Studies of climate change impacts in the Colorado River Basin have focused on the Upper Basin, finding that warming has exacerbated streamflow declines since 2000. Presently, it is unclear if the predicted high degree of interannual precipitation variability across the basin could overwhelm impacts of future warming and how this might vary in space. To address this, we employed an updated Variable Infiltration Capacity model framework to generate streamflow projections across the entire basin. Meteorological data were drawn from downscaled historical and future climate projections of eight climate models that best represent climatology. Results showed a high confidence of Far-Future (2066-2095) mean annual streamflow declines (relative to the Baseline, 1976-2005) at the Upper Basin, Lower Basin, and basin-wide scales (ensemble median trends of -5 % to -25 %). Streamflow declines were attributed to warming that diminished snowfall and melt during April to September and year-round increased soil evaporation from the Upper Basin, and overall precipitation declines in the Lower Basin. Widespread reductions in annual runoff and baseflow efficiencies indicated that precipitation increases did not offset the noted warming effects. Spatial coherence tests indicated that streamflow correlations among subbasins remained relatively constant in the future scenarios, though correlations with the Green River increased. This spatial attribution analysis is providing actionable information for water management in the Colorado River Basin under current and future conditions.

Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (<0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).

Irrigation water use associated with agricultural activities and urban green spaces provides substantial cooling effects and ameliorates heat in central Arizona. In this arid and semiarid area, evaluating the effect of irrigation on land surface temperature (LST) for different types of land use can improve decision making related to water resources management. In this work, we improved the simulation of urban and agricultural irrigation in the Variable Infiltration Capacity model through remotely sensed vegetation and irrigation parameters applied at high spatiotemporal resolution. We then conducted a multiyear (2004-2013) assessment of simulated LST with respect to ground observations and remotely sensed products finding overall good agreement. Overall, results show that irrigation of about 2 mm/day is required to reduce average daily LST by 1 degrees C across the region. Numerical experiments with the validated model also reveal that irrigation leads to LST reductions of higher magnitude and greater spatial variability in croplands than in urban areas. Furthermore, we found that the role of interannual variations in cropping practices is more critical than year-to-year differences in climatic conditions for the evaluation of irrigation cooling capacity. Thus, remotely sensed vegetation products can serve a valuable purpose in quantifying LST reductions and irrigation requirements to achieve a target of heat amelioration.

The Colorado River Basin (CRB) has experienced widespread and prolonged drought in the 21st century with recent precipitation (P) up to 25% below historical means and air temperature (T) up to 0.8 oC warmer. The extent that continued warming will lead to streamflow (Q) decline is unclear given the high interannual variability of P. Here we explore physically plausible ways that climate change could impact Q using the Variable Infiltration Capacity (VIC) model. We integrated advances in VIC using Landsat- and MODIS-based products to produce more realistic land surface conditions and used this setup to simulate long-range Q projections. Meteorological datasets were sourced from gridded daily observations (1950-2013) and downscaled historical (1950-2005) and future projections (2006-2099) derived from multiple CMIP5 models under a low and a high emission scenario to explore forcing uncertainties and cases where P increase could offset warming. We compared the impacts of anticipated climate change on hydrologic responses in subbasins key for water management to gauge their importance for basin-wide water budgets and how these relationships could evolve in time, as this has been a largely unexplored aspect in the CRB. Results showed that spatial gradients in seasonal P changes led to contrasting seasonal responses in runoff (R) across the CRB. Whereas most of the Upper Basin had a shift to greater R during the winter, summer R declined over most of the CRB due to heightened evapotranspiration in the northwest (Green, Upper Colorado, Glen Canyon, and Grand Canyon subbasins) and large P decline in the southeast (San Juan, Little Colorado, and Gila subbasins). The strength of seasonal runoff signals across different climate models and their impacts to annual Q were dependent on subbasin area and emission scenario. Annual Q at the CRB outlet declined in most cases, however, reflecting the pervasive drying effect of warming.

Accurate characterization of precipitation P at subdaily temporal resolution is important for a wide range of hydrological applications, yet large-scale gridded observational datasets primarily contain daily total P. Unfortunately, a widely used deterministic approach that disaggregates P uniformly over the day grossly mischaracterizes the diurnal cycle of P, leading to potential biases in simulated runoff Q. Here we present Precipitation Isosceles Triangle (PITRI), a two-parameter deterministic approach in which the hourly hyetograph is modeled with an isosceles triangle with prescribed duration and time of peak intensity. Monthly duration and peak time were derived from meteorological observations at U.S. Climate Reference Network (USCRN) stations and extended across the United States, Mexico, and southern Canada at 6-km resolution via linear regression against historical climate statistics. Across the USCRN network (years 2000–13), simulations using the Variable Infiltration Capacity (VIC) model, driven by P disaggregated via PITRI, yielded nearly unbiased estimates of annual Q relative to simulations driven by observed P. In contrast, simulations using the uniform method had a Q bias of −11%, through overestimating canopy evaporation and underestimating throughfall. One limitation of the PITRI approach is a potential bias in snow accumulation when a high proportion of P falls on days with a mix of temperatures above and below freezing, for which the partitioning of P into rain and snow is sensitive to event timing within the diurnal cycle. Nevertheless, the good overall performance of PITRI suggests that a deterministic approach may be sufficiently accurate for large-scale hydrologic applications.

Earth systems models require gridded land surface properties to compute fluxes of water, energy, and carbon within the landscape and to the atmosphere. However, most parameter sets contain time-invariant properties despite their known variability. Here we present new MODerate Resolution Imaging Spectroradiometer (MODIS)-based land surface parameters (MOD-LSP) formatted for the Variable Infiltration Capacity (VIC) hydrologic model that account for seasonal and interannual variability and longer-term change over the continental United States, Mexico, and southern Canada at 0.0625° spatial resolution and monthly temporal resolution. MOD-LSP improves over previously-available parameter sets via: (1) land cover maps of higher native spatial resolution; (2) multiple versions corresponding to the land cover of years 1992, 2001, and 2011; (3) spatially-explicit mean annual cycles of land surface properties, including leaf area index, canopy fraction, and albedo, derived from 17 years of observations; and (4) additional 17-year time series of these properties. The MOD-LSP parameters are useful as inputs to the VIC model, as an example land surface scheme, to assess the hydrologic impacts of land cover change from interannual to decadal scales; and as stand-alone datasets characterizing the temporal variability of these properties as a function of land cover class.

The linkages between land and water use are often neglected when considering resource management. Here, we examined regional changes in land and water use along the US–Mexico border in the decades following the North American Free Trade Agreement, using bi-national land cover maps from 1992–2011, a process-based hydrology and irrigation model driven with long-term meteorological data, and agricultural production and urban water demand statistics. During the study period, land and water use in the region partially re-oriented around the needs of US cities, leading to crop to urban conversions and water savings in the US, while agricultural and urban expansion in Mexico resulted in local aquifer exploitation and reduced river flows. We identified that land uses with lower rates of water consumption (urban in US and agriculture in Mexico) expanded more than those with higher demands (irrigated agriculture in US and urban in Mexico) due to the water scarcity in the region. This resulted in divergent trends in the US and Mexico that in aggregate has led to an unsustainable trajectory in land and water resources.

Abstract. The Variable Infiltration Capacity (VIC) model is a macroscale semi-distributed hydrologic model. VIC development began in the early 1990s and the model has since been used extensively for basin- to global-scale applications that include hydrologic dataset construction, trend analysis of hydrologic fluxes and states, data evaluation and assimilation, forecasting, coupled climate modeling, and climate change impact assessment. Ongoing operational applications of the VIC model include the University of Washington's drought monitoring and forecasting systems and NASA's Land Data Assimilation System. This paper documents the development of VIC version 5 (VIC-5), which includes a major reconfiguration of the legacy VIC source code to support a wider range of modern hydrologic modeling applications. The VIC source code has been moved to a public GitHub repository to encourage participation by the broader user and developer communities. The reconfiguration has separated the core physics of the model from the driver source code, whereby the latter is responsible for memory allocation, preprocessing and post-processing, and input–output (I–O). VIC-5 includes four drivers that use the same core physics modules, but which allow for different methods for accessing this core to enable different model applications. Finally, VIC-5 is distributed with robust test infrastructure, components of which routinely run during development using cloud-hosted continuous integration. The work described here provides an example to the model development community for extending the life of a legacy model that is being used extensively. The development and release of VIC-5 represents a significant step forward for the VIC user community in terms of support for existing and new model applications, reproducibility, and scientific robustness.

Las recientes negociaciones del Tratado de Libre Comercio de America del Norte (TLCAN) entre Estados Unidos (EE.UU.), Mexico y Canada han hecho visible la relacion estrecha que existe entre la manufactura automotriz y el comercio de los productos agricolas. Desde su inicio, en 1994, el TLCAN ha transformado las condiciones socioeconomicas a lo largo de los 3,000 km de la frontera entre Mexico y EE.UU., en parte, por las inversiones grandes relacionadas con la infraestructura agricola y manufacturera. Si bien el TLCAN incluye disposiciones para la proteccion del medio ambiente en ciertos sectores, se ha prestado poca atencion al uso de los recursos naturales que han sido requeridos para la expansion economica. ?Que consecuencias ha tenido el TLCAN en los recursos de agua y uso del suelo en la frontera de EE.UU.-Mexico? Esta pregunta es de importancia vital para la region debido a su clima arido y al hecho de que la disponibilidad de agua esta en el centro de las actividades economicas.

For their investigation of the impact of irrigated agriculture on hydrometeorological fields in the North American monsoon (NAM) region, Mahalov et al. used the Weather Research and Forecasting (WRF) Model to simulate weather over the NAM region in the summer periods of 2000 and 2012, with and without irrigation applied to the regional croplands. Unfortunately, while the authors found that irrigated agriculture may indeed influence summer precipitation, the magnitude, location, and seasonality of their irrigation inputs were substantially inaccurate because of 1) the assumption that pixels classified as "irrigated cropland'' are irrigated during the summer and 2) an outdated land cover map that misrepresents known agricultural districts. The combined effects of these errors are 1) an overestimation of irrigated croplands by a factor of 3-10 along the coast of the Gulf of California and by a factor of 1.5 near the Colorado River delta and 2) a large underestimation of irrigation by a factor of 7-10 in Chihuahua, particularly in 2012. Given the sensitivity of the WRF simulations conducted by Mahalov et al. to the presence of irrigated agriculture, it is expected that the identified errors would significantly impact surface moisture and energy fluxes, resulting in noticeably different effects on precipitation. The authors suggest that the analysis of irrigation effects on precipitation using coupled land-atmospheric modeling systems requires careful specification of the spatiotemporal distribution of irrigated croplands.

Accelerated climate change and associated forest disturbances in the southwestern USA are anticipated to have substantial impacts on regional water resources. Few studies have quantified the impact of both climate change and land cover disturbances on water balances on the basin scale, and none on the regional scale. In this work, we evaluate the impacts of forest disturbances and climate change on a headwater basin to the Colorado River, the San Juan River watershed, using a robustly calibrated (Nash–Sutcliffe efficiency 0.76) hydrologic model run with updated formulations that improve estimates of evapotranspiration for semi-arid regions. Our results show that future disturbances will have a substantial impact on streamflow with implications for water resource management. Our findings are in contradiction with conventional thinking that forest disturbances reduce evapotranspiration and increase streamflow. In this study, annual average regional streamflow under the coupled climate–disturbance scenarios is at least 6–11 % lower than those scenarios accounting for climate change alone; for forested zones of the San Juan River basin, streamflow is 15–21 % lower. The monthly signals of altered streamflow point to an emergent streamflow pattern related to changes in forests of the disturbed systems. Exacerbated reductions of mean and low flows under disturbance scenarios indicate a high risk of low water availability for forested headwater systems of the Colorado River basin. These findings also indicate that explicit representation of land cover disturbances is required in modeling efforts that consider the impact of climate change on water resources.

The Colorado River Basin is a fundamentally important river for society, ecology, and energy in the United States. Streamflow estimates are often provided using modeling tools which rely on uncertain parameters; sensitivity analysis can help determine which parameters impact model results. Despite the fact that simulated flows respond to changing climate and vegetation in the basin, parameter sensitivity of the simulations under climate change has rarely been considered. In this study, we conduct a global sensitivity analysis to relate changes in runoff, evapotranspiration, snow water equivalent, and soil moisture to model parameters in the Variable Infiltration Capacity (VIC) hydrologic model. We combine global sensitivity analysis with a space-filling Latin Hypercube Sampling of the model parameter space and statistical emulation of the VIC model to examine sensitivities to uncertainties in 46 model parameters following a variance-based approach. We find that snow-dominated regions are much more sensitive to uncertainties in VIC parameters. Although baseflow and runoff changes respond to parameters used in previous sensitivity studies, we discover new key parameter sensitivities. For instance, changes in runoff and evapotranspiration are sensitive to albedo, while changes in snow water equivalent are sensitive to canopy fraction and Leaf Area Index (LAI) in the VIC model. It is critical for improved modeling to narrow uncertainty in these parameters through improved observations and field studies. This is important because LAI and albedo are anticipated to change under future climate and narrowing uncertainty is paramount to advance our application of models such as VIC for water resource management.

Developing interdisciplinary methods to measure ecosystem services is a scientific priority, however, progress remains slow in part because we lack ecological production functions (EPFs) to quantitatively link ecohydrological processes to human benefits. In this study, we tested a new approach, combining a process‐based model with regression models, to create EPFs to evaluate water storage and local climate regulation from a green infrastructure project on the Yongding River in Beijing, China. Seven artificial lakes and wetlands were established to improve local water storage and human comfort; evapotranspiration (ET) regulates both services. Managers want to minimize the trade‐off between water losses and cooling to sustain water supplies while lowering the heat index (HI) to improve human comfort. We selected human benefit indicators using water storage targets and Beijing's HI, and the Variable Infiltration Capacity model to determine the change in ET from the new ecosystems. We created EPFs to quantify the ecosystem services as marginal values [Δfinal ecosystem service/Δecohydrological process]: (1) Δwater loss (lake evaporation/volume)/Δdepth and (2) Δsummer HI/ΔET. We estimate the new ecosystems increased local ET by 0.7 mm/d (20.3 W/m2) on the Yongding River. However, ET rates are causing water storage shortfalls while producing no improvements in human comfort. The shallow lakes/wetlands are vulnerable to drying when inflow rates fluctuate, low depths lead to higher evaporative losses, causing water storage shortfalls with minimal cooling effects. We recommend managers make the lakes deeper to increase water storage, and plant shade trees to improve human comfort in the parks.