Accurate assessment of streamflow is crucial for operational water resources management projects. The aim of this study was to estimate the uncertainties in the surface runoff simulated by a monthly water balance model in a mountainous watershed of the Portaikos river, a tributary of the Pinios river, Thessaly, Greece. The University of Thessaly (UTHBAL) monthly water balance model was developed in the R statistical computing environment language, named ‘R-UTHBAL’, to estimate surface water balance in data-scarce watersheds. Two sources of uncertainties in hydrological modelling were considered: the uncertainties in input data estimation and in model parameters. The uncertainties were estimated with the use of the R-package ‘hydroPSO’, a global Particle Swarm Optimisation (PSO) algorithm for the calibration of environmental models. The R-UTHBAL was integrated with the hydroPSO algorithm and advanced sensitivity analyses, and user-friendly evaluation plots were estimated to facilitate the interpretation and assessment of the calibration results. Areal input datasets were estimated using typical engineering methods (i.e. precipitation/temperature gradients, Thiessen polygons) and several objective functions, (i.e. Nash-Sutcliffe Efficiency and variations or adaptations), addressing different parts of the hydrograph have been used to assess both the skill and the robustness of the R-UTHBAL model to perform consistent streamflow predictions. Confidence intervals in the simulated runoff due to input data uncertainty, parameter uncertainty and total uncertainty were calculated. Application of R-UTHBAL with the hydroPSO showed that the uncertainty in streamflow estimation should always be accounted for and evaluated in operational water resources management projects.

The Dniester river – one of the largest rivers in Europe – flows through territory of two countries: Ukraine and the Republic of Moldova. The flow is formed mainly in upper part of the basin, in Carpathian region, situated in Ukraine. Extreme climatic condition from mountains and boundary plateau generate floods that propagate downstream and cause damage to economy and population. In order to regulate the flood wave, reservoirs were constructed on the stream: two are included in the Dniester Hydropower Complex (DHC) situated at the border of Ukraine and the Republic of Moldova, and the other one is Dubasary reservoir, positioned in the limits of Moldova. Main aim of the research is to evaluate the impact of stream reservoirs on the Dniester floods wave dynamics. The investigations were performed in order to evaluate the changes that occur in flood characteristics for three periods: natural flow, flow regulated by Dubasary, flow regulated by DHC and Dubasary. Based on hydrological time series from stations situated upstream and downstream of reservoirs, there were calculated and compared: flood characteristics, peaks of 10, 5, 1, 0.5 and 0.1% probability, and Environment Flow Components: high-flow pulses, small floods, large floods. The results show that high flood protection is specific to DHC, while through the Dubasary reservoir the flood wave passes mainly in transit. The flood protection system has a greater effect in regulating the floods with medium probability, especially after the DHC construction. The reservoirs caused a slight increase of coefficient of attenuation of peak discharges from 0.30 to 0.40 (in natural conditions) to 0.50-0.60 (in regulated flow conditions). Due to flow regulating impact, small floods as well as their average peaks and duration were reduced in reservoirs downstream part. High-flow pulses increased in number after DHC construction due to hydropeaking effect, however downstream Dubasary reservoirs their reduction is observed. At present, large floods increase in number in the upper part but are transformed into small floods to the downstream, thus protecting the region from inundation. Increasing frequency and occurrence of floods in the Dniester river basin lead to improvement of flood management strategies, both in Ukraine and the Republic of Moldova. In this regard, in the Transboundary Diagnostic Analysis of the Dniester basin the floods and inundations were identified as cross-cutting issue, and, as a result, a set of measures was developed and included in the Strategic Action Program developed and approved at high level in common by both countries.

Design flood hydrograph estimation is a key problem in hydrology and is necessary for a variety of applications from the design of hydraulic structures to flood risk mapping processes. Furthermore, in large ungauged basins (>1000 km²) design flood estimation methods are mainly rely on single-event theories using digital elevation models, land use / land cover and soil type data and relevant meteorological information (temperature and rainfall data). The single event-based deterministic approach is adopted, based on three modelling components: (i) synthetic storm generator; (ii) hydrological simulation model; and (iii) hydraulic routing model. In this study the 100-year design flood (which is assumed equal with the 100-year extreme rainfall) is estimated for Pinios river basin, Thessaly, Greece at Larissa outlet station (upstream area about 6500 km²). The hydrological approach is based on semi-distributed modelling of the rainfall-runoff process (at sub-basin scale) using the HEC-HMS software and the SCS-CN method for estimating rainfall excess, as well as the unit hydrograph theory for propagating the surface runoff to the subbasin outlets. Design Rainfall at subbasin scale is estimated from 13 IDF rainfall point curves using thiessen polygons and adjusted to the mean elevation of the subbasin with the developed precipitation gradients. The design flood hydrograph is estimated by combining the Intensity-Duration-Frequency (IDF) approach with standard time profiles, for constructing synthetic rainfall events of a certain probability, the SCS-CN method for extracting the excess from the gross rainfall, and the unit hydrograph theory, for propagating the surface runoff to the basin outlet.

Understanding the rainfall-runoff relationships is one of the requirements and necessities in flood modeling, forecasting and grasping its contribution to the annual runoff. This study aims to evaluate the use the hydrological modeling and flood frequency analysis (FFA) in investigating the magnitude and the occurrence of floods in a complex terrain mountainous watershed and the effects of the dams on downstream floods.

The case study area, the N’fis sub-basin is located in the High Atlas of Morocco, drains a total area of 1700 km², and is characterized by an arid to semi-arid climate in plains and sub-humid in mountains. The flood modeling in this watershed constitutes a true challenge due to the lack of an adequate amount of spatial and temporal flood data for FFA. Therefore, the SWAT (Soil and Water Assessment Tool) which is a physically based, the continuous model was used to simulate and reproduce the hydrological behavior of the N’fis upstream. The model parameters were calibrated and validated with data collected from 2000 to 2016 and performed satisfactorily with statistical values of Nash-Sutcliffe for the calibration period 0.52 and validation 0.69. Finally, the daily flood data (1982-2016) was used to carry out FFA using the L-moment methods (Gumbel Normal and Log Pearson III). Furthermore, the comparison of the goodness-of-fit for Gumbel, GEV, and LP3 distributions for flood frequency analysis in the N’fis watershed highlights that the GEV distribution shows good results and appears to be the more suitable one. This study allowed for a better assessment of floods and can assist water managers and decision-makers in adequately planning and managing flood protection.

Porous pavements are considered an alternative to increasing the city's permeable surfaces with great potential for rainwater harvesting. Over time, the surface layer clogs, limiting porous pavement dissemination. This study aims to evaluate the maximum time for an intervention of a porous surface to recover its permeable properties before clogging. Through a constant load permeameter, rainwater runoff simulations were performed to measure the permeability coefficient of porous asphalt mixtures. Void content and void communication were also evaluated. The results show that the intervention by surface cleaning for the porous asphalt mixtures studied should occur every year.

River flow forecasting is an essential tool to manage the floods in current era especially for flash flooding scenarios in urban areas. This study focuses the flash flooding scenario on basin of Nullah Lai basin which comprises of twin cities of Islamabad & Rawalpindi. Steep slopes in Margalla hills & Islamabad creates high flash floods in lower reaches of Rawalpindi which are densely populated. High intensity rainfall when occurs in steep slopes of Margalla and Islamabad pour down high floods with high velocity which instantaneously reaches the Rawalpindi less sloppy regions in which causes raising of water level in the stream and flooding occurs. Nullah Lai Rawalpindi reach starting from Qatarian bridge to Gawalmandi bridge has always faced the flash flooding over the time. In the period of few hours the water level reaches the several fts level in the nullah that is why it is not possible to timely alert the people living on the banks that problem always ask for the need of forecasting system at Nullah Lai. In current research china metrological agency forecast center (CMA) ensemble forecast data has been utilized to get the forecasted stage in the Nullah Lai. For this purpose, two initial objectives were set to achieve which basic needs to process the data available in grib format at data centers. The digital model of Nullah Lai was made using hydrology tools available in ArcGIS 10.3. The digital equation was obtained from gene expression modeling (GEP) which was later used to generate the ensemble stage forecast against the ensemble rainfall forecast. The results obtained shows that the flash flooding phenomenon in Nullah Lai can be with some uncertainty be predicted well before time. Using 3 days ahead forecast data from CMA same floods were predicted 3-days before the happening of event. This research also provides the procedure to use the ensemble forecast data in developing the automated model to generate the ensemble stage forecast against coming events. This study will help the administrative authorities to better manage the upcoming floods and save live and capital cost lost in flash flooding phenomenon which continuously happen in the basin of Nullah Lai.

Crop water stress detection must be improved in agriculture at various times of the

growing season keeping in view to fulfill the need of global food production. Remote

sensing based plant stress indicators have the benefit of having spectral and spatial

resolutions, cheap cost, and short turnaround times. This study discusses current

advancements in agricultural water stress monitoring, irrigation scheduling, and

challenges encountered.We explore the use of remote-sensing systems in the

evaluation of crop water stress by looking at existing research, technologies, and data.

Remote sensing systems are prepared to handle the intricate and technical evaluation

of agricultural productivity, security, & crop water stress in such a quick and effective

manner. They offer quick and easy fixes for a variety of ecological zones. This critical

study focuses on cutting-edge techniques for assessing crop water stressed and its

relationship to certain measurably-based variables.The study examines the connection

between relative water content (RWC), equivalent water thickness (EWT), and

agricultural water stress. Optical, thermometric, and Li-DAR sensing systems, as well

as systems that measure land surface temperature are examined. Using remote sensing,

evapotranspiration and sun-induced chlorophyll content are examined in connection

to crop drought.

Floods are random and natural occurrences that are brought on by heavy rain, flash floods, storms, broken dams, and glacial lake eruptions. Numerous studies in the literature demonstrate that the use of artificial intelligence (AI) in modeling methodologies yields outcomes for linear, non-linear, and other systems that are close to the real data. In this paper, we analyzed the state-of-the-art advancements in artificial intelligence modeling for four different types of water management variables: precipitation, streamflow, temperature, and relative humidity. We will develop several Machine Learning models in this study, including a variety of membership functions, optimizing approaches, and data set resources for training and testing. This research aims to compare different models of Artificial Intelligence specifically Deep Learning techniques such as Long-Short term memory (LSTM), and Seasonal autoregressive integrated moving average (SARIMA) in forethought extreme climatic devastation events in the Indus basin. The correspondence coefficient, neat medium inaccuracy, and core average square inaccuracy will be used as pursuance measures to assess and compare the models given. Using the model that will be most accurate, we will estimate future flows using the data of CMIP6 Models. AI systems are nearing or surpassing human performance on a growing number of demanding tasks, thanks to increased database availability and recent advancements in deep learning approaches. Photo rating, emotional analysis, sound interpretation, and strategic gameplay are all examples of this progress. These very successful deep learning models are generally deployed in a black-box method, which implies no information is supplied on how they obtain their predictions, owing to their unique non-linear structure. The current study will help in the forecasting of high storms for effective water resources management.

The role of effective irrigation management for optimal food production is well recognized. This problem can be possibly solved through the improvement of water use efficiency (WUE) for irrigation to achieve sustainability in irrigated agriculture. At the farm level, to control the adequacy of the water applied to actual crop requirements, net irrigation water requirements (NIWR) are needed. Indeed, NIWR is the water that must be supplied through irrigation to satisfy evapotranspiration, leaching and additional water supply, which is not provided by water stored in the soil, as well as precipitation entering the soil. Specifically, computation of NIWR is based on the estimation of crop water requirements (CWR) and soil water balance, where crop evapotranspiration (ET_c) is the main component. There is a continuous research effort to estimate ET_c, CWR and NIWR. Irrigation water management generally requires continuous monitoring and reliable information at specific spatial and temporal scales about the soil and plant conditions across farms. Earth observation (EO) using remote sensing (RS) has already become an important tool for the quantification and the detection of the spatial and temporal distribution and variability of several environmental variables at different scales. Remotely sensed models are currently considered suitable for crop water use estimation at field, as well as regional scales. Having the knowledge of crop water requirements, irrigation can be supplied either to satisfy full requirements, or to manage a deficit-controlled irrigation.

At the present time, the most prevailing group of EO methodologies for the estimation of ET_c is the Energy Balance (EB) algorithms and more specifically the residual methods. Remotely sensed EB algorithms convert satellite sensed radiances into land surface characteristics, such as albedo, leaf area index, vegetation indices, surface roughness, surface emissivity and surface temperature to estimate ET as a “residual” of the land surface energy balance equation. Most recent EB models differ mainly in how Sensible Heat (H) is estimated. These models include the Two Source Model (TSM), where the energy balance of soil and vegetation are modeled separately and then combined to estimate total LE, the Surface Energy Balance Algorithm for Land (SEBAL), the Mapping Evapotranspiration with Internalized Calibration (METRIC) that uses hot and cold pixels within the satellite images to develop an empirical temperature difference equation and the Surface Energy Balance Index (SEBI) based on the contrast between wet and dry areas. Other variations of SEBI include the Simplified Surface Energy Balance Index (S-SEBI), and the Surface Energy Balance System (SEBS).

In this paper, a methodology is presented, which is developed for the estimation of the actual daily evapotranspiration (ETa). This is a contribution to a European-funded research project, namely "HubIS". The proposed methodology utilizes the combination of simulation programs and remote sensing. Indeed, obtaining useful spatial information and describing difficult physical processes through remote sensing is important for developing better agricultural practices. In this study, a combination of Sentinel-2 and Sentinel-3 images for daily crop evapotranspiration estimation is presented and applied in cotton fields in Thessaly Greece, which is considered as a water-limited Mediterranean agricultural area. The simulation program used is the Sen-ET SNAP software. Specifically, Sen - ET SNAP graphical user interface uses satellite images from Sentinel 2 and Sentinel 3 and meteorological data from the Weather Research and Forecast (WRF) model. The purpose of the Sen-ET SNAP plugin is to enable estimation of daily actual evapotranspiration (and other land-surface energy fluxes) at field scale. The applied approach has been first operated by the European Space Agency (ESA), using the Sen-ET plugin and the proposed methodology is an improvement of ESA’s method. The proposed methodology framework consists of seventeen (17) separate steps having as the outcome the actual daily evapotranspiration flows estimation at a 20x20 m spatial resolution. The proposed methodology is applied in cotton in Thessaly Greece for the 2021 and 2022 growing seasons. The results are very satisfactory and indicate the suitability of Sen-ET SNAP software to estimate daily actual evapotranspiration, as well as its spatial variability throughout the crop. The methodology can be applied for effective irrigation management in data-scarce rural regions.

The aim of the present study is a hydrological approach on Lithaios river (Central Greece) streamflow 2D modelling, using GIS and geostatistics in order to investigate flow velocity, discharge rate, stage, river bed variations and the hydraulic properties (water depth, flow area, wetted perimeter, hydraulic radius and depth, Manning’s coefficient of roughness, Froude Number, etc.). Also, the study aims to the compilation–validation of a rating curve (RC) from a series of stage h(t)–discharge Q(t) pairs measurements, in order to use them as tools to assist environmental and agricultural water resources management, support environmental flows estimation, monitoring and irrigation planning in local basin scale. The results and statistical analysis, showed that Froude number during the measurement period was Fr < 1 which means that streamflow of the River Lithaios, is classified as subcritical. The results of model’s validation using different statistical and geostatistical methods, model simulation, error statistics criteria, were converge to the same output that the data fitting the selected power model for the curve RC and the modelling and 2D GIS mapping of river discharge were very satisfactory since the stabilities of the developed relationships were robust. The resulted outputs are proposed to serve as hydrological assisting tools for environmental water resources and irrigation management at the study area. These assisting tools will help water authorities accurately and quickly estimate river’s water quantities and variation with a minimum cost and effort, and they could be used for irrigation management, environmental flow estimation, flood protection, groundwater recharge and other purposes.