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The summer polynya along the northern coast of Greenland has been observed only six months later after the winter polynya in 2018, which has prompted concerns about the stability of some of the thickest sea-ice in the Arctic region. This study combines retrospective remotely sensed sea-ice measurements with results from the Regional Arctic System Model (RASM) to examine the causes, eect, and evolution of open-water areas/polynyas in the region. RASM is a limited-domain, fully-coupled climate model, consisting of the atmosphere (Weather Research and Forecasting, WRF3.7), ocean (Los Alamos National Laboratory Parallel Ocean Program, POP2), sea-ice (Community Sea Ice Model, CICE5), land hydrology (Variable Inltration Capacity, VIC4) and streamow routing (RVIC) components. The ocean and sea- ice models are congured with the horizontal resolution of 1/12-degree with 45 vertical levels and 5 sea-ice thickness categories, respectively. The atmosphere and land hydrology components are set up on a 50-km grid with 40-vertical levels and 3-soil layers, respectively. The Climate Forecast System Reanalysis (CFSR) and version 2 (CFSv2) output are used as boundary conditions for dynamic downscaling. Analysis of the sea-ice conditions o the coast of northern Greenland revealed that RASM, in agreement with satellite measurements, has simulated ve summer polynya events, i.e. in August of 1984, 1985, 2002, 2004 and 2018, over the 39-year period (1980-2018). All these events were primarily dynamically forced, with the thermodynamic forcing playing the secondary, yet still important role. While the thermodynamically driven sea-ice melting exhibited a relatively little year-to-year variability, between 87 km3 and 115 km3, its relative contribution to the total sea-ice loss increased by 2.5 times, from 16% in 1984 to 40% in 2018. This implies that with continuing thinning of sea-ice, increasingly less mechanical forcing may be required to generate and maintain a polynya or open water nort

The Arctic is one of the most challenging regions to model climate change due to its complexity, including the cryosphere, small scale processes and feedbacks controlling its amplified response to global climate change. The combination of these factors defines the need for high spatial and temporal model resolution, which is commonly not practical for most state-of-the-art global Earth system models (ESMs), including those participating in the Coupled Model Intercomparison Project Phase 6. We offer an alternative approach to improve model physics and reduce uncertainties in modeling Arctic climate using a high resolution regional climate system model for dynamical downscaling of output from ESMs. The Regional Arctic System Model (RASM) has been developed to better understand the past and present operation of the Arctic climate system and to predict its change at time scales up to decades. RASM is a coupled model, consisting of the atmosphere, ocean, sea ice, land hydrology and river routing scheme components. Its domain is pan-Arctic, with 50-km or 25-km grids for the atmosphere and land components. The ocean and sea ice components are configured at ~9.3-km or ~2.4-km grids horizontally and with 45 or 60 vertical layers. For hindcast simulations, RASM derives boundary conditions from global atmospheric reanalyses, allowing comparison with observations in place and time, which is a unique capability not available with ESMs. We will discuss improvements to RASM model physics offered by high resolution and in generation of internally consistent realistic initial conditions for Arctic climate prediction. We will also discuss the need for fine-tuning of scale aware parameterizations of sub-grid physical processes in varying model configurations. Finally, selected results will be presented to demonstrate gains of dynamical downscaling in comparison with observations and with the global reanalysis and predictions.

Questions related to historical and future water resources and scarcity have been addressed by several macroscale hydrological models. One of these models is the Variable Infiltration Capacity (VIC) model. However, further model developments were needed to holistically assess anthropogenic impacts on global water resources using VIC. Our study developed VIC-WUR, which extends the VIC model using (1) integrated routing, (2) surface and groundwater use for various sectors (irrigation, domestic, industrial, energy, and livestock), (3) environmental flow requirements for both surface and groundwater systems, and (4) dam operation. Global gridded datasets on sectoral demands were developed separately and used as an input for the VIC-WUR model. Simulated national water withdrawals were in line with reported Food and Agriculture Organization (FAO) national annual withdrawals (adjusted R2 > 0.8), both per sector and per source. However, trends in time for domestic and industrial water withdrawal were mixed compared with previous studies. Gravity Recovery and Climate Experiment (GRACE) monthly terrestrial water storage anomalies were well represented (global mean root-mean-squared error, RMSE, values of 1.9 and 3.5 mm for annual and interannual anomalies respectively), whereas groundwater depletion trends were overestimated. The implemented anthropogenic impact modules increased simulated streamflow performance for 370 of the 462 anthropogenically impacted Global Runoff Data Centre (GRDC) monitoring stations, mostly due to the effects of reservoir operation. An assessment of environmental flow requirements indicates that global water withdrawals have to be severely limited (by 39 %) to protect aquatic ecosystems, especially with respect to groundwater withdrawals. VIC-WUR has potential for studying the impacts of climate change and anthropogenic developments on current and future water resources and sector-specific water scarcity. The additions presented here make the VIC mo

The Regional Arctic System Model (RASM) is a fully coupled limited-domain ice-ocean-atmosphere-land hydrology model. Its domain is pan-Arctic, with the atmosphere and land components configured on a 50-km or 25-km grid. The ocean and sea ice components are configured on rotated sphere meshes with four configuration options: 1/12o (~9.3km) or 1/48o (~2.4km) in the horizontal space and with 45 or 60 vertical layers. As a regional climate model, RASM requires boundary conditions along its lateral boundaries and in the upper atmosphere, which are derived either from global atmospheric reanalyses for simulations of the past to present or from Earth System models (ESMs) for climate projections. In the former case, this allow comparison of RASM results with observations in place and time, which is a unique capability not available in global ESMs. RASM has been developed and used to investigate critical processes controlling the evolution of the Arctic climate system under a diminishing sea ice cover. Several examples of key physical processes and coupling between different model components will be presented, that improve the representation of the past and present Arctic climate system. The impact of such processes and feedbacks will be discussed with regard to improving model physics and reducing biases in the representation of its initial state for prediction of Arctic climate at time scales from synoptic to intra-annual.

The Regional Arctic System Model version 1 (RASM1) has been developed to provide high-resolution simulations of the Arctic atmosphere–ocean–sea ice–land system. Here, we provide a baseline for the capability of RASM to simulate interface processes by comparing retrospective simulations from RASM1 for 1990–2014 with the Community Earth System Model version 1 (CESM1) and the spread across three recent reanalyses. Evaluations of surface and 2 m air temperature, surface radiative and turbulent fluxes, precipitation, and snow depth in the various models and reanalyses are performed using global and regional datasets and a variety of in situ datasets, including flux towers over land, ship cruises over oceans, and a field experiment over sea ice. These evaluations reveal that RASM1 simulates precipitation that is similar to CESM1, reanalyses, and satellite gauge combined precipitation datasets over all river basins within the RASM domain. Snow depth in RASM is closer to upscaled surface observations over a flatter region than in more mountainous terrain in Alaska. The sea ice–atmosphere interface is well simulated in regards to radiation fluxes, which generally fall within observational uncertainty. RASM1 monthly mean surface temperature and radiation biases are shown to be due to biases in the simulated mean diurnal cycle. At some locations, a minimal monthly mean bias is shown to be due to the compensation of roughly equal but opposite biases between daytime and nighttime, whereas this is not the case at locations where the monthly mean bias is higher in magnitude. These biases are derived from errors in the diurnal cycle of the energy balance (radiative and turbulent flux) components. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The development of RASM2 aims to address these biases. Still, an advantage of RASM1 is that it captures the int

The Regional Arctic System Model version 1 (RASM1) has been developed to provide high-resolution simulations of the Arctic atmosphere–ocean–sea ice–land system. Here, we provide a baseline for the capability of RASM to simulate interface processes by comparing retrospective simulations from RASM1 for 1990–2014 with the Community Earth System Model version 1 (CESM1) and the spread across three recent reanalyses. Evaluations of surface and 2 m air temperature, surface radiative and turbulent fluxes, precipitation, and snow depth in the various models and reanalyses are performed using global and regional datasets and a variety of in situ datasets, including flux towers over land, ship cruises over oceans, and a field experiment over sea ice. These evaluations reveal that RASM1 simulates precipitation that is similar to CESM1, reanalyses, and satellite gauge combined precipitation datasets over all river basins within the RASM domain. Snow depth in RASM is closer to upscaled surface observations over a flatter region than in more mountainous terrain in Alaska. The sea ice–atmosphere interface is well simulated in regards to radiation fluxes, which generally fall within observational uncertainty. RASM1 monthly mean surface temperature and radiation biases are shown to be due to biases in the simulated mean diurnal cycle. At some locations, a minimal monthly mean bias is shown to be due to the compensation of roughly equal but opposite biases between daytime and nighttime, whereas this is not the case at locations where the monthly mean bias is higher in magnitude. These biases are derived from errors in the diurnal cycle of the energy balance (radiative and turbulent flux) components. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The development of RASM2 aims to address these biases. Still, an advantage of RASM1 is that it captures the int

The coastal streamflow flux from the Arctic drainage basin is an important driver of dynamics in the coupled ice-ocean system. Comprising more than one-third of the total freshwater flux into the Arctic Ocean, streamflow is a key component of the regional and global freshwater cycle. To better represent the coupling of the streamflow flux to the ocean, we have developed and applied the RVIC streamflow routing model within the Regional Arctic System Model (RASM). The RASM is a high-resolution regional Earth System Model whose domain includes all of the Arctic drainage basin. In this paper, we introduce the RVIC streamflow routing model, detailing its application within RASM and its advancements in terms of representing high-resolution streamflow processes. We evaluate model simulated streamflow relative to in-situ observations and demonstrate a method for improving model performance using a simple optimization procedure. We also present a new, spatially and temporally consistent, high-resolution dataset of coastal freshwater fluxes for the Arctic drainage basin and surrounding areas that is based on a fully-coupled RASM simulation and intended for use in Arctic Ocean modeling applications. This dataset is evaluated relative to other coastal streamflow datasets commonly used by the ocean modeling community. We demonstrate that the RASM-simulated streamflow flux better represents the annual cycle than existing datasets, especially in ungauged areas. Finally, we assess the impact that streamflow has on the coupled ice-ocean system, finding that the presence of streamflow leads to reduced sea surface salinity, increased sea surface temperatures and decreased sea ice thickness.

The Regional Arctic System Model (RASM) is a fully coupled, regional Earth system model applied over the pan-Arctic domain. This paper discusses the implementation of the Variable Infiltration Capacity land surface model (VIC) in RASM and evaluates the ability of RASM, version 1.0, to capture key features of the land surface climate and hydrologic cycle for the period 1979-2014 in comparison with uncoupled VIC simulations, reanalysis datasets, satellite measurements, and in situ observations. RASM reproduces the dominant features of the land surface climatology in the Arctic, such as the amount and regional distribution of precipitation, the partitioning of precipitation between runoff and evapotranspiration, the effects of snow on the water and energy balance, and the differences in turbulent fluxes between the tundra and taiga biomes. Surface air temperature biases in RASM, compared to reanalysis datasets ERA-Interim and MERRA, are generally less than 2 degrees C; however, in the cold seasons there are local biases that exceed 6 degrees C. Compared to satellite observations, RASM captures the annual cycle of snow-covered area well, although melt progresses about two weeks faster than observations in the late spring at high latitudes. With respect to derived fluxes, such as latent heat or runoff, RASM is shown to have similar performance statistics as ERA-Interim while differing substantially from MERRA, which consistently overestimates the evaporative flux across the Arctic region.

The Regional Arctic System Model (RASM) is a highresolution regional Earth System Model whose domain includes the entire Arctic drainage basin. The development of RASM is motivated by the objective to advance understanding of the Arctic climate system and to improve decadal climate predictions in high northern latitudes. Land surface processes in RASM are simulated using the Variable Infiltration Capacity (VIC) hydrologic model and the RVIC streamflow routing model coupled within the CESM modeling framework.

In data-poor regions around the world, particularly in less-privileged countries, hydrologists cannot always take advantage of available hydrological models to simulate a hydrological system due to the lack of reliable measurements of hydrological variables, in particular rainfall and streamflows, needed to implement and evaluate these models. Rainfall estimates obtained with remotely deployed sensors constitute an excellent source of precipitation for these basins, however they are prone to errors that can potentially affect hydrologic simulations. Concurrently, limited access to streamflow measurements does not allow a detailed representation of the system's structure through parameter estimation techniques. This dissertation presents multiple studies that evaluate the usefulness of remotely sensed products for different hydrological applications and the sensitivity of simulated streamflow to parameter uncertainty across basins with different hydroclimatic characteristics with the ultimate goal of increasing the applicability of land surface models in ungauged basins, particularly in South America. Paper 1 presents a sensitivity analysis of daily simulated streamflows to changes in model parameters along a hydroclimatic gradient. Parameters controlling the generation of surface and subsurface flow were targeted for the study. Results indicate that the sensitivity is strongly controlled by climate and that a more parsimonious version of the model could be implemented. Paper 2 explores how errors in satellite-estimated precipitation, due to infrequent satellite measurements, propagate through the simulation of a basin's hydrological cycle and impact the characteristics of peak streamflows within the basin. Findings indicate that nonlinearities in the hydrological cycle can introduce bias in simulated streamflows with error-corrupted precipitation. They also show that some characteristics of peak discharges are not conditioned by errors in satellite-estimated precipi

Mathematical models of water distribution systems (WDS) serve as tools to represent the real systems for many different purposes. Calibration is the process of fine tuning the model parameters so that the real system is well-represented. In practice, calibration is performed considering all information is deterministic. Recent researches have incorporated uncertainties caused by field measurements into the calibration process. Parameter (D-optimality) and predictive (I-optimality) uncertainties have been used as indicators of how well a system is calibrated.This study focuses on a methodology that extends previous work by considering the impact of uncertainty on decisions that are made using the model. A new sampling strategy that would take into account the accuracy needed for different model objectives is proposed.The methodology uses an optimization routine that minimizes square differences between the observed and model calculated head values by adjusting the model parameters. Given uncertainty in measurements, the parameters from this nonlinear regression are imprecise and the model parameter uncertainties are computed using a first order second moment (FOSM) analysis. Parameter uncertainties are then propagated to model prediction uncertainties through a second FOSM analysis. Finally, the prediction uncertainty relationships are embedded in optimization problems to assess the effect of the uncertainties on model-based decisions. Additional data is collected provided that the monetary benefits of reducing uncertainties can be addressed.The proposed procedure is first applied on a small hypothetical network for a system expansion design problem using a steady state model. It is hypothesized that the model accuracy and data required calibrating WDS models with different objectives would require different amount of data. A real-scale network for design and operation problems is studied using the same methodology for comparison. The effect of a common practice, gro

Increasing population, diminishing supplies and variable climatic conditions can cause difficulties in meeting water demands. When this long range water supply plan is developed to cope with future water demand changes, accuracy and reliability are the two most important factors. To develop an accurate model, the water supply system has become more complicated and comprehensive structures. Future uncertainty also has been considered to improve system reliability as well as economic feasibility.In this study, a general large-scale water supply system that is comprised of modular components was developed in a dynamic simulation environment. Several possible scenarios were simulated in a realistic hypothetical system. In addition to water balances and quality analyses, construction and operation of system components costs were estimated for each scenario. One set of results demonstrates that construction of small-cluster decentralized wastewater treatment systems could be more economical than a centralized plant when communities are spatially scattered or located in steep areas.The Shuffled Frog Leaping Algorithm (SFLA), then, is used to minimize the total system cost of the general water supply system. Decisions are comprised of sizing decisions - pipe diameter, pump design capacity and head, canal capacity, and water/wastewater treatment capabilities - and flow allocations over the water supply network. An explicit representation of energy consumption cost for the operation is incorporated into the system in the optimization process of overall system cost. Although the study water supply systems included highly nonlinear terms in the objective function and constraints, a stochastic search algorithm was applied successfully to find optimal solutions that satisfied all the constraints for the study networks.Finally, a robust optimization approach was introduced into the design process of a water supply system as a framework to consider uncertainties of the correlated f

Water distribution system modeling can be used as a basis of planning and operation decisions. However, model accuracy and uncertainty will impact the model based decisions. Model prediction uncertainty results from uncertainty in model parameters that are determined through calibration or are based upon modeler judgment. The focus of this dissertation is the effect of uncertainties on water quality model estimates and calibration. The dissertation is centered around three journal articles and a technical note.In the first paper, the effect of parameter uncertainty on water quality in a distribution system under steady and unsteady conditions was analyzed by Monte Carlo simulation (MCS). Sources of uncertainties for water quality include decay coefficients, pipe diameter and roughness, and nodal spatial and temporal demands. The effect of individual parameter is discussed, as well as the combined effect of the parameters. It also describes the effect of flow patterns.A general calibration model is developed in the second paper for identifying wall decay coefficients. The problem is solved using the SFLA optimization algorithm that is coupled with hydraulic and water quality simulation models using the EPANET toolkit. The methodology is applied on two application networks. The study presents the effect of different field conditions such as the network with or without tanks, altering disinfectant injection policies, changing measurement locations, and varying the number of global wall decay coefficient on the estimated parameters. The numerical study also discusses whether the complexity of the system can be captured with fewer than the actual number of field parameters and if the number of the measurement locations is sufficient.The third paper conducts a study that considers a full calibration assessment for a water quality model in the distribution systems. The calibration process begins with estimating the the best fit wall decay coefficients. Next, the uncertaint

The spatial distribution of snowpack in relation to snow water equivalent (SWE) and covered extent is highly variable in time both seasonally and interannually. In order to assess basin water resources, SWE must be distributed to areal estimates. This spatially distributed SWE connects the point scale to the larger scale of the basin (i.e. macro-scale), requiring a combination approach of statistical interpolation techniques and snowpack extent constraint from remote sensing. This research connects those multiple spatial scales and applies the combined remote sensing and ground-based SWE products in a hydrologic model setting to aid in improving streamflow forecasting in the mountainous terrain of snowmelt-dominated basins, a current modeling gap. Four specific advancements were achieved: 1) a comprehensive assessment of spatial distribution techniques in interpolating point snow water equivalent (SWE) measurements at snow telemetry (SNOTEL) stations to the macro-scale was made and an optimal technique for distributing SWE on this scale was obtained; 2) differences between two major data sources of SWE (SNOTEL and snowcourse) were quantified for both point-scale variability and interpolated macro-scale variability to determine spatial and temporal differences in data sources for dry, average and wet years to better inform water resources management applications; 3) basin-scale estimates of ground-based SWE and snow covered area (SCA) from remote sensing were evaluated relative to equivalent fields calculated by a hydrologic model and the effect of assimilating the remote sensing products into the model were investigated; and 4) in the context of (3), improvements were made in macro-scale SCA estimates through both a canopy correction and a low pass statistical filter in an effort to correct for the relatively low resolution of remotely sensed estimates.

Land surface processes play an important role when modeling weather and climate, and understanding and representing such processes in South America is a particular challenge because of the large variations in regional climate and surface features such as vegetation and soil. Numerical models have been used to explore the climate and weather of continental South America, but without appropriate initiation of land surface conditions model simulations can rapidly diverge from reality. This initiation problem is exacerbated by the fact that conventional surface observations over South America are scarce and biased towards the urban centers and coastal areas. This dissertation explores issues related to the apt representation of land surface processes and their impacts in numerical simulations with a regional atmospheric model (specifically the Eta model) over South America. The impacts of vegetation heterogeneity in regional weather forecast were first investigated. A South American Land Data Assimilation System (SALDAS) was then created analogous to that currently used in North America to estimate soil moisture fields for initializing regional atmospheric models. The land surface model (LSM) used in this SALDAS is the Simplified Simple Biosphere (SSiB). Precipitation fields are critical when calculating soil moisture and, because conventional surface observations are scarce in South America, some of the most important remote sensed precipitation products were evaluated as potential precipitation forcing for the SALDAS. Spin up states for SSiB where then compared with climatological estimates of land surface fields and significant differences found. Finally, an assessment was made of the value of SALDAS-derived soil moisture fields on Eta model forecasts. The primary result was that model performance is enhanced over the entire continent in up to 72h forecasts using SALDAS surface fields

The processes controlling snowpack mass balance are highly variable in time and space, requiring remote sensing to observe regional processes and intensive field observations to observe hilislope-scale phenomena. This research aims to further understanding of the processes controlling snowpack mass balance through innovative applications of remotely sensed data and statistical interpolations of ground observations. Four advancements were obtained: 1) the sensitivity of regression tree snow distribution models to digital elevation data and independent variables was quanitified; 2) improved ability to upscale point snow water equivalent (SWE) measurements at snow telemetry (SNOTEL) stations was obtained by quantifying the small-scale SWE variability surrounding these stations; 3) spatially distributed snowmelt algorithms were improved by incorporating remotely sensed snow-surface albedo data into snowmelt modeling; and (4) the temporal and spatial continuity of regional-scale estimates of snow covered area (SCA) and SWE were improved by combining remotely sensed data and air temperature data to extend estimates beneath the cloud cover.