Changing Land Use a Threat to Surface Water Quality: a Vulnerability Assessment Approach in Zanjanroud Watershed,Central Iran

Maintaining the quality of surface water resources as one of the most vital water supplies has always been at the center of global concerns. A set of manifest and latent factors have yet been identified by researchers worldwide that are subject to affect the quality of surface water. Among which, the effect of land use change, due to a spatial and temporal complexity, is often not easily verifiable. The present study attempts to offer an index-based model to quantify vulnerability of surface water resources in a semi arid basin in central Iran against land use changes. For this, water quality data including Na, K, Mg, Ca, Sodium Adsorption Ratio (SAR), total anions (Sum. A), SO₄, Cl, HCO₃, EC, TDS, and pH were collected from hydrometric stations over a period of 26 years (1987?2013). In order to detect land use changes, the land use maps of the years 1987, 1998, 2002, 2009, and 2013 were prepared from TM satellite images using supervised classification method. At next step, changing patterns of different land uses were traced by Shanon’s Diversity Index (SHDI) as a metric of patch diversity indicating diversity and heterogeneity of a landscape over time. Relationship between the SHDI values and water quality indicators revealed the impact of land use changes on quality of surface water resources. Statistical analysis confirmed a significant relationship between nine water quality factors and water discharge in the basin during the study period. From 1998 to 2009, the greatest changes were visible in the total anions, Ca, SO₄, and HCO₃. These parameters along with salinity were increasing in almost all sub-basins. According to the results, conversion of poor rangelands to rain fed agriculture fields is the most apparent land use change occurred in the study area over the study period. In 1987, SHDI as an indicator of the diversity and changes in the basin, showed a significant relationship with good rangelands (R² =–0.835). This indicates that fragmentation of the entire watershed area was initiated in 1987, which reached its peak in 2013. Generally speaking, urbanization, poor rangeland, and irrigated agriculture were recognized as three influential land uses adversely affect the water quality in the study area.     

Estimating source regions for snowmelt runoff in a Rocky Mountain basin: tests of a data‐based conceptual modeling approach

In many mountain basins, river discharge measurements are located far away from runoff source areas. This study tests whether a basic snowmelt runoff conceptual model can be used to estimate relative contributions of different elevation zones to basin‐scale discharge in the Cache la Poudre, a snowmelt‐dominated Rocky Mountain river. Model tests evaluate scenarios that vary model configuration, input variables, and parameter values to determine how these factors affect discharge simulation and the distribution of runoff generation with elevation. Results show that the model simulates basin discharge well (NSCE and R >0.90) when input precipitation and temperature are distributed with different lapse rates, with a rain‐snow threshold parameter between 0 and 3.3 °C, and with a melt rate parameter between 2 and 4 mm °C^?1 d^?1 because these variables and parameters can have compensating interactions with each other and with the runoff coefficient parameter. Only the hydrograph recession parameter can be uniquely defined with this model structure. These non‐unique model scenarios with different configurations, input variables, and parameter values all indicate that the majority of basin discharge comes from elevations above 2900 m, or less than 25% of the basin total area, with a steep increase in runoff generation above 2600 m. However, the simulations produce unrealistically low runoff ratios for elevations above 3000 m, highlighting the need for additional measurements of snow and discharge at under‐sampled elevations to evaluate the accuracy of simulated snow and runoff patterns. Copyright © 2013 John Wiley & Sons, Ltd.     

Historic range of variability in geomorphic processes as a context for restoration: Rocky Mountain National Park,Colorado, USA

Evaluation of historic range of variability (HRV) is an effective tool for determining baseline conditions and providing context to researchers and land managers seeking to understand and enhance ecological function. Incorporating HRV into restoration planning acknowledges the dynamic quality of landscapes by allowing variability and disturbance at reasonable levels and permitting riverine landscapes to adapt to the physical processes of their watersheds. HRV analysis therefore represents a practical (though under‐utilized) method for quantifying process‐based restoration goals. We investigated HRV of aggradational processes in the subalpine Lulu City wetland in Rocky Mountain National Park to understand the impacts of two centuries of altered land use and to guide restoration planning following a human‐caused debris flow in 2003 that deposited up to 1 m of sand and gravel in the wetland. Historic aerial photograph interpretation, ground penetrating radar surveys, and trenching, coring, and radiocarbon dating of valley‐bottom sediments were used to map sediment deposits, quantify aggradation rates, and identify processes (in‐channel and overbank fluvial deposition, direct hillslope input, beaver pond filling, peat accumulation) creating alluvial fill within the wetland. Results indicate (i) the Lulu City wetland has been aggrading for several millennia, (ii) the aggradation rate of the past one to two centuries is approximately six times higher than long‐term pre‐settlement averages, (iii) during geomorphically active periods, short‐term aggradation rates during the pre‐settlement period were probably much higher than the long‐term average rate, and (iv) the processes of aggradation during the last two centuries are the same as historic processes of aggradation. Understanding the HRV of aggradation rates and processes can constrain management and restoration scenarios by quantifying the range of disturbance from which a landscape can recover without active restoration. Copyright © 2011 John Wiley & Sons, Ltd.     

Use of a derived distribution approach for flood prediction in poorly gauged basins: A case study in Italy

A derived distribution approach is developed for flood prediction in poorly gauged basins. This couples information on the expected storm scaling, condensed into Depth Duration Frequency curves, with soil abstractions modeled using Soil Conservation Service Curve Number method and hydrological response through Nash’s Instantaneous Unit Hydrograph. A simplified framework is given to evaluate critical duration for flood design. Antecedent moisture condition distribution is included. The method is tested on 16 poorly gauged Mediterranean watersheds in Tyrrhenian Liguria, North Western Italy, belonging to a homogeneous hydrological regions. The derived flood distribution is compared to the regional one, currently adopted for flood design. The evaluation of Curve Number is critical for peak flood evaluation and needs to be carefully carried out. This can be done including local Annual Flood Series data in the estimation of the derived distribution, so gathering the greatest available information. However, Curve Number influence decreases for the highest return periods. When considerable return periods are required for flood design and few years of data are available, the derived distribution provides more accurate estimates than the approach based on single site distribution fitting. A strategy based on data availability for application of the approach is then given. The proposed methodology contributes to the ongoing discussion concerning PUB (Prediction in Ungauged Basins) decade of the IAHS association and can be used by researchers and practitioners for those sites where no flood data, or only a few, are available, provided precipitation data and land use information are at hand.     

Limitations of ROI Testing for Venting Design: Description of an Alternative Approach Based on Attainment of a Critical Pore-Gas Velocity in Contaminated Media

We utilize data from a Superfund site where radius of influence (ROI) testing was conducted in support of a venting design to describe limitations of ROI evaluation in more detail than has been done previously, and to propose an alternative method of design based on specification and attainment of a critical pore-gas velocity in contaminated subsurface media. Since accurate gas permeability estimation is critical to pore-gas velocity computation, we assess the usefulness of ROI testing data on estimation of radial permeability, vertical permeability, and leakance. We apply information from published studies on rate-limited vapor transport to provide the basis for selection of a critical design pore-gas velocity for soils at this site. Using single-well gas flow simulations, we evaluate whether this critical pore-gas velocity was achieved at measured ROIs. We then conduct a series of multi-well gas flow simulations to assess how variation in anisotropy and leakance affect three-dimensional vacuum and pore-gas velocity profiles and determination of an ROI. Finally, when attempting to achieve a critical design pore-gas velocity we evaluate whether it is more efficient to install additional wells or pump existing wells at a higher flow rate.     

Use of multi‐platform,multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin,Italy

Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd.