Rainfall Depth Research Articles

A Multi-Dimensional Investigation on Water Quality of Urban Rivers with Emphasis on Implications for the Optimization of Monitoring Strategy

Water quality monitoring (WQM) of urban rivers has been a reliable method to supervise the urban water environment. Indiscriminate WQM strategies can hardly emphasize the concerning pollution and usually require high costs of money, time, and manpower. To tackle these issues, this work carried out a multi-dimensional study (large spatial scale, multiple monitoring parameters, and long time scale) on the water quality of two urban rivers in Jiujiang City, China, which can provide indicative information for the optimization of WQM. Of note, the spatial distribution of NH3-N concentration varied significantly both in terms of the two different rivers as well as the different sections (i.e., much higher in the northern section), with a maximal difference, on average greater, than five times. Statistical methods and machine learning algorithms were applied to optimize the monitoring objects, parameters, and frequency. The sharp decrease in water quality of adjacent sections was identified by Analytical Hierarchy Process of water quality assessment indexes. After correlation analysis, principal component analysis, and cluster analysis, the various WQM parameters could be divided into three principal components and four clusters. With the machine learning algorithm of Random Forest, the relation between concentration of pollutants and rainfall depth was fitted using quadratic functions (calculated Pearson correlation coefficients ≥ 0.89), which could help predict the pollution after precipitation and further determine the appropriate WQM frequency. Generally, this work provides a novel thought for efficient, smart, and low-cost water quality investigation and monitoring strategy determination, which contributes to the construction of smart water systems and sustainable water source management.

The role of morphology in the spatial distribution of short-duration rainfall extremes in Italy

Abstract. The dependence of rainfall on elevation has frequently been documented in the scientific literature and may be relevant in Italy, due to the high degree of geographical and morphological heterogeneity of the country. However, a detailed analysis of the spatial variability of short-duration annual maximum rainfall depths and their connection to the landforms does not exist. Using a new, comprehensive and position-corrected rainfall extreme dataset (I2-RED, the Improved Italian-Rainfall Extreme Dataset), we present a systematic study of the relationship between geomorphological forms and the average annual maxima (index rainfall) across the whole of Italy. We first investigated the dependence of sub-daily rainfall depths on elevation and other landscape indices through univariate and multivariate linear regressions. The results of the national-scale regression analysis did not confirm the assumption of elevation being the sole driver of the variability of the index rainfall. The inclusion of longitude, latitude, distance from the coastline, morphological obstructions and mean annual rainfall contributes to the explanation of a larger percentage of the variance, even though this was in different ways for different durations (1 to 24 h). After analyzing the spatial variability of the regression residuals, we repeated the analysis on geomorphological subdivisions of Italy. Comparing the results of the best multivariate regression models with univariate regressions applied to small areas, deriving from morphological subdivisions, we found that “local” rainfall–topography relationships outperformed the country-wide multiple regressions, offered a uniform error spatial distribution and allowed the effect of morphology on rainfall extremes to be better reproduced.

Automating rainfall recording: Ensuring homogeneity when instruments change

Sub-daily rainfall is used in modelling of small catchments, design of urban drainage, calculating soil erosion, understanding the water balance of vegetated catchments, and assessing the impact of climate change on short duration storms. Hence it is critical to ensure that sub-daily rainfall records are homogeneous, but with a global shift to automated measurement of rainfall, there has been a change in the rainfall recording instrumentation. Although changes in instrumentation should be documented, in many cases this is not the case and station metadata are missing. As result, studies of rainfall patterns, particularly those investigating trends, often cannot report their treatment of possible inhomogeneities and must assume the data is suitable for further analysis.Here, we argue that standard quality assurance methods for checking inhomogeneities in rainfall may not identify a change in the rainfall record due to a change in instrumentation. Through testing (1) an aggregation of rainfall to the coarsest instrument resolution, (2) removal of rainfall below a minimum rainfall depth, (3) removal of rainfall below a minimum accumulation (event) depth, and (4) a combination of the above adjustments, we present recommendations for ensuring sub-daily rainfall is homogenous with minimum alteration to the rainfall record.We assess the proposed methods using the case study of Australia’s sub-daily rainfall monitoring network, where, in 1996 the pluviograph network was switched to tipping buckets, creating a large-sample test bed with a known systematic instrumentation change. Our results show that event-based statistics are considerably more sensitive to instrument change than annually aggregated rainfall statistics and hence standard methods for quality assurance may not identify possible inhomogeneities. We suggest an aggregation of the data to the coarsest instrument resolution, with the removal of small rainfall accumulations, can alleviate inhomogeneities for event-based statistics, whereas, for annual statistics, aggregation to the coarsest instrument resolution is sufficient. In the absence of a known instrumentation change, removing rainfall at or below the instrument resolution is also a viable technique for improving rainfall record homogeneity for event-based statistics. These findings will aid future sub-daily rainfall studies by justifying the use of a minimum rainfall or event depth for subsequent analysis.

Regional Rainfall Regimes Affect the Sensitivity of the Huff Quartile Classification to the Method of Event Delineation

The widely used Huff quartile approach classifies rainfall events according to which quarter of their duration contains the largest rainfall depth. The rainfall events themselves are often delineated by specifying a minimum rainless interevent time (MIT) that must precede and follow a period of rainfall for it to be identified as a separate event. However, there is no standard or universally applicable value of this MIT criterion. Some studies have stipulated as little as 15 rainless minutes to mark the start of a new event, whilst others have required 24 h or more. The present work investigates how the adoption of different values of the MIT criterion, based, for instance, on the response time of a catchment or the drying time of a vegetation canopy, affects the Huff quartile classification. To date, this has not been explored. To address this issue, the Huff classification is herein applied to data from two Australian ground observing stations, one arid continental and one wet tropical. For each location, rainfall events were delineated using values of the MIT criterion ranging from 30 min to 24 h. In comparison with the 6 h MIT adopted by Huff (1967) as being appropriate for locations in the eastern USA, results show that, for instance, the proportion of events classified as 4Q ranges from 70.9% larger when events are delineated with a MIT = 30 min to 50.8% smaller when events are delineated using a MIT = 24 h. Moreover, the changes in the Huff classification that result from the use of different MIT values were not the same in wet tropical and arid locations. It is argued here that these findings reflect complexity in the arrival of rainfall, including differences in event duration and intraevent intermittency, that cannot be captured in the Huff classification system. Relevant rainfall regime characteristics such as these are likely to vary geographically, and the differences shown between the eastern USA and the two Australian locations are only examples of what is likely a more general effect. The results show that there is no single and generally applicable ‘Huff classification’ process, and that rather than a 6 h MIT being applicable everywhere, different MIT durations are needed in locations having differing rainfall regimes.

Improving flash flood risk assessment using a simple approach for extreme rainfall scaling and storms transposition

AbstractFlash flood risk management is among the key topics of the European Flood Directive. Design hydrographs of small river basins could underestimate the hydraulic risk in case of intense and short‐duration events because of the intensification of hourly rainfall and the design rainfall depths uncertainty in often ungauged river basins. The use of synthetic hyetographs obtained from past observed events can be useful for a better evaluation of the flash flood hazard at the river basin scale. For this reason, a simple approach based on rainfall scaling and storms transposition of occurred events is presented to identify areas where design discharges underestimate the flash flood risk. The methodology considers the spatial distribution of the observed storms over a specific zone and the areal reduction factors to scale the observed hyetographs for different river basins extensions. Hyetographs of past observed events are used as input of a hydrological model in a set of river basins located in Northern Tuscany (Italy). The results show how peak discharges of short‐duration events are usually greater than the design floods of the small river basins with an area generally less than 30 km2.

Projections of rainfall erosivity in climate change scenarios for the largest watershed within Brazilian territory

Global climate change can potentially threaten agricultural production due to endangered natural resources, such as rainfall patterns. Thus, extreme rainfall events can cause greater rainfall erosivity, consequently, greater soil erosion. Conversely, a reduction in rainfall amount can lead to water scarcity for the agriculture production process. This way, it is a foremost need to model climatic conditions under global climate change scenarios, particularly in places where rainfall data tends to increase. This work aimed to project rainfall erosivity in the major Brazilian watershed, the Tocantins-Araguaia river basin, throughout the 21st century under two Intergovernmental Panel for Climate Change Fifth Assessment Report (IPCC AR5) Scenarios, the Representative Concentration Pathways, RCP4.5 and RCP8.5 scenarios. This study uses the downscaling of four global climate models of the Coupled Model Intercomparison Project (CMIP5) by the Eta regional climate model, used by the Brazilian National Institute for Space Research. The average rainfall erosivity was calculated based on the Modified Fournier Index in three periods of 30-year length throughout the 21st century. Time series of R-factor were analyzed at rain gauge station points overlapping regional model grid cells over the basin for the 1961–2099 period. Projections indicated lower annual average rainfall erosivity values in comparison with historical data. Estimated mean rainfall erosivity values were 10,977.69 ± 526 MJ mm ha−1 h−1 yr−1 for the RCP4.5 scenario, and 10,379.71 ± 723 MJ mm ha−1 h−1 yr−1 for the most pessimistic climate change scenario, RCP8.5. The largest reductions of the mean R-factor reached 5,5% for the multi-model ensemble projections for near future, and 15.4% for the ensemble projections models for long-term, with the greatest decreasing trends under RCP8.5. Reductions greater than 2,000 MJ mm ha−1 h−1 are expected throughout the 21st century according to multi-model ensemble projections models under RCP8.5 scenario in most of the watershed. Decreasing rainfall erosivity factor in both RCP scenarios was due to a lower rainfall depth. However, the value of rainfall erosivity is still considered high and should be taken into account in soil conservation practices. Furthermore, the smaller rainfall amount indicates a possible reduction in water availability for crops of longer cycle, and increase in spatial variability of less intense rainfall.

Efficiency of Geospatial Technology and Multi-Criteria Decision Analysis for Groundwater Potential Mapping in a Semi-Arid Region

The increasing water demand in Egypt causes massive stress on groundwater resources. The high variability in the groundwater depth, aquifer properties, terrain characteristics, and shortage of rainfall make it necessary to identify the groundwater potentiality in semi-arid regions. This study used the possibilities of multi-criteria decision approaches (MCDA), geographical information system (GIS), and groundwater field data to delineate potential groundwater zones in the Tushka area, west of Lake Nasser, South Egypt. Furthermore, groundwater potentiality identification can help decision-makers better plan and manage the water resources in this promising area. Eight controlling factors were utilized to achieve the objective of the present work using multi-criteria decision analysis (MCDA) approaches, namely the analytical hierarchy process (AHP) and frequency ratio (FR) models. The controlling parameters were integrated with the geographic information system (GIS) to develop the zones of groundwater potentialities. The results revealed that high and moderate-potential zones cover approximately 61% and 52% of the total area in the AHP and FR models, respectively. A total of 44 groundwater production wells along with the well yield were collected and used to validate the models. The results were evaluated using the receiver operating characteristics (ROC) curve. The best-performing prediction rates achieved by AHP and FR were 83% and 81%, respectively. Finally, the obtained results indicated that the AHP model achieved better performance than the FR model.

Assessing Flood Early Warning Systems for Flash Floods

Flash floods are an increasing concern, especially in regions with abrupt topography and small areas where floods are rapid and energy-filled. That is the case of the El Guamo stream basin located in Manizales, Colombia. It has been proposed a duration-independent rainfall threshold for flash floods in this basin, using a hydrodynamic method that links critical water stages to cumulative rainfall. This paper presents a systematic literature review of 19 case studies from 2016 to 2021 to compare and highlight complexities and differences in the methods used in rainfall threshold estimation in both the El Guamo stream basin as in other case studies. The results identified four types of methods: (i) empirical, (ii) hydrological/hydrodynamic, (iii) probabilistic, and (iv) compound. Each method identified the principal indicators and their predictor variables. Each method uses different indicators, such as accumulated rain, accumulated antecedent rainfall, intensity-duration of the rain event, maximum cumulative or cumulative rainfall depth for a specific duration, and critical rainfall within given time periods. Scenario analysis of the predictor variables is a common approach used in rainfall threshold estimation. Some predicting variables found are antecedent moisture conditions, inundation criteria, and synthetic hyetographs. Some case studies include a probabilistic analysis of the predictor variables. This article concludes that indicators and their predicting variables can be adjusted to local flood early warning systems depending on the rainfall threshold method selected. Hydrodynamic models are solid in rainfall threshold estimation. However, it is highly advisable to include uncertainty analysis and new data sources to have more robust rainfall thresholds. Furthermore, probabilistic methods, including uncertainty analysis with utility functions, are a valuable tool to improve decision-making in early warning systems, which can help to refine the rainfall threshold estimation.

Application of short time series analysis for the hydrodynamic characterization of a coastal karst aquifer: the Salento aquifer (Southern Italy)

Abstract Daily precipitation and groundwater level data, registered at 7 rain gauge stations and 11 monitoring wells in the Salento coastal karst aquifer (Southern Italy) were subject to short time series analyses to evaluate the hydrodynamic response of the aquifer. Results show that the Salento karst system has in general great storage capacity, which suggests a poor degree of aquifer karstification, and dominance in the permeability structure of not well-developed hierarchical karst networks. Baseflow generally dominates on quick flow, which only occasionally occurs. The dominant hydraulic behaviour is unimodal baseflow with a bimodal baseflow and quick flow one from time to time. Some local specific features, which emerge depending on the rainfall frequency and depth, and the geological and structural characteristics, point out that some components of the permeability structure, as the epikarst and major faults, only activate depending on the characteristics of the input. The study is novel because the analyses concerned specific sets of hydrological years from 2007 to 2011; also, because the analysis was conducted for a coastal karst aquifer of significant size, characterized by a regional groundwater flow system with an unsteady lower boundary, without inland freshwater springs, and significant exploitation by wells.

Estimation of Groundwater Recharge from Rainfall for Arid Coastal Plain of Ninh Thuan Province, Vietnams

Estimation of groundwater recharge from rainfall is a key factor for determining groundwater resources in water development and management for supporting sustainable socio-economic development, especially for arid areas. The paper presents finite element modeling in the simulation of moisture transfer in unsaturated soils through the relationship between soil moisture, soil suction, unsaturated permeability and soil-moisture dispersivity. Those parameters required for soil moisture transfer are derived from the soil-water characteristic curve functions. Element sizes and time steps used in the modelling have been selected based on a detailed analysis of numerical simulation errors. The methodology had been applied to arid coastal plain area of Ninh Thuan province, Vietnam. Five subsurface soil types in the study area have been collected and analyzed, for saturated permeability, porosity, saturated soil water content, field moisture content, etc. Hourly rainfall data of the years 2014–2018 have been analyzed and grouped into different-duration rainfall events (1-hour, 2-hour, 3-hour and so on). The different rainfall durations and depths of rainfall events and temporal infiltration determined by the moisture transfer modelling have allowed determining the groundwater recharge from the rainfall data. The results show that during the rainy months from May to December 2014–2018, the groundwater recharge from the rainfall is very varying through the modeled soil profiles, from 0.280 m (silty clay) to 0.470 m (sandy silt), which is equivalent to 33.3%—55.2% of the rainfall depth during May—December. Lower infiltration in silty clay is due to low permeability and in the sand is due to low suction, and higher infiltration in silt and sandy silt is thanks to their higher moisture dispersivity. On average, in terms of annual rainfall and soil properties, the average infiltration during May—December is 0.380 m which is equivalent to 44.9% of the rainfall depth, which is about 289 106 m3 of rainwater infiltrated into the Quaternary aquifer over 760 km2 of the coastal plain of Ninh Thuan province. The results would be very useful for effective water resources development and management in a given specific hydrogeological condition for such a severe drought area where water is extremely essential.

Climatological features of high temporal resolution rainfall over the Hyderabad city, India

High spatial variability and continuous evolution of a city's land use and land cover makes the city's rainfall mechanism more complex and dynamic, thus climatological rainfall features have both scientific and engineering relevance. A high-resolution hourly rainfall data from 37 Automatic Weather Stations (AWSs) of the Hyderabad city (an area of 650 km2) was considered. Rainfall analysis was carried out on an event-based approach from a total of 118 rainfall events. Rainfall frequency, depths and intensities were higher in the oldest region of the city for all seasons with exception of rainfall depth in summer. Diurnal variations in rainfall amount suggest large monsoon rainfall in the evening to midnight in fewer 1-hour spells. An increasing trend in rainfall depths and intensities highlights non-stationarity. In addition, decreased association among stations with increasing distance and increased spatial extent of precipitation with decreased spatial variability were observed. Further, enhanced rainfall activity in the city's region was observed as compared to its surrounding suburban region (has 33 AWSs). Results suggest significant rainfall variability across space- and time- scales, which may be associated with urbanization, and have great implications in a city's planning and development particularly under climate change and exponential urbanization context.

Minimum Rainfall Inter-Event Time to Separate Rainfall Events in a Low Latitude Semi-Arid Environment

Water scarcity in dry tropical regions is expected to intensify due to climate change. Characterization of rainfall events is needed for a better assessment of the associated hydrological processes, and the proposition of adaptation strategies. There is still no consensus on the most appropriate method to separate rainfall events from a continuous database, although the minimum inter-event time (MIET) is a commonly used criterion. Semi-arid regions of low latitudes hold a distinct rainfall pattern compared to their equivalent at higher latitudes; these seasonally dry tropical forests experience strong spatial–temporal variability with intense short-duration rainfall events, which, in association with high energy surplus and potential evaporation, leads to an atmospheric water deficit. In this study, we identified the most adequate MIET based on rainfall data continuously measured at 5-min intervals over the last decade (2009–2020) in the semi-arid northeast of Brazil. The rainfall events were grouped according to different MIETs: 15 min, 1 h, 2 h, 3 h, 6 h, 12 h, and 24 h to determine rainfall depth, duration and intensity at intervals of 5, 30, and 60 min, time between events, and respective temporal distribution, with and without single tip events. Including single tip events in the dataset affected the number of rainfall events and respective characteristics up to a MIET of 3 h. A MIET of 6 h is the most appropriate to characterize the rainfall distribution in this tropical semi-arid region. Three classes were defined based on rainfall depth, duration, and intensity: I-small events (77% below 40 mm and 32 mm/h), II-high intensity events (3% between 36 and 76 mm/h), III-longer events of higher depth (20%). This study is useful for understanding how the MIET relates to other ecohydrological processes and provides more precise information on the rainfall characteristics at the event scale.

Assessment of Logistic Regression Model Performance and Physical Controls on January 9, 2018, Debris Flows, Thomas Fire, California

ABSTRACT The 2017–2018 Thomas Fire burned 281,893 acres of land in southeastern Santa Barbara County and southwestern Ventura County. An atmospheric river storm impacted the region on January 9, 2018, producing intense rainfall in the western and northern portions of the burned area and triggering numerous post-fire debris flows (PFDFs). The most destructive and deadly flows inundated the town of Montecito, where 23 people died. Debris flow source and inundation mapping data across the fire provide a rare opportunity to assess the interplay between rainfall intensity, watershed characteristics, geologic conditions, and resulting PFDF occurrence. Mapped data are compared to spatially explicit analyses of 857 drainage basins modeled with the U.S. Geological Survey (USGS) logistic regression model (LRM) for PFDF prediction using 15-minute rainfall thresholds at 50 and 90 percent (P50 and P90) probabilities of exceedance. Results indicate that the LRM successfully predicted nearly every PFDF reaching the basin pour point. However, overall model accuracy was lowered by numerous false-positive responses, even where rainfall depths were far above LRM thresholds. Analyses of basins where rainfall was above P50 thresholds reveal a strong correlation between high false-positive responses and basins experiencing rainfall of less than about 150 to 200 percent of USGS thresholds. These false positives occurred in basins with small (0.02–0.05 km2), steep (≥23°) burned areas and in basins underlain by relatively weak geologic units that weather to produce few boulders. Identified relationships provide a basis for refining and improving existing PFDF hazard assessment modeling.

Long Short-Term Memory Based Subsurface Drainage Control for Rainfall-Induced Landslide Prevention

Subsurface drainage has been widely accepted to mitigate the hazard of landslides in areas prone to flooding. Specifically, the use of drainage wells with pumping systems has been recognized as an effective short-term solution to lower the groundwater table. However, this method has not been well considered for long-term purposes due to potentially high labor costs. This study aims to investigate the idea of an autonomous pumping system for subsurface drainage by leveraging conventional geotechnical engineering solutions and a deep learning technique—Long-Short Term Memory (LSTM)—to establish a geotechnical cyber-physical system for rainfall-induced landslide prevention. For this purpose, a typical soil slope equipped with three pumps was considered in a computer simulation. Forty-eight cases of rainfall events with a wide range of varieties in duration, total rainfall depths, and different rainfall patterns were generated. For each rainfall event, transient seepage analysis was performed using newly proposed Python code to obtain the corresponding pump’s flow rate data. A policy of water pumping for maintaining groundwater at a desired level was assigned to the pumps to generate the data. The LSTM takes rainfall event data as the input and predicts the required pump’s flow rate. The results from the trained model were validated using evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and R2. The R2-scores of 0.958, 0.962, and 0.954 for the predicted flow rates of the three pumps exhibited high accuracy of the predictions using the trained LSTM model. This study is intended to make a pioneering step toward reaching an autonomous pumping system and lowering the operational costs in controlling geosystems.

Assessment of climate change impact on probable maximum floods in a tropical catchment

The increases in extreme rainfall could increase the probable maximum flood (PMF) and pose a severe threat to the critical hydraulic infrastructure such as dams and flood protection structures. This study is conducted to assess the impact of climate change on PMF in a tropical catchment. Climate and inflow data of the Tenmengor reservoir, located in the state of Perak in Malaysia, have been used to calibrate and validate the hydrological model. The projected rainfall from regional climate model is used to generate probable maximum precipitation (PMP) for future periods. A hydrological model was used to simulate PMF from PMP estimated for the historical and two future periods, early (2031 − 2045) and late (2060 − 2075). The results revealed good performance of the hydrological model with Nash–Sutcliffe efficiency, 0.74, and the relative standard error, 0.51, during validation. The estimated rainfall depths were 89.5 mm, 106.3 mm, and 143.3 mm, respectively, for 5, 10, and 50 years of the return period. The study indicated an increase in PMP by 162% to 507% and 259% to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating floods in the future in his tropical catchment due to climate change.

Formulation of Parsimonious Urban Flash Flood Predictive Model with Inferential Statistics

The curve number (CN) rainfall–runoff model is widely adopted. However, it had been reported to repeatedly fail in consistently predicting runoff results worldwide. Unlike the existing antecedent moisture condition concept, this study preserved its parsimonious model structure for calibration according to different ground saturation conditions under guidance from inferential statistics. The existing CN model was not statistically significant without calibration. The calibrated model did not rely on the return period data and included rainfall depths less than 25.4 mm to formulate statistically significant urban runoff predictive models, and it derived CN directly. Contrarily, the linear regression runoff model and the asymptotic fitting method failed to model hydrological conditions when runoff coefficient was greater than 50%. Although the land-use and land cover remained the same throughout this study, the calculated CN value of this urban watershed increased from 93.35 to 96.50 as the watershed became more saturated. On average, a 3.4% increase in CN value would affect runoff by 44% (178,000 m3). This proves that the CN value cannot be selected according to the land-use and land cover of the watershed only. Urban flash flood modelling should be formulated with rainfall–runoff data pairs with a runoff coefficient > 50%.

Detecting Extreme Rainfall Events Using the WRF-ERDS Workflow: The 15 July 2020 Palermo Case Study

In this work, we describe the integration of Weather and Research Forecasting (WRF) forecasts produced by CIMA Research Foundation within ITHACA Extreme Rainfall Detection System (ERDS) to increase the forecasting skills of the overall early warning system. The entire workflow is applied to the heavy rainfall event that affected the city of Palermo on 15 July 2020, causing urban flooding due to an exceptional rainfall amount of more than 130 mm recorded in about 2.5 h. This rainfall event was not properly forecasted by meteorological models operational at the time of the event, thus not allowing to issue an adequate alert over that area. The results highlight that the improvement in the quantitative precipitation scenario forecast skills, supported by the adoption of the H2020 LEXIS computing facilities and by the assimilation of in situ observations, allowed the ERDS system to improve the prediction of the peak rainfall depths, thus paving the way to the potential issuing of an alert over the Palermo area.

Intensity duration frequency curve analysis for selected meteorological stations in North Shoa, Amhara Region, Ethiopia

The estimation of rainfall intensity is required for the design of hydraulic and water resources engineering control structures. Rainfall data of 11 – 23 years long from four stations was used to generate Intensity-Duration-Frequency (IDF) curves and parameters for selected stations in the study area. The daily rainfall data set obtained was then subjected to frequency analysis to determine the distribution which best characterizes the data set. The result showed that Log Person Type III was the best fit probability distribution function. The annual extreme values of rainfall depth were computed for different rainfall durations and return periods. Ranked rainfall depths for each return period were converted to rainfall intensities. The corresponding rainfall intensities were computed for different rainfall durations and return periods. IDF curves were developed using MIDUSS 2.25 software for return periods of 2, 5, 10, 25, 50, and 100 years and for durations of 1, 2, 3, 6, 12, and 24 h, and determined IDF parameters. The results shown that for shorter durations (1 hour and 2 hours), the IDF curves gave higher intensities for the same return period while for longer durations (3 h, 6 h, 12 h and 24 h), they gave low intensities for the same return period. The IDF relationship developed can be used as valuable tool for the designing of hydraulic structures in the region. This study could serve as a spring board for other studies that imply the calculation of a peak flow and designing of hydraulic structures as an input to help improve flood resilience in the region.

黄土丘陵区坡耕地与撂荒地土壤水分对降雨的响应特征

为了揭示黄土丘陵区不同土地覆被方式下土壤含水量对不同强度降雨的响应过程，选取山西省冯家沟小流域农耕地和撂荒地为研究对象，基于2019年4-10月降雨和土壤含水量以小时为单位的观测数据，分析了土壤含水量对不同强度降雨的响应规律。结果表明：（1）小雨、中雨、大雨和暴雨对土层的影响深度分别为20、20、60 cm和60 cm，研究区降雨最主要的形式-小雨对土壤水分的补给作用较小。（2）各强度降雨过程中，除0-10 cm土层外，农耕地土壤含水量均大于撂荒地，且大雨条件下农耕地各土层土壤含水量变化较显著，而撂荒地仅0-10 cm和10-20 cm土层土壤含水量有所增加。（3）不同土层土壤含水量对降雨的响应不同，表层响应相对明显而深层相对减弱；农耕地土壤平均含水量最大值出现在50-60 cm土层，而撂荒地出现在30-40 cm土层。（4）本地区农耕地土壤含水量高于撂荒地，表明在该地区适当的作物种植有较好的储存土壤水分的作用。本研究的结果对地区水资源的高效利用和水土流失防控提供了一定的科学依据。;In order to reveal the response process of soil water content to different rainfall intensities under different land cover modes in Loess hilly region. This study selected the cultivated land and abandoned land in Fengjiagou small watershed of Shanxi Province as the research objects. Based on the observation data of rainfall and soil water content in hours from April to October 2019, the response of soil water content to different rainfall intensities was analyzed. The results showed that:(1) The influence depths of light rainfall, moderate rainfall, heavy rainfall and torrential rainfall on soil layer were 20, 20, 60 cm and 60 cm, respectively. The main form of rainfall-light rainfall had little effect on soil moisture in the study area. (2) During each rainfall intensity, except 0-10 cm soil layer, the soil water content of the cultivated land was higher than that of abandoned land, and the soil water content of each soil layer in the cultivated land changed significantly under heavy rainfall conditions, while the soil water content of abandoned land was only increased in 0-10 cm and 10-20 cm soil layers. (3) The content of soil water among soil layers had different response to rainfall, and the response of surface soil moisture was more responsive than that of the deep soil water content. The maximum average soil water content appeared in 50-60 cm soil layer in the cultivated land, while the abandoned land appeared in the 30-40 cm soil layer. (4) The soil water content of the cultivated land was higher than that of the abandoned land, indicating that appropriate crop planting in this region had a better role in storing soil moisture. The results of this study provide a scientific basis for the efficient utilization of regional water resources and the prevention and control of soil erosion.

GIS-Based Assessment of Suitability Area of Rainwater Harvesting in Daro Labu District, Oromia, Ethiopia

Rainwater harvesting is the process of intercepting, conveying and storing rainfall for future use as an alternative source of water in the drought prone areas of Ethiopia especially eastern Oromia. The aim of the research was to assess and mapping suitability area of water harvesting site in Daro Labu District. There are various methodologies and criteria to identify suitable sites and techniques for rainwater harvesting (RWH). Determining the best method or guidelines for site selection, however, is difficult. GIS is the recent technology of spatiotemporal data used to assess the factor of influences for rainfall and runoff depth estimation for identification of potential area of RWH for crop production and mapping of the potential site. The influence factors for consideration of the assessment was climate data, soil texture and depth, land use and land cover (LULC) type, slope difference were used. The total area of the catchment was about 156064.72 ha. The SCS–CN for rainfall used to runoff depth estimation compute runoff and volume from the land surface depend on the level of antecedent moisture condition (AMC.) The length of wet and dry season of the study area were known with antecedent moisture condition (AMC) of II, I III by having the values of 82, 67 and 91 respectively. The annual of 20 years average rainfall was 925.2 mm with maximum and minimum of 1134 mm and 737.3 mm respectively with average annual runoff depth of estimate was 185.3 mm. From the estimated annual runoff the volume of water harvested was about 2.89 × 10⁸ m³. The suitability map of the study area shows extremely potential, highly potential, potential and not potential accounts by coverage areas about (12,797.3 ha) 8.2%, (25,906.7 ha) 16.6%, (98,302.8 ha) 63.5%, and (19,040 ha) 7.5% respectively. The runoff in the study area was affected by geomorphological factors, particularly, land use change, topography, soil texture and depth, drainage density, and population density affects the runoff rate and volume significantly.