Component Of Water Cycle Research Articles

A novel approach for precipitation modeling using artificial intelligence-based ensemble models

Precipitation is the primary component of the hydrologic water cycle and its accurate prediction plays a significant role for the planning, management and design of hydraulic structures. The objective of the study intended to explore a new approach to increase prediction efficiency of precipitation in the arid, semiarid and humid zones. The approach was implemented using relevant data from stations in Iraq and Nigeria. Support vector regression (SVR), artificial neural network (ANN) and adaptive neuro-fuzzy inference system models (ANFIS) were applied for a single modeling of 30 years monthly average precipitation data. Thereafter, a nonlinear ensemble and 2 linear ensemble techniques were applied to improve prediction efficiency and reliability of the single models. Error measures as well as a goodness of fit measure were the criteria employed to assess the performance potential of the models. Based on the results of this work it was found that ensemble modeling could improve prediction accuracy of the single models as much as 38% in the validation phase. It was also confirmed that the performance of artificial intelligence-based models can efficiently be improved by the application of ensemble modeling in the arid, semiarid and humid study stations of Iraq and Nigeria.

Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision

Evapotranspiration (E) is one of the most uncertain components of the global water cycle (WC). Improving global E estimates is necessary to improve our understanding of climate and its impact on available surface water resources. This work presents a methodology for deriving monthly corrections to global E datasets at 0.25∘ resolution. A principled approach is proposed to firstly use indirect information from the other water components to correct E estimates at the catchment level, and secondly to extend this sparse catchment-level information to global pixel-level corrections using machine learning (ML). Several E satellite products are available, each with its own errors (both random and systematic). Four such global E datasets are used to validate the proposed approach and highlight its ability to extract seasonal and regional systematic biases. The resulting E corrections are shown to accurately generalize WC closure constraints to unseen catchments. With an average deviation of 14% from the original E datasets, the proposed method achieves up to 20% WC residual reduction on the most favorable dataset.

Dynamics of evapotranspiration from concurrent above- and below-canopy flux measurements in a montane Sierra Nevada forest

Evapotranspiration (ET) from the land surface to the atmosphere is a major component of Earth's water cycle, and comprises both transpiration (T) of xylem water from plants and evaporation (E) of water from soils and vegetation surfaces. These two component fluxes respond differently to changes in temperature, water availability and atmospheric CO2 concentrations. Concurrent eddy covariance (EC) measurements above and below forest canopies provide a promising approach to partition ET into E and T. However, below-canopy EC measurements are rare, and questions remain regarding their spatial variability, canopy coupling, and temporal dynamics. To address these challenges, we measured and partitioned ET over more than three years, using concurrent above- and below-canopy EC towers in a montane forest at Sagehen Creek in California's Sierra Nevada mountains. This is the establishing study for the AmeriFlux site US-SHC. The main environmental control for ET was available energy; other important controls were canopy & soil temperature, soil moisture, vapor pressure deficit, and wind speed. Below-canopy measurements at two locations within the above-canopy footprint were similar to one another, suggesting low spatial heterogeneity in understory ET near the creek at our Sagehen site. We observed a total forest ET of 606 ± 50 mm yr−1 with 275 ± 17 mm yr−1 measured in the understory (all mean ± SD) during the water years 2018–2020. Interannual variability in ET and T was small despite large variability in precipitation totals; thus the P–ET water balance was mainly driven by variations in water supply. Partitioning the components of total forest ET at Sagehen with concurrent EC measurements showed that on average, 67–74% of ET originated from T (47% from trees and 20–27% from understory vegetation), while 26–33% were from E (mostly from the understory). Our results demonstrate the potential of concurrent above- and below-canopy EC measurements for ET partitioning.

Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin

Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy.Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes.Specifically, we model each component of the water cycle over 39 years (1980–2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model.The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data).Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture.The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate.

On the reliability of 12 high-resolution precipitation products for process-based hydrological modeling in China

Numerous satellite- and gauge-based precipitation products (SBPs/GBPs) have emerged in recent decades, providing unprecedented opportunities for process-based hydrological modeling. However, their reliability in high-resolution hydrological modeling at continental scales, especially for variables other than streamflow, remains to be investigated. Factors influencing their hydrological performance (e.g., catchment sizes, precipitation products’ spatial resolution and raw observation information) are also under debate. Here, we provide a comprehensive analysis of the hydrological reliability of 12 state-of-the-art precipitation datasets over China using a process-based land surface model at 0.125° resolution. Simulations driven by different precipitation products are evaluated across ∼ 200 basins with different catchment sizes. Results show that all simulations well reproduce the streamflow, soil moisture, evapotranspiration, and snow-depth at monthly timescale. None of the precipitation products provides best results for all variables, but the gauge-based and infrared-microwave-merged satellite products are superior to the infrared satellite products. The gauge-based regional precipitation product based on the 0.25° China precipitation dataset and the 6-km China Land Data Assimilation System (CN051_CLDAS) provides the best performance generally, followed by two global precipitation products from the Global Precipitation Climatology Centre (GPCC V2022) and the Global Precipitation Measurement Integrated Multi-satellite Retrievals (IMERGE-F V6). However, the advantages of CLDAS_CN051 and GPCC V2022 against satellite-based precipitation products are scale-dependent and tend to disappear over large-scale catchments (>106 km2). The spatial resolutions of precipitation products show no relationship with their hydrological performance, but the number of stations used to generate/bias-correct the precipitation is positively correlated with the performance of streamflow and soil moisture simulations. Differences among the climatology of terrestrial water-cycle components caused by using different precipitation products are notable and tend to increase over the dry and cold regions. Snow depth is the variable most sensitive to precipitation datasets, followed by runoff, ET, and soil moisture. This study suggests that using more station observations is more important than increasing spatial resolutions to enhance the applications of precipitation products in high-resolution hydrological modeling.

Twenty-Year Spatiotemporal Variations of TWS over Mainland China Observed by GRACE and GRACE Follow-On Satellites

Terrestrial water storage (TWS) is a pivotal component of the global water cycle, profoundly impacting water resource management, hazard monitoring, and agriculture production. The Gravity Recovery and Climate Experiment (GRACE) and its successor, the GRACE Follow-On (GFO), have furnished comprehensive monthly TWS data since April 2002. However, there are 35 months of missing data over the entire GRACE/GFO observational period. To address this gap, we developed an operational approach utilizing singular spectrum analysis and principal component analysis (SSA-PCA) to fill these missing data over mainland China. The algorithm was demonstrated with good performance in the Southwestern River Basin (SWB, correlation coefficient, CC: 0.71, RMSE: 6.27 cm), Yangtze River Basin (YTB, CC: 0.67, RMSE: 3.52 cm), and Songhua River Basin (SRB, CC: 0.66, RMSE: 7.63 cm). Leveraging two decades of continuous time-variable gravity data, we investigated the spatiotemporal variations in TWS across ten major Chinese basins. According to the results of GRACE/GFO, mainland China experienced an average annual TWS decline of 0.32 ± 0.06 cm, with the groundwater storage (GWS) decreasing by 0.54 ± 0.10 cm/yr. The most significant GWS depletion occurred in the Haihe River Basin (HRB) at −2.07 ± 0.10 cm/yr, significantly substantial (~1 cm/yr) depletions occurred in the Yellow River Basin (YRB), SRB, Huaihe River Basin (HHB), Liao-Luan River Basin (LRB), and Southwest River Basin (SWB), and moderate losses were recorded in the Northwest Basin (NWB, −0.34 ± 0.03 cm/yr) and Southeast River Basin (SEB, −0.24 ± 0.10 cm/yr). Furthermore, we identified that interannual TWS variations in ten basins of China were primarily driven by soil moisture water storage (SMS) anomalies, exhibiting consistently and relatively high correlations (CC &gt; 0.60) and low root-mean-square errors (RMSE &lt; 5 cm). Lastly, through the integration of GRACE/GFO and Global Land Data Assimilation System (GLDAS) data, we unraveled the contrasting water storage patterns between northern and southern China. Southern China experienced drought conditions, while northern China faced flooding during the 2020–2023 La Niña event, with the inverse pattern observed during the 2014–2016 El Niño event. This study fills in the missing data and quantifies water storage variations within mainland China, contributing to a deeper insight into climate change and its consequences on water resource management.

Spatiotemporal variation of water cycle components in Minjiang River Basin based on a correction method for evapotranspiration products

Study regionMinjiang River Basin of China. Study focusThis paper aimed to study the spatiotemporal changes in water cycle components including evapotranspiration (ET) and terrestrial water storage in a watershed. To achieve this, we proposed a method that is based on the water balance and utilizes measured annual precipitation and runoff data to correct ET products. Subsequently, we derived the annual terrestrial water storage changes and performed trend analysis on the water cycle components by the Mann-Kendall trend test. New hydrological insights for the region(1) The Global Land Data Assimilation System (GLDAS) product overestimated ET from 2000 to 2019 in the Minjiang River Basin (MJB), which was 105.18 mm higher than ET. Through sub-basin validation, the relative error between the corrected ET and the ET is less than 5.1%. The proposed method can construct a reasonable annual dataset of water cycle components. (2) From 2000–2019, the Minjiang River Basin witnessed non-significant increasing trends in precipitation and runoff. However, significant growth was observed in ET, indicated by a Mann-Kendall trend test Z-value of 2.63. The variation in ET was primarily influenced by the normalized difference vegetation index (NDVI) (correlation coefficients of 0.37) and temperature (correlation coefficients of 0.48). The terrestrial water storage change fluctuated between − 400 and 355 mm. The relationships among the water cycle components, as represented by the runoff coefficient and evapotranspiration coefficient show non-significant changing trends. Both displayed a non-significant decreasing trend. From 2000–2019, the water cycle process remained relatively stable under changing environments in the MJB.

Dynamic saturation area and water balance modelling of karstic basins

AbstractWater balance models are known as major tools for simulation of basin‐wide water cycle components. Considering the complexity of the runoff generation processes in karstic basins, it is crucial to bring the model processes closer to the physical reality of karstic springs and their contribution to runoff formation. One of the challenges in basin‐scale modelling of karstic basins is that the basin response to precipitation is highly influenced by the soil saturation condition and its seasonal variability. One way to address this challenge is to include a dynamic structure for the water balance model. In this research, the structure of SAM‐KARST water balance model for estimation of monthly streamflows in the outlet of karst basins was presented. Moreover, to better simulate the basin response fluctuations in different seasons, the SAMdyn‐KARST model with a dynamic storage structure was introduced and developed. The results of the SAMdyn‐KARST model were compared with the saturation area model (SAM) and SAM‐KARST models for the Shapur river basin in southwest of Iran. The results confirmed the importance of considering larger saturation storage in the karst basins. The case study results also showed that SAMdyn‐KARST outperformed SAM‐KARST model and provided higher accuracy in estimating seasonal variations of surface streamflows at the basin outlet.

Hydrological model-based streamflow reconstruction for Indian sub-continental river basins, 1951–2021

Streamflow is a vital component of the global water cycle. Long-term streamflow observations are required for water resources planning and management, hydroclimatic extremes analysis, and ecological assessment. However, long-term streamflow observations for the Indian-Subcontinental (ISC) river basins are lacking. Using meteorological observations, state-of-the-art hydrological model, and river routing model, we developed hydrological model-simulated monthly streamflow from 1951–2021 for the ISC river basins. We used high-resolution vector-based routing model (mizuRoute) to generate streamflow at 9579 stream reaches in the sub-continental river basins. The model-simulated streamflow showed good performance against the observed flow with coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) above 0.70 for more than 60% of the gauge stations. The dataset was used to examine the variability in low, average, and high flow across the streams. Long-term changes in streamflow showed a significant decline in flow in the Ganga basin while an increase in the semi-arid western India and Indus basin. Long-term streamflow can be used for planning water management and climate change adaptation in the Indian sub-continent.

Enhanced atmospheric water cycle processes induced by climate warming over the three rivers source region

The relationship between the ocean, land, and atmosphere is greatly impacted by climate change, and the atmospheric water cycle constitutes the most active and crucial hub in this interaction. Two crucial components of the atmospheric water cycle are the precipitation recycle ratio (PRR) and the water vapor source. Accurate quantification of these components can greatly assist in a better understanding of the atmospheric water cycle processes and variations. In this study, measured data from radiosondes and meteorological stations are used to assess the accuracy of the ERA5 reanalysis dataset. The Mann-Kendall (M-K), empirical orthogonal function (EOF), and probability density function (PDF) are utilized to analyze the spatiotemporal variations in the components of the atmospheric water cycle and to trace the sources of water vapor. Based on the Brubaker model, the PRR is estimated, and the atmospheric water cycle process over the three rivers source region (TRSR) is quantified. The findings reveal that (1) there is a significant increase in temperature, precipitation, evaporation, and water vapor content over the TRSR from 1979 to 2020, and the distribution of precipitation and water vapor content decreases from southeast to northwest. (2) The South Asian monsoon and westerly winds are the predominant atmospheric cycle systems for water vapor transport over the TRSR. The water vapor input at the western, southern, and northern borders all exhibit an increasing trend (0.16 × 105 kg s−1 a−1, 0.18 × 105 kg s−1 a−1, 0.09 × 105 kg s−1 a−1, respectively, p > 0.05), while the water vapor output at the eastern border exhibits a decreasing trend (−0.009 × 105 kg s−1 a−1, p > 0.05), resulting in a significant increase in net water vapor influxes (0.43 × 105 kg s−1 a−1, p < 0.05). Water vapor convergence occurs in the southeast while water vapor dispersion occurs in the northwest regions of the TRSR. (3) The multi-year average PRR is 20.85%, with a slightly declining trend, and the contribution of external moisture transport to regional precipitation is dominant over the TRSR. Climate change has sped up the atmospheric water cycle over the TRSR between 1979 and 2020: for every 1 °C rise in temperature, water vapor content increased by 6.83%, precipitation increased by 4.71%, and evaporation increased by 3.62%.

Long-chain propagation pathways from meteorological to hydrological, agricultural and groundwater drought and their dynamics in China

Drought propagation is a complex process involving various components of water cycle. Clarifying the propagation pathways and mechanisms among multiple types of droughts is essential. However, the current drought propagation analysis mainly focuses on the relationship between two types of droughts, which highlights the urgence in studying the propagation among multiple types of droughts. To this end, this study will first develop a dynamic threshold framework to identify the propagation pathways among meteorological, hydrological, agricultural and groundwater droughts, and further investigate the risk probability, trigger threshold and their controlling factors in the long-chain drought propagation. The propagation pathways among multi-type droughts were identified as meteorological-hydrological-agricultural-groundwater drought. In the long-chain drought propagation, the spatial heterogeneity of risk probability and trigger threshold in China is significant. Among them, the risk probability triggering the next stage decreases progressively in south China, and the drought propagation is blocked. A significant upward trend exhibited in the trigger thresholds in east China, indicating that drought resistance is weakening. Temperature, aridity index and soil evaporation flux are the main drivers affecting the dynamics of trigger thresholds. This study contributes to the completeness and systematization of the propagation process of drought in the water cycle. It not only offers new insights into the process of drought propagation, but also provides scientific guidance for establishing drought early warning systems and reducing drought hazards.

Representation of Hydrological Components under a Changing Climate—A Case Study of the Uruguay River Basin Using the New Version of the Soil and Water Assessment Tool Model (SWAT+)

SWAT+ is a revised version of the SWAT model that has the capability to route flow across landscape units in the catchment, which is expected to improve the spatial representation of processes in watersheds. We applied the SWAT+ model in the Uruguay River Basin, an international river basin in South America with a total surface area of 370,000 km2, in order to (1) assess the water balance components, (2) represent their spatial distribution, and (3) examine their changes over time. The catchment was divided into uplands and floodplains and a decision table rule was developed based on streamflow data. The SPOTPY Python library was linked to SWAT+ and used as a tool to perform sensitivity analyses and calibration. The model represented the fluctuations of discharge well, although there was a general tendency to underestimate peak flows. Blue (precipitation and runoff) and green (evapotranspiration and soil water content) hydrological components were spatially plotted. Overall, SWAT+ simulated a realistic spatial distribution of the water cycle components. A seasonal Mann–Kendall test suggests a positive increasing trend in the average temperature (p-value = 0.007; Sen’s slope = 0.09), the soil water content (p-value = 0.02; Sen’s slope = 1.29), and evapotranspiration (p-value: 0.03; Sen’s slope = 1.97), indicating that the ecosystem experienced a changing climate during the simulation period. The findings presented in this study are of significant value for the impacts of sustainable management and the evaluation of climate change on water resources in the Uruguay River Basin.

Exploring the Best-Matching Precipitation Traits in Four Long-Term Mainstream Products over China from 1981 to 2020

As a major component of water cycle, the accuracy quantification of different precipitation products is critical for evaluating climate change and ecosystem functions. However, a lack of evidence is available to choose a precise precipitation product in relative applications. Here, to solve this limit, we analyze the spatiotemporal pattern and accuracy of four precipitation products, including CHIRPS V2.0, PERSIANN-CDR, ECMWF ERA5-Land, and GLDAS_NOAH025_3H, over China during the period of 1981–2020, based on the five precipitation traits (i.e., spatial pattern of multi-year average, annual trend, seasonality, frequency, and intensity), and meteorological gauge observations are taken as the benchmark. Our results show that, compared to other products, CHIRPS data has the strongest ability to present spatial pattern of multi-year average precipitation, especially in most parts of northeastern and southern China, and ERA5 has the weakest ability to simulate the multi-year average precipitation. All four precipitation products can accurately depict the spatial pattern of seasonality, among which CHIRPS and ERA5 have the highest and lowest fitting ability, respectively, but four products poorly describe the spatial pattern of precipitation intensity and frequency at a daily scale. These products only correctly predict the interannual precipitation trend in some local areas. Our findings provide evidences to select high-quality precipitation data, and could help to improve the accuracy of relative geophysical models.

Groundwater–Surface Water Exchange and Spatial Distribution of Arsenic in Arid and Semi-Arid Regions: The Case of Aksu River in Xinjiang, Northwestern China

The Aksu River, a quintessential inland river, exhibits elevated arsenic (As) concentrations in certain sections of its natural waters. Further investigation is necessary to determine the role of surface water and groundwater (SW-GW) exchanges in contributing to these high As concentrations. Both surface water and groundwater constitute crucial components of the basin water cycle, and the interaction between the two has been a central focus in basin water cycle research. In this study, a total of 59 groundwater samples and 41 surface water samples were collected along the river’s course within the basin. Among the groundwater samples, 18.64% exceeded the permissible drinking limit for As concentrations (10 μg/L), while 39.02% of the surface water samples exceeded this threshold. The water bodies in the Aksu River Basin are mildly alkaline, with total dissolved solids (TDSs) in surface water significantly surpassing those in groundwater. The chemical compositions of surface water and groundwater are strikingly similar, with the predominant anions being chloride (Cl−) and sulfate (SO42−) and the principal cations being sodium (Na+). The dissolution of silicate and carbonate minerals primarily influences the water chemistry characteristics of surface water and groundwater in the Aksu River Basin, followed by the dissolution of salt rocks. Human activities also play a major role in affecting the river’s water quality. The distribution of groundwater with elevated As content is entirely encompassed within the spatial distribution of surface water. Groundwater–surface water exchange plays a vital role in As enrichment in surface water.

Dynamic Changes of Terrestrial Water Cycle Components over Central Asia in the Last Two Decades from 2003 to 2020

The terrestrial water cycle is important for the arid regions of central Asia (CA). In this study, the spatiotemporal variations in the three climate variables [temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET)] and terrestrial water cycle components [soil moisture (SM), snow water equivalent (SWE), runoff, terrestrial water storage (TWS), and groundwater storage (GWS)] of CA are comprehensively analyzed based on multiple datasets from 2003 to 2020. The major results are as follows: (1) Significant decreasing trends were observed for the TWS anomaly (TWSA) and GWS anomaly (GWSA) during 2003–2020, indicating serious water resource depletion. The annual linear trend values of TWSA and GWSA are −0.31 and −0.27 mm/a, respectively. The depletion centers are distributed over most areas of western and southern Kazakhstan (KAZ) and nearly all areas of Uzbekistan (UZB), Kyrgyzstan (KGZ), and Tajikistan (TJK). (2) TMP and PET have the largest significant negative impacts on SM and SWE. The PRE has a positive impact on terrestrial water variations. (3) During 1999–2019, water withdrawal did not significantly increase, whereas TWS showed a significant decreasing trend. Our results provide a comprehensive analysis of the basic TWS variation that plays a significant role in the water resource management of CA.

Hydrological forecasting at impact scale: the integrated ParFlow hydrological model at 0.6 km for climate resilient water resource management over Germany

In the context of the repeated droughts that have affected central Europe over the last years (2018–2020, 2022), climate-resilient management of water resources, based on timely information about the current state of the terrestrial water cycle and forecasts of its evolution, has gained an increasing importance. To achieve this, we propose a new setup for simulations of the terrestrial water cycle using the integrated hydrological model ParFlow/CLM at high spatial and temporal resolution (i.e., 0.611 km, hourly time step) over Germany and the neighboring regions. We show that this setup can be used as a basis for a monitoring and forecasting system that aims to provide stakeholders from many sectors, but especially agriculture, with diagnostics and indicators highlighting different aspects of subsurface water states and fluxes, such as subsurface water storage, seepage water, capillary rise, or fraction of plant available water for different (root-)depths. The validation of the new simulation setup with observation-based data monthly over the period 2011–2020 yields good results for all major components of the terrestrial water cycle analyzed here, i.e., volumetric soil moisture, evapotranspiration, water table depth, and river discharge. As this setup relies on a standardized grid definition and recent globally available static fields and parameters (e.g., topography, soil hydraulic properties, land cover), the workflow could easily be transferred to many regions of the Earth, including sparsely gauged regions, since ParFlow/CLM does not require calibration.

Land use changes impact on selected chemical denudation element and components of water cycle in small mountain catchment using SWAT model

Land use changes are extremely important in the context of land and water management. This study examines impact of land use and land cover (LULC) changes on nitrate nitrogen, outflow and surface runoff in the agriculture catchment in the Polish Carpathians using the Soil and Water Assessment Tool (SWAT). The study covers 25-year (1995–2019) and was divided into three periods relating to different LULC patterns (1995–2004, 2005–2012, 2013–2019). The LULC maps used were based on aerial photos from 1997, 2009, and 2016. The SWAT was calibrated and applied to periods with variable LULC (in simulation 1) and by keeping the LULC constant during the period 1995–2019 (simulation 2; the LULC from 1997) to determine the impact of LULC changes on the analysed variables. The ongoing LULC changes were found to have resulted in a reduction of cultivated land area (by 72 %) and an increase of grassland (by 45 %), forest (by 2 %) and urbanised areas (by 17 %). The SWAT model achieved reasonable accuracy. The model results tend to overestimate the load of nitrate nitrogen and underestimate outflow and surface runoff. The LULC changes resulted in a decrease in outflow between 1995 and 2004 of 24 % in comparison to 2013–2019, and a decrease in surface runoff of 12 %. At the same time, there was a significant decrease in nitrate nitrogen load of 68 % (from 13.0 kg ha−1 in 1995–2004 to 4.2 kg ha−1 in 2013–2019). If the LULC of 2013–2019 was restored to 1997 conditions (as in simulation 2), the outflow and load of NO3-N would be higher by 16 % (12 % in simulation 1) and 67 % (55 % in simulation 1) in relation to the observed values. The impact of LULC on the analysed parameters was spatially differentiated between subbasins depending on the scale of the land use changes. The research conducted here indicates that planning and managing the effects of LULC changes is of key importance for sustainable development in terms of the quantity and quality of water.

Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years

Runoff is a crucial water cycle component that contributes to the water resources to sustain human life. Historical trends in runoff, when examining climate change scenarios, provide vital information about past variability and support the design of adaptation measures. However, hydrological models based on climate data, such as the Budyko model, can be biased in estimating annual runoff due to input data uncertainty. Therefore, it is vital to utilize advanced machine learning-based computing models to reduce uncertainty and reconstruct climate variables over a long period of time and sufficiently large spatial coverage, preferably at a continental scale. We propose and test a novel machine learning-based framework called Hybrid Ensemble Multi-Model Framework (HEMMF) to reconstruct the gridded runoff of Europe over a 500-year historical period (1500 to 1999). The HEMMF combines non-parametric extended data pattern recognition and data-driven methods. The extended data patterns are computed using Moran’s spatial autocorrelation (SPA) index of the climate variable fields and the Budyko models output, whereas the data-driven methods contain nine different machine learning (ML) algorithms and four ensembles of ML. The extended data patterns are jointly ingested with climate-reconstructed data (precipitation, temperature, Palmer’s drought severity index) as predictor variables, which serve as input for the data-driven methods. To assess the impact and contribution of SPA, the runoff is simulated based on three different input training datasets in the HEMMF: (1) a dataset containing only precipitation, temperature, Palmer’s drought severity index, and four different estimates of runoff from the Budyko model, (2) a dataset containing only SPA of the first input datasets, and (3) a dataset created by merging the first and second datasets. The HEMMF offers the best reconstruction performance when using the third input dataset. This reconstructed runoff helps to explain the runoff trend, drought propagation, and runoff’s link with the climate variables. The proposed methodology has the potential to be applied to past hydroclimatic data and related analyses across different temporal periods, climate scenarios, and geographical scales.

Development of a 60-year high-resolution water body evaporation dataset in China

Evaporation from water bodies (Ew) is a critical component of the global water cycle. However, existing evaporation products that include Ew often suffer from drawbacks such as coarse resolution, short time span, and high uncertainty. This study developed a 60-year (1960–2019) high-resolution (0.05º×0.05º) evaporation dataset for small shallow water bodies in China based on the Penman model. Two key factors affecting the accuracy of the Penman model were considered: the uncertainty of the empirical wind function and changes in heat storage in the water body. Specifically, we used large-size (20 or 100 m2) pan evaporation (Epan) observations from 21 sites as a benchmark to correct the wind function of the Penman model. A data-driven model was then developed to map the spatial distribution of the wind function coefficients across China. The corrected wind function significantly improved the accuracy of Epan estimates compared to the original wind function, with the Kling-Gupta efficiency (KGE) increased by 0.05∼0.10. To model the effect of heat storage changes on evaporation, an equilibrium temperature method was used. We also introduced an area-dependent scaling factor into the wind function to account for the effect of water body's size on Ew estimation. The reliability of the Ew algorithm was tested on two lakes using eddy-covariance flux observations, and simulations showed good agreement with observations. The Epan (20 m2 pan) dataset and its two components calculated from the radiative and aerodynamic terms of the Penman model can be accessed at https://osf.io/qd28m/. Users can utilize the two Epan components and the area-dependent scaling factor to estimate evaporation for water bodies of varying sizes. However, caution is needed when applying this dataset to deep water bodies, as it is designed for shallow water bodies.

Quantifying the impact of large-scale afforestation on the atmospheric water cycle during rainy season over the Chinese Loess Plateau

Afforestation is an efficient measure to mitigating climate change and achieving carbon neutrality. However, there is a growing concern regarding the impact of afforestation on water resource availability owing to the water consumption through evapotranspiration (ET). The Loess Plateau (LP) in China has implemented the largest afforestation program in the world, the Grain for Green Program (GFGP), to curtail severe soil erosion. However, the effects of large-scale afforestation on the atmospheric branch of the hydrological cycle (i.e., the atmospheric water cycle (AWC)) are poorly understood. Combining a regional climate model and an analytical precipitation recycling model, this study quantitatively investigated the responses of precipitation recycling and components of the atmospheric water cycle to the GFGP over the LP during the rainy season. Two long-term simulations with high resolution were conducted for the LP’s regional climate under two scenarios: with and without the implementation of the GFGP. Results show that the GFGP intensified the AWC and increased the precipitation and atmospheric water vapor. The GFGP enhanced the ET and precipitation recycling ratio of the internal branch of the AWC, which contributed 15.51% of the increased precipitation. Meanwhile, the contribution of the external branch was 84.49%. In addition, both the inflow and outflow moisture fluxes showed increases induced by the GFGP; the inflow was larger than the outflow, leading to more moisture being retained on the LP. Moreover, the enhanced ET reinforced surface diabatic heating and caused an abnormal cyclone, resulting in more moisture being advected from the southern boundary, which can partly counteract the weakening East Asian summer monsoon. Furthermore, the GFGP has remotely strengthened the water vapor advection being received by downwind areas. This work advances the current understanding of the way large-scale afforestation affects the hydrological cycle from the viewpoint of the AWC, and can help direct future afforestation plans towards science-informed carbon neutrality and sustainable ecosystem management.