scholarly journals Assessing climate and land-use change impacts on streamflow in a mountainous catchment

2018 ◽  
Vol 11 (2) ◽  
pp. 503-513 ◽  
Author(s):  
Qiang Liu ◽  
Liqiao Liang ◽  
Yanpeng Cai ◽  
Xuan Wang ◽  
Chunhui Li
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Field Capacity ◽  
Soil Parameters ◽  
Area Index ◽  
Mountainous Catchment ◽  
Vegetation Height ◽  
Streamflow Response ◽  
Model Efficiency ◽  
Surrounding Areas

AbstractIt is essential to assess streamflow response to climate and land-use change in catchment basins that serve cities and their surrounding areas. This study used the Distributed Hydrology Soil Vegetation Model (DHSVM) to simulate streamflow under different climate and land-use change scenarios in the Dashi River catchment, China. The most sensitive soil parameters were maximum infiltration, porosity, field capacity, and wilting point, while the most sensitive vegetation parameters were leaf area index (LAI) and vegetation height. The suitability of the DHSVM model was aligned with Nash–Sutcliffe model efficiency coefficients (NSE) greater than 0.41 and 0.84 at daily and monthly scales, respectively. Streamflow increased/decreased with increasing/decreasing precipitation, while it decreased with increasing air temperature. Furthermore, streamflow decreased with the increase in forestland due to higher water consumption, especially during summer. Results from this study could help us to better understand streamflow response to changes in climate and land use.

scholarly journals Impact of Land Use Change on the Local Climate over the Tibetan Plateau

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
Jiming Jin ◽  
Shihua Lu ◽  
Suosuo Li ◽  
Norman L. Miller
Keyword(s):  
Land Use ◽  
Tibetan Plateau ◽  
Land Use Change ◽  
Lower Surface ◽  
Warming Trend ◽  
The Tibetan Plateau ◽  
Area Index ◽  
Local Climate ◽  
Significant Downward Trend ◽  
Three Rivers

Observational data show that the remotely sensed leaf area index (LAI) has a significant downward trend over the east Tibetan Plateau (TP), while a warming trend is found in the same area. Further analysis indicates that this warming trend mainly results from the nighttime warming. The Single-Column Atmosphere Model (SCAM) version 3.1 developed by the National Center for Atmospheric Research is used to investigate the role of land use change in the TP local climate system and isolate the contribution of land use change to the warming. Two sets of SCAM simulations were performed at the Xinghai station that is located near the center of the TP Sanjiang (three rivers) Nature Reserve where the downward LAI trend is largest. These simulations were forced with the high and low LAIs. The modeling results indicate that, when the LAI changes from high to low, the daytime temperature has a slight decrease, while the nighttime temperature increases significantly, which is consistent with the observations. The modeling results further show that the lower surface roughness length plays a significant role in affecting the nighttime temperature increase.

Land use change effects on catchment streamflow response in a humid tropical montane cloud forest region, central Veracruz, Mexico

2020 ◽  
Vol 34 (16) ◽  
pp. 3555-3570 ◽  
Author(s):  
Sergio M. López‐Ramírez ◽  
Leonardo Sáenz ◽  
Alex Mayer ◽  
Lyssette E. Muñoz‐Villers ◽  
Heidi Asbjornsen ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Cloud Forest ◽  
Tropical Montane Cloud Forest ◽  
Montane Cloud Forest ◽  
Forest Region ◽  
Streamflow Response

scholarly journals Changes in Soil Hydro-Physical Properties and SOM Due to Pine Afforestation and Grazing in Andean Environments Cannot Be Generalized

Forests ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 17 ◽  
Author(s):  
Franklin Marín ◽  
Carlos Dahik ◽  
Giovanny Mosquera ◽  
Jan Feyen ◽  
Pedro Cisneros ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Physical Properties ◽  
Soil Properties ◽  
Water Retention ◽  
Soil Parameters ◽  
Water Retention Capacity ◽  
Sampling Depth ◽  
Site Specific ◽  
The Impact

Andean ecosystems provide important ecosystem services including streamflow regulation and carbon sequestration, services that are controlled by the water retention properties of the soils. Even though these soils have been historically altered by pine afforestation and grazing, little research has been dedicated to the assessment of such impacts at local or regional scales. To partially fill this knowledge gap, we present an evaluation of the impacts of pine plantations and grazing on the soil hydro-physical properties and soil organic matter (SOM) of high montane forests and páramo in southern Ecuador, at elevations varying between 2705 and 3766 m a.s.l. In total, seven study sites were selected and each one was parceled into undisturbed and altered plots with pine plantation and grazing. Soil properties were characterized at two depths, 0–10 and 10–25 cm, and differences in soil parameters between undisturbed and disturbed plots were analyzed versus factors such as ecosystem type, sampling depth, soil type, elevation, and past/present land management. The main soil properties affected by land use change are the saturated hydraulic conductivity (Ksat), the water retention capacity (pF 0 to 2.52), and SOM. The impacts of pine afforestation are dependent on sampling depth, ecosystem type, plantation characteristics, and previous land use, while the impacts of grazing are primarily dependent on sampling depth and land use management (grazing intensity and tilling activities). The site-specific nature of the found relations suggests that extension of findings in response to changes in land use in montane Andean ecosystems is risky; therefore, future evaluations of the impact of land use change on soil parameters should take into consideration that responses are or can be site specific.

scholarly journals How to predict hydrological effects of local land use change: how the vegetation parameterisation for short rotation coppices influences model results

2015 ◽  
Vol 12 (1) ◽  
pp. 405-448 ◽  
Author(s):  
F. Richter ◽  
C. Döring ◽  
M. Jansen ◽  
O. Panferov ◽  
U. Spank ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Groundwater Recharge ◽  
Regional Climate ◽  
Growing Season ◽  
Stomatal Resistance ◽  
Minor Role ◽  
Area Index ◽  
Short Rotation ◽  
Negative Effect

Abstract. Among the different bioenergy sources short rotation coppices (SRC) with poplar and willow trees are one of the mostly promising options in Europe. SRC not only provide woody biomass, but often additional ecosystem services. One known shortcoming is the possible negative effect on groundwater recharge, caused by potentially higher rates of evapotranspiration compared to annual crops. An assessment of land use change by means of hydrological models and taking into account the changing climate can help to minimize negative and maximize positive ecological effects at regional and local scales, e.g. to regional climate and/or to adjacent ecosystems. The present study implemented the hydrological model system WaSim for such assessment. The hydrological analysis requires the adequate description of the vegetation cover to simulate the processes like soil evaporation, interception evaporation and transpiration. The uncertainties in the vegetation parameterisations might result in implausible model results. The present study shows that leaf area index (LAI), stomatal resistance (Rsc) as well as the beginning and length of the growing season are the sensitive parameters when investigating the effects of an enhanced cultivation of SRC on water budget or on groundwater recharge. Mostly sensitive is the description of the beginning of the growing season. When this estimation is wrong, the accuracy of LAI and Rsc description plays a minor role. The analyses done here illustrate that the use of locally measured vegetation parameters like maximal LAI and meteorological variables like air temperature, to estimate the beginning of the growing season, produce better results than literature data or data from remote network stations. However the direct implementation of locally measured or literature data on e.g. stomatal resistance is not always advisable. The adjustment of locally vegetation parameterisation shows the best model evaluation. Additionally the adjusted course of LAI and Rsc is less sensitive to different estimates for leaf unfolding, due to a slower increase in spring compared to a step functional annual course.

Partitioning of daily evapotranspiration using a modified shuttleworthwallace model, random Forest and support vector regression, for a cabbage farmland

2021 ◽  
Author(s):  
Han Li ◽  
Han Chen ◽  
Jinhui Jeanne Huang ◽  
Edward McBean ◽  
Jiawei Zhang ◽  
...  
Keyword(s):  
Random Forest ◽  
Support Vector Regression ◽  
Carbon Dioxide Concentration ◽  
Support Vector ◽  
Soil Parameters ◽  
Observation Data ◽  
Source Modeling ◽  
Area Index ◽  
Vegetation Height ◽  
Precision Irrigation

<p>Prediction of vegetation transpiration (T) is of increasing importance in water resources management and agricultural practices, in particular to facilitate precision irrigation. Traditional evapotranspiration (ET) partitioning dual source modeling requires an extensive array of ground-level parameters and needs model correction and calibration to attain model certainty. In response, a quick and low-cost method is described to predict T using artificial intelligence (AI) modeling based on meteorological factors, status of crop growth factors and soil parameters. This study compares Random Forest (RF) and Support Vector Regression (SVR) in building AI models using three years (2014–2017) of continuous high-resolution monitoring data in a cabbage farmland.Input data included air temperature (Ta), solar radiation (Ra), relative humidity (RH), vapor pressure deficit(VPD), wind speed (Ws), soil moisture (SM), vegetation height (H), and leaf area index (LAI). The results show that soil surface resistance calculations by Monte Carlo iterative method and vegetation stomatal resistance calculations and carbon dioxide concentration and emission, improve performance of the original Shuttleworth–Wallace(S-W) model. In addition, the AI model indicates Ta and Ra are essential inputs for both model types. When there are sufficient observation data, or only lacking soil and vegetation data, the RF model is recommended for use. When there are only limited data or lack of critical Ta and Ra data, the SVR model is the preferred model. Scientific guidance is provided for agriculture precision irrigation, indicating which AI model can best estimate T and water demand for irrigation planning and water management.</p>

scholarly journals SWMM-UrbanEVA: A Model for the Evapotranspiration of Urban Vegetation

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 243
Author(s):  
Birgitta Hörnschemeyer ◽  
Malte Henrichs ◽  
Mathias Uhl
Keyword(s):  
Model Performance ◽  
Soil Parameters ◽  
Suitable Model ◽  
Area Index ◽  
Fully Integrated ◽  
Model Efficiency ◽  
Spatio Temporal ◽  
Shading Effects ◽  
Moisture Conditions

Urban hydrology has so far lacked a suitable model for a precise long-term determination of evapotranspiration (ET) addressing shading and vegetation-specific dynamics. The proposed model “SWMM-UrbanEVA” is fully integrated into US EPA’s Stormwater Management Model (SWMM) and consists of two submodules. Submodule 1, “Shading”, considers the reduction in potential ET due to shading effects. Local variabilities of shading impacts can be addressed for both pervious and impervious catchments. Submodule 2, “Evapotranspiration”, allows the spatio-temporal differentiated ET simulation of vegetation and maps dependencies on vegetation, soil, and moisture conditions which are necessary for realistically modeling vegetation’s water balance. The model is tested for parameter sensitivities, validity, and plausibility of model behaviour and shows good model performance for both submodules. Depending on location and vegetation, remarkable improvements in total volume errors Vol (from Vol = 0.59 to −0.04% for coniferous) and modeling long-term dynamics, measured by the Nash–Sutcliffe model efficiency (NSE) (from NSE = 0.47 to 0.87 for coniferous) can be observed. The most sensitive model inputs to total ET are the shading factor KS and the crop factor KC. Both must be derived very carefully to minimize volume errors. Another focus must be set on the soil parameters since they define the soil volume available for ET. Process-oriented differentiation between ET fluxes interception evaporation, transpiration, and soil evaporation, using the leaf area index, behaves realistically but shows a lack in volume errors. Further investigations on process dynamics, validation, and parametrization are recommended.

scholarly journals Land use change effects on catchment streamflow response in a humid tropical montane cloud forest region, central Veracruz, Mexico

Authorea ◽  
2019 ◽  
Author(s):  
Alex Mayer ◽  
Sergio Miguel L pez Ram rez ◽  
Leonardo S enz ◽  
Lyssette Mu oz Villers ◽  
Heidi Asbjornsen ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Cloud Forest ◽  
Tropical Montane Cloud Forest ◽  
Montane Cloud Forest ◽  
Forest Region ◽  
Streamflow Response

scholarly journals Estimation of Green Land to Urban Change Based on Cellular Automata (CA) Method in Singaraja City and its Surrounding Areas

2018 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Nyoman Arto Suprapto ◽  
Takahiro Osawa ◽  
I Dewa Nyoman Nurweda Putra
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Areas ◽  
Negative Impact ◽  
Positive Impact ◽  
Landsat 8 ◽  
Land Conversion ◽  
Landsat 7 ◽  
Surrounding Areas ◽  
Green Land

Singaraja city is the second largest city in Bali which have a fairly rapid growth. Growth and development of the region in urban areas of Singaraja give the positive impact on the economy of the community but also give the negative impact on the environment. Land use change and land conversion into one of the negative issues of the development of urban areas in Singaraja. This study intends to calculate the amount of land conversion occur on the green land into urban areas within 14 years (2001-2015) and predict land use change in 2020 and 2025 in Singaraja City and Its Sorrounding Areas. Landsat 7 and Landsat 8 imageries were used to determine the land use map. Land use map obtained through the process of image classification using supervised method then verified using data field. Land use maps in 2015 and 2001 used to obtain the amount of change of urban areas and green land during the period of 14 years. This results show increasing amount of urban areas as 11,37% (3.153,74 ha) whereas green land decreased by 11,17% (3.097,68 ha). Land use change was predicted by Markov method. The projection results show the amount of urban areas in 2020 was 27,40% (7.598,45 ha) and 35,97% (9.974,55 ha) in 2025. The results obtained with this prediction accuracy rate of 0.91.

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