Regional Water Budgets Research Articles

Spatiotemporal evolution of dry and wet and quantitative analysis of the influence of meteorological factors based on MI and the FAO P–M model

The frequent occurrence of extreme climate events disrupts the regional water budget balance and leads to changes in the dry and wet conditions of the surface, making the water surplus and deficit more complex and variable. To explore the quantitative relationship between the spatiotemporal evolution of dry and wet conditions and meteorological factors in the Hexi Corridor under changing environmental conditions, the relative moisture index (MI) and FAO Penman–Monteith (FAO P–M) model were combined to construct a partial differential quantitative attribution model for dry and wet variations affected by climate factors in the Hexi Corridor. The results show that: (1) MI in the Hexi Corridor increased significantly (Z = 2.341) during 1960–2019, showing a wet-trend change, and the degree of drought increased from southeast to northwest in the Hexi Corridor. (2) The order of drought degree in four seasons is as follows: winter (− 0.95), spring (− 0.93), autumn (− 0.89) and summer (− 0.83). (3) The frequency of extreme drought, severe drought, moderate drought, and mild drought within 60 years of 21 meteorological stations accounted for 28.38%, 50.48%, 8.85%, and 7.38%, respectively, and the frequency above severe drought was the highest. (4) The sensitivity of meteorological factors gradually increased from northwest to southeast, and MI was the most sensitive to the change of precipitation (P), followed by net radiation (Rn), wind speed (u2), mean temperature (Tmean), relative humidity (RH) and maximum temperature (Tmax). MI was the least sensitive to the change of minimum temperature (Tmin). P is the most important meteorological variable that contributes to the increase of MI, followed by u2, Tmean, and Tmin. Rn, Tmax, and RH have the least influence, and the total contribution of the seven meteorological factors is 85.59%. Compared with the reference evapotranspiration, P is the main factor affecting the dry and wet variations in Hexi Corridor.

Evapotranspiration increment was underestimated in China due to underrepresented land cover changes

Numerous evapotranspiration (ET) products have been produced using various approaches and diverse forcing data even as the magnitude and trends of ET show divergence. We simulated ET using updated land use and cover change (LUCC) data in China from 1900 to 2020. We found that China’s ET increased slightly from 1900 to 1980, but it increased rapidly after 1980 due to LUCC characterized by forest expansion (2.05 mm yr−1, P < 0.01). We also found that the ET trends derived from our simulation were significantly higher than other ET products (−0.70–1.47 mm yr−1, P < 0.01), implying that existing, long-term ET products might have underestimated ET trends in China during the post-1980 period because of underrepresented LUCC. These underestimated ET trends could introduce biases in the regional water budget and water resources management. We advocate for future studies to take into account the impacts of LUCC in global ET simulations.

A river runs through it: Robust automated mapping of riparian woodlands and land surface phenology across dryland regions

Riparian woodlands in drylands are critically important to human society, global biodiversity, and regional water and energy budgets. These sensitive ecosystems have experienced substantial degradation over the last several decades from climatic change and direct human activity. Nevertheless, quantifying long-term change in dryland riparian woodlands remains a major challenge, and much uncertainty exists in their remaining extent, historical breadth, and likely future trajectories. Dryland landscapes show large, fine-scale spatial heterogeneity in seasonal greenness patterns, driven in part by spatial variation in water availability. Riparian woodlands occur where water is concentrated in the landscape, either as aboveground streamflow or subsurface groundwater. In arid and semi-arid climates, this renders them phenologically distinctive from upland ecosystems. However, despite their importance and distinctiveness, there are currently no automated methods for delineating dryland riparian woodlands across regional extents in the cloud. Here we designed and implemented a cloud-based algorithm to retrieve dryland land surface phenology patterns from multispectral satellite imagery and conducted sensitivity analyses using real and simulated data to demonstrate that the approach is robust for MODIS, Sentinel-2, and Landsat over realistic ranges of noise and cloud cover. We then designed a series of random forest vegetation classifiers that integrate phenological and spectral information, vegetative structure from LiDAR, and topography from LiDAR or the Shuttle Radar Topography Mission. We implemented classifiers for three local study sites and then generalized our model to run regionally across the southwestern United States, with balanced accuracy for the riparian woodland class ranging from 94.5% to 97.5% when validated with local to regional datasets. Generally, phenological information proved more important than any other data source for mapping riparian woodlands, which showed more stability in interannual phenology than did upland vegetation types. To our knowledge, ours is the first regional, annual, automatically-generated and updated approach for mapping dryland riparian woodlands in the southwestern United States, paving the way for improved modeling and management efforts on watershed to regional scales. We also provide one of the first operational, exclusively cloud-based methods to extract dryland land surface phenology patterns using Landsat, Sentinel-2, MODIS, or other sensors, providing a framework for future studies investigating other aspects of long-term or spatial variation in dryland vegetative seasonality across the globe.

Streamflow Composition and Water “Imbalance” in the Northern Himalayas

AbstractThe Yarlung Zangbo River (YZR) is the largest river in the northern Himalayas, providing crucial water resources for downstream. A full understanding of the streamflow dynamics and regional water budget is critical to secure water security of the Himalayan water tower. Here we establish a comprehensive hydrological model to simulate the precipitation‐runoff‐evapotranspiration‐groundwater‐streamflow complex in the YZR basin. We decipher contributions of different water sources (e.g., precipitation, meltwater, groundwater) to YZR's streamflow and estimate that groundwater sustains ∼36% of annual streamflow in the YZR, while precipitation and melt surface runoff contribute 40% and 24%, respectively. Combining modeling, observation and reanalysis data, our results reveal a water “imbalance” that ∼31% of annual precipitation and meltwater (∼333 mm yr−1 or ∼85 km3 yr−1) is unaccounted for in the YZR basin. We propose that the “excess water” discharges to deep fractured bedrock aquifers, which is promoted by widespread permeable active structures (e.g., faults, fractures). This hypothesis is supported by groundwater storage (GWS) estimates where inclusion of the deep groundwater bridges the discrepancy between baseflow‐derived (shallow) GWS and those derived from the Gravity Recovery and Climate Experiment satellite data. The deep groundwater most likely flows across basins, bypasses streams, and finally discharges to downstream aquifers in the Indo‐Gangetic Plain as mountain block recharge. This study not only provides a comprehensive analysis of the streamflow composition in the YZR, but also contributes to shaping a more complete picture of the functionality of the Himalayan water tower, highlighting the importance of groundwater in regional water transfers.

Estimating GRACE terrestrial water storage anomaly using an improved point mass solution

The availability of terrestrial water storage anomaly (TWSA) data from the Gravity Recovery and Climate Experiment (GRACE) supports many hydrological applications. Five TWSA products are operational and publicly available, including three based on mass concentration (mascon) solutions and two based on the synthesis of spherical harmonic coefficients (SHCs). The mascon solutions have advantages regarding the synthesis of SHCs since the basis functions are represented locally rather than globally, which allows geophysical data constraints. Alternative new solutions based on SHCs are, therefore, critical and warranted to enrich the portfolio of user-friendly TWSA data based on different algorithms. TWSA data based on novel processing protocols is presented with a spatial re-sampling of 0.25 arc-degrees covering 2002–2022. This approach parameterizes the improved point mass (IPM) and adopts the synthesized residual gravitational potential as observations. The assay indicates that the proposed Hohai University (HHU-) IPM TWSA data reliably agree with the mascon solutions. The presented HHU-IPM TWSA data set would be instrumental in regional hydrological applications, particularly enabling improved assessment of regional water budgets.

Quantifying the Impact of Land Use and Land Cover Change on Moisture Recycling With Convection‐Permitting WRF‐Tagging Modeling in the Agro‐Pastoral Ecotone of Northern China

AbstractLand use and land cover change (LUCC) can influence the regional atmospheric water budget. In this study, the Weather Research and Forecasting model embedded with evapotranspiration (ET)‐tagging (WRF‐tagging) is used to investigate the atmospheric pathways of ET as well as where and to what extent ET returns as precipitation (P) in the agro‐pastoral ecotone of northern China (APENC). First, we updated the default land use and vegetation indices in the WRF‐tagging with high‐resolution and real‐time datasets. WRF‐tagging modeling reproduces the spatial distribution of P and ET reasonably after updating surface characteristics. Second, we analyzed and quantified the contribution of ET to atmospheric moisture and regional P in the APENC. The water vapor originating as ET is advected in the atmosphere below 600 hPa to hundreds of kilometers by the prevailing winds. Moisture recycling shows that 5.83% of P comes from local ET during the growing season in the core region of the APENC. Furthermore, to quantify the impact of LUCC on moisture recycling, we designed two different vegetation scenarios (Afforestation and Degradation) by changing land use and vegetation indices in the model. Results show that the precipitation recycling ratio increased to 6.31% in the Afforestation scenario, and decreased to 5.19% in the Degradation scenario, demonstrating the non‐neglectable positive feedback of vegetation on P. An analysis of land surface‐precipitation feedback processes indicates that the LUCC‐induced change in precipitation efficiency dominates P changes. Our findings highlight the importance of LUCC on local and regional moisture recycling in the APENC.

Water Availability in China's Oases Decreased Between 1987 and 2017

AbstractThe significant expansion of China's oases during recent decades has inevitably increased water demand, profoundly affecting the regional water budget. While streamflow and precipitation have increased in China's oases under climate change, it remains uncertain whether this augmented water supply will suffice to meet the increasing water demand of oases, presenting a great challenge to guarantee regional water security. Here we used a process‐based crop water model, oasis land use datasets, vegetation maps, and observational data on streamflow and grain production to estimate the impacts of China's oasis dynamics on regional water resources from 1987 to 2017. We found that the water demand in oases significantly increased during 1987–2017, and oasis areas that have existed since 1987 accounted for a greater proportion of the increased water demand than newly expanded oasis areas. Although increases in precipitation and streamflow largely offset the additional water demand, 60.3% of counties in oasis regions experienced a decrease in water availability after 1987 because of a spatiotemporal mismatch between water supply and demand, and some counties suffered water deficits. Our findings provide a quantified basis for identifying and optimizing water use structures for water security in oases.

An improved rescaling algorithm for estimating groundwater depletion rates using the GRACE satellite

ABSTRACT The Gravity Recovery And Climate Experiments (GRACE) satellite mission has been instrumental in estimating large-scale groundwater storage changes across the globe. GRACE observations include significant errors, so pre-processing is normally required before the data are used. In particular, the terrestrial water storage anomalies (TWSA) are usually filtered to reduce the effects of measurement errors and then rescaled to reduce the unintended impacts of the filtering. The scaling is typically selected to maximize the Nash-Sutcliffe Efficiency (NSE) between the rescaled filtered TWSA and the original TWSA from large-scale hydrologic models that represent an incomplete water budget. The objectives of this study are as follows (1) to evaluate the use of NSE in the current GRACE rescaling methodology, (2) develop an improved methodology that incorporates a complete regional water budget, and (3) examine the impacts of the rescaling methodology on regional assessments of groundwater depletion. To evaluate the use of NSE as a performance metric, we compare it to an analytical solution that restores the relative variability between the rescaled filtered and original GRACE TWSA series. The relative variability approach produces more reliable estimates when comparing to TWSA estimates from global positioning systems (GPS) for the Sacramento and San Joaquin River basins in California. Rescaling the complete regional water budget results in a larger scale factor than the scale factor from the large-scale hydrologic model outputs, and the new TWSA results are more consistent with those from GPS. The large scale factor also suggests that regional groundwater depletion is more severe than previously estimated.

Spatial distribution of oceanic moisture contributions to precipitation over the Tibetan Plateau

Abstract. Evaporation from global oceans is an important moisture source for glaciers and headwaters of major Asian rivers in the Tibetan Plateau (TP). Although the accelerated global hydrological cycle, the altered sea–land thermal contrast and the amplified warming rate over the TP during the past several decades are known to have profound effects on the regional water balance, the spatial distribution of oceanic moisture contributions to the vast TP remains unclear. This hinders the accurate quantification of regional water budgets and the reasonable interpretation of water isotope records from observations and paleo archives. Based on historical data and moisture tracking, this study systematically quantifies the absolute and relative contributions of oceanic moisture to long-term precipitation in the TP. Results show that the seasonal absolute and relative oceanic contributions are generally out of phase, revealing the previously underestimated oceanic moisture contributions brought by the westerlies in winter and the overestimated moisture contributions from the Indian Ocean in summer. Quantitatively, the relative contribution of moisture from the Indian Ocean is only ∼30 % in the south TP and further decreases to below 10 % in the northernmost TP. The absolute oceanic contribution exhibits a spatial pattern consistent with the dipole pattern of long-term precipitation trends across the Brahmaputra Canyon region and the central-northern TP. In comparison, relative oceanic contributions show strong seasonal patterns associated with the seasonality of precipitation isotopes across the TP.

An assimilated deep learning approach to identify the influence of global climate on hydrological fluxes

The rapid acceleration of the global water cycle caused by changes in global climate trigger complex processes that make conventional machine learning techniques limited in assessing impacts of such changes on terrestrial water storage (TWS). This study introduces an assimilated deep learning neural network to improve the modeling of TWS dynamics. Key predictors and inputs to this deep learning framework include runoff, rainfall, soil moisture, evapotranspiration, global teleconnection patterns and sea surface temperatures (SSTs). The proposed back propagation model in this study capitalizes on the availability of remotely sensed observations and model datasets to predict monthly TWS, a quantity that is difficult to observe in the field, but important for the estimation of regional water budget balance, and water resource management for agricultural purposes. By integrating pre-processed outputs from the Principal Component Analysis (PCA) and Independent Component Analysis (ICA) into our network using a deep neural pattern, we synthesized TWS from 2002 to 2017, and also made future predictions using our trained models. Results from these analyses showed that the ICA-BPNN model has a higher predictive accuracy compared to the PCA-BPNN. These models (ICA-BPNN and PCA-BPNN) were used to fit the three dominant temporal patterns of Gravity Recovery and Climate Experiment (GRACE) – observed TWS over Africa. Our simulation results from the testing phase indicate that the fits for the prediction of the first three leading modes of TWS for both models when compared to the observed GRACE-TWS were PCA-BPNN1 (89%), PCA-BPNN2 (82%), PCA-BPNN3 (84%) and ICA-BPNN1 (93%), ICA-BPNN2 (88%), ICA-BPNN3 (82%). The simulation fit of the BPNN corresponding to multi-annual time series, which are captured in the second and third orthogonal modes and localized patterns of TWS were lower than those of annual signals in both the PCA- and ICA-BPNN models. This was attributed to the fact that the multi-annual time series in GRACE-hydrological signals of our test-bed are complex compared to the annual patterns of TWS. On the one hand, this exemplifies the superior performance of our predictive framework in modeling naturalized system (annual changes in TWS driven by only climatic factors). On the other hand, the complexity in modelling multi-annual variations in TWS suggests heavily disturbed naturalized systems evidenced in the presence of human water management operations among other anthropogenic activities.

The effects of land‐use conversion on evapotranspiration and water balance of subtropical forest and managed tea plantation in Taihu Lake Basin, China

AbstractWidespread clearing of natural forests for tea plantations have profoundly altered the local water balance and energy budget. However, studies of comprehensive effects of land‐use conversion on energy and hydrological cycling are still rare. In this study, we used 5 years (2014–2018) of eddy covariance (EC) data, modelled estimates, and hydrological monitoring of different functional layers (canopy, litter and soil layers) to summarize the seasonal and inter‐annual variations and differences of evapotranspiration and water balance components in tea plantations (Camellia sinensis) and natural bamboo forests (Phyllostachysedulis). Results suggested the bamboo forest had higher actual evapotranspiration (ETa) (835 ± 5 mm) than the tea plantation (730 ± 19 mm), owing to differences in canopy conditions and plant physiology. Evident differences in ETa components were found between the tea plantation site and the bamboo forest site. The proportion of transpiration (Etr) was higher at the bamboo site (0.71–0.76) than the tea plantation site (0.60–0.65). Additionally, the soil evaporation ratio (0.10–0.13) was lower at the bamboo forest site than the tea plantation site (0.21–0.24) due to vegetation structure differences altering the radiation energy partitioning between the two sites. Moreover, the tea plantations were more susceptible to drought stress with low water storage in soil and simple rhizome‐root system. The comprehensive water holding capacity of canopy, litter, and soil layers of bamboo forest was 1.0–1.8 times of that in tea plantation. Compared with bamboo forest, pruning and losing of soil with intensive management activities enhanced drainage at the tea plantation site. This study provides insights into land‐use conversion of subtropical forest and managed tea plantation that have important implications on regional energy and water budgets.

Conceptual Framework for Modeling Dynamic Complexities in Produced Water Management

This research addresses a gap in the produced water management (PWM) literature by providing a conceptual framework to describe the connections of PWM to regional water budgets. We use southeastern New Mexico as a case study, because the region is facing looming shortfalls in water availability, and oil and gas production generate high volumes of produced water in the region. The framework was developed through expert interviews, analysis of industry data, and information gained at industry meetings; it is supported by detailed descriptions of material flows, information flows, and PWM decisions. Produced water management decisions may be connected to regional water budgets through dynamic complexities; however, modeling efforts exploring PWM often do not capture this complexity. Instead, PWM is most often based on the least expensive management and disposal alternatives, without considering short and long-term impacts to the regional water budget. On the other hand, regional water budgets do not include treated produced water as a potential resource, thus missing opportunities for exploring the impact of potential beneficial reuse. This is particularly important when there is a need to address water shortages in chronically water-short regions of the United States. At the same time, oil and gas production in the western United States is challenged by the need to dispose of large volumes of produced water. The framework is useful for developing improved models of PWM to identify the impact of alternative management decisions on regional water budgets.

Warming, increase in precipitation, and irrigation enhance greening in High Mountain Asia

High-Mountain Asia exhibits one of the highest increases in vegetation greenness on Earth, subsequently influencing the exchange of water and energy between the land surface and the atmosphere. Given the strong interactions between the hydrosphere, the biosphere, and the cryosphere, understanding the drivers of greening in this highly complex region with significant land cover heterogeneity is essential to assess the changes in the regional water budget. Here, we perform a holistic multivariate remote sensing analysis to simultaneously examine the primary components of the terrestrial water cycle from 2003 to 2020 and decipher the principal drivers of greening in High-Mountain Asia. We identified three drivers of greening: (1) precipitation drives greening in mid and low elevation areas covered by evergreen and mixed forests (e.g., Irrawaddy basin), (2) decreases in snow enhance greening in most of the hydrologic basins, and (3) irrigation induces greening in irrigated lands (Ganges–Brahmaputra and Indus).

Development of a “nature run” for observing system simulation experiments (OSSEs) for snow mission development

Abstract Snow is a fundamental component of global and regional water budgets, particularly in mountainous areas and regions downstream that rely on snowmelt for water resources. Land surface models (LSMs) are commonly used to develop spatially distributed estimates of snow water equivalent (SWE) and runoff. However, LSMs are limited by uncertainties in model physics and parameters, among other factors. In this study, we describe the use of model calibration tools to improve snow simulations within the Noah-MP LSM as the first step in an Observing System Simulation Experiment (OSSE). Noah-MP is calibrated against the University of Arizona (UA) SWE product over a Western Colorado domain. With spatially varying calibrated parameters, we run calibrated and default Noah-MP simulations for water years 2010-2020. By evaluating both simulations against the UA dataset, we show that calibration decreases domain averaged temporal RMSE and bias for snow depth from 0.15 to 0.13 m and from -0.036 to -0.0023 m, respectively, and improves the timing of snow ablation. Increased snow simulation performance also improves estimates of model-simulated runoff in four of six study basins, though only one has statistically significant improvement. Spatially distributed Noah-MP snow parameters perform better than default uniform values. We demonstrate that calibrating variables related to snow albedo calculations and rain-snow partitioning, among other processes, is a necessary step for creating a nature run that reasonably approximates true snow conditions for the OSSEs. Additionally, the inclusion of a snowfall scaling term can address biases in precipitation from meteorological forcing datasets, further improving the utility of LSMs for generating reliable spatiotemporal estimates of snow.

Effects of clouds and aerosols on ecosystem exchange, water and light use efficiency in a humid region orchard

Investigating the patterns of water and carbon dynamics in agro-ecosystems in response to clouds and aerosols can shed new insights in understanding the biophysical impacts of climate change on crop productivity and water consumption. In this study, the effects of clouds and aerosols as well as other environmental factors on ecosystem water and carbon fluxes were examined based on three-year eddy covariance measurements under different sky conditions (quantified as the clearness index, Kt, i.e., the ratio of global solar radiation to extraterrestrial solar radiation) in a kiwifruit plantation in the humid Sichuan Basin of China. Results showed that evapotranspiration (ET) and canopy transpiration (Tc, measured by sap flow sensors) increased, while ecosystem light use efficiency (eLUE) and ecosystem water use efficiency (eWUE) decreased with increasing Kt. GPP presented a parabolic relationship with increasing Kt. The path analysis revealed that surface conductance (Gs) and canopy conductance (Gc) were the most dominant variables directly regulated carbon (GPP) and water (ET and Tc) fluxes. The effect path of Kt on ET and Tc was converted from through diffuse photosynthetic active radiation (PARdif) to direct PAR (PARdir) when the sky became clearer. The effect path of Kt on GPP was primarily through PARdif under different sky conditions. The declined eWUE with increasing Kt was caused by the different responses of GPP and ET to PARdir under clear skies. The declined eLUE resulted from the sharp decrease in GPP/PARdir, which surpassed the slight increase of GPP/PARdif with increasing PAR. The Priestley-Taylor Jet Propulsion Laboratory ET model (PT-JPL) incorporating Kt with an exponential function produced more reliable Tc estimates but minor improvement in ET. Further, the LUE-GPP model incorporating Kt with a linear function obtained much better GPP estimates. Our study shed light on how sky conditions modulate water and carbon dynamics between the biosphere and atmosphere, highlighting the necessity of the inclusion of sky conditions for better modeling regional water and carbon budgets.

Evaluation of Six Satellite-Based Terrestrial Latent Heat Flux Products in the Vegetation Dominated Haihe River Basin of North China

In this study, six satellite-based terrestrial latent heat flux (LE) products were evaluated in the vegetation dominated Haihe River basin of North China. These LE products include Global Land Surface Satellite (GLASS) LE product, FLUXCOM LE product, Penman-Monteith-Leuning V2 (PML_V2) LE product, Global Land Evaporation Amsterdam Model datasets (GLEAM) LE product, Breathing Earth System Simulator (BESS) LE product, and Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD16) LE product. Eddy covariance (EC) data collected from six flux tower sites and water balance method derived evapotranspiration (WBET) were used to evaluate these LE products at site and basin scales. The results indicated that all six LE products were able to capture the seasonal cycle of LE in comparison to EC observations. At site scale, GLASS LE product showed the highest coefficients of determination (R2) (0.58, p &lt; 0.01) and lowest root mean square error (RMSE) (28.2 W/m2), followed by FLUXCOM and PML products. At basin scale, the LE estimates from GLASS product provided comparable performance (R2 = 0.79, RMSE = 18.8 mm) against WBET, compared with other LE products. Additionally, there was similar spatiotemporal variability of estimated LE from the six LE products. This study provides a vital basis for choosing LE datasets to assess regional water budget.

A CONUS-scale study of wildfire and evapotranspiration: Spatial and temporal response and controlling factors

Evapotranspiration (ET) accounts for a substantial portion of regional water budgets in much of the southeast and fire-prone western United States (US). Even small changes in ET rates can translate to meaningful shifts in runoff patterns and makes forecasting the direction and magnitude of wildfire-induced ET alteration of critical importance. We use 1 km ET estimates from the operational Simplified Surface Energy Balance (SSEBop) product for the conterminous US (CONUS) to evaluate post-fire ET and evaporation ratio (ET/P) shifts in the first five post-fire years in approximately 5500 unique fires. Pixels with similar ET/P responses to fire are grouped through k-means clustering and the resultant cluster distribution is explored over space and time. The largest changes in post-fire ET/P are observed in the southwestern CONUS where first-year ratios are reduced by 50 to 90% and pre-fire ratios are rarely recovered by post-fire year five. Regional and intra-fire ET/P response variability is also highest in the western CONUS where climatic, topographic, and ecologic gradients are steep. Post-fire ET/P modifications are small to negligible in the east-southeast CONUS, and 18% of all pixels analyzed exhibit small to moderate increases in post-fire year one ET/P. A comparison of burned and unburned pixel pairs confirms the role of fire in the shifts but also indicates a high degree of background variability in the ET and precipitation data. Although the biggest percent ET/P reductions occur in shrub/scrub landscapes in much of the west, the biggest magnitude ET changes often occur in evergreen forests. Higher burn severities are consistently correlated with greater post-fire ET/P reductions, while relationships between post-fire ET/P shifts and numerous other landscape attributes (e.g., pre-fire vegetation type) vary in both direction and magnitude in different parts of the CONUS. Further work can be conducted to refine controlling relationships within more homogeneous sub-regions.

Impacts of Variations in Caspian Sea Surface Area on Catchment‐Scale and Large‐Scale Climate

AbstractThe Caspian Sea (CS) is the largest inland lake in the world. Large variations in sea level and surface area occurred in the past and are projected for the future. The potential impacts on regional and large‐scale hydroclimate are not well understood. Here, we examine the impact of CS area on climate within its catchment and across the northern hemisphere, for the first time with a fully coupled climate model. The Community Earth System Model (CESM1.2.2) is used to simulate the climate of four scenarios: (a) larger than present CS area, (b) current area, (c) smaller than present area, and (d) no‐CS scenario. The results reveal large changes in the regional atmospheric water budget. Evaporation (e) over the sea increases with increasing area, while precipitation (P) increases over the south‐west CS with increasing area. P‐E over the CS catchment decreases as CS surface area increases, indicating a dominant negative lake‐evaporation feedback. A larger CS reduces summer surface air temperatures and increases winter temperatures. The impacts extend eastwards, where summer precipitation is enhanced over central Asia and the north‐western Pacific experiences warming with reduced winter sea ice. Our results also indicate weakening of the 500‐hPa troughs over the northern Pacific with larger CS area. We find a thermal response triggers a southward shift of the upper troposphere jet stream during summer. Our findings establish that changing CS area results in climate impacts of such scope that CS area variations should be incorporated into climate model simulations, including palaeo and future scenarios.

Complex effects of moisture conditions and temperature enhanced vegetation growth in the Arid/humid transition zone in Northern China

Ecosystems in the arid/humid transition zone (AHTZ) of northern China are highly sensitive to climate change and human activities. Accurately assessing the impact of climate change on these ecosystems is important for effectively reducing the risks faced by them under future climate change. In this study, the leaf area index during the selected growing season (LAIGS) was used as an indicator for vegetation activity. After comparison different potential indicators, the growing season temperature (TGS) was used to indicate temperature, and the growing season aridity index (AIGS), which considers the regional water budget, was used to indicate moisture rather than precipitation, which is used more commonly. Correlation analysis and residual trends were used to study the influence of climatic and non-climatic factors on vegetation activity in the AHTZ from 1982 to 2016. The results for regions where LAIGS increased significantly (0.037/10 yr, 53.58% of the study area), the regions where LAIGS dominated by non-climatic factors (18.40%) was larger than areas dominated by climatic factors (9.61%). However, most (25.57%) of the regions in the selected study area were mainly driven by both climatic and non-climatic factors. In about half (49.73%) of the climate-affected regions, significant changes in LAIGS were driven jointly by TGS and AIGS. These regions were mainly in the northern and western Loess Plateau. The regions where changes were driven mainly by AIGS, and those where changes were driven mainly by TGS, each accounted for nearly a quarter of climate-affected regions (24.87% and 25.40%, respectively). The former regions were on the western Songliao Plain, the northern North China Plain, and the northern Loess Plateau, and the latter regions were in the northern Greater Khingan Mountains, on the southern North China Plain, in the western mountains of North China, and on the southern Loess Plateau.

Regional Energy and Water Budget of a Precipitating Atmosphere over Ocean

AbstractEffects of water mass imbalance and hydrometeor transport on the enthalpy flux and water phase on diabatic heating rate in computing the regional energy and water budget of the atmosphere over ocean are investigated. Equations of energy and water budget of the atmospheric column that explicitly consider the velocity of liquid and ice cloud particles, and rain and snow are formulated by separating water variables from dry air. Differences of energy budget equations formulated in this study from those used in earlier studies are that 1) diabatic heating rate depends on water phase, 2) diabatic heating due to net condensation of nonprecipitating hydrometeors is included, and 3) hydrometeors can be advected with a different velocity from the dry-air velocity. Convergence of water vapor associated with phase change and horizontal transport of hydrometeors is to increase diabatic heating in the atmospheric column where hydrometeors are formed and exported and to reduce energy where hydrometeors are imported and evaporated. The process can improve the regional energy and water mass balance when energy data products are integrated. Effects of enthalpy transport associated with water mass transport through the surface are cooling to the atmosphere and warming to the ocean when the enthalpy is averaged over the global ocean. There is no net effect to the atmosphere and ocean columns combined. While precipitation phase changes the regional diabatic heating rate up to 15 W m−2, the dependence of the global mean value on the temperature threshold of melting snow to form rain is less than 1 W m−2.