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The 2015–2016 Amazon drought was characterized by below-average regional precipitation for an entire year, which distinguishes it from the dry-season-only droughts in 2005 and 2010. Studies of vegetation indices (VIs) derived from optical remote sensing over the Amazonian forests indicated three stages in canopy response during the 2015–2016 drought, with below-average greenness during the onset and end of the drought, and above-average greenness during the intervening months. To date, a satisfactory explanation for this broad temporal pattern has not been found. A better understanding of rainforest behaviors during this unusually long drought should help predict their response to future droughts. We hypothesized that negative VI anomalies could be caused by water and heat stress exceeding the tolerance ranges of the rainforest. To test our hypothesis, based on monthly observations of terrestrial water storage (TWS), land surface temperature (LST), and vapor pressure deficit (VPD) for January 2003 to December 2016, we proposed an approach to categorize regions into two groups: (1) those exceeding normal hydrological and thermal ranges and (2) those within normal ranges. Accordingly, regions exceeding normal ranges during different stages of the 2015–2016 event were delineated. The results showed a gradual southward shift in these regions: from the northeastern Amazon during August to October 2015 to the north–central part during November 2015 to February 2016 and finally to the southern Amazon in July 2016. Over these regions exceeding normal ranges during droughts, negative VI anomalies were expected, irrespective of radiation anomalies. Over the regions within normal ranges, VI anomalies were assumed to respond positively to radiation anomalies, as is expected under normal conditions. We found that our proposed approach can explain more than 70 % of the observed spatiotemporal patterns in VI anomalies during the 2015–2016 drought. These results suggest that our "exceeding normal ranges"-based approach combining (i) water storage, (ii) temperature, and (iii) atmospheric moisture demand drivers can reasonably identify the most likely drought-affected regions at monthly to seasonal timescales. Using observation-based hydrological and thermal condition thresholds can help with interpreting the response of the Amazon rainforest to future drought events. [ABSTRACT FROM AUTHOR]

Measuring the spatiotemporal dynamics of lake and reservoir water storage is fundamental for assessing the influence of climate variability and anthropogenic activities on water quantity and quality. Previous studies estimated relative water volume changes for lakes where both satellite-derived extent and radar altimetry data are available. This approach is limited to only a few hundred lakes worldwide and cannot estimate absolute (i.e. total volume) water storage. We increased the number of measured lakes by a factor of 300 by using high-resolution Landsat and Sentinel-2 optical remote sensing and ICESat-2 laser altimetry, in addition to radar altimetry from the Topex/Poseidon; Jason-1, Jason-2 and Jason-3; and Sentinel-3 and Sentinel-6 instruments. Historical time series (1984–2020) of water storage could be derived for more than 170 000 lakes globally with a surface area of at least 1 km 2 , representing 99 % of the total volume of all water stored in lakes and reservoirs globally. Specifically, absolute lake volumes are estimated based on topographic characteristics and lake properties that can be observed by remote sensing. In addition to that, we also generated relative lake volume changes solely based on satellite-derived heights and extents if both were available. Within this dataset, we investigated how many lakes can be measured in near real time (2020–current) in basins worldwide. We developed an automated workflow for near-real-time global lake monitoring of more than 27 000 lakes. The GloLakes historical and near-real-time lake storage dynamics data from 1984 to current are publicly available through 10.25914/K8ZF-6G46 (Hou et al., 2022c) and a web-based data explorer (http://www.globalwater.online , last access: 12 December 2023). [ABSTRACT FROM AUTHOR]

The 2015/16 Amazon drought was characterized by below-average regional precipitation for an entire year, which distinguishes it from the dry-season only droughts in 2005 and 2010. Studies of vegetation indices (VI) derived from optical remote sensing over the Amazonian forests indicated three stages in canopy response during the 2015/16 drought, with below-average greenness during the onset and end of the drought, and above-average greenness during the intervening months. So far, a satisfactory explanation for this broad temporal pattern, and spatial variation within the Amazonian forests of this broad response, has not been found. Better understanding of rainforest behaviors during this unusually long drought should help predict their response to future droughts. We hypothesized that below-average greenness could be explained by water deficit and heat stress occurring beyond the tolerance thresholds of rainforest. To test our hypothesis, we used monthly observations of terrestrial water storage (TWS), land surface temperature (LST) and vapor pressure deficit (VPD) for January 2003–December 2016. First, for each 1° grid cell, we determined the ‘normal’ range of monthly TWS, LST and VPD during non-drought years (i.e. 2003–2016, excluding 2005, 2010, 2015 and 2016), and identified the extreme values of ‘normal’ range, i.e. minimum TWS, maximum LST and maximum VPD. We considered the normal hydrological and thermal ranges to have been exceeded when (1) two or three of these variables were simultaneously beyond their extreme values, or (2) only one variable was beyond the extreme value, but the other two were significantly (p<0.05) different from the average for non-drought years. Using these criteria, regions experiencing hydrological and thermal conditions beyond the ‘normal’ range during different stages of the 2015/16 event were delineated. The results showed a gradual southward shift of these regions: from the north-eastern Amazon in August–October 2015, to the north-central part in November 2015–February 2016 and finally to the southern Amazon in July 2016. The majority of forests within the delimited regions experienced below-average greenness. Conversely, outside of these regions, greenness responded positively to radiation anomalies, as is expected under normal conditions. The opposing influences of drought and radiation anomalies together explained more than 70 % of the observed spatiotemporal patterns in greenness. These results suggest that our exceeding ‘normal’ ranges based approach, combining water storage, temperature and atmospheric moisture demand drivers, can reasonably identify the most likely drought-affected regions at monthly to seasonal time scales. Using observation-based hydrological and thermal condition thresholds can help with interpreting the response of Amazon rainforest to future drought events. [ABSTRACT FROM AUTHOR]

Measurements of the spatiotemporal dynamics of lake and reservoir water storage are fundamental in the assessment of the influence of climate variability and anthropogenic activities on water quantity and quality, as well as wetland ecology and the estimating greenhouse gas emissions from lakes. Previous studies estimated relative water volume changes for lakes where both satellite-derived extent and radar altimetry data are available. This approach is limited to only few hundreds of lakes worldwide. In this study, the number of measured lakes was increased by a factor 400 using high-resolution Landsat and Sentinel-2 optical remote sensing and ICESat-2 laser altimetry in addition to radar altimetry from the Topex/Poseidon, Jason-1, -2 and -3, and Sentinel-3 instruments. Time series of relative (i.e., storage change) or absolute (i.e., total stored volume) storage for more than 170,000 lakes globally with a surface area of at least 1 km² (representing 99% of the total volume of all water stored in lakes and reservoirs globally) were retrieved. Within these, we were able to develop an automated workflow for near real-time global lake monitoring of more than 27,000 lakes. The historical and near real-time lake storage dynamics data for 1984 to current are publicly available through https://doi.org/10.25914/K8ZF-6G46 (Hou et al., 2022). [ABSTRACT FROM AUTHOR]

Rainfall interception loss remains one of the most uncertain fluxes in the global water balance, hindering water management in forested regions and precluding an accurate formulation in climate models. Here, a synthesis of interception loss data from past field experiments conducted worldwide is performed, resulting in a meta-analysis comprising 166 forest sites and 17 agricultural plots. This meta-analysis is used to constrain a global process-based model driven by satellite-observed vegetation dynamics, potential evaporation and precipitation. The model considers subgrid heterogeneity and vegetation dynamics, and formulates rainfall interception for tall and short vegetation separately. A global, 40-year (1980-2019), 0.1° spatial resolution, daily temporal resolution dataset is created, analysed and validated against in situ data. The validation shows a good consistency between the modelled interception and field observations over tall vegetation, both in terms of correlations and bias. While an underestimation is found in short vegetation, the degree to which it responds to in situ representativeness errors and difficulties inherent to the measurement of interception in short vegetated ecosystems is unclear. Global estimates are compared to existing datasets, showing overall comparable patterns. According to our findings, global interception averages to 73.81 mm yr-1 or 10.96 × 103 km3 yr-1, accounting for 10.53% of continental rainfall, and approximately 14.06% of terrestrial evaporation. The seasonal variability of interception follows the annual cycle of canopy cover, precipitation, and atmospheric demand for water. Tropical rainforests show low intra-annual vegetation variability, and seasonal patterns are dictated by rainfall. Interception shows a strong variance among vegetation types and biomes, supported by both the modelling and the meta-analysis of field data. The global synthesis of field observations and the new global interception dataset will serve as a benchmark for future investigations, and facilitate large-scale hydrological and climate research. [ABSTRACT FROM AUTHOR]

We present Multi-Source Weather (MSWX), a seamless global gridded near-surface meteorological product featuring a high 3-hourly 0.1° resolution, near-real-time updates (∼3-h latency), and bias-corrected medium-range (up to 10 days) and long-range (up to 7 months) forecast ensembles. The product includes 10 meteorological variables: precipitation, air temperature, daily minimum and maximum air temperature, surface pressure, relative and specific humidity, wind speed, and downward shortwave and longwave radiation. The historical part of the record starts 1 January 1979 and is based on ERA5 data bias corrected and downscaled using high-resolution reference climatologies. The data extension to within ∼3 h of real time is based on analysis data from GDAS. The 30-member medium-range forecast ensemble is based on GEFS and updated daily. Finally, the 51-member long-range forecast ensemble is based on SEAS5 and updated monthly. The near-real-time and forecast data are statistically harmonized using running-mean and cumulative distribution function-matching approaches to obtain a seamless record covering 1 January 1979 to 7 months from now. MSWX presents new and unique opportunities for hydrological modeling, climate analysis, impact studies, and monitoring and forecasting of droughts, floods, and heatwaves (within the bounds of the caveats and limitations discussed herein). The product is available at www.gloh2o.org/mswx. [ABSTRACT FROM AUTHOR]

Hydromorphological attributes such as flow width, water extent and gradient play an important role in river hydrological, biogeochemical and ecological processes; helping to predict river conveyance capacity, discharge and flow routing. While there are some river width datasets at global or regional scales, they do not consider temporal variation in river width and do not cover all Australian rivers. We combined detailed mapping of 1.4 million river reaches across the Australian continent with inundation frequency mapping from 27-years of Landsat observations. From these, the average flow width at different recurrence frequencies was calculated for all reaches, having a combined length of 3.3 million km. A parameter γ was proposed to describe the shape of the frequency-width relationship and can be used to classify reaches by the degree to which flow regime tends towards permanent, frequent, intermittent or ephemeral. Proposed scaling rules relating river width to gradient and contributing catchment area and discharge were investigated, demonstrating that such rules capture relatively little of real-world variability. Uncertainties mainly occur in multi-channel reaches and reaches with unconnected water bodies. The calculated reach attributes are easily combined with the river vector data in GIS, which should be useful for research and practical applications such as water resource management, aquatic habitat enhancement, and river engineering and management. The dataset is available at https://doi.org/10.25914/5c637a7449353 (Hou et al. 2019). [ABSTRACT FROM AUTHOR]

A portion of globally generated surface and groundwater resources evaporates from wetlands, waterbodies and irrigated areas. This secondary evaporation of "blue" water directly affects the remaining water resources available for ecosystems and human use. At the global scale, a lack of detailed water balance studies and direct observations limits our understanding of the magnitude and spatial and temporal distribution of secondary evaporation. Here, we propose a methodology to assimilate satellite-derived information into the landscape hydrological model W3 at an unprecedented 0.05°, or ca. 5 km resolution globally. The assimilated data are all derived from MODIS observations, including surface water extent, surface albedo, vegetation cover, leaf area index, canopy conductance and land surface temperature (LST). The information from these products is imparted on the model in a simple but efficient manner, through a combination of direct insertion of the surface water extent, an evaporation flux adjustment based on LST and parameter nudging for the other observations. The resulting water balance estimates were evaluated against river basin discharge records and the water balance of closed basins and demonstrably improved water balance estimates compared to ignoring secondary evaporation (e.g., bias improved from +38 to +2mmyr-1). The evaporation estimates derived from assimilation were combined with global mapping of irrigation crops to derive a minimum estimate of irrigation water requirements (I0), representative of optimal irrigation efficiency. Our I0 estimates were lower than published countrylevel estimates of irrigation water use produced by alternative estimation methods, for reasons that are discussed. We estimate that 16% of globally generated water resources evaporate before reaching the oceans, enhancing total terrestrial evaporation by 6.1×1012 m³ yr-1 or 8.8%. Of this volume, 5% is evaporated from irrigation areas, 58% from terrestrial waterbodies and 37% from other surfaces. Model-data assimilation at even higher spatial resolutions can achieve a further reduction in uncertainty but will require more accurate and detailed mapping of surface water dynamics and areas equipped for irrigation. [ABSTRACT FROM AUTHOR]

An integrated understanding of the biogeochemical consequences of climate extremes and land use changes is needed to constrain land-surface feedbacks to atmospheric CO2 from associated climate change. Past assessments of the global carbon balance have shown particularly high uncertainty in Southeast Asia. Here, we use a combination of model ensembles to show that intensified land use change made Southeast Asia a strong source of CO2 from the 1980s to 1990s, whereas the region was close to carbon neutral in the 2000s due to an enhanced CO2 fertilization effect and absence of moderate-to-strong El Niño events. Our findings suggest that despite ongoing deforestation, CO2 emissions were substantially decreased during the 2000s, largely owing to milder climate that restores photosynthetic capacity and suppresses peat and deforestation fire emissions. The occurrence of strong El Niño events after 2009 suggests that the region has returned to conditions of increased vulnerability of carbon stocks. [ABSTRACT FROM AUTHOR]

In this paper, evapotranspiration (ET) and leaf area index (LAI) were used to calibrate the SWAT model, whereas remotely sensed precipitation and other climatic parameters were used as forcing data for the 6300 km² Day Basin, a tributary of the Red River in Vietnam. The efficacy of the Sequential Uncertainty Fitting (SUFI-2) parameter sensitivity and optimization model was tested with area specific remote sensing input parameters for every Hydrological Response Units (HRU), rather than with measurements of river flow representing a large set of HRUs, i.e., a bulk calibration. Simulated monthly ET correlations with remote sensing estimates showed an R² = 0.71, Nash-Sutcliffe Efficiency NSE = 0.65, and Kling Gupta Efficiency KGE = 0.80 while monthly LAI showed correlations of R² = 0.59, NSE = 0.57 and KGE = 0.83 over a five-year validation period. Accumulated modelled ET over the 5-year calibration period amounted to 5713 mm compared to 6015 mm of remotely sensed ET, yielding a difference of 302 mm (5.3%). The monthly flow at two flow measurement stations were adequately estimated (R² = 0.78 and 0.55, NSE = 0.71 and 0.63, KGE = 0.59 and 0.75 for Phu Ly and Ninh Binh, respectively). This outcome demonstrates the capability of SWAT model to obtain spatial and accurate simulation of eco-hydrological processes, also when rivers are ungauged and the water withdrawal system is complex. [ABSTRACT FROM AUTHOR]