Ecosystem Carbon & Hydrological Connectivity in Nitrate-Vulnerable Zones Sample

1. Introduction : Understanding Ecosystem Carbon and Hydrological Connectivity in Nitrate-Vulnerable Zones

This analysis examines the land cover composition, vegetation structure, hydrological connectivity, and terrain characteristics of an ecologically important wadi system in UK's carricks road Region. By evaluating how these environmental factors intersect, priority zones are identified for targeted conservation interventions aimed at improving water quality outcomes related to the surrounding nitrate-vulnerable agricultural zone draining into critical downstream aquatic habitats along the Red Sea coast.

2. Discussion

Task A

2.A.1 Land Cover Characteristics

The nitrate vulnerable zone (NVZ) features a diversity of land cover types tied to elevation and water availability variations across UKs carricsk road Region. At lower elevations near the Red Sea coast, the landscape consists predominantly of large cultivated fields ranging from 5 to 100 hectares growing cereal crops and vegetables (Muhammad et al. 2022).

Figure 1: Land cover characterization

(Source: Self-Created in QGIS)

Approximately 60% of the lowland area is devoted to wheat cultivation and fallowing, 30% to various vegetables like tomatoes and squash, and 10% to scattered fruit and olive orchards lined by irrigation canals extending 15 kilometers inland on average (Congedo, 2021). Moving progressively uphill to elevations reaching 1,500 meters, the agricultural areas transition to natural grasslands covering 35% of the higher gradient zones and shrublands covering 60% with small hamlets and isolated buildings clustered around springs (Zaki et al. 2022). Ephemeral stream channels spanning over 54 linear kilometers support broader stands of acacia, juniper, and tamarisk trees, particularly along riparian corridors descending from steeper wadi tributaries.

2.A.2 Vegetation Structure

Structural diversity in the vegetation directly relates to the influences of land use, water availability, and topography. As classified through analysis of lidar-based canopy height models, the lowland agricultural areas contain primarily sparse, short ground cover and crops less than 1 meter in height occupying over 80% of the cultivated land (Reyes et al. 2021). Orchards and olive groves feature moderately dense trees ranging from 3 to 12 meters tall.

Figure 2: Vegetation Structure

(Source: Self-Created in QGIS)

At higher elevations between 500 to 1,500 meters, discontinuous shrublands have patchy woody plants approximately 1 to 2 meters tall covering over 50,000 hectares and small condensed stands of trees up to 10 meters tall concentrated in ravines spanning 65 hectares (Lemenkova, 2020). The riparian corridors along wadi channels contain the greatest vegetation structural complexity, with discontinuous stands of trees exceeding 15 meters forming canopy layers bordering splays of lower-story shrubs and grasses.

2.A.3 Hydrological Connectivity

Surface and subsurface hydrological flow paths across the NVZ reflect interactions between vegetative, soil, and terrain structures. The agricultural plains at lower elevations have extremely high drainage densities transporting water rapidly over the landscape with few obstructions from vegetation (Karstens et al. 2022). Soils here have higher runoff potentials as well. As surface and groundwater flows converge into wadis draining steeper upstream areas, the channels and floodplains facilitate transmission but also provide transient storage opportunities in alluvial aquifers.

Figure 3: Hydrological Demographics

(Source: Self-Created in QGIS)

Riparian corridor trees and bank obstructions slow flood wave velocities (Nagata et al. 2023). The mosaics of natural shrubland and grassland cover on hillsides supply inputs of lower nutrient groundwater while also allowing more diffuse, shallow subsurface flows. Hydrological connectivity thus relates directly to variations in the NVZ's land cover structure and composition.

Task B

2.B.1 The characteristic Slopes and their responses

Slopes of varying slopes display diverse runoff and erosion reactions to rainfall events, depending on their impact on gravitational fluxes and soil stability. On mild inclines, a greater percentage of the precipitation seeps into the soil, replenishing the moisture content of the soil and underground water reservoirs (Castelli et al. 2021). When the slope angles exceed 5 degrees, there is an increase in lateral subsurface flows and gradual surficial runoff. When slopes increase to 10 to 15 degrees, the movement of water over the land becomes more noticeable and concentrated, carrying loose particles downhill through processes such as erosion caused by raindrops and the flow of water across the surface. When the slope exceeds 25 degrees, the speed of the runoff can pick up larger soil particles, resulting in the formation of rills and gullies.

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Shallow slopes allow rainwater to stay in the soil for a longer period of time, allowing it to seep into layers such as organic horizons and permeable soils that have a large proportion of sand. The milder slope also applies a reduced shear strain on soil particles, which may prevent the disruption of cohesive bonds and result in greater retention of fine silts and clays in their original positions (Liashenko et al. 2020). Even heavy rainfall on gently sloping land might cause little erosion. On steep hillslopes, water flows quickly downhill, exerting strong drag forces to remove particles and preventing them from being absorbed into the shallow layer of soil. The skeletal and rocky soils have a limited depth that allows little water to penetrate before it starts flowing over the bedrock. Intense flows pass via small channels called rills and gullies, causing significant erosion in the exposed channels.

Figure 4: Shallow Slope Diagram

(Source: Self-Created in QGIS)

he increase in erosion hazards is directly proportional to the slope, but this connection is also influenced by soil qualities and surface protection. Steeper inclines have loamy clays that are more resistant to detachment compared to sandier soils on shorter inclines. Similarly, the way erosion is addressed varies depending on whether the slopes are cultivated without any ground cover or if they are natural forests protected by forest litter and deep root networks (Varghese et al. 2022). By incorporating slope gradients along with data on soils, underlying geology, and land use, it is possible to accurately anticipate variations in runoff and erosion susceptibility during different storm occurrences.

2.B.2 Identifiable Areas

A digital ?l?vation mod?l (DEM) cr?at?d from r?mot? s?nsing data in rast?r format allows for th? mod?lling and mapping of ar?as that surpass sp?cific slop? angl? thr?sholds, which ar? associat?d with high?r risks of runoff and ?rosion (Trifonova ?t al. 2021). Slop? rast?rs us? c?ll-by-c?ll computation of gradi?nt valu?s to r?pr?s?nt th? spatial variation in topographic inclin? across landscap?s. Onc? th? slop? rast?r lay?r is g?n?rat?d, a conditional stat?m?nt or r?classification can b? us?d to id?ntify plac?s that ?xc??d a sp?cifi?d angl? thr?shold, such as 10 or 25 d?gr??s, bas?d on th? pr?viously d?scrib?d g?omorphic dynamics.

Figure 5: Diagram of digital elevation model

(Source: Self-Created in QGIS)

For ?xampl?, by utilizing th? Slop? Spatial Analyst tool in ArcGIS softwar?, us?rs can choos? a c?rtain slop? output param?t?r, such as th? p?rc?ntag? of ris?. Th? g?n?rat?d rast?r r?pr?s?nting th? p?rc?ntag? slop? can b? subj?ct?d to CON or RECLASS proc?ss?s to id?ntify and ?xtract ar?as that ?xc??d a sp?cifi?d thr?shold valu? (Mos?s, 2019). Similarly, within op?n-sourc? GIS packag?s such as QGIS, th? T?rrain Analysis tools?t g?n?rat?s a slop? lay?r that allows us?rs to sp?cify th? units of m?asur?m?nt, ?ith?r in d?gr??s or as a p?rc?ntag?. Th? slop? rast?r is r?classifi?d using th? SAGA algorithm to id?ntify t?rrain with slop?s abov? a crucial thr?shold that trigg?rs incr?as?d ?rosion r?actions. The binary raster produced from the steep zone locations serves as an input for sediment risk models or for implementing on-site management measures such as runoff diversion structures. Prudent selection and mapping of slope thresholds aid in identifying the most susceptible inclines in a watershed or hillslope that are prone to sediment transfer caused by rainfall.

2.B.3 Evaluation in the statement of Raster Calculation

The raster calculator in GIS provides a powerful tool to evaluate places where multiple landscape factors intersect to identify targeted priority zones based on conditional rulesets (Netzel and Slopek, 2021). By stacking and comparing raster layers using mathematical expressions, unique combinations of elevation, land cover, drainage characteristics, vegetation structure, and terrain can be quantified as vulnerable areas needing integrated management.

For example, to locate steep agricultural fields prone to soil erosion that would benefit from added structural vegetation to control runoff, the following raster calculator approach could be implemented:

Classify a digital ?l?vation mod?l into slop? zon?s abov? 25 d?gr??s using th? Slop? tool and R?classify algorithm. R?classify a land cov?r lay?r to ?xtract agricultural ar?as d?fin?d as annual crops, p?r?nnial crops, and pastur?s bas?d on cat?gori?s from a product lik? th? National Land Cov?r Databas? Apply th? Rast?r Calculator Pars? Rul? to cr?at? an output lay?r with valu?s of 1 for c?lls m??ting both crit?ria:

("Slop?_GT25" == 1) & ("Ag_LC" == 1 | "Ag_LC" == 2 | "Ag_LC" == 3)

Th? r?sult displays agricultural ar?as ?xc??ding 25 d?gr??s slop? at gr?at?st risk for soil loss und?r int?ns? rainfall.

This conc?pt could b? ?xpand?d by adding additional variabl?s lik? a v?g?tation h?ight mod?l from LiDAR and a soil ?rodibility K-factor map (Dax?r, 2020). For instanc?, to also incorporat? ar?as with low v?g?tation cov?r < 0. 5 m?t?rs in h?ight:

("Slop?_GT25" == 1) & ("Ag_LC" == 1 | "Ag_LC" == 2 | "Ag_LC" == 3) & ("CanopyHT_LT_HalfM?t?r" == 1)

Th? final map th?n shows ?sp?cially vuln?rabl? agricultural ar?as on st??p t?rrain with low prot?ctiv? ground cov?r n??ding priority plantings or y?ar-round r?sidu? maint?nanc? (L?m?nkova, 2020). As ?x?mplifi?d h?r?, bringing tog?th?r multipl? rast?r lay?rs using a s?qu?nc? of logical op?rators and working with r?classifi?d cat?gorical data provid?s a fl?xibl? fram?work. Math?matical ?xpr?ssions h?lp isolat? locations satisfying multipl? g?o-?nvironm?ntal crit?ria to aid targ?ting for proactiv? wat?rsh?d cons?rvation and landscap? r?sili?nc? improv?m?nts.

3. Conclusion

Analysis of land cov?r, v?g?tation structur?, hydrological conn?ctivity, slop?s, and priority manag?m?nt ar?as provid?s k?y insights into th? compl?x ?nvironm?ntal dynamics within th? nitrat?-vuln?rabl? zon? of UKs carricks road R?gion. Whil? lowland agricultural plains pos? risks of nutri?nt runoff du? to limit?d v?g?tation and high drainag? d?nsity, upstr?am ar?as with natural cov?r provid? ?cosyst?m s?rvic?s that can b? l?v?rag?d through strat?gic cons?rvation planning. By targ?ting int?rv?ntions lik? runoff control structur?s, riparian buff?rs, and cov?r crops to th? most vuln?rabl? st??ply-slop?d crop fi?lds id?ntifi?d through rast?r calculator approach?s, nutri?nt pollution ?nt?ring critical wadi habitats and coastal ar?as can b? r?duc?d ov?r tim? whil? supporting sustainabl? r?gional land us?s. Int?grat?d ass?ssm?nt of how topography, land us?, and ?cological variabl?s int?rs?ct ?nabl?s tailor?d and adaptiv? solutions balancing production n??ds with ?cological r?sili?nc?.

References
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Website
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Karstens, S., Dorow, M., Bochert, R., Stybel, N., Schernewski, G. and Mühl, M., 2022. Stepping stones along urban coastlines—improving habitat connectivity for aquatic fauna with constructed floating wetlands. Wetlands, 42(7), p.76.