Abstract. The diversity in hydrologic models has historically led to great controversy on the "correct" approach to processbased hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.

This study evaluates regional‐scale hydrological simulations of the newly developed community Noah land surface model (LSM) with multiparameterization options (Noah‐MP). The model is configured for the Mississippi River Basin and driven by the North American Land Data Assimilation System Phase 2 atmospheric forcing at 1/8° resolution. The simulations are compared with various observational data sets, including the U.S. Geological Survey streamflow and groundwater data, the AmeriFlux tower micrometeorological evapotranspiration (ET) measurements, the Soil Climate Analysis Network (SCAN)‐observed soil moisture data, and the Gravity Recovery and Climate Experiment satellite‐derived terrestrial water storage (TWS) anomaly data. Compared with these observations and to the baseline Noah LSM simulations, Noah‐MP shows significant improvement in hydrological modeling for major hydrological variables (runoff, groundwater, ET, soil moisture, and TWS), which is very likely due to the incorporation of some major improvements into Noah‐MP, particularly an unconfined aquifer storage layer for groundwater dynamics and an interactive vegetation canopy for dynamic leaf phenology. Noah‐MP produces soil moisture values consistent with the SCAN observations for the top two soil layers (0–10 cm and 10–40 cm), indicating its great potential to be used in studying land‐atmosphere coupling. In addition, the simulated groundwater spatial patterns are comparable to observations; however, the inclusion of groundwater in Noah‐MP requires a longer spin‐up time (34 years for the entire study domain). Runoff simulation is highly sensitive to three parameters: the surface dryness factor (α), the saturated hydraulic conductivity (k), and the saturated soil moisture (θmax).

This study assesses the hydrologic performance of four land surface models (LSMs) for the conterminous United States using the North American Land Data Assimilation System (NLDAS) test bed. The four LSMs are the baseline community Noah LSM (Noah, version 2.8), the Variable Infiltration Capacity (VIC, version 4.0.5) model, the substantially augmented Noah LSM with multiparameterization options (hence Noah-MP), and the Community Land Model version 4 (CLM4). All four models are driven by the same NLDAS-2 atmospheric forcing. Modeled terrestrial water storage (TWS), streamflow, evapotranspiration (ET), and soil moisture are compared with each other and evaluated against the identical observations. Relative to Noah, the other three models offer significant improvements in simulating TWS and streamflow and moderate improvements in simulating ET and soil moisture. Noah-MP provides the best performance in simulating soil moisture and is among the best in simulating TWS, CLM4 shows the best performance in simulating ET, and VIC ranks the highest in performing the streamflow simulations. Despite these improvements, CLM4, Noah-MP, and VIC exhibit deficiencies, such as the low variability of soil moisture in CLM4, the fast growth of spring ET in Noah-MP, and the constant overestimation of ET in VIC.

Accurate simulation of energy, water, and carbon fluxes exchanging between the land surface and the atmosphere is beneficial for improving terrestrial ecohydrological and climate predictions. We systematically assessed the Noah land surface model (LSM) with mutiparameterization options (Noah‐MP) in simulating these fluxes and associated variations in terrestrial water storage (TWS) and snow cover fraction (SCF) against various reference products over 18 United States Geological Survey two‐digital hydrological unit code regions of the continental United States (CONUS). In general, Noah‐MP captures better the observed seasonal and interregional variability of net radiation, SCF, and runoff than other variables. With a dynamic vegetation model, it overestimates gross primary productivity by 40% and evapotranspiration (ET) by 22% over the whole CONUS domain; however, with a prescribed climatology of leaf area index, it greatly improves ET simulation with relative bias dropping to 4%. It accurately simulates regional TWS dynamics in most regions except those with large lakes or severely affected by irrigation and/or impoundments. Incorporating the lake water storage variations into the modeled TWS variations largely reduces the TWS simulation bias more obviously over the Great Lakes with model efficiency increasing from 0.18 to 0.76. Noah‐MP simulates runoff well in most regions except an obvious overestimation (underestimation) in the Rio Grande and Lower Colorado (New England). Compared with North American Land Data Assimilation System Phase 2 (NLDAS‐2) LSMs, Noah‐MP shows a better ability to simulate runoff and a comparable skill in simulating Rn but a worse skill in simulating ET over most regions. This study suggests that future model developments should focus on improving the representations of vegetation dynamics, lake water storage dynamics, and human activities including irrigation and impoundments.

No abstract