Searching For Empirical Evidence Of Complex Hydrological Behavior In Urbanizing Basins

Urban living accounts for approximately 50% of the worldwide population and this percentage will continue to rise in the decades to come. Urbanizing landscapes and their associated processes represent key aspects of coupled natural and human systems. Additionally, they can influence and cause impacts on regional broad-scale basin hydrology, stream and riparian ecosystems, and water quality. For this research, it is hypothesized that streamflow and water quality time series data for urbanizing basins may exhibit signs of complexity such as long range correlations, state transitions, and chaotic dynamics. To explore this hypothesis, we will use tools from statistical physics and ecology such as multifractal detrended fluctuation analysis, critical slowing down, and chaos-based time series analysis. We will apply these tools to streamflow, rainfall, and water quality data for nearly 40 stream gauges located in the metropolitan areas of cities across the US. It is expected that this analysis will reveal signs of complex behavior different from that already observed in natural basins, as well as new and unique behavior. The results from this research could help support and provide empirical evidence for developing improved data-driven models

Probablistic Rainfall Forecasting Using Single-Valued Rainfall Forecasts for Risk-Based Water Management at the South Florida Water Management District

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Improving Flood Forecasting in the Susquehanna River Basin Using a Probabilistic Approach

Accurate and reliable flood forecasts are important for flood prevention, minimizing flood damages, decision making and sustainable watershed management. Uncertainty in flood forecasting may arise from meteorological variables (i.e. precipitation and temperature) as well as different hydrologic sources, such as, hydrologic model structure, parameters, initial and boundary conditions. Probabilistic flood forecasting using ensembles can reduce the uncertainty in flood forecasting and improve accuracy. In this study, we use meteorological forecast ensembles (precipitation and land surface temperature) from the National Centers for Environmental Prediction (NCEP) 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2) to force a distributed hydrologic model and produce streamflow forecasts. The quality of streamflow forecasts are verified in a small headwater basin and a large basin in the north branch of Susquehanna River. The verification is done based on the streamflow amount, forecast lead time, season, and aggregation period for various forecasting scenarios. Ultimately, the verification results provide valuable and useful guidance regarding the potential application and accuracy of probabilistic flood forecasting in the Susquehanna River basin

Machine Learning for Postprocessing Medium-range Ensemble Streamflow Forecasts

Skillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles and a distributed hydrological model to generate ensemble streamflow forecasts at medium-range lead times (1 - 7 days). We demonstrate a case study for machine learning application in postprocessing ensemble streamflow forecasts in the Upper Susquehanna River basin in the eastern United States. For forecast verification, we use different metrics such as skill score and reliability diagram conditioned upon the lead time, flow threshold, and season. The verification results show that the machine learning postprocessor can improve streamflow forecasts relative to low complexity forecasts (e.g., climatological and temporal persistence) as well as deterministic and raw ensemble forecasts. As compared to the raw ensembles, relative gain in forecast skill from postprocessor is generally higher at medium-range timescales compared to shorter lead times; high flows compared to low-moderate flows, and warm-season compared to the cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts

Impacts of Global Warming of 1.5, 2.0 and 3.0 °C on Hydrologic Regimes in the Northeastern U.S.

Regional climate change impacts show a wide range of variations under different levels of global warming. Watersheds in the northeastern region of the United States (NEUS) are projected to undergo the most severe impacts from climate change in the forms of extreme precipitation events, floods and drought, sea level rise, etc. As such, there is high possibility that hydrologic regimes in the NEUS may be altered in the future, which can be absolutely devastating for managing water resources and ecological balance across different watersheds. In this study, we present a comprehensive impact analysis using different hydrologic indicators across selected watersheds in the NEUS under different thresholds of global temperature increases (1.5, 2.0 and 3.0 °C). Precipitation and temperature projections from fourteen downscaled Global Circulation Models (GCMs) under the representative concentration pathway (RCP) 8.5 greenhouse gas concentration pathway are used as inputs into a distributed hydrological model to obtain future streamflow conditions. Overall, the results indicate that the majority of the selected watersheds will enter a wetter regime, particularly during the months of winter, while flow conditions during late summer and fall indicate a dry future under all three thresholds of temperature increase. The estimation of time of emergence of new hydrological regimes show large uncertainties under 1.5 and 2.0 °C global temperature increases; however, most of the GCM projections show a strong consensus that new hydrological regimes may appear in the NEUS watersheds under 3.0 °C temperature increase

Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short-to medium-range forecast lead times (day 1-7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised of the following components: (i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); (ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Re-forecast version 2 (GEFSRv2); (iii) NOAA's Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); (iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; (v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and (vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the US Middle Atlantic region, ranging in size from 381 to 12 362 km(2). Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (>3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble streamflow forecasts, particularly in the cool season, but QR outperforms ARX(1,1). The scenarios that implement preprocessing and postprocessing separately tend to perform similarly, although the postprocessing-alone scenario is often more effective. The scenario involving both preprocessing and postprocessing consistently outperforms the other scenarios. In some cases, however, the differences between this scenario and the scenario with postprocessing alone are not as significant. We conclude that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.NOAA/NWS NA14NWS468001

Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

In this study, a new hybrid model integrated adaptive neuro fuzzy inference system with Firefly Optimization algorithm (ANFIS-FFA), is proposed for forecasting monthly rainfall with one-month lead time. The proposed ANFIS-FFA model is compared with standard ANFIS model, achieved using predictor-predictand data from the Pahang river catchment located in the Malaysian Peninsular. To develop the predictive models, a total of fifteen years of data were selected, split into nine years for training and six years for testing the accuracy of the proposed ANFIS-FFA model. To attain optimal models, several input combinations of antecedents’ rainfall data were used as predictor variables with sixteen different model combination considered for rainfall prediction. The performances of ANFIS-FFA models were evaluated using five statistical indices: the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), Willmott’s Index (WI), root mean square error (RMSE) and mean absolute error (MAE). The results attained show that, the ANFIS-FFA model performed better than the standard ANFIS model, with high values of R2, NSE and WI and low values of RMSE and MAE. In test phase, the monthly rainfall predictions using ANFIS-FFA yielded R2, NSE and WI of about 0.999, 0.998 and 0.999, respectively, while the RMSE and MAE values were found to be about 0.272 mm and 0.133 mm, respectively. It was also evident that the performances of the ANFIS-FFA and ANFIS models were very much governed by the input data size where the ANFIS-FFA model resulted in an increase in the value of R2, NSE and WI from 0.463, 0.207 and 0.548, using only one antecedent month of data as an input (t-1), to almost 0.999, 0.998 and 0.999, respectively, using five antecedent months of predictor data (t-1, t-2, t-3, t-6, t-12, t-24). We ascertain that the ANFIS-FFA is a prudent modelling approach that could be adopted for the simulation of monthly rainfall in the present study region

Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments

Climate patterns, including rainfall prediction, is one of the most complex problems for hydrologist. It is inherited by its natural and stochastic phenomena. In this study, a new approach for rainfall time series forecasting is introduced based on the integration of three stochastic modelling methods, including the seasonal differencing, seasonal standardization and spectral analysis, associated with the genetic algorithm (GA). This approach is specially tailored to eradicate the periodic pattern effects notable on the rainfall time series stationarity behaviour. Two different climates are selected to evaluate the proposed methodology, in tropical and semi-arid regions (Malaysia and Iraq). The results show that the predictive model registered an acceptable result for the forecasting of rainfall for both the investigated regions. The attained determination coefficient (R2) for the investigated stations was approx. 0.91, 0.90 and 0.089 for Mosul, Baghdad and Basrah (Iraq), and 0.80, 0.87 and 0.94 for Selangor, Negeri Sembilan and Johor (Malaysia)