@article{fang_johnson_yeghiazarian_sankarasubramanian_2024, title={Improved National-Scale Above-Normal Flow Prediction for Gauged and Ungauged Basins Using a Spatio-Temporal Hierarchical Model}, volume={60}, ISSN={["1944-7973"]}, url={https://doi.org/10.1029/2023WR034557}, DOI={10.1029/2023WR034557}, abstractNote={AbstractFloods cause hundreds of fatalities and billions of dollars of economic loss each year in the United States. To mitigate these damages, accurate flood prediction is needed for issuing early warnings to the public. This situation is exacerbated in larger model domains flood prediction, particularly in ungauged basins. To improve flood prediction for both gauged and ungauged basins, we propose a spatio‐temporal hierarchical model (STHM) using above‐normal flow estimation with a 10‐day window of modeled National Water Model (NWM) streamflow and a variety of catchment characteristics as input. The STHM is calibrated (1993–2008) and validated (2009–2018) in controlled, natural, and coastal basins over three broad groups, and shows significant improvement for the first two basin types. A seasonal analysis shows the most influential predictors beyond NWM streamflow reanalysis are the previous 3‐day average streamflow and the aridity index for controlled and natural basins, respectively. To evaluate the STHM in improving above‐normal streamflow in ungauged basins, 20‐fold cross‐validation is performed by leaving 5% of sites. Results show that the STHM increases predictive skill in over 50% of sites' by 0.1 Nash‐Sutcliffe efficiency (NSE) and improves over 65% of sites' streamflow prediction to an NSE > 0.67, which demonstrates that the STHM is one of the first of its kind and could be employed for flood prediction in both gauged and ungauged basins.}, number={1}, journal={WATER RESOURCES RESEARCH}, author={Fang, Shiqi and Johnson, J. Michael and Yeghiazarian, Lilit and Sankarasubramanian, A.}, year={2024}, month={Jan} }
 @article{johnson_fang_sankarasubramanian_rad_cunha_jennings_clarke_mazrooei_yeghiazarian_2023, title={Comprehensive Analysis of the NOAA National Water Model: A Call for Heterogeneous Formulations and Diagnostic Model Selection}, volume={128}, ISSN={["2169-8996"]}, url={https://doi.org/10.1029/2023JD038534}, DOI={10.1029/2023JD038534}, abstractNote={AbstractWith an increasing number of continental‐scale hydrologic models, the ability to evaluate performance is key to understanding uncertainty and making improvements to the model(s). We hypothesize that any model, running a single set of physics, cannot be “properly” calibrated for the range of hydroclimatic diversity as seen in the contenintal United States. Here, we evaluate the NOAA National Water Model (NWM) version 2.0 historical streamflow record in over 4,200 natural and controlled basins using the Nash‐Sutcliffe Efficiency metric decomposed into relative performance, and conditional, and unconditional bias. Each of these is evaluated in the contexts of meteorologic, landscape, and anthropogenic characteristics to better understand where the model does poorly, what potentially causes the poor performance, and what similarities systemically poor performing areas share. The primary objective is to pinpoint traits in places with good/bad performance and low/high bias. NWM relative performance is higher when there is high precipitation, snow coverage (depth and fraction), and barren area. Low relative skill is associated with high potential evapotranspiration, aridity, moisture‐and‐energy phase correlation, and forest, shrubland, grassland, and imperviousness area. We see less bias in locations with high precipitation, moisture‐and‐energy phase correlation, barren, and grassland areas and more bias in areas with high aridity, snow coverage/fraction, and urbanization. The insights gained can help identify key hydrological factors underpinning NWM predictive skill; enforce the need for regionalized parameterization and modeling; and help inform heterogenous modeling systems, like the NOAA Next Generation Water Resource Modeling Framework, to enhance ongoing development and evaluation.}, number={24}, journal={JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES}, author={Johnson, J. Michael and Fang, Shiqi and Sankarasubramanian, Arumugam and Rad, Arash Modaresi and Cunha, Luciana Kindl and Jennings, Keith S. and Clarke, Keith C. and Mazrooei, Amir and Yeghiazarian, Lilit}, year={2023}, month={Dec} }
 @article{chen_zhou_fang_li_wang_cao_2022, title={Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty}, volume={809}, ISSN={["1879-1026"]}, DOI={10.1016/j.scitotenv.2021.151152}, abstractNote={With the rapid growth of population and economy, shortage and mismatch of land and water resources have deepened the need for cropping pattern optimization. In the context of the sustainable development of agriculture, cropping pattern optimization should not only pursue economic benefits, but the consequent environmental effects also deserve equal attention. Meanwhile, climate change increases the complexity of balancing conflicts of economic-environmental system by cropping pattern optimization. Therefore, this paper builds a multi-objective programming model for Economic-Environmental Synergistic Optimization for Cropping Pattern under Climate Change (EESO-CP-CC) model, with the goals of economic benefit increment and environmental pollutants emission reduction. The EESO-CP-CC model couples a non-point source pollution input-output model, a one-dimensional water quality model and an economic benefit function into an integrated framework. Fuzzy method was used to solve the optimization model, and the stochastic uncertainty of water supply under climate change was quantified by the integration of Bayesian approach and interval linear regression. The model was applied to Jinxi Irrigation District (JXID) in Heilongjiang Province, northeast of China. Results show that by adjusting the acreage of rice, corn and soybean, the harmony degree of economy-society-environment system increased by 10.7% compared to the current situation, indicating that the model tends to achieve the best possible economic benefits while ensuring the environmental effects. Compared with actual cropping pattern, the pollutants emissions reduced by 24.7% and 3% from corn and soybean, respectively. However, this led to a decrease of economic benefit by 8% in exchange, showing the trade-off between environmental pollution reduction and economic benefits improvement. The output coefficients of nitrogen and phosphorus pollutants were optimized, with the optimal output reducing by 20% compared to the standard. Cropping pattern and water resources allocation vary with different climate change conditions, however, the amplitude of variation is modest, indicating that the model can cope well with the changing environment. The developed model can help achieve synergistic development of economic benefits and environmental effects, and thus promote sustainable development of irrigation areas, and improve the coping capacity of agricultural water and land under climate change, by cropping pattern optimization and planning.}, journal={SCIENCE OF THE TOTAL ENVIRONMENT}, author={Chen, Yingshan and Zhou, Yan and Fang, Shiqi and Li, Mo and Wang, Yijia and Cao, Kaihua}, year={2022}, month={Feb} }