Hydrologic modeling and uncertainty analysis of an ungauged watershed using mapwindow-swat

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial TechnologiesModeling of an ungauged watershed with the associated uncertainties of the input data is presented. The MapWindow versions of the Soil and Water Assessment Tool (SWAT) have been applied to a complex and ungauged watershed of about 248,000ha in an area close to the Niger River, Nigeria. The Kwara State Government of Nigeria in collaboration with the newly relocated former Zimbabwean farmers now occupied the largest portion of this watershed for an “Agricultural Estate Initiative ”. The government and these farmers are decision makers who need to take appropriate actions despite little or no data availability. SWAT being a physically based model, allow the use of Geographical Information System (GIS) inputs like the Digital Elevation Model(DEM), landuse and soil maps. The MapWindow-SWAT(MSWAT) involves processes like the Watershed Delineation, Hydrological Response Units (HRUs) Process and the SWAT run. The watershed was delineated into 11 subbasins and 28 HRUs. There were 8 landuse classes and 5 soil types. The model was able to simulate and forecast for several years(1990-2016). The results look 'reasonable' since there is no observed data from the watershed for statistical validation. However, using the Water Balance equation as a validation criteria, the correlation coefficient between the simulated rainfall and runoff was 0.84 for the subbasin 11 (outlet). Thereafter, the uncertainties in the continuous numerical input (i.e. rainfall) was examined using the Data Uncertainty Engine (DUE). One parameter exponential probability model was used for the daily rainfall amount based on the histogram. 700 realizations were generated from this uncertain input. Randomly selected numbers of the realizations were prepared and used as inputs into the MWSWAT model. It was surprising that there were no changes in the results when compared to the initial 'real' value (outflows from outlet) although other parameters of the model were kept constant

Modelling Opeki River flow for sustainable rural development

Modelling Opeki River flow for sustainable rural developmen

Spatial-Temporal Assessment of Satellite-Based Rainfall Estimates in Different Precipitation Regimes in Water-Scarce and Data-Sparse Regions

Accurate precipitation measurement is very important for socio-hydrological resilience in the face of frequent extreme weather events such as cyclones. This study evaluates the performance of two satellite products: the Tropical Rainfall Measuring Mission (TRMM 3B43V7) Multi-satellite Precipitation Analysis (TMPA, hereafter: TRMM) and the Integrated Multi-satellite Retrievals for GPM (IMERG, Final Run V06, hereafter: GPM) in the Sultanate of Oman. Oman is an arid country that generally has few rainy days, but has experienced significant flash floods, tropical storms and cyclones recently, leading to the loss of lives and millions of dollars in damage. Accurate precipitation analysis is crucial in flood monitoring, hydrologic modeling, and the estimation of the water balance of any basin, and the lack of a sufficient weather monitoring network is a barrier to accurate precipitation measurement. Satellite rainfall estimates can be a reliable option in sparse network areas, especially in arid and semi-arid countries. This study evaluated monthly rainfall (hereafter: OBSERVED) levels at 77 meteorological stations from January 2016 to December 2018. The capacity of the TRMM and GPM satellite products to detect monthly rainfall amounts at varying precipitation thresholds was also evaluated. Findings included (1) overall and across the 11 Governorates of Oman, both satellite products show different spatial variability and performance to the OBSERVED at the monthly, seasonal, and annual temporal scales; (2) from the perspective of precipitation detection and frequency bias, GPM showed a similar performance to TRMM at detecting low precipitation (2 mm/month) but was poorer at detecting high precipitation (>30 mm/month) across the entire country as well as in the Northern, Interior, and Dhofar regions; (3) both products show similarities to the OBSERVED through the partitioning of their seasonal time series into a distinct number of homogenous segments; and (4) both products had difficulty reproducing OBSERVED levels in the Dhofar and Interior regions, which is consistent with studies conducted in mountainous and coastal regions. With the aim of reproducing the spatial and temporal structure of OBSERVED in a rugged terrain, the study shows that both satellite products can be used in areas of sparse rain gauges or as additional observation for studies of extreme weather events. Overall, this study suggests that for Oman, both satellite products can be used as proxies for OBSERVED with appropriate bias corrections and GPM is also a reliable replacement for TRMM as a precipitation satellite product. The findings will be useful to the country’s flood resilience and mitigation efforts, especially in areas where there is sparse rain gauge coverage

Spatial modeling of soil heterogeneities and their impacts on runoff, sediment and total phosphorus loss

Located in southern Quebec, at the northeastern extremity of Lake Champlain, the Missisquoi Bay is subject to eutrophication arising from excess nutrients, predominantly phosphorus (P), contributed by agricultural runoff. Land use patterns, agronomic practices, soil properties, and geomorphology have an impact on soil P. Studies have used hydrologic models [e.g., the Soil and Water Assessment Tool (SWAT)] to characterize P loadings from the region's agricultural watersheds. The lack of a proper understanding of the impact of spatial variability and heterogeneity of soil properties on the prediction of runoff, sediment and nutrient movement has proven a major challenge. In order to overcome this, field surveys, spatial variability characterization of P through geostatistics, heterogeneity quantification and hydrologic modeling using SWAT were undertaken. An extensive geostatistic study of soil properties was followed by the use of SWAT to predict runoff, sediment and total phosphorus (TP). Soil surveys carried out in the summers of 2011 and 2012 measured soils physical and chemical properties. Variogram analysis characterized the spatial variability of soil test phosphorus (STP). Ordinary kriging (OK) was used to estimate STP values at unsampled locations. Due to OK's smoothing effect, some high value areas were underestimated, while some low areas were overestimated. Compared to OK, sequential Gaussian simulation (SGS) helped characterize the uncertainty and provides better estimates at non-sampled locations. Areas above the STP threshold at which P has the potential to move to freshwater after precipitation events, combined with topographic factors, were identified. The uncertainty in variogram parameters (sill, nugget and range) was characterized using a Bayesian Hierarchical framework, aiding in understanding the complexity and heterogeneity in the STP dataset attributable to land use patterns. The Posterior mean and 95% credible confidence intervals of the variogram parameters and STP values were developed. An Independent Component Analysis (ICA) technique, which overcame the problem of matrix inversion in co-simulation, served in the decomposition of spatially-correlated geochemical variables. This implementation was tested on three correlated variables: magnesium, calcium and iron. The measured soil properties required by SWAT were regionalized and clustered using a Regionalization with Constrained Clustering and Partitioning (REDCAP) algorithm. Five maps were created based on 5, 10, 15, 20 and 24 part partitioning. Each of these maps had different measures of heterogeneity and each was used as inputs for five different configurations of SWAT. Mean monthly flow, sediment and total P load from April 2001 to December 2002 were used to assess model performance before and after calibration. Overall, there was no significant difference in runoff simulation between any of the five map configurations, which might be due to the impacts of the SCS-CN (soil conservation service-curve number) method in simulating runoff. In the study watershed, using a higher resolution (number of regions) of soil data did not improve predictions of monthly streamflow, sediment or TP.Situé dans le sud du Québec, à l'extrémité nord-est du Lac Champlain, la Baie Missisquoi est sujette à une eutrophisation attribuable à un excès d'éléments nutritifs, principalement le phosphore (P), provenant du ruissellement agricole. L'utilisation des terres, les pratiques agronomiques, les propriétés du sol et la géomorphologie influencent la teneur en P du sol. Employant des modèles hydrologiques tel le Soil and Water Assessment Tool (SWAT), une série d'études ont déjà servi à caractériser les charges en P provenant des bassins versants agricoles de la région. Une compréhension inadéquate de l'impact de la variabilité spatiale et de l'hétérogénéité des propriétés du sol sur la prédiction du ruissellement et du déplacement des sédiments et éléments nutritifs, rend la tâche difficile. Afin de surmonter cette difficulté, des relevés sur le terrain, une caractérisation de la variabilité spatiale du P par géostatistique, une quantification de son hétérogénéité, et une modélisation hydrologique avec SWAT furent entrepris. Une étude géostatistique approfondie des propriétés du sol fut suivie par l'utilisation de SWAT pour prédire le ruissellement, et les charges en sédiment et phosphore total (TP). Des relevés pédologiques entrepris durant les étés de 2011 et 2012 permirent de mesurer les propriétés physiques et chimiques des sols. Une analyse par variogramme caractérisa la variabilité spatiale de la teneur en P du sol (TSP). Le krigeage ordinaire (OK) servit à l'estimation de la TSP à des sites non-échantillonnés. Etant donné l'effet de lissage de l'OK, certaines valeurs élevées furent sous-estimées, tandis que certaines petites valeurs furent surestimées. Comparé au OK, la simulation séquentielle gaussienne (SSS) permit d'évaluer l'incertitude et fournit de meilleures estimations pour les sites non-échantillonnées. Les régions en deçà du seuil auquel le P a le potentiel de se déplacer jusqu'au cours d'eau après une pluie, et les facteurs topographiques qui y sont liés furent identifiés. L'incertitude des paramètres de variogramme (palier, pépite et portée) fut évaluée dans un cadre hiérarchique bayésien, permettant une analyse plus approfondie de la complexité et de l'hétérogénéité des données de TSP pouvant être attribués à l'utilisation des terres. Des moyennes a posteriori, avec intervalle de crédibilité de 95%, furent générées pour les paramètres de variogramme et les valeurs de TSP. Une technique d'analyse en composantes indépendantes (ICA), surmonta le problème d'inverser une matrice lors de la co-simulation, et servit à la décomposition de variables géochimiques corrélées dans l'espace. Cette méthode fut éprouvée pour trois variables corrélées, soit le magnésium, le calcium et le fer. Les propriétés du sol mesurées et requises par SWAT furent régionalisées et groupe en utilisant un algorithme de régionalisation avec groupement et partitionnement sous contraintes (REDCAP). Cinq cartes furent crées selon que le territoire fut partitionné en 5, 10, 15, 20 ou 24 parties. Chacune de ces cartes différa en son hétérogénéité, et chacune servit comme donnée d'entrée à une configuration particulière de SWAT. Les débits moyens mensuels et les charges en sédiment et phosphore totales mesurés entre avril 2001 et décembre 2002 servirent à évaluer la performance du modèle avant et après son étalonnage. Le fait qu'aucune des cinq configurations (partitions de la carte d'entrée) du modèle ne se démarqua, s'explique par le fait que la méthode des numéros de courbe du Soil Conservation Service, servant à simuler le ruissellement, ne permet pas de retenir de petites mais importantes différences des propriétés physiques des sols. Dans le bassin versant étudié, l'utilisation d'une résolution (nombre de régions) plus élevée avec le modèle SWAT n'améliora ni la prédiction des débits moyens mensuels, ni celle des charges en sédiment et phosphore total

Joint Simulation of Spatially Correlated Soil Health Indicators, Using Independent Component Analysis and Minimum/Maximum Autocorrelation Factors

Soil health plays a major role in the ability of any nation to meet the Sustainable Development Goals. Understanding the spatial variability of soil health indicators (SHIs) may help decision makers develop effective policy strategies and make appropriate management decisions. SHIs are often spatially correlated, and if this is the case, a geostatistical model is required to capture the spatial interactions and uncertainty. Geostatistical simulation provides equally probable realizations that can account for uncertainty in the variables. This study used the following SHIs extracted from the Africa Soil Information Service “Legacy Database” for Nigeria: bulk density, organic matter, and total nitrogen. Maximum and minimum autocorrelation factors (MAF) and independent component analysis (ICA) are two techniques that can be used to transform correlated SHIs into uncorrelated factors/components that can be simulated independently. To confirm spatial orthogonality, the relative deviation from orthogonality, τ(h), and spatial diagonalization efficiency, k(h), approach 0 and 1 for both techniques. To validate the performance of each technique, 100 equally probable realizations were simulated by using MAF and ICA. Direct and cross-variograms showed adequate reproduction, using E-type, where E was defined as the “conditional expectation” of realizations (i.e., average estimate of realizations). It should be noted that only direct variograms of MAF and ICA were independently simulated. The average of 100 back-transformed simulated realizations and randomly selected realizations compared well with the original variables, in terms of spatial distribution, correlation, and pattern. Overall, both techniques were able to reproduce important geostatistical features of the original variables, making them important in joint simulations of spatially correlated variables in soil management

Towards Quantifying the Coastal Vulnerability due to Natural Hazards using the InVEST Coastal Vulnerability Model

Coastal areas and coastal communities are facing threats due to the impacts of climate change. Therefore, assessing their vulnerabilities and the potential for natural habitats to contribute to protecting coastal areas and communities is essential for effective long-term planning, sustainability, and resilient coastal management. This study modeled and mapped coastal vulnerability using the InVEST 3.9.1 model developed by the Natural Capital Project Coastal Vulnerability model to explore the role of natural habitats in mitigating coastal hazards in Southern Al Sharqiya and Al Wusta Governorates of the Sultanate of Oman. The results showed that the highest hazard classification > 2.67 represented 18% of the coastal distribution, the intermediate hazard classification ranging between 2.31 and 2.66 represented 38% of the coastal distribution, and the lowest hazard classification ranging between 1.22 and 2.30) represented 44% of the coastal distribution. These results, however, did not account for the role of natural habitats in coastal protection. In terms of the role of natural habitats in mitigating coastal hazards, the presence of natural habitats reduced the extent of the highest exposed shoreline by 14% and 8% for the highest and intermediate areas, respectively. Under the natural habitat’s scenario, the habitats could provide 59% protection for the coastal communities under the highest exposure category and 41% under the intermediate category. Under a no-habitat scenario, about 75% of the coastal communities are exposed and vulnerable to coastal hazards under the highest hazard exposure category and 25% under the intermediate category. These results demonstrate that it is critical, especially for policymakers, to enhance the protection of coastal ecosystems to achieve coastal resilience. This study buttresses the importance of coastal ecosystem assessments in ensuring coastal resilience and climate change adaptation processes for any coastal countries

Independent principal component analysis for simulation of soil water content and bulk density in a Canadian Watershed

Accurate characterization of soil properties such as soil water content (SWC) and bulk density (BD) is vital for hydrologic processes and thus, it is importance to estimate θ (water content) and ρ (soil bulk density) among other soil surface parameters involved in water retention and infiltration, runoff generation and water erosion, etc. The spatial estimation of these soil properties are important in guiding agricultural management decisions. These soil properties vary both in space and time and are correlated. Therefore, it is important to find an efficient and robust technique to simulate spatially correlated variables. Methods such as principal component analysis (PCA) and independent component analysis (ICA) can be used for the joint simulations of spatially correlated variables, but they are not without their flaws. This study applied a variant of PCA called independent principal component analysis (IPCA) that combines the strengths of both PCA and ICA for spatial simulation of SWC and BD using the soil data set from an 11 km2 Castor watershed in southern Quebec, Canada. Diagnostic checks using the histograms and cumulative distribution function (cdf) both raw and back transformed simulations show good agreement. Therefore, the results from this study has potential in characterization of water content variability and bulk density variation for precision agriculture

Assimilation of precipitation Estimates from the Integrated Multisatellite Retrievals for GPM (IMERG, early Run) in the Canadian Precipitation Analysis (CaPA)

Study region: The study area covers the whole Canada at 10-km grid. Analysis was further done at the regional scale by dividing the study area into four climatic zones: Atlantic, Central, Prairie, and West Coast. Study focus: The Canadian Precipitation Analysis (CaPA) platform produces 6 h and 24 h precipitation accumulations on a 10-km grid over Canada by combining gauge observations with a background field provided by the Global Environmental Multiscale (GEM) numerical weather prediction model. In this study, precipitation data from the Global Precipitation Measurement (GPM) mission are included as an additional data source and are compared with CaPA benchmark estimates obtained without the Integrated Multisatellite Retrievals for GPM (IMERG Early Run, version 03 (V03)). The data used are for the summer of 2014 and we specifically considered the 6 h accumulations. The frequency bias indicator (FBI) and the equitable threat score (ETS) are used as performance criteria. Results were analyzed at four climatic regions. New hydrological insights for the region: Results show that IMERG improves the ETS and FBI for all regions, with the Central and Prairie regions showing the most improvements over the benchmark. In these two regions, statistically significant improvements in ETS are obtained for all precipitation thresholds considered. In order to assess the value of IMERG in more remote areas that are nonetheless important for water resources management in Canada, a fifth zone that has a lower gauge density was considered. In this region, ETS was significantly improved for precipitation thresholds up to 10-mm/6-h. We believe that combining satellite information with other remotely sensed product such as radar will provide a significant increase in skill, especially for mountainous regions where there can be beam blockages that can affect the quality of radar data. Keywords: Canadian Precipitation Analysis, IMERG, Satellite observation, Global Precipitation Measurement, Precipitation assimilatio

Assessing the Impact of Climate Change on the Distribution of Lime (16srii-B) and Alfalfa (16srii-D) Phytoplasma Disease Using MaxEnt

Witches’ broom disease has led to major losses in lime and alfalfa production in Oman. This paper identifies bioclimatic variables that contribute to the prediction of distribution of witches’ broom disease in current and future climatic scenarios. It also explores the expansion, reduction, or shift in the climatic niche of the distribution of the disease across the different geographical areas of the entire country (309,501 km²). The maximum entropy model (MaxEnt) and geographical information system were used to investigate the potential suitability of habitats for the phytoplasma disease. This study used current (1970–2000) and future projected climatic scenarios (2021–2040, 2041–2060, 2061–2080, and 2081–2100) to model the distribution of phytoplasma for lime trees and alfalfa in Oman. Bioclimatic variables were downloaded from WorldClim with ± 60 occurrence points for lime trees and alfalfa. The area under the curve (AUC) was used to evaluate the model’s performance. Quantitatively, the results showed that the mean of the AUC values for lime (16SrII-B) and alfalfa (16SrII-D) future distribution for the periods of 2021–2040, 2041–2060, 2061–2080, and 2081–2100 were rated as “excellent”, with the values for the specified time periods being 0.859, 0.900, 0.931, and 0.913 for 16SrII-B; and 0.826, 0.837, 08.58, and 0.894 for 16SrII-D respectively. In addition, this study identified the hotspots and proportions of the areas that are vulnerable under the projected climate-change scenarios. The area of current (2021–2040) highly suitable distribution within the entire country for 16SrII-D was 19474.2 km2 (7.1%), while for 16SrII-B, an area of 8835 km2 (3.2%) was also highly suitable for the disease distribution. The proportions of these suitable areas are very significant from the available arable land standpoint. Therefore, the results from this study will be of immense benefit and will also bring significant contributions in mapping the areas of witches’ broom diseases in Oman. The results will equally aid the development of new strategies and the formulation of agricultural policies and practices in controlling the spread of the disease across Oman