NS ). A more detailed overview of the SWAT calibrating procedure was
given in Phomcha et al.  and Phomcha et al. .
2.6 Identifying Erosion Critical Source Areas
In order to implement suitable soilconservation measures, critical source areas in the study watershed need to be identified. According to LDD, degrees of soil loss have been categorized into five major classes consisting of very slight (< 15 tons/ha/year), slight (15-30 tons/ha/year), moderate (31-90 tons/ha/year), severe (91-125 tons/ha/year), and very severe (>125 tons/ha/year). For this study, sub- watersheds which produced soil loss more than 91 tons/ha/year were identified as critical areas based on the average annual sediment yield during the period (between 1997 and 2002). During this determination, the variables of the calibration stage remained unchanged. After the simulation, the sediment yield values for each sub-watershed were obtained.
Climate and landuse change impacts are related to the hydrology of a watershed. An effective watershed management integrates these complex relationships among climate, landuse cover, soil, water and the environment. Under changing conditions, hydrologic models can be employed as management tool to create scenario and simulate hydrological components in a watershed (Mango et al., 2011). Different hydrologic models have been developed and applied to help understand such interactions. They provide a framework for investigating the complex effects of landuse change (Wang et al., 2014; Khadka, 2012; Gyamfi et al., 2016), climate change (Aich et al., 2014; Yira et al., 2017; Fang et al., 2015) and combined impacts of climate and landuse changes on watershed hydrology (Legesse et al., 2003; Qi et al., 2009; Sead et al., 2010; Mango et al., 2011; Khoi and Suetsugi, 2014). Sead et al. (2010) studied the impacts of land use and climate change on streamflow in the Blue Nile River using the Soil and Water Assessment Tool (SWAT) model. The simulated results showed that, in the future, changes in climate and land use will have a significant impact on streamflow. Mango et al. (2011) applied the SWATmodel to assess the hydrological impacts to changes in landuse and climate change in the Mara river basin, Kenya. They reported that further deforestation would increase peak flows and reduce dry season flows while rising temperature caused by climate change will increase evapotranspiration and reduced runoff. Qi et al. (2009) applied the Precipitation Runoff Modeling System (PRMS) model to evaluate the hydrologic response to changes in climate and landuse in North Carolina. They reported that the catchment is more sensitive to changes in climate than landuse change, though both can cause significant water quantity and quality problems. Gyamfi et al. (2016) reports the impacts of various land use and cover change in the olifants Basin, South Africa. They reported a 46.97% increase in runoff as a result of decrease in rangeland while agricultural land, urban and forest lands were increased. Khoi and Suetsugi (2014) studied the impacts of climate and land-use changes in Be
Vegetative buffers, wetlands and management of ferti- lizer and manure have been suggested by prior studies to be more effective in reducing sediment and P loss into streams [11,12]. In addition, crop rotation (for instance corn-soybean instead of continuous corn) is known to reduce sediment, nutrient and pesticide loss . Soy- bean has the ability to fix N and requires low amounts of P fertilizer. Soybean improves soil texture, making it an excellent candidate for no-till, and has low weed and insect nuisance which reduces herbicide and insecticide use. Also, soybean fields dry faster, which in wet areas helps prepare land for corn planting . Agricultural tillage methods also affect water quality differently; switching from conventional tillage to conservation till- age (e.g. 30% residue) or to no-till reduces soil erosion, labor and carbon footprint . Residue left by conser vation tillage improves soil resistance to erosion, espe- cially during non-crop growth periods when canopy that reduces raindrop impact is absent. However, in some places, no-till can increase weeds and insects, reducing crop yield . Soil temperature reduction associated with no-till corn can result in slowing early season growth and lower yields. Leaving residue on poorly drained soils, especially in the wet upper Midwest, can prevent fields from drying out fast enough for April or early May planting of corn . To study the effect of non-point source pollution, water quality and quantity from various US streams is monitored by agencies such as the USGS. However, due to the cost involved in long- term monitoring, there are only a limited number of streams with data. Non-point source watershed models are useful to estimate water quality for non-monitored areas and periods, and to evaluate alternative crop man- agement scenarios . Future scenarios could be im- portant tools for policy makers. The objectives of this study were to: 1
SUBMITTED xxxx REVISED xxxxx ACCEPTED xxxx 6
ABSTRACT Soil erosion in highly gullied regions of Kashmir valley is a serious global issue due to its impacts on economic productivity and environmental consequences leading to land disintegration. Further, Lolab is a flood prone area and has witnessed many disastrous floods in the past due to which the assessment of hydrological behavior becomes an utmost priority and identification of most problematic sub-basins contributing to the erosion and excessive runoff needs to be identified so that proper management strategies can be applied. In this study, SWAT (Soil and Water Assessment Tool) was integrated with Arc software to simulate the runoff and sediment yield of Lolab Watershed due to its flexibility in input data requirements and capability of modeling larger catchments and mountainous areas. While carrying out sensitivity analysis four most sensitive parameters were found for runoff estimation of which Initial soilconservation service Curve number II was the most sensitive one and two most sensitive parameters were found for sediment estimation of which channel
layers including digital elevation model (DEM), soil data, and land use were used to characterize the watershed for modeling. Data from eleven weather stations for eleven years (2000-2010) were used for simulation. Model output was used to assess hydrological process by estimating runoff coefficient (rainfall: runoff) in each sub-watershed and hydrologic response unit (HRU). Runoff coefficients at HRUs varied from zero to 0.23, with significant variation among the four watersheds. Areas where RWH and SI can be implemented can be identified based on runoff coefficient at field scale, while other techniques such as large dams can be placed on the stream network where high runoff is generated. These results were integrated with GIS layers of land use, soil and community distribution to identify potential sites for RWH and SI techniques. Results also indicate variable areas as suitable for RWH and SI for different watersheds. The results of the hydrological models were integrated with biophysical and socio-economic information to provide a useful tool to target RWH and SI as part of sustainable planning at watersheds level. This approach appreciate the role of spatial variability within watersheds and the importance of multi-disciplinary integration as viable tools for sustainable resource planning in the dry environment and under climate change scenarios.
This study was made possible with the help of many people and organizations. First I would like to recognize the Conservation Effects Assessment Program (CEAP) and the Cooperative State Research Education and Extension Service (CSREES) for funding this research. The United States Geological Survey (USGS) provided funding to support related field work. I would like to thank Catherine Kling, Phil Gassman, Manoj Jha, Rebecca Olson and the staff at the Center for Agriculture and Rural Development (CARD) for their support of and assistance with this research. The USGS, the Iowa Geological Survey (IGS) and the National Laboratory for Agriculture and the Environment (NLAE) provided support with instrumentation and data collection. I would like to individually thank Mark Tomer for sharing his personal knowledge of the South Fork watershed and Deb Quade for providing geologic descriptions of the sediment cores. Data collection was also made possible due to cooperation of the South Fork watershed landowners and assistance from Team Hydro. I would also like to thank the individuals and organizations that contributed SWATmodeling results; Cole (Green) Rossi with the ARS Grassland Soil and Water Research Laboratory and Calvin Wolter with the IGS. I also thank Keith Schilling and Calvin Wolter (IGS) for
SWAT is a river basin or watershed, scale model. It is a contin- uous time model that operates on daily time steps and uses a command structure for routing runoff and chemical through watershed. It developed by Agricultural Research Services of United States Department of Agriculture to predict the impact of land management practices on water, sediment, and agricul- ture chemical yields in large and complex watersheds with vary- ing soil, land use, and management conditions over long periods of time, Arnold et al.  . ArcSWAT (Arc GIS- SWAT) is the latest available version which is used as an inter- face between ArcGIS and the SWATmodel. ArcSWAT version 2.3.4 which was built for ArcMap 9.3 is used in this study, Winchell et al.  . Spatial data (DEM, soil and land use) are used in the preprocessing phase and fed into the SWATmodel through the interface. The soil and land cover make important responding units and the same is accomplished by SWATmodel by subdividing the watershed into areas having unique land use and soil combination which are called Hydrological Response Units (HRU) during the process of runoff genera- tion. SWAT requires an assortment of input data layers for model setup and watershed simulations. The topography of watershed is deﬁned by a Digital Elevation Model (DEM). It is used to calculate sub-basin parameters such as slope and to deﬁne the stream network. The soil data are required to deﬁne soil characteristics and attributes. The land-cover data provide vegetation information on ground and their ecological pro- cesses in lands and soils. Climate, precipitation and stream ﬂow data are sourced and prepared according to SWAT input requirements. Fig. 2 shows the global view of SWATmodel components including input, output, the spatial datasets, and GIS parts and summarizes its methodology.
2.4 DESCRIPTION OF SWATMODELSWAT stands for Soil and Water Assessment Tool, developed by the United States Department of Agriculture - Agricultural Research Service (USDA- ARS). SWAT is developed to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions over long periods of time (Neitch et. al., 2005). SWAT is a physically based model, therefore the watershed is divided into a number of sub-basins based on a given digital elevation model (DEM) map instead of using regression equations to describe the input-output relationship. Within each sub-basin, soil and landuse maps are overlaid to create a number of unique hydrological response units (HRU) (Yang et. al., 2007). SWAT simulates surface and subsurface processes, accounting for vadose processes (i.e. infiltration, evaporation, plant uptake, lateral flows, and percolation into aquifer). Runoff volume is calculated using the Curve Number Method (SCS, 1972). Sediment yield from each sub-basin is generated using the MUSLE equation (Williams, 1995). The model updates the C factor of the MUSLE on a daily basis using information from the crop growth module. The routing phase controls the movement of water using the variable storage method or the Muskingum method (Cunge, 1969; Chow et. al., 1988; Yang et. al., 2007). Figure 2 shows the workflow of the modules of SWAT.
the case of the Merguellil catchment
Meriem Jouini 1 , Julien Burte 1 and Carole Sinfort 2
Semi-arid agricultural areas are fragile territories where water and soil resources must be preserved. In such zones impact evaluation is difficult due to the lack of data. We focused on the upstream Merguellil watershed, located in central Tunisia, where several water and soilconservation works were built since 1990 to control water erosion and to protect the downstream area. The rapid expansion of such conser- vation measures raised the issue of their impact on soil and water resources. Our main goal is the impact assessment by LCA of the most relevant farming systems in our territory, taking into account on-site and off-site contributions to local and global impacts. Our strategy is to combine LCA with a partic- ipatory approach to integrate knowledge and perceptions of local actors and to provide elements on environmental impacts for all stakeholders. The first step was a territorial systemic participatory diag- nosis to characterize the dynamics of the territory, to identify the natural resources and their uses, the developments of the agriculturalpractices and the characterization of the existing farming systems. This diagnosis was achieved through technical field visits and interviews with farmers. The second step was a territorial LCA of representative systems, mapping the different systems to consider the characteristics of their location (access to water, soil type…). Systemic territorial participatory diagnosis allowed to define a typology of production systems and to model the territory considering the interactions between these systems. Four types of production systems were identified to proceed with territorial LCA: olive and apricot system and olive and cereals system both in rainfed and irrigated combinations. LCA results are discussed for the most important midpoint indicators. This study demonstrated two major issues of LCA use for sustainable development in semi-arid watersheds: i) LCA results communication with stakeholders to fit with their understanding of the system and ii) localized impacts on soil and water resources, taking into account Water and SoilConservation Works.
The SWAT (Soil and Water Assessment Tool) is one of the most recent models developed jointly by the United States Department of Agriculture - Agricultural Research Services (USDA-ARS) and Agricultural Experiment Station in Temple, Texas . It is a physically based, continuous time, long-term simulation, lumped parame- ter, deterministic, and originated from agricultural mod- els. The computational components of SWAT can be placed into eight major divisions: hydrology, weather, sedimentation, soil temperature, crop growth, nutrients, pesticides, and agricultural management. The SWATmodel uses physically based inputs such as weather variables, soil properties, topography, and vegetation and land management practices occurring in the catchment. The physical processes associated with water flow, sediment transport, crop growth, nutrient cycling, etc. are directly modeled by SWAT [2,3]. Some of the advan- tages of the model include: modeling of ungauged
SPATIAL OPTIMIZATION OF SIX CONSERVATIONPRACTICES USING SWAT IN TILE-DRAINED AGRICULTURAL WATERSHEDS 1
Margaret M. Kalcic, Jane Frankenberger, and Indrajeet Chaubey 2
ABSTRACT: Targeting of agriculturalconservationpractices to the most effective locations in a watershed can promote wise use of conservation funds to protect surface waters from agricultural nonpoint source pollution. A spatial optimization procedure using the Soil and Water Assessment Tool was used to target six widely used conservationpractices, namely no-tillage, cereal rye cover crops (CC), filter strips (FS), grassed waterways (GW), created wetlands, and restored prairie habitats, in two west-central Indiana watersheds. These water- sheds were small, fairly flat, extensively agricultural, and heavily subsurface tile-drained. The targeting approach was also used to evaluate the model’s representation of conservationpractices in cost and water qual- ity improvement, defined as export of total nitrogen, total phosphorus, and sediment from cropped fields. FS, GW, and habitats were the most effective at improving water quality, while CC and wetlands made the greatest water quality improvement in lands with multiple existing conservationpractices. Spatial optimization resulted in similar cost-environmental benefit tradeoff curves for each watershed, with the greatest possible water qual- ity improvement being a reduction in total pollutant loads by approximately 60%, with nitrogen reduced by 20-30%, phosphorus by 70%, and sediment by 80-90%.
Erosion and sediment delivery are currently problems of interest for the Lakes Prespa basin. The potential for global climate changes to increase the risk of soil erosion is clear, but the actual damage is not. A model analysis of climate change impacts on runoff and erosion in this basin was not performed previously. The objective of this study was to investigate the effects of climate change and agricultural land manage- ment on channel and soil surface erosion, as well as sediment yield in streams in this basin. For this reason, in this study, the DHSVM (Distributed Hydrology Soil Vegeta- tion Model) model was used. The model was first calibrated using data for the period of (2010 - 2016), and then was used to predict results for the year 2045 using statisti- cally downscaled global climate data. Three tillage scenarios were incorporated into DHSVM: conventional till, reduced till, and no till. Results have shown that climate change and agriculturalpractices, particularly surface treatments to the land, can im- pact surface runoff and suspended sediment generation. Runoff and sediment genera- tion are strongly related, and runoff flows in rills and gullies typically carry suspended sediment loads downstream. Another factor that can affect formation of these channels and overland flow is land use. The results also showed that as the projected climate– driven intensity of storms increase, more runoff is predicted in the Lakes Prespa basin. Sensitivity of the model to surface erosion and changes in channel sediment bed depth were both evaluated for several parameters that relate to erosion. Observations have shown that suspended sediment concentrations can drastically increase, but model re- sults do not yet display large fluctuations in suspended sediment concentrations which are typically observed in nature as a result of storm and erosion events. In the long term, continued improvements to this preliminary model of the Lakes Prespa basin can provide better insight into the effects of climate change on the riparian habitat of fish in the basin and the sediment budget of the surrounding area.
ArcSWAT, the standard ArcGIS input interface for SWAT, can spatially identify potential HRUs within a sub- basin based on the above-mentioned HRU definition prin- ciples. However, ArcSWT-generated HRUs do not necessarily correspond spatially to individual farm fields in a given standard SWAT application. In other words, multiple potential HRUs (farm fields) with the same landuse/land- cover, soil, and slope, but located at different places of a subbasin, are considered as one HRU. This characteristic of HRUs is referred, herein, as spatial non-uniqueness. More- over, if lumping thresholds for HRU definition are used, spatial identification of individual HRUs will often be impractical. This is a key weakness in finding a one-to-one match between farm fields and HRUs, and hence assigning and presenting various inputs (e.g., rotation, tile drainage, manure and fertilizer application rates, etc.) and outputs at the HRU level. In this study, however, we introduce a data pre-processing procedure, discussed below, to create HRUs that are spatially unique so that there is a one-to-one match between HRUs and farm fields in a subbasin.
Water erosion is the process by which the land surface is worn away by water flowing over exposed soil. In the process, water picks up detached soil particles and debris that may contain chemicals harmful to receiving waters. Erosive forces increase as the velocity of flowing water increases resulting in small channels and eventually gullies of varying widths and depths. Soil erosion, therefore, should be avoided for two reasons: first, because it entails a loss and degradation of soil onsite; second, because the sediment and chemicals associated with the sediment particles can be harmful if it enters surface water bodies. Sedimentation is the process where soil particles settle out of suspension as the velocity of water decreases. Larger and heavier particles (gravel and sand) settle out more rapidly than fine silt and clay particles. It is difficult to totally eliminate the transportation of these fine particles even with the most effective erosion control program. A well-designed nursery facility will help reduce erosion from both irrigation and rain events.
The growing concern for food security through improved soil management techniques demands identi ﬁcation of
an environmental friendly and crop yield sustainable system of tillage.
Tillage is de ﬁned as the mechanical manipulation of the soil for the purpose of crop production affecting
signi ﬁcantly the soil characteristics such as soil water conservation, soil temperature, inﬁltration and evapotranspira- tion processes. This suggests that tillage exerts impact on the soil purposely to produce crop and consequently affects the environment. As world population is increasing so the demand for food is increasing and as such the need to open more lands for crop production arises. The yearning for yield increases to meet growing demand must be done in a way that soil degradation is minimal and the soil is prepared to serve as a sink rather than a source of atmospheric pollutants. Thus, conservation tillage, along with some complimentary practices such as soil cover and crop diversity ( Corsi, Friedrich, Kassam, Pisante, & de Moraes Sà, 2012 ) has emerged as a viable option to ensure sustainable food production and maintain environmental integrity. This implies that conservation tillage is a component of conservation agriculture (CA).
Watershed as an entry point acts as a beginning to address the issues of sustainable rainwater management for improving livelihoods. Extraction of watershed parameters using Geographical Information System (GIS) and use of simulation models is the current trend for hydrologic evalua- tion of watersheds. In the present study, the open Source Tool Quantum GIS 2.2.0 was used for preparation of maps to verify the spatial extent of the area. The Soil and Water Assessment Tool (SWAT) having an interface with Arc-View GIS software (ArcGIS 10.1 with Arc SWAT 2012 exten- sion) was selected for the estimation of runoff and sediment yield from Kaneri watershed, located in Western Maharashtra region. The coefficient of determination (R 2 ) for the monthly and yearly
As described earlier in section 126.96.36.199, this method should be performed for one parameter at a time only while the other parameters are fixed at a value of the best iteration. Then the parameter was varied independently and its effect on the model output was evaluated. Based on the analysis result groundwater delay (GW_DELAY), deep aquifer percolation fraction (RCHRG_DP), groundwater revap coefficient (GW_REVAP), average slope length (SLSUBBSN), soil evaporation compensation factor (ESCO), Base flow alpha factor (ALPHA_BF), runoff curve number (CN) and saturated hydraulic conductivity of soil layers were found to be most sensitive parameters in the order appearance. On the other hand parameters such as surface runoff lag time (surlag), available water content the soil (SOL_AWC), plant uptake compensation factor (EPCO), average slope steepness (HRU_SLP), threshold depth of water in the shallow aquifer required for return flow to occur (GWQMN) and threshold depth of water in the shallow aquifer for revap to occur (REVAPMN) were found to be least sensitive. The remaining parameters have moderate effect on the model output. In general, the global sensitivity analysis and the local sensitivity analysis produce different result. Therefore, attention was given to most sensitive parameters during model calibration process.
Abstract Streamflow simulation is often challenging in mountainous watersheds because of irregular topography and complex hydrological processes. Rates of change in precipitation and temperature with respect to elevation often limit the ability to reproduce stream runoff by hydrological models. Anthropogenic influence, such as water transfers in high altitude hydro- power reservoirs increases the difficulty in modeling since the natural flow regime is altered by long term storage of water in the reservoirs. The Soil and Water Assessment Tool (SWAT) was used for simulating streamflow in the upper Rhone watershed located in the south western part of Switzerland. The catchment area covers 5220 km 2 , where most of the land cover is dominated by forest and 14 % is glacier. Streamflow calibration was done at daily time steps for the period of 2001 –2005, and validated for 2006–2010. Two different approaches were used for simulating snow and glacier melt process, namely the temperature index approach with and without elevation bands. The hydropower network was implemented based on the intake points that form part of the inter-reservoir network. Subbasins were grouped into two major categories DOI 10.1007/s11269-012-0188-9
Sediments are one of the major problems for the op- eration of dams. They reduce the storage capacity of the reservoir and they can cause serious problems concerning the operation and stability of the dam (Srinivasan 1996). One of the important factors in reservoir design and operation is the sedimentation problem. Sediment delivered to the reservoir comes from two main sources. The first source is the main river entering the reservoir and the second source is the valleys on both sides of the reservoir. Due to the importance of the problem several empirical meth- ods were developed and later modeling techniques were adopted (U.S. Department of Interior 1972). Several types of models are used to predict sediment load among these (Srinivasan et al. 1998; Fernandez et al. 2003; Kim 2006; Yüksel et al. 2008; Baigorria & Romero 2007; Engda 2009; Gitas et al. 2009;, Nangia 2010; Jain 2010). Kim et al. 2008 developed the Soil and Water Assessment Tool (SWAT) ArcView GIS Patch II for steep slope watersheds. SWAT is a physically based model was developed to simulate and predict the runoff, sediment load, and agricultural chemical yields for large and com- plex watersheds having different soil type (Ashagre 2009). Santos et al. 2010 applied these models and got good results on Apucaraninha River watershed in southern of Brazil. Doughlas-Mankin et al. 2010 reviewed and introduced a number of selected pa-
The physical and chemical parameterization of the soil maps was adapted from the WLRC soil report (Belay, 2014) and, where WLRC data were missing, from the doctoral dissertation of Zeleke (2000), from the SCRPs Soil Conser- vation Research Report 27 for the Minchet catchment (Kejela, 1995), and from Hurni (1985). The land use and soil data contained 19 soil and 12 land use classes (see Figure 2 for details) The model setup comprised 2,349 HRUs within 12 sub-basins. The model was created using a zero per cent threshold, meaning all HRUs were accounted for in modeling. Daily precipitation records combined with minimum and maximum temperature records for the Minchet watershed were used to run the model. Weather station data from Yechereka were added for the years 2013 and 2014. Solar radiation, potential evapotranspiration and wind speed were generated by the ArcSWAT weather generator. Storm-based sediment concentrations measured at the Minchet and the Yechereka outlets were used for model calibration and validation. Flow observations were avail- able for the entire year, while sediment data were only available during rainfall events. The sediment concentration in the Gerda watershed is measured only during the rainy season, which is from June to October and assumed to be negligible during the remaining months. This is a realistic assumption given the extremely low sediment concentration during the dry season (Easton et al, 2010; Betrie et al, 2011).