Improved water and land management in the Ethiopian highlands and its impact on downstream stakeholders dependent on the Blue Nile

Improved water and land management in the Ethiopian highlands and its impact on downstream stakeholders dependent on the Blue Nile – short title Upstream-Downstream in Blue Nile River project is one of the projects in the Nile Basin supported by the CPWF. It was implemented during from 2007 to 2009 through a partnership of 8 institutions. The Blue Nile is the major tributary of the Nile River, contributing about 62% of the Nile flow at Aswan. About two thirds of the area of this densely populated basin is in the highlands and hence receives fairly high levels of annual rainfall of 800 to 2,200 mm. However, the rainfall is erratic in terms of both spatial and temporal distribution with prolonged dry spells and drought often leading to crop failures. Currently, water resources are only marginally exploited in the upper basin but are much more developed in the downstream reaches. The population, located in the downstream part of the Blue Nile, is dependent on the river water for supplementary irrigation and energy production. Canal and reservoir siltation is a major problem, adding the burdens of poor riparian farmers. This project was envisaged to improve the scientific understanding of the land and water resources of the basin, and hypothesized that with increased scientific knowledge of the hydrological, watershed, and institutional processes of the Blue Nile in Ethiopia (Abbay), constraints to up-scaling adaptable best practices and promising technologies (technical, socio-economic, institutional) could be overcome, which will result in significant positive impacts for both upstream and downstream communities and state

Improved water and land management in the Ethiopian highlands: its impact on downstream stakeholders dependent on the Blue Nile; Intermediate Results Dissemination Workshop February 5-6, 2009, Addis Ababa, Ethiopia

River basin management, Watershed management, Farming systems, Water balance, Reservoirs, Water supply, Irrigation requirements, Irrigation programs, Simulation models, Sedimentation, Rainfall-Runoff relationships, Erosion, Soil water, Water balance, Soil conservation, Institutions, Organizations, Policy, Water governance, International waters, Institutional and Behavioral Economics, Land Economics/Use, Resource /Energy Economics and Policy,

Application of soft computing models with input vectors of snow cover area in addition to hydro-climatic data to predict the sediment loads

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R$^{2}$ value of 0.85 and 0.74 during the training and testing period, respectively

Sustainable Reservoir Management Approaches under Impacts of Climate Change - A Case Study of Mangla Reservoir, Pakistan

Reservoir sedimentation is a major issue for water resource management around the world. It has serious economic, environmental, and social consequences, such as reduced water storage capacity, increased flooding risk, decreased hydropower generation, and deteriorated water quality. Increased rainfall intensity, higher temperatures, and more extreme weather events due to climate change are expected to exacerbate the problem of reservoir sedimentation. As a result, sedimentation must be managed to ensure the long-term viability of reservoirs and their associated infrastructure. Effective reservoir sedimentation management in the face of climate change necessitates an understanding of the sedimentation process and the factors that influence it, such as land use practices, erosion, and climate. Monitoring and modelling sedimentation rates are also useful tools for forecasting future impacts and making management decisions. The goal of this research is to create long-term reservoir management strategies in the face of climate change by simulating the effects of various reservoir-operating strategies on reservoir sedimentation and sediment delta movement at Mangla Reservoir in Pakistan (the second-largest dam in the country). In order to assess the impact of the Mangla Reservoir's sedimentation and reservoir life, a framework was developed. This framework incorporates both hydrological and morphodynamic models and various soft computing models. In addition to taking climate change uncertainty into consideration, the proposed framework also incorporates sediment source, sediment delivery, and reservoir morphology changes. Furthermore, the purpose of this study is to provide a practical methodology based on the limited data available. In the first phase of this study, it was investigated how to accurately quantify the missing suspended sediment load (SSL) data in rivers by utilizing various techniques, such as sediment rating curves (SRC) and soft computing models (SCMs), including local linear regression (LLR), artificial neural networks (ANN) and wavelet-cum-ANN (WANN). Further, the Gamma and M-test were performed to select the best-input variables and appropriate data length for SCMs development. Based on an evaluation of the outcomes of all leading models for SSL estimation, it can be concluded that SCMs are more effective than SRC approaches. Additionally, the results also indicated that the WANN model was the most accurate model for reconstructing the SSL time series because it is capable of identifying the salient characteristics in a data series. The second phase of this study examined the feasibility of using four satellite precipitation datasets (SPDs) which included GPM, PERSIANN_CDR, CHIRPS, and CMORPH to predict streamflow and sediment loads (SL) within a poorly gauged mountainous catchment, by employing the SWAT hydrological model as well as SWAT coupled soft computing models (SCMs) such as artificial neural networks (SWAT-ANN), random forests (SWAT-RF), and support vector regression (SWAT-SVR). SCMs were developed using the outputs of un-calibrated SWAT hydrological models to improve the predictions. The results indicate that during the entire simulation, the GPM shows the best performance in both schemes, while PERSIAN_CDR and CHIRPS also perform well, whereas CMORPH predicts streamflow for the Upper Jhelum River Basin (UJRB) with relatively poor performance. Among the best GPM-based models, SWAT-RF offered the best performance to simulate the entire streamflow, while SWAT-ANN excelled at simulating the SL. Hence, hydrological coupled SCMs based on SPDs could be an effective technique for simulating streamflow and SL, particularly in complex terrain where gauge network density is low or uneven. The third and last phase of this study investigated the impact of different reservoir operating strategies on Mangla reservoir sedimentation using a 1D sediment transport model. To improve the accuracy of the model, more accurate boundary conditions for flow and sediment load were incorporated into the numerical model (derived from the first and second phases of this study) so that the successive morphodynamic model could precisely predict bed level changes under given climate conditions. Further, in order to assess the long-term effect of a changing climate, a Global Climate Model (GCM) under Representative Concentration Pathways (RCP) scenarios 4.5 and 8.5 for the 21st century is used. The long-term modelling results showed that a gradual increase in the reservoir minimum operating level (MOL) slows down the delta movement rate and the bed level close to the dam. However, it may compromise the downstream irrigation demand during periods of high water demand. The findings may help the reservoir managers to improve the reservoir operation rules and ultimately support the objective of sustainable reservoir use for societal benefit. In summary, this study provides comprehensive insights into reservoir sedimentation phenomena and recommends an operational strategy that is both feasible and sustainable over the long term under the impact of climate change, especially in cases where a lack of data exists. Basically, it is very important to improve the accuracy of sediment load estimates, which are essential in the design and operation of reservoir structures and operating plans in response to incoming sediment loads, ensuring accurate reservoir lifespan predictions. Furthermore, the production of highly accurate streamflow forecasts, particularly when on-site data is limited, is important and can be achieved by the use of satellite-based precipitation data in conjunction with hydrological and soft computing models. Ultimately, the use of soft computing methods produces significantly improved input data for sediment load and discharge, enabling the application of one-dimensional hydro-morphodynamic numerical models to evaluate sediment dynamics and reservoir useful life under the influence of climate change at various operating conditions in a way that is adequate for evaluating sediment dynamics.:Chapter 1: Introduction Chapter 2:Reconstruction of Sediment Load Data in Rivers Chapter 3:Assessment of The Hydrological and Coupled Soft Computing Models, Based on Different Satellite Precipitation Datasets, To Simulate Streamflow and Sediment Load in A Mountainous Catchment Chapter 4:Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan Chapter 5:Conclusions and Recommendation

Prognoza nanosa putem suspendiranog nanosa pomoću metoda mekog računarstva

Appropriate and acceptable prediction of bed load being carried by streams is vitally important for water resources quantity and quality studies. Although measuring the rate of bed load in situ is the most consistent method, it is very expensive and cannot be conducted for as many streams as the measurement of suspended sediment load. Therefore, in this study the role of suspended load on bedload prediction was examined by using sensitivity analysis. On the other hand, conventional sediment rating curves and equations can not predict sediment load accurately so recently the usage of machine learning algorithms increase rapidly. Accordingly, soft computational methods are used in the study. These are; artificial neural network (ANN), support vector machine (SVM) models and a decision tree (CHAID) model that is not used before in sediment studies. Some particular parameters are frequently used in these soft computational methods to form input sets. Hence, well known and commonly used three input sets and a new generated set are used as inputs to predict bedload and then the suspended load variable is added in these input sets. The performances of models with respect to input sets are compared to each other. To generate the results and to push the limits of models a very skewed and heterogeneous data is col¬lected from distributed locations. The results indicate that the performance of ANN and CHAID tree models are good when compared to SVM models. The usage of a suspended load as an additional input for the models boosts the model performances and the suspended load has significant contributions to all models.Za kvantitativne i kvalitativne studije vodnih resursa od ključne je važnosti prikladna i prihvatljiva prognoza nanosa prenošenog vodotocima. Premda je najkonzistentnija metoda za određivanje nanosa in situ mjerenje stope nanosa, takva su mjerenja veoma skupa te se ne mogu provoditi na velikom broju vodotoka poput mjerenja nanosa suspendiranog sedimenta. Stoga je u ovoj studiji ispitana uloga suspendiranog nanosa u prognozi nanosa, pri čemu je primijenjena analiza osjetljivosti. Kako se konvencionalnim krivuljama stope sedimentacije i konvencionalnim jednadžbama ne mogu točno prognozirati sedimentni nanosi, u posljednje vrijeme jako porasla upotreba algoritama strojnog učenja. U skladu s tim, u ovoj studiji su primjenjene metode mekog računarstva. Poimence, primijenjeni su ovi modeli: umjetne neuronske mreže (ANN), metoda potpornih vektora (SVM) i modeli stabla odlučivanja (CHAID), koji su po prvi put upotrijebljeni u istraživanju sedimenata. Pojedini parametri često se koriste u metodama mekog računarstva pri kreiranju ulaznih skupova podataka. Ovdje su upotrijebljena tri uobičajena ulazna skupa te novi generirani skup, koji su najprije poslužili kao ulazni podaci za prognozu nanosa, a zatim je tim ulaznim skupovima dodana varijabla suspendiranog nanosa. Međusobno su uspoređene performanse modela s obzirom na ulazne skupove. Kako bi se generirali rezultati i smanjila ograničenja modela, s različitih lokacija prikupljeni su vrlo pristrani i heterogeni podaci. Rezultati pokazuju da su performanse ANN i CHAID modela stabla odlučivanja dobre u usporedbi sa SVM modelima. Upotreba suspendiranog nanosa kao dodatne ulazne varijable poboljšava performanse svih modela i značajno im doprinosi

Prediction of Suspended Sediment Concentration in Kinta River Using Soft Computing Techniques

The prediction of suspended sediment concentration in hyperconcentrated rivers is crucial in modeling and designing hydraulic structures such as dams and water intake inlets. In this study, suspended sediment concentration in Kinta River is predicted using soft computing technique, specifically radial basis function. Suspended sediment concentration and stream discharge from the year of 1992 to 1995 and data from the year of 2009 are used as input. The data are divided into three sections, namely training, testing and validation. 824 data are allocated for training, 313 data are allocated for testing purpose and 342 data are allocated for validation purpose. All data are normalized to reduce error. The determination of input neuron is based on correlation analysis. The number of hidden neurons is determined by the application of trial and error method. As for the output, only one output neuron is required which is the predicted value of suspended sediment concentration. The results obtained from the radial basis function model are evaluated to identify the performance of radial basis function model. Performance of the prediction is measured using statistical parameters namely root mean square error (RMSE), mean square error (MSE), Coefficient of efficiency (CE) and coefficient of determination ( ). Radial basis function model performed well producing the value of (0.9856 & 0.9884) for training and testing stages, respectively. However the performance of RBF model in the prediction of suspended sediment concentration for the year 2009 is poor, with the value of of 0.6934. Recommendations to improve the prediction accuracy are by incorporating a wider data span and by including other hydrology parameters that may impact the changes in the value of suspended sediment concentratio

Modeling of Soil Erosion and Sediment Transport

The Special Issue entitled “Modeling of Soil Erosion and Sediment Transport” focuses on the mathematical modeling of soil erosion caused by rainfall and runoff at a basin scale, as well as on the sediment transport in the streams of the basin. In concrete terms, the quantification of these phenomena by means of mathematical modeling and field measurements has been studied. The following mathematical models (software) were used, amongst others: AnnAGNPS, SWAT, SWAT-Twn, TUSLE, WRF-Hydro-Sed, CORINE, LCM-MUSLE, EROSION-3D, HEC-RAS, SRC, WA-ANN. The Special Issue contains 14 articles that can be classified into the following five categories: Category A: “Soil erosion and sediment transport modeling in basins”; Category B: “Inclusion of soil erosion control measures in soil erosion models”; Category C: “Soil erosion and sediment transport modeling in view of reservoir sedimentation”; Category D: “Field measurements of gully erosion”; Category E: “Stream sediment transport modeling”. Most studies presented in the Special Issue were applied to different basins in Europe, America, and Asia, and are the result of the cooperation between universities and/or research centers in different countries and continents, which constitutes an optimistic fact for the international scientific communication

Assessment of climate change and development of data based prediction models of sediment yields in Upper Indus Basin

Hohe Raten von Sedimentflüssen und ihre Schätzungen in Flusseinzugsgebieten erfordern die Auswahl effizienter Quantifizierungsansätze mit einem besseren Verständnis der dominierten Faktoren, die den Erosionsprozess auf zeitlicher und räumlicher Ebene steuern. Die vorherige Bewertung von Einflussfaktoren wie Abflussvariation, Klima, Landschaft und Fließprozess ist hilfreich, um den geeigneten Modellierungsansatz zur Quantifizierung der Sedimenterträge zu entwickeln. Einer der schwächsten Aspekte bei der Quantifizierung der Sedimentfracht ist die Verwendung traditioneller Beziehung zwischen Strömungsgeschwindigkeit und Bodensatzlöschung (SRC), bei denen die hydrometeorologischen Schwankungen, Abflusserzeugungsprozesse wie Schneedecke, Schneeschmelzen, Eisschmelzen usw. nicht berücksichtigt werden können. In vielen Fällen führt die empirische Q-SSC Beziehung daher zu ungenauen Prognosen. Heute können datenbasierte Modelle mit künstlicher Intelligenz die Sedimentfracht präziser abschätzen. Die datenbasierten Modelle lernen aus den eingespeisten Datensätzen, indem sie bei komplexen Phänomenen wie dem Sedimenttransport die geeignete funktionale Beziehung zwischen dem Output und seinen Input-Variablen herstellen. In diesem Zusammenhang wurden die datenbasierten Modellierungsalgorithmen in der vorliegenden Forschungsarbeit am Lehrstuhl für Wasser- und Flussgebietsmanagement des Karlsruher Instituts für Technologie in Karlsruhe entwickelt, die zur Vorhersage von Sedimenten in oberen unteren Einzugsgebieten des oberen Indusbeckens von Pakistan (UIB) verwendet wurden. Die dieser Arbeit zugrunde liegende Methodik gliedert sich in vier Bearbeitungsschritte: (1) Vergleichende Bewertung der räumlichen Variabilität und der Trends von Abflüssen und Sedimentfrachten unter dem Einfluss des Klimawandels im oberen Indus-Becken (2) Anwendung von Soft-Computing-Modellen mit Eingabevektoren der schneedeckten Fläche zusätzlich zu hydro-klimatischen Daten zur Vorhersage der Sedimentfracht (3) Vorhersage der Sedimentfracht unter Verwendung der NDVI-Datensätze (Hydroclimate and Normalized Difference Vegetation Index) mit Soft-Computing-Modellen (4) Klimasignalisierung bei suspendierten Sedimentausträge aus Gletscher und Schnee dominierten Teileinzugsgebeiten im oberen Indus-Becken (UIB). Diese im UIB durchgeführte Analyse hat es ermöglicht, die dominiertenden Parameter wie Schneedecke und hydrologischen Prozesses besser zu und in eine verbesserte Prognose der Sedimentfrachten einfließen zu lassen. Die Analyse der Bewertung des Klimawandels von Flüssen und Sedimenten in schnee- und gletscherdominierten UIB von 13 Messstationen zeigt, dass sich die jährlichen Flüsse und suspendierten Sedimente am Hauptindus in Besham Qila stromaufwärts des Tarbela-Reservoirs im ausgeglichenen Zustand befinden. Jedoch, die jährlichen Konzentrationen suspendierter Sedimente (SSC) wurden signifikant gesenkt und lagen zwischen 18,56% und 28,20% pro Jahrzehnt in Gilgit an der Alam Bridge (von Schnee und Gletschern dominiertes Becken), Indus in Kachura und Brandu in Daggar (von weniger Niederschlag dominiertes Becken). Während der Sommerperiode war der SSC signifikant reduziert und lag zwischen 18,63% und 27,79% pro Jahrzehnt, zusammen mit den Flüssen in den Regionen Hindukush und West-Karakorum aufgrund von Anomalien des Klimawandels und im unteren Unterbecken mit Regen aufgrund der Niederschlagsreduzierung. Die SSC während der Wintersaison waren jedoch aufgrund der signifikanten Erwärmung der durchschnittlichen Lufttemperatur signifikant erhöht und lagen zwischen 20,08% und 40,72% pro Jahrzehnt. Die datenbasierte Modellierung im schnee und gletscherdominierten Gilgit Teilbecken unter Verwendung eines künstlichen neuronalen Netzwerks (ANN), eines adaptiven Neuro-Fuzzy-Logik-Inferenzsystems mit Gitterpartition (ANFIS-GP) und eines adaptiven Neuro-Fuzzy-Logik-Inferenzsystems mit subtraktivem Clustering (ANFIS) -SC), ein adaptives Neuro-Fuzzy-Logik- Inferenzsystem mit Fuzzy-C-Mittel-Clustering, multiplen adaptiven Regressionssplines (MARS) und Sedimentbewertungskurven (SRC) durchgeführt. Die Ergebnisse von Algorithmen für maschinelles Lernen zeigen, dass die Eingabekombination aus täglichen Abflüssen (Qt), Schneedeckenfläche (SCAt), Temperatur (Tt-1) und Evapotranspiration (Evapt-1) die Leistung der Sedimentvorhersagemodelle verbesserne. Nach dem Vergleich der Gesamtleistung der Modelle schnitt das ANN-Modell besser ab als die übrigen Modelle. Bei der Vorhersage der Sedimentfrachten in Spitzenzeiten lag die Vorhersage der ANN-, ANIS-FCM- und MARS-Modelle näher an den gemessenen Sedimentbelastungen. Das ANIS-FCM-Modell mit einem absoluten Gesamtfehler von 81,31% schnitt bei der Vorhersage der Spitzensedimente besser ab als ANN und MARS mit einem absoluten Gesamtfehler von 80,17% bzw. 80,16%. Die datenbasierte Modellierung der Sedimentfrachten im von Regen dominierten Brandu-Teilbecken wurde unter Verwendung von Datensätzen für Hydroklima und biophysikalische Eingaben durchgeführt, die aus Strömungen, Niederschlag, mittlerer Lufttemperatur und normalisiertem Differenzvegetationsindex (NDVI) bestehen. Die Ergebnisse von vier ANNs (Artificial Neural Networks) und drei ANFIS-Algorithmen (Adaptive Neuro-Fuzzy Logic Inference System) für das Brandu Teilnbecken haben gezeigt, dass der mittels Fernerkundung bestimmte NDVI als biophysikalische Parameter zusätzlich zu den Hydroklima-Parametern die Leistung das Modell nicht verbessert. Der ANFIS-GP schnitt in der Testphase besser ab als andere Modelle mit einer Eingangskombination aus Durchfluss und Niederschlag. ANN, eingebettet in Levenberg-Marquardt (ANN-LM) für den Zeitraum 1981-2010, schnitt jedoch am besten mit Eingabekombinationen aus Strömungen, Niederschlag und mittleren Lufttemperaturen ab. Die Ergebnisgenauigkeit R2 unter Verwendung des ANN-LM-Algorithmus verbesserte sich im Vergleich zur Sedimentbewertungskurve (SRC) um bis zu 28%. Es wurde gezeigt, dass für den unteren Teil der UIB-Flüsse Niederschlag und mittlere Lufttemperatur dominierende Faktoren für die Vorhersage von Sedimenterträgen sind und biophysikalische Parameter (NDVI) eine untergeordnete Rolle spielen. Die Modellierung zur Bewertung der Änderungen des SSC in schnee- und gletschergespeiste Gilgit- und Astore-Teilbecken wurde unter Verwendung des Temp-Index degree day modell durchgeführt. Die Ergebnisse des Mann-Kendall-Trendtests in den Flüssen Gilgit und Astore zeigten, dass der Anstieg des SSC während der Wintersaison auf die Erwärmung der mittleren Lufttemperatur, die Zunahme der Winterniederschläge und die Zunahme der Schneeschmelzen im Winter zurückzuführen ist. Während der Frühjahrssaison haben die Niederschlags- und Schneedeckenanteile im Gilgit-Unterbecken zugenommen, im Gegensatz zu seiner Verringerung im Astore-Unterbecken. Im Gilgit-Unterbecken war der SSC im Sommer aufgrund des kombinierten Effekts der Karakorum-Klimaanomalie und der vergrößerten Schneedecke signifikant reduziert. Die Reduzierung des Sommer-SSC im Gilgit Fluss ist auf die Abkühlung der Sommertemperatur und die Bedeckung der exponierten proglazialen Landschaft zurückzuführen, die auf erhöhten Schnee, verringerte Trümmerflüsse Trümmerflüsse und verringerte Schneeschmelzen von Trümmergletschern zurückzuführen sind. Im Gegensatz zum Gilgit River sind die SSC im Astore River im Sommer erhöht. Der Anstieg des SSC im Astore-Unterbecken ist auf die Verringerung des Frühlingsniederschlags und der Schneedecke, die Erwärmung der mittleren Sommerlufttemperatur und den Anstieg des effektiven Niederschlags zurückzuführen. Die Ergebnisse zeigen ferner eine Verschiebung der Dominanz von Gletscherschmelzen zu Schneeschmelzen im Gilgit-Unterbecken und von Schnee zu Niederschlägen im Astore-Unterbecken bei Sedimenteden Sedimentfrachten in UIB. Die vorliegende Forschungsarbeit zur Bewertung der klimabedingten Veränderungen des SSC und seiner Vorhersage sowohl in den oberen als auch in den unteren Teilbecken des UIB wird nützlich sein, um den Sedimenttransportprozess besser zu verstehen und aufbauen auf dem verbessertenProzessverständnis ein angepasstes Sedimentmanagement und angepasste Planungen der zukünftigen Wasserinfrastrukturen im UIB ableiten zu können