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Abstract. We apply airborne measurements across three seasons(summer, winter and spring 2017–2018) in a multi-inversion framework toquantify methane emissions from the US Corn Belt and Upper Midwest, a keyagricultural and wetland source region. Combing our seasonal results withprior fall values we find that wetlands are the largest regional methanesource (32 %, 20 [16–23] Gg/d), while livestock (enteric/manure; 25 %,15 [14–17] Gg/d) are the largest anthropogenic source. Naturalgas/petroleum, waste/landfills, and coal mines collectively make up theremainder. Optimized fluxes improve model agreement with independentdatasets within and beyond the study timeframe. Inversions reveal coherentand seasonally dependent spatial errors in the WetCHARTs ensemble meanwetland emissions, with an underestimate for the Prairie Pothole region butan overestimate for Great Lakes coastal wetlands. Wetland extent andemission temperature dependence have the largest influence on predictionaccuracy; better representation of coupled soil temperature–hydrologyeffects is therefore needed. Our optimized regional livestock emissionsagree well with the Gridded EPA estimates during spring (to within 7 %) butare ∼ 25 % higher during summer and winter. Spatial analysisfurther shows good top-down and bottom-up agreement for beef facilities (withmainly enteric emissions) but larger (∼ 30 %) seasonaldiscrepancies for dairies and hog farms (with > 40 % manureemissions). Findings thus support bottom-up enteric emission estimates butsuggest errors for manure; we propose that the latter reflects inadequatetreatment of management factors including field application. Overall, ourresults confirm the importance of intensive animal agriculture for regionalmethane emissions, implying substantial mitigation opportunities throughimproved management. 

Abstract Large‐scale models often use a single grid to represent an entire catchment assuming homogeneity; the impacts of such an assumption on simulating evapotranspiration (ET) and streamflow remain poorly understood. Here, we compare hydrological dynamics at Shale Hills (PA, USA) using a complex model (spatially explicit, >500 grids) and a simple model (spatially implicit, two grids using “effective” parameters). We asked two questions:What hydrological dynamics can a simple model reproduce at the catchment scale? What processes does it miss by ignoring spatial details?Results show the simple model can reproduce annual runoff ratios and ET, daily discharge peaks (e.g., storms, floods) but not discharge minima (e.g., droughts) under dry conditions. Neither can it reproduce different streamflow from the two sides of the catchment with distinct land surface characteristics. The similar annual runoff ratios between the two models indicate spatial details are not as important as climate in reproducing annual scale ET and discharge partitioning. Most of the calibrated parameters in the simple model are within the ranges in the complex model, except that effective porosity has to be reduced to 40% of the average porosity from the complex model. The form of the storage‐discharge relationship is similar. The effective porosity in the simple model however represents the dynamic and mobile water storage in the effective drainage area of the complex model that connects to the stream and contributes to high streamflow; it does not represent the passive, immobile water storage in the often disconnected uphill areas. This indicates that an additional uphill functioning unit is needed in the simple model to simulate the full spectrum of high‐low streamflow dynamics in natural catchments.