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The quantity and quality of surface water are inherently connected yet are overwhelmingly studied separately in the field of remote sensing. Remotely observable water quantity (e.g., water extent, water elevation, lake/reservoir volume, and river discharge) and water quality (e.g., color, turbidity, total suspended solids, chlorophyll a, colored dissolved organic matter, and temperature) parameters of inland waterbodies interact through a series of hydrological and biogeochemical processes. In this review, we analyzed trends in remote sensing publications to understand the prevalence of studies on the quantity versus quality of open‐surface inland waterbodies (rivers, streams, lakes, and reservoirs) as well as identified opportunities for integrating both water quality and quantity sensing in future work. Our bibliometric analysis found that despite the increasing number of publications using remote sensing for inland waterbodies, few studies to date have used remote sensing tools or approaches to simultaneously study water quantity and quality. Ultimately, by providing insights into potential integration of the water quality and quantity studies, we aim to identify a pathway to advance the understanding of inland water dynamics and freshwater resources through remote sensing. This article is categorized under:Water and Life > MethodsScience of Water > Water QualityWater and Life > Nature of Freshwater EcosystemsScience of Water > Methods [ABSTRACT FROM AUTHOR]

Forest litter decomposition is an essential component of global carbon and nutrient turnover. Invertebrates play important roles in litter decomposition, but the regional pattern of their effects is poorly understood. We examined 476 case studies across 93 sites and performed a meta‐analysis to estimate regional effects of invertebrates on forest litter decomposition. We then assessed how invertebrate diversity, climate and soil pH drive regional variations in invertebrate‐mediated decomposition. We found that (1) invertebrate contributions to litter decomposition are 1.4 times higher in tropical and subtropical forests than in forests elsewhere, with an overall contribution of 31% to global forest litter decomposition; and (2) termite diversity, together with warm, humid and acidic environments in the tropics and subtropics are positively associated with forest litter decomposition by invertebrates. Our results demonstrate the significant difference in invertebrate effects on mediating forest litter decomposition among regions. We demonstrate, also, the significance of termites in driving litter mass loss in the tropics and subtropics. These results are particularly pertinent in the tropics and subtropics where climate change and human disturbance threaten invertebrate biodiversity and the ecosystem services it provides. [ABSTRACT FROM AUTHOR]

Branch growth is an important aspect of the tree growth process. Studying the branch growth pattern is important for understanding the growth of trees and optimizing forest management decisions. A total of 48 planted Korean pine sample trees from northeast China were selected, and a total of 327 sample branches were measured for branch diameter growth model development and 323 sample branches for branch length. Based on the aforementioned research objectives and the obtained data, the impact of individual tree variables, branch variables, and forest stand competition variables on branch growth were initially investigated in this study. Finally, we constructed nonlinear mixed-effects models at the individual tree level. The results show that the Mitscherlich growth equation had the best fitting accuracy and was selected as the base model for developing the Korean pine branch growth model. Forest stand competition of the ratio of the basal area of the subject tree to the mean basal area of the stand (CI2) and branch variables of the branch length and branch diameter had the most significant contributions and were selected and introduced into the branch growth models. The branch growth became larger with increasing CI2, branch length and branch diameter, but the effects of the branch height and diameter at the breast height on branch growth should be considered in combination with their parameters and other variables together. In addition, the introduction of random effects effectively improves the fitting accuracy of the branch growth model. Overall, the Korean pine branch growth models developed in this study have good fitting performance and have important theoretical and practical value. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the American Association of Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Open water evaporation, which often consumes a large fraction of annual storage (especially in arid and semi‐arid regions), is a controlling variable for modern water resource management. Developing a daily reservoir evaporation data set is necessary for reservoir operations to consider the influence of evaporation in a timely manner. However, over the past few decades, the quantification of reservoir evaporation has primarily relied on monthly Class A pan evaporation observations, which might lead to largely biased estimations because they do not incorporate the effects of heat storage and fetch. In this study, we developed a general Daily Lake Evaporation Model (DLEM) based on Penman's equation combined with daily atmospheric reanalysis data sets, to improve the validity and frequency of reservoir evaporation monitoring. Compared with daily evaporation estimates from eddy covariance systems, the DLEM showed good quality and reliability, with R2 values ranging from 0.50 to 0.73 and RMSE values ranging from 1.13 to 1.97 mm day−1 in four different sites. By applying DLEM to all 188 major reservoirs in Texas, we generated a long‐term data set of (1 January 1981–31 December 2021) daily evaporation rates. The results reveal a clear geographic distribution and strong seasonality of evaporation throughout Texas, where the mean evaporation rate is highest during July, with 6.85 mm day−1. Trend analysis indicates that the annual average evaporation rate has significantly increased since 1981 at a rate of 0.076 mm day−1 decade−1. This study provides sufficient data support for water resource operations and management, and regional water planning. Key Points: A general Daily Lake Evaporation Model (DLEM) is developed on modified‐Penman evaporation and compared with in situ eddy covariance measurementsA first long‐term (40+ years) regional scale daily reservoir evaporation data set is generated for 188 major reservoirs in Texas (US)While shortwave radiation affects the long‐term spatial distribution and trends, high wind speed is likely to cause daily extremes [ABSTRACT FROM AUTHOR]

Reservoirs play a crucial role in regulating water availability and enhancing water security. Here, we develop NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) based Global Water Reservoir (GWR) product, consisting of measurements of reservoir area, elevation, storage, evaporation rate, and evaporation loss for 164 large global reservoirs. The dataset is available at 8-day and monthly temporal resolutions. Since the Moderate Resolution Imaging Spectroradiometer (MODIS) is close to the end of its life, we further evaluated the consistency between MODIS and VIIRS-based GWR to ensure continuity to the 20+ year MODIS GWR product. Independent assessment of VIIRS reservoir storage (8-day) retrievals against in-situ measurements shows an average of R2 = 0.84, RMSE = 0.47 km3, and NRMSE = 16.45%. The evaporation rate has an average of R2 = 0.56, RMSE = 1.32 mm/day, and NRMSE = 28.14%. Furthermore, results show good consistency (R2 ≥ 0.90) between the VIIRS and MODIS-based product components, confirming that long-term data continuity can be achieved. This dataset can provide valuable insights for long-term trend analysis, hydrological modeling, and understanding hydroclimatic extremes in the context of reservoirs. [ABSTRACT FROM AUTHOR]

Evacuation planning and emergency routing systems are crucial in saving lives during disasters. Traditional emergency routing systems, despite their best efforts, often struggle to accurately capture the dynamic nature of flood conditions, road closures, and other real-time changes inherent in urban disaster logistics. This paper introduces the ReinforceRouting model, a novel approach to optimizing evacuation routes using reinforcement learning (RL). The model incorporates a unique RL environment that considers multiple criteria, such as traffic conditions, hazardous situations, and the availability of safe routes. The RL agent in this model learns optimal actions through interaction with the environment, receiving feedback in the form of rewards or penalties. The ReinforceRouting model excels in executing prompt and accurate route planning on large road networks, outperforming traditional RL algorithms and shortest-path-based algorithms. A higher safety score and episode reward of the model are demonstrated when compared to these classical methods. This innovative approach to disaster evacuation planning offers a promising avenue for enhancing the efficiency, safety, and reliability of emergency responses in dynamic urban environments. [ABSTRACT FROM AUTHOR]

Although reservoirs serve as a lifeline for drought mitigation and adaptation, reservoir‐based droughts (RBD) have received limited attention. Especially RBD that incorporate reservoir evaporation have remained overlooked. Here, we develop an Integrated Reservoir Drought Index (IRDI) using a Frank copula by combining the effects of reservoir storage and evaporation rate for 164 global reservoirs. Results show that Africa, South America, and Australia have experienced relatively longer RBD, whereas Asia and Africa have experienced more intense RBD. Furthermore, when significant (p‐value < 0.05) trends in inflow and IRDI have shown opposite signs at a given reservoir, it is implied that human influence was the dominant driver of the long‐term changes in RBD. We found that RBD was driven by climate in 71% of the 38 reservoirs examined and by human activities in the remaining 29% of reservoirs. Our study provides reservoir specific drought outlook that has significant environmental and sustainability implications. Plain Language Summary: Reservoirs are the prime source of water during droughts. However, water supply from reservoirs depends on how much water reservoirs have. Reservoirs lose significant amounts of water through evaporation. Hence, understanding reservoir droughts by incorporating evaporation information—will help water managers to better regulate water resources and optimize reservoir operations. With the ongoing trends of an increasing population and a changing climate, reservoirs will undergo more frequent and intense droughts in the future. Here, we developed an integrated reservoir drought index (IRDI) based on reservoir storage and evaporation that can help to identify, detect, and characterize droughts more efficiently. We found that reservoirs in Africa, South America, and Australia experienced relatively longer droughts, whereas reservoirs in Asia and Africa experienced more intense droughts. While comparing trends of IRDI and inflow coming into the reservoirs, 29% of the 38 reservoirs examined showed opposite trends. This highlights the fact that how reservoirs are operated or managed can also influence drought characteristics within them. Key Points: Evaporation information should be included in reservoir‐based drought frameworksThe Integrated Reservoir Drought Index (IRDI) helps to identify, detect, and characterize drought characteristics efficientlyReservoir‐based droughts were driven by climate in 71% of the 38 reservoirs analyzed, and by humans in the remaining 29% of the reservoirs [ABSTRACT FROM AUTHOR]

Key message: Korean pine trees expressed significant crown displacement. Nearest neighbor competition had the largest effect on the crown displacement compared to the slope direction and mean direction of wind. The crown displacement of individual trees exhibits an adaptive response driven by neighbors, but it can also be the result of wind force and slope effects. In our research, we focused on the crown displacement of planted Korean pine trees in northeast China. A total of 51 trees aged 8–56 years were destructively harvested to measure branch attributes. The gravity centers of the upper, lower and entire crown were calculated. The Markov chain Monte Carlo (MCMC) algorithm, generalized mixed effects model, and circular statistics were used to determine the response of crown displacement to biotic and abiotic factors. The results showed that the dominant tree had the largest absolute crown displacement, and the suppressed tree expressed the largest relative crown displacement. The lower crown showed more apparent crown displacement than the upper crown. The Korean pine was displaced in a northeastern direction. Nearest neighbor competition had the largest effect on the crown displacement compared to the slope direction. Slope direction had a slightly larger effect than the mean direction of wind. The crown displacement for the lower crown was more affected by the interaction of the wind, slope and neighbor competition than the upper crown. No relationship between crown displacement and stem eccentricity for the planted Korean pine was detected. [ABSTRACT FROM AUTHOR]

This study addresses how site index may affect crown shape by developing a crown shape regression model for the planted L. kaempferi at high altitudes in the subtropical climate of China. A total of 9241 branches from 78 trees, including 39 dominant trees and 39 intermediate trees, were measured in Hubei Province, southern China. The branch characteristics, including branch length, branch angle, and branch chord length of all living branches, were measured by felling trees. The models that showed good performance in crown shape modeling were used and compared in the present study. The goodness of fit statistics and stability of parameter estimation of the modified Kozak equation were better than those of the segmented polynomial equation. A nonlinear mixed-effect crown shape model was developed based on the modified Kozak equation. In addition to the tree level variables of diameter at the breast height (DBH), crown ratio (CR), and tree height-to-DBH ratio, an attempt was made to incorporate the site index into the crown shape model for the planted L. kaempferi. However, the site index was not a significant variable in the crown shape model. The purpose of our study was to lay the foundation for further study of the growth of the trees and the effects of crown morphology on stem growth in the future. [ABSTRACT FROM AUTHOR]

Climate change and increasing water demand due to population growth pose serious threats to surface water availability. The biggest challenge in addressing these threats is the gap between climate science and water management practices. Local water planning often lacks the integration of climate change information, especially with regard to its impacts on surface water storage and evaporation as well as the associated uncertainties. Using Texas as an example, state and regional water planning relies on the use of reservoir "Firm Yield" (FY)—an important metric that quantifies surface water availability. However, this existing planning methodology does not account for the impacts of climate change on future inflows and on reservoir evaporation. To bridge this knowledge gap, an integrated climate‐hydrology‐management (CHM) modeling framework was developed, which is generally applicable to river basins with geographical, hydrological, and water right settings similar to those in Texas. The framework leverages the advantages of two modeling approaches—the Distributed Hydrology Soil Vegetation Model (DHSVM) and Water Availability Modeling (WAM). Additionally, the Double Bias Correction Constructed Analogues method is utilized to downscale and incorporate Coupled Model Intercomparison Project Phase 6 GCMs. Finally, the DHSVM simulated naturalized streamflow and reservoir evaporation rate are input to WAM to simulate reservoir FY. A new term—"Ratio of Firm Yield" (RFY)—is created to compare how much FY changes under different climate scenarios. The results indicate that climate change has a significant impact on surface water availability by increasing reservoir evaporation, altering the seasonal pattern of naturalized streamflow, and reducing FY. Plain Language Summary: Under conditions of climate change, it is important to have actionable climate information for local water management and surface water planning. By investigating the impacts of climate change on surface water availability over a representative metropolitan area (and its water supply reservoirs), this research helps to bridge the gap between climate science and the necessary information for sound water management decisions. First, the coarse resolution information from global climate models was translated into the finer spatial scales suitable for application into the long‐range water supply planning tools commonly used in Texas. Second, a climate‐hydrology‐management (CHM) modeling framework was developed to simulate future surface water availability under different climate scenarios. The results indicate that the changes in reservoir evaporation rates and streamflow patterns play an important role in reducing future surface water availability. The projected hydrological and water availability information can guide decisions related to local and long‐range water planning. The study framework is also applicable to other river basins for improving the estimation of future water availability under climate change. Key Points: The assessment of future available surface water is improved by explicitly accounting for projected reservoir evaporation and naturalized streamflowAn open‐water evaporation module was used to represent the enhanced reservoir evaporation loss in future climate scenariosAn ensemble of future reservoir Firm Yield (FY) values under different climate change scenarios is generated for supporting water supply risk management decisions [ABSTRACT FROM AUTHOR]

Surface water reservoirs are increasingly being relied upon to meet rising demands in the context of growing population and changing climate. However, the amount of water available in reservoirs (and the corresponding trends) have not been well quantified at the global scale. Here we use satellite observations to estimate the storage variations of 7245 global reservoirs from 1999 to 2018. Total global reservoir storage has increased at a rate of 27.82 ± 0.08 km3/yr, which is mainly attributed to the construction of new dams. However, the normalized reservoir storage (NS)—the ratio of the actual storage to the storage capacity—has declined by 0.82 ± 0.01%. The decline of NS values is especially pronounced in the global south, while the global north mainly exhibits an NS increase. With predicted decreasing runoff and increasing water demand, these observed diminishing storage returns of reservoir construction will likely persist into the future. Despite the continually increasing global reservoir storage, the normalized storage—the ratio of the actual storage to the storage capacity—has declined over the past two decades, indicating diminishing storage returns from reservoir construction. [ABSTRACT FROM AUTHOR]

The characteristics and distribution of foliage biomass are important aspects of the crown structures of trees. In this study, we developed a foliage biomass distribution model at the branch level and annual shoot level (i.e., all branches on an annual increment of the main tree bole) using a sample of 51 Korean pine trees that were destructively sampled in Liaoning Province, northeast China. A variety of independent variables, including branches, trees, and forest competition, were used as explanatory variables in the model development. Our results indicated that foliage biomass at the branch level increased with an increasing relative distance from the tree tip into the crown. The peak of the foliage biomass distribution shifted to the tree tip with increasing tree age. A power function, including branch diameter and competition variables, showed excellent performance in modeling branch foliage at the branch level. The final model, which included branch diameter, competition variables, and relative distance to branch base for the relative foliage density, showed best performance. The trees that showed advantageous growth ability also had a larger amount of foliage biomass. [ABSTRACT FROM AUTHOR]

Enhanced reservoir evaporation has become an emerging concern regarding water loss, especially when compounded with the ever‐increasing water demand. In this study, we evaluated the evaporation rates and losses for 678 major reservoirs (representing nearly 90% of total storage capacity) in the Contiguous United States over historical baseline (1980–2019), near‐term (2020–2039), and mid‐term (2040–2059) future periods. The evaporation rate was estimated using the Lake Evaporation Model (LEM), an advanced lake evaporation model that addresses both heat storage and fetch effects, driven by multi‐ensemble downscaled Coupled Model Intercomparison Projects 6 (CMIP6) projections under the SSP585 emission scenario. The results project that the evaporation loss may increase by 2.5 × 107 m3/yr through the research period (1980–2059). Among all regions, the Rio Grande is projected to have the largest increasing rate in the near‐term and mid‐term future, with values of 7.11% of 10.25%, respectively. At the seasonal scale, the most significant increase in the evaporation rate is projected during the fall. The evaporation is projected to increase faster than the streamflow over many of the regions in the southwestern US during the summer/fall, suggesting that the shortage of water will be further exacerbated. The climate models contribute the most to the variance, as compared to the other components related to the projection of evaporation losses (e.g., hydrological model, downscaling method, and historical meteorological data set). These findings demonstrate the need to consider accelerated water loss through open water evaporation in long‐term water resources planning across various spatiotemporal scales. Plain Language Summary: Evaporative water loss from reservoirs is unavoidable in arid/semi‐arid regions worldwide. The expected exacerbation of evaporation losses under climate change has become an emerging issue in water resource planning. We project the future evaporation rate and losses with Lake Evaporation Model under CMIP6‐based future climate and hydrological scenario. For 678 major reservoirs over the Contiguous United States, both evaporation rates and losses are projected to increase, with much more severe conditions during the mid‐term future (2040–2059) than in the near‐term future (2020–2039). Future exacerbation of evaporation will be much more substantial in the southwestern US, and is expected to be more severe in the fall. This research supports both short‐term extreme water event responses and long‐term water supply management strategies. Key Points: Reservoir evaporation losses in the Contiguous United States are projected to increase by 2.5 × 107 m3/yr under the SSP585 emission scenarioThe increase of reservoir evaporation will further exacerbate the water shortage in the southwestern regionsCompared to the other components related to the projection of the evaporation losses, the climate models contribute the most to the variance [ABSTRACT FROM AUTHOR]

The level of C-reactive protein (CRP) in the human body is closely associated with cardiovascular diseases and inflammation. In this study, a label-free functionalized aptamer sensor was attached to an electrode trimmed with in-gold nanoparticles and carboxylated graphene oxide (AuNPs/GO-COOH) to achieve sensitive measurements relative to CRP. Gold nanoparticles were selected for this study due to super stability, remarkably high electrical conductivity, and biocompatibility. In addition, carboxylated graphene oxide was utilized to promote the anchorage of inducer molecules and to increase detection accuracies. The sensing signal was recorded using differential pulse voltammetry (DPV), and it produced a conspicuous peak current obtained at approximately −0.4 V. Furthermore, the adapted sensor manifested a broad linear span from 0.001 ng/mL to 100 ng/mL. The results also demonstrated that this aptamer sensor had superior stability, specificity, and reproducibility. This aptamer-based electrochemical sensor has enormous potential in complex application situations with interfering substances. [ABSTRACT FROM AUTHOR]

Aptamers as the recognition element of the stochastic nanopore sensors have been under intense investigation. This paper reviews recent research advances in aptamer‐based nanopore sensing techniques, including the classification and selection of nanopores (biological nanopores, solid‐state nanopores, and nanopipettes), different strategies of aptamer‐based nanopore sensing, and their values and outlook for applications in areas such as environmental analysis, precision diagnosis, pharmaceutical industry, and security. Furthermore, the single‐molecule nanopore sensors have been applied to reveal the aptamer‐target interactions, such as recognition orientation, binding sites, and conformational heterogeneity dissociation kinetics at the single‐molecule level. In this review, recent research efforts to develop aptamer‐based single‐molecule nanopore sensors with high selectivity and sensitivity are highlighted, and some perspectives are drawn. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The evaporative loss from global lakes (natural and artificial) is a critical component of the terrestrial water and energy balance. However, the evaporation volume of these water bodies—from the spatial distribution to the long-term trend—is as of yet unknown. Here, using satellite observations and modeling tools, we quantified the evaporation volume from 1.42 million global lakes from 1985 to 2018. We find that the long-term average lake evaporation is 1500 ± 150 km3 year−1 and it has increased at a rate of 3.12 km3 year−1. The trend attributions include an increasing evaporation rate (58%), decreasing lake ice coverage (23%), and increasing lake surface area (19%). While only accounting for 5% of the global lake storage capacity, artificial lakes (i.e., reservoirs) contribute 16% to the evaporation volume. Our results underline the importance of using evaporation volume, rather than evaporation rate, as the primary index for assessing climatic impacts on lake systems. While the evaporative water loss from global lakes is invisible, the volume is substantial. In recent decades, lake evaporation volume has been significantly increasing due to enhanced evaporation rate, melting lake ice, and expansion of water extent. [ABSTRACT FROM AUTHOR]

Korean pine (Pinus koraiensis Sieb. et Zucc.) is the most important forest vegetation in northeast China. The timber quality of this tree species is largely driven by branch growth and distribution within the crown. Thus, developing branch diameter and length models, especially those that include competition indices, is essential. A total of 48 Korean pine trees were selected to conduct destructive measurements of branch characteristics. This was carried out on all live branches, and a branch diameter and length model was developed. Various indices, including the absolute depth into the branch base (DINC) from tree tip, were used. The equation with the largest Radj2 and smallest root mean square error (RMSE) values was selected as the best model. Each parameter from the best model was reparameterized to the tree variables and competition indices. Finally, the branch diameter model that included diameter at the breast height (DBH), tree height (HT), and the crown length index (CLI), and the branch length model that included DBH and HT exhibited the best performance. The Radj2 and RMSE values were 0.42 and 4 mm, respectively, for the branch diameter model, and 0.77 and 63 cm, respectively, for the branch length model. Branch diameter and length increased as DBH increased and decreased as HT increased. Furthermore, branch diameter decreased as the CLI increased. [ABSTRACT FROM AUTHOR]

Using branch data from 63 L.kaempferi and 36 L.gmelinii sampled trees in northeastern China, the responses of crown profiles with different tree classes (dominant, intermediate and suppressed trees) and aspects (north, east, south and west) to competition were studied. Crown displacement, defined as the distance between the centre of gravity of the crown and the stem base, was calculated for all the sampled trees to estimate the crown plasticity. The horizontal crown overlapping area (COAH) between sampled trees and the closest neighbouring trees was calculated to estimate the neighbour competition. A crown profile model that includes COAH as the explanatory variable was developed, and the effect of COAH on the crown profile of two tree species was analysed. The results showed that more than half of the trees had the largest crown radius and upper crown surface area on the north and east sides for L.kaempferi and on the north and south sides for L.gmelinii. The crown displacement of L.kaempferi was greater on the north and east sides, and that of L.gmelinii was greater on the south and east sides. COAH had a significant effect on the crown profile model for both species. The mean square error reduction (MSER) for the crown profile model including COAH relative to the model without COAH was 1.68% and 2.10% for L.kaempferi and L.gmelinii, respectively. The COAH had a greater impact on the crown profile of L.gmelinii than that of L.kaempferi. The inflexion point of the dominant and intermediate trees for both species was sensitive to COAH and gradually moved towards the crown base as the neighbour competition intensified, while the inflexion point of the suppressed tree showed a weak response to COAH. The crown profile in the four directions was closely related to the current radial stem growth. [ABSTRACT FROM AUTHOR]

Long‐range water planning is complicated by factors that are rapidly changing in the 21st century, including climate, population, and water use. Here, we analyze climate factors and drought projections for Texas as an example of a diverse society straddling an aridity gradient to examine how the projections can best serve water stakeholder needs. We find that climate models are robust in projecting drying of summer‐season soil moisture and decreasing reservoir supplies for both the eastern and western portions of Texas during the 21st century. Further, projections indicate drier conditions during the latter half of the 21st century than even the most arid centuries of the last 1,000 years that included megadroughts. To illustrate how accounting for drought nonstationarity may increase water resiliency, we consider generalized case studies involving four key stakeholder groups: agricultural producers, large surface water suppliers, small groundwater management districts, and regional water planning districts. We also examine an example of customized climate information being used as input to long‐range water planning. We find that while stakeholders value the quantitative capability of climate model outputs, more specific climate‐related information better supports resilience planning across multiple stakeholder groups. New suites of tools could provide necessary capacity for both short‐ and long‐term, stakeholder‐specific adaptive planning. Key Points: Water stakeholders should prepare for future droughts that will be unlike past droughtsInformation available from climate projections often does not align with the detailed information needed for water planningBetter awareness of the mismatch between available and needed information will help inform efforts to close this gap [ABSTRACT FROM AUTHOR]

Hydrological drought quantification using surface water information has been lacking at a large scale due to limited in situ data. We introduce a framework for monitoring hydrological droughts using a global, long‐term, monthly, remotely sensed reservoir surface area dataset. At regional scales, a new index—the reservoir area drought index (RADI)—is defined as the monthly normalized reservoir area time series. RADI was validated using an in situ reservoir storage based index (average R2 = 0.83). RADI not only helps to characterize drought propagation from meteorological and agricultural droughts to hydrological droughts but also fills the information gap between streamflow/runoff based and groundwater based drought indices. The surface area dataset was further used to characterize the recovery rate (and to estimate the recovery time) at the individual reservoir scale during droughts. This across‐scale drought monitoring framework can help mitigate drought impacts and increase water use efficiency among multi‐reservoir systems. Plain language summary: Drought is a global phenomenon that can have substantial impacts on regional economies and ecosystems. Quantification of droughts is of great importance for mitigating losses. Leveraging an existing remotely sensed global reservoir area dataset, we introduce a new hydrological drought index–the reservoir area drought index, RADI—that can serve as an indicator of surface water availability at a regional scale. Nine drought‐prone regions were chosen to evaluate the performance of RADI. Results suggest that RADI can contribute new information to hydrological drought monitoring and can complement other indices to fully depict the propagation of different kinds of droughts. In addition, reservoir area time series can also be used for quantifying the responses of individual reservoirs to droughts. Key Points: A new hydrological drought index was developed using remotely sensed reservoir surface area time seriesThe drought evaluation framework permits drought quantification at both regional and local scalesThe new index is informative about reservoir water availability and contributes to the evaluation of drought propagation. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Knowledge of reservoir bathymetry is essential for many studies on terrestrial hydrological and biogeochemical processes. However, there are currently no cost-effective approaches to derive reservoir bathymetry at the global scale. This study explores the potential of generating high-resolution global bathymetry using elevation data collected by the 532-nm Advanced Topographic Laser Altimeter System (ATLAS) onboard the Ice, Cloud, and Land Elevation Satellite (ICESat-2). The novel algorithm was developed and tested using the ICESat-2 airborne prototype, the Multiple Altimeter Beam Experimental Lidar (MABEL), with Landsat-based water classifications (from 1982 to 2017). MABEL photon elevations were paired with Landsat water occurrence percentiles to establish the elevation–area (E–A) relationship, which in turn was applied to the percentile image to obtain partial bathymetry over the historic dynamic range of reservoir area. The bathymetry for the central area was projected to achieve the full bathymetry. The bathymetry image was then embedded onto the digital elevation model (DEM). Results were validated over Lake Mead against survey data. Results over four transects show coefficient of determination (${R} ^{2}$) values from 0.82 to 0.99 and root-mean-square error (RMSE) values from 1.18 to 2.36 m. In addition, the E–A and elevation–storage (E–S) curves have RMSEs of 1.56 m and 0.08 km3, respectively. Over the entire dynamic reservoir area, the derived bathymetry agrees very well with independent survey data, except for within the highest and lowest percentile bands. With abundant overpassing tracks and high spatial resolution, the newly launched ICESat-2 should enable the derivation of bathymetry over an unprecedented number of reservoirs. [ABSTRACT FROM AUTHOR]

Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of large-scale observations, model simulation uncertainties and biases related to errors in model structure and uncertain inputs (e.g., hydrologic parameters and atmospheric forcings). The availability of long-term and global remotely sensed soil moisture offers the opportunity to improve model estimates through data assimilation with complete spatiotemporal coverage. In this study, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) derived soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period 2000–2006 with the Community Land Model, version 3.5 (CLM3.5), integrated with the Parallel Data Assimilation Framework (PDAF) at a spatial resolution of 0.0275 ∘ (∼3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans Ertel Centre for Weather Research (HErZ). The performance of assimilation was assessed against open-loop model simulations and cross-validated with independent ESA CCI-derived soil moisture (CCI-SM) and gridded runoff observations. Our results showed improved estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. The assimilation experiment results also showed overall improvements in runoff, although some regions were degraded, especially in central Europe. The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring. [ABSTRACT FROM AUTHOR]

Abstract: The potential of using Landsat for assessing long‐term water surface dynamics of individual reservoirs at a global scale has been significantly hindered by contaminations from clouds, cloud shadows, and terrain shadows. A novel algorithm was developed toward the automatic correction of these contaminated image classifications. By applying this algorithm to the data set by Pekel et al. (2016, https://doi.org/10.1038/nature20584), time series of area values for 6,817 global reservoirs (with an integrated capacity of 6,099 km3) were generated from 1984 to 2015. The number of effective images that can be used in each time series has been improved by 81% on average. The long‐term average area for these global reservoirs was corrected from 1.73 × 105 km2 to 3.94 × 105 km2. The results were proven to be robust through validation using observations, synthetic data, and visual inspection. This continuous reservoir surface area data set can provide benefit to various applications (both at continental and local scales). [ABSTRACT FROM AUTHOR]

Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of observations at large scales and the uncertainties of model simulations due to errors in model structure and inputs (e.g. hydrologic parameters and atmospheric forcings). In this study, we assimilated ESA CCI soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period from 2000 to 2006 with the Community Land Model, version 3.5 (CLM3.5) integrated with the Parallel Data Assimilation Framework (PDAF) at spatial resolution of 0.0275° (∼ 3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans-Ertel Centre for Weather Research (HErZ). Our results show that estimates of soil moisture have improved, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. On average, the mean bias in soil moisture was reduced from 0.1 mm3/mm3 in open-loop simulations to 0.004 mm3/mm3 with SM assimilation. The assimilation experiment also shows overall improvements in runoff, particularly during peak runoff. The results demonstrate the potential of assimilating satellite soil moisture observations to improve high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring. [ABSTRACT FROM AUTHOR]

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3- 5 m) resolution sensing of the Earth on a daily basis. Startup companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via highaltitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems. [ABSTRACT FROM AUTHOR]

Freshwater availability and agricultural production are key factors for sustaining the fast growing population and economy in the state of Texas, which is the third largest state in terms of agricultural production in the United States. This paper describes a long-term (1918–2011) grid-based (1/8°) surface hydrological dataset for Texas at a daily time step based on simulations from the Variable Infiltration Capacity (VIC) hydrological model. The model was calibrated and validated against observed streamflow over 10 Texas river basins. The simulated soil moisture was also evaluated using in situ observations. Results suggest that there is a decreasing trend in precipitation and an increasing trend in temperature in most of the basins. Droughts and floods were reconstructed and analyzed. In particular, the spatially distributed severity and duration of major Texas droughts were compared to identify new characteristics. The modeled flood recurrence interval and the return period were also compared with observations. Results suggest the performance of extreme flood simulations needs further improvement. This dataset is expected to serve as a benchmark which may contribute to water resources management and to mitigating agricultural drought, especially in the context of understanding the effects of climate change on crop yield in Texas. [ABSTRACT FROM AUTHOR]

In just the past five years, the field of Earth observation has evolved from the relatively staid approaches of government space agencies into a plethora of sensing opportunities afforded by CubeSats, Unmanned Aerial Vehicles (UAVs), and smartphone technologies that have been embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity 5 Research and Climate Experiment (GRACE) system, with costs typically on the order of one billion dollars per satellite and with concept-to-launch timelines on the order of two decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturise sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing 3-5 m resolution sensing of the Earth on a daily basis. Start-up companies that did not exist five years ago now operate more satellites in orbit than any space agency and at costs that are a mere fraction of an 10 agency mission. With these advances come new space-borne measurements, such as high-definition video for understanding real-time cloud formation, storm development, flood propagation, precipitation tracking, or for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-meter resolution, pushing back on spatiotemporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal 15 attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizenscience to record photos of environmental conditions, estimate daily average temperatures from battery state, and enable the measurement of other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the Internet of Things as a new measurement domain. Such global access will enable 20 real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is not clear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this 25 array of novel and game-changing sensing platforms presents our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilise and exploit these new observation platforms to enhance our understanding of the Earth system. [ABSTRACT FROM AUTHOR]

A thorough understanding of the peak flows under urbanization and climate change-with the associated uncertainties-is indispensable for mitigating the negative social, economic, and environmental impacts from flooding. In this paper, a case study was conducted by applying the Distributed Hydrology Soil Vegetation Model (DHSVM) to the San Antonio River basin (SARB), Texas. Historical and future land-cover maps were assembled to represent the urbanization process. Future climate and its uncertainties were represented by a series of designed scenarios using the Change Factor (CF) method. The factors were calculated by comparing the model ensemble from phase 5 of the Coupled Model Intercomparison Project (CMIP5) with baseline historical climatology during two future periods (2020-49, period 1; 2070-99, period 2). It was found that with urban impervious areas increasing alone, annual peak flows may increase from 601 (period 1) to 885 m3 s−1 (period 2). With regard to climate change, annual peak flows driven by forcings from maximum, median, and minimum CFs under four representative concentration pathways (RCPs) were analyzed. While the median values of future annual peak flows-forced by the median CF values-are very similar to the baseline under all RCPs, in each case the uncertainty range (calculated as the difference between annual peak flows driven by the maximum and minimum CFs) is very large. When urbanization and climate change coevolve, these averaged annual peak flows from the four RCPs will increase from 447 (period 1) to 707 m3 s−1 (period 2), with the uncertainties associated with climate change more than 3 times greater than those from urbanization. [ABSTRACT FROM AUTHOR]

Man-made reservoirs play a key role in the terrestrial water system. They alter water fluxes at the land surface and impact surface water storage through water management regulations for diverse purposes such as irrigation, municipal water supply, hydropower generation, and flood control. Although most developed countries have established sophisticated observing systems for many variables in the land surface water cycle, long-term and consistent records of reservoir storage are much more limited and not always shared. Furthermore, most land surface hydrological models do not represent the effects of water management activities. Here, the contribution of reservoirs to seasonal water storage variations is investigated using a large-scale water management model to simulate the effects of reservoir management at basin and continental scales. The model was run from 1948 to 2010 at a spatial resolution of 0.25° latitude-longitude. A total of 166 of the largest reservoirs in the world with a total capacity of about 3900 km3 (nearly 60% of the globally integrated reservoir capacity) were simulated. The global reservoir storage time series reflects the massive expansion of global reservoir capacity; over 30 000 reservoirs have been constructed during the past half century, with a mean absolute interannual storage variation of 89 km3. The results indicate that the average reservoir-induced seasonal storage variation is nearly 700 km3 or about 10% of the global reservoir storage. For some river basins, such as the Yellow River, seasonal reservoir storage variations can be as large as 72% of combined snow water equivalent and soil moisture storage. [ABSTRACT FROM AUTHOR]

Land surface temperatures (LSTs) within tropical forests contribute to climate variations. However, observational data are very limited in such regions. This study used passive microwave remote sensing data from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS), providing observations under all weather conditions, to investigate the LST over the Amazon and Congo rainforests. The SSM/I and SSMIS data were collected from 1996 to 2012. The morning and afternoon observations from passive microwave remote sensing facilitate the investigation of the interannual changes of LST anomalies on a diurnal basis. As a result of the variability of cloud cover and the corresponding reduction of solar radiation, the afternoon LST anomalies tend to vary more than the morning LST anomalies. The dominant spatial and temporal patterns for interseasonal variations of the LST anomalies over the tropical rainforest were analyzed. The impacts of droughts and El Niños on this LST were also investigated. Differences between early morning and late afternoon LST anomalies were identified by the remote sensing product, with the morning LST anomalies controlled by humidity (according to comparisons with the National Centers for Environmental Prediction (NCEP) reanalysis data). [ABSTRACT FROM AUTHOR]

A total of 89 trees of Korean pine (Pinus koraiensis) were destructively sampled from the plantations in Heilongjiang Province, P.R. China. The sample trees were measured and calculated for the biomass and carbon stocks of tree components (i.e., stem, branch, foliage and root). Both compatible biomass and carbon stock models were developed with the total biomass and total carbon stocks as the constraints, respectively. Four methods were used to evaluate the carbon stocks of tree components. The first method predicted carbon stocks directly by the compatible carbon stocks models (Method 1). The other three methods indirectly predicted the carbon stocks in two steps: (1) estimating the biomass by the compatible biomass models, and (2) multiplying the estimated biomass by three different carbon conversion factors (i.e., carbon conversion factor 0.5 (Method 2), average carbon concentration of the sample trees (Method 3), and average carbon concentration of each tree component (Method 4)). The prediction errors of estimating the carbon stocks were compared and tested for the differences between the four methods. The results showed that the compatible biomass and carbon models with tree diameter (D) as the sole independent variable performed well so that Method 1 was the best method for predicting the carbon stocks of tree components and total. There were significant differences among the four methods for the carbon stock of stem. Method 2 produced the largest error, especially for stem and total. Methods 3 and Method 4 were slightly worse than Method 1, but the differences were not statistically significant. In practice, the indirect method using the mean carbon concentration of individual trees was sufficient to obtain accurate carbon stocks estimation if carbon stocks models are not available. [ABSTRACT FROM AUTHOR]

Reservoir storage information is essential for accurate flood monitoring and prediction. South Asia, however, is dominated by international river basins where communications among neighboring countries about reservoir storage and management are extremely limited. A suite of satellite observations were combined to achieve high-quality estimation of reservoir storage and storage variations in South Asia from 2000 to 2012. The approach used water surface area estimations from the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices product and the area-elevation relationship to estimate reservoir storage. The surface elevation measurements were from the Geoscience Laser Altimeter System (GLAS) on board the Ice, Cloud, and land Elevation Satellite (ICESat). In order to improve the accuracy of water surface area estimations for relatively small reservoirs, a novel classification algorithm was developed. In this study, storage information was retrieved for a total of 21 reservoirs, which represents 28% of the integrated reservoir capacity in South Asia. The satellite-based reservoir elevation and storage were validated by gauge observations over five reservoirs. The storage estimates were highly correlated with observations (i.e., coefficients of determination larger than 0.9), with normalized root mean square error (NRMSE) ranging from 9.51% to 25.20%. Uncertainty analysis was also conducted for the remotely sensed storage estimations. For the parameterization uncertainty associated with surface area retrieval, the storage mean relative error was 3.90%. With regard to the uncertainty introduced by ICESat/GLAS elevation measurements, the storage mean relative error was 0.67%. [ABSTRACT FROM AUTHOR]

This paper presents an application of the Variable Infiltration Capacity (VIC) model to the Fraser River basin (FRB) of British Columbia (BC), Canada, over the latter half of the twentieth century. The Fraser River is the longest waterway in BC and supports the world's most abundant Pacific Ocean salmon populations. Previous modeling and observational studies have demonstrated that the FRB is a snow-dominated system, but with climate change, it may evolve to a pluvial regime. Thus, the goal of this study is to evaluate the changing contribution of snow to the hydrology of the FRB over the latter half of the twentieth century. To this end, a 0.25° atmospheric forcing dataset is used to drive the VIC model from 1949 to 2006 (water years) at a daily time step over a domain covering the entire FRB. A model evaluation is first conducted over 11 major subwatersheds of the FRB to quantitatively assess the spatial variations of snow water equivalent (SWE) and runoff ( R). The ratio of the spatially averaged maximum SWE to R ( RSR) is used to quantify the contribution of snow to the runoff in the 11 subwatersheds of interest. From 1949 to 2006, RSR exhibits a significant decline in 9 of the 11 subwatersheds (with p < 0.05 according to the Mann-Kendall test statistics). To determine the sensitivity of RSR, the air temperature and precipitation in the forcing dataset are then perturbed. The ratio RSR decreases more significantly, especially during the 1990s and 2000s, when air temperatures have warmed considerably compared to the 1950s. On the other hand, increasing precipitation by a multiplicative factor of 1.1 causes RSR to decrease. As the climate continues to warm, ecological processes and human usage of natural resources in the FRB may be substantially affected by its transition from a snow to a hybrid (nival/pluvial) and even a rain-dominated system. [ABSTRACT FROM AUTHOR]

The implementation of a multimodel drought monitoring system is described, which provides near-real-time estimates of surface moisture storage for the global land areas between 50°S and 50°N with a time lag of about 1 day. Near-real-time forcings are derived from satellite-based precipitation estimates and modeled air temperatures. The system distinguishes itself from other operational systems in that it uses multiple land surface models (Variable Infiltration Capacity, Noah, and Sacramento) to simulate surface moisture storage, which are then combined to derive a multimodel estimate of drought. A comparison of the results with other historic and current drought estimates demonstrates that near-real-time nowcasting of global drought conditions based on satellite and model forcings is entirely feasible. However, challenges remain because hydrological droughts are inherently defined in the context of a long-term climatology. Changes in observing platforms can be misinterpreted as droughts (or as excessively wet periods). This problem cannot simply be addressed through the addition of more observations or through the development of new observing platforms. Instead, it will require careful (re)construction of long-term records that are updated in near-real time in a consistent manner so that changes in surface meteorological forcings reflect actual conditions rather than changes in methods or sources. [ABSTRACT FROM AUTHOR]

Human alteration of the land surface hydrologic cycle is substantial. Recent studies suggest that local water management practices including groundwater pumping and irrigation could significantly alter the quantity and distribution of water in the terrestrial system, with potential impacts on weather and climate through land-atmosphere feedbacks. In this study, the authors incorporated a groundwater withdrawal scheme into the Community Land Model, version 4 (CLM4). To simulate the impact of irrigation realistically, they calibrated the CLM4 simulated irrigation amount against observations from agriculture censuses at the county scale over the conterminous United States. The water used for irrigation was then removed from the surface runoff and groundwater aquifer according to a ratio determined from the county-level agricultural census data. On the basis of the simulations, the impact of groundwater withdrawals for irrigation on land surface and subsurface fluxes were investigated. The results suggest that the impacts of irrigation on latent heat flux and potential recharge when water is withdrawn from surface water alone or from both surface and groundwater are comparable and local to the irrigation areas. However, when water is withdrawn from groundwater for irrigation, greater effects on the subsurface water balance are found, leading to significant depletion of groundwater storage in regions with low recharge rate and high groundwater exploitation rate. The results underscore the importance of local hydrologic feedbacks in governing hydrologic response to anthropogenic change in CLM4 and the need to more realistically simulate the two-way interactions among surface water, groundwater, and atmosphere to better understand the impacts of groundwater pumping on irrigation efficiency and climate. [ABSTRACT FROM AUTHOR]

We studied 34 global reservoirs for which good quality surface elevation data could be obtained from a combination of five satellite altimeters for the period from 1992 to 2010. For each of these reservoirs, we used an unsupervised classification approach using the Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day 250 m vegetation product to estimate the surface water areas over the MODIS period of record (2000 to 2010). We then derived elevation-area relationships for each of the reservoirs by combining the MODIS-based estimates with satellite altimeter-based estimates of reservoir water elevations. Through a combination of direct observations of elevation and surface area along with documented reservoir configurations at capacity, we estimated storage time histories for each reservoir from 1992 to 2010. We evaluated these satellite-based data products in comparison with gauge observations for the five largest reservoirs in the United States (Lakes Mead, Powell, Sakakawea, Oahe, and Fort Peck Reservoir). The storage estimates were highly correlated with observations (R = 0.92 to 0.99), with values for the normalized root mean square error (NRMSE) ranging from 3% to 15%. The storage mean absolute error (expressed as a percentage of reservoir capacity) for the reservoirs in this study was 4%. The multidecadal reconstructed reservoir storage variations are in accordance with known droughts and high flow periods on each of the five continents represented in the data set. [ABSTRACT FROM AUTHOR]

The potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis real-time product 3B42RT (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product and through evaluation of streamflow simulations over four tributaries of La Plata basin (LPB) in South America using the two precipitation products, is investigated. Assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February 2005, which include use of additional microwave sensors [Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and Advanced Microwave Sounding Unit-B (AMSU-B)] and implementation of different calibration schemes. This study suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse. [ABSTRACT FROM AUTHOR]

We calculate evapotranspiration (E) from remote sensing (RS) data using the Penman-Monteith model over continental USA for four years (2003-2006) and explore, through an ensemble generation framework, the impact of input dataset (meteorological, radiation and vegetation) selection on performance (uncertainty) at the monthly time-scale. The impact of failed or missed RS retrievals and algorithmic assumptions are also quantified. To evaluate bias, we inter-compare RS-E with three independent sources of E: Variable Infiltration Capacity (VIC)-model simulated, North American Regional Reanalysis (NARR) inferred, and Gravity Recovery and Climate Experiment (GRACE) inferred. Overall, we find that the choice of vegetation parameterization, followed by surface temperature, has the greatest impact on RS-E uncertainty. Additional uncertainty (4-18%) is linked to sources of net radiation—used to scale instantaneous RS-E under the assumption of constant daytime evaporative fraction—including the Surface Radiation Budget (SRB), International Satellite Cloud Climatology Project (ISCCP), and North American Land Data Assimilation System (NLDAS)-VIC. The ensemble median agrees to within 21% of VIC-modelled E, except for the Colorado and Great Basins for which the need for a soil moisture constraint on RS-E becomes evident. [ABSTRACT FROM AUTHOR]

Nine satellite-based products, each of which provides information about land surface water budget terms, are used to estimate seasonal and annual variations in the water budget of the major river basins of the conterminous USA from 2003 to 2006. The remotely sensed terms are compared with gridded gauge precipitation, and estimates of evapotranspiration (E) and total water storage (TWS) derived from the Variable Infiltration Capacity (VIC) macroscale hydrology model. Among the remote sensing estimates, precipitation has the largest uncertainties. In general, apparent errors for E and TWS show substantial spatial variations, but the consistencies among these remote sensing products are greater than among precipitation products, possibly due in part to similarities in methodology, especially for TWS. Inferred run-off (as a residual of remote sensing estimates of precipitation, E, and TWS) is generally overestimated, due both to excessive precipitation and underestimation of combined E and terrestrial water storage change (TWSC) from remote sensing. [ABSTRACT FROM AUTHOR]