Modification of the National Weather Service Distributed Hydrologic Model for subsurface water exchanges between grids

To account for spatial variability of precipitation, as well as basin physiographic properties, the National Weather Service (NWS) has developed a distributed version of its hydrologic component, termed the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Because channels are the only source of water exchange between neighboring computational elements, the absence of such exchange has been identified as a weakness in the model. The primary objective of this paper is to modify the model structure to account for subsurface water exchanges without dramatically altering the conceptual framework of the water balance module. The subsurface exchanges are established by partitioning the slow response components released from the lower layer storages into two parts: the first part involves the grid's conceptual channel, while the second is added to the lower layer storages of the downstream pixel. Realizing the deficiency of the water balance module to locate the lower zone layers in sufficient depths, a complementary study is conducted to test the feasibility of further improvement in the modified model by equally shifting downward the lower zone layers of all pixels over the basin. The Baron Fork at Eldon, Oklahoma, is chosen as the test basin. Ten years of grid-based multisensor precipitation data are used to investigate the effects of the modification, plus shifting the lower zone layers on model performance. The results show that the modified-shifted HL-RDHM can markedly improve the streamflow simulations at the interior point, as well as very high peak-flow simulations at the basin's outlet. Copyright 2011 by the American Geophysical Union

Geographic Information Systems (GIS)-based spatially distributed model for runoff routing

A method is proposed for routing spatially distributed excess precipitation over a watershed to produce runoff at its outlet. The land surface is represented by a (raster) digital elevation model from which the stream network is derived. A routing response function is defined for each digital elevation model cell so that water movement from cell to cell can be convolved to give a response function along a flow path and responses from all cells can be summed to give the outlet hydrograph. An example application of analysis of runoff on Waller Creek in Austin, Texas, is presented.Waller Creek Working Grou

Toward improved streamflow forecasts: Value of semidistributed modeling

The focus of this study is to assess the performance improvements of semidistributed applications of the U.S. National Weather Service Sacramento Soil Moisture Accounting model on a watershed using radar-based remotely sensed precipitation data. Specifically, performance comparisons are made within an automated multicriteria calibration framework to evaluate the benefit of "spatial distribution" of the model input (precipitation), structural components (soil moisture and streamflow routing computations), and surface characteristics (parameters). A comparison of these results is made with those obtained through manual calibration. Results indicate that for the study watershed, there are performance improvements associated with semidistributed model applications when the watershed is partitioned into three subwatersheds; however, no additional benefit is gained from increasing the number of subwatersheds from three to eight. Improvements in model performance are demonstrably related to the spatial distribution of the model input and streamflow routing. Surprisingly, there is no improvement associated with the distribution of the surface characteristics (model parameters)

Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km-2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash-Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of page1506 the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins

A GIS based 3D-Routing-Model to estimate and reduce CO2-emissions of distribution transports

Schröder, M., & Cabral, P. (2019). Eco-friendly 3D-Routing: A GIS based 3D-Routing-Model to estimate and reduce CO2-emissions of distribution transports. Computers, Environment and Urban Systems, 73, 40-55. DOI: 10.1016/j.compenvurbsys.2018.08.002Road freight transportation accounts for a significant share of the worldwide CO2-Emissions, indicating that respective operations are not sustainable. Regarding the forecasted increase in CO2-Emissions from this sector, undertaking responsibilities for its environmental impact are needed. Although technical and strategic solutions to reduce emissions have been introduced, or are in development, these rarely yield instant emission reduction potentials. A strategic approach to reducing them instantly, based on the given infrastructure and existing vehicle fleet, may be achieved through route optimization. Route optimization is a well-researched topic in the transportation domain. However, it is mainly used to reduce transportation times and expenses. Rising expectations towards sustainability by authorities and consumers led to an increased interest in route optimization in which environmental externalities, such as fuel consumption and CO2-Emissions are minimized. This paper introduces a Geographic Information System (GIS) based 3D-Routing-Model, which incorporates models to estimate vehicle fuel consumption while taking effects, such as road inclination and varying velocities into account. The proposed model utilizes a Digital Elevation Model (DEM) to enrich a road network with elevation data. The 3D-Routing-Model is applied in different distribution scenarios within the framework of an artificial company in the Lisbon Metropolitan Area, Portugal to evaluate the effects of road inclination on vehicles fuel consumption and its proportional CO2-Emissions. Results indicate that eco-friendly routes can yield significant fuel and emission saving potentials of up to 20% in the tested scenarios. However, eco-friendly routes are characterized by longer distances as well as operation times, which leads to increased expenses. The question remains if companies within the transportation sector are more interested in maximizing their profits, or investing in a sustainable future.authorsversionpublishe

Eco-friendly 3d-routing : a GIS based 3d-routing-model to estimate and reduce co2-emissions of distribution transports

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesRoad Freight Transportation accounts for a significant share of the worldwide CO2-Emissions, indicating that respective operations are not sustainable. Regarding the forecasted increase in CO2-Emissions from Road Freight Transportation, this sector needs to undertake responsibilities for its environmental impact. Although technical and strategic solutions to reduce emissions have been introduced or are in development, such solutions rarely yield instant emission reduction potentials. A strategic approach to reduce them instantly, based on the given infrastructure and existing vehicle fleet, is represented through route optimization. Route optimization is a well-researched topic in the transportation domain. However, it is mainly used to reduce transportation times and expenses. Rising expectation towards sustainability through stakeholders such as authorities and consumers, let to an increased interest in route optimization where environmental externalities as fuel consumption and CO2-Emissions are minimized. This paper introduces a Geographic Information System (GIS) based 3D-Routing-Model, which incorporates models to estimate vehicle fuel consumption while taking effects as road inclination and varying velocities into account. The proposed model utilizes a Digital Elevation Model to enrich a Road Network with elevation data – An approach which is applicable to any area where respective data is available. To evaluate the effects of road inclination on a vehicles fuel consumption and its proportional CO2-Emissions, the 3D-Routing-Model is applied in different distribution scenarios within the framework of an artificial company in the Lisbon Metropolitan Area. The obtained results indicate that eco-friendly routes can yield significant fuel and emission saving potentials of up to 20 % in the tested scenarios. However, the results also indicate that eco-friendly routes are characterized through longer distances as well as operation times, which eventually leads to increased expenses. It remains the question if companies within the transportation sector are more interested in maximizing their profits, or to invest in a sustainable future