Terrestrial Water Cycle Research Articles

A western United States snow reanalysis dataset over the Landsat era from water years 1985 to 2021

Water stored in mountain snowpacks (i.e., snow water equivalent, SWE) represents an important but poorly characterized component of the terrestrial water cycle. The Western United States snow reanalysis (WUS–SR) dataset is novel in its combination of spatial resolution (~500 m), spatial extent (31°–49° N; 102°–125° W), and temporal continuity (daily over 1985–2021). WUS–SR is generated using a Bayesian framework with model-based snow estimates updated through the assimilation of cloud-free Landsat fractional snow-covered area observations. Over the WUS, the peak SWE verification with independent in situ measurements show correlation coefficient, mean difference (MD), and root mean squared difference (RMSD) of 0.77, −0.15 m, and 0.28 m, respectively. The effects of forest cover and Landsat image availability on peak SWE are assessed. WUS–SR peak SWE is well correlated (ranging from 0.75 to 0.91) against independent lidar-derived SWE taken near April 1st, with MD <0.15 m and RMSD <0.38 m. The dataset is useful for characterizing WUS mountain snow storage, and ultimately for improving snow-derived water resources management.

Contribution of water rejuvenation induced by climate warming to evapotranspiration in a Siberian boreal forest

Water age is a useful metric to evaluate the influence of anthropogenic and natural forcings on the terrestrial water cycle. Current climate warming is enhancing the warming of permafrost soil in the Arctic. Although permafrost is a crucial component of the Arctic terrestrial water cycle, its influence on processes regulating the fluxes and ages of Arctic terrestrial water, particularly soil storage and evapotranspiration, is not well understood. In this study, a water age calculation scheme was implemented into the coupled hydrological and biogeochemical model (CHANGE) to assess the mechanisms through which climate warming affects the soil water storage–evapotranspiration–water age feedback cycle in a boreal forest. Continuous air temperature increase from 1980 to 2016 caused earlier snowmelt and soil thawing, inducing decreasing age trends in snow- and rain-sourced water. The younger water contributed to higher spring evapotranspiration. In summer, the higher evapotranspiration dried the surface soil layer. In turn, the drier surface layer increased the loss of fresh rainwater. Autumn precipitation, preserved in the frozen winter soil until the following spring, became an additional source of water and enhanced plant transpiration in the following summer. This increase accounted for 4.2% of the annual total transpiration. These results suggest that permafrost warming, characterized by earlier soil thawing and later freezing, induced higher evapotranspiration, thereby shortening the residence time of precipitation-sourced water in the active layer and further rejuvenating water in soil layers and in evapotranspiration. Under future climate warming conditions, this effect is expected to intensify and the water cycle will accelerate.

Changes in and driving factors of the lake area of Huri Chagannao’er Lake in Inner Mongolia

Inland lakes are an important component of the terrestrial water cycle in Inner Mongolia's typical steppe region, and their variations have far-reaching implications for the sustainable development of water resources in this region. Huri Chagannao'er Lake, one of Inner Mongolia's four major freshwater lakes and the second largest inland lake in the typical steppe region, was chosen as the research object. In this study, the spatiotemporal changes in the area of Huri Chagannao’er Lake over the last 30 years were analyzed using the modified normalized difference water index method and Landsat data. Then, we used regression analysis, correlation analysis, gray relational analysis, and Geodetector to investigate the potential causes of lake area changes. Changes in the lake's water balance, meteorological and climatic changes near the lake, and changes in land use and land cover in the drainage basin are all possible driving factors. Finally, the main driving factors of the lake area change are discussed in conjunction with the literature and field investigation, and measures and suggestions for the lake's sustainable utilization and protection are proposed. The findings revealed that i) from 1988 to 2017, the lake shrank from 98.99 km2 to 29.81 km2, with year 2000 marking the start of the dramatic changes in lake areas; ii) the lake shrinkage was primarily concentrated in the western part of the lake, while the eastern part of the lake remained stable; iii) human activities, such as water interception and storage, as well as excessive exploitation of water resources, were the most significant causes of the dramatic fluctuation in the area of the lake's western part; iv) climate change also had some influence on the lake area changes. The regional climate became warmer and drier, reducing the amount of water entering the drainage basin. The findings of this study highlight the dominant role of human activity intensity in lake area changes and provide a theoretical foundation and technical support for the study of inland lakes in Inner Mongolia's typical steppe region.

Effects of Artificial Water Withdrawal on the Terrestrial Water Cycle in the Yangtze River Basin

Clarifying the response of the terrestrial water cycle to the influence of climate change and human activities and accurately grasping the variations in the water cycle and water resources under the changing environment are the scientific basis for achieving the sustainable development of the Yangtze River Economic Belt. In this paper, a dataset of rasterized water consumption in the Yangtze River Basin was constructed, and an artificial water withdrawal module considering the process of water intake, water consumption and drainage was designed, which was coupled with the land surface model CLM4.5. Based on the multi-scale validation in the Yangtze River Basin, two numerical simulation experiments were carried out to reveal the impact of artificial water withdrawal on the water cycle process in the Yangtze River Basin. The results indicate that artificial water withdrawal leads to an 0.1–0.3 m increase in groundwater table depth in most areas of the basin, and agricultural irrigation leads to a 0–0.03 mm3/mm3 increase in soil moisture in most areas. Climate change dominates the variation of discharge in the Yangtze River basin and leads to an increase in discharge at most stations.

Non-linear interactions between vegetation and terrestrial water storage in Australia

• Revisit analysis shows that vegetation growth is water-limited in 60% of Australia. • Non-linear interactions between NDVI and TWS occur in 58% of Australia. • The area where high temperatures limit vegetation growth has nearly tripled recently. Vegetation conditions are deeply dependent on water availability. Conversely, vegetation can also affect terrestrial water conditions by modifying evapotranspiration and other hydrological processes. Knowledge of the interactions between vegetation and land water conditions are required for understanding and simulating terrestrial water and carbon budgets in response to climate change. For the first time, in this study, a non-linear Granger causality approach is applied to investigate the water-vegetation interactions over Australia. They are characterized by respectively the normalized difference vegetation index (NDVI) and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS). The results indicate that non-linear interactions between TWS and NDVI occur in over half (58.4%) of the continent, which are more likely to take place for grasslands, followed by shrublands, agricultural lands, savannas, and forests. A revisit-analysis of the dominant driver among water, temperature, and radiation for vegetation conditions confirms that Australia is mostly water limited. Although the total area of primarily water-limited regions has slightly decreased from 67.0% in the earlier sub-period (1985–1999) to 65.2% in the more recent sub-period (2000–2015), it accounts for a much larger proportion of the continent than that of temperature- and radiation-limited regions in both sub-periods. The spatial coverage of regions where temperature is the dominant factor has nearly tripled for the recent sub-period. This could most likely be due to the warmer climate that exacerbates the limitation of high temperature on vegetation growth and functioning. These results contribute to an improved understanding of Australia’s terrestrial water and carbon cycles. The GRACE TWS shows to be highly relevant data for such investigations at a continental scale.

Evaluating the Effects of Climate Change and Human Activities on the Seasonal Trends and Spatial Heterogeneity of Soil Moisture

Soil moisture (SM), as a crucial variable in the soil–vegetation–atmosphere continuum, plays an important role in the terrestrial water cycle. Analyzing SM’s variation and driver factors is crucial to maintaining ecosystem diversity on the Tibetan Plateau (TP) and ensuring food security as well as water supply balance in developing countries. Gradual wetting of the soil has been detected and attributed to precipitation in this area. However, there is still a gap in understanding the potential mechanisms. It is unclear whether the greening, glacier melting, and different vegetation degradation caused by asymmetrical climate change and intensified human activities have significantly affected the balance of SM. Here, to test the hypothesis that heterogeneous SM caused by precipitation was subject to temperatures and anthropogenic constraints, GLDAS-2.1 (Global Land Data Assimilation System-2.1) SM products combined with the statistical downscaling and Geographic detectors were applied. The results revealed that: (1) Seasonal SM gradually increased (p &lt; 0.05), while SM deficit frequently appeared with exposure to extreme climates, such as in the summer of 2010 and 2013, and changed into a pattern of precipitation transport to western dry lands in autumn. (2) There was a synergistic reaction between greening and local moisture in autumn. SM was dominated by low temperature (TMN) in winter, warming indirectly regulated SM by exacerbating the thawing of glaciers and permafrost. The spatial coupling between the faster rising rate of TMN and the frozen soil might further aggravate the imbalance of SM. (3) The land cover’s mutual transformation principally affected SM in spring and autumn, and degradation accelerated the loss of SM replenished by precipitation. (4) Land cover responses were different; SM in grassland was less affected by external disturbance, while degraded woodland and shrub performed adaptive feedback under dry environments, SM increased by 0.05 and 0.04 m3/(m3 10a), respectively. Our research provides a scientific basis for improving hydrological models and developing vegetation restoration strategies for long-term adaptation to TP-changing environments.

Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest

Abstract. Temperate forest ecosystems play a crucial role in governing global carbon and water cycles. However, unprecedented global warming presents fundamental alterations to the ecological functions (e.g., carbon uptake) and biophysical variables (e.g., leaf area index) of forests. The quantification of forest carbon uptake, gross primary productivity (GPP), as the largest carbon flux has a direct consequence on carbon budget estimations. Part of this assimilated carbon stored in leaf biomass is related to the leaf area index (LAI), which is closely linked to and is of critical significance in the water cycle. There already exist a number of models to simulate dynamics of LAI and GPP; however, the level of complexity, demanding data, and poorly known parameters often prohibit the model applicability over data-sparse and large domains. In addition, the complex mechanisms associated with coupling the terrestrial carbon and water cycles poses a major challenge for integrated assessments of interlinked processes (e.g., accounting for the temporal dynamics of LAI for improving water balance estimations and soil moisture availability for enhancing carbon balance estimations). In this study, we propose a parsimonious forest canopy model (PCM) to predict the daily dynamics of LAI and GPP with few required inputs, which would also be suitable for integration into state-of-the-art hydrologic models. The light use efficiency (LUE) concept, coupled with a phenology submodel, is central to PCM (v1.0). PCM estimates total assimilated carbon based on the efficiency of the conversion of absorbed photosynthetically active radiation into biomass. Equipped with the coupled phenology submodel, the total assimilated carbon partly converts to leaf biomass, from which prognostic and temperature-driven LAI is simulated. The model combines modules for the estimation of soil hydraulic parameters based on pedotransfer functions and vertically weighted soil moisture, considering the underground root distribution, when soil moisture data are available. We test the model on deciduous broad-leaved forest sites in Europe and North America, as selected from the FLUXNET network. We analyze the model's parameter sensitivity on the resulting GPP and LAI and identified, on average, 10 common sensitive parameters at each study site (e.g., LUE and SLA). The model's performance is evaluated in a validation period, using in situ measurements of GPP and LAI (when available) at eddy covariance flux towers. The model adequately captures the daily dynamics of observed GPP and LAI at each study site (Kling–Gupta efficiency, KGE, varies between 0.79 and 0.92). Finally, we investigate the cross-location transferability of model parameters and derive a compromise parameter set to be used across different sites. The model also showed robustness with the compromise single set of parameters, applicable to different sites, with an acceptable loss in model skill (on average ±8 %). Overall, in addition to the satisfactory performance of the PCM as a stand-alone canopy model, the parsimonious and modular structure of the developed PCM allows for a smooth incorporation of carbon modules to existing hydrologic models, thereby facilitating the seamless representation of coupled water and carbon cycle components, i.e., prognostic simulated vegetation leaf area index (LAI) would improve the representation of the water cycle components (i.e., evapotranspiration), while GPP predictions would benefit from the simulated soil water storage from a hydrologic model.

Emergence of the physiological effects of elevated CO2 on land-atmosphere exchange of carbon and water.

Elevated atmospheric CO2 (eCO2 ) influences the carbon assimilation rate and stomatal conductance of plants, thereby affecting the global cycles of carbon and water. Yet, the detection of these physiological effects of eCO2 in observational data remains challenging, because natural variations and confounding factors (e.g., warming) can overshadow the eCO2 effects in observational data of real-world ecosystems. In this study, we aim at developing a method to detect the emergence of the physiological CO2 effects on various variables related to carbon and water fluxes. We mimic the observational setting in ecosystems using a comprehensive process-based land surface model QUINCY to simulate the leaf-level effects of increasing atmospheric CO2 concentrations and their century-long propagation through the terrestrial carbon and water cycles across different climate regimes and biomes. We then develop a statistical method based on the signal-to-noise ratio to detect the emergence of the eCO2 effects. The eCO2 effect on gross primary productivity (GPP) emerges at relatively low CO2 increase (∆[CO2 ] ~ 20 ppm) where the leaf area index is relatively high. Compared to GPP, the eCO2 effect causing reduced transpiration water flux (normalized to leaf area) emerges only at relatively high CO2 increase (∆[CO2 ] >> 40 ppm), due to the high sensitivity to climate variability and thus lower signal-to-noise ratio. In general, the response to eCO2 is detectable earlier for variables related to the carbon cycle than the water cycle, when plant productivity is not limited by climatic constraints, and stronger in forest-dominated rather than in grass-dominated ecosystems. Our results provide a step toward when and where we expect to detect physiological CO2 effects in in-situ flux measurements, how to detect them and encourage future efforts to improve the understanding and quantification of these effects in observations of terrestrial carbon and water dynamics.

Global land surface evapotranspiration monitoring by ETMonitor model driven by multi-source satellite earth observations

Evapotranspiration (ET) is an essential ecohydrological process linking the land surface energy, water and carbon cycles, and plays a critical role in the earth system. ET remains one of the most problematic components of the water cycle to be determined due to the heterogeneity of the landscape and the complexity of driving factors. The satellite-based earth observation is expected to provide ET information at large-scales. However, accurate global ET information, with spatially and temporally continuous coverage at moderate-to-high resolution, is still scarce. In this paper, a combined model, called ETMonitor, with multi-process parameterizations, was improved and applied to estimate the global ET, mainly using the biophysical and hydrological parameters/variables retrieved from satellite observations. The ETMonitor model was improved in several aspects in this study to generate the global ET datasets during 2000–2019 at daily/1-km resolution, including: 1) adopting high temporal resolution surface water cover and snow/ice cover as input, to simulate the impact of their seasonal change on ET variation; 2) parameterizing the impact of soil moisture on plant transpiration and soil evaporation using high resolution soil moisture, which was downscaled to 1-km resolution from the coarse resolution data retrieved from microwave remote sensing observation; 3) involving a better soil heat flux estimation to reduce its impact on the uncertainty of estimated ET; 4) being calibrated based on global ground flux observations to achieve better accuracy. The estimated daily ET was validated based on the global in situ observations at site scale across various ecosystems, with overall high correlation (0.75), low bias (0.08 mm d-1), and low root mean square error (0.93 mm d-1). It had good ability to partition total ET to plant transpiration and soil evaporation indicated by the good agreement with the ground isotope measurements in a growing season at one site in the northwest China. The estimated global ET was cross-validated by comparing with other existing ET products, and it showed the global ET estimated by ETMonitor could capture the expected global ET patterns both in space and in time. It also indicated the superiority of the ET product by ETMonitor in the following aspects: capability in capturing the seasonal dynamics of waterbody evaporation and sublimation; better performance in capturing the spatial variation of ET in the irrigated cropland regions and mountain regions with complex terrain than other global ET products, e.g., the GLEAM and MOD16 ET products; capability of ET component partitioning at high spatial (1-km) and high temporal (daily) resolutions with good accuracy. The estimated plant transpiration, soil evaporation, canopy rainfall interception loss, and water body evaporation and snow sublimation accounted for 61.54 % (±0.44 %), 19.08 % (±0.54 %), 13.54 % (±0.49 %), and 5.84 % (±0.24 %), respectively, of the total ET on global average. The ETMonitor global ET dataset could provide important information on studies of global terrestrial water and energy cycles and climate change studies, and water resources management at global and regional scales.

Evaluation of Surface Water Resource Availability under the Impact of Climate Change in the Dhidhessa Sub-Basin, Ethiopia

Climate change, with its reaching implications, has become a popular topic in recent years. Among the many aspects of climate change, one of the most pressing concerns has been identified as the impact on the terrestrial water cycle, which has a direct impact on human settlement and ecosystems. The paper begins by reviewing previous studies, and then identifies their flaws and future research directions. The effects of climate change on surface water resources in the Dhidhessa Sub-basin, Abbay Basin, Ethiopia, were studied as practices. For future potential climate change, the results of global climate models (GCMs) and high-resolution regional climate models (RCMs) from multiple climate models were combined with data from Representative Concentration Pathways (RCPs) prepared by the Intergovernmental Panel on Climate Change from the CCAFS Data Distribution Center web page. To evaluate the impacts on water resources, various distributed hydrologic models based on local underlying surfaces were developed. The future potential climate change of the Dhidhessa Sub-basin Province was evaluated by integrating RCP outputs, whereas the climate change of the Dhidhessa River was directly derived from the results of different RCP. Dhidhessa stream flow will decrease in the future compared to the baseline era. The predictions of future discharge (stream flow) were based on climate scenarios data from 1991 to 2020 and for the future with two time windows, 2044 (2030–2059) and 2084 (2070–2099), on a monthly time-step after bias correction to both precipitation and temperature in the future climate described in the under each RCP. According to model results, the quantity of surface water resources in the Dhidhessa river region will decrease over the next 100 years, the percent decrease in mean annual stream flow by 10%, in 2044, and 6.3% in 2084, respectively, making the impact of temperature increase on runoff greater than that of precipitation. The distribution of runoff would be more even across years but more uneven across years in the long-term window, implying a higher possibility of drought and flooding. In general, this study discovered that any effect on this river that results in a decrease in flow will have a direct impact on the area’s ongoing water resource development and socioeconomic development.

Uncertainty quantification in land surface temperature retrieved from Himawari-8/AHI data by operational algorithms

Himawari-8, a new-generation geostationary satellite, can retrieve sub-hourly land surface temperatures (LSTs) with moderate spatial resolution, providing a new scale for monitoring the thermal environment in Asia and Oceania. This study evaluated uncertainties of LSTs retrieved by three operational algorithms from Advanced Himawari Imager (AHI) data. We compared two nonlinear split-window algorithms (SOB and WAN algorithms) and one nonlinear three-band algorithm (YAM algorithm). First, the error characteristics of the retrieved LSTs caused by the input parameter errors were simulated under various land-atmospheric conditions using an atmospheric radiative transfer model. Thereafter, retrieved LSTs from actual AHI data were evaluated using in-situ observations from AsiaFlux and OzFlux networks and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LSTs. The simulated results showed that the YAM algorithm maintained the highest accuracy, whereas the WAN algorithm had the highest robustness to input errors. The YAM algorithm had the smallest total error including input errors over a wide range of retrieval conditions. Validation of the three algorithms via in-situ LSTs from 12 sites revealed nighttime mean RMSEs for all sites of ∼1.7 °C, and daytime mean RMSEs for semi-arid and humid sites of approximately 3.0 °C and 2.0 °C, respectively. These are comparable to the accuracies reported for LST products with higher spatial resolutions, such as the Moderate Resolution Imaging Spectroradiometer and Landsat. Within the Himawari-8 disk, the estimation error of the YAM algorithm was ∼1.0 °C lower than those of the SOB and WAN algorithms in regions with extremely high viewing angle, temperature, and humidity (e.g., northern China, Australia, and Southeast Asia). Furthermore, AHI LSTs showed closer agreement with ECOSTRESS compared to in-situ LSTs, suggesting the usefulness of ECOSTRESS for assessing the diurnal LSTs derived from geostationary satellites. The resulting LST products and the knowledge of their error characteristics have the potential to improve the collective understanding of terrestrial energy and water cycles based on improved accuracy and robustness.

Spatiotemporal variations in evapotranspiration and transpiration fraction following changes in climate and vegetation in a karst basin of southwest China

Evapotranspiration (ET) is a vital factor in terrestrial water and energy cycles. Exploring the spatiotemporal variations in ET and transpiration (T) can improve our understanding of the role of vegetation restoration in the context of climate change. Karst ecosystems are particularly sensitive and vulnerable to environmental change. In this study, we investigated the spatiotemporal changes of ET, T, and the transpiration fraction (T/ET) in the Longjiang River Basin (LRB) of southwest China from 1987 to 2018 using the variable infiltration capacity (VIC) model. In karst areas, the original third soil layer in the VIC model was modified to an epikarst zone and a reservoir module was integrated with the model. Model validation and evaluation showed that the modified VIC model performed well in fitting the observations of runoff and ET. During the 32-year study period, ET, T, and T/ET increased by 2.30 mm·yr−1, 2.21 mm·yr−1, and 0.0006, respectively, with the significant increase concentrated in areas with increased forestland. High positive correlations between NDVI and ET, T, and T/ET were observed in areas where forestland and grassland increased. Temperature and wind speed were positively correlated with ET, T, and T/ET in areas with low altitude and abundant precipitation, where large areas of cropland are present. There was a positive correlation between precipitation and ET in karst areas, whereas the negative correlation between precipitation and T was mainly occurred in non-karst areas. The scenario analysis based on different land use data revealed that ET and T/ET exhibited significant differences in karst areas, but not in non-karst areas, indicating that karst areas were more affected by ecological restoration. Moreover, T did not show a significant increase after vegetation restoration, suggesting that increased ET was mainly caused by evaporation. This study provides insights into the effects of vegetation restoration and climate change on ET, T, and T/ET under different geological conditions and can help implement appropriate afforestation strategies in different geological settings.

Improving predictions of evapotranspiration by integrating multi-source observations and land surface model

Accurate estimation of evapotranspiration (ET) is essential for understanding terrestrial energy, water, and carbon cycles. This study proposes a hybrid model integrating in-situ and remote sensing-derived soil moisture (SM) observations and remote sensing leaf area index (LAI) with the Noah-MP model. The ensemble Kalman filter (EnKF) approach updates the leaf biomass and specific leaf area (SLA) by assimilating the remotely sensed LAI. A machine learning (ML) surrogate model is used to integrate multi-site SM profile observations and remote sensing SM products to estimate the three-layer SM. An iterative coupling of two parts implements the hybrid model: optimization of leaf biomass and SLA by assimilation of LAI in the Noah-MP model and simulation of three-layer SM in the ML surrogate model. The performance of the hybrid model is evaluated in the Heihe River Basin (HRB) in northwest China. The estimated ET from the hybrid model is compared with observations from the large aperture scintillometer (LAS) at the Arou, Daman, and Sidaoqiao sites and up-scaled watershed ET over the HRB. The findings indicate that the hybrid model performs well in vegetated areas but underestimates ET in extreme arid deserts. The three-site unbiased root mean squared errors (ubRMSEs) of ET estimates from the hybrid model are 29.06%, 42.76%, and 50.00% lower than Noah-MP at the Arou, Daman, and Sidaoqiao sites, respectively. The coupling of data assimilation (DA) and ML methods can improve vegetation dynamics and SM transport estimation in the Noah-MP model. The hybrid model can take advantage of DA and ML methods and integrate multi-source observations to improve the accuracy of ET estimation. The results also indicate that the ET predictions are more sensitive to root zone SM (0–40 cm) over croplands, grasslands, and shrublands, while the ET simulations are more affected by deeper rooting depths SM (0–100 cm) and groundwater over forests.

The terrestrial water cycle in a warming world

The terrestrial water cycle in a warming world

Multivariate assimilation of satellite-based leaf area index and ground-based river streamflow for hydrological modelling of irrigated watersheds using SWAT+

• Performance of the SWAT + model with and without data assimilation (DA) is assessed. • Some modules of SWAT + source code are modified to be connected to the DA routine. • Multivariate and univariate assimilations of discharge and LAI data are compared. • High-resolution crop mapping is essential for LAI assimilation in irrigated watersheds. • Joint assimilation of streamflow and Sentinel-2′s LAI results in better simulation of streamflow, vegetation, and irrigation. Vegetation dynamics have different effects on the terrestrial water cycle and therefore play an important role in catchment-scale biophysical and hydrological modeling. Meanwhile, validation of hydrological models of irrigated watersheds concerning vegetation dynamics has rarely been studied. In this study, we propose a combinatorial approach for modelling irrigated watersheds; at first, based on Sentinel-2 (S2), we provide crop/land-use mapping; then we retrieve S2-based leaf area index (LAI) through the neural network algorithm and the PROSAIL model; finally, we employ sequential particle filter (PF) technique to explore the benefits of jointly assimilating high-resolution S2-LAI as well as in-situ river discharge observations for improving the predictive accuracy of SWAT + model. Moreover, we explain how SWAT + source code must be modified to implement the assimilation procedure. The developed methodology is applied to an irrigated watershed in Iran; we provide 66 raster LAI maps with a spatial resolution of 20 m for the study area related to January to December of 2019 as well as crop/land-use mapping of 2019 with a spatial resolution of 10 m. Results show that improvements in the hydrological simulation of LAI, evapotranspiration, and river discharge are achieved when we apply multivariate assimilation of S2-LAI and streamflow observations, compared to univariate assimilation scenarios. Results also reveal that LAI assimilation has a significant influence on the estimation of irrigation volume and timing.

Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1

Abstract. Runoff is a critical component of the terrestrial water cycle, and Earth system models (ESMs) are essential tools to study its spatiotemporal variability. Runoff schemes in ESMs typically include many parameters so that model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at a global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using a surrogate-assisted Bayesian framework. First, the polynomial chaos expansion machinery with Bayesian compressed sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5∘ × 0.5∘ for 1991–2010. The error metric between the ELM simulations and the benchmark data is selected to construct the surrogates, which facilitates efficient calibration and avoids the more conventional, but challenging, construction of high-dimensional surrogates for the ELM simulated runoff. Second, the Sobol' index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that, in most regions, ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multimodel ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

Constant hydraulic supply enables optical monitoring of transpiration in a grass, a herb, and a conifer.

Plant transpiration is an inevitable consequence of photosynthesis and has a huge impact on the terrestrial carbon and water cycle, yet accurate and continuous monitoring of its dynamics is still challenging. Under well-watered conditions, canopy transpiration (Ec) could potentially be continuously calculated from stem water potential (Ψstem), but only if the root to stem hydraulic conductance (Kr-s) remains constant and plant capacitance is relatively small. We tested whether such an approach is viable by investigating whether Kr-s remains constant under a wide range of daytime transpiration rates in non-water-stressed plants. Optical dendrometers were used to continuously monitor tissue shrinkage, an accurate proxy of Ψstem, while Ec was manipulated in three species with contrasting morphological, anatomical, and phylogenetic identities: Tanacetum cinerariifolium, Zea mays, and Callitris rhomboidea. In all species, we found Kr-s to remain constant across a wide range of Ec, meaning that the dynamics of Ψstem could be used to monitor Ec. This was evidenced by the close agreement between measured Ec and that predicted from optically measured Ψstem. These results suggest that optical dendrometers enable both plant hydration and Ec to be monitored non-invasively and continuously in a range of woody and herbaceous species. This technique presents new opportunities to monitor transpiration under laboratory and field conditions in a diversity of woody, herbaceous, and grassy species.

Model Estimates of China's Terrestrial Water Storage Variation Due To Reservoir Operation

AbstractUnderstanding the role of reservoirs in the terrestrial water cycle is critical to support the sustainable management of water resources especially for China where reservoirs have been extensively built nationwide. However, this has been a scientific challenge due to the limited availability of continuous, long‐term reservoir operation records at large scales, and a process‐based modeling tool to accurately depict reservoirs as part of the terrestrial water cycle is still lacking. Here, we develop a continental‐scale land surface‐hydrologic model over the mainland China by explicitly representing 3,547 reservoirs in the model with a calibration‐free conceptual operation scheme for ungauged reservoirs and a hydrodynamically based two‐way coupled scheme. The model is spatially calibrated and then extensively validated against streamflow observations, reservoir storage observations and GRACE‐based terrestrial water storage anomalies. A 30‐year simulation is then performed to quantify the seasonal dynamics of China’s reservoir water storage (RWS) and its role in China's terrestrial water storage (TWS) over recent decades. We estimate that, over a seasonal cycle, China's RWS variation is 15%, 16%, and 25% of TWS variation during 1981–1990, 1991–2000, and 2001–2010, respectively, and one‐fifth of China’s reservoir capacity are effectively used annually. In most regions, reservoirs play a growing role in modulating the water cycle over time. Despite that, an estimated 80 million people have faced increasing water resources challenges in the past decades due to the significantly weakened reservoir regulation of the water cycle. Our approaches and findings could help the government better address the water security challenges under environmental changes.

Evaluating a reservoir parametrization in the vector-based global routing model mizuRoute (v2.0.1) for Earth system model coupling

Abstract. Human-controlled reservoirs have a large influence on the global water cycle. While global hydrological models use generic parameterizations to model dam operations, the representation of reservoir regulation is still lacking in many Earth system models. Here we implement and evaluate a widely used reservoir parametrization in the global river-routing model mizuRoute, which operates on a vector-based river network resolving individual lakes and reservoirs and is currently being coupled to an Earth system model. We develop an approach to determine the downstream area over which to aggregate irrigation water demand per reservoir. The implementation of managed reservoirs is evaluated by comparing them to simulations ignoring inland waters and simulations with reservoirs represented as natural lakes using (i) local simulations for 26 individual reservoirs driven by observed inflows and (ii) global-domain simulations driven by runoff from the Community Land Model. The local simulations show the clear added value of the reservoir parametrization, especially for simulating storage for large reservoirs with a multi-year storage capacity. In the global-domain application, the implementation of reservoirs shows an improvement in outflow and storage compared to the no-reservoir simulation, but a similar performance is found compared to the natural lake parametrization. The limited impact of reservoirs on skill statistics could be attributed to biases in simulated river discharge, mainly originating from biases in simulated runoff from the Community Land Model. Finally, the comparison of modelled monthly streamflow indices against observations highlights that including dam operations improves the streamflow simulation compared to ignoring lakes and reservoirs. This study overall underlines the need to further develop and test runoff simulations and water management parameterizations in order to improve the representation of anthropogenic interference of the terrestrial water cycle in Earth system models.

An Estimation of Precipitation Retention Time Using Depth Metres in the Northern Basin of Lake Biwa

To facilitate climate change adaptations and water management, estimates of precipitation retention time (time required for precipitation to reach a lake) can help to accurately determine a water body’s terrestrial water storage capacity and water cycle. Although estimating the precipitation retention time on land is difficult, estimating the lag between precipitation on land and a rise in lake water levels is possible. In this study, the delay times (using a depth metre installed in the mooring system in the northern basin of Lake Biwa from August 2017 to October 2018) were calculated using response functions, and it evaluated the precipitation retention time in the catchment. However, as several delays between the river surface flow (&lt;1 d) and shallow subsurface flow (≈45 d) remained unidentified, the delay times resulting from direct precipitation on the lake as well as from internal seiches were determined. The results suggest that delay times of approximately 20 d correspond to the paddy–waterway system between the river inflow and the subsurface flow, and that this effect corresponds to that of large rivers such as the Ane River. These findings can enhance water management strategies regarding the regulation of river flows, adapting to climate change-induced fluctuations in precipitation.