Identification of Hydrologically Sensitive Areas Considering Watershed Process Dynamics

2018 ◽  
Vol 61 (6) ◽  
pp. 1891-1906
Author(s):  
Younggu Her ◽  
Conrad Heatwole

Abstract. Hydrologically sensitive areas (HSAs) largely control watershed response to rainfall, along with water and pollutant transport processes. Thus, their identification is critical in watershed management planning. Although watershed processes have been studied enough to provide a good understanding of HSA dynamics, only a few concepts and methods are available for HSA delineation, and they rely on spatial indices that do not consider temporal variation in hydrologic processes. This study introduces alternative concepts and methods to delineate HSAs. Three unique maps showing watershed dynamics were created using the outputs of a long-term hydrologic simulation implemented with a grid-based distributed model. The spatial distributions of HSAs identified using the newly proposed methods were compared with those of topographic indices. Results demonstrated that the new methods highlight transport processes, such as routing (or connection) and travel time, and the roles of soil and land covers, which have not been the focus of other concepts and approaches for HSA identification. In contrast to topographic index-based approaches, the proposed methods provided HSA boundaries with clear physical meanings to improve the interpretability and applicability of HSA maps. The methods are expected to enhance our ability to tackle water issues for improved water resource management by providing unique concepts and alternative ways to explicitly delineate HSAs. Keywords: Grid-based distributed model, Hydrologic connectivity, Hydrologically sensitive area, HYSTAR, Time-area method, Topographic wetness index.

2015 ◽  
Vol 61 (228) ◽  
pp. 763-775 ◽  
Author(s):  
L.M. Andreassen ◽  
M. Huss ◽  
K. Melvold ◽  
H. Elvehøy ◽  
S.H. Winsvold

AbstractGlacier volume and ice thickness distribution are important variables for water resource management in Norway and the assessment of future glacier changes. We present a detailed assessment of thickness distribution and total glacier volume for mainland Norway based on data and modelling. Glacier outlines from a Landsat-derived inventory from 1999 to 2006 covering an area of 2692 ± 81 km2 were used as input. We compiled a rich set of ice thickness observations collected over the past 30 years. Altogether, interpolated ice thickness measurements were available for 870 km2 (32%) of the current glacier area of Norway, with a total ice volume of 134 ± 23 km3. Results indicate that mean ice thickness is similar for all larger ice caps, and weakly correlates with their total area. Ice thickness data were used to calibrate a physically based distributed model for estimating the ice thickness of unmeasured glaciers. The results were also used to calibrate volume–area scaling relations. The calibrated total volume estimates for all Norwegian glaciers ranged from 257 to 300 km3.


Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5763 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Indrajit Chowdhuri ◽  
Zhaleh Siabi ◽  
...  

Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.


2006 ◽  
Vol 78 (1) ◽  
pp. 63-76 ◽  
Author(s):  
Laura J. Agnew ◽  
Steve Lyon ◽  
Pierre Gérard-Marchant ◽  
Virginia B. Collins ◽  
Arthur J. Lembo ◽  
...  

Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 132 ◽  
Author(s):  
Yuqing Li ◽  
Zuhao Zhou ◽  
Kang Wang ◽  
Chongyu Xu

Flow and transport processes in soil and rock play a critical role in agricultural non-point source pollution (ANPS) loads. In this study, we investigated the ANPS load discharged into rivers from an irrigation district in the Tibetan Plateau and simulated ANPS load using a distributed model. Experiments were conducted for two years to measure soil water content and nitrogen concentrations in soil and the quality and quantity of subsurface lateral flow in the rock and at the drainage canal outlet during the highland barley growing period. A distributed model, in which the subsurface lateral flow in the rock was described using a stepwise method, was developed to simulate flow and ammonium nitrogen (NH4+-N) and nitrate nitrogen (NO3−-N) transport processes. Sobol’s method was used to evaluate the sensitivity of simulated flow and transport processes to the model inputs. The results showed that with a 21.2% increase of rainfall and irrigation in the highland barley growing period, the average NH4+-N and NO3−-N concentrations in the soil layer decreased by 10.8% and 14.3%, respectively, due to increased deep seepage. Deep seepage of rainfall water accounted for 0–52.4% of total rainfall, whereas deep seepage of irrigation water accounted for 36.6–45.3% of total irrigation. NH4+-N and NO3−-N discharged into the drainage canal represented 19.9–30.4% and 19.4–26.7% of the deep seepage, respectively. The mean Nash–Sutcliffe coefficient value, which was close to 0.8, and the lowest values of root mean square errors, the fraction bias, and the fractional gross error indicated that the simulated flow rates and nitrogen concentrations using the proposed method were very accurate. The Sobol’s sensitivity analysis results demonstrated that subsurface lateral flow had the most important first-order and total-order effect on the simulated flow and NH4+-N and NO3−-N concentrations at the surface drainage outlet.


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