Historical Rainfall Research Articles

Climate Change Impacts on Rainwater Tank’s Potential Water Savings, Efficiency and Reliability Presenting Relationship Between ‘Seasonality Index’ and Water Savings Efficiency

The paper presents potential impacts of climate change on rainwater tanks outcomes such as water savings, reliability and water savings efficiency under two projected climate change scenarios; RCP4.5 and RCP8.5 utilising a case study using rainfall data from four stations within the city of Brisbane, Australia. Historical rainfall data were collected from the Australian Bureau of Meteorology website for the selected stations. Projected daily rainfall data were collected from Australian government data portal for the same stations within Brisbane. Using an earlier developed daily water balance model, eTank potential annual water savings were calculated for several weather and climate change scenarios with varied roof areas and rainwater demandValues of ‘Seasonality Index (SI)’, a commonly used factor representing rainfall variability within a year, were explored to validate earlier developed relationships between SI and water savings efficiency through rainwater tanks. It is found that in most cases water savings in future periods are expected to decrease and such decrease is not necessarily attributed to the expected decrease in rainfall amounts in future, rather also affected by future reductions in rainwater tank reliability. Linear relationships between potential future water savings and reliabilities for all the stations are found. Relationship between SI and water savings efficiency was established, and it is found that the relationship slightly varies with an earlier developed relationship using historical data. Compared to earlier established relationship, for lower SI values water savings efficiency is expected to become better, while for higher SI values the water savings efficiency is expected to become worse. Also, an increase of roof area from 100 m2 to 200 m2 will cause an average increase of water savings efficiency by 25% and an increase of rainwater usage from 200 L/day to 300 L/day will cause an average increase of water savings efficiency by 20%.

Assessment of the applicability of large‐scale climate ensemble simulations for analysing future extreme rainfall

AbstractExtreme rainfall events are a major cause of natural disasters and have been projected to increase in frequency and intensity under future climate change scenarios. Previous studies have used outputs of climate model simulations to evaluate changes in future extreme rainfall, but results have been uncertain due to the use of only a limited number of climate simulations. To address this issue, this study aims to evaluate the applicability of the d4PDF database (Database for Policy Decision‐Making for Future Climate Change), produced through large‐scale climate ensemble simulations, for analysing changes in extreme rainfall values between historical and future climatic conditions. A large‐scale sample size of the d4PDF allows the investigation of high‐magnitude rainfalls with long return periods. To utilize the d4PDF as a large‐scale sample for estimating extreme rainfall, the homogeneity between ensembles was assessed using the t test and Mann–Whitney U test, and the historical extreme rainfall with the d4PDF dataset was validated through comparison with observed data and using the Kolmogorov–Smirnov test. It was confirmed that the d4PDF ensembles could be used as a large‐scale sample, and the historical rainfall series of the d4PDF showed a similar distribution pattern to the observed rainfall series. Subsequent investigation of extreme rainfall for future ensembles during 2051–2110 indicated an increase by 20%–30% compared to historical climatic conditions in South Korea due to climate change. The results demonstrate the applicability of large‐scale climate ensemble simulations, such as d4PDF, for analysing changes in extreme rainfall and suggest that such information can be used to counter future extreme rainfall‐related disasters. Therefore, this study contributes to the knowledge gap in this area and highlights the need for further assessment and utilization of large‐scale climate ensemble simulations for future climate change research.

Rainfall analysis over 31 years of Chintapalle, Visakhapatnam, High Altitude and Tribal zone, Andhra Pradesh, India

Climate change and variability, particularly which of the annual rainfall, has received a great deal of interest to researchers worldwide. The extent of the variability of rainfall varies according to locations. Consequently, investigating the dynamics of rainfall variable in the perspective of changing climate is important to evaluate the impact of climate change and adapt potential mitigation strategies. To gain insight, trend analysis has been employed to inspect and quantify the rainfall distribution in the Chintapalli, Visakhapatnam district of Andhra Pradesh, India. Thirty-one years for a period of 1990–2020 long historical rainfall data series for different temporal scales (Monthly, Seasonal and Annual) of the study region was used for the analysis. Statistical trend analysis techniques namely Mann–Kendall (MK) test was used to detect the trend. To compute trend magnitude, Theil–Sen approach (TSA) was used for calculation of Sen’s slope. The detailed analysis of the data for 31 years indicates positive increasing trend with 2.13mm per year derived from the linear regression. MK test detected that there were rising and falling trends for various time scales in the study area. Departure analysis of rainfall indicated that a possible chance of normal rainfall, more frequently in the area. Rainfall Anomaly Index (RAI) analysis revealed that normal for most of the years, however, 2002 is the very dry year. While last ten years, the frequency of drought occurrence is thrice, but the magnitude is low. The study results will help in persuading the rainfall risks with effective use of water resources which can increase crop productivity and likely to manage natural resources for sustainability at HAT zone of Andhra Pradesh.

Non-stationarity in extreme rainfalls across Australia

Future flooding is likely to exceed current design flood levels which are based on historical extreme rainfall characteristics. The Clausius-Clapeyron relationship explains the intensification of extreme rainfalls as approximately 7% per one degree warming as atmospheric water holding capacity increases with temperature. Therefore, to prepare for a future warmer climate, we need to develop methodologies to project future rainfall intensities across the range of durations and exceedance probabilities used in engineering design. However, the studies that have investigated changes in extreme rainfalls across Australia have had disparate results and are not spatially or temporally comprehensive – hampering our understanding of changes in extreme rainfalls across different durations and exceedance probabilities.This study investigates the impact of climate change on rainfalls from the annual maximum to the 1 in 100-year rainfall across a range of storm durations for the continent of Australia. We find increases in short duration (<1 h) annual maximum rainfall are greater than increases in long duration (>1 h) annual maxima across Australia from 1967 to 2021. These results are consistent regardless of the data period or data set chosen for analysis. We estimate events rarer than the annual maxima through fitting non-stationary Generalize Extreme Value models. We find that events of rarer severity have increased more than frequent events. Further, we identify the parameterisation of a model with non-stationary location and scale parameters to capture the changes in historic design quantiles that are consistent with our physical understanding of rainfall intensification, empirical quantile changes, and historical trends. We conclude that trends in annual maxima are best represented by non-stationary models that incorporate changes in both location and scale parameters, not by solely varying either location or scale parameters.

From theory to practice: optimisation of available information for landslide hazard assessment in Rome relying on official, fragmented data sources

The definition of landslide hazard is a step-like procedure that encompasses the quantification of its spatial and temporal attributes, i.e., a reliable definition of landslide susceptibility and a detailed analysis of landslide recurrence. However, available information is often incomplete, fragmented and unsuitable for reliable quantitative analysis. Nevertheless, landslide hazard evaluation has a key role in the implementation of risk mitigation policies and an effort should be done to retrieve information and make it useful for this purpose. In this research, we go through this topic of optimising the information available in catalogues, starting from landslide inventory review and constitution of a boosted training dataset, propaedeutic for susceptibility analysis based on machine learning methods. The temporal recurrence of landslide events has been approached here either through the definitions of large-scale quantitative hazard descriptors or by analysis of historical rainfall (i.e., the main triggering factor for the considered shallow earth slope failures) databases through the definition of rainfall probability curves. Spatial and temporal attributes were integrated, selecting potential landslide source areas ranked in terms of hazard. Data integration was also pursued through persistent scatterer interferometry analysis which pointed out areas of interest within potential landslide source areas featured by ongoing ground movement. The consequential approach led to the definition of the first hazard product of the city of Rome at a local scale functional for advisory purposes or the statutory level, representing a thematic layer able to orient the risk managers and infrastructure stakeholders.

Rainfall shocks and risk aversion: Evidence from Southeast Asia

AbstractWe analyze how individual risk aversion changes in response to shocks in an agrarian setting, and the role of changes in yields and prices as two potential channels. To do so we specify a theoretical model that describes temporal alterations in risk aversion. Empirically, we test the model's proposition by combining individual‐level panel data with historical rainfall data for rural Thailand and Vietnam. We find that rainfall shocks increase individuals risk aversion, whereby the largest effects are observed among households that are net buyers of food commodities. Regarding potential channels, only prices seem to explain–and even then just to a very small extent–the increase in net buyers' risk aversion. Our findings imply that shocks can increase risk aversion, and, in the absence of functioning credit and insurance markets, may ultimately lead to decisions that perpetuate poverty.

Intermodel Spread of Historical Indian Monsoon Rainfall Change in CMIP6: The Role of the Tropical Pacific Mean State

Abstract Robust projections of the Indian summer monsoon rainfall (ISMR) are critical as it provides 80% of the annual precipitation to more than 1 billion people who are very vulnerable to climate change. However, even over the historical period, state-of-the-art climate models have difficulties in reproducing the observed ISMR trends and are affected by a large intermodel spread, which questions the reliability of ISMR projections. Such uncertainty could come from internal variability or model biases. Here, we study the impact of the latter on the historical forced change of ISMR in 34 models from CMIP6. First, we show that models’ biases over India do not significantly impact how they simulate the historical change of ISMR. However, we do find statistically significant relationships between ISMR historical forced changes and remote rainfall and temperature biases within the tropics by using a maximum covariance analysis (MCA). Our results highlight the key role of tropical Pacific sea surface temperature (SST) mean state biases as an important source of intermodel spread in the ISMR change. The physical mechanisms underlying these statistical relationships between ISMR change and the intermodel spread of Pacific SST biases are finally explored. We found that models having El Niño/La Niña–like mean SST bias in the Pacific tend to exhibit El Niño/La Niña–like changes over the historical period, impacting ISMR through a shift in the Walker circulation and Rossby wave propagation across the Pacific.

Rainfall Shocks, Livelihood Diversification and Welfare: Evidence from Rural Togo

Abstract Theory suggests that rural farm households exposed to greater risk should diversify their income portfolios to reduce variation in welfare caused by adverse events such as rainfall shocks. Rainfall shocks, however, can also degrade asset stocks and make diversification more costly. Using a panel dataset of small-scale cattle farmers from rural Togo and long-term historical rainfall data, we first examine whether and in what direction rural portfolio diversification is related to historical rainfall shocks. Second, we test whether diversification is associated with stabilised welfare in the face of recent rainfall shocks. Our results show that historical rainfall shock exposure reduces income diversification. In terms of mitigation, we find that diversification is generally not effective in insulating against welfare losses. We conclude that there is a need to stimulate rural diversification as a means of building resilient livelihoods to cope with increasing weather variability by strengthening credit, agricultural and market institutions.

Comparing the Performance of Three Decision Tree Models for Precipitation Prediction in Cengkareng Soekarno Hatta

This study evaluates and compares the performances of advanced decision tree algorithms, namely LightGBM, XGBoost, and Catboost, for precipitation prediction in Cengkareng Soekarno Hatta, a region in Jakarta, Indonesia that experiences heavy rainfall and flooding. Using historical rainfall data, the study evaluates the algorithm based on various metrics such as accuracy, precision, recall, F1 score, and ROC/AUC. The study aims to provide insight into the potential of advanced machine learning algorithms for precipitation prediction in the region. This study illustrates various visualization and analysis techniques for exploring and understanding data. The analysis employs three decision tree models, including LightGBM, XGBoost, and Catboost, to predict rain patterns. Results indicate that all models perform well in predicting no rain, but struggle with moderate and heavy rain. AUC values for all models are similar, indicating their equal ability to distinguish between positive and negative instances. The study highlights the importance of using appropriate visualization techniques to gain insights into the data and explores the correlations between different features to better understand the underlying relationships.

KAJIAN PERUBAHAN IKLIM DI DKI JAKARTA BERDASARKAN DATA CURAH HUJAN

A common annual problem that often occurs in DKI Jakarta is flooding. Extreme rainfall is one of the most dominant factors that trigger flooding in DKI Jakarta. Global warming causes climate change and rainfall characteristics. This study aims to understand the characteristics of the climate rainfall in DKI Jakarta at this time and the potential for changes in the future. In this study, the characteristics of rainfall which is analyzed were rainfall variabilities such as annual rainfall, maximum rainfall, and the number of rainy days as indicated by analysis of rainfall trends or the tendency of changes in rainfall characteristics over time. Rainfall prediction simulation is carried out using the Statistical Downscaling method. The climate model used is CanESM5 (The Canadian Earth System Model version 5), which is one of the climate models in the Assessment Report (AR6) issued by the IPCC in 2022. The future rainfall at each station is projected for the future period (FP), namely FP-1 (2025-2049), FP-2 (2050-2074), and FP-3 (2075-2100) with the climate scenario Shared Socio-economic Pathways (SSP) 3-7,0. Predictive rainfall analysis yields information that the average annual rainfall, average maximum rainfall and the number of rainy days generally increase in each future period when compared to the historical annual average rainfall. In general, climate change does not result in changes in monsoon rainfall patterns. However, global warming has the potential to increase future rainfall and speed up the start of the rainy season.

The multiplicity of impact: how social marginalization compounds climate disasters

ABSTRACT This study advances and examines the proposition that social marginalization, especially along racial and ethnic lines, produces compound disadvantages that accumulate across a wide range of personal, social and political domains when climate disasters strike, producing a multiplicity of impact often missed by quantitative research on social vulnerability. To test this claim, we use data collected by the Houston Area Survey after the historic rainfall brought by Hurricane Harvey in 2017. Analyses reveal that impacts to Black residents were much more pervasive than for any other group, including a disproportionate likelihood of impact to their income, transportation and personal networks in addition to their housing. Results also indicate that this multiplicity of impact across one’s personal and social domains associates with greater scrutiny of local government’s role in the disaster, net of one’s general political ideology. The implication is that we cannot fully understand the social impacts of a changing climate through social vulnerability metrics and property damage assessments, alone. More comprehensive frameworks and impact accounting are needed.

Assessing the influence of the El Niño–Southern Oscillation phase on rainfall variability in the Gauteng province of South Africa

South Africa is subject to large rainfall variability, with El Niño–Southern Oscillation (ENSO) being the most important driver across the summer rainfall region. The aim of this study was to relate ENSO phase to historical rainfall variability over the densely populated and economically important Gauteng province of South Africa. Monthly rainfall totals for the period 1960–2021 were used to calculate Standardised Precipitation Index (SPI) values at different timescales for five stations. The Oceanic Niño Index (ONI) was used to classify seasons according to ENSO phase. For each station, the number of wet, near-normal or dry seasons falling in a particular ENSO phase was tallied and used to calculate probabilities of occurrence. Results revealed spatial inhomogeneities despite the study area being relatively small. Analysis of 3- and 6-month periods revealed that dry conditions occur more frequently during spring and autumn El Niño events, but are very unlikely during summer and autumn La Niña events. Analysis of 12-month periods revealed that near-normal conditions occur most frequently regardless of ENSO phase, with an increased chance of experiencing wet as opposed to dry conditions under La Niña. This information can aid various decision-makers to make better use of seasonal climate predictions.

Study on the time conversion method of early warning indicators of flash flood disaster: A case of Qufu City, Shandong Province.

To solve the problem of imperfect flash flood warning indicators in mountainous watersheds, this study proposes a conversion method of critical rainfall for different warning periods on the basis of the existing stormwater calculation formulae and applies it to typical mountainous watersheds. The specific method is to use the multiplicative power function method to interpolate and extend the early warning indicators of other time periods through the known early warning indicators of some time periods, apply them to Hengmiao Village and Gaolou Village, typical disaster prevention objects in Qufu City, Shandong Province, China, and verify the rationality of the results. The results show that the multiplicative power function method interpolates and extends the early warning indexes of 0.5, 1, 3, 6, 12, and 24 h under the same soil moisture condition. Based on the historical actual mountain flood disaster rainfall, the correlation coefficient R2 and Nash coefficient Ens are calculated to be 0.85 and 0.86, respectively, which verifies the applicability of this method. This study provides a convenient and quick way to unify the time series of regional early warning indicators and a feasible way for other regions to study regional overall early warning.

Drivers of low-frequency Sahel precipitation variability: comparing CMIP5 and CMIP6 ensemble means with observations

Phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) both grossly underestimate the magnitude of low-frequency Sahel rainfall variability; but unlike CMIP5, CMIP6 mean historical precipitation does not even correlate with observed multi-decadal variability. We demarcate realms of simulated physical processes that may induce differences between these ensembles and prevent both from explaining observations. We partition all influences on simulated Sahelian precipitation variability into (1) teleconnections from sea surface temperature (SST); (2) atmospheric and (3) oceanic variability internal to the climate system; (4) the SST response to external radiative forcing; and (5) the “fast” (not mediated by SST) precipitation response to radiative forcing. In a vast improvement from previous ensembles, the mean spectral power of Sahel rainfall in CMIP6 atmosphere-only simulations is consistent with observed low-frequency variance. Low-frequency variability is dominated by teleconnections from observed global SST, and the fast response only hurts the performance of simulated precipitation. We estimate that the strength of simulated teleconnections is consistent with observations using the previously-established North Atlantic Relative Index (NARI) to approximate the role of global SST, and apply this relationship to the coupled ensembles to infer that both fail to explain low-frequency historical Sahel rainfall variability mostly because they cannot explain the observed combination of forced and internal variability in North Atlantic SST. Yet differences between CMIP5 and CMIP6 in mean Sahel precipitation and its correlation with observations do not derive from differences in NARI, but from the fast response or the role of other SST patterns.

Farmers’ perceptions of climate variability and adaptation strategies in the rural areas of Dire Dawa administration, eastern Ethiopia

Climate variability has significant impact on agricultural production especially in low-income countries where agriculture largely relies on rainfall, but only a few studies explored this issue at local scale. Therefore, this study was conducted to characterize local climate and assess farmers' perceptions and adaptation strategies to climate variability in the rural areas of Dire Dawa administration. Historical rainfall and temperature data (1987–2017) were obtained from National Meteorological Agency (NMA) of Ethiopia, while data of farmers' perceptions and adaptation strategies were collected from a total of 120 household heads through survey questionnaire, key informant interviews and focus group discussions. The results revealed that the area received an average annual rainfall of 568.3 mm with main rainy season (kiremt) contributing 70.7% to annual rainfall. The earliest and latest onset dates of kiremt season were 15th of April and 2nd of August, respectively. The amount of annual and kiremt rainfall totals showed low and medium variability with a coefficient variability (CV) of 18.3% and 27.7%, respectively, whereas short rainy season (belg) rainfall had high variability with a CV of 43.9%. Climate variability perception analysis showed that an overwhelming majority of the respondents (90%) perceived a decrease in the annual rainfall and 91.7% detected an increase in annual average temperature in the study area. Farmers of the study area were well aware of the changes in rainfall and temperature and thus employed a range of adaptation practices. Soil and water conservation practices (100%), off-farm income diversification (63%), planting drought-tolerant varieties (50%) and changing of planting date (45%) were the main adaptation strategies employed in the study area to avert the negative effects of climate variability. The findings imply that the area has been experiencing palpable changes in climate variables during the study period against which farmers exercised multiple adaptation strategies. However, farmers in the area are still face hardship as a result of climate variability which necessitates improving farmers’ resilience through innovative mechanisms and better extension services.

Influence of Climate Change on precipitation and the formulation of Rain Design in Brazil

This work aims to answer whether climate change is a precursor to updating the rain equations developed in different regions of Brazil. The methodology was based on a Systematic Review of the existing literature, made through the “Periódicos Capes” portal. The results showed that in some cities, there are no rain equations and not enough rain measurements available, so, in these cases it is not possible to study Climate Change and the formulations of Rain Designs. However, in places where data are available, the need to update the Rain Design equations are evaluated using recent historical rainfall data, and it was found that these changes could be explained by climate change.

Analysis of the Triggering and Maintenance Mechanisms of a Record-Breaking Warm-Sector Extreme-Rainfall Process in Front of an Upper-Level Trough in Tianjin, China

A short-time rainstorm exceeding the extreme historical rainfall occurred in the Jinnan District of Tianjin, China, on 3 July 2022. Due to the concentrated time period of precipitation, it caused serious water accumulation in the Jinnan District. The purpose of this paper is to study the weather mechanism of this extreme rainstorm in the Jinan District of Tianjin. By analyzing the fine observation facts, we can obtain the mesoscale weather characteristics and environmental conditions of the process. The results provide a reference for similar weather forecasting and warning in the future. Based on the 1 min interval precipitation observation data, the ERA5 reanalysis data, the CINRAD-SA radar reflectivity data of Tanggu, the cloud-top brightness temperature data of the Fengyun-4A satellite, and the Variational Doppler Radar Analysis System data, we comprehensively analyzed a record-breaking extreme rainfall process in Tianjin on 3 July 2022. The results show that the extreme rainfall process presents prominent mesoscale weather characteristics, with high precipitation intensity in a short-term period. This process is influenced by multi-scale weather systems, including the 500 hPa upper-level trough and the long-distance water vapor transport by Typhoon Chaba. The rainstorm event is caused by the combined actions of cold pool outflow produced by the upstream precipitation, the easterly disturbance in the boundary layer, the mesoscale temperature front, and the ground convergence line. Specifically, the ground convergence line is formed by the northerly wind of the cold pool outflow and the warm and moist southerly airflow from the ocean, and the temperature front is caused by the horizontal thermal difference of the underlying surface. Both the ground convergence line and temperature front contribute considerably to the triggering of mesoscale convection. The mesoscale secondary circulation is formed in the meridional direction by the meso-γ-scale convergence and its interaction with strong velocity in front of the trough, contributing to the development and maintenance of vertical motion in the Jinnan region of Tianjin and thereby leading to the occurrence and development of this extreme heavy rainfall process.

Different Characteristics and Drivers of the Extraordinary Pakistan Rainfall in July and August 2022

The unprecedented and long-lasting abnormal monsoon rainfall attacked Pakistan in the summer of 2022, causing severe flooding. This study investigated the sub-seasonal characteristics and mechanisms of this distinctively extreme precipitation event. The historical rainfall in July and August and extreme precipitation mainly occurred in northern Pakistan. Both the monthly rainfall in July and August 2022 and the extreme precipitation during the summer were far exceeding the historical record and involved unique spatial distribution. The rainfall in July 2022 is nationwide and mainly located in northern Pakistan, while the rainfall in August and extreme precipitation occurred in southern Pakistan. Different physical processes are responsible for the precipitation in July and August 2022. In July, the South Asian high (SAH) and Iranian high extended eastward. Meanwhile, the anticyclonic circulation anomalies occurred in northwestern Pakistan and the easterly winds enhanced in the south side of the Tibetan Plateau (TP), which strengthened water vapor transporting from the Bay of Bengal and cooperated with the cyclonic system over the Arabian Sea to enhance the precipitation over Pakistan. In August, the SAH further extended eastward and the Western Pacific Subtropical High extended westward to the TP. Meanwhile, the European blocking (EB) developed, and a deep trough appeared over northwestern Pakistan. This weakened the easterly flow along southern TP but enhanced the southerly flow accompanying the cyclone over the Bay of Bengal and the Arabian Sea, and thus guided the water vapor transporting to southern Pakistan and enhanced the precipitation. The extreme precipitation in July was mainly attributed to the unusually strong Indian monsoon, while the extreme precipitation in August was the result of a combination of the Indian monsoon and EB. The study provided important information about extreme precipitation in Pakistan, which will help policymakers take measures to deal with the effects of flooding.

July 2020 heavy rainfall in Japan: effect of real-time river discharge on ocean circulation based on a coupled river-ocean model

In July 2020, a stationary atmospheric front over Japan caused persistent, nearly continuous rain for most of the month that resulted in new historical highest rainfall records in several areas and caused serious river floods, landslides, and debris-flow events. An existing hindcast and forecast ocean circulation model that includes climatological discharge information of major rivers failed to represent the extreme river discharge under heavy rainfall. New experiments were conducted using real-time river discharge information based on Today's Earth CaMa-Flood simulation that includes 368 rivers in Japan. The inclusion of real-time river discharge improved the salinity bias in the near-surface waters. The differences were significant compared to the observations in the heavy rain region (i.e., Ariake Bay and Tosa Bay), and insignificant at offshore stations. The ensemble experiments of real-time river discharge cases suggested the difference between the climatological and the real-time river discharge experiments was not random, but was robust. The freshening water changed the shelf circulation, and its far-reaching effect appeared hundreds of kilometers away from the shore. Passive particle tracking was conducted for examining the cross-shelf exchange. More particles released from Bungo Channel went offshore in near-surface water when the real-time river discharge was used compared to using the climatology discharge. Particles released in Tosa Bay, Seto Inland Sea, and Kii Channel showed the opposite tendency. The real-time river discharge not only changed the modeled coastal salinity distribution, but also the coastal and offshore currents. The role of the real-time river discharge on modeling normal flow periods or drought events, and its influence on a longer time scale model simulation remain to be explored.

Water environment characteristics and control countermeasures of typical hilly cities in Southeast China

Abstract Due to population growth and economic development, problems such as uneven distribution of freshwater resources and degradation of water quality are becoming increasingly prominent. Many measures have been adopted to address water resources issues; however, few studies have focused on specific changes in water quality after policy implementation. In this study, Yiwu City, Zhejiang Province, a typical hilly city with a well-developed water system, was selected to analyze the changes in water quality. By analyzing the rainfall series and hydrology characteristics, and water quality characteristics, this paper revealed the water environment characteristics and put forward some suggestions for water management in hilly cities. Results showed that the historical rainfall time distribution is uneven, and the water environment capacity of urban inland rivers exhibits strong variability in Yiwu City. The contents of ammonia nitrogen (NH3-N) and total phosphorus (TP) decreased significantly since the policy implementation of ‘Five water cohabitation’. Urban rivers presented regional pollution and surface source pollution characteristics, and the water quality fluctuated significantly with the increase in rainfall. At the same time, the study confirmed that scientific water replenishment measures can effectively improve water quality, providing reference ideas for the protection and management of China's urban water environment.