Melt runoff (MR) contributes significantly to the total runoff in many river basins. Knowledge of the meltwater contribution (MCR, defined as the ratio of MR to the total runoff) to the total runoff benefits water resource management and flood control. A process-based land surface model, Noah-MP, was used to investigate the spatiotemporal characteristics of MR and MCR in the Upper Changjiang River (as known as Yangtze River) Basin (UCRB) located in southwestern China. The model was first calibrated and validated using snow cover fraction (SCF), runoff, and evapotranspiration (ET) data. The calibrated model was then used to perform two numerical experiments from 1981 to 2010: control experiment that considers MR and an alternative experiment that MR is removed. The difference between two experiments was used to quantify MR and MCR. The results show that in the entire UCRB, MCR was approximately 2.0% during the study period; however, MCR exhibited notable spatiotemporal variability. Four sub-regions over the Qinghai-Tibet Plateau (QTP) showed significant annual MCR ranging from 3.9% to 6.0%, while two sub-regions in the low plain regions showed negligible annual MCR. The spatial distribution of MCR was generally consistent with the distribution of glaciers and elevation distribution. Mann-Kendall (M-K) tests of the long-term annual MCR indicated that the four sub-regions in QTP exhibited increasing trends ranging from 0.01%/year to 0.21%/year during the study period but only one displayed statistically significant trend. No trends were found for the peak time (PT) of MR and MCR, in contrast, advancing trend were observed for the center time (CT) of MR, ranging from 0.01 months/year to 0.02 months/year. These trends are related to the changes of air temperature and precipitation in the study area.

Accurate simulation of energy, water, and carbon fluxes exchanging between the land surface and the atmosphere is beneficial for improving terrestrial ecohydrological and climate predictions. We systematically assessed the Noah land surface model (LSM) with mutiparameterization options (Noah-MP) in simulating these fluxes and associated variations in terrestrial water storage (TWS) and snow cover fraction (SCF) against various reference products over 18 United States Geological Survey two-digital hydrological unit code regions of the continental United States (CONUS). In general, Noah-MP captures better the observed seasonal and interregional variability of net radiation, SCF, and runoff than other variables. With a dynamic vegetation model, it overestimates gross primary productivity by 40% and evapotranspiration (ET) by 22% over the whole CONUS domain; however, with a prescribed climatology of leaf area index, it greatly improves ET simulation with relative bias dropping to 4%. It accurately simulates regional TWS dynamics in most regions except those with large lakes or severely affected by irrigation and/or impoundments. Incorporating the lake water storage variations into the modeled TWS variations largely reduces the TWS simulation bias more obviously over the Great Lakes with model efficiency increasing from 0.18 to 0.76. Noah-MP simulates runoff well in most regions except an obvious overestimation (underestimation) in the Rio Grande and Lower Colorado (New England). Compared with North American Land Data Assimilation System Phase 2 (NLDAS-2) LSMs, Noah-MP shows a better ability to simulate runoff and a comparable skill in simulating R-n but a worse skill in simulating ET over most regions. This study suggests that future model developments should focus on improving the representations of vegetation dynamics, lake water storage dynamics, and human activities including irrigation and impoundments.

A mesoscale evaluation is performed to determine the impacts of climate change on terrestrial hydrological components and the Net Irrigation Water Requirement (NIWR) throughout the Chesapeake Bay watershed in the mid-Atlantic region of the United States. The Noah-MP land surface model is calibrated and evaluated against the observed datasets of United States Geological Survey (USGS) streamflow gages, actual evapotranspiration from USGS Simplified Surface Energy Balance (SSEBop) Model and soil moisture from Soil Analysis Climate Network (SCAN). Six best performing Global Climate Models (GCM) based on Multivariate Adaptive Constructed Analogs (MACA) scheme are included for two future scenarios (RCP 4.5 and RCP 8.5), to assess the change in water balance components, change in NIWR for two dominant crops (corn and soybeans) and uncertainty in GCM projections. Using these long-term simulations, the flood inundation maps are developed for future scenarios along the Susquehanna River including the City of Harrisburg in Pennsylvania. The HEC-RAS 2D model is calibrated and evaluated against the high-water marks from major historical flood events and the stage-discharge relationship of the available USGS streamgages. Finally, the impacts of climate change are assessed on flood inundation depth and extent by comparing a 30-yr and 100-yr flood event based on the historical and future (scenario-based) peak discharge estimates at the USGS streamgages. Interestingly, flood inundation extent and severity predicted by the model along the Susquehanna River near Harrisburg is expected to rise in the future climate scenarios due to the greater frequency of extreme events increasing total precipitation. / Master of Science / Climate change is inevitable due to increased greenhouse gas emissions, with impacts varying in space and time significantly throughout the globe. The impacts are strongly driven by the change in precipitation and temperature which affect the control of the movement of water on the surface of the Earth. These changes in the water cycle require an understanding of hydrological components like streamflow, soil moisture, and evapotranspiration. Development of long-term climate models and computational hydrological models (based on mathematical equations and governed by laws of physics) has helped us in understanding this climate variability in space and time. This study performs a long-term simulation using the datasets from six different climate models to analyze the change in terrestrial hydrological components for the entire Chesapeake Bay watershed in the mid-Atlantic region of the United States. The simulations provide an understanding of the interplay between various land surface processes due to climate change and can help determine future water availability and consumption. To illustrate the usefulness of such long-term simulations, the crop water requirement is quantified for the dominant crops in Chesapeake Bay watershed to project water availability and support the development of mitigation strategies. Flood inundation maps are also developed for a section of Susquehanna River near the City of Harrisburg in south-central Pennsylvania using the streamflow from long-term simulations. The flood inundation depth and extent for major flood events such as Tropical Storm Agnes (1972) and Tropical Storm Lee (2011) are compared along the Susquehanna River, which can aid in managing flood operations, reduce the future flood damages and prioritize the mitigation efforts for endangered communities near the City of Harrisburg.

Land surfaces have changed as a result of human and natural processes, such asdeforestation, urbanization, desertification and natural disasters like wildfires. Land use and landcover change impacts local and regional climates through various bio geophysical processes acrossmany time scales. More realistic representation of land surface parameters within the land surfacemodels are essential to for climate models to accurately simulate the effects of past, current andfuture land surface processes. In this study, we evaluated the sensitivity and accuracy of theWeather Research and Forecasting (WRF) model though the default MODIS land cover data andannually updated land cover data over southeast of United States. Findings of this study indicatedthat the land surface fluxes, and moisture simulations are more sensitive to the surfacecharacteristics over the southeast US. Consequently, we evaluated the WRF temperature andprecipitation simulations with more accurate observations of land surface parameters over thestudy area. We evaluate the model performance for the default and updated land cover simulationsagainst observational datasets. Results of the study showed that updating land cover resulted insubstantial variations in surface heat fluxes and moisture balances. Despite updated land use andland cover data provided more representative land surface characteristics, the WRF simulated 2- m temperature and precipitation did not improved due to use of updated land cover data. Further,we conducted machine learning experiments to post-process the Noah-MP land surface modelsimulations to determine if post processing the model outputs can improve the land surfaceparameters. The results indicate that the Noah-MP simulations using machine learning remarkablyimproved simulation accuracy and gradient boosting, and random forest model had smaller meanerror bias values and larger coefficient of determination over the majority of stations. Moreover,the findings of the current study showed that the accuracy of surface heat flux simulations byNoah-MP are influenced by land cover and vegetation type.