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

Effects of Stress from Mine Drainage on Ecosystem Functions in Rocky Mountain Streams

Research in this dissertation tested a hypothesis that relates biodiversity, community biomass, and ecosystem function to a gradient of stress. Biodiversity was predicted to have a low threshold of response to stress, while biomass and function were predicted to be stable or increase under low to moderate stress and decrease only under high stress. This hypothesis was tested by examination of biological communities and ecosystem functions in mountain streams under stress from mine drainage. Mine drainage presents both chemical (low pH, dissolved metals) and physical (deposition of metal oxides) stresses on stream biota

Agriculture intensifies soil moisture decline in Northern China

Northern China is one of the most densely populated regions in the world. Agricultural activities have intensified since the 1980s to provide food security to the country. However, this intensification has likely contributed to an increasing scarcity in water resources, which may in turn be endangering food security. Based on in-situ measurements of soil moisture collected in agricultural plots during 1983–2012, we find that topsoil (0–50cm) volumetric water content during the growing season has declined significantly (p < 0.01), with a trend of −0.011 to −0.015 m3 m−3 per decade. Observed discharge declines for the three large river basins are consistent with the effects of agricultural intensification, although other factors (e.g. dam constructions) likely have contributed to these trends. Practices like fertilizer application have favoured biomass growth and increased transpiration rates, thus reducing available soil water. In addition, the rapid proliferation of water-expensive crops (e.g., maize) and the expansion of the area dedicated to food production have also contributed to soil drying. Adoption of alternative agricultural practices that can meet the immediate food demand without compromising future water resources seem critical for the sustainability of the food production system

How Stream Fungal Biodiversity Affects Ecosystem Functions

There was an observable impact on leaf decomposition and respiration rate. Respiration was much higher among the biodiverse microcosm compared to the other treatments, and the biodiverse treatment had the least amount of biomass remaining

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

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

Machine Learning and Dynamics based Error-Index Method for the Detection of Monsoon Onset Vortex over the Arabian Sea: Climatology and Composite Structures

Monsoon onset vortex (MOV) forms over the Arabian Sea near the northern flank of the low-level jet during the monsoon onset over Kerala (MOK). the study concerns the development and evaluation of an algorithm for detecting and tracking MOVs in regional/global analyses. the first step involves preparing the first-guess database of MOV locations based on geopotential height, surface and 850 hPa wind magnitude and circulation from ERA5 reanalysis for 1982–2020. Three different approaches: (a) error-index of MOV, (b) machine-learning (ML), and (c) combination of error-index and ML models, are employed to detect MOV. the error-index method, in which the detected vortex is compared with the idealized vortex, achieves an accuracy of 0.6 with a 0.95 true-positive-rate and 0.55 false-positive-rate. the best ML models can identify the MOVs in the training samples with maximum accuracy of 0.99. However, their accuracy is limited in tracking the MOVs continuously in the global analyses as they are not trained with wind circulation. the combined error-index and ML models could detect all the 27 observed MOVs in the ERA5 reanalysis with 5 false-positives. This approach is tested on IMDAA reanalysis, and the success rate is 0.79 with 6 false positives. Temporal analyses show that ∼95% of the MOVs occur during −10 to +20 days from MOK. Composite structures indicate that the MOVs exhibit higher sea-surface temperatures (\u3e 0.3 °C) in the forward sector with 85% cloud cover in the left-rear sector. Rainfall of 4–5 mm·hr−1 is seen in the left sector. Upper-level (700–200 hPa) warm core (\u3e 3.5°C) and lower-level (1000–700 hPa) cold-core (\u3c 1°C) is evident. the composite structures of MOVs are almost similar to that of monsoon depressions with higher asymmetry in the forward-rear sectors. This study may help explore future projections of MOV activity from climate models and its relationship with monsoon rainfall activity

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

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