scholarly journals Quantifying the effect of land use and land cover changes on green water and blue water in northern part of China

2009 ◽  
Vol 13 (6) ◽  
pp. 735-747 ◽  
Author(s):  
X. Liu ◽  
L. Ren ◽  
F. Yuan ◽  
V. P. Singh ◽  
X. Fang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Hydrological Model ◽  
Green Water ◽  
Model Parameters ◽  
Blue Water ◽  
Distributed Hydrological Model ◽  
Daily Streamflow ◽  
Runoff Model ◽  
Hydrological Effects

Abstract. Changes in land use and land cover (LULC) have been occurring at an accelerated pace in northern parts of China. These changes are significantly impacting the hydrology of these parts, such as Laohahe Catchment. The hydrological effects of these changes occurring in this catchment were investigated using a semi-distributed hydrological model. The semi-distributed hydrological model was coupled with a two-source potential evaportranspiration (PET) model for simulating daily runoff. Model parameters were calibrated using hydrometeorological and LULC data for the same period. The LULC data were available for 1980, 1989, 1996 and 1999. Daily streamflow measurements were available from 1964 to 2005 and were divided into 4 periods: 1964–1979, 1980–1989, 1990–1999 and 2000–2005. These periods represented four different LULC scenarios. Streamflow simulation was conducted for each period under these four LULC scenarios. The results showed that the change in LULC influenced evapotranspiration (ET) and runoff. The LULC data showed that from 1980 to 1996 grass land and water body had decreased and forest land and crop land had increased. This change caused the evaporation from vegetation interception and vegetation transpiration to increase, whereas the soil evaporation tended to decrease. Thus during the period of 1964–1979 the green water or ET increased by 0.95%, but the blue water or runoff decreased by 8.71% in the Laohahe Catchment.

scholarly journals Quantifying the effect of land use and land cover change on green water and blue water in northern part of China

2008 ◽  
Vol 5 (4) ◽  
pp. 2425-2457 ◽  
Author(s):  
X. Liu ◽  
L. Ren ◽  
F. Yuan ◽  
V. P. Singh ◽  
X. Fang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Hydrological Model ◽  
Soil Evaporation ◽  
Green Water ◽  
Blue Water ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Land Cover Data ◽  
Saturation Excess

Abstract. In order to investigate the effect of land use and land cover changes on hydrological process in northern parts of China, a distributed hydrological model was developed and applied in the Laohahe catchment. The direct evaporation from the intercepted water, potential canopy transpiration and potential soil evaporation were computed using a physically-based two-source potential evapotranspiration model, which would be regarded as input to the distributed hydrological model for the computation of actual evaportranspiration. Runoff generation was based on mixed runoff mechanisms of infiltration excess runoff and saturation excess runoff and the Muskingum-Cunge method was adopted for flow routing. The land cover data were available for 1980, 1989, 1996 and 1999. Daily streamflow measurements were available from 1964 to 2005 and were divided into 4 periods: 1964–1979, 1980–1989, 1990–1999 and 2000–2005, based on the land cover scenarios. The distributed hydrological model was coupled with a two-source potential evaportranspiration model for simulating daily runoff. The result of runoff simulation showed that the saturation excess runoff generation was dominant in the catchment. Model parameters were calibrated using hydrometeorological and land cover data corresponding to the same period. Streamflow simulation was conducted for each period under these four land cover scenarios. The results showed that the change of land use and land cover had a significant influence on evapotranspiration and runoff. The land cover data showed that forest land and water body had decreased from 1980 through 1999 and farm land and grass land had increased. This change caused the vegetation interception evaporation and vegetation transpiration to decrease, whereas the soil evaporation tended to increase. Thus the green water decreased but the blue water increased over the Laohahe catchment. This result was inconsistent with the fact that runoff ratio had a tendency of decrease in the catchment in 2000. It is this reason that water use out of stream channel has been increasing in recent years.

scholarly journals Regionalizing rainfall–runoff model parameters to predict the daily streamflow of ungauged catchments in the dry tropics

2009 ◽  
Vol 40 (5) ◽  
pp. 433-444 ◽  
Author(s):  
David A. Post
Keyword(s):  
Input Data ◽  
Water Yield ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Wet Season ◽  
Daily Streamflow ◽  
Total Length ◽  
Dry Tropics ◽  
Rainfall Runoff Model ◽  
Runoff Model

A methodology has been derived which allows an estimate to be made of the daily streamflow at any point within the Burdekin catchment in the dry tropics of Australia. The input data requirements are daily rainfall (to drive the rainfall–runoff model) and mean average wet season rainfall, total length of streams, percent cropping and percent forest in the catchment (to regionalize the parameters of the rainfall–runoff model). The method is based on the use of a simple, lumped parameter rainfall–runoff model, IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data). Of the five parameters in the model, three have been set to constants to reflect regional conditions while the other two have been related to physio-climatic attributes of the catchment under consideration. The parameter defining total catchment water yield (c) has been estimated based on the mean average wet season rainfall, while the streamflow recession time constant (τ) has been estimated based on the total length of streams, percent cropping and percent forest in the catchment. These relationships have been shown to be applicable over a range of scales from 68–130,146 km2. However, three separate relationships were required to define c in the three major physiographic regions of the Burdekin: the upper Burdekin, Bowen and Suttor/lower Burdekin. The invariance of the relationships with scale indicates that the dominant processes may be similar across a range of scales. The fact that different relationships were required for each of the three major regions indicates the geographic limitations of this regionalization approach. For most of the 24 gauged catchments within the Burdekin the regionalized rainfall–runoff models were nearly as good as or better than the rainfall–runoff models calibrated to the observed streamflow. In addition, models often performed better over the simulation period than the calibration period. This indicates that future improvements in regionalization should focus on improving the quality of input data and rainfall–runoff model conceptualization rather than on the regionalization procedure per se.

scholarly journals Diagnostic calibration of a hydrological model in an alpine area

2014 ◽  
Vol 11 (1) ◽  
pp. 1253-1300 ◽  
Author(s):  
Z. He ◽  
F. Tian ◽  
H. C. Hu ◽  
H. V. Gupta ◽  
H. P. Hu
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Generation Mechanism ◽  
Spatiotemporal Variability ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Alpine Area ◽  
Long Time ◽  
Tailan River Basin

Abstract. Hydrological modeling depends on single- or multiple-objective strategies for parameter calibration using long time sequences of observed streamflow. Here, we demonstrate a diagnostic approach to the calibration of a hydrological model of an alpine area in which we partition the hydrograph based on the dominant runoff generation mechanism (groundwater baseflow, glacier melt, snowmelt, and direct runoff). The partitioning reflects the spatiotemporal variability in snowpack, glaciers, and temperature. Model parameters are grouped by runoff generation mechanism, and each group is calibrated separately via a stepwise approach. This strategy helps to reduce the problem of equifinality and, hence, model uncertainty. We demonstrate the method for the Tailan River basin (1324 km2) in the Tianshan Mountains of China with the help of a semi-distributed hydrological model (THREW).

scholarly journals Fourier Landscape Pattern Indices for Predicting South Carolina Watershed Fecal Coliform

2017 ◽  
Vol 10 (1) ◽  
pp. 20-34
Author(s):  
Alvin Spivey ◽  
Anthony Vodacek
Keyword(s):  
Land Use ◽  
South Carolina ◽  
Land Cover ◽  
Landscape Pattern ◽  
Fecal Coliform ◽  
Model Parameters ◽  
Residential Areas ◽  
Ecological Processes ◽  
Bare Rock ◽  
Decadal Scale

AbstractExtending the Landscape Pattern Metric (LPM) model analysis in Smith et al. (2001) into a LPM decision model, decadal scale prediction of fecal coliform compromised South Carolina watersheds is developed. The model’s parameter variability identifies the greatest contributors to a compromised watershed’s prediction. The complete set of model parameters include Land Cover Land Use (LCLU) & slope,along stream proportion, Fourier Metric of Fragmentation (FMF), Fourier Metric of Proportion (FMP), and Least Squares Fourier Transform Fractal Dimension (LsFT). The 1992 National Land Cover Data (NLCD) Land Cover Land Use (LCLU) within fecal coliform compromised watersheds is used to train the model parameters, and the 2001 NLCD LCLU is used to test the LPM model. The most significant model parameters arealong stream bare rock LsFT,FMF between urban/recreational grasses and evergreen forests, andFMF between deciduous forests and high density residential areas. These metrics contribute significantly more than the bestproportiondescriptor:proportion of urban/recreational grasses. In training, the proposed model correctly identified 92 % of the compromised watersheds; while the Smith et al. (2001) model 94 % of the compromised watersheds were correctly identified. This study reveals the ability of Fourier metrics to interpret ecological processes, and the need for more appropriate landscape level models.

scholarly journals Bayesian estimation of parameters in a regional hydrological model

2002 ◽  
Vol 6 (5) ◽  
pp. 883-898 ◽  
Author(s):  
K. Engeland ◽  
L. Gottschalk
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Hydrological Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Daily Streamflow ◽  
Likelihood Model ◽  
Simulation Errors

Abstract. This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC) analysis. The Bayesian method requires formulation of a likelihood function for the parameters and three alternative formulations are used. The first is a subjectively chosen objective function that describes the goodness of fit between the simulated and observed streamflow, as defined in the GLUE framework. The second and third formulations are more statistically correct likelihood models that describe the simulation errors. The full statistical likelihood model describes the simulation errors as an AR(1) process, whereas the simple model excludes the auto-regressive part. The statistical parameters depend on the catchments and the hydrological processes and the statistical and the hydrological parameters are estimated simultaneously. The results show that the simple likelihood model gives the most robust parameter estimates. The simulation error may be explained to a large extent by the catchment characteristics and climatic conditions, so it is possible to transfer knowledge about them to ungauged catchments. The statistical models for the simulation errors indicate that structural errors in the model are more important than parameter uncertainties. Keywords: regional hydrological model, model uncertainty, Bayesian analysis, Markov Chain Monte Carlo analysis

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