Hydrologic Modeling

The study demonstrates the application of GIS technologies and digital model for watershed management in Amlasole Micro-watershed, India. Extensive processing of spatial and attribute data for the study area were performed using GIS technologies for use in WEAP model. DEM for the study area has been created from SRTM data and where Survey of India topographic map was used for validating. Satellite interpreted data for the land use and District level meteorological data were used to prepare the land use and climatic input files for the model run. Various other inputs are used based on field data and experience. With this setup, model was run successfully and the runoff results could be tested against the observed discharged data from the main stream which shows small variation from the model results. Other results could not be verified at field level. However the trend in simulated monthly inflow outflow from the catchment followed the trend in observed rainfall which indicates that the framework can be used for assessing monthly demand supply of the water in the catchment for watershed management and implementation. The model shows alternative results in “what if” questions satisfactorily for deciding more acceptable options for the watershed management purpose.

The Hindukush Karakoram Himalayan mountains contain some of the largest glaciers of the world, and supply melt water from perennial snow and glaciers to the Upper Indus Basin (UIB) upstream of Tarbela dam, which constitutes greater than 80% of the annual flows, and caters to the needs of millions of people in the Indus Basin. It is therefore important to study the response of perennial snow and glaciers in the UIB under changing climatic conditions, using improved hydrological modeling, glacier mass balance, and observations of glacier responses. However, the available glacier inventories and datasets only provide total perennial-snow and glacier cover areas, despite the fact that snow, clean ice and debris covered ice have different melt rates and densities. This distinction is vital for improved hydrological modeling and mass balance studies. This study, therefore, presents a separated perennial snow and glacier inventory (perennial snow-cover on steep slopes, perennial snow-cover...

Bangladesh is situated at the confluence of GBM basins, with 90% of the basin area locating outside the country. Future climate change will lead to intense, prolonged, and frequent floods in Bangladesh. An integrated flood risk assessment that transforms flood risks from transboundary river basins to the local administrative level is necessary. A 1D/2D hydrodynamic model is developed for flood vulnerable Arial Khan River feed by basin-scale hydrologic model for low (RCP2.6) and high (RCP8.5) climate scenarios. An increasing trend in flood depth, duration, and area is observed from the early (2020s) to the end (2080s) of the century for both scenarios. The difference between both RCPs is minimal from the 2020s to 2050s but becomes very pronounced in the 2080s. The depth-duration-area with equal weightage provides better hazard results for the area. Flood risk is assessed using the IPCC AR5 framework incorporating vulnerability and exposure. Some medium-hazard zones fall into high-ris...

The hydrologic response of different climate-change emission scenarios for the twenty-first century were evaluated in 14 basins from different hydroclimatic regions across the United States using the Precipitation-Runoff Modeling System (PRMS), a process-based, distributed-parameter watershed model. This study involves four major steps: 1) setup and calibration of the PRMS model in 14 basins across the United States by local U.S. Geological Survey personnel; 2) statistical downscaling of the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3 climate-change emission scenarios to create PRMS input files that reflect these emission scenarios; 3) run PRMS for the climate-change emission scenarios for the 14 basins; and 4) evaluation of the PRMS output. This paper presents an overview of this project, details of the methodology, results from the 14 basin simulations, and interpretation of these results. A key finding is that the hydrological response of the ...

Permasalahan umum yang seringkali dihadapi dalam analisis hidrologi adalah keterbatasan data hujan maupun data debit. Pemodelan hidrologi hujan-aliran menggunakan data satelit merupakan salah satu alternatif untuk mengatasi permasalahan tersebut. Integrated Flood Analysis System (IFAS) merupakan salah satu alat dan metode yang bisa digunakan untuk pemodelan hidrologi dengan memanfaatkan data satelit. IFAS dikembangkan berbasis sistem informasi geografis untuk menentukan sistem daerah aliran sungai (DAS) dan mengestimasi parameter-parameter standar dalam analisis limpasan sehingga hasilnya bisa ditampilkan berdasarkan data-data satelit yang ada. Ada sebelas parameter hidrologi yang dikalibrasikan pada pemodelan ini. Sensitivitas parameterparameter tersebut terhadap respon hidrologi pada tahap kalibrasi model dikaji dalam penelitian ini. Penelitian ini mengambil studi kasus di DAS Rokan dengan stasiun AWLR Lubuk Bendahara yang mempunyai luas DAS 3196 km2. Datadata satelit yang dipakai untuk pemodelan adalah data pada periode waktu 2003 hingga 2006. Kalibrasi dilakukan dengan menggunakan satu seri data dari tahun 2005 hingga 2006. Hasil validasi model didapatkan nilai-nilai koefisien korelasi (R), kesalahan volume (VE), dan koefisien efisiensi (CE) masing-masing adalah 0.647, 16.385, dan 0.631. Nilai-nilai parameter tersebut menunjukkan bahwa penggunaan data satelit cukup handal untuk pemodelan hidrologi hujan-aliran
dan bisa dijadikan salah satu alternatif untuk analisis hidrologi pada daerah yang tidak terdapat data pencatatan dari stasiun hidrologi.

Gandaki province has the good potentiality of hydroelectricity generation with existing twenty-nine hydroelectricity projects. Since the Province is rich in water resources, analysis of watersheds needs to be done for management, planning and identification of water as well as natural resources. GIS offers integration of spatial and no spatial data to understand and analyze the watershed processes and helps in drawing a plan for integrated watershed development and management. The Digital Elevation Model (DEM) available on the NASA-Earth data has been taken as a primary data for morphometric analysis of watershed in Gandaki Province using QGIS. Delineation of watershed was conducted from a DEM by computing the flow direction and using it in the Watershed tool. Necessary fill sink correction was made before proceeding to delineation. A raster representing the direction of flow was created using Flow Direction tool to determine contributing area. Flow accumulation raster was created f...

L’érosion du sol est un phénomène naturel qui rend compte de certains processus (détachement des particules, transport et dépôt) induits par différents agents érosifs. Dans les zones semi-arides, les précipitations sont souvent intenses sur les sols secs à végétation pauvre, ce qui peut entraîner une forte susceptibilité à l'érosion des sols. Par conséquent, une évaluation appropriée de l'érosivité des précipitations revêt une importance particulière en raison des effets négatifs causés par l'épuisement de la couche superficielle du sol et la charge excessive de sédiments dans les eaux réceptrices des réservoirs. La présente étude a été menée sur le bassin versant de l’Oued K'sob situé au Nord-Est de l'Algérie, dans le but d’étudier l’influence la variabilité climatique sur la production des sédiments. Le bassin draine une superficie de 1480 km² située entre les altitudes 585 et 1888 m. L’Oued principal s’écoule sur une longueur 73 km et alimente le barrage K’sob d’une capacité initiale de 29.5 Mm3, mis en service en 1940. La région d’étude présente un climat semi-aride à tendance continentale avec un hiver relativement pluvieux et un été sec et chaud, avec une pluie moyenne interannuelle est de 340 mm. La construction des courbes IDF a permis de déterminer un exposant climatique de la région, b=0.75. Pour l’érosivité des précipitations, le calcul d'un tel indice est basé sur les pluies dépassant un seuil spécifique et nécessite des données pluviométriques avec une fine résolution temporelle, qui, souvent, sont rares ou difficiles à acquérir. L'examen des pluies quotidiennes survenant avant une inondation portant une charge sédimentaire a montré une variabilité spatiale des seuils d'érosivité des précipitations. Les valeurs saisonnières des seuils sont faibles et se situent entre 2 mm en été et 6 mm en hiver, ce qui met en évidence un processus d'érosivité typiquement élevé dans les régions semi-arides. Des relations empiriques, établies à l'échelle saisonnière, ont été proposées comme solution alternative au calcul de l'indice R dérivé de l'équation révisée des pertes en sol. Les modèles déterminés ont permis de simuler l'érosivité des événements pluvieux en fonction de la pluie quotidienne. Entre 68 et 78 % de la variance de l'érosivité des précipitations s'explique par la pluie quotidienne donnant lieu à un événement pluvieux érosif. Ensuite, la moyenne spatiale de l'indice d'érosivité annuel a fluctué entre 228 et 386 MJ mm ha-1 h-1 an-1 avec une moyenne interannuelle de 302 MJ mm ha-1 h-1 an-1, ce qui a sous-estimé de 6% l'indice d'érosivité quantifié selon l'équation universelle révisée de perte de sol. L'érosivité des précipitations est le facteur déterminant du rendement en sédiments avec un degré de fixation différent au cours de l'année. En automne, 68 % de la variance de la production de sédiments s'explique par l'érosivité des pluies, contre seulement 42 % au printemps en raison des changements des conditions du sol, notamment la présence d'une couverture végétale qui protège le sol contre l'érosivité des pluies.

— In this project prepared a working model on green society which not only provides occupant an ecofriendly environment but it also focuses on re-use of waste materials. Minimal CO2 emission, Less electricity consumption and gives zero discharge. Majority it has solar panels to produce electricity but even biogas plant which emit methane gas (used as cooking fuel) and other gases also produce electricity. Also windmill installed contributes to electricity production .The hydraulic jump in the canal behind the society also helps in electricity production hence the generated electricity can be used to run the appliances in the house of the society .Here the sand filters are used to filter out grey water and sewage water which can be used in watering the plants in the garden.Most importantly green techniques like double coated window , cavity wall, cooling pipes , cooling tower incommercial building , porous pipe , rain water harvesting , zigzag LED light arrangements, low flow fixture in washroom etc... Have been included for thermal insulation, acoustics, proper ventilation and lighting and reduces use of water. This whole society is ultimately created on the basis of three important techniques i.e. 3R (Reduce, Reuse, and Recycle)

Abastract The ideal allotment of controlled water assets for diversified schemes is needed for continuous and controlled advancement. In present study SWAT (Soil and Water Assessment Tool), a physically based semi-distributed hydrological model was applied in the Satluj Basin (Suni to Kasol) for hydrologic modeling. 23 sub-basins were demarcated from the study area, which consists of 325 Hydrological Response Units (HRUs) with regard to specific slope, soil and land use combination. SWAT-CUP (SWAT-Calibration and Uncertainty Programs) is one of the new developments for calibration/sensitivity analysis of watershed models that incorporate a semi-automated approach SUFI-2 (Sequential Uncertainty Fitting) which includes both manual and automatic calibration and incorporating global sensitivity analysis. Parameters for Sensitivity Analysis used to measure statistics for goodness-of-fit. For the study area, CN2, SOIL_K, RCHRG_DP, CH_K2 were considered as the most sensitive parameters. The model Calibration (1986-1998) and Validation (1999-2011) were executed with initial 3 years considered as a warm-up period (1983-1985) at Kasol site which is considered as anoutlet of the study area. The statistical performance of the model was assessed using the coefficient of variation (R 2), and Nash-Sutcliffe efficiency (NSE), during model Calibration, R² for the daily runoff was obtained as 0.98 with Nash-Sutcliff of 0.96 respectively. The validation also signified satisfactory results with R 2 of 0.96 and NSE of 0.94 respectively. The results have shown excellent correlation between observed and simulated discharge on daily time steps. The results and ensuing inferences would be of immense help to the hydrological fraternity and the water resource managers.

This paper presents an application dedicated to the modeling of reservoir inflow and
management of reservoirs in the Souss-Massa basin in Morocco. This application is
included in the integrated decision support system (SIAD_TIGERS) developed for the
needs of the Souss-Massa Hydraulic Basin Agency (ABHSM). The Souss-Massa has
experienced over the last twenty years a very important development of its economic
activities and rapid change of the habitat. These multiple developments have a direct
impact and increase the pressure on the already scarce water resources. The watershed
is the site of eight major dams for a storage capacity of more than 450 Mm3. Presently,
to assess the global budget, the loss (through evaporation and seepage) and the release
of water (for drinking water and agriculture), ABHSM conducts periodic (daily, monthly
and yearly) reservoirs budget, from measurements of water level in the pool. Thus, the
reservoir inflow is deducted as a byproduct of the reservoir balance. The approach
proposed in this paper is progressive and is part of a proactive planning for the
management and monitoring of the reservoirs water storage. This approach models the
flow using a hydrological rainfall-runoff model coupling with a reservoir simulation
model; the flood mapping is done by coupling the hydrologic model with a hydraulic
simulation model integrated in the SIAD_TIGERS.
Several hydrological models were analyzed according to a set of criteria determined by
the constraints of the project and the requirements of the Agency (spatial and temporal
scale, process modeled, cost, user interface and so on.). The reservoir management
model has been selected on the basis of its good performance, its ability to meet the
requirements of the ABHSM and its integration with the rainfall-runoff model for
estimation of the reservoir inflow. The reservoir model simulates exactly the elevation
and storage estimated in the reservoir in the same conditions of inflow, losses and water
releases. The model can simulate different scenarios (pessimistic, conservative,
optimistic, and variations) and to prioritize the conflicting water needs. This project was
made possible through a grant from the Canada Space Agency (CSA).

Ce papier présente une application dédiée à la modélisation des apports des réservoirs
et la gestion des barrages localisés dans le bassin du Souss-Massa (Maroc). Cette
application constitue l’une des réalisations insérées dans le système intégré d’aide à la
décision (SIAD_TIGERS) réalisé pour accompagner l’Agence (de gestion) du Bassin
hydraulique du Souss-Massa (ABHSM). Le Sous-Massa connaît depuis ces dernières
décennies un développement très important de ses activités économiques et une
évolution rapide de l’habitat. Ces multiples développements s’accompagnent d'une forte
pression sur les ressources en eau. Le bassin du Souss-Massa est le site de huit
importants barrages permettant un stockage total supérieur à 450 Mm3. Actuellement,
pour réaliser les bilans globaux, évaluer les pertes (par évaporation ou par infiltration)
et estimer les volumes des lâchers d’eau, l’ABHSM effectue des bilans périodiques des
réservoirs, à partir des mesures de niveau d’eau dans le barrage. Ainsi, l’apport est
déduit comme un sous-produit du bilan du réservoir.
Dans cet article, nous proposons une approche progressive qui peut s’inscrire dans le
cadre d’une planification proactive pour la gestion et le suivi des réservoirs. Il consiste à
modéliser les apports en utilisant un modèle hydrologique pluie-débit couplé à un
modèle de simulation de réservoir; les zones d’inondation et la prévision des crues sont
réalisées en couplant le modèle hydrologique à un modèle de simulation hydraulique, le
tout intégré au sein de la plate-forme SIAD_TIGERS. Plusieurs modèles hydrologiques
ont été analysés selon un ensemble de critères déterminés par les contraintes de
gestion et les besoins de l’ABHSM (échelle spatio-temporelle, processus modélisés,
coût, interface utilisateur etc.). Le modèle de gestion de réservoir du groupe HEC a été
retenu en fonction de sa bonne performance, de sa capacité à répondre aux besoins de
l’ABHSM et de son intégration avec le modèle pluie-débit d’estimation des apports. Le
modèle de réservoir simule exactement la hauteur et le volume d’eau dans le
réservoir en tenant compte des apports, des pertes et des lâchers d’eau. Le modèle
permet de simuler différents scénarios (pessimiste, conservateur, optimiste, et
variantes) et d’établir des priorités dans les besoins à satisfaire. Ce projet a été
réalisé grâce à une subvention paritaire de l’Agence Spatiale du Canada (ASC).

"This study is an attempt to seek a relatively optimal data-driven model for rainfall forecasting from three aspects: model inputs, modeling methods, and data preprocessing techniques. Four rain data records from different regions, namely two monthly and two daily series, are examined. A comparison of seven input techniques, either linear or nonlinear, indicates that linear correlation analysis (LCA) is capable of identifying model inputs reasonably. A proposed model, modular artificial neural network (MANN), is compared with three benchmark models, viz. artificial neural network (ANN), K-nearest-neighbors (K-NN), and linear regression (LR). Prediction is performed in the context of two modes including normal mode (viz., without data preprocessing) and data preprocessing mode. Results from the normal mode indicate that MANN performs the best among all four models, but the advantage of MANN over ANN is not significant in monthly rainfall series forecasting. Under the data preprocessing mode, each of LR, K-NN and ANN is respectively coupled with three data preprocessing techniques including moving average (MA), principal component analysis (PCA), and singular spectrum analysis (SSA). Results indicate that the improvement of model performance generated by SSA is considerable whereas those of MA or PCA are slight. Moreover, when MANN is coupled with SSA, results show that advantages of MANN over other models are quite noticeable, particularly for daily rainfall forecasting. Therefore, the proposed optimal rainfall forecasting model can be derived from MANN coupled with SSA.

Keywords: Rainfall prediction; Modular artificial neural network; Moving average; Principal component analysis; Singular spectral analysis; Fuzzy C-means clustering"

For determining of Muskingum model coefficients, requires to an output hydrograph. Such hydrograph is not available in most rivers. In this research, the Muskingum's new coefficients are determined by the method that which is not require to output hydrograph and its accuracy is high. This coefficient is determined based on kinematic wave model with suitable scheme. The comparison between results of Muskingum model with new coefficients and dynamic wave model, showed that the new coefficients, are valid at special conditions. The new coefficients were adjusted by optimization technique for all conditions. The new adjusted coefficients are function of bed slop, bottom width and Manning's roughness coefficient for the river. The results of these coefficients were validated by dynamic wave model.

"Developing a hydrological forecasting model based on past records is crucial to effective hydropower reservoir management and scheduling. Traditionally, time series analysis and modeling is used for building mathematical models to generate hydrologic records in hydrology and water resources. Artificial intelligence (AI), as a branch of computer science, is capable of analyzing long-series and large-scale hydrological data. In recent years, it is one of front issues to apply AI technology to the hydrological forecasting modeling. In this paper, autoregressive moving-average (ARMA) models, artificial neural networks (ANNs) approaches, adaptive neural-based fuzzy inference system (ANFIS) techniques, genetic programming (GP) models and support vector machine (SVM) method are examined using the long-term observations of monthly river flow discharges. The four quantitative standard statistical performance evaluation measures, the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (E), root mean squared error (RMSE), mean absolute percentage error (MAPE), are employed to evaluate the performances of various models developed. Two case study river sites are also provided to illustrate their respective performances. The results indicate that the best performance can be obtained by ANFIS, GP and SVM, in terms of different evaluation criteria during the training and validation phases.

Keywords: Monthly discharge time series forecasting; ARMA; ANN; ANFIS; GP; SVM"

"Data-driven techniques such as Auto-Regressive Moving Average (ARMA), K-Nearest-Neighbors (KNN), and Artificial Neural Networks (ANN), are widely applied to hydrologic time series prediction. This paper investigates different data-driven models to determine the optimal approach of predicting monthly streamflow time series. Four sets of data from different locations of People’s Republic of China (Xiangjiaba, Cuntan, Manwan, and Danjiangkou) are applied for the investigation process. Correlation integral and False Nearest Neighbors (FNN) are first employed for Phase Space Reconstruction (PSR). Four models, ARMA, ANN, KNN, and Phase Space Reconstruction-based Artificial Neural Networks (ANN-PSR) are then compared by one-month-ahead forecast using Cuntan and Danjiangkou data. The KNN model performs the best among the four models, but only exhibits weak superiority to ARMA. Further analysis demonstrates that a low correlation between model inputs and outputs could be the main reason to restrict the power of ANN. A Moving Average Artificial Neural Networks (MA-ANN), using the moving average of streamflow series as inputs, is also proposed in this study. The results show that the MA-ANN has a significant improvement on the forecast accuracy compared with the original four models. This is mainly due to the improvement of correlation between inputs and outputs depending on the moving average operation. The optimal memory lengths of the moving average were three and six for Cuntan and Danjiangkou respectively when the optimal model inputs are recognized as the previous twelve months.

Keywords: Hydrologic time series; Auto regressive moving average; K nearest neighbors; Artificial neural networks; Phase space reconstruction; False nearest neighbors; Dynamics of chaos"

"Flood disaster is one of the most damaging natural disasters in China, with annual average losses more than 200 billion yuan in recent years. After 1995 floods in the Liaohe River and 1998 floods in the Yangtze River, the governments from national to local have realized that the flood control operation of reservoirs can play a major role in alleviating flood losses but there are some problems in flood control management for reservoirs. Most of the existing flood control management systems for reservoir were established for special purposes and are lack of data share and communication with governments, it is very difficult for decision-making departments to get real-time information in short time. Thus, a national programming about flood control management system for reservoirs is presented. The paper is a summary of the outcome of national programming about the flood control management system for reservoirs in China. The background, objectives, main challenges and main contents of the programming are introduced. The main focus is on the issues of the software integration flood control management system for reservoirs. Emphasis is concentrated on the flowchart design of the system and its core components. The current system can be applied to a river control center or a single reservoir because of using the national standard databases and easily integrated into the national flood control system in the future. An application system is briefly introduced in order to understand the system.

Keywords: flood control; integrated management; database; reservoir"

This paper deals with the geoenvironmental politics of early-modern Venice as a case
study of geological agency that enlightens the entanglements of geo-history and human
history. It focuses on a controversy that was sparked by Galileo’s pupil Benedetto
Castelli, as he claimed that his mathematical treatment of running waters could solve all
of the most urgent problems linked to the management of the Lagoon of Venice. From
an epistemological viewpoint, the controversy is relevant as a case of clashing ‘styles of
thought’, as it constituted a disciplinary conflict that pitted Galileian physico-
mathematical abstraction (which resulted from the isolation of a set of quantifiable data)
against ‘geological’ concreteness (a form of comprehensive knowledge which aimed to
cope with systemic complexity). Castelli was not able to convince the Venetian
authorities that his method could solve the main problems relative to the conservation of
the lagoon at a time when its depth and navigability were worryingly diminishing. While
the Venetian authorities invested in diverting rivers away from the lagoon to reduce
sediment supply, Castelli argued, to the contrary, that it was precisely the diversion of
the rivers that caused shoaling because of the loss of the great quantity of water
discharged by the rivers, which he accurately calculated. His analytical approach was
dismissive of the comprehensive knowledge and complex methods that Venetian water
experts and engineers had developed towards a systemic understanding of the
hydrogeology and the environment of the lagoon with the active involvement of citizens
and fishermen in the assessment of the state of the waters.
Keywords: Benedetto Castelli, Early Modern Science, Renaissance hydrogeology,
Lagoon of Venice

Aam Talaab also known as Maavina Kere or mango beach is one of the major attractions of Raichur. It is located between Latitude 16º10′49.5′′ N to 16º11′27.8′′ N Latitude and 77º20′36′′ E to 77º23′26′′ E Longitude and an area of 4.77 km 2 , having maximum length of 3.98 km. The maximum and minimum elevation of the basin is 491 m and 404 m above MSL, respectively. The values of Stream frequency is 6.50, Form factor 0.30, Shape factor 3.32, Elongation Ratio 0.62, Circularity Ratio 0.39, Drainage density 2.66, Length of overland flow 0.19. The study of hypsometric properties of Aam Talab watershed using hypsometric integral (HI) and hypsometric curve retrieved in that, HI value is 0.52 and hence watershed falls under the Mature Stage. Rainfall was estimated by considering precipitation data from 1998 to 2016 it was found that maximum percentage of runoff took place in 2009 and minimum in the year 2012.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

Pengalihragaman curah hujan menjadi limpasan pada daerah aliran sungai adalah suatu fenomena hidrologi yang sangat kompleks, dimana  ini merupakan proses nonlinear, dengan waktu yang berubah-ubah dan terdistribusi secara spasial. Untuk mendekati fenomena tersebut, maka telah dikembangkan suatu analisa sistem hidrologi dengan menggunakan model yang merupakan penyederhanaan kenyataan alam yang sebenarnya. Model tersebut dibentuk oleh satu set persamaan matematis yang mencerminkan perilaku dari parameter dalam hidrologi.
Pada penelitian ini, analisis mengenai hubungan curah hujan-limpasan untuk daerah aliran sungai (DAS) digunakan model Artificial Neural Networks (ANN). ANN merupakan salah satu bentuk kecerdasan buatan yang mempunyai kemampuan untuk belajar dari data dan tidak membutuhkan waktu yang lama dalam eksekusi model. ANN multi layer perceptron dengan metode back propagation dengan perbandingan 2 (dua) algoritma pelatihan (gradient descent dan adaptive learning rate) digunakan untuk mempelajari hubungan hujan-limpasan pada DAS Tukad Jogading serta memverifikasi model tersebut secara statistik berdasarkan nilai mean square error (MSE), root mean square error (RMSE), kesalahan absolute rata-rata (KAR) dan nilai koefisien korelasi (r).
Hasil penelitian menunjukkan bahwa penggunaan model ANN metode back propagation dapat diterapkan dalam modelisasi hubungan curah hujan limpasan. Dari enam model arsitektur jaringan yang diterapkan, model 6 dengan pelatihan gradient descent berarsitektur 4-10-5-1 memberikan hasil yang paling optimum dengan tingkat nilai kesalahan abolute rata-rata (KAR) 1,7548 dengan nilai koefisien korelasi 91,64 % pada proses pembelajaran. Pada proses pengujian, model 5 beraksitektur 3-10-5-1 memberikan nilai KAR 1,0241dengan nilai koefisien korelasi 69,77 %. Sedangkan model 6 dengan pelatihan adaptive learning rate berarsitektur 4-10-5-1 memberikan hasil yang paling optimum nilai KAR 1,3217 dengan nilai koefisien korelasi 90,78% pada proses pembelajaran. Pada proses pengujian, model 3 berarsitektur 4-5-1 memberikan nilai KAR 1,4771dengan nilai koefisien korelasi 69,01%. Dari hasil tersebut terlihat bahwa ANN memiliki kemampuan yang cukup baik dalam mereplikasi fluktuasi debit yang acak ke dalam bentuk model buatan yang memiliki fluktuasi yang hampir sama dan juga dapat diterapkan dalam modelisasi curah hujan limpasan walaupun hasil pengujian (testing) hasilnya tidak terlalu akurat karena masih terjadi penyimpangan.

Kata kunci: pemodelan curah hujan limpasan, DAS, artificial neural networks, metode back propagation, gradient descent, adaptive learning rate

Flood management alternatives are often evaluated on the basis of flood parameters such as depth and velocity. As these parameters are uncertain, so is the evaluation of the alternatives. It is thus important to incorporate the uncertainty of flood parameters into the decision making frameworks. This research develops a spatial probabilistic multi-criteria decision making (SPMCDM) framework to demonstrate the impact of the design rainfall uncertainty on evaluation of flood management alternatives. The framework employs a probabilistic rainfall-runoff transformation model, a two-dimensional flood model and a spatial MCDM technique. Thereby, the uncertainty of decision making can be determined alongside the best alternative. A probability-based map is produced to show the discrete probability distribution function (PDF) of selecting each competing alternative. Overall the best at each grid cell is the alternative with the mode parameter of this PDF. This framework is demonstrated on the Swannanoa River watershed in North Carolina, USA and its results are compared to those of deterministic approach. While the deterministic framework fails to provide the uncertainty of selecting an alternative, the SPMCDM framework showed that in overall, selection of flood management alternatives in the watershed is “moderately uncertain”. Moreover, three comparison metrics, F fit measure, κ statistic, and Spearman rank correlation coefficient (ρ), are computed to compare the results of these two approaches. An F fit measure of 62.6%, κ statistic of 15.4% to 45.0%, and spatial mean ρ value of 0.48, imply a significant difference in decision making by incorporating the design rainfall uncertainty through the presented SPMCDM framework. The SPMCDM framework can help decision makers to understand the uncertainty in selection of flood management alternatives.

This article contributes to better understanding of the precipitation data, analyzing several measurement errors in Romania. Based on the influence of wind speed, solid precipitation rate, and wetting losses, we adjusted the monthly amounts registered at 159 weather stations through 1961–2006. The results emphasize distinct temporal and spatial distributions of the adjustment magnitude. In general, the correction factors increase with altitude and they have high values in the cold season, as they highly depend on wind speed and solid precipitation percentage. In Romania, bias corrections increase monthly precipitation by less than 10% from June to September, by 10–20% in the transition months, and by higher values during the winter. Copyright © 2010 Royal Meteorological Society

Soil erosion is a very serious problem from agricultural as well as environmental point of view. Various computer models have been used to estimate soil erosion and assess erosion control practice. Universal Soil Loss Equation (USLE) is one of the most frequently used soil loss estimation models which have been used in many countries around the world. Erosivity (USLE R-factor) is one of the USLE input parameters to reflect impacts of rainfall in computing soil loss. R factor for a specific rainfall event depends upon maximum rainfall intensity of specific period and kinetic energy of that event. Annual R factor is calculated as the sum of erosivities of such rainfall events that occurred. It is usually calculated from rainfall data having higher temporal resolution but the process of calculation is very tedious and also the higher temporal resolution data are not readily available in many parts of the world. Various regression models have been developed to estimate monthly R factor as well as annual R factor using monthly/yearly rainfall amount. However, it is rarely allowed to estimate R factor with higher accuracy using these models since they were developed from obsolete dataset and also only the rainfall amount was used for an input parameter without rainfall intensity. In this study, a web-based Erosivity estimation system (Web ERosivity Module-WERM) was developed to compute R factor using 10 min interval rainfall data. The model was then tested for 75 different cities in Korea using the rainfall data of 15 to 18 years from 1997 to 2014 obtained from Korea Meteorological Administration (KMA). Using the monthly rainfall data and R factor values obtained from the model, regression equation for 25 cities was developed to estimate monthly R factor from the monthly rainfall with amount and intensity of rainfall considered. The coefficient of determination (R 2) of the regression equation ranged from 0.75 to 0.92. This indicated that these regression equations can be used to estimate the value of R-factor from the monthly rainfall data with more than 75% accuracy. The WERM is very simple to use and it can be a very effective tool to compute R factor using higher temporal resolution rainfall data. Along with this, it is possible to calculate R factor using local daily rainfall with the help of regression equations which are available for 25 cities in South Korea till now.

Landuse change has significant impacts on hydrologic processes in a watershed. Assessment of the impacts of
landuse changes is essential for watershed management and sustainable development. In this study, a soil and water
assessment tool (SWAT) model was used to assess the landuse changes impacts on runoff generation in Simly dam watershed
located 30 km east of Islamabad, Pakistan with a catchment area of 156 km2.The simulation was performed for three time
periods 1991-92, 2000-01, 2009-10 on a monthly time step. The model was calibrated using the auto-calibration tool SWATCUP. Model predicted surface runoff volumes with Nash Sutcliffe Efficiency (NSE) ranges from 0.81 to 0.83. The results of the
scenarios revealed that expansion of urban areas from 5% to 10% indicated 1.2% and 0.2% increase in the surface runoff and
water yield respectively, expansion of forests areas from 46% to 66% indicated 9.3% and 2.5% decrease in the surface runoff
and water yield respectively and deforestation of the catchment from 46% to 36% indicated 6.7% and 2.2% increase in the surface runoff and water yield respectively. The study has presented that the Soil and Water Assessment Tool (SWAT) model can be a suitable tool for evaluating the impacts of landuse changes in Simly watershed.

"Genetic algorithms are among of the global optimization schemes that have gained popularity as a means to calibrate rainfall-runoff models. However, a conceptual rainfall-runoff model usually includes 10 or more parameters and these are interdependent, which makes the optimization procedure very time-consuming. This may result in the premature termination of the optimization process which will prejudice the quality of the results. Therefore, the speed of optimization procedure is crucial in order to improve the calibration quality and efficiency. A hybrid method that combines a parallel genetic algorithm with a fuzzy optimal model in a cluster of computers is proposed. The method uses the fuzzy optimal model to evaluate multiple alternatives with multiple criteria where chromosomes are the alternatives, whilst the criteria are flood performance measures. In order to easily distinguish the performance of different alternatives and to address the problem of non-uniqueness of optimum, two fuzzy ratios are defined. The new approach has been tested and compared with results obtained by using a two-stage calibration procedure. The current single procedure produces similar results, but is simpler and automatic. Comparison of results between the serial and parallel genetic algorithms showed that the current methodology can significantly reduce the overall optimization time and simultaneously improve the solution quality.

Keywords: calibration; cluster; fuzzy evaluation; multiple criteria; parallel genetic algorithms; rainfall-runoff model"