Alterations in historical patterns of rain and snowfall are severely impacting existing infra- structure that provides water to communities, agriculture, and industry. At the same time, warming trends are compromising the use of water as a coolant in thermoelectric energy production and increasing the risk of extreme weather damage to critical energy and water treatment facilities. To develop robust and sustainable options for adaptation, scientists, policymakers, and practitioners need highly accurate, tightly-coupled models of Earth system, hydrological, and operational water and energy resources. CERC-WET researchers will combine scenario generators, models of Impacts, Adaptation and Vulnerabilities (IAV), and comprehensive climate models to develop integrated Earth Systems Models (iESMs) that take into account the interactions of climate change with the energy, water, and agricultural resource sectors and simulate how water demands to produce energy and energy demands to provision water will evolve in future.
Context: Hydrological impact analyses require data inputs at resolutions finer than provided by global climate models. However, current methods for downscaling climate model output to fine resolutions produce highly uncertain results.
Objective: We will use a global climate model with fine resolution over the regions of interest and coarse resolution elsewhere to improve climatological inputs to hydrological impact assessment. We will work with the other Topic Area 4 projects to evaluate our model and to generate appropriate inputs for the various impact analyses.
Many atmospheric phenomena with the greatest potential impact in future warmer climates are inherently multiscale; these phenomena include hurricanes and tropical cyclones, atmospheric rivers, and other hydrometeorological extremes. In addition, many critical human systems span local to continental scales and are also inherently multiscale. Both types of systems are challenging to simulate in conventional climate models due to the relatively coarse resolutions of the models relative to the native length-scales of these human and natural structures. To create Earth system models with sufficient local resolution to capture these key systems yet sufficient speed to enable long-duration climate-change simulations, we are leading a Scientific Discovery through Advanced Computing (SciDAC) Project “Multiscale Methods for Accurate, Efficient, and Scale-Aware Models of the Earth System” that is developing new global climate codes with variable spatial and temporal resolutions. These codes allocate computational resources where greater resolution is needed to resolve critical human and natural systems. The resulting codes are designed and proven to be both highly accurate and highly scalable for the massively parallel supercomputers required to simulate water resources from local to global scales. These codes will serve as the foundation for Projects 4.1 and 4.2.
Context: As climate changes power plants and electric utility grid networks will likely need to handle large dynamic variations and perturbations. What operating characteristics will be required and how will reliability and availability of power be assured?
Objective: (1) Characterize drought-susceptible regions in the U.S. and China, (2) Determine climate impacts for these regions, (3) Evaluate climate change impacts on renewable capacity & dynamics, (4) determine required power plant operation dynamics, and (5) Determine overall dynamics grid operation.
Climate change will impact a wide variety of environmental conditions, many of which have implications for the different components of the energy system. For example, many regions depend upon steam-turbine based power cycles to meet the electricity demand, which consumes or withdraws large amounts of surface water for cooling which will be limited by drought conditions. Climate change could also impact the potential of renewable resources that are based on steam-turbine thermal cycles (solar thermal, geothermal) or potentially water-intensive bio-power types. Many regions rely on hydropower for a significant portion of their electricity mix and significant dynamic response to grid needs; shortfalls must be compensated for by other resources, often conventional power generation, as has been occurring in California. While the potential impact of climate change conditions on some of these components has been examined for hydropower, the implications for resource procurement planning and grid operability have not yet been examined in detail. We will address this gap using dynamic power plant simulation tools, dynamic utility grid network simulation, and climate and energy system modeling.
To determine the major impacts of climate change on the dynamic operation, control, and dispatch of power plants in drought-susceptible regions of the U.S. and China, we will: (1) identify and characterize the major regions of interest in both nations that are vulnerable to drought, and characterize the electricity grid technology mix and renewable potential of each region; (2) conduct climate simulations of the IPCC AR5 report scenarios and obtain future climate data for each region that is sufficient to capture the broad range of impacts that will affect power plant and/or grid operations; (3) simulate the impact of climate change-affected conditions on the dynamic operation of individual power plant types using physical dynamic models and knowledge of physical operating limits; (4) simulate the impact of climate change-affected conditions on bio-power, solar thermal, wind, solar, and geothermal resource availability, as applicable; and (5) determine the overall sustainability of energy and water operations in the selected climate change-affected regions by simulating utility grid network dynamics.
Context: using a state-of the art connectivity algorithm from the field of computer science, a storm tracking and prediction system can be constructed, which allows the users to investigate the cross0correlations between extreme events in climate phenomena, such as AO, MJO and ENSO
Objective: Segment precipitation events into 4-D "objects" including x,y,z locations and time. A Graphical User Interface (GUI) will be developed to study the impacts of hurricanes atmospheric rivers, and droughts on hydropower facilities.
With the ever-growing quantity and quality of climate-related model outputs and observations (including remote sensing data), along with advancements in machine learning, statistical modeling, and data driven techniques, there are unprecedented opportunities and challenges for engaging these computational tools and harnessing observational data to improve our understanding of the complex nature of the interactions between the environment and society. Our approach harnesses a state-of-the-art connectivity algorithm, which segments a target precipitation variable into 4D “objects,” calculates key features for each object, and stores them in a PostgreSQL database. To construct the 4D objects, we use PERSIANN data to create a new dataset, the CONNected precipitation objECT (CONNECT), or PERSIANN-CONNECT database. With these segmented objects and features, we can investigate the joint interaction of three climate phenomena: the AO, MJO and ENSO. One of the most unique aspects of the object-oriented approach is objects can be described by physically based features and then stored in a searchable database. By reorganizing the traditional grid-based global and high resolution satellite precipitation dataset (PERSIANN) into individual 4D objects, the PERSIANN-CONNECT dataset can display: a wealth of spatial and temporal features (e.g., average intensity (mm/hr), starting and ending location (latitude and longitude), duration (hr) and speed (km/hr), etc.), environmental features (e.g., whether or not the event occurred during an El Nino event), and more. This dataset can be used to study hurricanes, mesoscale convective systems, atmospheric rivers, and drought conditions the U.S. and the Asian monsoon system that causes severe floods and droughts in China.
Nguyen, P., Shearer, E. J., Tran, H., Ombadi, M., Hayatbini, N., Palacios, T., ... & Kuligowski, B. (2019). The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data. Scientific data, 6, 180296.
Hayatbini, N., Hsu, K. L., Sorooshian, S., Zhang, Y., & Zhang, F. (2019). Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS. Journal of Hydrometeorology, (2019).
Hayatbini, N., Kong, B., Hsu, K. L., Nguyen, P., Sorooshian, S., Stephens, G., ... & Ganguly, S. (2019). Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN. Remote Sensing, 11(19), 2193.
Nguyen, P., Shearer, E. J., Ombadi, M., Gorooh, V. A., Hsu, K., Sorooshian, S., ... & Ralph, M.(2019). PERSIANN Dynamic Infrared-Rain rate model (PDIR) for high-resolution, real-time satellite precipitation estimation. Bulletin of the American Meteorological Society, (2019).
Afzali Gorooh, V., Kalia, S., Nguyen, P., Hsu, K. L., Sorooshian, S., Ganguly, S., & Nemani, R. R. (2020). Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS. Remote Sensing, 12(2), 316.
Context: Climate Change may alter the frequency, intensity, and duration of extreme hydrological events. It is important to (1) understand the climate variability in decades scale; (2) evaluate the performance of climate models for simulating historical climate extremes.
Objective: A long-term (over 33+ years), global, high-resolution (~4 km), remotely sensed precipitation dataset will be used to examine many kinds of indicators for historical precipitation extremes. Then, we will assess the future climate variability of extreme events and develop adapting strategies for management of energy and water resources.
The IPCC reports and the research work of international programs such as the GEWEX project of the WCRP have established how the hydrologic cycle is intensifying due to global warming. We thus know that the nature and the frequency of hydroclimatic extremes such as floods and droughts will increase, significantly impacting water resources system management, agriculture, urban water supply, and hazards. Understanding the changing baseline conditions and characteristics of extreme events can help illuminate system thresholds and tipping points. This approach relies on GCMs to estimate future climate variability and changes based on specific scenarios. To be useful, GCMs predictions should be evaluated to show consistency with historical events, especially for the extreme We will use the newly released, 33+ year, global high resolution (daily, 25km) precipitation climate data record PERSIANN-CDR with two separate, but interconnected, objectives: (1) to evaluate the changing patterns of precipitation extremes over the past three decades and at regional scales across China, focusing especially on river basins with extensive hydropower and reservoir systems; and (2) to conduct retrospective simulation studies to evaluate the performance of all CMIP5 climate models, downscaled at 25 km regional spatial scales that capture the regional hydrologic conditions; this will allow us to identify which of these climate models are better able to simulate the heterogeneous patterns of precipitation, as compared to PERSIANN-CDR. Identifying which of the selected models pass the evaluation criteria will allow their use in ensemble studies of future precipitation over the region.
Tao, Y., Yang, T., Faridzad, M., Jiang, L., He, X. and Zhang, X. (2017), "Non-stationary bias correction of monthly CMIP5 temperature projections over China using a residual-based bagging tree model." Int. J. Climatol. doi:10.1002/joc.5188
Liu, X., Yang, T., Hsu, K., Liu, C., & Sorooshian, S. "Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau." Hydrology and Earth System Sciences, (2017) 21(1), 169.
Katiraie-Boroujerdy, P. S., Ashouri, H., Hsu, K. L., & Sorooshian, S. "Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR." Theoretical and Applied Climatology, (2017) 130(1-2), 249-260.
Nguyen P, Sorooshian S, Thorstensen A, Tran H, Huynh P, Pham T, Braithwaite D, Hsu K, AghaKouchak A, Ashouri H. Exploring trends through “RainSphere”: Research data transformed into public knowledge. Bulletin of the American Meteorological Society. 2016 Nov 2(2016).
Ashouri, H., Nguyen, P., Thorstensen, A., Hsu, K. L., Sorooshian, S., & Braithwaite, D. (2016). Assessing the efficacy of high-resolution satellite-based PERSIANN-CDR precipitation product in simulating streamflow. Journal of Hydrometeorology, 17(7), 2061-2076.
Rahnamay Naeini, M., Yang, T., Tavakoly, A., Analui, B., AghaKouchak, A., Hsu, K. L., & Sorooshian, S. (2020). A Model Tree Generator (MTG) Framework for Simulating Hydrologic Systems: Application to Reservoir Routing. Water, 12(9), 2373.
Sadeghi, M., Nguyen, P., Hsu, K., & Sorooshian, S. (2020). Improving near real-time precipitation estimation using a U-Net convolutional neural network and geographical information. Environmental Modelling & Software, 134, 104856.
Context: Groundwater quality in irrigated basins is not sustainable, not only because of salt accumulation, but also due to imbalanced hydrological budgets.
Objective: Develop an integrated framework to simulate the physical and geochemical processes of basin-scale groundwater salinization, and examine alternative management strategies that will be essential for reversing ongoing declines in groundwater quality.
Climate change and drought are greatly increasing dependence on groundwater and, in turn, accelerating overdraft in many parts of the world. Overdraft is converting many groundwater systems into closed hydrologic basins, in which groundwater salinity increases because evaporation becomes the sole or dominant exit for water. The relevant time scale for overdraft-induced, deep groundwater salinization is long, on the order of centuries, but results in irreversible destruction of key, fresh groundwater resources that are relied upon for driving the 'green revolution' that has been crucial for the world food supply. Consequently, increasing food production to match population growth, let alone maintaining production at current levels, will potentially require massive amounts of desalinization of irrigation water.
To make society aware of the ultimate costs of groundwater overdraft, we will model the future-time evolution of groundwater salinity in typical, overdrafted groundwater basins and compute the energy costs of desalinating the water for irrigation. This will have a number of scientific and societal benefits, including leading the way for new technologies for groundwater management to sustain groundwater quality and a truer valuation of groundwater worth, the value of preventing groundwater salinization.
Context: California's water and hydrolelectirc energy supply are tightly coupled to the Sierra Nevada snowpack.
Objective: This project aims to use new methods for characterizing snowpack, and its connection to water and energy supply.
In many regions of the world, including China, much of the water and energy resources come from runoff from snow-dominated river basins, which are often remote with limited to non-existent monitoring networks yet they provide key dry-season water supply and hydropower. We will apply previously developed data assimilation methods to derive a high-resolution snow reanalysis dataset over high-elevation snow-dominated river basins in China. The methods, previously described (Girotto et al., (2014) and Margulis et al. (2015)), use Bayesian concepts to generate a posterior estimate of space-time continuous estimates of snowpack over a basin from prior model-based estimates and time series of fractional snow covered area (fSCA) derived from Landsat (Cortes et al., 2014). The spatially-distributed estimates of snow water equivalent (SWE) over a basin will provide new insight to characterize the snowpack climatology, how it is distributed, and how it might be changing. Previous results in the Western U.S. and the Andes indicate that the reanalysis dataset will provide comparable accuracy to in situ measurements. The reanalysis dataset would be of unprecedented space-time resolution and extend over basins that have a deficit of in-situ sampling. This will allow for better water/energy management and provide a useful validation dataset to improve climate models used to predict how these water/energy resources will change in the future.
Li, D., Lettenmaier, D. P., Margulis, S. A., & Andreadis, K. (2019). The value of accurate high-resolution and spatially continuous snow information to streamflow forecasts. Journal of Hydrometeorology, (2019).
Margulis, S. A., Fang, Y., Li, D., Lettenmaier, D. P., & Andreadis, K. (2019). The Utility of Infrequent Snow Depth Images for Deriving Continuous Space‐Time Estimates of Seasonal Snow Water Equivalent. Geophysical Research Letters, 46(10), 5331-5340.