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Lateral subsurface flow plays an essential role in sustaining the terrestrial ecosystem, but it is not explicitly represented in most Earth System Models. In this study, we implemented an explicit lateral saturated flow model into the E3SM land model (ELM). The model explicitly describes lateral flow in the saturated zone by representing, for each model grid, an idealized hillslope consisting of five hydrologically connected soil columns. We conducted three model experiments driven by 0.125° atmospheric forcing data during 1980–2015 over California using models of the default ELM, a modified version of ELM to enhance infiltration, and the model with the lateral saturated flow model. The simulated runoff, evapotranspiration, and terrestrial water storage anomaly (TWSA) from the three simulations were evaluated against available observations, and the model explicitly representing lateral flow performs best. The new model produces greater gridcell‐averaged evapotranspiration especially over the mountainous regions with moderate relief and seasonally dry climates. Most importantly, it improves the modeled seasonal variations, interannual variabilities, and the recent decadal decline of TWSA. Many of these improvements can be attributed to the enhanced ecosystem resilience to droughts as demonstrated by transpiration increases caused by lateral flow. Model sensitivity experiments suggest that subsurface runoff is most sensitive to the ratio between horizontal and vertical saturated hydraulic conductivity, followed by hillslope planforms (convergent, divergent, and uniform), number of columns, and lower boundary conditions. Future work should effectively characterize hillslopes in global models and explore the long‐term influences of lateral water movement on modeled biogeochemical cycle. Plain Language Summary: In this study, we implemented a lateral saturated flow scheme into the Energy Exascale Earth System Model's land model (ELM) to explicitly represent lateral groundwater movement. We applied our newly developed model over California and found better model performance against the original and a revised version of ELM through the explicit yet simplified representation of lateral flow along hillslopes. Most importantly, our new model does a better job at reproducing the seasonal variations, interannual variabilities, and a declining trend of terrestrial water storage anomaly in California. Given the intensified coupling among water, energy, and carbon cycles associated with climate change, our study highlights the need to implement lateral flow in Earth System Models for better climate projections. Key Points: A land model with an explicit lateral saturated flow model produces runoff and evapotranspiration reasonably well in the California basinIt simulates a better declining terrestrial water storage trend during droughts as lateral flow enhances ecosystem resilienceLateral subsurface flow is most sensitive to the ratio between vertical and horizontal hydraulic conductivity followed by hillslope shape [ABSTRACT FROM AUTHOR]

The article discusses the First International Noah-MP Annual Users' Workshop, which brought together over 200 participants from 16 countries to discuss advancements in the Noah-MP land surface model. The workshop focused on enhancing the model's capabilities, applicability, and interoperability in Earth system applications. The article highlights the various applications of the Noah-MP model, including weather prediction, climate projection, hydrology, agriculture, and urban heat island studies. It also identifies current challenges and limitations of the model, such as uncertainties in certain processes and a lack of communication and coordination within the Noah-MP community. The article concludes with recommendations for future model development and the establishment of a Noah-MP Academia Collaboratory to support community collaborations and enhance research-to-operation activities. [Extracted from the article]

The widely used open-source community Noah with multi-parameterization options (Noah-MP) land surface model (LSM) is designed for applications ranging from uncoupled land surface hydrometeorological and ecohydrological process studies to coupled numerical weather prediction and decadal global or regional climate simulations. It has been used in many coupled community weather, climate, and hydrology models. In this study, we modernize and refactor the Noah-MP LSM by adopting modern Fortran code standards and data structures, which substantially enhance the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization as a result of re-organizing model physics into individual process-level Fortran module files, (2) an enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) an enhanced code structure and calling workflow as a result of leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standards, and (5) enhanced driver and interface structures to be coupled with the host weather, climate, and hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather, climate, and hydrology models in the future. Overall, the modernized Noah-MP allows a more efficient and convenient process for future model developments and applications. [ABSTRACT FROM AUTHOR]

The widely-used open-source community Noah-MP land surface model (LSM) is designed for applications ranging from uncoupled land-surface and ecohydrological process studies to coupled numerical weather prediction and decadal global/regional climate simulations. It has been used in many coupled community weather/climate/hydrology models. In this study, we modernize/refactor the Noah-MP LSM by adopting modern Fortran code and data structures and standards, which substantially enhances the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization and interoperability by re-organizing model physics into individual process-level Fortran module files, (2) enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) enhanced code structure and calling workflow by leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standard, and (5) enhanced driver and interface structures to couple with host weather/climate/hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather/climate/hydrology models in the future. Overall, the modernized Noah-MP will allow a more efficient and convenient process for future model developments and applications. [ABSTRACT FROM AUTHOR]

Despite plentiful evidence of frozen ground effects on snowmelt infiltration from lab experiments at pedon scales, streamflow observations show a weaker or no effect in terms of timing and magnitude at larger scales. We aim to understand this conflicting phenomenon through modeling using the Noah land surface model with multi‐physics (MP; Noah‐MP) options and the Routing Application for Parallel computatIon of Discharge (RAPID) over the Mississippi River Basin. We conduct 16 experiments with combinations of two supercooled liquid water (SLW) parameterization schemes and four soil hydraulic property schemes in Noah‐MP driven by two gridded precipitation products of the North American Land Data Assimilation System (NLDAS) and the Integrated Multi‐satellitE Retrievals for GPM (IMERG) Final. We then use RAPID to route Noah‐MP modeled surface runoff and groundwater discharge to predict daily streamflow at 52 United States Geological Survey gauges from 2015 to 2019. A model with the highest permeability performs better than other schemes on daily streamflow predictions by 20%–57% throughout a water year and 29%–113% for the spring as measured by the mean Kling‐Gupta Efficiency of the 52 gauges. Different SLW schemes demonstrate negligible effects on streamflow predictions. Models forced by IMERG show a better prediction skill compared with those forced by NLDAS at most of the gauges. Both precipitation products confirm that a scheme of higher permeability yields more accurate streamflow predictions over frozen ground. Future models should represent preferential flows through macropore networks to improve the understanding of frozen soil effects on infiltration and discharge across scales. Plain Language Summary: Frozen ground presumably affects the discharge of snowmelt water into rivers during winter and spring due to the apparent effects of ice "blockage." The presence of ice in the soil affects the release of soil liquid water and the time to release the water to local streams and rivers through the effects of soil ice on water flow and capacity to hold snowmelt water. At present, it is not fully understood how the soil ice affects the soil's capability of holding and releasing liquid water to rivers at river‐basin to continental scales. We use a computer model to test competing hypotheses through combinations of optional schemes of water holding capacity and water flow. The modeling results over major sub‐basins in the Mississippi River show that a model with higher permeable frozen soil results in higher skill in streamflow predictions at river basin scales. This study highlights the need to represent water flow through macropores that may be formed due to ice expansion during freezing/thawing cycles. Key Points: Streamflow predictions are substantially sensitive to the choice of frozen soil hydraulic property parameterizationsIrrespective of precipitation product used, a scheme of higher frozen soil permeability yields more skillful streamflow predictionsStreamflow prediction improvement with a scheme of higher frozen soil permeability is pronounced in basins dominated by frozen ground [ABSTRACT FROM AUTHOR]

The Tigris–Euphrates dryland river basin has experienced a declining trend in terrestrial water storage (TWS) from April 2002 to June 2017. Using satellite observations and a process-based land surface model, we find that climate variations and direct human interventions explain ∼61% (−0.57 mm month−1) and ∼39% (−0.36 mm month−1) of the negative trend, respectively. We further disaggregate the effects of climate variations and find that interannual climate variability contributes substantially (−0.27 mm month−1) to the negative TWS trend, slightly greater than the decadal climate change (−0.25 mm month−1). Interannual climate variability affects TWS mainly through the nonlinear relationship between monthly TWS dynamics and aridity. Slow recovery of TWS during short wetting periods does not compensate for rapid depletion of TWS through transpiration during prolonged drying periods. Despite enhanced water stress, the dryland ecosystems show slightly enhanced resilience to water stress through greater partitioning of evapotranspiration into transpiration and weak surface "greening" effects. However, the dryland ecosystems are vulnerable to drought impacts. The basin shows straining ecosystem functioning after experiencing a severe drought event. In addition, after the onset of the drought, the dryland ecosystem becomes more sensitive to variations in climate conditions. Significance Statement: The purpose of the research is to better understand climate impacts on terrestrial water storage over dryland regions with declining water storage. In our study, we disaggregate three components of climate impacts, namely, decadal climate change, interannual variability, and intra-annual variability. We then use observational datasets and a process-based model to quantify their individual effects on water storage. We find that interannual variability is the most significant climatic contributor to the declining water storage, mainly caused by prolonged drought periods and corresponding quick drying rates due to plant transpiration. We also find that the dryland ecosystem is sensitive and vulnerable to severe drought events. This study is important because 1) it provides a framework to investigate climate impacts on water fluxes and storages, 2) it highlights the importance of vegetation dynamics on dryland hydrology, and 3) it emphasizes the negative impacts of extreme hydroclimatological events on ecosystem functioning. [ABSTRACT FROM AUTHOR]

This study aimed to improve runoff simulations and explore deep soil hydrological processes for a watershed in the center of the Loess Plateau (LP), China. This watershed, the Wuding River Basin (WRB), has very complex topography, with soil depths ranging from 0 to 197 m. The hydrological model used for our simulations was Community Land Model version 5 (CLM5) developed by the National Center for Atmospheric Research. Actual soil depths and river channels were incorporated into CLM5 to realistically represent the physical features of the WRB. Through sensitivity tests, CLM5 with 150 soil layers with the observed variable soil depths produced the most reasonable results and was adopted for this study. Our results showed that CLM5 with actual soil depths significantly suppressed unrealistic variations of the simulated subsurface runoff when compared to the default simulations. In addition, when compared with the default version with 20 soil layers, CLM5 with 150 soil layers slightly improved runoff simulations but generated simulations with much smoother vertical water flows that were consistent with the uniform distribution of soil textures in our study watershed. The runoff simulations were further improved by the addition of river channels to CLM5, where the seasonal variability of the simulated runoff was reasonably captured. Moreover, the magnitude of the simulated runoff remarkably decreased with increased soil evaporation by lowering the soil water content threshold, which triggers surface resistance. The lowered threshold was consistent with the loess soil, which has a high sand component. Such soils often generate stronger soil evaporation than soils dominated by clay. Finally, with the above changes in CLM5, the simulated total runoff matched very closely with observations. When compared with those for the default runoff simulations, the correlation coefficient, root mean square error, and Nash–Sutcliffe coefficient for the improved simulations changed dramatically from 0.02, 10.37 mm, and - 12.34 to 0.62, 1.8 mm, and 0.61. The results in this study provide strong physical insight for further investigation of hydrological processes in complex terrain with deep soils. [ABSTRACT FROM AUTHOR]

Groundwater-based irrigation has dramatically expanded over the past decades. It has important implications for terrestrial water, energy fluxes, and food production, as well as local to regional climates. However, irrigation water use is hard to monitor at large scales due to various constraints, including the high cost of metering equipment installation and maintenance, privacy issues, and the presence of illegal or unregistered wells. This study estimates irrigation water amounts using machine learning to integrate in situ pumping records, remote sensing products, and climate data in the Kansas High Plains. We use a random forest regression to estimate the annual irrigation water amount at a reprojected spatial resolution of 6 km based on various data, including remotely sensed vegetation indices and evapotranspiration (ET), land cover, near-surface meteorological forcing, and a satellite-derived irrigation map. In addition, we assess the value of ECOSTRESS ET products for irrigation water use estimation and compare with the baseline results by using MODIS ET. The random forest regression model can capture the temporal and spatial variability of irrigation amounts with a satisfactory accuracy ( R 2 = 0.82). It performs reasonably well when it is calibrated on the western portion of the study area and tested on the eastern portion that receives more rain than the western one, suggesting its potential transferability to other regions. ECSOTRESS ET and MODIS ET yield a similar irrigation estimation accuracy. [ABSTRACT FROM AUTHOR]

The interannual variability (IAV) of gross primary productivity (GPP) reflects the sensitivity of GPP to climate variations and contributes substantially to the variations and long‐term trend of the atmospheric CO2 growth rate. Analyses of three observation‐based GPP products indicate that their IAVs are consistently correlated to terrestrial water storage anomaly over the central US, where episodic droughts occur. A land surface model explicitly representing plant hydraulics and groundwater capillary rise with an adequate soil hydraulics well captures the observed GPP IAV. Our sensitivity experiments indicate that, without representations of plant hydraulics and groundwater capillary rise or using an alternative soil hydraulics, the land model substantially overestimates the GPP IAV and the GPP sensitivity to water in the central US. This study strongly suggests the use of the van Genuchten water retention model to replace the most commonly used Brooks–Corey model, which generally produces too strong matric suction of soil water especially in dry conditions, in land surface modeling. This study highlights the importance of plant and soil hydraulics and surface–groundwater interactions to Earth system modeling for projections of future climates that may experience more intense and frequent droughts. Plain Language Summary: The interannual variability (IAV) of land carbon uptake contributes substantially to the fluctuations of atmospheric CO2 growth rate. Consistent with previous studies, our data analyses of various observation‐based gross primary productivity (GPP) products and land water storage change data reveal a positive GPP–water relationship. This relationship also has been used to evaluate and constrain climate model projections. Our model sensitivity experiments suggest that current land surface models may overestimate the GPP–water sensitivity and potentially degrade the credibility of future climate projections, due to a lack of appropriate plant and soil hydraulics and surface–groundwater interactions. Our results highlight the importance of key ecohydrological processes on IAV of GPP as well as CO2 projections. Key Points: Observation‐based estimates of annual gross primary productivity (GPP) show a strong dependence on water availability over the central USA land surface model with adequate representations of plant and soil hydraulics can capture the observed interannual variability of GPPA model with a low plant drought resilience substantially overestimates GPP sensitivity to water availability [ABSTRACT FROM AUTHOR]

To represent the physical processes at hillslope scales for hyper‐resolution land surface modeling, we propose a hierarchical, catchment‐based spatial tessellation method. The land surface is divided into a hierarchical structure: catchments, height bands along hillslopes within a catchment, and land cover patches within a height band. This catchment‐based structure explicitly represents hillslope drainage networks and can be applied at various resolutions determined by a pre‐defined maximum height band size. The proposed tessellation method is superior to the conventional grid‐based structure in representing land surface heterogeneity, resulting in a higher aggregation skill through the height band representation. The spatial variations in air temperature, leaf area index, saturated soil hydraulic conductivity, and soil porosity are generally lower within a height band than those in a conventional rectangular grid, reflecting the nature of topographic control on climate, vegetation, and soil distribution. The improvement in aggregation skill depends on resolutions and terrain slope angle, more pronounced at 1/6° model resolution and over steeper terrains. Finally, we demonstrate that our proposed catchment‐based structure performs better than the grid‐based structure through modeling tests over the Columbia River basin at resolutions of 1/2°, 1/6°, and 1/20° and a global test at 1/2° using the ILAMB model evaluation metrics. Plain Language Summary: This paper develops a catchment‐based spacing approach for high‐resolution land surface modeling. The land surface is divided into a hierarchical structure: catchments, height bands along hillslopes within a catchment, and land cover patches within a height band. We demonstrates that the catchment‐based approach can better represent the heterogeneous distributions of water, soils, plants, and climates, especially over mountainous regions than does the conventional, rectangular grid‐based approach. When used in a land surface model, the catchment‐based approach also performs better than the grid‐based approach through modeling tests over the Columbia River basin at resolutions of 1/2°, 1/6°, and 1/20° and a global test at 1/2°. Key Points: A catchment‐based hierarchical spatial tessellation: catchments, height bands, and land cover patches, is proposedThe catchment‐based tessellation method is superior to the conventional grid‐based structure in representing land surface heterogeneitiesThe proposed catchment‐based structure performs better than the grid‐based structure in land surface modeling [ABSTRACT FROM AUTHOR]

This study examines the overall performance of the Noah with multiparameterization (Noah‐MP) land surface model in simulating key land‐atmosphere variables at a global scale and explores the feasibility of running Noah‐MP with regionally different combinations of parameterization schemes. We conducted Noah‐MP ensemble simulations and evaluated the annual means and seasonal cycles of the simulated latent heat flux, net radiation (RN), runoff, soil moisture, snow water equivalent, land surface temperature (LST), leaf area index (LAI), and gross primary productivity (GPP) against a wide variety of global products. The results show that the global patterns of the modeled annual means of these variables generally agree with those of the reference data sets. By evaluating the best simulations in the ensemble, we show that Noah‐MP performs very well in simulating global LST and RN but produces biases in annual mean LAI and GPP by more than 40% in most herbaceous regions. Overall, the main disagreements between Noah‐MP and the reference data sets occurred in the tropical, polar, high‐altitude, and hyperarid regions. This study also highlights the potential of land‐cover‐specific combinations of parameterization schemes to produce optimal modeling results over different land‐cover types. In addition, we strongly suggest the use of multi‐objective optimization of the key parameterizations and parameters to further improve the Noah‐MP's overall performance. Key Points: Performance of Noah with multiparameterization (Noah‐MP) in simulating key land‐atmosphere variables was comprehensively evaluated at a global scaleMain disagreements between Noah‐MP and references occur in the tropical, polar, high‐altitude, and hyperarid regionsIt is feasible to run Noah‐MP with land‐cover‐specific combinations of parameterization schemes [ABSTRACT FROM AUTHOR]

Accurate estimation of the spatio‐temporal distribution of snow water equivalent is essential given its global importance for understanding climate dynamics and climate change, and as a source of fresh water. Here, we explore the potential of using the Long Short‐Term Memory (LSTM) network for continental and regional scale modeling of daily snow accumulation and melt dynamics at 4‐km pixel resolution across the conterminous US (CONUS). To reduce training costs (data are available for ∼0.31 million snowy pixels), we combine spatial sampling with stagewise model development, whereby the network is first pretrained across the entire CONUS and then subjected to regional fine‐tuning. Accordingly, model evaluation is focused on out‐of‐sample predictive performance across space (analogous to the prediction in ungauged basins problem). We find that, given identical inputs (precipitation, temperature, and elevation), a single CONUS‐wide LSTM provides significantly better spatio‐temporal generalization than a regionally calibrated version of the physical‐conceptual temperature‐index‐based SNOW17 model. Adding more meteorological information (dew point temperature, vapor pressure deficit, longwave radiation, and shortwave radiation) further improves model performance, while rendering redundant the local information provided by elevation. Overall, the LSTM exhibits better transferability than SNOW17 to locations that were not included in the training data set, reinforcing the advantages of structure learning over parameter learning. Our results suggest that an LSTM‐based approach could be used to develop continental/global‐scale systems for modeling snow dynamics. Plain Language Summary: Understanding the spatio‐temporal distribution of water in the snowpack (known as snow water equivalent) is very important for understanding climate dynamics and climate change, and for forecasting and management of global water supplies. In this study, we use Deep Learning (DL) to model snow accumulation and melt at 4‐km pixel‐scale resolution across the conterminous US (CONUS). Long Short‐Term Memory (LSTM) networks are developed at both continental‐ and regional‐scale, by combining spatial pixel sampling and stagewise network pre‐training/fine‐tuning. We benchmark out‐of‐sample predictive performance against the physical‐conceptual temperature‐index‐based SNOW17 model, and find that LSTM networks significantly outperform calibrated versions of the SNOW17 model when given identical information. Further, when provided with additional meteorological information, performance of the LSTM is improved. The LSTM models also exhibits better transferability than the SNOW17, indicating the potential for future development of a DL‐based system for modeling continental/global‐scale snow dynamics. Key Points: A trained continental‐scale Long Short‐Term Memory (LSTM) network is capable of providing almost as good performance as a regionally trained oneThe continent‐scale LSTM outperforms a regionally trained SNOW17, and a SNOW17 model calibrated locally to each pixel across the domainThe LSTM exhibits better spatial transferability than SNOW17, and exhibits a trade‐off between transferability and model complexity [ABSTRACT FROM AUTHOR]

Explicit representations of microbial processes in soil organic carbon (SOC) decomposition models have received increasing attention, because soil heterotrophic respiration remains one of the greatest uncertainties in climate‐carbon feedbacks projected by Earth system models (ESMs). Microbial‐explicit models have been developed and applied in site‐ and global‐scale studies. These models, however, lack the ability to represent microbial respiration responses to drying‐wetting cycles, and few of them have been incorporated in land surface models (LSMs) and validated against field observations. In this study, we developed a multi‐layer, microbial‐explicit soil organic carbon decomposition model (MESDM), based on two main assumptions that (a) extracellular enzymes remain active at dry reaction microsites, and (b) microbes at wet microsites are active or potentially active, while microbes at the dry microsites are dormant, by dividing the soil volume into wet and dry zones. MESDM with O2 and CO2 gas transport models was coupled with Noah‐MP LSM and tested against half‐hourly field observations at a semiarid grassland site in the southwest US characterized by pulsed precipitation. The results show MESDM can reproduce the observed soil respiration pulses of various sizes in response to discrete precipitation events (Birch effect) and thus improve the simulation of net ecosystem exchange. Here, both microbial accessibility to accumulated dissolved organic carbon and reactivation of dormant microbes at the dry microsites upon rewetting are critical to reproducing the Birch effect. This study improves our understanding of and ability to simulate complex soil carbon dynamics that experience drying‐wetting cycle in climate‐carbon feedbacks. Plain Language Summary: Soil microbial respiration represented in Earth system models remains one of the greatest uncertainties in predicting the interactions between climate change and global carbon cycle. Explicit representations of soil microbial activities and respiration processes in soil organic carbon decomposition models (or the so called microbial‐explicit models) have received increasing attention, and advancements have been made in related studies. However, these microbial‐explicit models still fail to simulate soil microbial respiration responses to drying‐rewetting cycles and need to be incorporated into land surface models (LSMs) for further validation against field observations. In this study, we developed a new microbe‐explicit model accounting for soil moisture control on microbial physiological state and activity and extracellular enzyme activity at reaction microsites in soil pores. We then incorporated the new model into the Noah‐MP LSM in place of the conventional first‐order decay model. The results show that Noah‐MP coupled with the new microbial‐explicit model reproduces observed soil respiration pulses of various sizes in response to precipitation pulses in a semiarid grassland in the southwest US. Our study helps improve the projection of the interactions between climate changes and land carbon cycle. Key Points: We develop a microbial‐explicit soil organic carbon decomposition model considering dry enzyme activities and microbial dormancyThe model incorporated into a land surface model is tested against half‐hourly field observations at a semiarid grassland siteThe model reproduces the observed respiration pulses in response to rain pulses and improves the simulation of net ecosystem exchange [ABSTRACT FROM AUTHOR]

Accurate snow albedo simulation is a prerequisite for climate models to produce reliable climate prediction. Climate models would benefit from schemes of snowpack radiative transfer that are responsive to changing atmospheric conditions. However, the uncertainties in the narrowband snow optical parameters used by these schemes have not been evaluated. Conventional methods typically compute these narrowband parameters as irradiance‐weighted averages of the spectral snow optical parameters, with the single scattering albedo being additionally weighted by the optically thick snowpack albedo. We first evaluate the effectiveness of the conventional methods as adopted by the widely used Community Land Model (CLM). Snow albedo calculations using the CLM narrowband optical parameters are relatively accurate for very thin snow (e.g., a bias of 0.01 for a 2‐cm snowpack). The error, however, becomes larger as snowpack thickens (with biases of up to 0.05 for semi‐infinite snowpack), because the snow radiative transfer is highly nonlinear and is most significant at wavelengths <1 μm. In this study, we propose a novel method to retrieve broadband optical parameters according to snow radiative transfer theory, reducing the albedo biases to <0.003 for 2 cm snowpacks and <0.005 for thick snowpacks. We find little impact in changing incident spectra on narrowband snow albedo. These newly derived narrowband optical parameters improve snow albedo accuracy by a factor of 10, allowing to trace the impacts of aerosol pollution in snow. The parameters are independent of which two‐stream approximation is used, and are thus applicable to sea ice, glaciers, and seasonal snow cover. Plain Language Summary: Snow albedo describes how much sunlight is reflected at the snow surface, which depends on how deep the sunlight penetrates the snowpack. Radiative transfer schemes describe sunlight absorption with snow optical depth. Snow radiative transfer schemes used in climate models make approximations using narrow‐band snow optical properties for computational efficiency. A conventional way to derive the narrowband parameters is to average the wavelength‐dependent values weighted by the incident solar spectrum. This approach produces snow albedo biases of up to 0.01 for shallow snowpacks and biases of up to 0.05 for thick snow. Such precision is not accurate enough for resolving the strongly positive snow‐climate feedback when albedo decreases due to light‐absorbing particles. This can amount to 0.01 over some "hot spots," which are climatically significant and have received increasing attention. Here, we provide a new set of narrowband optical parameters that improve the snow albedo accuracy by a factor of 10. Key Points: The semi‐empirical method used by Community Land Model to calculate narrowband snow optical parameters can produce errors that grow with snow massThe albedo errors stem from the relatively small biases in narrowband optical parameters that are amplified by nonlinear radiative transferWe propose a new set of narrowband snow optical parameters based on snow radiative transfer theory to improve albedo calculation accuracy [ABSTRACT FROM AUTHOR]

Water availability in the dry western United States (US) under climate change and increasing water use demand has become a serious concern. Previous studies have projected future runoff changes across the western US but ignored the impacts of ecosystem response to elevated CO2 concentration. Here, we aim to understand the impacts of elevated CO2 on future runoff changes through ecosystem responses to both rising CO2 and associated warming using the Noah‐MP model with representations of vegetation dynamics and plant hydraulics. We first validated Noah‐MP against observed runoff, leaf area index (LAI), and terrestrial water storage anomaly from 1980 to 2015. We then projected future runoff with Noah‐MP under downscaled climates from three climate models under Representative Concentration Pathway 8.5. The projected runoff declines variably from the Pacific Northwest by −11% to the Lower Colorado River basin by −92% from 2016 to 2099. To discern the exact causes, we conducted an attribution analysis of the modeled evapotranspiration from two additional sensitivity experiments: one with constant CO2 and another one with static monthly LAI climatology. Results show that surface "greening" (due to the CO2 fertilization effect) and the stomatal closure effect are the second largest contributors to future runoff change, following the warming effect. These two counteracting CO2 effects are roughly compensatory, leaving the warming effect to remain the dominant contributor to the projected runoff declines at large river basin scales. This study suggests that both surface "greening" and stomatal closure effects are important factors and should be considered together in water resource projections. Plain Language Summary: Water shortage in the western United States (US) is becoming increasingly serious due to increasing socioeconomic demands and climate change. Although previous studies have projected various degrees of runoff changes, they neglect the impact of rising CO2 on runoff projections. To explore the possible role that CO2 may play in the hydrologic cycle, we conducted three experiments with the newly improved Noah‐MP land model including vegetation dynamics and plant hydraulics. Consistent with previous studies, the western US tends to be drier toward the end of the 21st Century. CO2‐induced leaf area index increases (surface "greening") contribute considerably to the projected widespread transpiration increases and runoff reductions; however, these changes are nearly compensated by the stomatal closure effect of CO2 on transpiration, leaving the warming effect to remain the major cause to these transpiration and runoff changes. Therefore, the dual roles of CO2 in the hydrologic cycle through interactions with vegetation processes need to be considered in water resource projections. Key Points: Annual runoff of the major western US rivers is projected to decline variably from −11% to −92% by 2099 under Representative Concentration Pathway 8.5The counteracting CO2 fertilization and stomatal closure effects on runoff are roughly compensatoryDue to the two offsetting CO2 effects, warming remains the dominant driver for the projected runoff decline at river basin scales [ABSTRACT FROM AUTHOR]

Aims The vertical distribution of plant roots is a comprehensive result of plant adaptation to the environment. Limited knowledge on fine vertical root distributions and complex interactions between roots and environmental variables hinders our ability to reliably predict climatic impacts on vegetation dynamics. This study attempts to understand the drought adaptability of plants in arid areas from the perspective of the relationship between vertical root distribution and surroundings. Methods By analyzing root profiles compiled from published studies, the root vertical profiles of two typical phreatophytes, Tamarix ramosissima and Populus euphratica , and their relationships with environmental factors were investigated. A conceptual model was adopted to link the parameter distribution frequency with plant drought adaptability. Important Findings The strong hydrotropism (groundwater-dependent) and flexible water-use strategy of T. ramosissima and P. euphratica help both species survive in hyperarid climates. The differences in the developmental environments between T. ramosissima and P. euphratica can be explained well by the different distribution characteristics of root profiles. That is, higher root plasticity helps T. ramosissima develop a more efficient water-use strategy and therefore survive in more diverse climatic and soil conditions than P. euphratica. We conclude that the higher variation in root profile characteristics of phreatophytes can have greater root adaptability to the surroundings and thus wider hydrological niches and stronger ecological resilience. The inadequacy of models in describing root plasticity limits the accuracy of predicting the future response of vegetation to climate change, which calls for developing process-based dynamic root schemes in Earth system models. [ABSTRACT FROM AUTHOR]

In this study, we developed a realistic irrigation scheme in version 3.6 of the Weather Research and Forecasting model (WRF3.6) with version 4 of the Community Land Model (CLM4) land surface scheme to investigate the effects of cropland irrigation on regional climate in China. Irrigation may occur throughout the year in most croplands with good thermal conditions to cultivate crops for more grain production, known as multiple cropping (MC). However, MC has been considered less in previous studies investigating the climatic effects of irrigation. In addition, the effects of cropland irrigation on seasonal climate in China have been less studied. The climatic effects of irrigation are assessed by comparing observations and model simulations with and without irrigation from 2001 through 2010. Results showed that the simulation with irrigation reduced mainly biases of land surface temperature (LST), surface air temperature (SAT), and precipitation over the irrigated areas. The simulation with irrigation also reproduced reliable annual irrigation water use and reasonable spatial distribution patterns of seasonal irrigation amounts. Both annual LST and SAT decreased 0.6 °C averaged over irrigated areas due to the irrigation‐induced cooling effect. Additionally, the decreased surface temperature in the spring led to a reduced land‐sea heat contrast that suppressed summer precipitation. The results indicated that a realistic irrigation scheme is important for accessing the climatic effects of irrigation. More broadly, including MC in the irrigation scheme may be useful for other assessments of the climatic effects of irrigation. Key Points: A realistic irrigation scheme was proposed including multiple cropping to study the effects of irrigation on regional climateLand surface temperature and surface air temperature decreased mainly in irrigated areas due to the irrigation‐induced cooling effectSpring irrigation could suppress summer precipitation over China via the decreased land‐sea heat contrast [ABSTRACT FROM AUTHOR]

Catchment‐scale response functions, such as transit time distribution (TTD) and evapotranspiration time distribution (ETTD), are considered fundamental descriptors of a catchment's hydrologic and ecohydrologic responses to spatially and temporally varying precipitation inputs. Yet, estimating these functions is challenging, especially in headwater catchments where data collection is complicated by rugged terrain, or in semi‐arid or sub‐humid areas where precipitation is infrequent. Hence, we developed practical approaches for estimating both TTD and ETTD from commonly available tracer flux data in hydrologic inflows and outflows without requiring continuous observations. Using the weighted wavelet spectral analysis method of Kirchner and Neal [2013] for δ18O in precipitation and stream water, we calculated TTDs that contribute to streamflow via spatially and temporally variable flow paths in a sub‐humid mountain headwater catchment in Arizona, United States. Our results indicate that composite TTDs (a combination of Piston Flow and Gamma TTDs) most accurately represented this system for periods up to approximately 1 month, and that a Gamma TTD was most appropriate thereafter during both winter and summer seasons and for the overall time‐weighted TTD; a Gamma TTD type was applicable for all periods during the dry season. The TTD results also suggested that old waters, i.e., beyond the applicable tracer range, represented approximately 3% of subsurface contributions to streamflow. For ETTD and using δ18O as a tracer in precipitation and xylem waters, a Gamma ETTD type best matched the observations for all seasons and for the overall time‐weighted pattern, and stable water isotopes were effective tracers for the majority of vegetation source waters. This study addresses a fundamental question in mountain catchment hydrology; namely, how do the spatially and temporally varying subsurface flow paths that support catchment evapotranspiration and streamflow modulate water quantity and quality over space and time. [ABSTRACT FROM AUTHOR]

Plants are able to adapt to changing environments and thus survive droughts. However, most land surface models produce unrealistically low ecosystem resiliency to droughts, degrading the credibility of the model‐predicted ecohydrological responses to climate change. We aim to enhance the Noah‐MP modeled ecosystem resilience to droughts with an explicit representation of plant water storage supplied by dynamic root water uptake through hydrotropic root growth to meet the transpiration demand. The new model represents plant stomatal water stress factor as a function of the plant water storage and relates the rate of root water uptake to the profile of model‐predicted root surface area. Through optimization of major leaf, root, and soil parameters, the new model improves the prediction of leaf area index, ecosystem productivity, evapotranspiration, and terrestrial water storage variations over most basins in the contiguous United States. Sensitivity experiments suggest that both dynamic root water uptake and groundwater capillary rise extend the plants' "memory" of antecedent rainfall. The modeled plants enhance their efficiency to use antecedent rain water stored in shallow soils mainly through more efficient root water uptake over the U.S. Southwest drylands while use that stored in deep soils and aquifers with the aid of groundwater capillary rise in the Central United States. Future plant hydraulic models should not ignore soil water retention model uncertainties and the soil macropore effects on soil water potential and infiltration. Plain Language Summary: Plants are able to adapt to changing environments and thus survive droughts. However, the plants represented in current computer models do not well survive droughts for lacking a representation of adaptation mechanisms. This study develops explicit representations of plant water storage and plant water availability, which are enhanced by root water uptake that is linked to the predicted vertical distribution of fine root biomass in response to soil water content. The new model enhances ecosystem productivity and transpiration under droughts in most large river basins in the contiguous United States. Virtual experiments reveal two "pumping" mechanisms for plants under droughts to use antecedent rain water. The plants tend to more efficiently use antecedent rain water stored in shallow soils through more efficient root water uptake over the U.S. Southwest drylands and that stored in deeper soils or aquifers with the aid of groundwater capillary rise in the Central U.S. basins. Soil water pressure becomes critically important for pushing the soil water into plant tissues and up to the leaves in the new model. Therefore, uncertainties in soil water retention models and the effects of soil macropores on soil water potential and infiltration should be well treated in future models. Key Points: We developed a model of plant water storage supplied by dynamic root water uptake through hydrotropic growth in Noah‐MPIt enhances plants' efficiency to use antecedent rain water stored in shallow soils and that in aquifers with the aid of capillary riseFuture plant hydraulic models should consider soil water retention model uncertainties and soil macropore effects on water retention [ABSTRACT FROM AUTHOR]