Research Publications (Food Safety)

Water Resources Research, Accepted Article. The use of hydro-meteorological forecasts in water resources management holds great promise as a soft pathway to improve system performance.

Despite a multitude of small catchment studies, we lack a deep understanding of how variations in critical zone architecture lead to variations in hydrologic states and fluxes. This study characterizes hydrologic dynamics of fifteen catchments of the US Critical Zone Observatory (CZO) Network where we hypothesized that our understanding of subsurface structure would illuminate patterns of hydrologic partitioning.

Sub‐seasonal streamflow forecasts, with lead times of 1‐30 days, provide valuable information for operational water resource management. This paper introduces the Multi‐Temporal Hydrological Residual Error model (MuTHRE) to address the challenge of obtaining “seamless” sub‐seasonal forecasts, i.e., daily forecasts with consistent high‐quality performance over multiple lead times (1‐30 days) and aggregation scales (daily to monthly).

Use of Real‐Time Control (RTC) technology in Rainwater Harvesting Systems (RWH) can improve performance across water supply, flood protection, and environmental flow provision. Such systems make the most of rainfall forecast information, to release water prior to storm events and thus minimise uncontrolled overflows. To date, most advanced applications have adopted 24‐hr forecast information, leaving longer‐term forecasts largely untested.

Radar rainfall nowcasting, the process of statistically extrapolating the most recent rainfall observation, is increasingly used for very‐short‐range rainfall forecasting (less than six hours ahead). We performed a large‐sample analysis of 1533 events, systematically selected for four event durations and twelve lowland catchments (6.5—957 km2), to determine the predictive skill of nowcasting.

A flood forecasting system commonly consists of at least two essential components, i.e., a numerical weather prediction (NWP) model to provide rainfall forecasts and a hydrological/hydraulic model to predict the hydrological response. Whilst being widely used for flood forecasting, hydrological models only provide a simplified representation of the physical processes of flooding due to negligence of strict momentum conservation.

Recent technological advances in representation of processes in numerical climate models have led to skilful predictions, which can consequently increase the confidence of hydrological predictions and usability of hydro‐climatic services. Given that many water‐related stakeholders are affected by seasonal hydrological variations, there is a need to manage such variations to their advantage through better understanding of the drivers that influence hydrological predictability.

The quality of the forecasts, i.e., the accuracy in predicting the observed streamflow, affects the decisions that can be taken thus determining the success or failure of hydropower operations, i.e., the so‐called forecast value.

The identification of natural fractures and the wells they connect is crucial for the development of geological reservoirs because it may have an important impact on reservoir model construction and hydraulic fracture propagation. In this study we investigated the use of a novel data source, the microbial community composition in the reservoir formation fluids, for identification of interwell connectivity caused by natural fractures.

Cooperation in transboundary river basins can make water resources systems more efficient and benefit riparian stakeholders. However, in a basin with upstream and downstream stakeholders that have different interests, non‐cooperative outcomes have often been observed. These can be described by a one‐shot prisoners’ dilemma game where non‐cooperation (defection) is a dominant equilibrium strategy.

Increasingly variable hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide. These trends emphasize the need of accurate medium‐ to long‐term predictions to timely prompt anticipatory operations.

In a recent article in this journal, Luby, Polasky, and Swackhamer come to the provocative conclusion that urban water prices in the United States are “cheaper when drier.” They also argue that utilities fail to provide affordable water and that they charge less for “additional” use compared to “essential” use. We challenge these claims.

We propose a time series modeling approach based on nonlinear dynamical systems to recover the underlying dynamics and predictability of streamflow and to produce projections with identifiable skill. First, a wavelet spectral analysis is performed on the time series to identify the dominant quasiperiodic bands. The time series is then reconstructed across these bands and summed to obtain a signal time series.

Phase five of the Coupled Model Intercomparison Project enabled a range of decadal modeling experiments where climate models were initialized with observations and allowed to evolve freely for 10–30 years. However, climate models struggle to realistically simulate rainfall and the skill of rainfall prediction in decadal experiments is poor.