Assessment of vulnerability to water shortage in semi-arid river basins: The value of demand reduction and storage capacity

Interest in securing reliable water supplies has increased due to climate change and rapid population growth. This challenge is significant in growing areas with limited water supplies. To meet water demands, water managers are considering new storage infrastructure to increase the reliability of water supplies while also identifying opportunities to reduce water use per person. Although these strategies change water consumption patterns, their success at reducing shortages across space and time for different climate change scenarios remains unclear. In this paper, population- and climate-dependent future water supply and demand models are developed and integrated into a water allocation model calibrated for the South Platte River Basin of Colorado. Eight future climate scenarios are simulated using four statistically downscaled models from the Coupled Model Inter-Comparison Project Phase 5 (CMIP5) with two Representative Concentration Pathways (RCP). Lastly, findings from the water allocation model simulations are generalized beyond the study area using a novel approach by introducing dimensionless indices to characterize water shortage and basin conditions. Results reveal a threshold ratio of total storage capacity to mean water supply with a value of 0.64 above which additional storage has no effect on total water shortages. This threshold communicates the limitation of building storage infrastructure as a strategy to adapt to decreasing average water supplies for basins considering increasing storage capacity. However, basins with low current capacity are likely to fall below the threshold and could invest in reservoirs to mitigate future shortages.

Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework

In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input–output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out with a Bayesian procedure based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and, hence, all of the input parameters, via the associated posterior probabilities.

Improved Streamflow Calibration of a Land Surface Model by the Choice of Objective Functions—A Case Study of the Nakdong River Watershed in the Korean Peninsula

Long-term streamflow simulations of the Land Surface Models (LSMs) are necessary for the comprehensive evaluation of hydrological responses to climate change. The high complexity and uncertainty in the LSM modelling require the model calibration to improve the simulation performance and stability. Objective functions are commonly used in the calibration process, and the choice of objective functions plays a crucial role in model performance identification. The Kling and Gupta Efficiency (KGE) has been widely used in the hydrological model calibration by the measure of the three components (variability, bias, and correlation) decomposed from the Nash and Sutcliffe Efficiency (NSE). However, there is a clear tendency of systematic errors in the peak flow and/or water balance of streamflow time series optimized by the KGE calibration when the correlation between simulations and observations is relatively low. For a more balanced optimal solution of the KGE, this study has proposed the adjusted KGE (aKGE) by substituting the normalized correlation score in the KGE. The proposed aKGE was assessed by long-term daily streamflow simulation results from the Common Land Model (CoLM) for the calibration (2000–2009) and validation (2010–2019) periods in the Nakdong River Watershed, Korea. The case study demonstrated that the aKGE calibration can improve the simulation performance of high flow and annual average flow with a slightly inferior correlation of flows compared with the KGE and NSE criteria.

Flood risk behaviors of United States riverine metropolitan areas are driven by local hydrology and shaped by race

Flooding risk results from complex interactions between hydrological hazards (e.g., riverine inundation during periods of heavy rainfall), exposure, vulnerability (e.g., the potential for structural damage or loss of life), and resilience (how well we recover, learn from, and adapt to past floods). Building on recent coupled conceptualizations of these complex interactions, we characterize human-flood interactions (collective memory and risk-enduring attitude) at a more comprehensive scale than has been attempted to date across 50 US metropolitan statistical areas with a sociohydrologic (SH) model calibrated with accessible local data (historical records of annual peak streamflow, flood insurance loss claims, active insurance policy records, and population density). A cluster analysis on calibrated SH model parameter sets for metropolitan areas identified two dominant behaviors: 1) "risk-enduring" cities with lower flooding defenses and longer memory of past flood loss events and 2) "risk-averse" cities with higher flooding defenses and reduced memory of past flooding. These divergent behaviors correlated with differences in local stream flashiness indices (i.e., the frequency and rapidity of daily changes in streamflow), maximum dam heights, and the proportion of White to non-White residents in US metropolitan areas. Risk-averse cities tended to exist within regions characterized by flashier streamflow conditions, larger dams, and larger proportions of White residents. Our research supports the development of SH models in urban metropolitan areas and the design of risk management strategies that consider both demographically heterogeneous populations, changing flood defenses, and temporal changes in community risk perceptions and tolerance.

Impacts of climate change, land-use change and phosphorus reduction on phytoplankton in the River Thames (UK)

Potential increases of phytoplankton concentrations in river systems due to global warming and changing climate could pose a serious threat to the anthropogenic use of surface waters. Nevertheless, the extent of the effect of climatic alterations on phytoplankton concentrations in river systems has not yet been analysed in detail. In this study, we assess the impact of a change in precipitation and temperature on river phytoplankton concentration by means of a physically-based model. A scenario-neutral methodology has been employed to evaluate the effects of climate alterations on flow, phosphorus concentration and phytoplankton concentration of the River Thames (southern England). In particular, five groups of phytoplankton are considered, representing a range of size classes and pigment phenotypes, under three different land-use/land-management scenarios to assess their impact on phytoplankton population levels. The model results are evaluated within the framework of future climate projections, using the UK Climate Projections 09 (UKCP09) for the 2030s. The results of the model demonstrate that an increase in average phytoplankton concentration due to climate change is highly likely to occur, with the magnitude varying depending on the location along the River Thames. Cyanobacteria show significant increases under future climate change and land use change. An expansion of intensive agriculture accentuates the growth in phytoplankton, especially in the upper reaches of the River Thames. However, an optimal phosphorus removal mitigation strategy, which combines reduction of fertiliser application and phosphorus removal from wastewater, can help to reduce this increase in phytoplankton concentration, and in some cases, compensate for the effect of rising temperature.

The challenge of predicting flash floods from thunderstorm rainfall

A major characteristic of the hydrometeorology of semi-arid regions is the occurrence of intense thunderstorms that develop very rapidly and cause severe flooding. In summer, monsoon air mass is often of subtropical origin and is characterized by convective instability. The existing observational network has major deficiencies for those regions in providing information that is important to run-off generation. Further, because of the complex interactions between the land surface and the atmosphere, mesoscale atmospheric models are currently able to reproduce only general features of the initiation and development of convective systems. In our research, several interrelated components including the use of satellite data to monitor precipitation, data assimilation of a mesoscale regional atmospheric model, modification of the land component of the mesoscale model to better represent the semi-arid region surface processes that control run-off generation, and the use of ensemble forecasting techniques to improve forecasts of precipitation and run-off potential are investigated. This presentation discusses our ongoing research in this area; preliminary results including an investigation related to the unprecedented flash floods that occurred across the Las Vegas valley (Nevada, USA) in July of 1999 are discussed.