shrubland.

A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.

Estimating transpiration globally by integrating the Priestley-Taylor model with neural networks

Abstract Transpiration (T), the component of evapotranspiration (ET) controlled by the vegetation, dominates terrestrial ET in many ecosystems; however, estimating it accurately, especially at the global scale, remains a considerable challenge. Existing approaches mostly rely on the relationship between T and photosynthesis, but untangling this relationship is difficult and leads to diverging T estimates. Limited in-situ measurements and the inability to directly measure transpiration from space further complicate the reliable assessment of this crucial process in the terrestrial water cycle. Here, we developed a new hybrid Priestley-Taylor (PT) model combined with an Artificial Neural Network (ANN) using globally available remote sensing and reanalysis data of soil moisture, vapor pressure deficit and windspeed. We also take advantage of the newly released global sap flow measurement network SAPFLUXNET. In the proposed approach, we avoid the parameterization of stomatal conductance by training the ANN on the PT-Coefficient α, obtained by inverting the PT equation. The results showed that our model framework can estimate T in different forest ecosystems based on few predictors. By utilizing forcings from independent datasets, we eliminate the reliance on in-situ measurements for predicting T. Through upscaling actual observations to a larger scale, this model framework helps alleviate the scarcity of T products. Intercomparison of T with ET partitioning methods based on eddy covariance data, shows high performances (KGE of 0.69 in Europe and 0.60 in North America), slightly improving estimates compared to other models. Analysis of contribution of T to ET across 100 FLUXNET sites result in a global mean of 55.2%. We believe that modelling T independent from the carbon cycle can support our understanding of land-atmosphere feedbacks and climate extremes in future research.

Tackling emerging contaminants from water streams: the LIFE PRISTINE project

Tackling emerging contaminants from water streams: the LIFE PRISTINE project The LIFE PRISTINE project aims to develop the PRISTINE Integrated Solution to remove contaminants of emerging concern (CECs) from water streams. This technology will be adaptable on a case-by-case basis for drinking water, to protect humans from CECs, and for wastewater, to protect the environment and remove these contaminants from the water cycle.

Ensemble Machine-Learning-Based Framework for Estimating Surface Soil Moisture Using Sentinel-1/2 Data: A Case Study of an Arid Oasis in China

Soil moisture (SM) is a critical parameter in Earth’s water cycle, significantly impacting hydrological, agricultural, and meteorological research fields. The challenge of estimating surface soil moisture from synthetic aperture radar (SAR) data is compounded by the influence of vegetation coverage. This study focuses on the Weigan River and Kuche River Delta Oasis in Xinjiang, employing high-resolution Sentinel-1 and Sentinel-2 images in conjunction with a modified Water Cloud Model (WCM) and the grayscale co-occurrence matrix (GLCM) for feature parameter extraction. A soil moisture inversion method based on stacked ensemble learning is proposed, which integrates random forest, CatBoost, and LightGBM. The findings underscore the feasibility of using multi-source remote sensing data for oasis moisture inversion in arid regions. However, soil moisture content estimates tend to be overestimated above 10% and underestimated below 5%. The CatBoost model achieved the highest accuracy (R2 = 0.827, RMSE = 0.014 g/g) using the top 16 feature parameter groups. Additionally, the R2 values for Stacking1 and Stacking2 models saw increases of 0.008 and 0.016, respectively. Thus, integrating multi-source remote sensing data with Stacking models offers valuable support and reference for large-scale estimation of surface soil moisture content in arid oasis areas.

How Well Does the F-10 Australian Curriculum Prepare Students to be Water Literate Citizens?

Abstract Global water availability and management are persistent challenges to sustainable futures, yet people may have limited understandings of water systems and may hold negative attitudes towards sustainable solutions. With education a mechanism for realising a water literate citizenry, this study asks: How well does the Australian curriculum prepare students to be water literate citizens? McCarroll and Hamann’s (2020) Dimensions of Water Literacy guided a document analysis of Version 9.0 of the F-10 Australian Science and Humanities and Social Sciences (HASS) curricula. Findings revealed that concepts related to water literacy were largely confined to the Year 4 Science and Year 7 Geography curricula. In Science, the dimensions of Science and Systems Knowledge and Local Knowledge were through concepts related to the natural and urban water cycle. In HASS, the Hydrosocial Knowledge dimension was privileged, owing to people’s interactions with water. While there were occurrences of Functional Knowledge in both curricula, the organisation of the curriculum according to knowledge and skills does not explicitly focus on the development of students’ positive attitudes and values towards water conservation, nor engage them in individual or collective decision-making and action. Implications for the Australian curriculum and what it means to be a water literate citizen are discussed.