Climate Change Impact on Flood Frequency and Source Area in Northern Iran under CMIP5 Scenarios

Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin

Against the backdrop of accelerated global climate change and urbanization, the frequency and severity of flood disasters have been increasing. In recent years, influenced by climate change, the Hai-River Basin (HRB) has experienced multiple large-scale flood disasters. During the widespread extraordinary flood event from July 28th to August 1st, 2023, eight rivers witnessed their largest floods on record. These events caused significant damage and impact on economic and social development. The development of hydrological models with better performance can help researchers understand the impacts of climate change, provide risk information on different disaster events within watersheds, support decision-makers in formulating adaptive measures, urban planning, and improve flood defense mechanisms to address the ever-changing climate environment. This study examines the potential for enhancing streamflow simulation accuracy in the HRB located in Northeast China by combining the physically-based hydrological model with the data-driven model. Three hybrid models, SWAT-D-BiLSTM, SWAT-C-BiLSTM and SWAT-C-BiLSTM with SinoLC-1, were constructed in this study, in which SWAT was used as a transfer function to simulate the base flow and quick flow generation process based on weather data and spatial features, and BiLSTM was used to directly predict the streamflow according to the base flow and quick flow. In the SWAT-C-BiLSTM model, SWAT parameters with P values less than 0.4 in each hydrological station-controlled watershed were calibrated, while the SWAT-D-BiLSTM model did not undergo calibration. Additionally, this study utilizes both 30 m resolution land use and land cover (LULC) map and the first 1 m resolution LULC map SinoLC-1 as input data for the models to explore the impact on streamflow simulation performance. Among five models, the NSE of SWAT-C-BiLSTM with SinoLC-1 reached 0.93 and the R2 reached 0.95 during the calibration period, and both of them stayed at 0.92 even in the validation period, while the NSE and R2 of the other four models were all below 0.90 in the validation period. The potential impact of climate change on streamflow in the HRB was evaluated by using predicted data from five global climate models from CMIP6 as input for the best-performing SWAT-C-BiLSTM with SinoLC-1. The results indicate that climate change exacerbates the uneven distribution of streamflow in the HRB, particularly during the concentrated heavy rainfall months of July and August. It is projected that the monthly streamflow in these two months will increase by 34% and 49% respectively in the middle of this century. Furthermore, it is expected that the annual streamflow will increase by 5.6% to 9.1% during the mid-century and by 6.7% to 9.3% by the end of the century. Both average streamflow and peak streamflow are likely to significantly increase, raising concerns about more frequent urban flooding in the capital economic region within the HRB.

Spatiotemporal rainfall and temperature variability in Suha watershed, Upper Blue Nile Basin, Northwest Ethiopia

The aim of the study is to analyze the spatio-temporal variability and projection of rainfall and temperature (2021-2040) in Suha watershed, North West highlands of Ethiopia. The study used 4 km × 4 km reconstructed (blended) monthly rainfall and temperature data from National Meteorological Agency of Ethiopia (NMAE) and Royal Netherlands Meteorological Institute (KNMI) of Cen Trends v1 (0.25° × 0.25°), respectively. Mann-Kendall test, Sen's slope estimator, coefficient of variation, autoregressive integrated model , and the inverse distance weighted tool were used to analysis the rainfall and temperature variability for different timescale and agroecology. The coefficient of variation showed the existence of high seasonal variability than the year-to-year in the watershed. Kiremt (main rainy season) rainfall showed substantial inter-annual variability throughout the time of observation (1990-2020). Besides, the Mann-Kendall test and the Sen's slope estimator detected that except, Belg (little rainy season) rainfall, all the others including the annual exhibited a nonsignificant increasing trend at p < 0.05. Spatially, the upper part of the watershed is wetter than the lower portion and a large portion of it (40%) received 930-1024 mm of rainfall per year. The minimum temperature showed a decreasing trend (0.01 °C per year), while the maximum temperature exhibited the opposite result (0.02 °C per year). The seasonal variability in both cases showed mixed outcomes. Forty-two percent of the watershed experiences a minimum temperature of 10.6-12.0 °C, whereas 45% experiences a maximum temperature between 24.1 and 26.0 °C. Meanwhile, the projected result showed that there is a decreasing trend for mean annual rainfall and an increasing trend for mean annual temperature from 2021 to 2040. Hence, policymakers have to give priority to the designing and application of area-specific adaptive strategies to curb the impact of climate variability. Rainwater harvesting and small-scale irrigation practice could be possible options.

Making Room for Our Forthcoming Rivers

This paper provides a schematic, conceptual trip across a set of paradigms that can be adopted to design flood control actions and the associated river setting, including the space allocated to the river. By building on such paradigms, it eventually delineates an integrated approach to identify a socially desirable river setting, under a climate changing reality. The key point addressed is that when residual Risk and Operation, Management and Replacement costs are considered to their full extent, even a basic economic analysis may suggest alternative river settings that can be more attractive, particularly if accompanied by suitable economic-administrative management measures. Emphasis is put on the deep uncertainty characterizing the whole decision problem and on the need for a drastic change of paradigm. The approach proposed can greatly improve current Flood Risk Management Plans responding to the European Flood Directive (Directive 2007/60/EC). It can also help to develop constructive dialogues with stakeholders, while enhancing the understanding of the problem. Although mainly intended to address a conceptual level, it also aims at providing an applicable method.

Impact of Climate Change on the Hydrology of the Forested Watershed That Drains to Lake Erken in Sweden: An Analysis Using SWAT+ and CMIP6 Scenarios

Precipitation and temperature around the world are expected to be altered by climate change. This will cause regional alterations to the hydrological cycle. For proper water management, anticipating these changes is necessary. In this study, the basin of Lake Erken (Sweden) was simulated with the recently released software SWAT+ to study such alterations in a short (2026–2050), medium (2051–2075) and long (2076–2100) period, under two different climate change scenarios (SSP2-45 and SSP5-85). Seven global climate models from the latest projections of future climates that are available (CIMP 6) were compared and ensembled. A bias-correction of the models’ data was performed with five different methods to select the most appropriate one. Results showed that the temperature is expected to increase in the future from 2 to 4 °C, and precipitation from 6% to 20%, depending on the scenario. As a result, water discharge would also increase by about 18% in the best-case scenario and by 50% in the worst-case scenario, and the surface runoff would increase between 5% and 30%. The floods and torrential precipitations would also increase in the basin. This trend could lead to soil impoverishment and reduced water availability in the basin, which could damage the watershed’s forests. In addition, rising temperatures would result in a 65% reduction in the snow water equivalent at best and 92% at worst.

Urban Flood Management through Urban Land Use Optimization Using LID Techniques, City of Addis Ababa, Ethiopia

In recent years, many urban areas in Ethiopia have experienced frequent flood events as a result of climate change and urban sprawl. Unplanned and unsustainable poor urban storm water management strategies will aggravate the impact and frequency of flood occurrence. In this study, impacts of urbanization and climate change on generated flood magnitude are analyzed using the urban hydrological model of Storm Water Management Model (SWMM) and Low Impact Development (LID) sustainable land use optimization techniques. Three rainfall distribution patterns (TS1, TS2 and TS3) in combination with rainfall duration periods of 10, 30 and 60 min and a pessimistic climate change scenario of RCP 4.5 compared to RCP 8.5 are used for the analysis purpose for selected infiltration and storage LID techniques (Bio-Retention Cell, Infiltration Trench and Rain Barrel). The study results showed that combined LID techniques have a significant impact on urban flood reduction of up to 75%. This significant amount of flood reduction is greater than the amount of excess flood magnitude which occurred as a result of climate change using the most pessimistic climate change scenario. The study results also confirmed that rainfall patterns have a significant impact on peak discharge for shorter rainfall durations. This study highly recommends using cost effective, easy and environmental adaptive and sustainable LID techniques for urban flood management in addition to existing drainage structures.

An In-Depth Analysis of Physical Blue and Green Water Scarcity in Agriculture in Terms of Causes and Events and Perceived Amenability to Economic Interpretation

An analytical review of physical blue and green water scarcity in terms of agricultural use, and its amenability to economic interpretation, is presented, employing more than 600 references. The main definitions and classifications involved and information about reserves and resources are critically analyzed, blue and green water scarcity are examined along with their interchange, while their causal connection with climate in general is analyzed along with the particular instances of Europe, Africa, Asia and the WANA region. The role of teleconnections and evaporation/moisture import-export is examined as forms of action at a distance. The human intervention scarcity driver is examined extensively in terms of land use land cover change (LULCC), as well as population increase. The discussion deals with following critical problems: green and blue water availability, inadequate accessibility, blue water loss, unevenly distributed precipitation, climate uncertainty and country level over global level precedence. The conclusion singles out, among others, problems emerging from the inter-relationship of physical variables and the difficulty to translate them into economic instrumental variables, as well as the lack of imbedding uncertainty in the underlying physical theory due to the fact that country level measurements are not methodically assumed to be the basic building block of regional and global water scarcity.

Fuzzy clustering and distributed model for streamflow estimation in ungauged watersheds

This paper proposes a regionalization method for streamflow prediction in ungauged watersheds in the 7461 km² area above the Gharehsoo Hydrometry Station in the Ardabil Province, in the north of Iran. First, the Fuzzy c-means clustering method (FCM) was used to divide 46 gauged (19) and ungauged (27) watersheds into homogenous groups based on a variety of topographical and climatic factors. After identifying the homogenous watersheds, the Soil and Water Assessment Tool (SWAT) was calibrated and validated using data from the gauged watersheds in each group. The calibrated parameters were then tested in another gauged watershed that we considered as a pseudo ungauged watershed in each group. Values of R-Squared and Nash–Sutcliffe efficiency (NSE) were both ≥ 0.70 during the calibration and validation phases; and ≥ 0.80 and ≥ 0.74, respectively, during the testing in the pseudo ungauged watersheds. Based on these metrics, the validated regional models demonstrated a satisfactory result for predicting streamflow in the ungauged watersheds within each group. These models are important for managing stream quantity and quality in the intensive agriculture study area.

Hydrological Modelling for Water Resource Management in a Semi-Arid Mountainous Region Using the Soil and Water Assessment Tool: A Case Study in Northern Afghanistan

To address the issues of water shortages and the loss of agricultural products at harvest in northern Afghanistan, the Soil and Water Assessment Tool (SWAT) was applied for agricultural water resource management by simulating surface runoff in the Balkhab River basin (BRB) on a monthly basis from 2013 to 2018. Elevation, slope, land cover data, soil maps, and climate data such as temperature, precipitation, relative humidity, wind speed, and solar radiation were used as inputs in the SWAT modelling. During the dry season from July to September, the water resources downstream were basically attributed to baseflow from groundwater. In the calibration, the groundwater baseflow was estimated by analyzing station-recorded discharges for 190 springs. With the estimated baseflow, the SWAT results were markedly improved, with R2 values of 0.70, 0.86, 0.67, and 0.80, Nash-Sutcliff efficiency (NSE) values of 0.52, 0.83, 0.40, and 0.57, and percent bias (PBIAS) values of 23.4, −8.5, 23.4, and 17.5 in the four different subbasins. In the validation, the statistics also indicated satisfactory results. The output of this study can be used in agricultural water resource management with irrigation practices and further in the assessment of climate change effects on the water resources in the BRB.

Quantifying the cooling effect of rain events on outdoor thermal comfort in the southern coastal stations of the Caspian Sea

Pleasant outdoor thermal conditions depend on a wide range of climatic elements. The impact of rainfall events, as important climatic elements, on providing thermal comfort, has been less explored in the available literature. The work presented herein investigates the impact of Rainy Days as well as a Day Prior to (D_prior) and a Day Post rain (D_post) events on thermal conditions in the southern coastal region of the Caspian Sea. In this study, rainfall events during 1961-2017 observational period were categorized based on their intensity. Then, human thermal comfort during non-rainy (sunny) and rainy days was estimated and compared by using the radiation-driven Physiological Equivalent Temperature (PET) index, Universal Thermal Climate Index (UTCI) and Perceived Temperature (PT) index. Furthermore, difference between the average of thermal conditions in rainy days compared to a day prior and a day post rain events was calculated separately for comfort, cold and heat stress thresholds of each bioclimatic index. Finally, the correlation between the average of indices for rainy days and the frequency of rainfall events of each specific year was computed. Results suggested that overall average of studied indices for all rainy days is lower than the average for days prior and post the rain events. PET index has shown to be most impacted and reduced as a result of rain events and therefore more indicative of a cool ing effect. The observed difference in total average of PET in rainy days compared to non-rainy days were 8.30 °C, 5.86 °C and 8.85 °C for Babolsar, Rahst and Gorgan stations, respectively. Generally, the cooling effect of rain events on the temperature for a day prior rain events is higher than a day post rainfall. Finally, the trend analysis on rainy days in the studied period revealed that the average of bioclimatic indices in western stations (Babolsar and Rasht) are increasing whereas a decreasing trend was observed for Gorgan as more of an eastern station.

Impact of Indo-Pacific Climate Variability on High Streamflow Events in Mahanadi River Basin, India

The potential impact of climate variability on the hydrological regime in the Mahanadi river basin is of great importance for sustainable water resources management. The impact of climate variability on streamflow is analyzed in this study. The impact of climate variability modes on extreme events of Mahanadi basin during June, July, and August (JJA), and September, October, and November (SON) seasons were analyzed, with daily streamflow data of four gauge stations for 34 years from 1980 to 2013 found to be associated with the sea surface temperature variations over Indo-Pacific oceans and Indian monsoon. Extreme events are identified based on their persistent flow for six days or more, where selection of the stations was based on the fact that there was no artificially regulated streamflow in any of the stations. Adequate scientific analysis was done to link the streamflow variability with the climate variability and very significant correlation was found with Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO), El Nino Modoki Index (EMI), and Indian monsoon. Agriculture covers major portion of the basin; hence, the streamflow is very much essential for agriculture as well as population depending on it. Any disturbances in the general flow of the river has subjected an adverse impact on the inhabitants’ livelihood. While analyzing the correlation values, it was found that all stations displayed a significant positive correlation with Indian Monsoon. The respective correlation values were 0.53, 0.38, 0.44, and 0.38 for Andhiyarkore, Baronda, Rajim, and Kesinga during JJA season. Again in the case of stepwise regression analysis, Monsoon Index for the June, July, and August (MI-JJA) season (0.537 for Andhiyarkore) plays significant role in determining streamflow of Mahanadi basin during the JJA season and Monsoon Index for July, August, and September (MI-JAS) season (0.410 for Baronda) has a strong effect in affecting streamflow of Mahanadi during the SON season. Flood frequency analysis with Weibull’s plotting position method indicates future floods in the Mahanadi river basin in JJA season.

Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models

The present study is carried out in the context of the continuous increase, worldwide, of the number of flash-floods phenomena. Also, there is an evident increase of the size of the damages caused by these hazards. Bâsca Chiojdului River Basin is one of the most affected areas in Romania by flash-flood phenomena. Therefore, Flash-Flood Potential Index (FFPI) was defined and calculated across the Bâsca Chiojdului river basin by using one bivariate statistical method (Statistical Index) and its novel ensemble with the following machine learning models: Logistic Regression, Classification and Regression Trees, Multilayer Perceptron, Random Forest and Support Vector Machine and Decision Tree CART. In a first stage, the areas with torrentiality were digitized based on orthophotomaps and field observations. These regions, together with an equal number of non-torrential pixels, were further divided into training surfaces (70%) and validating surfaces (30%). The next step of the analysis consisted of the selection of flash-flood conditioning factors based on the multicollinearity investigation and predictive ability estimation through Information Gain method. Eight factors, from a total of ten flash-floods predictors, were selected in order to be included in the FFPI calculation process. By applying the models represented by Statistical Index and its ensemble with the machine learning algorithms, the weight of each conditioning factor and of each factor class/category in the FFPI equations was established. Once the weight values were derived, the FFPI values across the Bâsca Chiojdului river basin were calculated by overlaying the flash-flood predictors in GIS environment. According to the results obtained, the central part of Bâsca Chiojdului river basin has the highest susceptibility to flash-flood phenomena. Thus, around 30% of the study site has high and very high values of FFPI. The results validation was carried out by applying the Prediction Rate and Success Rate. The methods revealed the fact that the Multilayer Perceptron - Statistical Index (MLP-SI) ensemble has the highest efficiency among the 3 methods.

Landscape Management through Change Processes Monitoring in Iran

The presented research investigated and predicted landscape change processes (LCPs) in the Talar watershed, northern Iran. The Land Change Modeler was used for change analysis, transition potential modeling, and prediction of land use/land cover (LULC) map. The evaluation of projected LULC map was performed by comparing the real and predicted LULC maps for the reference year, 2014. Landscape metrics and change processes were investigated for the period 1989–2014 and for exploring the situation in 2030. Results illustrated that the increase in agricultural land and residential areas took place at the expense of forest and rangeland. The distance from forests was the most sensitive parameter for modeling the transition potentials. The modelling of the LULC change projected the number of patches, the landscape shape index, interspersion and juxtaposition index, and edge density, Euclidean nearest-neighbor distance, and area-weighted shape index will amount to 65.3, 7.63, 20.1, 8.77, −1.35, and 0.61% as compared to 2014, respectively. Our findings indicated that the type of change processes that occurred was not entirely the same in 1989–2000 and 2000–2014. In addition, change processes in the creation of dry farming, orchard, and residential classes, attrition of forest and rangeland categories, and dissection in irrigated farming are projected. The dynamics of landscape metrics and change processes combined in one analytical framework can facilitate understanding and detection of the relationship between ecological processes and landscape pattern. The finding of current research will provide a roadmap for improved LULC management and planning in the Talar watershed, southern coast of the Caspian Sea.

Social Acceptability of Flood Management Strategies under Climate Change Using Contingent Valuation Method (CVM)

Floods are natural hazards with serious impact on many aspects of human life. The Intergovernmental Panel on Climate Change (IPCC) reported that climate change already has significant impact on magnitude and frequency of flood events worldwide. Thus, it is suggested to adopt strategies to manage damage impacts of climate change. For this, involving the local community in the decision-making process, as well as experts and decision-makers, is essential. We focused on assessing the social acceptability of flood management strategies under climate change through a socio-hydrological approach using the Contingent Valuation Method (CVM). For this purpose as well, hydro-climate modelling and the Analytical Network Process (ANP) were used. Among twelve investigated flood management strategies, “river restoration”, “agricultural management and planning”, and “watershed management” were the publicly most accepted strategies. Assessment of the social acceptability of these three strategies was carried out by use of the CVM and Willingness to Pay (WTP) methodology. Generally, 50%, 38%, and 18% were willing to pay and 44%, 48%, and 52% were willing to contribute flood management strategy in zones 1, 2, and 3, respectively. Overall, peoples’ WTP for flood management strategies decreased with increasing distance from the river. Among different investigated dependent variables, household income had the highest influence on WTP.