Terrestrial Water Cycle Research Articles

Nonstationarity in the global terrestrial water cycle and its interlinkages in the Anthropocene

Climate change and human activities alter the global freshwater cycle, causing nonstationary processes as its distribution shifting over time, yet a comprehensive understanding of these changes remains elusive. Here, we develop a remote sensing-informed terrestrial reanalysis and assess the nonstationarity of and interconnections among global water cycle components from 2003 to 2020. We highlight 20 hotspot regions where terrestrial water storage exhibits strong nonstationarity, impacting 35% of the global population and 45% of the area covered by irrigated agriculture. Emerging long-term trends dominate the most often (48.2%), followed by seasonal shifts (32.8%) and changes in extremes (19%). Notably, in mid-latitudes, this encompasses 34% of Asia and 27% of North America. The patterns of nonstationarity and their dominant types differ across other water cycle components, including precipitation, evapotranspiration, runoff, and gross primary production. These differences also manifest uniquely across hotspot regions, illustrating the intricate ways in which each component responds to climate change and human water management. Our findings emphasize the importance of considering nonstationarity when assessing water cycle information toward the development of strategies for sustainable water resource usage, enhancing resilience to extreme events, and effectively addressing other challenges associated with climate change.

Summer evapotranspiration-cloud feedbacks in land-atmosphere interactions over Europe

AbstractLand-atmosphere (L-A) feedbacks are important for understanding regional climate functioning. However, the accurate quantification of feedback strength is challenging due to complex, nonlinear interactions and varying background atmospheric conditions. In particular, the role of cloud water in the terrestrial water cycle is often ignored or simplified in previous L-A feedback studies, which overlook the relationship between evapotranspiration (ET) and cloud water (TQC). This study diagnoses the interactions between $$\:ET$$, $$\:TQC$$ and its dynamics ($$\:\varDelta\:TQC/\varDelta\:t$$) under different atmospheric conditions by conducting correlation and a novel scaling analysis, based on a coupled regional climate model simulation. Contrasting correlation relationships between $$\:ET$$, $$\:TQC$$ and $$\:\varDelta\:TQC/\varDelta\:t$$ were identified, indicating the positive feedback between $$\:ET$$ and the dynamics in cloud water. Two types of positive scaling relationships between $$\:ET$$ and $$\:\varDelta\:TQC/\varDelta\:t$$ were identified by K-means clustering. The analysis shows a contrasting north-south distribution of the scaling relationship that is similar to the spatial distribution of energy-limited and water-limited $$\:ET$$ regimes, highlighting the role of ET regimes in modulating the $$\:ET$$ - $$\:\varDelta\:TQC/\varDelta\:t$$ scaling relationships. Moreover, the feedback strength and scaling relationship are affected by atmospheric moisture flux dynamics, providing remote moisture sources and altering dry/wet conditions. Our results highlight the role of cloud water in the atmospheric part of the L-A process chain and reveal the effect of different atmospheric conditions on L-A interactions based on the new analysis framework.

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.

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.

Air and Surface Temperatures Differently Drive Terrestrial Carbon and Water Cycles in the High Latitudes

AbstractHigh‐latitude vegetation experience different temperatures than the ambient air temperature. While lacking a regional plant temperature product, we drove the dynamic ecosystem model, LPJ‐GUESS, with widely used ERA5‐land surface temperature (Tsurf, at radiative equilibrium) and air temperature to understand ecosystem process responses to these two temperatures. We show that tundra plants' growth is stimulated by warmer Tsurf in the summer, but in the boreal forests, colder Tsurf in the non‐summer months constrains leaf development and enzyme activity the following growing season. Tsurf drives higher productivity of tundra plant individuals but leads to less productive individuals in the boreal forest, although with compensatory changes (almost 68%) in vegetation structure. We demonstrate the importance of forcing temperature in simulating high‐latitude ecosystem processes and call for a community effort to measure plant temperatures across canopy heights and seasons to reduce uncertainties in estimating high‐latitude plant responses and feedback to climate.

Enhancing long-term vegetation monitoring in Australia: a new approach for harmonising the Advanced Very High Resolution Radiometer normalised-difference vegetation (NVDI) with MODIS NDVI

Abstract. Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia, which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global Advanced Very High Resolution Radiometer (AVHRR) normalised-difference vegetation index (NDVI) products for their suitability for long-term vegetation monitoring in Australia. Comparisons with the MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce the interannual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA's Climate Data Record of AVHRR NDVI to the MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors (“AusENDVI-clim”: Australian Empirical NDVI-climate) and another that is independent of climate data (“AusENDVI-noclim”). These datasets, spanning 1982–2013 at a spatial resolution of 0.05° and with a monthly time step, exhibit strong correlations (r2=0.89–0.94) and low mean errors compared with MODIS MCD43A4 NDVI (mean absolute error (MAE) = 0.014–0.028, RMSE = 0.021–0.046), accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO2 concentration, and woody-cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with the MODIS MCD43A4 NDVI and the recalibrated AVHRR NDVI time series (r2=0.82–0.95, MAE = 0.016–0.029, RMSE = 0.039–0.041). Finally, we provide a complete 41-year dataset where the gap-filled AusENDVI-clim from January 1982 to February 2000 is joined with the MODIS MCD43A4 NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia's annual maximum NDVI revealed increasing values, and shifts in the timing, of the annual peak NDVI across most of the continent, underscoring the dataset's potential to address crucial questions regarding the changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on the terrestrial carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at https://doi.org/10.5281/zenodo.10802703 (Burton et al., 2024).

Inter- and intra-annual variability and climatic responses of ecosystem water use efficiency in a cool-temperate freshwater wetland

Wetland ecosystems play a pivotal role in terrestrial carbon and water cycles, thereby possessing great potential to regulate terrestrial water use efficiency (WUE), which is calculated as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, it remains unclear the possible changes in wetland WUE under present and future climate conditions. In this research, WUE variations in a Phragmites australis-dominated freshwater wetland were determined by the eddy covariance method during 2020–2023. Further, we projected future GPP, ET, and WUE under four Representative Concentration Pathway (RCP) scenarios based on five Earth System Models. The 3-year average field observation suggested that the P. australis marsh exhibited high GPP (1149 g C m−2), but consumed large amounts of water through ET (611 mm H2O), resulting in relatively low WUE (1.89 g C mm−1 H2O). During wet years, the studied marsh consumed much more water through evaporation than through transpiration, thus exhibiting lower WUE. Contrarily, large amounts of water were utilized to maintain high primary productivity through transpiration in 2021–2022 dry year, leading to higher WUE. In future scenarios, GPP in the P. australis marsh shows consistently faster uptrends compared to ET from 2020 to 2100, consequently yielding persistent growths of WUE. Future growths of WUE indicate that P. australis marsh tends consume more water for maintaining productivity levels rather than loss via evaporation under future climate conditions, thereby intensifying the carbon–water interaction. Driven by the most rapid growth of environmental drivers, the RCP8.5 scenario shows the fastest increasing trends in GPP, ET, and WUE among four RCP scenarios, whereas the reverse ocurres under RCP2.6 scenario. Overall, our study advocates for more comprehensive research encompassing field observation and model simulation to address the current knowledge gap regarding the response of wetland WUE to contemporary and future climate change.

Water controls the divergent responses of terrestrial plant photosynthesis under nitrogen enrichment

Abstract Quantifying leaf photosynthetic response to nitrogen (N) deposition under contrasting water conditions is important for reliably modelling terrestrial carbon and water cycles, a topic that has not been well understood. Here, we analysed 737‐paired observations from 102 publications to assess the response of 11 leaf photosynthesis‐related properties to N addition under different water conditions. Our research includes global experiments, with 19 conducted in the field and 83 in greenhouses. Treatments without water reduction were classified as ‘no water change’, while those with reduced water or precipitation causing physiological drought were categorized as ‘drought’. We found that, compared to the control group, N addition significantly increased leaf photosynthetic rate (Pn; 20.9%), leaf transpiration (E; 8.3%) and stomatal conductance (gs; 14.1%). However, the decrease in Pn (−11.6%), E (−24.7%) and gs (−23.9%) under the combination of N addition and drought indicated that N addition could not offset the negative effects of drought. Furthermore, N addition significantly enhanced water‐use efficiency (WUE) by 19.8% under no water change conditions and by 21.1% under drought conditions. Within plant functional groups, herbaceous species exhibited greater susceptibility to N addition than woody species, especially under drought conditions. The observed patterns of increase in Pn with longer experimental duration and WUE with higher N rate under drought conditions showed that plants would gradually adapt to long‐term water stress in the context of N deposition. Furthermore, our results showed that drought could strengthen the correlations between leaf photosynthetic properties. Lastly, our study demonstrated that N addition and drought significantly impacted leaf nitrogen content and SPAD, respectively, and further affected gs, Pn and WUE. Synthesis. Our results emphasize the crucial role of water conditions in shaping the response of leaf photosynthesis to nitrogen (N) enrichment and also acknowledge the significance of leaf functional traits in regulating the dynamics of leaf photosynthetic processes.

Spatially varying effect of soil moisture-atmosphere feedback on spring streamflow under future warming in China

Soil moisture–atmosphere feedback (SAF) is expected to accelerate warming in the future, but its influences on the terrestrial water cycle over non-traditional hotspots of land–atmosphere coupling (e.g., East Asia) remains elusive due to strong heterogeneity and uncertainties at model grid scales. Here we show robust evidence of the integral effects of SAF on streamflow under future warming across major river basins in China. Based on high-resolution hydrological modelings driven by CMIP6 simulations with/without land–atmosphere coupling, SAF is projected to significantly increase spring streamflow by 28–58% in mid-latitude China, but decrease it by 12–48% in other regions. Such contrary effects are attributed to the soil moisture-precipitation interaction, which causes spatially opposite changes in precipitation through thermodynamic (e.g., ascending/descending vertical motions) and thermal (e.g., weakened meridional water vapor transport) atmospheric adjustments. Our result highlights the importance of SAF in altering freshwater resources even in locations not typically considered to be SAF hotspots.

Recycling the rain: Exploring forest management practices in a western Mediterranean watershed to recouple the atmospheric and terrestrial water cycle

Current knowledge of the role that forests play in recycling rainwater has led to the proposal of forest management practices to mitigate desertification in the western Mediterranean basin. Building upon this hypothesis, we present the Mijares watershed in eastern Spain as a detailed case study. We then perform a scientific literature review in the context of the western Mediterranean basin to identify the physical and biophysical uncertainties associated with forest management practices that aim to strengthen the atmospheric and terrestrial components of the water cycle. The review identified eleven interconnected uncertainties that result from complex vegetation, climate, soil, and water relationships. We discuss the need to deepen our understanding of the atmospheric dynamics of the Mijares by accounting for the role of forests in recycling rainfall and by adopting an adaptive management approach to apply the lessons learned in the territory. Based on these considerations, we propose forest management practices to reinforce the water cycle of the watershed. Our work contributes to understanding the uncertainties arising from forest management practices aimed at mitigating desertification, thereby supporting decision-making. These insights apply to other western Mediterranean coastal watersheds.

Understanding Terrestrial Water and Carbon Cycles and Their Interactions Using Integrated SMAP Soil Moisture and OCO‐2 SIF Observations and Land Surface Models

AbstractRecently, more advanced synchronous global‐scale satellite observations, the Soil Moisture Active Passive enhanced Level 3 (SMAP L3) soil moisture product and the Orbiting Carbon Observatory 2 (OCO‐2) solar‐induced chlorophyll fluorescence (SIF) product, provide an opportunity to improve the predictive understanding of both water and carbon cycles in land surface modeling. The Simplified Simple Biosphere Model version 4 (SSiB4) was coupled with the Top‐down Representation of Interactive Foliage and Flora Including Dynamics Model (TRIFFID) and a mechanistic representation of SIF. Incorporating dynamic vegetation processes reduced global SIF root‐mean‐squared error (RMSE) by 12%. Offline experiments were conducted to understand the water and carbon cycles and their interactions using satellite data as constraints. Results indicate that soil hydraulic properties, the soil hydraulic conductivity at saturation (Ks) and the water retention curve, significantly impact soil moisture and SIF simulation, especially in the semi‐arid regions. The wilting point and maximum Rubisco carboxylation rate (Vmax) affect photosynthesis and transpiration, then soil moisture. However, without atmospheric feedback processes, their effects on soil moisture are undermined due to the compensation between soil evaporation and transpiration. With optimized parameters based on SMAP L3 and OCO‐2 data, the global RMSE of soil moisture and SIF simulations decreased by 15% and 12%, respectively. These findings highlight the importance of integrating advanced satellite data and dynamic vegetation processes to improve land surface models, enhancing understanding of terrestrial water and carbon cycles.

Synergizing AI and Physical Expertise to Close the Water Budget from Satellite Data

Abstract A multitude of Earth observation (EO) products are available for monitoring the terrestrial water cycle. These EO datasets have resulted in a multiplicity of datasets for the same geophysical variable. Furthermore, inconsistencies between the water components prevent the water budget closure. A maximum a posteriori (MAP) estimator has been used in the past to optimally combine EO datasets. This framework has many advantages, but it can only be utilized when all four water components are available (precipitation P, evapotranspiration E, total water storage change dS, and river discharge R) and solely at the basin scale. By combining physical expertise with the statistical inference of neural networks (NNs), we designed a custom deep learning scheme to optimize EO data. This hybrid approach benefits from the optimization capabilities of NNs to estimate the parameters of interconnected physical modules. The NN is trained using basin-scale data (from MAP results) over 38 basins to obtain optimized EOs globally. The NN integration offers several enhancements compared to MAP: Independent calibration/mixing models are obtained with imbalance reduction and optimization at the pixel level, and environmental variables can be used to extrapolate results to unmonitored regions. The NN integration enables combining EO estimates of individual water components (P, E, dS, and R) in a hydrologically coherent manner, resulting in a significant decrease in the water budget imbalance at the global scale. Mean imbalance errors can be significant on raw EOs, but they become negligible when EOs are integrated. The standard deviation (STD) of the imbalance is around 26 mm month−1 for raw EOs, and they decrease to 21 when combined and 19 when mixed.

Irrigation increases and stabilizes mosquito populations and increases West Nile virus incidence

Humans have greatly altered earth’s terrestrial water cycle with the majority of fresh water being used for agriculture. Irrigation changes spatial and temporal water availability and alters mosquito abundance and phenology. Previous studies evaluating the effect of irrigation on mosquito abundance and mosquito-borne disease have shown inconsistent results and little is known about the effect of irrigation on variability in mosquito abundance. We examined the effect of irrigation, climate and land cover on mosquito abundance and human West Nile virus (WNV) disease cases across California. Irrigation made up nearly a third of total water inputs, and exceeded precipitation in some regions. Abundance of two key vectors of several arboviruses, including WNV, Culex tarsalis and the Culex pipiens complex, increased 17–21-fold with irrigation. Irrigation reduced seasonal variability in C. tarsalis abundance by 36.1%. Human WNV incidence increased with irrigation, which explained more than a third (34.2%) of the variation in WNV incidence among California counties. These results suggest that irrigation can increase and decouple mosquito populations from natural precipitation variability, resulting in sustained and increased disease burdens. Shifts in precipitation due to climate change are likely to result in increased irrigation in many arid regions which could increase mosquito populations and disease.

How climate change and groundwater extraction will shape the global terrestrial water cycle pattern

Human groundwater extraction alters hydrological processes, thereby influencing climate through land–atmosphere interactions. As socioeconomic development progresses, the escalating water demand exacerbates conflicts with limited water resources. Nevertheless, potential response of the global terrestrial water cycle to the combined impact of climate change and groundwater extraction under various future scenarios remain vaguely understood. Therefore, this study developed human water regulation projection schemes based on Shared Socio-economic Pathways (SSPs) and Representative Concentration Pathways (RCPs) integrated into a global climate model (CAS-FGOALS-g3). The results show that global water demand in 2100 under the SSP370 scenario almost doubled relative to 2010. The proportion of surface soil drying due to warming is 3%-11% more in the medium- and high-emission scenarios than in the low one. In major irrigated areas, excessive groundwater extraction changed the groundwater storage trend, especially in the Southern Plains of the United States, where the increasing groundwater storage trend became negative compared to scenarios without groundwater extraction. This study underscores the complex interplay between human water use and hydroclimate within an evolving environment, emphasizing the critical need for effective water resource management to mitigate future water stress.

Two sub-annual timescales and coupling modes for terrestrial water and carbon cycles.

To bridge the knowledge gap between (a) our (instantaneous-to-seasonal-scale) process understanding of plants and water and (b) our projections of long-term coupled feedbacks between the terrestrial water and carbon cycles, we must uncover what the dominant dynamics are linking fluxes of water and carbon. This study uses the simplest empirical dynamical systems models-two-dimensional linear models-and observation-based data from satellites, eddy covariance towers, weather stations, and machine-learning-derived products to determine the dominant sub-annual timescales coupling carbon uptake and (normalized) evaporation fluxes. We find two dominant modes across the Contiguous United States: (1) a negative correlation timescale on the order of a few days during which landscapes dry after precipitation and plants increase their carbon uptake through photosynthetic upregulation. (2) A slow, seasonal-scale positive covariation through which landscape drying leads to decreased growth and carbon uptake. The slow (positively correlated) process dominates the joint distribution of local water and carbon variables, leading to similar behaviors across space, biomes, and climate regions. We propose that vegetation cover/leaf area variables link this behavior across space, leading to strong emergent spatial patterns of water/carbon coupling in the mean. The spatial pattern of local temporal dynamics-positively sloped tangent lines to a convex long-term mean-state curve-is surprisingly strong, and can serve as a benchmark for coupled Earth System Models. We show that many such models do not represent this emergent mean-state pattern, and hypothesize that this may be due to lack of water-carbon feedbacks at daily scales.

Autumn phenology consistently delays in subtropical forests in China based on a new process-based model integrating temperature, photoperiod and precipitation

Abstract Climate warming has delayed vegetation autumn phenology, which in turn influences terrestrial carbon and water cycles and their feedback to the climate. However, the performance of autumn phenology models, especially for subtropical forests, remains poor. In this study, we extracted the end-of-photosynthetic-growing-season (EOPS) dates in subtropical China over the period 2001-2018 based on high-resolution solar-induced chlorophyll fluorescence (SIF) dataset using three fitting methods. We developed a new autumn phenology model (DMP model) that integrates precipitation and photoperiod into the classic cold-degree days model, and the new model outperformed the classic model reducing the RMSE by approximately 2 days. We found that the EOPS date was delayed by an average of 4.1 days per decade in the subtropical forests of China from 2001 to 2018, and the precipitation (partial correlation coefficient, r = 0.45), rather temperature (r = 0.29) determined the EOPS processes. We further studied future EOPS using the DMP model and found the EOPS will delay by 1.0 days per decade under the SSP2-4.5 scenario and 2.7 days per decade under the SSP5-8.5 scenario from 2030 to 2100. Our study highlighted the role of precipitation in regulating EOPS in the subtropical forests of China and provided valuable insight for integrating multiple climatic determinants into autumn phenology models.

Reconstructing high-resolution gridded precipitation data in the southwest China highland canyon area using an improved (MGWR) downscaling method

High-spatiotemporal-resolution rainfall data are vital for investigating local terrestrial water cycles. Although remote-sensing satellite retrieval of precipitation products effectively reproduces spatial patterns of rainfall, it suffers from low spatial resolution. To overcome such limitations, a two-step downscaling approach is proposed here. First, 80 % of the meteorological-station data is utilized to calibrate the original Global Precipitation Measurement (GPM) data, enhancing the correlation between GPM and station data. Subsequently, utilizing elevation, slope, aspect, the normalized difference vegetation index (NDVI), wind direction, water vapor, and land surface temperature, as well as slope and aspect correction factors, as independent variables, multiscale geographically weighted regression (MGWR) and temporal lag MGWR (TL-MGWR) models were constructed. Through the aforementioned steps, downscaled monthly and daily precipitation data for the geographic region under investigation in 2022 at a spatial resolution of 0.01° were obtained.Our findings indicate that selectively employing suitable MGWR or TL-MGWR models on a monthly basis can effectively downscale monthly GPM rainfall data. The downscaled (original) monthly precipitation data exhibited a correlation of 0.94 (0.768), with a mean absolute error (MAE) of 16.233 mm/month, root-mean-square error (RMSE) of 27.106 mm/month, and bias of −0.043. Similar enhancement was likewise noted in daily precipitation, displaying a correlation coefficient of 0.863 (0.318) for downscaled (original) data, and a RMSE of 3.209 mm/day, MAE of 1.082 mm/day, and bias of −0.06. The downscaled results show a correlation increase of 0.172 monthly and 0.545 daily, with MAE reductions of 18.43 mm/month and 1.658 mm/day, RMSE reductions of 26.172 mm/month and 4.183 mm/day, and bias reductions of 82.7 % and 56.8 %. In summary, the data after downscaling, both for monthly and daily datasets, was markedly improved in accuracy. The proposed downscaling method is applicable for reconstructing high-resolution grid data in the complex terrain of the southwest China highland canyon area.

Dynamic Monitoring and Change Analysis of Lake Area on the Inner-Mongolian Plateau over the Past 22 Years

Lakes are essential components of the terrestrial water cycle. Their size and quantity reflect natural climate change and anthropogenic activities in time. Lakes on the Inner-Mongolian Plateau (IMP) have experienced significant changes in recent decades, but the current situation remains elusive. In this study, we conducted multi-decadal intensive monitoring of lake area and performed comprehensive variation analysis on the IMP. The study involved pre-processing, lake area extraction, post-processing, and lake area analysis procedures using multi-source satellite images. The results reveal the detailed variation in the lake from various aspects. The temporal analysis indicates that the lake area has undergone two distinct periods of decline followed by subsequent increase, and the variation pattern from January to December was diverse. The spatial analysis suggests that the lake area expanded slightly in the western and southern parts, while the area shrank in the other two. Variations in lakes of different sizes exhibited spatial heterogeneity. To some extent, effective actions have led to lake rehabilitation, but it has not yet reached or surpassed the level of 2000. This study provides a substantial data basis and technological guidance for conducting lacustrine surveys. The results can play important roles in fostering further analyses of the water cycle and the carbon cycle.

Divergent determinants on interannual variability of terrestrial water cycle across the globe

Intensifying variability in precipitation under a changing climate is projected to amplify fluctuation in terrestrial hydrological cycle, leading to more severe water-related disasters. The connections between interannual variability of hydrological components and factors influencing these connections have not been clearly defined yet. Based on terrestrial water budget from Climate Data Record, we identify dominant factors influencing partitioning interannual variability of precipitation (P) into that of evapotranspiration (E), runoff (Q), and water storage deviation (ΔS) across the globe by employing geographical detector model (GDM). Sensitivities of the variability partitioning to dominant factors are quantified for different hydroclimate regions by linear regression model and law of total differential. Results show that dominant factors influencing precipitation variability partitioning (VP) are different across distinct hydroclimate conditions. Comparing the statistical index (q value) of the GDM, it can be seen that surface air temperature (Ta), snow water equivalent (SWE) and water storage capacity (Smax) are dominant factors of VP in humid, semi-arid and arid regions, respectively. Changes in P variability largely can transfer into Q variability in humid region. The P variability partitioned into Q variability is dramatically reduced in semi-arid region with SWE decreasing, while P variability partitioned into ΔS variability increases with Smax increasing in arid region. Joint effects of Ta and coefficient of variation of precipitation (Pcv) are found to be the most important interaction in determining VP across the globe. Furthermore, warmer temperatures in humid region cause >90 % of the change in precipitation variability to be transferred to Q variability change. In semi-arid region with snowfall, decreased SWE has strong effect on changes in ΔS (30–40 %) and Q (20–40 %) variability. Our findings imply a changing VP and more severe impacts of hydrological extremes under future climate, where intensive changes in Ta, SWE and land cover are projected.

Evaluation of root zone soil moisture products over the Huai River basin

Abstract. Root zone soil moisture (RZSM) is critical for water resource management, drought monitoring and sub-seasonal flood climate prediction. While RZSM is not directly observable from space, several RZSM products are available and widely used at global and continental scales. This study conducts a comprehensive and quantitative evaluation of eight RZSM products using observations from 58 in situ soil moisture stations over the Huai River basin (HRB) in China. Attention is drawn to the potential factors that contribute to the uncertainties of model-based RZSM, including the errors in atmospheric forcing, vegetation parameterizations, soil properties and spatial scale mismatch. The results show that the Global Land Data Assimilation System Catchment Land Surface Model (GLDAS_CLSM) outperforms the other RZSM products with the highest correlation coefficient (R= 0.69) and the lowest unbiased root mean square error (ubRMSE = 0.018 m3 m−3), while SMOS Level 4 (L4) RZSM shows the worst performance among eight RZSM products. The RZSM products based on land surface models generally perform better in the wet season than in the dry season due to the enhanced ability to capture of the temporal dynamics of in situ observations in the wet season and the inertia of remaining high soil moisture values even in the dry season, while the SMOS L4 RZSM product, derived from SMOS L3 surface moisture (SSM) combined with an exponential filter method, performs better in the dry season due to the attenuated ground microwave radiation signal caused by the increased water vapour absorption and scattering in the wet season. The underestimated SMOS L3 SSM triggers the underestimation of RZSM in SMOS L4. The overestimated RZSM products based on land surface models could be associated with the overestimated precipitation amounts and frequency, the underestimated air temperature, and the underestimated ratio of transpiration to the total terrestrial evapotranspiration. In addition, the biased soil properties and flawed vegetation parameterizations affect the hydrothermal transport processes represented in different land surface models (LSMs) and lead to inaccurate soil moisture simulation. The scale mismatch between point and footprint also introduces representative errors. The comparison of frequency of normalized soil moisture between RZSM products and in situ observations indicates that the LSMs should focus on reducing the frequency of wet soil moisture, increasing the frequency of dry soil moisture and the ability to capture the frequency peak of soil moisture. The study provides some insights into how to improve the ability of land surface models to simulate the land surface states and fluxes by taking into account the issues mentioned above. Finally, these results can be extrapolated to other regions located in similar climate zones, as they share similar precipitation patterns that dominate the terrestrial water cycle.