Search

We develop a novel framework for rigorously evaluating land surface models (LSMs) against observations by recognizing the asymmetry between verification‐ and falsification‐oriented approaches. The former approach cannot completely verify LSMs even though it exhausts every case of consistency between the model predictions and observations, whereas the latter only requires a single case of inconsistency to reveal that there must be something wrong. We argue that it is such an inconsistency that stimulates further development of the models and enhancement of the observations. We therefore propose a falsification‐oriented signature‐based evaluation framework to identify cases of inconsistency between model predictions and observations by extracting signatures based on a set of key assumptions. We apply this framework to evaluate an ensemble of simulations from the Noah‐MP LSM against observations over the continental United States under the three assumptions of water mass conservation, no lateral water flow, and a sufficiently long period of time. Regions showing inconsistencies between the Noah‐MP ensemble simulations and the observations are located in the western mountainous areas, the Yellowstone river basin, the lower Floridan aquifer, the Niobrara river basin at the north tip of the Ogallala aquifer, and the basins downstream of the Balcones fault zones in Texas. These regions coincide with the sites where both advances in theoretical modeling and new observational data (e.g., from the Critical Zone Observatories) have emerged. Plain Language Summary: We propose a framework for locating regions that require substantial efforts in modeling and observations. The framework is based on the fact that a single case of inconsistency between model predictions and observations always indicates that there must be something wrong. Such an consistency can stimulate and guide further development of models and enhancement of observations. An application over the continental United States shows that the identified regions encompass the areas where both theoretical modeling advancements and observational data from the Critical Zone Observatories have emerged. The results also suggest the need to extend the observatories over the Balcones fault zones in Texas and the Floridan aquifer in Florida. Key Points: A novel framework of falsification‐oriented signature‐based evaluation is proposedThe framework is demonstrated to reveal the regions of inconsistencies between model predictions and observationsThe identified regions coincide with the sites where both theoretical modeling advancements and new observational data (e.g., from the Critical Zone Observatories) have emerged [ABSTRACT FROM AUTHOR]

Long‐range water planning is complicated by factors that are rapidly changing in the 21st century, including climate, population, and water use. Here, we analyze climate factors and drought projections for Texas as an example of a diverse society straddling an aridity gradient to examine how the projections can best serve water stakeholder needs. We find that climate models are robust in projecting drying of summer‐season soil moisture and decreasing reservoir supplies for both the eastern and western portions of Texas during the 21st century. Further, projections indicate drier conditions during the latter half of the 21st century than even the most arid centuries of the last 1,000 years that included megadroughts. To illustrate how accounting for drought nonstationarity may increase water resiliency, we consider generalized case studies involving four key stakeholder groups: agricultural producers, large surface water suppliers, small groundwater management districts, and regional water planning districts. We also examine an example of customized climate information being used as input to long‐range water planning. We find that while stakeholders value the quantitative capability of climate model outputs, more specific climate‐related information better supports resilience planning across multiple stakeholder groups. New suites of tools could provide necessary capacity for both short‐ and long‐term, stakeholder‐specific adaptive planning. Key Points: Water stakeholders should prepare for future droughts that will be unlike past droughtsInformation available from climate projections often does not align with the detailed information needed for water planningBetter awareness of the mismatch between available and needed information will help inform efforts to close this gap [ABSTRACT FROM AUTHOR]

This work introduced a method to study river flow variability in response to climate change by using remote sensing precipitation data, downscaled climate model outputs with bias corrections, and a land surface model. A meteorological forcing dataset representing future climate was constructed via the delta change method in which the modeled change was added to the present-day conditions. The delta change was conducted at a fine spatial and temporal scale to contain the signals of weather events, which exhibit substantial responses to climate change. An empirical transformation technique was further applied to the constructed forcing to ensure a realistic range. The meteorological forcing was then used to drive the land surface model to simulate the future river flow. The results show that preserving fine-scale processes in response to climate change is a necessity to assess climatic impacts on the variability of river flow events. [ABSTRACT FROM AUTHOR]

We evaluate the sensitivity of biogenic emissions simulated by a land-surface model (LSM) to different representations of land-cover vegetation. We drive the community land model on a 0.1° grid over Texas, USA, from 1993 to 1998 using bilinearly interpolated North American Regional Reanalysis data. Two land-cover datasets provide the starting point for analysis: (1) a satellite-derived vegetation and soil-color database and (2) a vegetation-distribution dataset derived from ground surveys. These datasets help us to qualitatively characterize the uncertainty in land-cover representations. We systematically vary the datasets to examine the sensitivity of modeled emissions to variation in representation of bare-soil fraction, vegetation-type distribution, and phenology. Different datasets’ representation of vegetation-type distribution leads to simulated mean statewide total biogenic emissions that vary by a factor of 3. Variation in specified bare-soil fraction causes simulated statewide average emissions that vary by a factor of 1.7. Scaling leaf area index values within reasonable bounds causes a near-linear change in simulated emissions. Differences in simulated values are the largest for major metropolitan regions and for eastern and central Texas, where biogenic emissions are the highest and where tropospheric ozone pollution is a significant concern. Changing bare-soil fraction alters simulated vegetation temperature and consequently indirectly affects modeled emissions (⩽16% of inherent emissions capacity). Our estimates of the model sensitivity to land-cover representation are consistent with those for other regions. [Copyright &y& Elsevier]

Abstract: This paper introduces a method to incorporate species-based variation of the emission of biogenic volatile organic compounds (BVOCs) into regional climate and weather models. We convert a species-based land-cover database for Texas into a database compatible with the community land model (CLM) and a database compatible with the Noah land-surface model (LSM). We link the LSM-compatible land-cover databases to the original species-based data set as a means to derive region-specific BVOC emission capacities for each plant functional type (in the CLM database) and for each land-cover type (in the Noah database). The spatial distribution of inherent BVOC flux (defined as the product of the BVOC emission capacity and the leaf biomass density) derived using the Texas-specific BVOC emission capacities is well correlated with the spatial distribution of inherent BVOC flux calculated using the original species data (). The mean absolute error for the emission-capacity-derived inherent flux distribution is an order of magnitude lower than the statewide range of inherent fluxes. The ground-referenced land-cover databases derived here are likely more accurate than their satellite-derived counterparts; they can be used for a variety of regional model simulations in Texas. The inherent BVOC flux distributions derived using region-specific BVOC emission capacities are more consistent with observations than the BVOC flux distribution derived using the CLM3 standard BVOC emission capacities, which are top-down estimates based on the literature. When used in conjunction with detailed land-cover data sets, region-specific BVOC emission capacities produce reasonably accurate inherent BVOC fluxes. [Copyright &y& Elsevier]

This study investigates how gross primary production (GPP) estimates can be improved with the use of solar-induced chlorophyll fluorescence (SIF) based on the interdependence between SIF, precipitation, soil moisture and GPP itself. We have used multi-year datasets from Global Ozone Monitoring Experiment-2 (GOME-2), Tropical Rainfall Measuring Mission (TRMM), European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM), and FLUXNET observations from ten stations in the continental United States. We have employed a GPP quantification framework that makes use of two factors whose influence on the SIF–GPP relationship was not evaluated previously—namely, differential plant sensitivity to water supply at different stages of its lifecycle and spatial variability patterns in SIF that are in contrast to those of GPP, precipitation, and soil moisture. It was found that over the Great Plains and Texas, fluorescence emission levels lag behind precipitation events from about two weeks for grasses to four weeks for crops. The spatial variability of SIF and GPP is shown to be characterized by different patterns: SIF demonstrates less variation over the same spatial extent as compared to GPP, precipitation and soil moisture. Thus, using newly introduced SIF–precipitation lead–lag relationships, we estimate GPP using SIF, precipitation and soil moisture data for grasses and crops over the US by applying the multiple linear regression technique. Our GPP estimates capture the drought impact over the US better than those from Moderate Resolution Imaging Spectroradiometer (MODIS). During the drought year of 2011 over Texas, our GPP values show a decrease by 50–75 gC/m 2 /month, as opposed to the normal yielding year of 2007. In 2012, a drought year over the Great Plains, we observe a significant reduction in GPP, as compared to 2007. Hence, estimating GPP using specific SIF–GPP relationships, and information on different plant functional types (PFTs) and their interactions with precipitation and soil moisture over the Great Plains and Texas regions can help produce more reasonable GPP estimates. [ABSTRACT FROM AUTHOR]

• Identifying agricultural drought in Texas during 2010-2013 by joint assimilation of TB and TWS. • Multi-source observation assimilation improves the estimation of soil moisture profile. • Improvement for soil moisture at deep layers is mainly due to the assimilation of TWS. • Soil moisture with assimilation indicate more severe drought in spatial extent and magnitude. Texas experienced an extreme drought on record in 2011. Model simulations or satellite observations have been used to assess and analyze the drought. In this study, a method based on multi-source remote sensing data assimilation is proposed to evaluate the drought in Texas, which combines the advantages of model simulations and satellite observations. Brightness temperature from the Soil Moisture and Ocean Salinity (SMOS), the Advanced Microwave Scanning Radiometer for EOS/Advanced Microwave Scanning Radiometer 2 (AMSR-E/AMSR2) and terrestrial water storage anomalies from the Gravity Recovery and Climate Experiment (GRACE) are assimilated into the Noah-Multiparameterization model (Noah-MP) to improve the soil moisture and drought estimation. The Ensemble Kalman Filter (EnKF) was used as a data assimilation method with observation aggregation (dealing with the multi-resolution observations) and observation bias correction (dealing with the systematic bias between observations and simulations). Data from ground stations and satellites are used for the validation of soil moisture, terrestrial water storage anomalies, and evapotranspiration. With assimilation, slight improvement can be found for surface soil moisture over the open-loop configuration, but some notable degradations are also observed. More obvious improvement can be found at deep layers, which is mainly due to the assimilation of terrestrial water storage anomalies. In addition, the improvement for terrestrial water storage anomalies and evapotranspiration is more statistically significant in most regions of Texas. Overall, the assimilation experiment plays a positive role in the estimation of essential variables relate to drought. The drought is estimated using the soil moisture percentile and classified into five categories. The results from the assimilation experiment tend to predict a more severe drought in Texas for both magnitude and spatial patterns, which indicates not only the well-known drought in 2011 but also the continuous severe drought in 2012 and 2013. Moreover, the spatial distribution of drought reveals that drought occurred more frequently in the North, West, and Southwest. [ABSTRACT FROM AUTHOR]

This article presents a methodology aimed at improving mid-term power system resilience at transmission substations in areas potentially affected by floods, combining hardening strategies and quantitative metrics. It takes into account flood forecasts from a hydrological model and the location of electrical equipment to perform impact assessment "as is" and with resilience planning strategies. Thus, the impact of floods on the grid is evaluated over a range of realistic flood scenarios, based on the accumulated cost and load energy unserved as metrics together with future transmission system expansion capacity projections. The mixed-integer linear programming formulation is aimed at minimizing accumulated cost and load energy unserved with optimal hardening of substations, assuming that any non-hardened substation disabled by flooding must be repaired. Furthermore, the methodology is demonstrated in the coastal area of Texas with simulations of floods based on the rainfall of Hurricane Harvey in 2017. Ultimately, the choice of the most appropriate mitigation strategies shall optimize resilience metrics and/or cost indicators with robustness over a range of scenarios. • Mid-term power system resilience improvements to floods at transmission substations. • Impact assessment considering hydrological model and location of electrical equipment. • Accumulated cost and load energy unserved used as metrics separately. • Mixed-integer linear programming formulation for optimal hardening of substations. • Case study: floods based on Hurricane Harvey's rainfall in the coastal area of Texas. [ABSTRACT FROM AUTHOR]