A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation

In regions with lack of hydrological and hydraulic data, a spatial flood modeling and mapping is an opportunity for the urban authorities to predict the spatial distribution and the intensity of the flooding. It helps decision-makers to develop effective flood prevention and management plans. In this study, flood inventory data were prepared based on the historical and field surveys data by Sari municipality and regional water company of Mazandaran, Iran. The collected flood data accompanied with different variables (digital elevation model and slope have been considered as topographic variables, land use/land cover, precipitation, curve number, distance to river, distance to channel and depth to groundwater as environmental variables) were applied to novel hybridized model based on neural network and swarm intelligence-grey wolf algorithm (NN-SGW) to map flood-inundation. Several confusion matrix criteria were used for accuracy evaluation by cutoff-dependent and independent metrics (e.g., efficiency (E), positive predictive value (PPV), negative predictive value (NPV), area under the receiver operating characteristic curve (AUC)). The accuracy of the flood inundation map produced by the NN-SGW model was compared with that of maps produced by four state-of-the-art benchmark models: random forest (RF), logistic model tree (LMT), classification and regression trees (CART), and J48 decision tree (J48DT). The NN-SGW model outperformed all benchmark models in both training (E = 90.5%, PPV = 93.7%, NPV = 87.3%, AUC = 96.3%) and validation (E = 79.4%, PPV = 85.3%, NPV = 73.5%, AUC = 88.2%). As the NN-SGW model produced the most accurate flood-inundation map, it can be employed for robust flood contingency planning. Based on the obtained results from NN-SGW model, distance from channel, distance from river, and depth to groundwater were identified as the most important variables for spatial prediction of urban flood inundation. This work can serve as a basis for future studies seeking to predict flood susceptibility in urban areas using hybridized machine learning (ML) models and can also be applied in other urban areas where flood inundation presents a pressing challenge, and there are some problems regarding required model and availability of input data

Analysis of lake and river flow regime alteration to assess impacts of hydraulic structures

Abstract A key challenge in Integrated Water Resources Management (IWRM) is determination of environmental flow (EF). This is relevant in all water use scenarios and river regulation work. Water use and management alter water availability for ecosystems and the timing and distribution of runoff. Increased water consumption and allocation of water to different types of consumption impose pressures on aquatic ecosystems, affecting their status and ability to deliver important services, well–known examples being the Aral Sea in Asia and Lake Chad in Africa. This thesis presents new methods to determine the impacts of hydraulic structures on the flow regime of lakes and rivers. Methods to quantify different characteristics of flow in a non–dimensionless way are also presented. These tools allow more environment–based regulation of flow regimes. By using three main flow characteristics of river regime (magnitude, timing and intra–annual), three impact factors, MIF (magnitude impact factor), TIF (timing impact factor) and VIF (variation impact factor), were developed. Combining these impact factors produced a new river impact (RI) index to assess the impacts of hydraulic structure using monthly flow data. Based on RI variations, a classification was developed rating impacts along a scale from ‘Low’ to ‘Drastic’. The importance of climate patterns and river flow regime in controlling lake levels was examined. The lake simulation results were compared using a new index, Degree of Lake Wetness (DLW) and lake response time to changes in hydrology or climate was evaluated. Environmental flow allocation and optimisation of annual EF distribution are critical for ecosystem health. Flow release from reservoirs can be partly supplemented or compensated for by natural runoff from downstream (residual) catchment areas. In a new hydrological approach, optimal intra–annual flow regime for EF can be estimated while considering water inflow from the downstream residual sub-catchment. This thesis provides methods and indices to help quantify river and lake regimes, better understand the possible impacts of changes and manage these impacts optimally. This knowledge is crucial for decision making about EF regimes and achieving water release patterns from dams and hydropower that minimise the hydrological, morphological and biological impacts.Tiivistelmä Integroidun vesivarojen suunnittelun ja hallinnan (IWRM) yhtenä haasteena on ympäristövirtaaman määrittäminen valuma–alue-tasolla. Tämä on tärkeää arvioitaessa säännöstelyn ja vesirakentamisen ympäristövaikutuksia. Vedenkäyttö ja hallinta muuttavat veden saatavuutta jokiekosysteemissä ja virtaaman vuosittaista ajoittumista sekä jakautumista eri kuukausien välillä. Vesivarojen lisääntyvä käyttö eri tarkoituksiin voi vaikuttaa vesiekosysteemeihin ja niiden tuottamiin ekosysteemipalveluihin. Aral–järvi Aasiassa ja Chad–järvi Afrikassa ovat hyviä esimerkkejä veden liiallisesta käytöstä ja ympäristönäkökulman huomiotta jättämisestä. Väitöstyön keskeisin tavoite oli kehittää menetelmiä, joilla voidaan arvioida miten vesirakentaminen vaikuttaa jokien virtaamiin ja järvien vedenpintoihin. Jotta vesistövaikutuksia voidaan yleistää, menetelmät kehitettiin dimensiottomiksi. Nämä menetelmät luovat perustan ympäristöystävällisemmällä vesistöjen virtaamien säännöstelylle. Käyttäen kuukausittaista keskivirtaamaa ja kolmea tyypillisintä virtaamaluokkaa (suuruus, ajoittuminen ja vuodenaikainen vaihtelu), määritettiin uusi yhdistetty jokivaikutusindeksi (RI). Tämän indeksin avulla voitiin lopulta arvioida vesirakentamisen vaikutusta. Perustuen RI-indeksiin, usean joen vesirakentamisen vaikutuksia arvioitiin luokittelemalla vaikutukset vähäisiksi tai merkittäviksi. Työssä tarkasteltiin ilmaston vaihtelun ja jokien virtaamaolosuhteiden vaikutusta järvien vedenpintoihin. Järvisimuloinnin tuloksia verrattiin puolestaan käyttäen indeksiä, joka kuvaa järvessä tapahtuvia muutoksia suhteessa hydrologisiin ja ilmastollisiin olosuhteisiin. Väitöskirja käsittelee myös ympäristövirtaamien (EF) keskeisiä kysymyksiä: vedenkäytön jakautumista ja vuosittaisen virtaaman optimointia ympäristövirtaaman näkökulmasta. Työssä käytetään uutta hydrologista lähestymistapaa arvioimaan ympäristövirtaaman optimoitua kausivirtaamavaihtelua. Tässä lähestymistavassa vesivarastoaltaista lähtevää virtaamaa voidaan osittain täydentää tai kompensoida alapuoliselta valuma–alueelta tulevalla virtaamalla. Väitöstyön tulokset lisäävät ymmärrystä vesivarojen kestävästä käytöstä. Lisäksi työssä kehitetyillä menetelmillä voidaan määrittää ja optimoida jokien ja järvien virtaamaolosuhteita erilaisissa tilanteissa. Väitöstyö tarjoaa uusia käytäntöjä päätöksentekoon liittyen ympäristövirtaamaolosuhteisiin ja -jakaumiin vesivoima- ja vedenkäyttökysymyksissä ottaen huomioon hydrologiset, morfologiset ja biologiset rajoitteet

Satellite-based agricultural water consumption assessment in the ungauged and transboundary Helmand Basin between Iran and Afghanistan

Abstract Hamun Lake, the greatest (>8500 km²) freshwater in the Iran plateau, has almost entirely dried over the last 20 years. The flow of the Hirmand (or Helmand) River, the most important feeding river, has decreased from 4.0 to 1.9 km³ in the border of Iran-Afghanistan. In this river basin, the annual water consumption for irrigation is over 90% of the total consumed water. This study aims to calculate the increase in agricultural water consumption in the last two decades. Due to the lack of in-situ data across Afghanistan (including ∼80% of the studied area), this research utilizes remote-sensing. Using Google Earth Engine, land use maps for the years 2002, 2008, 2013, 2017, and 2021 were developed by a supervised classification scheme. Since 2002, it was found that the cropland area has increased from 2008 to 5475 km². Most cropland has been developed around the Kajaki dam. Based on the Penman-Monteith-Leuning Evapotranspiration version 2 (PML V2) actual evapotranspiration (AET) data (our model assumes the irrigation efficiency equal to 0.3), the annual consumed water has increased from 2 to over 6 km³ in the last two decades. The presented framework in this study can be recommended for other ungauged basins

A sensitivity analysis of lake water level response to changes in climate and river regimes

Abstract Lake water level regimes are influenced by climate, hydrology and land use. Intensive land use has led to a decline in lake levels in many regions, with direct impacts on lake hydrology, ecology and ecosystem services. This study examined the role of climate and river flow regime in controlling lake regimes using three different lakes with different hydraulic characteristics (volume-inflow ratio, CIR). The regime changes in the lakes were determined for five different river inflows and five different climate patterns (hot-arid, tropical, moderate, cold-arid, cold-wet), giving 75 different combinations of governing factors in lake hydrology. The input data were scaled to unify them for lake comparisons. By considering the historical lake volume fluctuations, the duration (number of months) of lake volume in different ‘wetness’ regimes from ‘dry’ to ‘wet’ was used to develop a new index for lake regime characterisation, ‘Degree of Lake Wetness’ (DLW). DLW is presented as two indices: DLW₁, providing a measure of lake filling percentage based on observed values and lake geometry, and DLW₂, providing an index for lake regimes based on historical fluctuation patterns. These indices were used to classify lake types based on their historical time series for variable climate and river inflow. The lake response time to changes in hydrology or climate was evaluated. Both DLW₁ and DLW₂ were sensitive to climate and hydrological changes. The results showed that lake level in high CIR systems depends on climate, whereas in systems with low CIR it depends more on river regime

Design of environmental flow regimes to maintain lakes and wetlands in regions with high seasonal irrigation demand

Abstract In arid regions, the construction of dams has led to an increase in irrigated agriculture, resulting in the desiccation of vulnerable lakes and wetlands. In many arid mountainous regions, such as in the Middle East, upstream dams typically feed rivers that flow into lowland terminal (closed) lakes or wetlands. The release of water for environmental purposes is a widely recognised option for reducing such impacts. The present study used monthly hydrological data from the Kor river in southern Iran, its main reservoirs and data above and below the Korbal irrigation system. The Kor river is a major source for feeding the Bakhtegan and Tashk lakes, which have recently started to disappear. An analysis of the water resource system before the dam construction (before 1973) showed that the monthly lake inflow depended on available water in river above the irrigation system (for eight months) and, during the irrigation season, water consumed for irrigation as well (for four months). However, in the post-development period (after 1997), the flow rate to the lake depended almost entirely on the Korbal irrigation system, except during some winter months when little irrigation was needed. Environment flow release has not been effective as it has led to greater water availability in the river, which results in more water being consumed for irrigation, as demonstrated here. To overcome this management mismatch, a new environmental flow release strategy (regime) was designed in which water is released from the upstream reservoirs during periods of low irrigation demand (e.g. winter months)

Energy analysis in Water-Energy-Food-Carbon Nexus

Abstract This study evaluated the comprehensive Water-Energy-Food-Carbon Nexus (WEFC) by focusing on energy assessment in northwest Iran. The energy evaluation indices for different products were calculated by estimating the total input and output energies. Multi-objective optimization based on five individual objectives and WEFC Nexus policies was used to identify the optimal land-use allocation of wheat, barley, rapeseed, and sugar beet, silage corn, and potato while minimizing water and energy consumption and CO₂ emissions, and maximizing food production and profit. The results indicate that the suggested framework provides a practical methodology for determining the optimal land-use allocation considering quantitative WEFC Nexus. To increase economic efficiency and reduce energy consumption, agricultural practices and policy recommendations should be adopted, including promoting renewable energy sources, implementing energy-saving technologies, improving fertilizer management, improving crop rotation practices, conservation tillage, and improving water management and adoption of sustainable farming practices. The results allow policymakers to optimize multiple resources and recommend the best resource allocation under recommendation policy, technology, and constraints to achieve sustainable development in agriculture

Three-decade assessment of dry and wet spells change across Iran, a fingerprint of climate change

Abstract Extended periods of hydro-climate extremes with excessive or scarce rainfall associated with high or low temperatures have resulted in an imbalanced water cycle and inefficient socio-economic systems in several regions of Iran. However, there is a lack of comprehensive investigations on short-term to long-term variations in timing, duration, and temperature of wet/dry spells. This study bridges the current gap through a comprehensive statistical analysis of historical climatic data (1959–2018). Results indicated that the negative tendency of the accumulated rainfall (− 0.16/ − 0.35 mm/year during the past 60/30 years) in 2- to 6-day wet spells had made significant contributions to the ongoing downward trend in annual rainfall (− 0.5/ − 1.5 mm/year during the past 60/30 years) owing to a warmer climate condition. Warmer wet spells are likely responsible for precipitation patterns changes in snow-dominated stations since their wet spells temperature has more than threefold growth with increasing distance to coasts. The most detected trends in climatic patterns have started in the last two decades and become more severe from 2009 to 2018. Our results confirm the alteration of precipitation features across Iran due to anthropogenic climatic change, and suggest expected increase in air temperature would likely result in further dry and warm conditions over the coming decades

Assessment of reservoir inflow prediction through constraining SWAT parameters to remotely sensed ET data in data scarce region of Chennai, India

Abstract Prediction of reservoir inflow is an important aspect of water supply management in an urbanized region. In this regard, this study aims to improve the reservoir inflow prediction using the calibration of the MODIS (Moderate resolution Imaging Spectroradiometer) evapotranspiration (ET) data in addition to the streamflow in the SWAT (Soil and Water Assessment Tool) model. The results of this study show that constraining SWAT parameters to the ET combined with streamflow helps to improve the simulation of ET. Thereby, it enhances the representation of vertical fluxes in regional hydrology. The reservoir inflow was calibrated with streamflow alone with Nash-Sutcliffe Efficiency (NSE) of 0.63, whereas the inclusion of ET provides an NSE of 0.59. However, the simulation of ET has improved by 10%. The results of this study demonstrate that the inclusion of ET data helps to improve the simulation of hydrologic processes in the region

A multi-criteria approach for improving streamflow prediction in a rapidly urbanizing data scarce catchment

Abstract This study advocates a multi-criteria approach to improve the streamflow predictions in a data-scarce catchment of Chennai metropolitan city of India using the Soil Water and Assessment Tool (SWAT). The remotely sensed evapotranspiration (ET) data, groundwater recharge estimation, and parameter regionalization were used to improve model prediction. Dynamic change of Land Use and Land Cover (LULC) was accounted for along with multi-parameter calibration for reducing the uncertainty in model parameters. The results revealed an improved streamflow prediction accuracy by 10%, especially in the prediction of medium and high flows with the Nash-Sutcliffe efficiency of 0.60. The enhanced parameters were regionalized to ungauged sub-basins and validated using a measured flow event downstream of regionalization with 15% prediction uncertainty. This semi-arid catchment is dominated by ET (58%) and runoff (27%) in the region’s hydrology. The finding of this study can be applied to improve the hydrological modelling and predictions in data-scarce regions