In the present work, the sensitivity of near-surface air temperature and building energy consumption to different rooftop mitigation strategies in the urban environment is evaluated by means of numerical simulations in idealized urban areas, covering a large spectra of possible urban structures, for typical summer and winter conditions. Rooftop mitigation stategies considered include cool roofs, green roofs and rooftop photovoltaic panels. In particular, the latter two rooftop technologies are simulated using two novel parameterization schemes, incorporated in the mesoscale model Weather Research and Fore-5 casting (WRF), coupled with a multilayer urban canopy parameterization and a building energy model (BEP+BEM). Results indicate that near-surface air temperature within the city is reduced by all the RMSs during the summer period: cool roofs are the most efficient in decreasing air temperature (up to 1°C on average), followed by irrigated green roofs with grass vegetation and photovoltaic panels. Green roofs reveal to be the most efficient strategy in reducing the energy consumption by air conditioning systems, up to 45%, because of their waterproof insulating layer, while electricity produced by photovoltaic 10 panels overcomes energy demand by air conditioning systems. During wintertime, green roofs maintain a higher near-surface air temperature than standard roofs, because of their higher thermal capacity and the consequent release of sensible heat during nighttime. On the other hand, photovoltaic panels (during nighttime) and cool roofs (during daytime) reduce near-surface air temperature, resulting in a reduced thermal comfort. Green roofs are the most efficient rooftop mitigation strategy in reducing energy consumption by heating, and are able to reduce the energy demand up to 40% for low rise buildings, while cool roofs 15 always increase consumption due to the decreased temperature. The results presented here show that the novel parameterization schemes implemented in the WRF model can be a valuable tool to evaluate the effects of mitigation strategies in the urban environment. Moreover, this study demonstrates that all rooftop technologies present multiple benefits for the urban environment , showing that green roofs are the most efficient in increasing thermal comfort and diminish energy consumption, while photovoltaic panels can reduce the dependence on fossil fuel consumption through electricity generation.

Wetlands are an important land type – they provide vital ecosystem services such as regulating floods, storing carbon, and providing wildlife habitat. The ability to simulate their spatial extent and hydrological processes is important for valuing wetlands’ function. The purpose of this study is to dynamically simulate wetlands’ hydrological processes and their feedback to the regional climate in the Prairie Pothole Region (PPR) of North America, where a large number of wetlands exist. In this study, we incorporated a wetland scheme into the Noah-MP Land Surface Model with two major modifications: (1) modifying the sub-grid saturation fraction for spatial wetland extent; (2) incorporating a dynamic water storage to simulate hydrological processes. This scheme was tested at a fen site in central Saskatchewan, Canada and applied regionally in the PPR with 13-year climate forcing produced by a high-resolution convection-permitting model. The differences between wetland and no-wetland simulations are significant, with increasing latent heat and evapotranspiration while decreasing sensible heat and runoff. Finally, the dynamic wetland scheme was tested using the coupled WRF model, showing an evident cooling effect of 1~3℃ in summer where wetlands are abundant. In particular, the wetland simulation shows reduction in the number of hot days for more than 10 days over the summer of 2006, when a long-lasting heatwave occurred. This research has great implications for land surface/regional climate modeling, as well as wetland conservation, for valuing wetlands in providing a moisture source and mitigating extreme heatwaves, especially under climate change.

This paper describes and evaluates novel parameterizations for accounting for the effect of rooftop mitigation strategies on the urban environment, in the context of the mesoscale model Weather Research and Forecasting (WRF), coupled with a urban canopy parameterization and a building energy model (BEP+BEM). Through the new implementation, the sensitivity of near-surface air temperature and building energy consumption to different rooftop mitigation strategies is evaluated by means of numerical simulations in idealized urban areas, for typical summer and winter conditions. Rooftop mitigation strategies considered include cool roofs, green roofs and rooftop photovoltaic panels. Results indicate that near-surface air temperature is reduced by all the RMSs during the summer period: cool roofs are the most efficient in decreasing air temperature (up to 1 °C on average), followed by green roofs and photovoltaic panels. Green roofs reveal to be the most efficient strategy in reducing the energy consumption by air conditioning systems, up to 45%, while electricity produced by photovoltaic panels overcomes energy demand by air conditioning systems. During wintertime, green roofs maintain a higher near-surface air temperature than standard roofs. On the other hand, photovoltaic panels and cool roofs reduce near-surface air temperature, resulting in a reduced thermal comfort. The results presented here show that the novel parameterization schemes implemented in the WRF model can be a valuable tool to evaluate the effects of mitigation strategies in the urban environment. The new model is available as part of the public release of WRF in version 4.3.

Representing climate-crop interactions is critical to earth system modeling. Despite recent progress in modeling dynamic crop growth and irrigation in land surface models (LSMs), transitioning these models from field to regional scales is still challenging. This study applies the Noah-MP LSM with dynamic crop-growth and irrigation schemes to jointly simulate the crop yield and irrigation amount for corn and soybean in the central U.S. The model performance of crop yield and irrigation amount are evaluated at county-level against the USDA reports and USGS water withdrawal data, respectively. The bulk simulation (with uniform planting/harvesting management and no irrigation) produces significant biases in crop yield estimates for all planting regions, with root-mean-square-errors (RMSEs) being 28.1% and 28.4% for corn and soybean, respectively. Without an irrigation scheme, the crop yields in the irrigated regions are reduced due to water stress with RMSEs of 48.7% and 20.5%. Applying a dynamic irrigation scheme effectively improves crop yields in irrigated regions and reduces RMSEs to 22.3% and 16.8%. In rainfed regions, the model overestimates crop yields. Applying spatially-varied planting and harvesting dates at state-level reduces crop yields and irrigation amount for both crops, especially in northern states. A “nitrogen-stressed” simulation is conducted and found that the improvement of irrigation on crop yields are limited when the crops are under nitrogen stress. Several uncertainties in modeling crop growth are identified, including yield-gap, planting date, rubisco capacity, and discrepancies between available datasets, pointing to future efforts to incorporating spatially-varying crop parameters to better constrain crop growing seasons.

Subsurface tile drainage (TD) is a dominant agriculture water management practice in the United States (US) to enhance crop production in poorly-drained soils. Assessments of field- or watershed-level (<50 km2) hydrologic impacts of tile drainage are becoming common; however, a major gap exists in our understanding of regional (>105 km2) impacts of tile drainage on hydrology. The National Water Model (NWM) is a distributed 1-km resolution hydrological model designed to provide accurate streamflow forecasts at 2.7 million reaches across the US. The current NWM lacks tile drainage representation which adds considerable uncertainty to streamflow forecasts in tile-drained areas. In this study, we quantify the performance of the NWM with a newly incorporated tile drainage scheme over the heavily tile-drained Midwestern US. Implementing a tile drainage scheme enhanced the uncalibrated model performance by about 20% to 50% of the calibrated NWM (Calib). The calibrated NWM with tile drainage (CalibTD) showed enhanced accuracy with higher event hit rates and lower false alarm rates than Calib. CalibTD showed better performance in high-flow estimations as tile drainage increased streamflow peaks (14%), volume (2.3%), and baseflow (11%). Regional water balance analysis indicated that tile drainage significantly reduced surface runoff (-7% to -29%), groundwater recharge (-43% to -50%), evapotranspiration (-7% to -13%), and soil moisture content (-2% to -3%). However, infiltration and soil water storage potential significantly increased with tile drainage. Overall, our findings highlight the importance of incorporating the tile drainage process into the operational configuration of the NWM.