Droughts impede water balance recovery from fires in the Western United States.

A steady rise in fires in the Western United States, coincident with intensifying droughts, imparts substantial modifications to the underlying vegetation, hydrology and overall ecosystem. Drought can compound the ecosystem disturbance caused by fire, although how these compound effects on hydrologic and ecosystem recovery vary among ecosystems is poorly understood. Here we use remote sensing-derived high-resolution evapotranspiration (ET) estimates from before and after 1,514 fires to show that ecoregions dominated by grasslands and shrublands are more susceptible to drought, which amplifies fire-induced ET decline and, subsequently, shifts water flux partitioning. In contrast, severely burned forests recover from fire slowly or incompletely, but are less sensitive to dry extremes. We conclude that moisture limitation caused by droughts influences the dynamics of water balance recovery in post-fire years. This finding explains why moderate to extreme droughts aggravate impacts on the water balance in non-forested vegetation, while moisture accessed by deeper roots in forests helps meet evaporative demands unless severe burns disrupt internal tree structure and deplete fuel load availability. Our results highlight the dominant control of drought on altering the resilience of vegetation to fires, with critical implications for terrestrial ecosystem stability in the face of anthropogenic climate change in the West.

Interconnected hydrologic extreme drivers and impacts depicted by remote sensing data assimilation.

Hydrologic extremes often involve a complex interplay of several processes. For example, flood events can have a cascade of impacts, such as saturated soils and suppressed vegetation growth. Accurate representation of such interconnected processes while accounting for associated triggering factors and subsequent impacts of flood events is difficult to achieve with conceptual hydrological models alone. In this study, we use the 2019 flood in the Northern Mississippi and Missouri Basins, which caused a series of hydrologic disturbances, as an example of such a flood event. This event began with above-average precipitation combined with anomalously high snowmelt in spring 2019. This series of anomalies resulted in above normal soil moisture that prevented crops from being planted over much of the corn belt region. In the present study, we demonstrate that incorporating remote sensing information within a hydrologic modeling system adds substantial value in representing the processes that lead to the 2019 flood event and the resulting agricultural disturbances. This remote sensing data infusion improves the accuracy of soil moisture and snowmelt estimates by up to 16% and 24%, respectively, and it also improves the representation of vegetation anomalies relative to the reference crop fraction anomalies.

Effects of Amazonian flying rivers on frog biodiversity and populations in the Atlantic rainforest.

Given the speed at which humans are changing the climate, species with high degrees of endemism may not have time to avoid extinction through adaptation. We investigated through teleconnection analysis the origin of rainfall that determines the phylogenetic diversity of rainforest frogs and the effects of microclimate differences in shaping the morphological traits of isolated populations (which contribute to greater phylogenetic diversity and speciation). We also investigated through teleconnection analysis how deforestation in Amazonia can affect ecosystem services that are fundamental to maintaining the climate of the Atlantic rainforest biodiversity hotspot. Seasonal winds known as flying rivers carry water vapor from Amazonia to the Atlantic Forest, and the breaking of this ecosystem service could lead Atlantic Forest species to population decline and extinction in the short term. Our results suggest that the selection of morphological traits that shape Atlantic Forest frog diversity and their population dynamics are influenced by the Amazonian flying rivers. Our results also suggest that the increases of temperature anomalies in the Atlantic Ocean due to global warming and in the Amazon forest due to deforestation are already breaking this cycle and threaten the biodiversity of the Atlantic Forest hotspot.

Efectos de los ríos voladores de la Amazonía sobre la diversidad y las poblaciones de ranas en la Mata Atlántica Resumen Con la velocidad a la que la humanidad está alterando el clima, puede que las especies con un nivel elevado de endemismo no cuenten con tiempo suficiente para adaptarse y evitar la extinción. Usamos un análisis de teleconexión para investigar el origen de las precipitaciones que determinan la diversidad filogenética de las ranas selváticas y los efectos de las diferencias microclimáticas sobre la determinación de las características morfológicas de las poblaciones aisladas, las cuales contribuyen a una mayor especiación y diversidad filogenética. También utilizamos este análisis para investigar cómo la deforestación en la Amazonía puede afectar los servicios ambientales que son fundamentales para mantener el punto caliente de biodiversidad que es la Mata Atlántica. Los ríos voladores son vientos estacionales que transportan vapor de agua desde la Amazonía hasta la Mata Atlántica; la interrupción de este servicio ambiental podría derivar en la declinación poblacional y la extinción a corto plazo de las especies en este ecosistema. Nuestros resultados sugieren que los ríos voladores de la Amazonía influyen sobre la selección de las características morfológicas que determinan la diversidad de ranas y sus dinámicas poblacionales en la Mata Atlántica. Nuestros resultados también sugieren que el incremento de anomalías térmicas en el Océano Atlántico, causadas por el calentamiento global, y en la Amazonía, causadas por la deforestación, ya están interrumpiendo este ciclo y son una amenaza para la biodiversidad del punto caliente que es la Mata Atlántica.

Changes in land use enhance the sensitivity of tropical ecosystems to fire-climate extremes.

The Pantanal, the largest contiguous wetland in the world with a high diversity of ecosystems and habitat for several endangered species, was impacted by record-breaking wildfires in 2020. In this study, we integrate satellite and modeling data that enable exploration of natural and human contributing factors to the unprecedented 2020 fires. We demonstrate that the fires were fueled by an exceptional multi-year drought, but dry conditions solely could not explain the spatial patterns of burning. Our analysis reveals how human-caused fires exacerbated drought effects on natural ecosystem within the Pantanal, with large burned fractions primarily over natural (52%), and low cattle density areas (44%) in 2020. The post-fire ecosystem and hydrology changes also had strong ecological effects, with vegetation productivity less than - 1.5 σ over more than 30% of the natural and conservation areas. In contrast to more managed areas, there was a clear decrease in evaporation (by ~ 9%) and an increase in runoff (by ~ 5%) over the natural areas, with long-term impacts on ecosystem recovery and fire risk. This study provides the first tropical evidence outside rainforests of the synergy between climate, land management and fires, and the associated impacts on the ecosystem and hydrology over the largest contiguous wetlands in the world.

GRACE improves seasonal groundwater forecast initialization over the U.S.

We evaluate the impact of Gravity Recovery and Climate Experiment data assimilation (GRACE-DA) on seasonal hydrological forecast initialization over the U.S., focusing on groundwater storage. GRACE-based terrestrial water storage (TWS) estimates are assimilated into a land surface model for the 2003-2016 period. Three-month hindcast (i.e., forecast of past events) simulations are initialized using states from the reference (no data assimilation) and GRACE-DA runs. Differences between the two initial hydrological condition (IHC) sets are evaluated for two forecast techniques at 305 wells where depth-to-water-table measurements are available. Results show that using GRACE-DA-based IHC improves seasonal groundwater forecast performance in terms of both RMSE and correlation. While most regions show improvement, degradation is common in the High Plains, where withdrawals for irrigation practices affect groundwater variability more strongly than the weather variability, which demonstrates the need for simulating such activities. These findings contribute to recent efforts towards an improved U.S. drought monitor and forecast system.

Influence of global climate on freshwater changes in Africa's largest endorheic basin using multi-scaled indicators.

The poor investments in gauge measurements for hydro-climatic research in Africa has necessitated the need to investigate how decision makers can leverage on sophisticated space-borne measurements to improve knowledge on surface water hydrology that can feed directly into water accounting processes, and risk assessment from extreme droughts and its impacts. To demonstrate such potential, a suite of satellite earth observations (Sentinel-2, altimetry, Landsat, GRACE, and TRMM) and model data are combined with the standardized precipitation evapotranspiration index to assess the impacts of global climate on freshwater dynamics over the LCB (Lake Chad basin), Africa's largest endorheic basin. As shown in the results of this study, the significant relationship of climate modes (AMO; r=0.68 and 0.59; and AMM; r=0.42 and 0.47) with drought patterns in the LCB highlights the evidence of global climate influence in the region. The significant declines in drought extents and their intensities (2004 - 2015) over LCB coincide with the rise in surface water extent of the Lake Chad during the same period. Change detection analysis of open water features in the southern pool of Lake Chad during the 2015 - 2019 period shows that on the average, only 28.4% of inundated areas within the vicinity of the Lake persisted during the period. While the association of terrestrial water storage (TWS) with model-derived surface water storage (SWS) is strongest (r=0.89) in the catchments that provide the most nourishment to the Lake Chad, the relationship of rainfall (2002 - 2017) with TWS (r=0.85), model TWS (r=0.87) and SWS (r=0.88) confirm that the LCB's hydrology is predominantly climate-driven. This notion is further reinforced as the predicted SWS over the LCB using a support vector machine regression scheme was found to be strongly correlated (r=0.95 at α=0.05) with observed SWS.

Hydropower dam operation strongly controls Lake Victoria's freshwater storage variability.

River impoundments strongly modify the global water cycle and terrestrial water storage (TWS) variability. Given the susceptibility of global water cycle to climate change and anthropogenic influence, the synthesis of science with sustainable reservoir operation strategy is required as part of an integrated approach to water management. Here, we take advantage of new approaches combining state-of-the-art computational models and a novel satellite-based reservoir operation scheme to spatially and temporally decompose Lake Victoria's TWS, which has been dam-controlled since 1954. A ground-based lake bathymetry is merged with a global satellite-based topography to accurately represent absolute water storage, and radar altimetry data is integrated in the hydrodynamic model as a proxy of reservoir operation practices. Compared against an idealized naturalized system (i.e., no anthropogenic impacts) over 2003-2019, reservoir operation shows a significant impact on water elevation, extent, storage and outflow, controlling lake dynamics and TWS. For example, compared to Gravity Recovery and Climate Experiment (GRACE) data, reservoir operation improved correlation and root mean square error of basin-wide TWS simulations by 80% and 54%, respectively. Results also show that lake water storage is 20% higher under dam control and basin-wide surface water storage contributes 64% of TWS variability. As opposed to existing reservoir operation schemes for large-scale models, the proposed model simulates spatially distributed surface water processes and does not require human water demand estimates. Our proposed approaches and findings contribute to the understanding of Lake Victoria's water dynamics and can be further applied to quantify anthropogenic impacts on the global water cycle.

Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model.

The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE) through the assimilation of MODIS SCF observations. Results show that data assimilation (DA) improved SCF estimates compared to the open-loop model without assimilation (OL), especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL). A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from -0.017 m for OL to -0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD) (0.095 m for OL, 0.093 m for DA). The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from -0.111 m for OL to -0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA.

Evaluation of Simulated Snow and Snowmelt Timing in the Community Land Model Using Satellite-based Products and Streamflow Observations.

The purpose of this study was to evaluate snow and snowmelt simulated by version 4 of the Community Land Model (CLM4). We performed uncoupled CLM4 simulations, forced by Modem-Era Retrospective Analysis for Research and Applications Land-only (MERRA-Land) meteorological fields. GlobSnow snow cover fraction (SCF), snow water equivalent (SWE) and satellite-based passive microwave (PMW) snowmelt-off day of year (MoD) data were used to evaluate SCF, SWE, and snowmelt simulations. Simulated runoff was then fed into a river routing scheme and evaluation was performed at 408 snow-dominated catchments using gauge observations. CLM4 and GlobSnow snow cover extent showed a strong agreement, especially during the peak snow cover months. Overall there was a good correlation between simulated and observed SWE (correlation coefficient, R = 0.6). Simulated and observed SWE were similar over areas with relatively flat terrain and moderate forest density. The simulated MoD agreed (MoD differences (CLM4-PMW) = +/-7 days) with observations over 39.4% of the study domain. Snowmelt-off occurred earlier in the model compared to the observations over 39.5 % of the domain and later over 21.1% of the domain. Large differences of MoD were seen in the areas with complex terrain and dense forest cover. We also found that, although streamflow seasonal phase was accurately modeled (R=0.9), the peaks controlled by snowmelt were underestimated. Routed CLM4 streamflow tended to occur early (by 10 days on average).

Upper Blue Nile Basin Water Budget from a Multi-Model Perspective.

Improved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, in-situ streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

Tradeoff between cost and accuracy in large-scale surface water dynamic modeling.

Recent efforts have led to the development of the local inertia formulation (INER) for an accurate but still cost-efficient representation of surface water dynamics, compared to the widely used kinematic wave equation (KINE). In this study, both formulations are evaluated over the Amazon basin in terms of computational costs and accuracy in simulating streamflows and water levels through synthetic experiments and comparisons against ground-based observations. Varying time steps are considered as part of the evaluation and INER at 60-second time step is adopted as the reference for synthetic experiments. Five hybrid (HYBR) realizations are performed based on maps representing the spatial distribution of the two formulations that physically represent river reach flow dynamics within the domain. Maps have fractions of KINE varying from 35.6% to 82.8%. KINE runs show clear deterioration along the Amazon river and main tributaries, with maximum RMSE values for streamflow and water level reaching 7827m3.s-1 and 1379cm near the basin's outlet. However, KINE is at least 25% more efficient than INER with low model sensitivity to longer time steps. A significant improvement is achieved with HYBR, resulting in maximum RMSE values of 3.9-292m3.s-1 for streamflows and 1.1-28.5cm for water levels, and cost reduction of 6-16%, depending on the map used. Optimal results using HYBR are obtained when the local inertia formulation is used in about one third of the Amazon basin, reducing computational costs in simulations while preserving accuracy. However, that threshold may vary when applied to different regions, according to their hydrodynamics and geomorphological characteristics.

Hydrological modeling of the Peruvian-Ecuadorian Amazon basin using GPM-IMERG satellite-based precipitation dataset.

In the last two decades, rainfall estimates provided by the Tropical Rainfall Measurement Mission (TRMM) have proven applicable in hydrological studies. The Global Precipitation Measurement (GPM) mission, which provides the new generation of rainfall estimates, is now considered a global successor to TRMM. The usefulness of GPM data in hydrological applications, however, has not yet been evaluated over the Andean and Amazonian regions. This study uses GPM data provided by the Integrated Multi-satellite Retrievals (IMERG) (product/final run) as input to a distributed hydrological model for the Amazon Basin of Peru and Ecuador for a 16-month period (from March 2014 to June 2015) when all datasets are available. TRMM products (TMPA V7, TMPA RT datasets) and a gridded precipitation dataset processed from observed rainfall are used for comparison. The results indicate that precipitation data derived from GPM-IMERG correspond more closely to TMPA V7 than TMPA RT datasets, but both GPM-IMERG and TMPA V7 precipitation data tend to overestimate, compared to observed rainfall (by 11.1% and 15.7 %, respectively). In general, GPM-IMERG, TMPA V7 and TMPA RT correlate with observed rainfall, with a similar number of rain events correctly detected (~20%). Statistical analysis of modeled streamflows indicates that GPM-IMERG is as useful as TMPA V7 or TMPA RT datasets in southern regions (Ucayali basin). GPM-IMERG, TMPA V7 and TMPA RT do not properly simulate streamflows in northern regions (Marañón and Napo basins), probably because of the lack of adequate rainfall estimates in northern Peru and the Ecuadorian Amazon.

Modeling surface water dynamics in the Amazon Basin using MOSART-Inundation-v1.0: Impacts of geomorphological parameters and river flow representation.

Surface water dynamics play an important role in water, energy and carbon cycles of the Amazon Basin. A macro-scale inundation scheme was integrated with a surface-water transport model and the extended model was applied in this vast basin. We addressed the challenges of improving basin-wide geomorphological parameters and river flow representation for large-scale applications. Vegetation-caused biases embedded in the HydroSHEDS DEM data were alleviated by using a vegetation height map of about 1-km resolution and a land cover dataset of about 90-m resolution. The average elevation deduction from the DEM correction was about 13.2 m for the entire basin. Basin-wide empirical formulae for channel cross-sectional geometry were adjusted based on local information for the major portion of the basin, which could significantly reduce the cross-sectional area for the channels of some subregions. The Manning roughness coefficient of the channel varied with the channel depth to reflect the general rule that the relative importance of riverbed resistance in river flow declined with the increase of river size. The entire basin was discretized into 5395 subbasins (with an average area of 1091.7 km2), which were used as computation units. The model was driven by runoff estimates of 14 years (1994 - 2007) generated by the ISBA land surface model. The simulated results were evaluated against in situ streamflow records, and remotely sensed Envisat altimetry data and GIEMS inundation data. The hydrographs were reproduced fairly well for the majority of 13 major stream gauges. For the 11 subbasins containing or close to 11 of the 13 gauges, the timing of river stage fluctuations was captured; for most of the 11 subbasins, the magnitude of river stage fluctuations was represented well. The inundation estimates were comparable to the GIEMS observations. Sensitivity analyses demonstrated that refining floodplain topography, channel morphology and Manning roughness coefficients, as well as accounting for backwater effects could evidently affect local and upstream inundation, which consequently affected flood waves and inundation of the downstream area. It was also shown that the river stage was sensitive to local channel morphology and Manning roughness coefficients, as well as backwater effects. The understanding obtained in this study could be helpful to improving modeling of surface hydrology in basins with evident inundation, especially at regional or larger scales.