scholarly journals Advancing stream classification and hydrologic modeling of ungaged basins for environmental flow management in coastal southern California

2022 ◽  
Author(s):  
Stephen Adams ◽  
Brian Bledsoe ◽  
Eric Stein
Keyword(s):  
Southern California ◽  
Environmental Flow ◽  
Hydrologic Model ◽  
Stratify Flow ◽  
Model Errors ◽  
Streamflow Prediction ◽  
Flow Management ◽  
Stream Classification ◽  
Model Based ◽  
Error Metric

Abstract. Environmental streamflow management can improve the ecological health of streams by returning modified flows to more natural conditions. The Ecological Limits of Hydrologic Alteration (ELOHA) framework for developing regional environmental flow criteria has been implemented to reverse hydromodification across the heterogenous region of coastal southern California (So. CA) by focusing on two elements of the flow regime: streamflow permanence and flashiness. Within ELOHA, classification groups streams by hydrologic and geomorphic similarity to stratify flow-ecology relationships. Analogous grouping techniques are used by hydrologic modelers to facilitate streamflow prediction in ungaged basins (PUB) through regionalization. Most watersheds, including those needed for stream classification and environmental flow development, are ungaged. Furthermore, So. CA is a highly heterogeneous region spanning a gradient of urbanization, which presents a challenge for regionalizing ungaged basins. In this study, we develop a novel classification technique for PUB modeling that uses an inductive approach to group regional streams by modeled hydrologic similarity followed by deductively determining class membership with hydrologic model errors and watershed metrics. As a new type of classification, this “Hydrologic Model-based Classification” (HMC) prioritizes modeling accuracy, which in turn provides a means to improve model predictions in ungaged basins, while complementing traditional classifications and improving environmental flow management. HMC is developed by calibrating a regional catalog of process-based rainfall-runoff models, quantifying the hydrologic reciprocity of calibrated parameters that would be unknown in ungaged basins, and grouping sites according to hydrologic and physical similarity. HMC was applied to 25 USGS streamflow gages in the south coast region of California and was compared to other hybrid PUB approaches combining inductive and deductive classification. Using an Average Cluster Error metric, results show HMC provided the most hydrologically similar groups according to calibrated parameter reciprocity. Hydrologic Model-based Classification is relatively complex and time-consuming to implement, but it shows potential for advancing ungaged basin management. This study demonstrates the benefits of thorough stream classification using multiple approaches, and suggests that Hydrologic Model-based Classification has advantages for PUB and building the hydrologic foundation for environmental flow management.

scholarly journals On the sources of global land surface hydrologic predictability

2013 ◽  
Vol 17 (7) ◽  
pp. 2781-2796 ◽  
Author(s):  
S. Shukla ◽  
J. Sheffield ◽  
E. F. Wood ◽  
D. P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Forecast Skill ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Forecast Period ◽  
Data Set ◽  
Model Based ◽  
Snow Water

Abstract. Global seasonal hydrologic prediction is crucial to mitigating the impacts of droughts and floods, especially in the developing world. Hydrologic predictability at seasonal lead times (i.e., 1–6 months) comes from knowledge of initial hydrologic conditions (IHCs) and seasonal climate forecast skill (FS). In this study we quantify the contributions of two primary components of IHCs – soil moisture and snow water content – and FS (of precipitation and temperature) to seasonal hydrologic predictability globally on a relative basis throughout the year. We do so by conducting two model-based experiments using the variable infiltration capacity (VIC) macroscale hydrology model, one based on ensemble streamflow prediction (ESP) and another based on Reverse-ESP (Rev-ESP), both for a 47 yr re-forecast period (1961–2007). We compare cumulative runoff (CR), soil moisture (SM) and snow water equivalent (SWE) forecasts from each experiment with a VIC model-based reference data set (generated using observed atmospheric forcings) and estimate the ratio of root mean square error (RMSE) of both experiments for each forecast initialization date and lead time, to determine the relative contribution of IHCs and FS to the seasonal hydrologic predictability. We find that in general, the contributions of IHCs to seasonal hydrologic predictability is highest in the arid and snow-dominated climate (high latitude) regions of the Northern Hemisphere during forecast periods starting on 1 January and 1 October. In mid-latitude regions, such as the Western US, the influence of IHCs is greatest during the forecast period starting on 1 April. In the arid and warm temperate dry winter regions of the Southern Hemisphere, the IHCs dominate during forecast periods starting on 1 April and 1 July. In equatorial humid and monsoonal climate regions, the contribution of FS is generally higher than IHCs through most of the year. Based on our findings, we argue that despite the limited FS (mainly for precipitation) better estimates of the IHCs could lead to improvement in the current level of seasonal hydrologic forecast skill over many regions of the globe at least during some parts of the year.

scholarly journals Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data

2012 ◽  
Vol 16 (9) ◽  
pp. 3083-3099 ◽  
Author(s):  
H. Xie ◽  
L. Longuevergne ◽  
C. Ringler ◽  
B. R. Scanlon
Keyword(s):  
Regional Scale ◽  
Water Storage ◽  
Hydrologic Model ◽  
Sub Saharan Africa ◽  
Model Parameters ◽  
Total Water ◽  
Discharge Data ◽  
Model Based ◽  
Grace Data ◽  
Sub Saharan

Abstract. Irrigation development is rapidly expanding in mostly rainfed Sub-Saharan Africa. This expansion underscores the need for a more comprehensive understanding of water resources beyond surface water. Gravity Recovery and Climate Experiment (GRACE) satellites provide valuable information on spatio-temporal variability in water storage. The objective of this study was to calibrate and evaluate a semi-distributed regional-scale hydrologic model based on the Soil and Water Assessment Tool (SWAT) code for basins in Sub-Saharan Africa using seven-year (July 2002–April 2009) 10-day GRACE data and multi-site river discharge data. The analysis was conducted in a multi-criteria framework. In spite of the uncertainty arising from the tradeoff in optimising model parameters with respect to two non-commensurable criteria defined for two fluxes, SWAT was found to perform well in simulating total water storage variability in most areas of Sub-Saharan Africa, which have semi-arid and sub-humid climates, and that among various water storages represented in SWAT, water storage variations in soil, vadose zone and groundwater are dominant. The study also showed that the simulated total water storage variations tend to have less agreement with GRACE data in arid and equatorial humid regions, and model-based partitioning of total water storage variations into different water storage compartments may be highly uncertain. Thus, future work will be needed for model enhancement in these areas with inferior model fit and for uncertainty reduction in component-wise estimation of water storage variations.

scholarly journals Seasonal streamflow forecasts in the Ahlergaarde catchment, Denmark: effect of preprocessing and postprocessing on skill and statistical consistency

2017 ◽  
Author(s):  
Diana Lucatero ◽  
Henrik Madsen ◽  
Jens C. Refsgaard ◽  
Jacob Kidmose ◽  
Karsten H. Jensen
Keyword(s):  
Hydrological Model ◽  
Predictive Distribution ◽  
Low Flow ◽  
Model Errors ◽  
Streamflow Prediction ◽  
Initial State ◽  
Lower Accuracy ◽  
Spatially Distributed ◽  
Streamflow Forecasts ◽  
Statistical Consistency

Abstract. In the present study we analyze the effect of bias adjustments in both meteorological and streamflow forecasts on skill and reliability of monthly average streamflow and low flow forecasts. Both raw and pre-processed meteorological seasonal forecast from the European Center for Medium-Range Weather Forecasts (ECMWF) are used as inputs to a spatially distributed, coupled surface – subsurface hydrological model based on the MIKE SHE code in order to generate streamflow predictions up to seven months in advance. In addition to this, we postprocess streamflow predictions using an empirical quantile mapping that adjusts the predictive distribution in order to match the observed one. Bias, skill and statistical consistency are the qualities evaluated throughout the forecast generating strategies and we analyze where the different strategies fall short to improve them. ECMWF System 4-based streamflow forecasts tend to show a lower accuracy level than those generated with an ensemble of historical observations, a method commonly known as Ensemble Streamflow Prediction (ESP). This is particularly true at longer lead times, for the dry season and for streamflow stations that exhibit low hydrological model errors. Biases in the mean are better removed by postprocessing that in turn is reflected in the higher level of statistical consistency. However, in general, the reduction of these biases is not enough to ensure a higher level of accuracy than the ESP forecasts. This is true for both monthly mean and minimum yearly streamflow forecasts. We highlight the importance of including a better estimation of the initial state of the catchment, which will increase the capability of the system to forecast streamflow at longer leads.

scholarly journals Catering Information Needs from Global to Local Scales—Potential and Challenges with National Forest Inventories

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 800 ◽  
Author(s):  
Kangas ◽  
Räty ◽  
Korhonen ◽  
Vauhkonen ◽  
Packalen
Keyword(s):  
Information Needs ◽  
National Forest ◽  
Model Errors ◽  
Sampling Errors ◽  
Small Areas ◽  
Model Based ◽  
Forest Inventories ◽  
National Forest Inventories ◽  
Biased Estimators ◽  
National Information

Forest information is needed at global, national and local scales. This review aimed at providing insights of potential of national forest inventories (NFIs) as well as challenges they have to cater to those needs. Within NFIs, the authors address the methodological challenges introduced by the multitude of scales the forest data are needed, and the challenges in acknowledging the errors due to the measurements and models in addition to sampling errors. Between NFIs, the challenges related to the different harmonization tasks were reviewed. While a design-based approach is often considered more attractive than a model-based approach as it is guaranteed to provide unbiased results, the model-based approach is needed for downscaling the information to smaller scales and acknowledging the measurement and model errors. However, while a model-based inference is possible in small areas, the unknown random effects introduce biased estimators. The NFIs need to cater for the national information requirements and maintain the existing time series, while at the same time providing comparable information across the countries. In upscaling the NFI information to continental and global information needs, representative samples across the area are of utmost importance. Without representative data, the model-based approaches enable provision of forest information with unknown and indeterminable biases. Both design-based and model-based approaches need to be applied to cater to all information needs. This must be accomplished in a comprehensive way In particular, a need to have standardized quality requirements has been identified, acknowledging the possibility for bias and its implications, for all data used in policy making.

Model-Based Air Traffic Flow Management

2008 ◽  
Author(s):  
Padmanabhan Menon ◽  
Monish Tandale ◽  
Jason Kwan ◽  
Victor Cheng ◽  
Robert Windhorst ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Air Traffic ◽  
Flow Management ◽  
Air Traffic Flow Management ◽  
Model Based ◽  
Air Traffic Flow ◽  
Traffic Flow Management

scholarly journals A Method to Account for QPF Spatial Displacement Errors in Short-Term Ensemble Streamflow Forecasting

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3505
Author(s):  
Bradley Carlberg ◽  
Kristie Franz ◽  
William Gallus
Keyword(s):  
Heavy Precipitation ◽  
Hydrologic Model ◽  
Probability Of Detection ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Spatial Displacement ◽  
North Central ◽  
Distributed Hydrologic Model ◽  
Streamflow Forecasts ◽  
Made In

To account for spatial displacement errors common in quantitative precipitation forecasts (QPFs), a method using systematic shifting of QPF fields was tested to create ensemble streamflow forecasts. While previous studies addressed spatial displacement using neighborhood approaches, shifting of QPF accounts for those errors while maintaining the structure of predicted systems, a feature important in hydrologic forecasts. QPFs from the nine-member High-Resolution Rapid Refresh Ensemble were analyzed for 46 forecasts from 6 cases covering 17 basins within the National Weather Service North Central River Forecast Center forecasting region. Shifts of 55.5 and 111 km were made in the four cardinal and intermediate directions, increasing the ensemble size to 81 members. These members were input into a distributed hydrologic model to create an ensemble streamflow prediction. Overall, the ensemble using the shifted QPFs had an improved frequency of non-exceedance and probability of detection, and thus better predicted flood occurrence. However, false alarm ratio did not improve, likely because shifting multiple QPF ensembles increases the potential to place heavy precipitation in a basin where none actually occurred. A weighting scheme based on a climatology of displacements was tested, improving overall performance slightly compared to the approach using non-weighted members.

Classification of natural flow regimes in Australia to support environmental flow management

2010 ◽  
Vol 55 (1) ◽  
pp. 171-193 ◽  
Author(s):  
MARK J. KENNARD ◽  
BRADLEY J. PUSEY ◽  
JULIAN D. OLDEN ◽  
STEPHEN J. MACKAY ◽  
JANET L. STEIN ◽  
...  
Keyword(s):  
Flow Regimes ◽  
Environmental Flow ◽  
Flow Management ◽  
Natural Flow ◽  
Natural Flow Regimes

Quantifying parameter uncertainty in reservoir operation associated with environmental flow management

2018 ◽  
Vol 176 ◽  
pp. 1271-1282 ◽  
Author(s):  
Shan He ◽  
Xin'an Yin ◽  
Chunxue Yu ◽  
Zhihao Xu ◽  
Zhifeng Yang
Keyword(s):  
Parameter Uncertainty ◽  
Reservoir Operation ◽  
Environmental Flow ◽  
Flow Management

scholarly journals Seasonal Forecasting of Global Hydrologic Extremes: System Development and Evaluation over GEWEX Basins

2015 ◽  
Vol 96 (11) ◽  
pp. 1895-1912 ◽  
Author(s):  
Xing Yuan ◽  
Joshua K. Roundy ◽  
Eric F. Wood ◽  
Justin Sheffield
Keyword(s):  
North American ◽  
Land Surface ◽  
Climate Models ◽  
Water Cycle ◽  
System Development ◽  
Research Programme ◽  
Hydrologic Model ◽  
Streamflow Prediction ◽  
Climate Services ◽  
Hydrologic Extremes

Abstract Seasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales are among the grand challenges proposed by the World Climate Research Programme (WCRP) and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Cycle Experiment (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed that is based on coupled climate forecast models participating in the North American Multimodel Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX RHP river basins by comparing with ensemble streamflow prediction (ESP). The multimodel seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high f low ensemble forecasts, and better “real time” prediction for the 2012 North American extreme drought. The association of the onset of extreme hydrologic events with oceanic and land precursors is also investigated based on the joint distribution of forecasts and observations. Climate models have a higher probability of missing the onset of hydrologic extremes when there is no oceanic precursor. But oceanic precursor alone is insufficient to guarantee a correct forecast—a land precursor is also critical in avoiding a false alarm for forecasting extremes. This study is targeted at providing the scientific underpinning for the predictability of hydrologic extremes over GEWEX RHP basins and serves as a prototype for seasonal hydrologic forecasts within the Global Framework for Climate Services (GFCS).

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