Small-Scale Catchment Analysis of Water Stress in Wet Regions of the U.S.: An Example from Louisiana

Groundwater is increasingly being overdrafted in the Southeastern U.S., despite abundant rainfall and the apparent availability of surface water. Using the state of Louisiana as an example, the current study quantifies the stresses on water resources and investigates the potential for opportunities to use surface water in lieu of groundwater pumping. The assessment is based on a fine watershed scale (12-digit Hydrological Unit Code [HUC] boundaries) water balance between the availability of surface and groundwater and surface water and groundwater demand. Water demand includes environmental flows, as well as public supply, rural domestic, industrial, power generation, agricultural, and aquaculture sectors. The seasonality of water stress is also addressed by incorporating monthly variations in surface water supply and irrigation demands. We develop several new weighting schemes to disaggregate the water withdrawals, provided by the U.S. Geological Survey on a county scale, to the HUC12 scale. The analysis on the smaller HUC12 scale is important for identifying areas with high water stress that would otherwise be masked at a larger scale (e.g. the county or HUC8 watershed scales). The results indicate that the annual water stress in Louisiana is below one (i.e. there is more water available than is used) for most watersheds; however, some watersheds (15 of the HUC12 units) show stresses greater than one, indicating an insufficient water supply to meet existing demands. The areas of the highest water stress are largely attributable to water consumption for power generating plants or irrigation. Moreover, estimating the stresses on surface water and groundwater sources separately confirms our speculation of abundant surface water and demonstrates a significant over-drafting/deficit of groundwater in many of the states aquifer systems. These results have implications for identifying new opportunities for reallocation of surface water use to reduce groundwater pumping and improve water sustainability in the region. Seasonal fluctuations in surface water supply and water withdrawals for irrigation highlight the fact that the water system is under more stress during the summer season. This observation underscores the need for infrastructure for shortterm surface water storage in agricultural regions. The water budget analysis presented here can be useful for stakeholders in developing water management plans and can also help to inform the development of a water code that will enable Louisiana to successfully manage and conserve its water resources for the future

Adaptive Reservoir Operation in the Transboundary Nile River Basin

Thesis (Ph.D.)--University of Washington, 2021The Nile River Basin (NRB) is home to more than 200 million people sharing the water resources for agriculture, industry, municipal uses, in-stream navigation, and hydropower generation. A central and existential water management issue for the region is maintaining a sustainable supply of water against increasing population, recurring drought, and climate change. Recent published datasets on future dams reveal an increasingly impounded NRB for hydropower development by upstream and transboundary nations, notably Ethiopia. The most downstream country, Egypt, therefore needs to adapt the operation of High Aswan Dam (HAD), which is key to the country’s water security, to planned dams, such as the Grand Ethiopian Renaissance Dam (GERD). The overarching goal of this dissertation is to derive an adaptive reservoir operating policy under the combined impacts from climate variability, population pressures and planned dams. First, a modeling framework was developed to simulate streamflow and understand reservoir operations in the NRB using satellite earth observations and macroscale hydrologic modeling. The satellite-based framework yielded a reasonable skill in deriving monthly HAD releases in good agreement with measured discharge downstream of the dam. Building upon this satellite-based modeling, the second study evaluated the hydrological potential of the Upper Blue Nile (UBN) basin for meeting the declared hydropower production design from the GERD (5150 MW). The results indicated the hydrology of the UBN limited the hydropower potential of GERD and thus the initial plans to upgrade the GERD capacity (from 5250MW to 6000MW to 6450 MW) have not been beneficial to improving the dam’s hydropower production. The third study presented a blueprint for adapting HAD operation under the impacts of filling/operation of the GERD based on a Water Supply Stress Index (WaSSI). To adapt to a faster GERD filling scenario (e.g., 3-year filling), HAD needs to modify its operation in summer months by elevating the downstream stress level (store more and release less), e.g., WaSSIAG=0.70. Such adaptation can also help HAD recover its normal operating level in four years after GERD is completely filled compared to 7 years with no adaptation scenario. Additionally, maintaining HAD storage at higher levels prior to GERD filling can significantly reduce the HAD recovery period to only 2 years. In the fourth study, a Forecast-based Adaptive Reservoir Operation (FARO) approach is introduced to explore how HAD can improve its operation by using long-term streamflow forecasts. The FARO results showed that the forecast horizon for HAD operation, using perfect forecasts, ranges between 5- and 12-month lead time in low and high demand scenarios, respectively, beyond which the forecast information no longer improves the release decision. . The forecast value to HAD operation is more pronounced in the months following the flooding season (October through December). The work presented in this dissertation provides a tangible way forward for existing dams to adapt their operations to real-world transboundary challenges while inspiring a win-win deal and considering the equitable rights of development in the Nile countries

The Use of Remote-Sensing Based Multi-Sensor Quantitative Precipitation Estimates in Deriving Extreme Precipitation Frequencies: Implications for Flash Flood Monitoring

The Second International Symposium on Flash Floods in Wadi Systems: 25-27 October 2016. Technische Universität Berlin, Campus El Gouna, Egypt

Carbon capture and sequestration in power generation: review of impacts and opportunities for water sustainability

Abstract This article reviews the use of carbon capture and sequestration (CCS) as a viable mitigation strategy for reducing greenhouse gas (GHG) emissions in fossil-fuel power plants and discusses the impacts on the sustainability of freshwater resources. While CCS technology can significantly mitigate anthropogenic GHG emissions, CCS installations are expected to impose new water stresses due to additional water requirements for chemical and physical processes to capture and separate CO2. In addition to these processes, the parasitic loads imposed by carbon capture on power plants will reduce their efficiency and thus require more water for cooling the plant. Groundwater contamination due to CO2 leakage during geologic sequestration is an additional concern when adapting CCS into power plants. Imposing such constraints on the quantity and quality of freshwater resources will influence decisions on the types of energy facilities and threaten the sustainability of water systems. A review of recent studies highlights three main challenges that would impact water sustainability due to CCS installation: (1) water requirements needed for different stages of CCS, (2) changes in groundwater quality due to carbon leakage into geologic formations, and (3) opportunities for using desalinated brine from saline sequestration aquifers to provide new freshwater sources and offset the CCS-induced water stresses. This article also reviews availability and gaps in datasets and simulation tools that are necessary for an improved CCS analysis. Illustrative analyses from two US states, Louisiana and Arizona, are presented to examine the possible consequences of introducing CCS technologies into existing power plants. A basin-scale, water stress framework is applied to estimate the added stresses on freshwater resources due to CCS installations. The scenario-based illustrative examples indicate the need for a full analysis of the inter-relationship between implementing different CCS technologies in the electric generation sector and the water system. Such analyses can be examined in future studies via an integrated energy-water nexus approach. Furthermore, the current article highlights the need for integrating the environmental, economic, and societal aspects of CCS deployment into future assessment of the viability of CCS operations and how to make water systems less vulnerable to CCS impacts

Examining the Robustness of a Spatial Bootstrap Regional Approach for Radar-Based Hourly Precipitation Frequency Analysis

Radar-based Quantitative Precipitation Estimates (QPE) provide rainfall products with high temporal and spatial resolutions as opposed to sparse observations from rain gauges. Radar-based QPE’s have been widely used in many hydrological and meteorological applications; however, using these high-resolution products in the development of Precipitation Frequency Estimates (PFE) is impeded by their typically short-record availability. The current study evaluates the robustness of a spatial bootstrap regional approach, in comparison to a pixel-based (i.e., at site) approach, to derive PFEs using hourly radar-based multi-sensor precipitation estimation (MPE) product over the state of Louisiana in the US. The spatial bootstrap sampling technique augments the local pixel sample by incorporating rainfall data from surrounding pixels with decreasing importance when distance increases. We modeled extreme hourly rainfall data based on annual maximum series (AMS) using the generalized extreme value statistical distribution. The results showed a reduction in the uncertainty bounds of the PFEs when using the regional spatial bootstrap approach compared to the pixel-based estimation, with an average reduction of 10% and 2% in the 2- and 5-year return periods, respectively. Using gauge-based PFE’s as a reference, the spatial bootstrap regional approach outperforms the pixel-based approach in terms of robustness to outliers identified in the radar-based AMS of some pixels. However, the systematic bias inherent to radar-based QPE especially for extreme rainfall cases, appear to cause considerable underestimation in PFEs in both the pixel-based and the regional approaches

Inferring the joint operation of high Aswan dam and Toshka depression using multi-sensor satellite approach

Remote sensing observations with high spatial and temporal resolutions have been successful in overcoming the challenge of data availability in ungauged basins. In this study, we applied a Multi-Sensor Satellite (MSS) approach to understand the reservoir operation in the Nile river basin (NRB) with the focus on the joint operation of High Aswan Dam (HAD) and Toshka depression, located in the southwestern part of HAD. The MSS data, which integrates Landsat (5-8), Sentinel-2A, MODIS with hydrological model outputs, are used in a water balance model to derive the operation of HAD reservoir and Toshka depression. The results show that the MSS approach has a reasonable skill when modelling the Toshka inflow (i.e. HAD spillway outflow) with a Relative Error and R2 of −19.14% and 0.79, respectively (for the period 1998–2002). Overall, our study provides a framework that harnesses free available sensors to infer the operation of lakes and reservoirs in the NRB

Accounting for Inter-Annual and Seasonal Variability in Assessment of Water Supply Stress: Perspectives from a Humid Region in the USA

Stresses on water systems can be quantitatively assessed through indices that account for water demand relative to water availability, e.g., the Water Supply Stress Index (WaSSI). However, as a result of adopting deterministic supply-driven approaches, limited attention is paid to the potential impacts of climatic variability on quantifying water stresses. The current study aimed to account for the impacts of inter-annual and intra-annual variability in the WaSSI stress index and to provide insights into potential opportunities for better water management practices. The results from our analysis indicate that looking only at average stresses can substantially mask the important impacts of climate variability. Louisiana, as a typical example of humid regions in the USA, is subjected to high levels of stresses (WaSSI exceeds 1.0) with higher inter-annual variability in watersheds where thermoelectric power plants exist and extensive water is used for cooling process. In addition, intra-annual variability in some watersheds shows periodicity in terms of seasonal stress distributions due to variability in surface water supply and water demand. Our analysis indicated that the stress variability grows as the median WaSSI increases but up to a certain threshold level and then the variability decreases for very high stress levels. For the annual and monthly scales, the peak variability, quantified as the width of the 2.5-97.5 stress percentiles, reached 68% for a median annual WaSSI of 1.00 and 100% for a median monthly WaSSI of 1.15, respectively. Various decisions related to water use and management can be driven by such variability, at both annual and intra-annual scales. Hence, these results have important implications for applied water resource studies aiming to formulate water management policies and improve water system sustainability under climate variability

A Framework for Incorporating the Impact of Water Quality on Water Supply Stress: An Example from Louisiana, USA

© 2017 The Authors. Journal of the American Water Resources Association published by Wiley Periodicals, Inc. on behalf of American Water Resources Association. Water of poor quality can directly impact the budget of water available for key user groups. Despite this importance, methods for quantifying the impact of water quality on water availability remain elusive. Here, we develop a new framework for incorporating the impact of water quality on water supply by modifying the Water Supply Stress Index (WaSSI). We demonstrate the usefulness of the framework by investigating the impact of high salinity waters on the availability of irrigation water for agriculture in Louisiana. The WaSSI was deconstructed into sectoral components such that the total available water supply could be reduced for a particular demand sector (agricultural irrigation in this example) based on available water quality information. The results for Louisiana highlight substantial impacts on water supply stress for farmers attributable to the landward encroachment of saline surface water and groundwater near the coast. Areas of high salinity near the coast also increased the competition for freshwater resources among the industrial, municipal, and agricultural demand sectors in the vicinities of the municipal areas of Lake Charles, Lafayette, and Baton Rouge, Louisiana. The framework developed here is easily adaptable for other water quality concerns and for other demand sectors, and as such can serve as a useful tool for water managers

Integrating multi-sensor observations and rainfall-runoff inundation modeling for mapping flood extents over the Nile River basin: example from the 2020 flooding in Sudan

Understanding the dynamics of flooding events is crucial to mitigate flood risks, particularly in developing nations like Sudan. This study combines multi-sensor approaches with Rainfall-Runoff-Inundation (RRI) modeling to predict flood inundation extent over the Nile River Basin (NRB). Building upon the RRI model, we firstly simulated the streamflow over the Blue Nile basin and the White Nile basin. Our results show a good agreement between the observed and the simulated streamflow at both daily and monthly scales, e.g. NSE = 0.72 and R2 = 0.85 for daily simulations at Khartoum station. Further, we compared the inundation extents from the RRI model with derived inundation maps from different satellite images (Sentinel-1, Sentinel-2, Landsat-8, and MODIS). The results indicate the potential to overcome the limitation of data scarcity in developing regions and hence provide a supportive assessment tool for flood risk maps in the NRB

Large Ensemble Diagnostic Evaluation of Hydrologic Parameter Uncertainty in the Community Land Model Version 5 (CLM5)

Abstract Land surface models such as the Community Land Model version 5 (CLM5) seek to enhance understanding of terrestrial hydrology and aid in the evaluation of anthropogenic and climate change impacts. However, the effects of parametric uncertainty on CLM5 hydrologic predictions across regions, timescales, and flow regimes have yet to be explored in detail. The common use of the default hydrologic model parameters in CLM5 risks generating streamflow predictions that may lead to incorrect inferences for important dynamics and/or extremes. In this study, we benchmark CLM5 streamflow predictions relative to the commonly employed default hydrologic parameters for 464 headwater basins over the conterminous United States (CONUS). We evaluate baseline CLM5 default parameter performance relative to a large (1,307) Latin Hypercube Sampling‐based diagnostic comparison of streamflow prediction skill using over 20 error measures. We provide a global sensitivity analysis that clarifies the significant spatial variations in parametric controls for CLM5 streamflow predictions across regions, temporal scales, and error metrics of interest. The baseline CLM5 shows relatively moderate to poor streamflow prediction skill in several CONUS regions, especially the arid Southwest and Central U.S. Hydrologic parameter uncertainty strongly affects CLM5 streamflow predictions, but its impacts vary in complex ways across U.S. regions, timescales, and flow regimes. Overall, CLM5's surface runoff and soil water parameters have the largest effects on simulated high flows, while canopy water and evaporation parameters have the most significant effects on the water balance