Spatial modeling of land subsidence using machine learning models and statistical methods

Land subsidence causes many problems every year and damages residential areas and agricultural lands. The purpose of this study is to prepare a susceptibility map to the phenomenon of land subsidence in the central and eastern plains of Fars province in Iran using statistical and machine learning models. Initially, with a wide inspection, the locations of land subsidence in the study region were recorded using the global positioning system (GPS), and a spatial distribution of subsidence was provided then for building and evaluating learning models; the data was partitioned into two sections of calibration (70%) and testing (30%) dataset. In the following stage, the maps of the factors affecting the land subsidence were prepared using basic information (geological and topographic maps and satellite images) in raster format, and the relationship between land subsidence locations and the effective factors including slope percentage, slope aspect, distance from the road, distance from the river, land use, plan curvature, topographic wetness index, geology (lithological units), distance from the faults, and groundwater level changes was considered in the study area. To investigate the multicollinearity between independent variables, tolerance and variance inflation factor (VIF) measures were used, and to prioritize the effective factors, the random forest (RF) algorithm was applied. The results indicated that the most important factors affecting land subsidence were groundwater level changes, land use, height, distance from the fault, distance from the river, and topographic wetness index, respectively. For further analysis, a land subsidence susceptibility zoning map was prepared using logistic regression (LR), random forest (RF), boosting regression tree (BRT), and support vector machine (SVM) models, and the results were evaluated. The evaluation results indicated that the models mentioned have high accuracy in modeling land subsidence such that the boosting regression tree and the logistic regression have high (0.873 and 0.853, respectively) and the random forest and support vector machine models have very high accuracy (0.953 and 0.926, respectively). The findings of this study indicated that the machine learning techniques and prepared maps can be applied for land use planning, groundwater management, and management of the study area for future agriculture tasks.

Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: A comparison between MCDM and SWAT models

Kangsabati basin located in tropical plateau region faces multiple problems of soil erosion susceptibility (SES), soil fertility deterioration, and sedimentation in reservoirs. Hence, identification of SES zones in thirty-eight sub-basins (SB) for basin prioritization is necessary. The present research addressed the issue by using four multi-criteria decision-making (MCDM) models: VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), technique for order preference by similarity to ideal solution (TOPSIS), simple additive weighing (SAW), compound factor (CF). To determine the best fitted method from MCDM for erosion susceptibility (ES), a comparison has been made with Soil and Water Assessment Tool (SWAT), where fifteen morphometric parameters were considered for MCDM, and meteorological data, soil, slope and land use land cover (LULC) were considered for SWAT model. Two validation indices of percentage change and intensity change were used for evaluation and comparison of MCDM results. With SWAT model performance, SWAT calibration and uncertainty analysis programs (CUP) was used for sensitive analysis of SWAT parameters on flow discharge and sediment load simulation. The results showed that 23, 16, 18 SB have high ES; therefore they were given 1 to 3 ranks, whereas 31, 37, 21SB have low ES, hence given 38 to 36 rank as predicted by MCDM methods and SWAT. MCDM validation results depict that VIKOR and CF methods are more acceptable than TOPSIS and SAW. Calibration (flow discharge R2 0.86, NSE 0.75; sediment load R2 0.87, NSE 0.69) and validation (flow discharge R2 0.79, NSE 0.55; sediment load R2 0.79, NSE 0.76) of SWAT model indicated that simulated results are well fitted with observed data. Therefore, VIKOR reflects the significant role of morphometric parameters on ES, whereas SWAT reflects the significant role of LULC, slope, and soil on ES. However, it could be concluded that VIKOR is more effective MCDM method in comparison to SWAT prediction.

Towards an operationalisation of nature-based solutions for natural hazards

Nature-based solutions (NBS) are being promoted as adaptive measures against predicted increasing hydrometeorological hazards (HMHs), such as heatwaves and floods which have already caused significant loss of life and economic damage across the globe. However, the underpinning factors such as policy framework, end-users' interests and participation for NBS design and operationalisation are yet to be established. We discuss the operationalisation and implementation processes of NBS by means of a novel concept of Open-Air Laboratories (OAL) for its wider acceptance. The design and implementation of environmentally, economically, technically and socio-culturally sustainable NBS require inter- and transdisciplinary approaches which could be achieved by fostering co-creation processes by engaging stakeholders across various sectors and levels, inspiring more effective use of skills, diverse knowledge, manpower and resources, and connecting and harmonising the adaptation aims. The OAL serves as a benchmark for NBS upscaling, replication and exploitation in policy-making process through monitoring by field measurement, evaluation by key performance indicators and building solid evidence on their short- and long-term multiple benefits in different climatic, environmental and socio-economic conditions, thereby alleviating the challenges of political resistance, financial barriers and lack of knowledge. We conclude that holistic management of HMHs by effective use of NBS can be achieved with standard compliant data for replicating and monitoring NBS in OALs, knowledge about policy silos and interaction between research communities and end-users. Further research is needed for multi-risk analysis of HMHs and inclusion of NBS into policy frameworks, adaptable at local, regional and national scales leading to modification in the prevalent guidelines related to HMHs. The findings of this work can be used for developing synergies between current policy frameworks, scientific research and practical implementation of NBS in Europe and beyond for its wider acceptance.

A review of soil carbon dynamics resulting from agricultural practices

Literature related to the carbon cycle and climate contains contradictory results with regard to whether agricultural practices increase or mitigate emission of greenhouse gases (GHGs). One opinion is that anthropogenic activities have distinct carbon footprints - measured as total emissions of GHGs resulting from an activity, in this case, "agricultural operations". In contrast, it is argued that agriculture potentially serves to mitigate GHGs emissions when the best management practices are implemented. We review the literature on agricultural carbon footprints in the context of agricultural practices including soil, water and nutrient management. It has been reported that the management practices that enhance soil organic carbon (SOC) in arid and semi-arid areas include conversion of conventional tillage practices to conservation tillage approaches. We found that agricultural management in arid and semi-arid regions, which have specific characteristics related to high temperatures and low rainfall conditions, requires different practices for maintenance and restoration of SOC and for control of soil erosion compared to those used in Mediterranean, tropical regions. We recommend that in order to meet the global climate targets, quantification of net global warming potential of agricultural practices requires precise estimates of local, regional and global carbon budgets. We have conducted and present a case study for observing the development of deep soil carbon profile resulting from a 10-year wheat-cotton and wheat-maize rotation on semi-arid lands. Results showed that no tillage with mulch application had 14% (37.2 vs 43.3 Mg ha^-1) higher SOC stocks in comparison to conventional tillage with mulch application. By implementing no tillage in conjunction with mulch application, lower carbon losses from soil can mitigate the risks associated with global warming. Therefore, it is necessary to reconsider agricultural practices and soil erosion after a land-use change when calculating global carbon footprints.

Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model

The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)—linear, relief, and shape—of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvārī drainage basin. The results show that in evolutionary terms this is a youthful landscape. Rejuvenation has influenced the erosional development of the basin, but lithology and relief, structure, and tectonics have determined the drainage patterns of the catchment. Results of the AHP model indicate that slope and drainage density influence erosion in the study area. The COPRAS-AHP ensemble model results reveal that sub-basin 1 is the most susceptible to soil erosion (SE) and that sub-basin 5 is least susceptible. The ensemble model was compared to the two individual models using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). To evaluate the prediction accuracy of the ensemble model, its results were compared to results generated by the modified Pacific Southwest Inter-Agency Committee (MPSIAC) model in each sub-basin. Based on SCCT and KTCCT, the ensemble model was better at ranking sub-basins than the MPSIAC model, which indicated that sub-basins 1 and 4, with mean sediment yields of 943.7 and 456.3 m 3 km − 2   year − 1 , respectively, have the highest and lowest SE susceptibility in the study area. The sensitivity analysis revealed that the most sensitive parameters of the MPSIAC model are slope (R2 = 0.96), followed by runoff (R2 = 0.95). The MPSIAC shows that the ensemble model has a high prediction accuracy. The method tested here has been shown to be an effective tool to improve sustainable soil management.

Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms

Gully erosion is one of the most effective drivers of sediment removal and runoff from highland areas to valley floors and stable channels, where continued off-site effects of water erosion occur. Gully initiation and development is a natural process that greatly impacts natural resources, agricultural activities, and environmental quality as it promotes land and water degradation, ecosystem disruption, and intensification of hazards. In this research, an attempt is made to produce gully erosion susceptibility maps for the management of hazard-prone areas in the Pathro River Basin of India using four well-known machine learning models, namely, multivariate additive regression splines (MARS), flexible discriminant analysis (FDA), random forest (RF), and support vector machine (SVM). To support this effort, observations from 174 gully erosion sites were made using field surveys. Of the 174 observations, 70% were randomly split into a training data set to build susceptibility models and the remaining 30% were used to validate the newly built models. Twelve gully erosion conditioning factors were assessed to find the areas most susceptible to gully erosion. The predisposing factors were slope gradient, altitude, plan curvature, slope aspect, land use, slope length (LS), topographical wetness index (TWI), drainage density, soil type, distance from the river, distance from the lineament, and distance from the road. Finally, the results from the four applied models were validated with the help of ROC (Receiver Operating Characteristics) curves. The AUC value for the RF model was calculated to be 96.2%, whereas for those for the FDA, MARS, and SVM models were 84.2%, 91.4%, and 88.3%, respectively. The AUC results indicated that the random forest model had the highest prediction accuracy, followed by the MARS, SVM, and FDA models. However, it could be concluded that all the machine learning models performed well according to their prediction accuracy. The produced GESMs can be very useful for land managers and policy makers as they can be used to initiate remedial measures and erosion hazard mitigation in prioritized areas.

Estimation of Sediment Yield and Maximum Outflow Using the IntErO Model in the Sarada River Basin of Nepal

Soil erosion is a severe environmental problem worldwide as it washes away the fertile topsoil and reduces agricultural production. Nepal, being a hilly country, has significant erosion disputes as well. It is important to cognise the soil erosion processes occurring in a river basin to manage the erosion severity and plan for better soil conservation programs. This paper seeks to calculate the sediment yield and maximum outflow from the Sarada river basin located in the western hills of Nepal using the computer-graphic Intensity of Erosion and Outflow (IntErO) model. Asymmetry coefficient of 0.63 was calculated, which suggests a possibility of large floods to come in the river basin in the future whereas the maximum outflow from the river basin was 1918 m³ s−1. An erosion coefficient value of 0.40 was obtained, which indicates surface erosion of medium strength prevails in the river basin. Similarly, the gross soil loss rate of 10.74 Mg ha−1 year−1 was obtained with the IntErO modeling which compares well with the soil loss from the erosion plot measurements. The IntErO model was used for the very first time to calculate soil erosion rates in the Nepalese hills and has a very good opportunity to be applied in similar river basins.

Simulated Runoff and Sediment Yield Responses to Land-Use Change Using the SWAT Model in Northeast China

Land-use change is one key factor influencing the hydrological process. In this study, the Hun River Basin (HRB) (7919 km2), a typical alpine region with only four gauge meteorological stations, was selected as the study area. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), widely adopted in East Asia, was used with the Soil and Water Assessment Tool (SWAT) model to simulate runoff and sediment yield responses to land-use change and to examine the accuracy of CMADS in the HRB. The criteria values for daily/monthly runoff and monthly sediment yield simulations were satisfactory; however, the validation of daily sediment yield was poor. Forestland decreased sediment yield throughout the year, increased water percolation, and reduced runoff during the wet season, while it decreased water percolation and increased runoff during the dry season. The responses of grassland and forestland to runoff and sediment yield were similar, but the former was weaker than the latter in terms of soil and water conservation. Cropland (urban land) generally increased (increased) runoff and increased (decreased) sediment yield; however, a higher sediment yield could occur in urban land than that in cropland when precipitation was light.

A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran

In north of Iran, flood is one of the most important natural hazards that annually inflict great economic damages on humankind infrastructures and natural ecosystems. The Kiasar watershed is known as one of the critical areas in north of Iran, due to numerous floods and waste of water and soil resources, as well as related economic and ecological losses. However, a comprehensive and systematic research to identify flood-prone areas, which may help to establish management and conservation measures, has not been carried out yet. Therefore, this study tested four methods: evidential belief function (EBF), frequency ratio (FR), Technique for Order Preference by Similarity To ideal Solution (TOPSIS) and Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) for flood hazard susceptibility mapping (FHSM) in this area. These were combined in two methodological frameworks involving statistical and multi-criteria decision making approaches. The efficiency of statistical and multi-criteria methods in FHSM were compared by using area under receiver operating characteristic (AUROC) curve, seed cell area index and frequency ratio. A database containing flood inventory maps and flood-related conditioning factors was established for this watershed. The flood inventory maps produced included 132 flood conditions, which were randomly classified into two groups, for training (70%) and validation (30%). Analytical hierarchy process (AHP) indicated that slope, distance to stream and land use/land cover are of key importance in flood occurrence in the study catchment. In validation results, the EBF model had a better prediction rate (0.987) and success rate (0.946) than FR, TOPSIS and VIKOR (prediction rate 0.917, 0.888, and 0.810; success rate 0.939, 0.904, and 0.735, respectively). Based on their frequency ratio and seed cell area index values, all models except VIKOR showed acceptable accuracy of classification.

Combining land preparation and vegetation restoration for optimal soil eco-hydrological services in the Loess Plateau, China

In semiarid terrestrial ecosystems, optimized eco-rehabilitation strategies, such as land preparations and planting vegetation, are keys to achieve a successful ecological restoration. Land preparations and vegetation are supposed to have the coupled and respective impacts on soil ecosystem services, which are still unclear now. In this study, eighteen experimental plots with six different combinations and repetitions of land preparations and vegetation were built in the Chinese Loess Plateau in 2014 and soil moisture storages (SMS), soil carbon stocks (SCS) and other soil nutrient stocks were calculated at 0-100 cm, also the effects of land preparations and planting vegetation on soil eco-hydrological services are analyzed by mathematical methods. The results show that leveled ditches-M. sativa had the highest SMS (125 mm) while zig terraces-P. tabulaeformis had the lowest values (88 mm). Fish-scale pits-P. tabulaeformis had the most SCS (9804 g/m²) and leveled ditches-M. sativa had the lowest values (8163 g/m²). For soil nutrient stocks, leveled benches-C. microphylla and fish-scale pits-P. tabulaeformis had the highest levels while leveled ditches-M. sativa had the lowest values. The partial redundancy analysis (pRDA) and variation partitioning (VP) analysis indicated that soil nutrient stocks were most affected by the coupling effects of land preparation and vegetation. SMS at surface (0-10 cm) were mainly affected by precipitation (58.8%). Furthermore, SMS at subsurface (10-60 cm) and deep soil layer (60-100 cm) were affected by the shared effects of vegetation and land preparation (61.3%), and vegetation (72.2%), respectively. The findings quantified the coupling and respective contributions of vegetation restoration and land preparation to soil eco-hydrological services and demonstrate that the optimal combination of eco-rehabilitation strategies can achieve a sustainable land restoration.

The impact of political, socio-economic and cultural factors on implementing environment friendly techniques for sustainable land management and climate change mitigation in Romania

Throughout the history of Romania, political decisions, socio-economic measures, and cultural (traditional) characters have affected the implementation of environment friendly techniques (EFTs) policies. In the context of this paper, EFTs can be defined as solutions for the use of land resources aiming the increasing of goods for meeting the changing human needs and with neutral or positive environmental impact. Changes in the political regime have always had a visible impact on the EFTs issue in Romania. EFTs has gone through several major phases. The political impact on EFTs implementation mainly affected sustainable land management (SLM) and to a small extent, at the end of the communist era and partly during the capitalist period, climate change mitigation. Throughout history, the political factor has dominated and influenced the capacity of the EFTs implementation process in responding to socio-economic stimuli. In addition, quality of life, rural-urban and urban-rural migrations, poverty, education level, and climate change adaptation have had impacts on the status of EFTs according to governance and political reflections. The agrarian reforms from the last two centuries, based on socio-economic demands, have strongly influenced the capacity to implement EFTs both positively and negatively. However, the cultural factor was least affected by political and socio-economic changes as a stability factor in ensuring continued implementation of the EFTs. Currently, there is a strong need to reconsider EFTs as sustainability tools for Romanian agriculture that can cope with climate change and sustainable land management (SLM) demands. This paper presents a brief history of EFTs in Romania and their benefits in achieving SLM equilibrium, describing the impacts of political decisions, socio-economic measures, and cultural features on implementing ETFs policies.

The Impact of Training Data Sequence on the Performance of Neuro-Fuzzy Rainfall-Runoff Models with Online Learning

The learning algorithms in many of conventional Neuro-Fuzzy Systems (NFS) are based on batch or global learning where all parameters of the fuzzy system are optimized off-line. Although these models have frequently been used, they suffer from a reduced flexibility in their architecture as the number of rules need to be predefined by the user. This study uses a Dynamic Evolving Neural Fuzzy Inference System (DENFIS) in which an evolving, online clustering algorithm, the Evolving Clustering Method (ECM), is implemented. This study focused on evaluating the performance of this model in capturing the rainfall-runoff process and rainfall-water level relationship. The two selected study catchments are located in an urban tropical and in a semi-urbanized area, respectively. The first catchment, Sungai Kayu Ara (23.22 km2), is located in Malaysia, with 10-min rainfall-runoff time-series from which 30 major events are used. The second catchment, Dandenong (272 km2), is located in Victoria, Australia, with daily rainfall and river stage (water level) data from which 11 years of data is used. DENFIS results were then compared with two groups of benchmark models: a regression-based data-driven model known as the Autoregressive Model with Exogenous Inputs (ARX) for both study sites, and physical models Hydrologic Engineering Center–Hydrologic Modelling System (HEC–HMS) and Storm Water Management Model (SWMM) for Sungai Kayu Ara and Dandenong catchments, respectively. DENFIS significantly outperformed the ARX model in both study sites. Moreover, DENFIS was found comparable if not superior to HEC–HMS and SWMM in Sungai Kayu Ara and Dandenong catchments, respectively. A sensitivity analysis was then conducted on DENFIS to assess the impact of training data sequence on its performance. Results showed that starting the training with datasets that include high peaks can improve the model performance. Moreover, datasets with more contrasting values that cover wide range of low to high values can also improve the DENFIS model performance.

A Bibliometric Analysis of Soil and Water Conservation in the Loess Tableland-Gully Region of China

The tableland-gully region is one of the main topographic-ecological units in the Chinese Loess Plateau (CLP), and the soil in this region suffers from serious water erosion. In recent years, much work has been conducted to control soil erosion in this area. This paper summarized the development of soil and water conservation researches in the CLP from the bibliometric perspective based on the Science Citation Index (SCI) and Chinese National Knowledge Infrastructure (CNKI) databases. The quantity of SCI literatures has increased rapidly since 2007, with an average annual growth rate of 21.4%, and the quantity of CNKI literatures in the last decade accounted for 62% of the past 30 years. The development trends showed that early SCI research was related to loess geology in the context of ecological remediation, while the CNKI literature focused on agricultural production under comprehensive management. Over time, the research themes of the two databases gradually became unified, i.e., the management of sloping farmland and the improvement of agricultural productivity. Subsequently, the themes gradually extended to the disposition of comprehensive control measures for soil erosion and the environmental effect of agro-fruit ecosystems. The highly cited papers mainly focused on soil reservoir reconstruction, soil erosion factors, and environmental effects of vegetation restoration. Two aspects need further study, including (i) the effect of soil erosion control under different ecological remediation patterns; and, (ii) the ecosystem maintenance mechanism and regulation approaches that are based on the sustainable utilization of soil and water resources in the tableland-gully region of the Loess Plateau.

The way forward: Can connectivity be useful to design better measuring and modelling schemes for water and sediment dynamics?

For many years, scientists have tried to understand, describe and quantify water and sediment fluxes, with associated substances like pollutants, at multiple scales. In the past two decades, a new concept called connectivity has been used by Earth Scientists as a means to describe and quantify the influences on the fluxes of water and sediment on different scales: aggregate, pedon, location on the slope, slope, watershed, and basin. A better understanding of connectivity can enhance our comprehension of landscape processes and provide a basis for the development of better measurement and modelling approaches, further leading to a better potential for implementing this concept as a management tool. This paper provides a short review of the State-of-the-Art of the connectivity concept, from which we conclude that scientists have been struggling to find a way to quantify connectivity so far. We adapt the knowledge of connectivity to better understand and quantify water and sediment transfers in catchment systems. First, we introduce a new approach to the concept of connectivity to study water and sediment transfers and the associated substances. In this approach water and sediment dynamics are divided in two parts: the system consists of phases and fluxes, each being separately measurable. This approach enables us to: i) better conceptualize our understanding of system dynamics at different timescales, including long timescales; ii) identify the main parameters driving system dynamics, and devise monitoring strategies which capture them; and, iii) build models with a holistic approach to simulate system dynamics without excessive complexity. Secondly, we discuss the role of system boundaries in designing measurement schemes and models. Natural systems have boundaries within which sediment connectivity varies between phases; in (semi-)arid regions these boundaries can be far apart in time due to extreme events. External disturbances (eg. climate change, changed land management) can change these boundaries. It is therefore important to consider the system state as a whole, including its boundaries and internal dynamics, when designing and implementing comprehensive monitoring and modelling approaches. Connectivity is a useful tool concept for scientists that must be expanded to stakeholder and policymakers.

Attribution of Runoff Reduction in the Juma River Basin to Climate Variation, Direct Human Intervention, and Land Use Change

Juma River, located in the Midwest of the Haihe River basin, is an important source of water supply to Beijing and Hebei. Over the past decades, the region has been seriously threatened by water shortages owing to complex climate conditions and intensive human activities. This study investigated the runoff characteristics of the Juma River by employing the Soil and Water Assessment Tool (SWAT) and stochastic methods for the period of 1961–2013. Accordingly, the runoff changes attributed to the climate variation and different types of anthropogenic activities (land use change and direct human intervention) were estimated, respectively, in conjunction with the improved quantitative response analysis. The results indicated that the annual runoff of both Zijingguan station and Zhangfang station has decreased significantly at the 0.001 significance level, and reduction rates were −0.054 billion m3 and −0.10 billion m3, respectively. Moreover, the persistency of this trend has been shown for decades (Hurst coefficient > 0.50). The SWAT model was calibrated and validated during the baseline period of 1961–1978. Significant rising temperatures and declining precipitation were the main reasons for runoff reduction, especially during the two periods of 1998–2002 and 2003–2008. Additionally, water withdrawal of Wuyi canal aggravated the runoff reduction and water scarcity conditions in the region. After 2009, the effects of direct human intervention exceeded those of climate change. However, the impact of land use change can be seen as negligible during the study period. Climate change had a greater effect on runoff reduction in winter, while the impact of human activities was more dramatic in summer.

Uncertainty in Rainfall Intensity Duration Frequency Curves of Peninsular Malaysia under Changing Climate Scenarios

This study developed a methodological framework to update the rainfall intensity-duration-frequency (IDF) curves under climate change scenarios. A model output statistics (MOS) method is used to downscale the daily rainfall of general circulation models (GCMs), and an artificial neural network (ANN) is employed for the disaggregation of projected daily rainfall to hourly maximum rainfall, which is then used for the development of IDF curves. Finally, the 1st quartiles, medians, and 3rd quartiles of projected rainfall intensities are estimated for developing IDF curves with uncertainty level. Eight GCM simulations under two radiative concentration pathways (RCP) scenarios, namely, RCP 4.5 and RCP 8.5, are used in the proposed framework for the projection of IDF curves with related uncertainties for peninsular Malaysia. The projection of rainfall revealed an increase in the annual average rainfall throughout the present century. The comparison of the projected IDF curves for the period 2006–2099 with that obtained using GCM hindcasts for the based period (1971–2005) revealed an increase in rainfall intensity for shorter durations and a decrease for longer durations. The uncertainty in rainfall intensity for different return periods for shorter duration is found to be 2 to 6 times more compared to longer duration rainfall, which indicates that a large increase in rainfall intensity for short durations projected by GCMs is highly uncertain for peninsular Malaysia. The IDF curves developed in this study can be used for the planning of climate resilient urban water storm water management infrastructure in Peninsular Malaysia.

Spatial Prediction of Erosion Risk of a Small Mountainous Watershed Using RUSLE: A Case-Study of the Palar Sub-Watershed in Kodaikanal, South India

An erosion model using the Revised Universal Soil Loss Equation (RUSLE) equation derived from the Advanced Spaceborne Thermal Emission and Reflection Global Digital Elevation Model (ASTER G-DEM) and LANDSAT 8 is presented in the study. This model can be a cost-effective, quick and less labor-intensive tool for assessing erosion in small watersheds. It can also act as a vital input for the primary assessment of environmental degradation in the region, and can aid the formulation of watershed development planning strategies. The Palar River, which drains into Shanmukha Nadi, is a small mountain watershed. The town of Kodaikanal, a popular tourist attraction in Tamilnadu, forms part of this sub-watershed. This quaint, hill-town has been subjected to intense urbanization and exhaustive changes in its land use practices for the past decade. The consequence of this change is manifested in the intense environmental degradation of the region, which results in problems such as increased numbers of landslides, intense soil erosion, forest fires and land degradation. The nature of the terrain, high precipitation, and intense agriculture exponentially increase the rate of soil erosion. Spatial prediction of soil erosion is thereby a valuable and mandatory tool for sustainable land use practices and economic development of the region. A comprehensive methodology is employed to predict the spatial variation of soil erosion using the revised soil loss equation in a geographic information system (GIS) platform. The soil erosion susceptibility map shows a maximum annual soil loss of 3345 Mg·ha−1·y−1, which correlates with scrub forests, degraded forests, steep slopes, high drainage density and shifting cultivation practices. The erosion map shows that the central region is subjected to intense erosion while the inhabited southern part is less prone to erosion. A small patch of severe soil loss is also visible on the eastern part of the northern fringe. About 4% of the sub-watershed is severely affected by soil erosion and 18% falls within a moderate erosion zone. The growing demand for land and infrastructure development forces the shift of urbanization and agriculture to these less-managed spaces. In light of this scenario, the spatial distribution of erosion combined with terrain and hydro-morphometry can aid in sustainable development and promote healthy land use practices in the region.

Multi-Objective Optimization for Reservoir Operation Considering Water Diversion and Power Generation Objectives

Due to the uneven distribution of water resources in time and space, the problem of water shortage has become increasingly serious in some areas. To optimize use of water resources, it is urgent to establish multi-objective models and apply effective optimization algorithms to guide reservoir management. This study proposed a model of multi-objective optimization for reservoir operation (MORO) with the objectives of maximizing water diversion and power generation. The multi-objective evolutionary algorithm based on decomposition with adaptive weight vector adjustment (MOEA/D-AWA) was applied to solve the MORO problem. In addition, the performance of the MOEA/D-AWA was compared with two other algorithms based on the hyper-volume index. Huangjinxia reservoir, which is located in Shaanxi, China, was selected as the case study. The results show that: (1) the proposed model is effective and reasonable in theory; (2) the optimization results obtained by MOEA/D-AWA demonstrate this algorithm can be applied to the MORO problem, providing a set of evenly distributed non-dominated solutions; and (3) water diversion and power generation are indeed contradictory objectives. The MORO strategy can be used to efficiently utilize water resources, improve the comprehensive benefits of reservoirs, and provide decision support for actual reservoir operation.

The Effect of the Gully Land Consolidation Project on Soil Erosion and Crop Production on a Typical Watershed in the Loess Plateau

The Gully Land Consolidation Project (GLCP) was launched to create more arable land by excavating soil from the slopes on both sides of gullies, combined with simultaneous comprehensive gully prevention and control measures. The purpose of the GLCP is to increase crop production and reduce soil erosion to achieve ecological and agricultural sustainability. In this study, we assess the effects of the GLCP on soil erosion and crop production by studying the BaoChengGou Watershed in the Loess Plateau, primarily by means of high spatial-resolution satellite images (taken by the GF-1 and ZY-3 satellites) combined with the InVEST model and field investigations. Sloping cropland, sparse forestland, and natural grassland are the main land use types in the study area. After implementing the GLCP, consolidated land in the cropland increased by 7.35%, an increase that has come largely at the expense of grassland and forestland. The GLCP has markedly reduced soil erosion in the BaoChengGou Watershed, especially in the sense that soil erosion intensity was also reduced significantly in the project region on the whole, despite intensifying in certain places, such as excavated slopes; furthermore, it has improved crop yields in the study area by 10.9%. Comprehensive measurement shows the GLCP to be scientific, reasonable, and clearly efficacious. This study presents findings regarding the positive significance of the GLCP in promoting ecological and agricultural sustainability in the Loess Plateau.

Evaluating indirect and direct effects of eco-restoration policy on soil conservation service in Yangtze River Basin

The conservation impacts of policies that promote large-scale ecological restoration of ecosystem services and socio-economic development are well documented around the world. However, the effect of socio-economic development resulting from such policies on ecosystem services is rarely analysed, although it is important to do so if these policies are to be sustainable. We analysed the socio-economic impacts of soil conservation services from 2000 to 2015 in the Yangtze River Basin under the Grain to Green Programme (GTGP). Also we assessed the driving forces behind the programme: conservation policies, urbanization, agricultural development, and population growth. Our results show that during 2000-2015, cultivated area decreased by 7.5%, urban area increased by 67.5%, forest area increased by 2.1%, and soil erosion was reduced by 19.5%. The programme not only contributed significantly to an improvement in soil conservation services but also enhanced them significantly through faster urbanization. Furthermore, vegetation cover and crop yields increased synergistically, mainly due to high-efficiency agriculture that reduced the negative effect of the GTGP on agricultural production. Overall determining the indirect and direct effects of the GTGP on soil conservation and agricultural production are important for furthering our understanding of the long-term effects of ecological restoration policies, and the present study offers practical insights for ecological restoration of other watersheds.

Effects of land use and seasonality on stream water quality in a small tropical catchment: The headwater of Córrego Água Limpa, São Paulo (Brazil)

Stream water quality is controlled by the interaction of natural and anthropogenic factors over a range of temporal and spatial scales. Among these anthropogenic factors, land cover changes at catchment scale can affect stream water quality. This work aims to evaluate the influence of land use and seasonality on stream water quality in a representative tropical headwater catchment named as Córrego Água Limpa (Sao Paulo, Brasil), which is highly influenced by intensive agricultural activities and urban areas. Two systematic sampling approach campaigns were implemented with six sampling points along the stream of the headwater catchment to evaluate water quality during the rainy and dry seasons. Three replicates were collected at each sampling point in 2011. Electrical conductivity, nitrates, nitrites, sodium superoxide, Chemical Oxygen Demand (DQO), colour, turbidity, suspended solids, soluble solids and total solids were measured. Water quality parameters differed among sampling points, being lower at the headwater sampling point (0m above sea level), and then progressively higher until the last downstream sampling point (2500m above sea level). For the dry season, the mean discharge was 39.5ls^-1 (from April to September) whereas 113.0ls^-1 were averaged during the rainy season (from October to March). In addition, significant temporal and spatial differences were observed (P<0.05) for the fourteen parameters during the rainy and dry period. The study enhance significant relationships among land use and water quality and its temporal effect, showing seasonal differences between the land use and water quality connection, highlighting the importance of multiple spatial and temporal scales for understanding the impacts of human activities on catchment ecosystem services.

Assessment of temporal and spatial water quality in international Gomishan Lagoon, Iran, using multivariate analysis

Coastal lagoon ecosystems are vulnerable to eutrophication, which leads to the accumulation of nutrients from the surrounding watershed over the long term. However, there is a lack of information about methods that could accurate quantify this problem in rapidly developed countries. Therefore, various statistical methods such as cluster analysis (CA), principal component analysis (PCA), partial least square (PLS), principal component regression (PCR), and ordinary least squares regression (OLS) were used in this study to estimate total organic matter content in sediments (TOM) using other parameters such as temperature, dissolved oxygen (DO), pH, electrical conductivity (EC), nitrite (NO₂), nitrate (NO₃), biological oxygen demand (BOD), phosphate (PO₄), total phosphorus (TP), salinity, and water depth along a 3-km transect in the Gomishan Lagoon (Iran). Results indicated that nutrient concentration and the dissolved oxygen gradient were the most significant parameters in the lagoon water quality heterogeneity. Additionally, anoxia at the bottom of the lagoon in sediments and re-suspension of the sediments were the main factors affecting internal nutrient loading. To validate the models, R², RMSECV, and RPDCV were used. The PLS model was stronger than the other models. Also, classification analysis of the Gomishan Lagoon identified two hydrological zones: (i) a North Zone characterized by higher water exchange, higher dissolved oxygen and lower salinity and nutrients, and (ii) a Central and South Zone with high residence time, higher nutrient concentrations, lower dissolved oxygen, and higher salinity. A recommendation for the management of coastal lagoons, specifically the Gomishan Lagoon, to decrease or eliminate nutrient loadings is discussed and should be transferred to policy makers, the scientific community, and local inhabitants.

Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping: A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models

Soil erosion, every year imposes extensive damages to human beings by means of reducing soil productivity and filling reservoirs from sedimentation in Ghaemshahr Basin in Mazandaran Province, (Iran); therefore, identifying prone areas to soil erosion for preventive measures is essential in this basin. In this research, erodibility prioritization of sub-watersheds of Ghaemshahr Basin has done using morphometric parameters analysis and different multi-criteria decision making (MCDM) models such as simple additive weighing (SAW), VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), technique for order preference by similarity to ideal solution (TOPSIS), and compound factor (CF). For this purpose, Advanced Space Thermal Emission Radiometer (ASTER), a Digital Elevation Model (DEM) with spatial resolution of 30m used for extraction and analysis of 23 morphometric parameters including basic, linear, shape, and landscape. For validation of the MCDM methods, the indices of percentage of changes and intensity of changes were used. The results of prioritization of sub-watersheds indicated that in TOPSIS and CF models, sub-watershed 30 with 0 and 13.33 scores are located in first rank, respectively, which is known as the most prone sub-watersheds to erosion. Also, results showed that sub-watersheds in terms of susceptibility to erosion, in CF model has an one category namely Low; meanwhile, in TOPSIS and VIKOR models show four classes including low, moderate, high, and very high. In contrast, for SAW model there are three classes of moderate, high, and very high susceptibility. In general, the results showed that morphometric parameters have high efficiency in identification of erosion-prone areas and also VIKOR method has higher predictive accuracy than TOPSIS, SAW, and CF models.

The superior effect of nature based solutions in land management for enhancing ecosystem services

The rehabilitation and restoration of land is a key strategy to recover services -goods and resources- ecosystems offer to the humankind. This paper reviews key examples to understand the superior effect of nature based solutions to enhance the sustainability of catchment systems by promoting desirable soil and landscape functions. The use of concepts such as connectivity and the theory of system thinking framework allowed to review coastal and river management as a guide to evaluate other strategies to achieve sustainability. In land management NBSs are not mainstream management. Through a set of case studies: organic farming in Spain; rewilding in Slovenia; land restoration in Iceland, sediment trapping in Ethiopia and wetland construction in Sweden, we show the potential of Nature based solutions (NBSs) as a cost-effective long term solution for hydrological risks and land degradation. NBSs can be divided into two main groups of strategies: soil solutions and landscape solutions. Soil solutions aim to enhance the soil health and soil functions through which local eco-system services will be maintained or restored. Landscape solutions mainly focus on the concept of connectivity. Making the landscape less connected, facilitating less rainfall to be transformed into runoff and therefore reducing flood risk, increasing soil moisture and reducing droughts and soil erosion we can achieve the sustainability. The enhanced eco-system services directly feed into the realization of the Sustainable Development Goals of the United Nations.