Hydrological assessment and biogeochemical advancement of the Noah-MP land surface model

textLand surface models (LSMs) simulate the energy, momentum, water, and carbon balance of the soil-vegetation-atmosphere system. As a key component of weather and climate models, LSMs play an important role in weather prediction and climate projections. Rapid growth in LSM development has resulted in both the improvement of existing process representation and the addition of new processes and functionalities. However, it is a challenge to evaluate the accuracy of energy, water, and nutrient fluxes simulated by LSMs, due to the lack of observational data and the complexity of interactions and feedback among different processes. Additionally, climate and terrestrial biosphere models consider nitrogen an important factor in limiting plant carbon uptake, while operational environmental models view nitrogen as the leading nutrient for causing eutrophication in water bodies. However, few LSMs include nitrogen dynamics and nitrogen leaching is usually not well parameterized; hence these LSMs are not feasible for applications-based modeling, particularly for land management and agricultural impacts. Therefore, this dissertation uses the Noah-MP LSM to study the following three questions. (1) How do recent developments in Noah-MP improve its performance in hydrological modeling, based on a case study for the Mississippi River Basin? (2) Compared to other similar LSMs, what are the advantages and disadvantages of Noah-MP in assessing the water balance over the conterminous U.S.? (3) After coupling the Fixation and Uptake of Nitrogen plant model and the Soil and Water Assessment Tool soil nitrogen dynamics into Noah-MP, can this coupled model characterize the major nitrogen fluxes and how the nitrogen dynamics affect the carbon and water simulations? The main scientific findings are as follows. (1) Noah-MP shows significant improvement in modeling the major hydrological variables such as runoff, groundwater, evapotranspiration, soil moisture, and terrestrial water storage (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. (2) Compared to other three LSMs, Noah-MP provides the best performance in simulating soil moisture and is among the best in simulating TWS. (3) The new Noah-MP with nitrogen dynamics performs well in capturing the major nitrogen state/flux variables (e.g., soil nitrate and nitrate leaching). (4) The addition of nitrogen dynamics in Noah-MP improves the modeling of the carbon and water cycles (e.g., net primary productivity and evapotranspiration).Geological Science

An initial assessment of SMAP soil moisture retrievals using high-resolution model simulations and in situ observations

At the end of its first year of operation, we compare soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission to simulations from a land surface model with meteorological forcing downscaled from observations/reanalysis and in situ observations from sparse monitoring networks within continental United States (CONUS). The radar failure limits the duration of comparisons for the active and combined products (~3 months). Nevertheless, the passive product compares very well against in situ observations over CONUS. On average, SMAP compares to the in situ data even better than the land surface model and provides significant added value on top of the model and thus good potential for data assimilation. At large scale, SMAP is in good agreement with the model in most of CONUS with less-than-expected degradation over mountainous areas. Lower correlation between SMAP and the model is seen in the forested east CONUS and significantly lower over the Canadian boreal forests.United States. National Aeronautics and Space Administration (NNX14AH92G)United States. National Aeronautics and Space Administration (NNX13AI44G

Development and evaluation of a physically-based lake level model for water resource management: A case study for Lake Buchanan, Texas

AbstractStudy regionLake Buchanan, a major reservoir for the City of Austin area, the Texas Hydrologic Region 12, USA.Numerical climate models are increasingly being used by climate scientists to inform water management. However, successful transitions from climate models (O(10–100km)) to water resources studies (O(100m–1km)) still need improved data structures and modeling strategies to resolve spatial scale mismatch. In this study, we introduce a mechanistic lake-level modeling framework that consists of a state-of-the-art land surface model – Noah-MP, a vector-based river routing scheme – RAPID, and a lake mass-balance model. By conducting a case study for Lake Buchanan, we demonstrate the capability of the framework in predicting lake levels at seasonal lead times. The experiments take into account different runoff resolutions, model initialization months, and multiple lead times. Uncertainty analyses and sensitivity tests are also conducted to guide future research.New hydrological insightsDifferent from traditional grid-based solutions, the framework is directly coupled on the vector-based NHDPlus dataset, which defines accurate hydrologic features such as rivers, dams, lakes and reservoirs. The resulting hybrid framework therefore allows for more flexibility in resolving “scaling-issues” between large-scale climate models and fine-scale applications. The presented hindcast results also provide insight into the influences of baseline LSM resolutions, initialization months, and lead times, which would ultimately help improve lake-level forecast skills

Exploring the Individualized Effect of Climatic Drivers on MODIS Net Primary Productivity through an Explainable Machine Learning Framework

Along with the development of remote sensing technology, the spatial–temporal variability of vegetation productivity has been well observed. However, the drivers controlling the variation in vegetation under various climate gradients remain poorly understood. Identifying and quantifying the independent effects of driving factors on a natural process is challenging. In this study, we adopted a potent machine learning (ML) model and an ML interpretation technique with high fidelity to disentangle the effects of climatic variables on the long-term averaged net primary productivity (NPP) across the Amazon rainforests. Specifically, the eXtreme Gradient Boosting (XGBoost) model was employed to model the Moderate-resolution Imaging Spectroradiometer (MODIS) NPP data, and the Shapley addictive explanation (SHAP) method was introduced to account for nonlinear relationships between variables identified by the model. Results showed that the dominant driver of NPP across the Amazon forests varied in different regions, with temperature dominating the most considerable portion of the ecoregion with a high importance score. In addition, light augmentation, increased CO2 concentration, and decreased precipitation positively contributed to Amazonia NPP. The wind speed for most vegetated areas was under the optimum, which benefits NPP, while sustained high wind speed would bring substantial NPP loss. We also found a non-monotonic response of Amazonia NPP to VPD and attributed this relationship to the moisture load in Amazon forests. Our application of the explainable machine learning framework to identify the underlying physical mechanism behind NPP could be a reference for identifying relationships between components in natural processes

Simulation and Driving Factor Analysis of Satellite-Observed Terrestrial Water Storage Anomaly in the Pearl River Basin Using Deep Learning

Accurate estimation of terrestrial water storage (TWS) and understanding its driving factors are crucial for effective hydrological assessment and water resource management. The launches of the Gravity Recovery and Climate Experiment (GRACE) satellites and their successor, GRACE Follow-On (GRACE-FO), combined with deep learning algorithms, have opened new avenues for such investigations. In this study, we employed a long short-term memory (LSTM) neural network model to simulate TWS anomaly (TWSA) in the Pearl River Basin (PRB) from 2003 to 2020, using precipitation, temperature, runoff, evapotranspiration, and leaf area index (LAI) data. The performance of the LSTM model was rigorously evaluated, achieving a high average correlation coefficient (r) of 0.967 and an average Nash–Sutcliffe efficiency (NSE) coefficient of 0.912 on the testing set. To unravel the relative importance of each driving factor and assess the impact of different lead times, we employed the SHapley Additive exPlanations (SHAP) method. Our results revealed that precipitation exerted the most significant influence on TWSA in the PRB, with a one-month lead time exhibiting the greatest impact. Evapotranspiration, runoff, temperature, and LAI also played important roles, with interactive effects among these factors. Moreover, we observed an accumulation effect of precipitation and evapotranspiration on TWSA, particularly with shorter lead times. Overall, the SHAP method provides an alternative approach for the quantitative analysis of natural driving factors at the basin scale, shedding light on the natural dominant influences on TWSA in the PRB. The combination of satellite observations and deep learning techniques holds promise for advancing our understanding of TWS dynamics and enhancing water resource management strategies

The Reliability of Global Remote Sensing Evapotranspiration Products over Amazon

As a key component of terrestrial water cycle, evapotranspiration (ET), specifically over the Amazon River basin, is of high scientific significance. However, due to the sparse observation network and relatively short observational period of eddy covariance data, large uncertainties remain in the spatial-temporal characteristics of ET over the Amazon. Recently, a great number of long-term global remotely sensed ET products have been developed to fill the observation gap. However, the reliabilities of these global ET products over the Amazon are unknown. In this study, we assessed the consistency of the magnitude, trend and spatial pattern of Amazon ET among five global remotely sensed ET reconstructions. The magnitudes of these products are similar but the long-term trends from 1982 to 2011 are completely divergent. Validation from the eddy covariance data and water balance method proves a better performance of a product grounded on local measurements, highlighting the importance of local measurements in the ET reconstruction. We also examined four hypotheses dealing with the response of ET to brightening, warming, greening and deforestation, which shows that in general, these ET products respond better to warming and greening than to brightening and deforestation. This large uncertainty highlights the need for future studies focusing on ET issues over the Amazon

Global terrestrial stilling: does Earth's greening play a role?

International audiencePrevious studies have documented that surface wind speed (u) has been increasing over the ocean but decreasing over land for the past several decades. The decreasing u at the surface over land has been referred to as terrestrial stilling. A plausible hypothesis for terrestrial stilling is an increase in surface roughness associated with changes in land surface (e.g. enhanced vegetation growth, landscape fragmentation or urbanization). One of the most widespread land surface changes is enhanced vegetation leaf area index (LAI) known as greening, particularly over the middle to high latitudes of the Northern Hemisphere where strong stilling is observed from weather station data. In this study, we examine the hypothesis that enhanced vegetation LAI is a key driver of global terrestrial stilling. We first characterized the trend in u over the ocean using long-term satellite altimeter measurements, and the trend in u over land using continuous wind records from 4305 in situ meteorological stations. We then performed initial condition ensemble Atmospheric Model Intercomparison Project-type simulations using two state-of-the-art Earth system models (IPSL-CM and CESM) to isolate the response of u to the historical increase in LAI (representing the greening) for the period 1982-2011. Both models, forced with observed sea surface temperature and sea ice and with LAI from satellite observation, captured the observed strengthening of Pacific trade winds and Southern Ocean westerly winds. However, these simulations did not reproduce the weakening of surface winds over land as significantly as it appears in the observations (−0.006 m s −1 versus −0.198 m s −1 during 1982-2011), indicating that enhanced LAI (greening) is not a dominant driver for terrestrial stilling

RCM Transponder: Transponder User Manual

The diversity in hydrologic models has historically led to great controversy on the "correct" approach to process-based 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

The evolution of process-based hydrologic models: Historical challenges and the collective quest for physical realism.

The diversity in hydrologic models has historically led to great controversy on the "correct" approach to process-based 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