Sediment Respiration Pulses in Intermittent Rivers and Ephemeral Streams

Intermittent rivers and ephemeral streams (IRES) may represent over half the global stream network, but their contribution to respiration and carbon dioxide (CO2) emissions is largely undetermined. In particular, little is known about the variability and drivers of respiration in IRES sediments upon rewetting, which could result in large pulses of CO2. We present a global study examining sediments from 200 dry IRES reaches spanning multiple biomes. Results from standardized assays show that mean respiration increased 32-fold to 66-fold upon sediment rewetting. Structural equation modeling indicates that this response was driven by sediment texture and organic matter quantity and quality, which, in turn, were influenced by climate, land use, and riparian plant cover. Our estimates suggest that respiration pulses resulting from rewetting of IRES sediments could contribute significantly to annual CO2 emissions from the global stream network, with a single respiration pulse potentially increasing emission by 0.2-0.7%. As the spatial and temporal extent of IRES increases globally, our results highlight the importance of recognizing the influence of wetting-drying cycles on respiration and CO2 emissions in stream network

Changes in moisture and energy fluxes due to agricultural land use and irrigation in the Indian Monsoon Belt

We present a conceptual synthesis of the impact that agricultural activity in India can have on land-atmosphere interactions through irrigation. We illustrate a “bottom up” approach to evaluate the effects of land use change on both physical processes and human vulnerability. We compared vapor fluxes (estimated evaporation and transpiration) from a pre-agricultural and a contemporary land cover and found that mean annual vapor fluxes have increased by 17% (340 km3) with a 7% increase (117 km3) in the wet season and a 55% increase (223 km3) in the dry season. Two thirds of this increase was attributed to irrigation, with groundwater-based irrigation contributing 14% and 35% of the vapor fluxes in the wet and dry seasons, respectively. The area averaged change in latent heat flux across India was estimated to be 9 Wm−2. The largest increases occurred where both cropland and irrigated lands were the predominant contemporary land uses

Lower Tropospheric Temperature Variability Over the USA: a GIS Approach

We use the high resolution North American Regional Analysis (NARR) dataset to build for the United States a Temperature Change Index (TCI) based on four contributing variables derived from the layer-averaged temperature and lapse rate of the 1000mb - 700mb layer (near-surface to 3000 meters) for the 1979-2008 period. The analysis uses Geographic Information Systems (GIS) methods to identify distinct regional patterns based on aggregate temperature trends and variability scores. The resulting index allows us to identify and compare regions that experience high (low) temperature trends and variability that are referred to as hot spots (cold spots). The upper Midwest emerges as the region that experiences the largest increases and variability, due to the large magnitude of variability and trends of all variables. In contrast, the lowest TCI scores are observed over southeastern regions and the Rocky Mountains. Regarding landscape characteristics, high TCI scores occur mostly over agricultural lands (thus implying the problem of temperature variability-dependant crop yields) while low scores generally prevail over forests. At a seasonal time scale, the largest and most contrasting TCI scores occur during the winter and, to a lesser extent, fall seasons. All variables used to build the TCI show well defined seasonal patterns and differences, especially between winter and summer. Our method, based on the use of thickness layers, provides a more complete analysis than methods based on monolevel data and confirms that temperature is a robust component of climate change in general and must be included in any study that deals with vulnerability assessment of climate change risks

CityTFT: Temporal Fusion Transformer for Urban Building Energy Modeling

Urban Building Energy Modeling (UBEM) is an emerging method to investigate urban design and energy systems against the increasing energy demand at urban and neighborhood levels. However, current UBEM methods are mostly physic-based and time-consuming in multiple climate change scenarios. This work proposes CityTFT, a data-driven UBEM framework, to accurately model the energy demands in urban environments. With the empowerment of the underlying TFT framework and an augmented loss function, CityTFT could predict heating and cooling triggers in unseen climate dynamics with an F1 score of 99.98 \% while RMSE of loads of 13.57 kWh

Calibration and Validation of the Hybrid-Maize Crop Model for Regional Analysis and Application over the U.S. Corn Belt

Detailed parameter sensitivity, model validation, and regional calibration of the Hybrid-Maize crop model were undertaken for the purpose of regional agroclimatic assessments. The model was run at both field scale and county scale. The county-scale study was based on 30-yr daily weather data and corn yield data from the National Agricultural Statistics Service survey for 24 locations across the Corn Belt of the United States. The field-scale study was based on AmeriFlux sites at Bondville, Illinois, andMead, Nebraska. By using the one-at-a-time and interaction-explicit factorial design approaches for sensitivity analysis, the study found that the five most sensitive parameters of the model were potential number of kernels per ear, potential kernel filling rate, initial light use efficiency, upper temperature cutoff for growing degree-days’ accumulation, and the grain growth respiration coefficient. Model validation results show that the Hybrid-Maize model performed satisfactorily for field-scale simulations with a mean absolute error (MAE) of 10 bu acre-1 despite the difficulties of obtaining hybrid-specific information. At the county scale, the simulated results, assuming optimal crop management, overpredicted the yields but captured the variability well. A simple regional adjustment factor of 0.6 rescaled the potential yield to actual yield well. These results highlight the uncertainties that exist in applying crop models at regional scales because of the limitations in accessing cropspecific information. This study also provides confidence that uncertainties can potentially be eliminated via simple adjustment factor and that a simple crop model can be adequately useful for regional-scale agroclimatic studies

Application of weather prediction models for hazard mitigation planning: a case study of heavy off-season rains in Senegal

Heavy off-season rains in the tropics pose significant natural hazards largely because they are unexpected and the popular infrastructure is ill-prepared. One such event was observed from January 9 to 11, 2002 in Senegal (14.00° N, 14.00°W), West Africa. This tropical country is characterized by a long dry season from November to April or May. During this period, although the rain-bearing monsoonal flow does not reach Senegal, the region can occasionally experience off-season rains. We conducted a numerical simulation of the January 9-11, 2002 heavy off-season rain using the Fifth-Generation NCAR/Pennsylvania State University Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) model. The objective was to delineate the meteorological set-up that led to the heavy rains and flooding. A secondary objective was to test the model's performance in Senegal using relatively simpler (default) model configurations and local/regional observations. The model simulations for both MM5 and WRF agree satisfactorily with the observations, particularly as regards the wind patterns, the intensification of the rainfall, and the associated drop in temperatures. This situation provided the environment for heavy rainfall accompanied by a cold wave. The results suggest that off-the-shelf weather forecast models can be applied with relatively simple physical options and modest computational resources to simulate local impacts of severe weather episodes. In addition, these models could become part of regional hazard mitigation planning and infrastructure

Litter Breakdown in Mountain Streams Affected by Mine Drainage: Biotic Mediation of Abiotic Controls

Breakdown of plant litter in streams was studied as an example of a major ecological process subject to change through multiple stresses associated with mine drainage. Rates of litter breakdown were measured at 27 sites in streams of the Rocky Mountains of Colorado, USA. Eight of the sites were pristine, and 19 were affected to varying degrees by mine drainage. The pH, concentrations of dissolved zinc, and deposition rates of metal oxides were measured in each stream. Rates of litter breakdown were estimated from changes in mass of willow leaves in litterbags. Biomass of shredding invertebrates in litterbags was monitored at each site, as was microbial respiration on litter. Of the abiotic variables, increased concentrations of zinc and increased deposition rates of metal oxides were most closely related to decreased rates of litter breakdown. Biomass of shredding invertebrates was negatively related to concentration of dissolved zinc and deposition of metal oxides and was more closely related to breakdown rates than was microbial respiration. Microbial respiration was related negatively to deposition rates of metal oxides and positively to nutrient concentrations. Shredder biomass and microbial respiration together accounted for 76% of the variation in breakdown rates. Remediation schemes for streams affected by mine drainage should take into account the distinct ecological effects of the multiple stresses caused by mine drainage (pH, high concentrations of dissolved metals, deposition of metal oxides); remediation of a single stress is likely to be ineffective

The Purdue Agro-climatic (PAC) Dataset for The U.S. Corn Belt: Development and Initial Results

This study is a result of a project titled ‘‘Useful to Usable (U2U): Transforming Climate Variability and Change Information for Cereal Crop Producers”. This paper responds to the project goal to improve farm resiliency and proftability in the U.S. Corn Belt region by transforming existing meteorological dataset into usable knowledge and tools for the agricultural community. A high-resolution agro-climatic dataset that covers the U.S. Corn Belt was built for the U2U project based on a Land Data Assimilation System (LDAS) framework. This data referred to as the Purdue Agro-climatic (PAC) dataset is a gridded, continuous dataset suitable for agrocli- matic and crop model studies over the U.S. Corn Belt. The dataset was created at 4 km, sub- daily spatiotemporal resolution and covers the period of 1981–2014. The dataset includes a range of variables such as daily maximum/minimum temperature, solar radiation, rainfall, evapotranspiration (ET), multilevel soil moisture and soil temperatures. The data were com- pared to feld measurements from Amerifux and the Soil Climate Analysis Network (SCAN), and with coarser but widely used atmospheric regional reanalysis data products. Validations indicate an overall good agreement between this dataset and feld measurements. The agree- ment is particularly high for radiation and temperature parameters and lesser for rainfall and soil moisture data. Despite the differences with observations, the data show improvements over the coarser resolution products and other available models and thus highlights the value of the dataset for agroclimatic and crop model studies. This high-resolution dataset is available to the wider community, and can fll gaps in observed data records and increase accessibility for the agricultural sector, and for conduct- ing variety of if-then assessments

Improved prediction of severe thunderstorms over the Indian Monsoon region using high-resolution soil moisture and temperature initialization

The hypothesis that realistic land conditions such as soil moisture/soil temperature (SM/ST) can significantly improve the modeling of mesoscale deep convection is tested over the Indian monsoon region (IMR). A high resolution (3 km foot print) SM/ST dataset prepared from a land data assimilation system, as part of a national monsoon mission project, showed close agreement with observations. Experiments are conducted with (LDAS) and without (CNTL) initialization of SM/ST dataset. Results highlight the significance of realistic land surface conditions on numerical prediction of initiation, movement and timing of severe thunderstorms as compared to that currently being initialized by climatological fields in CNTL run. Realistic land conditions improved mass flux, convective updrafts and diabatic heating in the boundary layer that contributed to low level positive potential vorticity. The LDAS run reproduced reflectivity echoes and associated rainfall bands more efficiently. Improper representation of surface conditions in CNTL run limit the evolution boundary layer processes and thereby failed to simulate convection at right time and place. These findings thus provide strong support to the role land conditions play in impacting the deep convection over the IMR. These findings also have direct implications for improving heavy rain forecasting over the IMR, by developing realistic land conditions

Design and Deployment of Photo2Building: A Cloud-based Procedural Modeling Tool as a Service

We present a Photo2Building tool to create a plausible 3D model of a building from only a single photograph. Our tool is based on a prior desktop version which, as described in this paper, is converted into a client-server model, with job queuing, web-page support, and support of concurrent usage. The reported cloud-based web-accessible tool can reconstruct a building in 40 seconds on average and costing only 0.60 USD with current pricing. This provides for an extremely scalable and possibly widespread tool for creating building models for use in urban design and planning applications. With the growing impact of rapid urbanization on weather and climate and resource availability, access to such a service is expected to help a wide variety of users such as city planners, urban meteorologists worldwide in the quest to improved prediction of urban weather and designing climate-resilient cities of the future.Comment: 7 pages, 7 figures, PEARC '20: Practice and Experience in Advanced Research Computing, July 26--30, 2020, Portland, OR, US