Magnitude Of Rainfall Research Articles

The Effect of Monsoon Rainfall Patterns on Epilithic Diatom Communities in the Hantangang River, Korea

Most of Korea’s rivers and lakes are subject to physico-chemical disturbances, such as increased water quantity and flow rates, and influx of nitrogen and phosphorus, due to intense rainfall concentrated in the Asian monsoon season. To examine the influence of rainfall on epilithic diatom communities, we measured the diatom distribution and river water quality at 29 sites along the main-stream and tributaries of the Hantangang River, Korea, in the period of 2012–2015. Water quality parameters in the polluted sites had improved following rainfall, but the response of dominant species varied with water quality; the dominant species Nitzschia fonticola decreased in abundance regardless of sampling sites, and the abundance of Achnanthidium minutissimum in the clean sites and Nitzschia palea in the polluted sites increased after rainfall, respectively. The community dynamic index (CDI) showed that the most obvious shift of epilithic diatom community occurred in the mid-polluted sites in 2013 with the highest rainfall. This suggest that the effect of rainfalls on the epilithic diatom community is dependent on various parameters, such as the magnitude of rainfall, water quality and its biotic compositions of diatom communities, but it also indicates that improving the water quality of rivers is important to promote the resilience of diatom communities to extremes of precipitation. Further investigation is needed to generalize the effects of monsoon rainfall on the epilithic diatom communities, considering rivers with different environmental characteristics.

Early Holocene Indian summer monsoon and its impact on vegetation in the Central Himalaya: Insight from δD and δ13C values of leaf wax lipid

Increase in rainfall during the early Holocene has been reported from different regions of Indian subcontinent, although the magnitude shows spatial variability with lower change in higher rainfall regions and vice versa. While the early-Holocene rainfall records are available from lowland areas of the Indian sub-continent, the record is poorly documented from the high-altitude Central Himalayan region where orography plays an important role in the spatial variability in the rainfall pattern. For the first time, δDC29, δ13CC29 values of alkane and δ13C values of organic matter from a relict lake of Benital area in the Central Himalaya have been used to reconstruct monsoonal rainfall and contemporary vegetation for last 10.5 ka with an emphasis on the early Holocene. The δDC29 values suggest that the early Holocene was characterized by a wet phase at ca. 9 ka with 25% higher rainfall compared to present while the middle-late-Holocene was relatively arid. The estimated increase in monsoonal rainfall for the early Holocene is the highest compared to the previous report from elsewhere. The magnitude of early-Holocene rainfall estimated in this study suggests that in addition to erosion of the bed-rocks at the head-water region, increased stream power reworked the older floodplain and resulted in 2.3 times higher sediment discharged into the Ganges-Brahmaputra (GB) delta compared to the present discharge. The δ13CC29 values of the lake sediments suggest a transition from woody to non-woody plant assemblages occurred at 7 ka. The poor correlation between δDC29 and δ13CC29 values suggests at higher rainfall, the δ13CC29 values of catchment vegetation were less responsive.

Extreme rainfall events in Amazonia: The Madeira river basin

Monitoring and understanding the behavior of precipitation is a major challenge of the hydrological cycle and a key issue with respect to the corresponding impact and assessment of risk management in watersheds, notably in the Amazon region. This work investigates the frequency of extreme rainfall events in the largest Amazon sub-basin, the Madeira river basin, using information from the CHIRPS product dataset (Climate Hazards Group Infrared Precipitation with Station) acquired at a spatial degree resolution of 0.05° in a daily basis. Images from 1981 to 2017 were used, spanning a historical time series period of 36 years. Subsequently, these data were analyzed by applying the Gumbel distribution, with trend estimates determined by the Mann-Kendal test at the 95% confidence level. The historical time series revealed that the average maximum rainfall of 1 day, examined throughout the basin, showed variations ranging from approximately 30 to 300 mm. day−1. It should be noted that there is a substantial influence of the El Niño/La Niña climatic phenomenon in the studied region. In addition, the results of the trend assessment showed a reduction in the magnitude of extreme rainfall in regions without vegetation cover in the study area accordingly to the scale of observation of the water balance.

RAINFALL VARIABILITY OVER SOUTHERN BIHAR, INDIA

In the present study Mann-Kendall (MK)/Modified Mann-Kendall (MMK) test, and Sen’s slope test were used to determine the trend over the Southern part of Bihar at seasonal and annual time scales for 11 stations using 110 years data (1901-2010). In MMK test all stations in annual and monsoon season showed a significant decrease in rainfall trend at the 10% significance level. The maximum decrease in the magnitude of rainfall observed for the annual season was - 2.861 mm/year followed by monsoon rainfall - 2.792 mm/year, and for winter rainfall - 0.085mm/year respectively. On the other hand, the maximum increase in rainfall observed for Post monsoon season was 0.225 mm/year and for summer season it was 0.138mm/year during the study period. Spatial trend depicted the Nalanda and Jamui has the greatest reduction of rainfall from the last 110 years. This study will be very helpful for policymakers, agriculturists, and framers to take a decision on water management, crop scheduling and patterns over this region.

Construction of land surface dynamic feedback for digital soil mapping considering the spatial heterogeneity of rainfall magnitude

The land surface dynamic feedback (LSDF) information captured by time-series remote sensing data during the soil-drying process after a rainfall event provides effective covariates for digital soil mapping over low-relief areas. However, current methods used to capture LSDF require a uniform rainfall magnitude in the geographic space; a condition that is not often met for large areas. Here, we propose a LSDF construction method considering the spatial heterogeneity of rainfall magnitudes by adjusting the evaporation variables in the LSDF. For this, the relationships between evaporation and rainfall magnitudes were first established. The LSDFs from various locations for rainfall events with different magnitudes were then adjusted based on these relationships. Using a case study, the adjusted LSDFs after two rainfall events were then used to predict soil texture over a low-relief area. The results showed that the cubic polynomial model performed best when constructing the relationship between evaporation adjustment and rainfall magnitude, giving the highest R2 value and a low Akaike information criterion. Adjustment to the LSDF decreases with increasing rainfall and the rate of change in the adjustment also decreases with increasing rainfall. For both rainfall events, prediction accuracies with the adjusted LSDFs were higher than those based on the original LSDFs. Furthermore, the greater the adjustment, the greater the improvement in the accuracy. We conclude that the proposed construction method for LSDF, accounting for the spatial heterogeneity of rainfall magnitudes, offers improved predictive power for digital soil mapping over large areas.

Spatio-Temporal Variability of Rainfall Indices and their Teleconnections with El Niño-Southern Oscillation for Tapi Basin, India

The long-term spatio-temporal variability in twelve rainfall indices across Tapi basin are investigated for period 1944–2013. The temporal trends in aforesaid indices, representing magnitude, intensity, frequency and duration of rainfall, are analysed using non-parametric tests. The total annual rainfall exhibit uniformly increasing trends over Lower Tapi basin (LTB), while heterogeneous trends are observed across Upper (UTB) and Middle (MTB) Tapi basins. The rainy days portrays decreasing trends at 75% stations in the basin, while rainfall intensity displays increasing trends across LTB and UTB. On other hand, LTB and Purna sub-catchment (in UTB) show coherent increasing trends in extreme rainfall, while decreasing trends are observed over Burhanpur sub-catchment (in UTB) and MTB. A considerable increase in duration of dry spells is reported at 92.5% stations in the basin, indicating a marked shift towards drier climatic regime. Further, Principal Component Analysis (PCA) is carried out to address the space-time variability of rainfall indices. The results of PCA were adopted in cluster analysis to identify the stations exhibiting similar characteristic variations in rainfall trends. The hydroclimatic teleconnections between rainfall indices and large-scale oceanic-atmospheric circulations of the tropical Pacific Ocean (viz., El Nino-Southern Oscillation) revealed the prevalence of wet conditions in the basin during moderate to strong La-Nina, while dry conditions were encountered during moderate to strong El-Nino and weak La-Nina events. The spatial estimates of rainfall indices are derived corresponding to 2-, 10- and 50-year return periods which would be helpful in hydrologic design and risk assessment of rainfall-induced disasters in near future.

An Intuitive Metric to Quantify and Communicate Tropical Cyclone Rainfall Hazard

AbstractRecent tropical cyclones (TCs) have highlighted the hazards that TC rainfall poses to human life and property. These hazards are not adequately conveyed by the commonly used Saffir–Simpson scale. Additionally, while recurrence intervals (or, their inverse, annual exceedance probabilities) are sometimes used in the popular media to convey the magnitude and likelihood of extreme rainfall and floods, these concepts are often misunderstood by the public and have important statistical limitations. We introduce an alternative metric—the extreme rain multiplier (ERM), which expresses TC rainfall as a multiple of the climatologically derived 2-yr rainfall value. ERM allows individuals to connect (“anchor,” in cognitive psychology terms) the magnitude of a TC rainfall event to the magnitude of rain events that are more typically experienced in their area. A retrospective analysis of ERM values for TCs from 1948 to 2017 demonstrates the utility of the metric as a hazard quantification and communication tool. Hurricane Harvey (2017) had the highest ERM value during this period, underlining the storm’s extreme nature. ERM correctly identifies damaging historical TC rainfall events that would have been classified as “weak” using wind-based metrics. The analysis also reveals that the distribution of ERM maxima is similar throughout the eastern and southern United States, allowing for both the accurate identification of locally extreme rainfall events and the development of regional-scale (rather than local-scale) recurrence interval estimates for extreme TC rainfall. Last, an analysis of precipitation forecast data for Hurricane Florence (2018) demonstrates ERM’s ability to characterize Florence’s extreme rainfall hazard in the days preceding landfall.

Evaluating functional connectivity in a small agricultural catchment under contrasting flood events by using UAV

ABSTRACTConcentrated erosion, a major feature of land degradation, represents a serious problem for soil and water resources management and a threat to ecosystems. Understanding the internal mechanisms (de‐)coupling sediment pathways can improve the management and resilience of catchments. In this study, concentrated erosion and deposition forms were mapped accurately through field and aerial unmanned aerial vehicle (UAV) campaigns, in order to assess the evolution of connectivity pathways over a series of three contrasted and consecutive flood events occurring between October 2016 and January 2017 (return period ranging from 0.5 to 25 years) in a small Mediterranean agricultural catchment (Can Revull, Mallorca, Spain; 1.4 km2). In addition, a morphometric index of connectivity (IC) was used to identify the potential trajectories of different concentrated erosion forms and deposition areas. IC predictions were calibrated by identifying the optimal critical thresholds, i.e. those most consistent with field observations after each of the events studied. The results found that the index performed well in predicting the occurrence and the length/area of the different type of landforms, giving kappa (κ) coefficients of variation ranging between 0.21 and 0.92 and linear correlations R2 between 0.33 and 0.72. The type of landform affected the correspondence of IC predictions and field observations, with lower thresholds the greater the magnitude of their associated geomorphic processes. Rainfall magnitude proved to be a very important factor controlling the development of erosion and deposition landforms, with large differences in length/area between the contrasted events. The evolution of the observed trajectories revealed feedback dynamics between the structural and functional connectivity of the catchment, in which morphological changes determined the spatial distribution of the processes’ activity in the successive events and vice versa. © 2020 John Wiley & Sons, Ltd.

Spatio-temporal trend analysis of rainfall using parametric and non-parametric tests: case study in Uttarakhand, India

This study investigates the spatial and temporal patterns of trends and magnitude of rainfall on monthly, seasonal and annual time scales of 13 districts of Uttarakhand State located in Central Himalayan region of India. The temporal trend was analyzed using Mann-Kendall (MK), Modified Mann-Kendall (MMK), and Kendall Rank Correlation (KRC) tests at 10%, 5%, and 1% significance levels. The magnitude (slope) of rainfall trend (mm/year) was determined using Theil-Sen’s Slope (TSS) and Simple Linear Regression (SLR) tests. The autocorrelation coefficient (ACC) of three different time series was calculated at one-time lag and were tested at 10%, 5%, and 1% levels of significance for the application of MMK test. The results of analysis revealed significant positive and negative trends were observed in monthly, seasonal, and annual rainfall time series in all 13 districts of Uttarakhand state. The spatial variation of the trends based on monthly, seasonal, and annual rainfall time series data was interpolated using the Thiessen polygon (TP) method in ArcGIS 10.2 environment. The maps of spatial variability of rainfall trends were developed to help local stakeholders and water resource managers to understand the risk and vulnerability related to climate change in the region.

Altered water uptake patterns of Populus deltoides in mixed riparian forest stands

Plant water uptake plays an important role in regulating ecosystem water balance and its productivity. However, previous studies regarding plant water uptake were primarily conducted in wet areas under seasonal drought conditions, with a limited understanding of the proportion and drivers of plant water uptake under humid conditions. Actually, climate change and variations in global precipitation patterns could simultaneously trigger seasonal drought and flooding. Therefore, it is critical to explore patterns and mechanisms for plant water uptake under humid conditions in wet regions. Here, we employed dual stable isotopes of hydrogen and oxygen coupled with a Bayesian mixing model (MixSIAR) to explore the water uptake patterns of Populus deltoides in two types of riparian forests (pure vs. mixed stand of P. deltoides), under different magnitudes of rainfall (7.9, 15.4 and 34.1 mm), in the Middle-Lower Reaches of the Yangtze River in China. We further used both partial correlation and variation partitioning analyses to determine the relative importance of soil variables and plant properties affecting the proportion of P. deltoides water uptake from different soil layers. Our results revealed that compared to pure stands, P. deltoides in mixed stands had a lower water uptake proportion from deep soil layers (60–80, 80–100 cm) and had higher water uptake from shallow soil layers (0–20, 20–40 cm) under 15.4 mm and 34.1 mm rainfall events. Our results also revealed that plant properties such as leaf biomass, fine root biomass, and diameter at breast height were the primary factors influencing water uptake by P. deltoides. This suggests that P. deltoides in mixed stands could increase the proportion of water uptake from shallow soil layers through altering plant attributes. These findings indicate that mixed stands could restrain frequent extreme rainfall events and subsequent flooding, suggesting more resilience towards future climatic variability.

Crop Planning of the Warangal District of Telangana State using Probability Distribution of 40 Years Rainfall Data

Rain is a major component of the water cycle that deposits most of the fresh water on the earth. The determination of the frequency of occurrence of extreme hydrological events is a prerequisite for planning and execution of many water resource projects. A comprehensive statistical analysis on annual, monthly and seasonal rainfall for Warangal District, Telangana was performed using rainfall data for 40 years (1962-2001). The current investigation was conducted with the ultimate aim of determining the type of Probability distribution that best fits the rainfall data of that particular area. The plotting position and probabilistic methods of probability distribution functions were used for analysis of rainfall data. Rainfall magnitude were evaluated for different return periods. As well as the rainfall pattern of that area has been studied with help of standard deviation and co-efficient of variation. The difference in results obtained from the methods of plotting position were found to be insignificant. Chi-square test was used to measure the Goodness of fit for the seasonal and monthly rainfall. Gumbel’s (Extreme value type-I) method and Normal method was found to be the best method of distribution for the rainfall data of this region. A detailed study was conducted on the crop planning of this region. Rainfall amount is decreasing gradually due to urbanization, global climatic change and hence a decrease in crop productivity. Despite the growth in percentage of gross-irrigated area over Rain-fed farming, Farmers are still rainfall dependent. Crop planning is done with the average effective rainfall of Warangal. Economic analysis is carried out for the crops cultivated in this region and farmers get 23% increase in their yield according to the rates available in Warangal market.

Regional scale trend and variability of rainfall pattern over agro-climatic zones in the mid-Mahanadi river basin of eastern India

Investigation of trends and variability in rainfall patterns is of paramount importance for the effective planning and management of rainfed cropping systems. In this study, a detailed analysis is carried out to understand the changes in rainfall patterns and characteristics at a regional scale on agro-climatic zone basis, in the mid-Mahanadi river basin. Daily rainfall data of India Meteorological Department (IMD) at 0.25° × 0.25° spatial resolution has been analyzed for the period of 1979–2013 on a monthly, seasonal, and annual timescale to investigate variability, trends, and seasonality of rainfall. Results of analysis reveal that there is large spatio-temporal variation in the river basin with a shift in seasonality of rainfall, with increased prolonged dry spells, and significant increase in extreme weather events over the agro-climatic zones (AZ). Most of the agro-climatic zones are found to have significant decreasing trends in the magnitude of rainfall during the non-monsoon season. In particular, agro-climatic zones nearer to the coastal region are more vulnerable to the high rainfall, whereas the North-Eastern-Ghat (AZ67) and Western-Central-Table (AZ68) regions are susceptible to the moisture stress. Further, analysis of groundwater level fluctuation shows a significant decline in groundwater level during the monsoon as well as non-monsoon seasons. The resulted effect of rainfall variability can be seen in terms of an increase in the prolonged dry spells as well as decline in the groundwater level. Moreover, the results of this study indicate a climate change signal at the regional scale that will help the policymakers and water managers to identify the critical agro-climatic zones in the mid-Mahanadi river basin for the formulation of efficient water resources management and planning strategies, particularly for the major crops of the rainfed agriculture regions.

Application of Machine Learning to Model Wetland Inundation Patterns Across a Large Semiarid Floodplain

AbstractInundation is a primary driver of floodplain ecology. Understanding temporal and spatial variability of inundation patterns is critical for optimum resource management, particularly in striking an appropriate balance between environmental water application and extractive use. Nevertheless, quantifying inundation at the fine resolution required of ecological modeling is an immense challenge in these environments. In this study, Random Forest, a machine learning technique, was implemented to predict the inundation pattern in a section of the Darling River Floodplain, Australia, at a spatial scale of 30 m and daily temporal resolution. The model achieved very good performance with an average accuracy of 0.915 based on the area under the receiver operating characteristic curve over 10 runs of the model in testing data sets. Six variables explained 70% of the total contribution to inundation occurrence, with the most influential being landscape shape (local deviation from global mean elevation), elevation‐weighted distance to the river, the magnitude of river flow (10‐ and 30‐day accumulated river discharge), local rainfall, and soil moisture. This approach is applicable to other floodplains across the world where understanding of fine‐scale inundation pattern is for operational ecological management and scenario testing.

Impact of Climate Variability on Crop Yield in Kalahandi, Bolangir, and Koraput Districts of Odisha, India

Most tropical regions in the world are vulnerable to climate variability, given their dependence on rain-fed agricultural production and limited adaptive capacity owing to socio-economic conditions. The Kalahandi, Bolangir, and Koraput districts of the south-western part of Odisha province of India experience an extreme sub-humid tropical climate. Based on the observed changes in the magnitude and distribution of rainfall and temperature, this study evaluates the potential impact of climate variation on agricultural yield and production in these districts. The study is conducted by taking into account meteorological data like rainfall and temperature from 1980 to 2017 and crop productivity data from 1980–81 to 2016–17. Additionally, climate variability indices like Monsoon Index, Oceanic Nino Index, and NINO-3 and NINO 3.4 are used. To analyse the data, various statistical techniques like correlation and multiple linear regression are used. The amount of monsoon rainfall is found to have a significant impact on crop productivity, compared to temperature, in the study area, and as a result the Monsoon Index has a determining impact on crop yield among various indices.

Identifying the origins of extreme rainfall using storm track classification

Abstract Identifying patterns in data relating to extreme rainfall is important for classifying and estimating rainfall and flood frequency distributions routinely used in civil engineering design and flood management. This study demonstrates the novel use of several self-organising map (SOM) models to extract the key moisture pathways for extreme rainfall events applied to example data in northern Spain. These models are trained using various subsets of a backwards trajectory data set generated for extreme rainfall events between 1967 and 2016. The results of our analysis show 69.2% of summer rainfall extremes rely on recirculatory moisture pathways concentrated on the Iberian Peninsula, whereas 57% of winter extremes rely on deep-Atlantic pathways to bring moisture from the ocean. These moisture pathways have also shown differences in rainfall magnitude, such as in the summer where peninsular pathways are 8% more likely to deliver the higher magnitude extremes than their Atlantic counterparts.

Evaluation of meteorological drought using the Standardized Precipitation Index (SPI) in the High Ziz River basin, Morocco

Abstract One of the adverse impacts of climate change is drought, and the complex nature of droughts makes them one of the most important climate hazards. Drought indices are generally used as a tool for monitoring changes in meteorological, hydrological, agricultural and economic conditions. In this study, we focused on meteorological drought events in the High Ziz river Basin, central High Atlas, Morocco. The application of drought index analysis is useful for drought assessment and to consider methods of adaptation and mitigation to deal with climate change. In order to analyze drought in the study area, we used two different approaches for addressing the change in climate and particularly in precipitation, i) to assess the climate variability and change over the year, and ii) to assess the change within the year timescale (monthly, seasonally and annually) from 1971 to 2017. In first approach, precipitation data were used in a long time scale e.g. annual and more than one-year period. For this purpose, the Standardized Precipitation Index (SPI) was considered to quantify the rainfall deficit for multiple timescales. For the second approach, trend analysis (using the Mann-Kendall (M-K) test) was applied to precipitation in different time scales within the year. The results showed that the study area has no significant trend in annual rainfall, but in terms of seasonal rainfall, the magnitude of rainfall during summer revealed a positive significant trend in three stations. A significant negative and positive trend in monthly rainfall was observed only in April and August, respectively.

Numerical Simulations of Typhoon Hagupit (2008) Using WRF

Abstract We used a mesoscale atmospheric model to simulate Typhoon Hagupit (2008) in the South China Sea (SCS). First, we chose optimized parameterization schemes based on a series of sensitivity tests. The results suggested that a combination of the Kain–Fritsch cumulus scheme and the Goddard microphysics scheme was the best choice for reproducing both the track and intensity of Typhoon Hagupit. Next, the simulated rainfall was compared with microwave remote sensing products. This comparison validated the model results for both the magnitude of rainfall and the location of heavy rain relative to the typhoon’s center. Furthermore, the potential vorticity and vertical wind speed displayed the asymmetric horizontal and tilted vertical structures of Typhoon Hagupit. Finally, we compared the simulation of air–sea turbulent fluxes with estimations from an in situ buoy. The time series of momentum fluxes were roughly consistent, while the model still overestimated heat fluxes, especially right before the typhoon’s arrival at the buoy.

Temporal dynamics of urban heat island correlated with the socio-economic development over the past half-century in Seoul, Korea

Urban heat island (UHI), an iconic consequence of anthropogenic activities and climate condition, affects air pollution, energy use, and health. Therefore, better understanding of the temporal dynamics of UHI is required for sustainable urban planning to mitigate air pollution under a changing climate. Here, we present the evolution of UHI intensity (UHIi) and its controlling factors in the Seoul metropolitan area, Korea, over the last 56 years (1962–2017), which has experienced unique compressed economic growth and urban transformation under monsoon climate. The analysis demonstrated an inverted U-shape long-term variation of UHIi with the progress of urban transformation and economic climate which has not been reported in Asian cities before. Meanwhile, short-term variations in UHIi are related to both diurnal temperature range and duration after rainfall event unlike previous studies, and the UHIi was exacerbated by heat waves. Our findings suggest that the UHIi will exhibit different temporal dynamics with future changes in the monsoon climate, and heat waves in the urban area will be reinforced if current rapid urbanization continues without a shift toward sustainable and equitable development. Asian cities that are likely to face the similar urbanization trajectory and the implications are that urban (re)development strategy considers changes in rainfall magnitude and timing due to monsoon system variation under changing climate and plans to mitigate synergy between heat wave and UHI in this area.

The Effect of Urban Expansion in Metro Manila on the Southwest Monsoon Rainfall

Model simulations of historical climate changes often do not take into account the climatic effects of changes in land use. Modifications in land cover such as urbanization, affect the partitioning of the water and energy balances, which in turn influences temperature and rainfall magnitudes and patterns. The local climate impacts of major urban expansion in metropolitan Manila (Metro Manila) in the Philippines is examined, particularly during the peak of the Southwest monsoon (SWM) rainfall season from July to September. MM5 is used to perform an ensemble simulation of experiments consisting of three-month runs for ten different years using two land cover scenarios representative of the urban cover of Metro Manila in 1972 and its expanded urban area in 2001. Results show that the SWM rainfall may be enhanced by up to 20% due to extensive urban growth. Comparison of model results with observation data indicates that without urban expansion, rainfall increases only by about 17%. When expanded urban impacts are included, the modeled increase in rainfall is about 29%, which is much closer to the observed value. This study therefore illustrates the significance of incorporating land use changes together with CO2 changes in climatic change modeling especially at the scale of local impacts.

The Challenges of Water Management and Governance in Cities

Combined impacts of sea-level rise, river flooding, increased frequency and magnitude of extreme rainfall, heatwaves, water scarcity, water pollution, ageing or lacking infrastructures for water, wastewater and solid waste in rapidly urbanising regions in the world call for improved water management and governance capacity in cities to accelerate the transition to water-wise cities. The sixteen contributions to this Special Issue create further awareness and present solutions on integrated approaches, advanced water management practices and water governance strategies. It is concluded that cities require a long-term strategy and a multilevel water governance approach. Research has shown how important it is to involve the civil society and private parties early on in this process to create success. Collaboration among cities and regions by sharing best practices for rapid implementation are crucial to cope with nearly all Sustainable Development Goals.