4 Faculty of Natural Resources, University of Jiroft, Kerman 7867161167, Iran; firstname.lastname@example.org * Correspondence: email@example.com; Tel.: +351-289800900; Fax: +351-289818419
Received: 20 May 2019; Accepted: 13 June 2019; Published: 17 June 2019 Abstract: Climate and landusechange 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 landuse and climatechange effects on soilerosion at an important damwatershed occupying a strategic position on the narrow Strait of Hormuz. The future climatechange at the study area was inferred using statistical downscaling and validated by the Canadian earth system model (CanESM2). The future landusechange 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 landusechangescenarios. 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 landuse/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 soilerosion 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 soilerosion 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 climatechange.
Tillage and residue management scenarios
Tillage and residue management was considered for the agricultural areas of the basin. Tillage refers to tilling the soil and altering the surface before planting or after harvest. Residue management refers to the amount of residue that is left on the surface of the field after harvest. This thesis examined a combination of these factors. The conventional tillage (CT) scenario involves two primary assumptions. The first assumption is that the fields are tilled such that most, if not all, of surface vegetation is removed. The second assumption is that little to no additional sur- face residue is left on the surface. These result in a disturbed top soil that is more susceptible to erosion and raindrop impact, and reduced soil cohesion. The most conservative scenario is no–till (NT). No–Till assumes that the soil is not tilled and that soil disturbance is largely limited to planting and harvest activities designed to have a minimal impact. The NT scenario also assumes that 90% of the field surface is covered by residue such as straw. Reduced till (RT) is a compromise be- tween CT and NT, with moderate disturbance of the soil and a 60% residue cover. The model was run to examine sensitivity to tillage practices. For CT, RT, and NTscenarios there are two assumed seasons: winter and growing season. Winter season is for the time period after harvest and before spring plant growth begins to dominate the field surface. For this project, the winter season was defined as October – April.
SWAT model was applied to simulate the impact of landuse and climatechange on water balance in the Krueng Jreu Sub Watershed, Aceh Besar district. The model was good enough to predict streamflow recorded at two stations located on the River Krueng Jreu and Krueng Meuleusong based on analysis on the models performance. The coefficients of determination varied between 0.58 and 0.72, while the Nash- Sutcliffe coefficients (E NS ) ranged between 0.65-0.72 and the percentage bias were in the range of -0.36 to 2.30. Under landusechangescenarios, the model predicted increases in both runoff and water yield as the impacts of increasing 15% of settlement area. Converting all agricultural land (14.26% of watershed area) into forest increased water yield during the dry month period by approximately 1% and decreased runoff contribution to water yield by 5%. The increases in runoff (23.6%), water yield (15.1%) and evapotranspiration (2.3%) were found under the scenario of 10% increase in precipitation. The model also predicted an 1.2% increase in annual evapotranspiration and the decreases in both runoff (1.3%) and water yield (0.8%) as the response to the 1.5 ° increase in daily air temperature. A combination of increasing 10% in daily precipitation and 1.5⁰C in daily air temperature caused increases in all observed water balance components.
quantitative knowledge. This has been addressed in differ- ent ways: (i) field suspended sediment load measurements and historical sedimentary archives (sediment accumulated in lakes) showed that deforestation and changes in agricul- tural practices have greatly influenced erosion and sediment transport (e.g. Valero-Garc´es et al., 2000); (ii) experimental catchments have been monitored worldwide in order to un- derstand the factors that control runoff generation and sedi- ment transport (e.g. Bosch and Hewlett, 1982), and to obtain detailed information on different parameters for hydrological modeling and to assess the influence of LULC change on ero- sion rates and sediment yield (e.g. Garc´ıa-Ruiz et al., 2008). All these studies have provided a deep insight into the in- teraction between LULC change and geomorphic processes. Experimental approaches, however, are resource-intensive and very limited in their ability to address the effects of fu- ture changes in LULC or other drivers such as the climate.
interlinked (Mwangi et al., 2015a). Agroforestry, for example, additionally provides other environmental services e.g. soilerosion control, provision of wood products such as timber and fuelwood, carbon sequestration, modification of microclimate (Ong et al., 2006; Nair, 1993). Soilerosion control is directly related to the findings reported here. The decrease in surface runoff due to agroforestry as reported in this study would consequently reduce soilerosion which is still a major problem in the MRB (Defersha and Melesse, 2012; Defersha et al., 2012; Kiragu, 2009). Reduced soilerosion would essentially reduce loss of top fertile soils in farmlands and hence control decline in land productivity for improved crop production. Decline in land productivity in the upper Mara has led to increased encroachment of the Mau forest by the local communities whose main economic activity is subsistence farming (Mati et al., 2008). Reduction in soilerosion would also minimize sedimentation in the rivers and thus improving the water quality. This is very important because the majority of people living in the watershed consume the water directly from the stream without any form of treatment (Ngugi et al., 2014; Dessu et al., 2014). For the few who live in towns within the watershed and who have the privilege of using treated water, reduced sediment loads would lower water treatment costs. Another key benefit of agroforestry is the provision of timber and fuelwood which would lower the pressure on the native forests. In Kenya, about 89% of people living in rural areas rely on fuelwood for their energy needs (World Resources Institute, 2007; Nyaga et al., 2015) which demonstrates the importance of agroforestry in the livelihoods of rural communities. Agroforestry would also be a means of restoring back some of the degraded parts of the watershed that was initially under forest.
Abstract: Quantifying the hydrological response due to changes in climate and land-use is imperative for the proper management of water resources within a watershed. The impact of climate and land-use changes on the hydrology of the Upper Ebonyi river (UER) watershed, South East Nigeria, was studied using the Soil and Water Assessment Tool (SWAT) hydrological model. A climatological time series analysis from 1985-2014 using non-parametric test showed significant negative trends in precipitation and relative humidity trend while minimum and maximum temperature, solar radiation and wind speed showed significant positive trends. Future hypothetical land-usechangescenarios representing urbanization and conversion of forest to agricultural land were combined with future downscaled climate model (CSIRO-Mk3-6-0) and simulated in SWAT model. Scenario 1 represents urbanization and climate data of 2020-2030; Scenario 2 represents urbanization and climate data of 2040-2050; Scenario 3 represents conversion of forest to agricultural land and climate data of 2020-2030 and Scenario 4 represents conversion of forest to agricultural land and climate data of 2040-2050 while the Baseline Scenario is the present land-use and climate data of 2005-2014. Relative to the Baseline (2005-2014), the results showed a decrease in streamflow by 10.3%, 26.2%, 11.8% and 26.72% for Scenarios 1, 2, 3, and 4 respectively, while sediment yield decreased by 7.54%, 19.4%, 11.1% and 9.01% for Scenarios 1, 2, 3, and 4 respectively. The results suggest development of adaptation strategies to cope with the predicted hydrological conditions under future climate and land-usechange in the watershed.
sensed data may affect the accuracy level this method definitely fills a gap in studying landuse/land cover changes resource distribution and their utilization as well. The Nepalese statistical data on landuse/land cover changes is very questionable (Shrestha et al. 2004). The present data shall certainly help to update the information and create a baseline database for future data collection. Spatial and Temporal databases were used to assess the changes in landuse and land cover patterns in the eleven-year period. Technological institutional and natural resource policy forces also play an important role in changing landuse pattern. Landusechange is therefore often modeled as a function of a selection of socio-economic and biophysical variables that act as the so-called „driving forces‟ of landusechange. At different scales of analysis different driving forces have a dominant influence on the landuse system. Driving forces are most often considered exogenous to the landuse system to facilitate modeling. However, in some cases this assumption hampers the proper description of the landuse system. Population pressure is often considered to be an important driver of landuse/land cover changes.
GIBSI is an integrated modelling system designed to assist stakeholders in decision making process for water manage- ment at the watershed scale (Rousseau et al., 2000; Vil- leneuve et al., 1998). It is basically composed of a MySQL® database management server, a GIS and a graphical user interface (GUI). The modeling part is based on the semi- distributed, physically based hydrological model HYDRO- TEL (Fortin et al., 2001a). HYDROTEL integrates six com- putational modules that are run in a cascade (i.e. in a decou- pled manner): weather data interpolation, snow cover dy- namic, potential evapotranspiration, soil moisture balance, surface runoff and streamflow. Each module offers more than one computational algorithm based on the availability of data for the studied watershed. Some algorithms, devel- oped from physically based principles, retain some empirical aspects while others are still fully empirical. Rainfall–runoff processes can be modeled on a 3–24-h time step basis. The hydrological model is sensitive to landuse configuration by the mean of the Manning coefficient (for surface runoff rout- ing), leaf area index and root depth (for actual evapotranspi- ration calculation). Other models can be used (i.e. erosion, nitrogen, phosphorus and pathogens transport), but they were not considered in this study. All models run on a daily time step with meteorological data (precipitation, minimum and maximum temperatures) as inputs. Outputs are daily stream- flow and water quality data at any computational river seg- ment. Pre- and post-processing tools enable to easily define management scenarios, run simulations and analyse results. The 1995 landuse configuration is used by default in the database and for simulations. It was determined based on a satellite image processed and validated with 1994 survey data (Villeneuve et al., 1998).
The Automated Geospatial Watershed Assessment (AGWA) tool was used to build input parameter files for the Soil and Water Assessment Tool (SWAT). Three different land cover scenariosLand cover of 2007, land cover of 2020, and full urbanization land cover as shown in Fig- ure 6, were used to parameterize the watershed inde- pendently, and runoff was generated for 31 years of con- tinuous simulation for the watersheds group in the study area. The same soil and rainfall data were used as input to each of the three simulation runs, so all changes in the runoff can be traced solely to changes in land cover. . AGWA delineated Gaza Strip region into small sub- basins to get a higher accuracy in the model result which shown in Figure 7. AGWA made a digitizing stream depend on DEM map. There is no continuity of surface water between Gaza region and the region outside it due to the Israeli dams. In the light of this fact, The natural extension of these sub-basins through the region outside Gaza region was neglected.
This study presents an approach to improve water and land used allocation, taking into consideration the direction of prevailing winds, with the intention of increasing the water availability of a basin or basins and for a better adaptation to changes in climate.
This paper is divided into 5 sections. Section 2 presents the two different approached for Land, Water and Wind Watershed Cycle. One focuses on an individual basin (Intrabasin L3WC) and the other on several basins (Interbasin L3WC). Section 3 presents two case studies in the Rio São Francisco basin in Brazil, one for each approach. Firstly, a probabilistic distribution of wind patterns in Brazil is presented, focusing especially on the São Francisco river. The Intrabasin L3WC case study is presented focusing on the comparison the water requirements and impact on hydropower and biomass electricity generation. A quick overview of the needs for water transposition in Brazil is presented. This is followed by a case study of the Interbasin L3WC, which is divided in four parts: the transposition of water, the consumption of water with irrigation, the return of the moisture with the wind and the use of L3WC as a climatechange adaptation strategy. The benefits and challenges of the methodology are discussed in Section 4. Section 5 concludes the paper.
For decades the world has experienced extensive tropical deforestation. In some countries there are now signs of a forest transition whereby loss of natural forest continues while land previously used for agriculture or grazing is being converted into tree plantations and cash crops. This watershed study explores drivers and impacts of such developments to socio-economy and carbon storage and its policy relevance. The study was based on satellite image analysis, field data and interviews regarding landuse and forest cover, socio-economic situation and policy over 14 years. It found that intensified land management and drivers of market, infrastructure and household tenure security, have contributed to increased production of food and tree crops and increased forest cover. This forest transition is partly related to policy and driven by the households’ response to a changing market and socio-economic situation. Understanding drivers of change can contribute to sustainable climate and resource management policies.
Human land degradation which removes the vegetation cover increases the vulnerability of the region for wind erosion (Cook et al., 2009). The loss of vegetation, accompanied by cropland expansion, plays the main role in exposing the topsoil of Kaftahumera for wind erosion. The severity of loss of vegetal cover is aggravating in most woodland areas of Kaftahumera. Due to the extended landuse changes, there might be a reduction in the capacity of the land to provide ecosystem goods and services. Image analysis confirmed the conversion of about 47% of the woody vegetation to other landuse types affecting the natural vegetation. The intensive mechanized farming, mainly for production of oil crops for international markets, has contributed to the shrinking of the woodlands (Lemenih et al. 2014; Zewdie & Csaplovics, 2014). In addition, the expansion of subsistence agriculture with ever increasing population pressure competes with the natural vegetation of the region (Zewdie & Csaplovics, 2015). The continuous exposure of the landscape for degradation eases the loss of soil from the region. The soils being blown away from the landscape drain the soil through transporting sediments and nutrients. Studies on climatechange also show a stronger link between vegetation change and dust aerosols in which precipitation of the Sahel region is reduced due to changes in vegetation cover and increased occurrence of dust storms (Yoshioka et al., 2007).
Climate changes can strongly affect the river flows in Pakistan. The analysis of climatechangeimpact is very complex as it includes many climate parameters and there could also be a lot of uncertainties. The main parameters accountable for climatechange are change in temperature, precipitation and glacier storage. The past trends of flows for different rivers in Pakistan are quite different from each other with respect to the specific climate changes in related watersheds which occurred in the past. The assessment for the future changes in climate parameters is also difficult, especially for precipitation. The available projections about the expected future changes in different climatic parameters are not quite sufficient and it needs further investigations to have a clear understanding of their impact on river flows in Pakistan. The available research studies provided some indication of future climate changes in Pakistan and their impact on river flows. As an attempt, the impact of climate changes in terms of assumed change in temperature and glaciated area have been modeled for the river flows of Chitral watershed. The model outputs show a meaningful impact of assumed climate changes on the flows of Chitral river. For more accurate and reliable results, such analyses require detailed input and realistic estimates of future changes in climate parameters. Finally, it is concluded that there is a strong need of further research about the possible impacts of climate changes on river flows in Pakistan. References
In Ethiopia, there are several studies on landuse and land cover change that showed the different facets of change. Gete and Hurni (2001) in Dembecha area of Gojjam, Belay (2002) in Derekolli catchment of South Wello, Amare (2007) in Eastern Escarpment of Wello, Nyssen et al (2008) in Wag zone of Amhara Region, Northern Ethiopia, studied landuse and land cover dynamics. Gessesse and Kleman (2007) in Southern part of Ethiopia (Central rift valley), Berhan Gessesse (2010 ) in western part of Ethiopia and Diress et al (2010) in North eastern Afar range lands have also studied LULCC. Most of these studies found cropland has expanded at the expense of woody vegetation cover. But from 1984- 2003, area of forest and shrub land was increased while area of agricultural land was decreased in Simen Mountains National Park, North western Ethiopia (Menale et al 2011). So that region- specific information of changes in LULCC is essential for land resource management. Though LULCC research is undergoing at different places and at different scale, still a lot more studies are needed to cover the country. The present study investigates the landuse and land cover dynamics in Ameleke watershed in middle catchment of Gidabo River, South Ethiopia. This watershed is inhabited by the Gedeo Guji Oromo ethnic groups. The Gedeo community change the natural forest and grassland to agroforestry while in Guji Oromos’ land charcoal production, expansion of croplands and overgrazing on grass lands and shrub lands are the major landuse problems. Cultivation and grazing of marginal lands has also a desperate effect on the resources of the watershed. In association with this problem, this study tried to document the spatial and temporal landuse and land cover dynamics of Ameleke watershed.
population size of ecologically similar species (functional types). Habitat status ranges from “untransformed” in protected areas to “extreme transformation”, such as in urba- nized areas. The index is aggregated by weighting the area subject to each activity and the number of species occurring in the particular area. To estimate the reduction in populations caused by a predefined set of landuse types, highly experienced experts for each taxonomic group were independently asked. Estimations were made relative to populations in a large protected area in the same ecosystem type in South Africa (divi- ded into six types). Taxonomic groups were divided into functional types such as body size, trophic niche, and reproduction strategy, which respond in similar ways to human activities. Expert-derived estimates were validated against measurements available in literature. All analyses were carried out in South Africa (Scholes and Biggs, 2005). The NCI and MSA are implemented in analyses done by GLOBIO3 (Alkemade et al., 2009). GLOBIO3 describes biodiversity as the remaining mean species abundance (MSA) of original species (depending on literature summarizing field data on species occurrences), relative to their abundance in pristine or primary vegetation. The MSA represents the average response of the total set of species to any landuse activity occur- ring in the specific ecosystem. Such a response is, e.g., the decline of species numbers in a defined area. Individual species responses are not modelled (Alkemade et al., 2009). The main difference between the BII and the MSA is that every area is weighted equally in MSA, whereas BII gives more weight to species rich areas. With the BII, ecosystems are weighted by their species richness, and the population sizes (abundances) are esti- mated for each landuse class in each ecosystem. The MSA is also similar to the Living Planet Index (LPI) (Loh et al., 2005) provided by the World Wide Fund For Nature (WWF). The LPI aims to measure the average changing state of populations of verte- brate species from around the world since 1970 (3000 population time series for over 1100 species).
The experimental areas of the current study were limited to three sites in Borneo, one per forest type. Due to this limita- tion, we demonstrated different bacterial communities in Borneo tropical forest soil with different land-use types, which is affected by the depth of soil. Certain bacterial genera and species were found selectively distributed in vertical soil ho- rizon which was reflected in their α- and β-diversity. Conversion of primary or logged forest into oil palm planta- tion may have resulted in higher bacterial species diversity as indicated in higher numbers of observed OTUs and α- diversity indices. In addition, β-diversity analysis of OTUs also indicated that changes in land-use have impact on the vertical bacterial community structure as shown in oil palm plantation organic (O-Horizon) and organic-mineral (A- Horizon) soils. Findings presented here are only snapshot in time and long term study is needed to enable an elucidation of whether the changes in soil bacteria across land-use conver- sion are a more permanent effect or simply temporary distur- bance. However, additional research with continuous monitor- ing at a larger experimental area is needed to provide a better understanding on the relationship between the tropical soil properties and bacterial communities. Finally, we propose that the implementation of environmentally friendly practices by the oil palm industry may help to maintain the microbial di- versity and ecosystem functions in natural habitats.
The number of days available for field operations is frequently central, either directly or indirectly, to farm planning decisions. The number, and distribution, of working days influences the type and acreage of crops grown, and the corresponding labour and machinery requirements. The condition of land for field operations can been classified in terms of trafficability and workability. Trafficability is concerned with the ability of soil to provide adequate traction for vehicles, and withstand traffic without excess compaction or structural damage. If land is considered trafficable, then it is deemed suitable for non-soil-engaging operations (e.g. fertilizer application and crop protection). Workability is concerned with soil-engaging operations and can be considered to be a combination of trafficability and the ability of soil to be manipulated in a desired way without causing significant damage or compaction. The most influential factor in determining the suitability of land for field operations is the soil moisture status. When a soil is trafficked or worked in an unsuitable condition, damage to the soil's structure and the consequent effect on crop production can persist for many years . In mechanised agriculture, high axle loads are cause of major concern regarding the risk of soil compaction, especially if wheeling and tillage are conducted at high soil moisture content [2, 3]. Tillage is a fundamental factor influencing soil quality, crop performance and the sustainability of cropping systems  because it represents the most influential manipulation or alteration of soil physical properties due to repetitive application, its depth range extending up to tens of centimeter, and because it influences the type of residue management applied.
In terms of the results for climatechange for the strips – there is greater variability E-W as opposed to N-S. This suggests the influence of Atlantic derived frontal systems and how these may change in the RCMs.
In terms of the effect of landusechange then variation due to subtle ‘real changes’ in historic landuse (between LCM 2000 and LCM 2007) is small. Extremes of landusechange are predicted to result in significant change but these scenarios are very unlikely to be realised. For the Dee, Hampshire Avon, Tees and the Usk the change in recharge for landusechange to climatechange is comparable with the Ely-Ouse and Trent less and the Stour and England and Wales as a whole greater. This was investigated further by swapping out different landuse types, i.e. arable to forested and showed much less variation than for the single landuse runs. The original question that the modelling work was to address relates to the relative changes in recharge related to climatechange as opposed to landusechange. Taking England and Wales as a whole then the order of change in recharge due to landuse variation is: socio-economic landuse (LCM2000 w.r.t. LCM2007) is less than spatial replacement whose magnitude of change in recharge is less than wholesale replacement (i.e. all one landuse type for England and Wales). Comparing the magnitude of these changes with those resulting from climatechange show that variation of recharge related to climatechange variation falls in the middle of landusechange. However, the variation of recharge due to the use of different RCMs is comparable with the overall variation of landusechange, although this is tempered by the underestimation of recharge by the RCMs.
Motivated by the awareness of the urgency and comple- xity of the challenges presented by climatechange and its interaction with social systems, the Conference inten- ds to offer to the international debate its contribution in identifying possible solutions to deal with the conse- quences of the changing climate.
We use the data described in section 3.1.2 to estimate (1) and (2). For each equation we use a log-linear specification, and a one-way fixed effects model to capture unobserved cross- sectional heterogeneity 9 . In the pond equation (1), we include temperature (T) one- and two-year lags for precipitation (P) because prairie wetlands are dependent on accumulated soil moisture (Sorenson et al. 1998), and the change in the crop share ( Δ CS) as changes in crop area better capture potential wetland loss. In the duck equation (2), we include the current year and one-year lag of ponds, the crop share and the lagged harvest (H) since birds are harvested in the fall and thus affect the following spring migration. The regressions fit the data well with R 2 of 0.75 and 0.83 for the pond and waterfowl models, respectively, and highly significant F-statistics. The estimated equations, with fixed effects omitted for simplicity and p-values in parentheses, are: (3) ln(Ponds) = 13.76 – 0.04*T + 0.01*P + 0.09*P t-1 + 0.14*P t-2 – 1.49* Δ CS