Understanding nitrogen transfer dynamics in a small agricultural catchment: Comparison of a distributed (TNT2) and a semi distributed (SWAT) modeling approaches

The coupling of an hydrological and a crop model is an efficient approach to study the impact of the interactions between agricultural practices and catchment physical characteristics on stream water quality. We analyzed the consequences of using different modeling approaches of the processes controlling the nitrogen (N) dynamics in a small agricultural catchment monitored for 15 years. Two agro-hydrological models were applied: the fully distributed model TNT2 and the semi-distributed SWAT model. Using the same input dataset, the calibration process aimed at reproducing the same annual water and N balance in both models, to compare the spatial and temporal variability of the main N processes. The models simulated different seasonal cycles for soil N. The main processes involved were N mineralization and denitrification. TNT2 simulated marked seasonal variations with a net increase of mineralization in autumn, after a transient immobilization phase due to the burying of the straw with low C:N ratio. SWAT predicted a steady humus mineralization with an increase when straws are buried and a decrease afterwards. Denitrification was mainly occuring in autumn in TNT2 because of the dynamics of N availability in soil and of the climatic and hydrological conditions. SWAT predicts denitrification in winter, when mineral N is available in soil layers. The spatial distribution of these two processes was different as well: less denitrification in bottom land and close to ditches in TNT2, as a result of N transfer dynamics. Both models simulate correctly global trend and inter-annual variability of N losses in small agricultural catchment when a sufficient amount data is available for calibration. However, N processes and their spatial interactions are simulated very differently, in particular soil mineralization and denitrification. The use of such tools for prediction must be considered with care, unless a proper calibration and validation of the different N processes is carried out

Using combined prediction models to quantify and visualize stormwater runoff in an urban watershed

Stormwater runoff can transport nutrients, sediments, chemicals, and pathogens to surface waterbodies. Managing runoff is crucial to preserving water quality in rapidly developing urban watersheds like those in Northwest Arkansas. A watershed containing the majority of the University of Arkansas campus was designated as the study area because stormwater from it drains into the West Fork of the White River, designated as an impaired waterbody due to siltation. The project objective was to develop methodology to test existing stormwater drainage infrastructure, identify potential areas of improvement, and estimate potentially contaminated runoff by combining two widely used prediction models. The U.S. Department of Agriculture’s Natural Resource Conservation Service’s curve number (CN) method was used to estimate runoff depths and volumes, while a flow-direction model was created that integrated topography, land use, and stormwater drainage infrastructure in a geographic information system. This study combined the CN and flow-direction models in a single geodatabase to develop flow direction/quantity models. Models were developed for 5-, 10-, 25-, 50-, and 100-year floods and varied by the antecedent moisture content. These models predicted flow directions within existing drainage infrastructure and runoff volumes for each flood, and served as a hypothetical flood analysis model. Results showed that between 24,000 m3 (5-year flood) and 60,000 m3 (100-year flood) of runoff would be transported to the West Fork of the White River. The methodology developed and results generated will help stormwater planners visualize localized runoff, and potentially adapt existing drainage networks to accommodate runoff, prevent flooding and erosion, and improve the quality of runoff entering nearby surface waterbodies

Natural disasters and household welfare : evidence from Vietnam

As natural disasters hit with increasing frequency, especially in coastal areas, it is imperative to better understand how much natural disasters affect economies and their people. This requires disaggregated measures of natural disasters that can be reliably linked to households, the first challenge this paper tackles. In particular, a methodology is illustrated to create natural disaster and hazard maps from first hand, geo-referenced meteorological data. In a second step, the repeated cross-sectional national living standard measurement surveys (2002, 2004, and 2006) from Vietnam are augmented with the natural disaster measures derived in the first phase, to estimate the welfare effects associated with natural disasters. The results indicate that short-run losses from natural disasters can be substantial, with riverine floods causing welfare losses of up to 23 percent and hurricanes reducing welfare by up to 52 percent inside cities with a population over 500,000. Households are better able to cope with the short-run effects of droughts, largely due to irrigation. There are also important long-run negative effects, in Vietnam mostly so for droughts, flash floods, and hurricanes. Geographical differentiation in the welfare effects across space and disaster appears partly linked to the functioning of the disaster relief system, which has so far largely eluded households in areas regularly affected by hurricane force winds.Natural Disasters,Hazard Risk Management,Disaster Management,Climate Change Mitigation and Green House Gases,Adaptation to Climate Change

Occurrence of metolachlor and trifluralin losses in the Save river agricultural catchment during floods

Rising pesticide levels in streams draining intensively managed agricultural land have a detrimental effect on aquatic ecosystems and render water unfit for human consumption. The Soil and Water Assessment Tool (SWAT) was applied to simulate daily pesticide transfer at the outlet from an agriculturally intensive catchment of 1110 km2 (Save river, south-western France). SWAT reliably simulated both dissolved and sorbed metolachlor and trifluralin loads and concentrations at the catchment outlet from 1998 to 2009. On average, 17 kg of metolachlor and 1 kg of trifluralin were exported at outlet each year, with annual rainfall variations considered. Surface runoff was identified as the preferred pathway for pesticide transfer, related to the good correlation between suspended sediment exportation and pesticide, in both soluble and sorbed phases. Pesticide exportation rates at catchment outlet were less than 0.1% of the applied amount. At outlet, SWAT hindcasted that (i) 61% of metolachlor and 52% of trifluralin were exported during high flows and (ii) metolachlor and trifluralin concentrations exceeded European drinking water standards of 0.1 µg L−1 for individual pesticides during 149 (3.6%) and 17 (0.4%) days of the 1998–2009 period respectively. SWAT was shown to be a promising tool for assessing large catchment river network pesticide contamination in the event of floods but further useful developments of pesticide transfers and partition coefficient processes would need to be investigated

Assessment of hydrology, sediment and particulate organic carbon yield in a large agricultural catchment using the SWAT model

The Soil and Water Assessment Tool (SWAT, 2005) was used to simulate discharge and sediment transport at daily time steps within the intensively farmed Save catchment in south-west France (1110 km2). The SWAT model was applied to evaluate catchment hydrology and sediment and associated particulate organic carbon yield using historical flow and meteorological data for a 10-years (January 1999–March 2009). Daily data on sediment (27 months, January 2007–March 2009) and particular organic carbon (15 months, January 2008–March 2009) were used to calibrate the model. Data on management practices (crop rotation, planting date, fertiliser quantity and irrigation) were included in the model during the simulation period of 10 years. Simulated daily discharge, sediment and particulate carbon values matched the observed values satisfactorily. The model predicted that mean annual catchment precipitation for the total study period (726 mm) was partitioned into evapotranspiration (78.3%), percolation/groundwater recharge (14.1%) and abstraction losses (0.5%), yielding 7.1% surface runoff. Simulated mean total water yield for the whole simulation period amounted to 138 mm, comparable to the observed value of 136 mm. Simulated annual sediment yield ranged from 4.3 t km−2 y−1 to 110 t km−2 y−1 (annual mean of 48 t km−2 y−1). Annual yield of particulate organic carbon ranged from 0.1 t km−2 y−1 to 2.8 t km−2 y−1 (annual mean of 1.2 t km−2 y−1). Thus, the highest annual sediment and particulate carbon yield represented 25 times the minimum annual yield. However, the highest annual water yield represented five times the minimum (222 mm and 51 mm, respectively). An empirical correlation between annual water yield and annual sediment and organic carbon yield was developed for this agricultural catchment. Potential source areas of erosion were also identified with the model. The range of the annual contributing erosive zones varied spatially from 0.1 to 6 t ha−1 according to the slope and agricultural practices at the catchment scale

A coupled terrestrial and aquatic biogeophysical model of the Upper Merrimack River watershed, New Hampshire, to inform ecosystem services evaluation and management under climate and land-cover change

Accurate quantification of ecosystem services (ES) at regional scales is increasingly important for making informed decisions in the face of environmental change. We linked terrestrial and aquatic ecosystem process models to simulate the spatial and temporal distribution of hydrological and water quality characteristics related to ecosystem services. The linked model integrates two existing models (a forest ecosystem model and a river network model) to establish consistent responses to changing drivers across climate, terrestrial, and aquatic domains. The linked model is spatially distributed, accounts for terrestrial–aquatic and upstream–downstream linkages, and operates on a daily time-step, all characteristics needed to understand regional responses. The model was applied to the diverse landscapes of the Upper Merrimack River watershed, New Hampshire, USA. Potential changes in future environmental functions were evaluated using statistically downscaled global climate model simulations (both a high and low emission scenario) coupled with scenarios of changing land cover (centralized vs. dispersed land development) for the time period of 1980–2099. Projections of climate, land cover, and water quality were translated into a suite of environmental indicators that represent conditions relevant to important ecosystem services and were designed to be readily understood by the public. Model projections show that climate will have a greater influence on future aquatic ecosystem services (flooding, drinking water, fish habitat, and nitrogen export) than plausible changes in land cover. Minimal changes in aquatic environmental indicators are predicted through 2050, after which the high emissions scenarios show intensifying impacts. The spatially distributed modeling approach indicates that heavily populated portions of the watershed will show the strongest responses. Management of land cover could attenuate some of the changes associated with climate change and should be considered in future planning for the region

Modeling the impact of climate change and land use change scenarios on soil erosion at the Minab Dam Watershed

Climate and land use change can influence susceptibility to erosion and consequently land degradation. The aim of this study was to investigate in the baseline and a future period, the land use and climate change effects on soil erosion at an important dam watershed occupying a strategic position on the narrow Strait of Hormuz. The future climate change at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future land use change was also simulated using the Markov chain and artificial neural network, and the Revised Universal Soil Loss Equation was adopted to estimate soil loss under climate and land use change scenarios. Results show that rainfall erosivity (R factor) will increase under all Representative Concentration Pathway (RCP) scenarios. The highest amount of R was 40.6 MJ mm ha(-1) h(-1)y(-1) in 2030 under RPC 2.6. Future land use/land cover showed rangelands turning into agricultural lands, vegetation cover degradation and an increased soil cover among others. The change of C and R factors represented most of the increase of soil erosion and sediment production in the study area during the future period. The highest erosion during the future period was predicted to reach 14.5 t ha(-1) y(-1), which will generate 5.52 t ha(-1) y(-1) sediment. The difference between estimated and observed sediment was 1.42 t ha(-1) year(-1) at the baseline period. Among the soil erosion factors, soil cover (C factor) is the one that watershed managers could influence most in order to reduce soil loss and alleviate the negative effects of climate change.FCT-Foundation for Science and Technology - PTDC/GES-URB/31928/2017; FEDER ALG-01-0247-FEDER-037303info:eu-repo/semantics/publishedVersio

Analysis of Rob Flood Risk on The Coast of East Luwu District Using GIS

Rob floods caused by rising sea levels are a natural disaster that can potentially threaten coastal areas, especially in Indonesia. Tidal floods seriously threaten coastal areas, especially East Luwu Regency. Environmental factors and rapid growth on the coast of East Luwu Regency influence the vulnerability and complexity of the environment. This research aims to identify the spatial distribution of tidal flood risk levels and predict tidal flood inundation in 2050 at the highest tide on the coast of Luwu Timur District. This effort is part of a disaster mitigation strategy due to rising sea levels. The modeling approach involves Geographic Information Systems (GIS) overlaying data and integrating DEM, HHWL, and SLR data for 28 years (1992-2020). The research results show that the coastal areas studied have a high risk related to tidal flooding, with locations closest to the coastline being at the highest risk. In contrast, the risk decreases as you move away from the coastline. Apart from that, the modeling results also estimate that in 2050, inundation will reach a height of 1,570 meters. The area affected by tidal flood inundation has increased in each sub-district. The inundation will spread evenly along the coastline and extend inland due to seawater intrusion. Coastal areas dominated by production land, such as ponds and agricultural areas, are predicted to experience the most extensive impact of inundation compared to other land uses. Emphasizes the need for mitigation efforts to minimize the impacts that may be caused by tidal floods in the future

A Flood Risk Management Program of Wadi Baysh Dam on the Downstream Area: An Integration of Hydrologic and Hydraulic Models, Jizan Region, KSA

For public safety, especially for people who dwell in the valley that is located downstream of a dam site, as well as the protection of economic and environmental resources, risk management programs are urgently required all over the world. Despite the high safety standards of dams because of improved engineering and excellent construction in recent times, a zero-risk guarantee is not possible, and accidents can happen, triggered by natural hazards, human actions, or just because the dam is aging. In addition to that is the impact of potential climate change, which may not have been taken into account in the original design. A flood risk management program, which is essential for protecting downstream dam areas, is required. Part of this program is to prepare an inundation map to simulate the impact of dam failure on the downstream areas. The Baysh dam has crucial importance both to protect the downstream areas against flooding, to provide drinking water to cities in the surrounding areas, and to use the excess water for irrigation of the agricultural areas located downstream of the dam. Recently, the Kingdom of Saudi Arabia (KSA) was affected by extraordinary rainstorm events causing many problems in many different areas. One of these events happened along the basin of the Baysh dam, which raised the alarm to the decision makers and to the public to take suitable action before dam failure occurs. The current study deals with a flood risk analysis of Wadi Baysh using an integration of hydrologic and hydraulic models. A detailed field investigation of the dam site and the downstream areas down to the Red Sea coast has been undertaken. Three scenarios were applied to check the dam and the reservoir functionality; the first scenario at 100-and 200-year return period rainfall events, the second scenario according to the Probable Maximum Precipitation (PMP), and the third scenario if the dam fails. Our findings indicated that the Baysh dam and reservoir at 100-and 200-year rainfall events are adequate, however, at the PMP the water will spill out from the spillway at ~8900 m3/s causing flooding to the downstream areas; thus, a well-designed channel along the downstream wadi portion up to the Red Sea coast is required. However, at dam failure, the inundation model indicated that a vast area of the section downstream of the dam will be utterly devastated, causing a significant loss of lives and destruction of urban areas and agricultural lands. Eventually, an effective warning system and flood hazard management system are imperative

Flood Mapping And Flood Damage Estimation Using GIS

Flood mapping has been used widely in many countries nowadays as a method to encounter this natural hazard phenomenon. In the past few years, studies about the flood analysis and modeling used to take years. But now, with the facilitation of Geographical Information System (GIS) automated process, the studies are faster and even accurate in modeling the flood inundation area. The used of GIS is essential as a planning tool for floods, educating the populations at risk and managing floods as they actually rise. Features in the GIS itself such as the 3-D Visualization and detailed hydrological characteristic help a lot in defining areas which is vulnerable to flood. Generally, flood mapping can identify the effects of flooding and those effects are represented in hazard and risk mapping. The main advantage of using GIS for flood management is that it not only generates a visualization of flooding but also creates potential to further analyze this product to estimate probable damage due to flood. This paper is to demonstrateth e use of GIS in constructing the flood map at studies area. The map then are derive into Digital Terrain Model (DTM) to make the identification in flood vulnerable areas easier which provide high detailed representation of the topographical variations in the Earth's surface