Seasonal Rainfall Variability Research Articles

Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study

The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community.

Variability in soil moisture using AMSR-E product- A regional case study in the province of Marathwada division, India

ABSTRACT Soil moisture plays a crucial role in the assessment of weather patterns and analyzing the precipitation. The moisture analysis in the soil surface has been most efficient with passive remote sensing. The study attempts extraction of soil moisture and quantifying it with rainfall. The Marathwada division was selected as the study area, the region corresponds to low rainfall and is a drought-prone area. The data used were AMSR-E/Aqua Daily L3 gridded soil moisture and CHRS rainfall data, the daily product of 25 km×25 km resolution. The methodology was demonstrated for the years 2003, 2004, and 2005. A cumulative 122 AMSR-E daily ascending and descending scenes were retrieved and analyzed for the rainy season. The spatial analysis depicts peak soil moisture of 25% for the year 2005, greater than 20%, and 18% during 2003 and 2004, respectively. Peak moisture corresponding to all years is 20% in July compared to 13%, 17%, and 16% in June, August, and September, respectively. The temporal analysis revealed that during the onset of monsoon, soil moisture is 4% with 1.5 mm rainfall and it increases by 25% at peak rainfall of 39 mm. The soil moisture variation is in line with seasonal changes and rainfall variations.

Temporal trends and source apportionment of water pollution in Honghu Lake, China.

Honghu Lake, the largest shallow lake in Jianghan Plain of China, is essential for maintaining ecosystem functioning in this region. However, water pollution and high disturbance are seriously threatening the ecological security of this lake. To explore the causes of water quality fluctuations in Honghu Lake, the water quality index method (CCME-WQI), multivariate statistical, and source apportionment techniques were adopted to characterize temporal trends in lake water quality (2004-2017), identify the main driving factors of water quality indicators, and quantify the contribution of various pollution sources. Besides, the water periods of the lake have been reclassified due to the seasonal variation of rainfall in the study area. The results of CCME-WQI showed that the water quality in Honghu Lake initially improved over 2004-2011, with better water quality in the wet period than in the dry periods, while the results over 2012-2017 were found to be opposite. Correlation analysis identified untreated industrial wastewater (UIW) as the main pollution source affecting CODMn concentrations in Honghu Lake, while untreated domestic sewage discharge (UDS) was identified as the main pollution source affecting BOD and F. coli concentrations. The main pollution sources affecting nutrient indicators were rainfall and enclosure aquaculture (EA). Principal component analysis (PCA) combined with absolute principal component score-multiple linear regression model (APCS-MLR) further appointed the source contribution of each pollution source to water quality indicators. The results showed that EA in 2012 was reduced by 81% compared with 2004, resulting in the contribution of EA to NH3-N, TP, and TN decreased by 0.2 mg L-1, 0.039 mg L-1, and 0.37 mg L-1, respectively. Compared with 2012, UIW was reduced by 65% in 2016, resulting in the contribution of UIW to CODMn decreased by 1.17 mg L-1. In addition, compared with 2004, UDS decreased by 85% in 2016, and the contribution of UDS to BOD and F. coli decreased by 0.7 mg L-1 and 887 cfu L-1, respectively. Based on the results of APCS-MLR, it was predicted that the concentrations of COD and TP in Honghu Lake would meet the water quality requirements after 2017. However, the rainfall non-point source pollution must be further controlled to achieve the desired level of TN concentration. This study provided an accurate method for analyzing lake water pollution, and the results can provide a valuable reference for optimizing water quality management and pollution control strategies within Honghu Lake.

Analysis of Climate Variability and Trends in Southern Ethiopia

This study investigated the trends and variability of seasonal and annual rainfall and temperature data over southern Ethiopia using time series analysis for the period 1983–2016. Standard Anomaly Index (SAI), Coefficient of Variation (CV), Precipitations Concentration Index (PCI), and Standard Precipitation Index (SPI) were used to examine rainfall variability and develop drought indices over southern Ethiopia. Temporal changes of rainfall trends over the study period were detected using Mann Kendall (MK) trend test and Sen’s slope estimator. The results showed that the region experienced considerable rainfall variability and change that resulted in extended periods of drought and flood events within the study period. Results from SAI and SPI indicated an inter-annual rainfall variability with the proportions of years with below and above normal rainfall being estimated at 56% and 44% respectively. Results from the Mann Kendall trend test indicated an increasing trend of annual rainfall, Kiremt (summer) and Bega (dry) seasons whereas the Belg (spring) season rainfall showed a significant decreasing trend (p < 0.05). The annual rate of change for mean, maximum and minimum temperatures was found to be 0.042 °C, 0.027 °C, and 0.056 °C respectively. The findings from this study can be used by decision-makers in taking appropriate measures and interventions to avert the risks posed by changes in rainfall and temperature variability including extremes in order to enhance community adaptation and mitigation strategies in southern Ethiopia.

Microphysical Characteristics of Rainfall Observed by a 2DVD Disdrometer during Different Seasons in Beijing, China

The seasonal variations of raindrop size distribution (DSD) and rainfall are investigated using three-year (2016–2018) observations from a two-dimensional video disdrometer (2DVD) located at a suburban station (40.13° N, 116.62° E, ~30 m AMSL) in Beijing, China. The annual distribution of rainfall presents a unimodal distribution with a peak in summer with total rainfall of 966.6 mm, followed by fall. Rain rate (R), mass-weighted mean diameter (Dm), and raindrop concentration (Nt) are stratified into six regimes to study their seasonal variation and relative rainfall contribution to the total seasonal rainfall. Heavy drizzle/light rain (R2: 0.2~2.5 mm h−1) has the maximum occurrence frequency throughout the year, while the total rainfall in summer is primarily from heavy rain (R4: 10~50 mm h−1). The rainfall for all seasons is contributed primarily from small raindrops (Dm2: 1.0~2.0 mm). The distribution of occurrence frequency of Nt and the relative rainfall contribution exhibit similar behavior during four seasons with Nt of 10~1000 m−3 registering the maximum occurrence and rainfall contributions. Rainfall in Beijing is dominated by stratiform rain (SR) throughout the year. There is no convective rainfall (CR) in winter, i.e., it occurs most often during summer. DSD of SR has minor seasonal differences, but varies significantly in CR. The mean values of log10Nw (Nw: mm−1m−3, the generalized intercept parameter) and Dm of CR indicate that the CR during spring and fall in Beijing is neither continental nor maritime, at the same time, the CR in summer is close to the maritime-like cluster. The radar reflectivity (Z) and rain rate (?) relationship (Z = ?R?) showed seasonal differences, but were close to the standard NEXRAD Z-R relationship in summer. The shape of raindrops observed from 2DVD was more spherical than the shape obtained from previous experiments, and the effect of different axis ratio relations on polarimetric radar measurements was investigated through T-matrix-based scattering simulations.

Mapping dynamic non-perennial stream networks using high-resolution distributed hydrologic simulation: A case study in the upper blue river basin

More than half of all streams globally are non-perennial, and thus dynamic due to their expanding and retracting nature. Field mapping in conjunction with observational data from gauges and/or in-situ loggers is a typical approach for studying non-perennial stream dynamics, but these approaches underrepresent their spatiotemporal variability. High-resolution distributed hydrological modeling promises to bridge this gap, thanks to advances in model physics, remote sensing, and computational power. As the first step towards this goal, we investigate the capability of distributed hydrologic modeling to capture stream dynamics in Upper Blue River Basin, OK. Coupled Routing and Excess STorage (CREST), a distributed hydrological model, is used to simulate spatiotemporally varied streamflow at 10-meter spatial resolution and daily time steps. USGS stream gauge data and in-situ state logger data are used to calibrate and validate the simulation at the watershed outlet and small headwater tributaries, respectively. Dynamic Surface Water Estimate (DSWE), a LANDSAT product is also compared with simulated water presence in high-order streams. Results show that the CREST model can capture low-moderate streamflow values at the watershed outlet with a log-NSE value over 0.7 in the validation period, while underestimating high flow values due to the daily time step. Also, the calibrated model can accurately estimate wet/dry status as monitored by in-situ state loggers in nine headwater catchments. The dynamic stream networks are mapped over 2510 stream segments using the CREST simulation. Non-perennial streams are the most dynamic in small headwater tributaries (contributing area <2 km2) and high-order streams are sustained by perennial flow. As hydrologic interpretation of the stream dynamics, the interannual and seasonal variability in rainfall and evapotranspiration is well reflected by the water occurrence in streams. Across various catchments, a consistent threshold behavior is found between drainage density and unit discharge, indicating the control of runoff generation on the flowing stream networks. The mapping also identifies differences in stream dynamics caused by heterogeneities in land cover and soil properties. Given the prevalence of dynamical streams worldwide, our analysis illustrates the potential for mapping them using distributed hydrologic models.

Understanding the Temporal Variability of Rainfall for Estimating Agro-Climatic Onset of Cropping Season over South Interior Karnataka, India

Annual, seasonal and intra-seasonal variations in rainfall affect crop production from land preparation to the realization of potential crop yield in a region. Particularly, the onset of the rainy season is most crucial for determining the sowing period. Statistical analysis (Modified Mann-Kendall aka MMK-test for trend and likelihood ratio test for shifting pattern) of 60 years rainfall of south interior Karnataka (SIK) inferred the presence of temporal variability in rainfall. There was a monotonic increase in rainfall of February, March, April, June and August months (a positive sign of MM-K (tau) value), with a negligible rate of change (Sen’s slope towards zero). Upon seasonal analysis, there was a significant increase in winter, pre-monsoon and monsoonal rainfall as compared to post-monsoonal rainfall (higher Sen’s slope for pre-monsoon), indicating a need for agronomic interventions for estimating an effective date of sowing for reducing risks of crop production. Further, the agro-climatic onset of cropping season was estimated by considering soil–crop–water relations. Earlier onset of cropping season was estimated based on thresholds of soil–crop–water relations, which highlights sowing of crops in advance (May 1st fortnight) instead of late (June 1st fortnight) to avoid crop losses due to early-season drought.

Seasonal rainfall variability and its correlation with potential drivers over Western Oromia

Characterizing seasonal rainfall and identifying its association with the climate drivers are important to produce reliable seasonal climate forecasts of a given area. This study examined the rainfall variability and its relationships with SST, SLP and sunspot indices by statistical methods over Western Oromia. The regional is characterized by monomodal pattern of 60% seasonal rainfall contribution in Kiremt season. High temporal anomaly rainfall but no extremes were observed from 1981-2017. An insignificant decreasing Kiremt and Belg but, an increasing Bega trend was observed. ENSO and PDO/ DMI and AMO showed negative/ positive correlation with kiremt rainfall. ENSO, DMI and AMO showed positive correlation for belg rainfall whereas, PDO showed associated negatively for the most parts of the region. PSI to kiremt/Belg rainfall correlation showed significant positive/Negative spatial association over the western part (West Wolega and Illubabor) / the East part (Jimma &amp; East wollega) of the region. The results showed that there is decreasing trends in rainfall anomalies for main rainy seasons of region and good correlations these various indices. Therefore, it is suggested that seasonal climate predictions may consider these large-scale determinants oscillation indices like AMO and PDO in predicting climatic events. Â

Influence of hydrological factors on bacterial community structure in a tropical monsoonal estuary in India.

In the present study, we analyzed variations in bacterial community structure along a salinity gradient in a tropical monsoonal estuary (Cochin estuary [CE]), on the southwest coast of India, using Illumina next-generation sequencing (NGS). Water samples were collected from eight different locations thrice a year to assess the variability in the bacterial community structure and to determine the physico-chemical factors influencing the bacterial diversity. Proteobacteria was the most dominant phyla in the estuary followed by Bacteroidetes, Cyanobacteria, Actinobacteria, and Firmicutes. Statistical analysis indicated significant variations in bacterial communities between freshwater and mesohaline and euryhaline regions, as well as between the monsoon (wet) and nonmonsoon (dry) periods. The abundance of Betaproteobacteria was higher in the freshwater regions, while Alphaproteobacteria and Epsilonproteobactera were more abundant in mesohaline and euryhaline regions of the estuary. Gammaproteobacteria was more abundant in regions with high nutrient concentrations. Various bacterial genera indicating the presence of fecal contamination and eutrophication were detected. Corrplot based on Pearson correlation analysis demonstrated the important physico-chemical variables (temperature, salinity, dissolved oxygen, and inorganic nutrients) that influence the distribution of dominant phyla, class, and genera. The observed spatio-temporal variations in bacterial community structure in the CE were governed by regional variations in anthropogenic inputs and seasonal variations in monsoonal rainfall and tidal influx.

Global connections between El Nino and landslide impacts

El Nino is a critical part of global inter-annual climate variability, and the intensity of El Nino has major implications for rainfall-induced natural hazards in many vulnerable countries. The impact of landslides triggered by rainfall is likely to be modulated by the strength of El Nino, but the nature of this connection and the places where it is most relevant remains unconstrained. Here we combine new satellite rainfall data with a global landslide exposure model to show that El Nino has far-reaching effects on landslide impacts to people and infrastructure. We find that the impact of El Nino on landslide exposure can be greater in parts of Southeast Asia and Latin America than that due to seasonal rainfall variability. These findings improve our understanding of hazard variability around the world and can assist disaster mitigation efforts on seasonal timescales.

Interpreting the influence of rainfall and reservoir water level on a large-scale expansive soil landslide in the Danjiangkou Reservoir region, China

Expansive soils are widely distributed in the Danjiangkou Reservoir region (DRR), China. In recent years, with a rise in the reservoir water level (RWL), reservoir-related expansive soil landslides have gradually become a serious issue in this area. However, few studies on reservoir-related expansive landslides have been reported. Taking the Jingang landslide as a case study, a detailed analysis was performed to interpret the movement characteristics of the landslide under the influence of rainfall and the RWL, based on the comprehensive in-situ monitoring data and laboratory soil tests. The Jingang landslide is a large-scale expansive soil landslide triggered by reservoir operation. Available data indicate that the displacement of the Jingang landslide displays step-like trends and mainly occurs during high RWL or filling operation periods. Both seasonal rainfall and RWL variation are important factors that cause the ground movement of a landslide. The onset of rapid ground movement usually corresponded to an RWL of 161–163 m a.s.l.. The motion pattern is primarily retrogressive, and the displacement velocity increases towards the toe, which could reach 41 mm/month. The free swelling rates of the soils in the Jingang landslide are approximately 40–60%. The stability calculation indicates that the factor of safety of the Jingang landslide decreases from 1.049 to 1.038 due to the swell pressure of expansive soil, and this has a significant adverse effect on the landslides.

Intense precipitation events during the monsoon season in Bangladesh as captured by satellite-based products

AbstractExtreme precipitation events are a serious threat to societal well-being over rainy areas such as Bangladesh. The reliability of studies of extreme events depends on data quality and their spatial and temporal distribution, although these subjects remain knowledge gaps in many countries. This work focuses on the analysis of four satellite-based precipitation products for monitoring intense rainfall events: the Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), the PERSIANN-Climate Data Record (PERSIANN-CDR), the Integrated Multisatellite Retrievals (IMERG), and the CPC Morphing Technique (CMORPH). Five indices of intense rainfall were considered for the period 2000-2019 and a set of 31 rain gauges for evaluation. The number and amount of precipitation associated with intense rainfall events are systematically underestimated or overestimated throughout the country. While random errors are higher over the wetter and higher-elevation north- and southeastern parts of Bangladesh, biases are more homogeneous. CHIRPS, PERSIANN-CDR and IMERG perform similar capturing total seasonal rainfall, but variability is better represented by CHIRPS and IMERG. Better results were obtained by IMERG, followed by PERSIANN-CDR and CHIRPS, in terms of climatological intensity indices based on percentiles, although the three products exhibited systematic errors. IMERG and CMORPH systematically overestimate the occurrence of intense precipitation events. IMERG showed the best performance representing events over a value of 20 mm/day; CMORPH exhibited random and systematic errors strongly associated with a poor representation of interannual variability in seasonal total rainfall. The results suggest that the datasets have different potential use and such differences should be considered in future applications regarding extreme rainfall events and risk assessment in Bangladesh.

Wet and dry spells in Senegal: comparison of detection based on satellite products, reanalysis, and in situ estimates

Abstract. In this study, the detection and characteristics of dry/wet spells (defined as episodes when precipitation is abnormally low or high compared to usual climatology) drawn from several datasets are compared for Senegal. Here, four datasets are based on satellite data (TRMM-3B42 V7, CMORPH V1.0, TAMSAT V3, and CHIRPS V2. 0), two on reanalysis products (NCEP-CFSR and ERA5), and three on rain gauge observations (CPC Unified V1.0/RT and a 65-rain-gauge network regridded by using two kriging methods, namely ordinary kriging, OK, and block kriging, BK). All datasets were converted to the same spatio-temporal resolution: daily cumulative rainfall on a regular 0.25∘ grid. The BK dataset was used as a reference. Despite strong agreement between the datasets on the spatial variability in cumulative seasonal rainfall (correlations ranging from 0.94 to 0.99), there were significant disparities in dry/wet spells. The occurrence of dry spells is less in products using infrared measurement techniques than in products coupling infrared and microwave, pointing to more frequent dry spell events. All datasets show that dry spells appear to be more frequent at the start and end of rainy seasons. Thus, dry spell occurrences have a major influence on the duration of the rainy season, in particular through the “false onset” or “early cessation” of seasons. The amplitude of wet spells shows the greatest variation between datasets. Indeed, these major wet spells appear more intense in the OK and Tropical Rainfall Measuring Mission (TRMM) datasets than in the others. Lastly, the products indicate a similar wet spell frequency occurring at the height of the West African monsoon. Our findings provide guidance in choosing the most suitable datasets for implementing early warning systems (EWSs) using a multi-risk approach and integrating effective dry/wet spell indicators for monitoring and detecting extreme events.

Quantifying monthly water balance to estimate water deficit in Mayurakshi River basin of Eastern India

Recorded seasonal variation and uneven distribution of rainfall is one of the major issues to the agrarian society and the domestic water users today. For strategic planning of water use, scientific estimation of water balance in different spatio-temporal scale is necessary. For this, the present paper intends to investigate monthly and annual water balance state of Mayurakshi River basin (including 45 sub-basins) using precipitation (P), runoff (Q), evapotranspiration (ET) and recharge/soil moisture. P, Q, ET, respectively, vary from 1625.97 mm to 2037.93 mm, 182.19 mm to 1503.15 mm and 1050 mm to 1500 mm for the entire basin. The monthly pattern shows significant variation attributed to strong seasonality of P and temperature (T). Monsoon months recorded heavy maximum P, Q and actual ET and on the other hand, all these are very poor during pre-monsoon season. Groundwater recharge annually differs spatially, but annual average recharge is 307mmwith main contribution in monsoon months. Water deficit condition prevails in pre-monsoon season; strong groundwater lowering and drying of top soil are some explicit evidences of the same. This seasonal water scarcity withstands against multi-cropping strategies of the agrarian communities. To cope with this emerging situation, pressure on groundwater is mounting but it is the fact that this resource is also highly limited and precious. Quantification of monthly water balance and water deficit will be inputs to the strategy development for sustainable water resource management.

Analysis of Seasonal rainfall variability in Rainfed Agriculture in Homa Bay County

Analysis of Seasonal rainfall variability in Rainfed Agriculture in Homa Bay County

Seasonality of pluviosity and saline intrusion drive meiofauna and nematodes on an Amazon freshwater-oligohaline beach

Salinity in estuarine environments is one of the most important influencing factors for meiofauna. The effects of seasonal variation in rainfall and saline intrusion on meiofauna/nematodes diversity and abundance on a mesotidal beach located in the mixing zone of an Amazon estuary were analyzed. Samplings occurred bimonthly over one year at four stations in the intertidal zone. Density variation in meiofauna community had a direct relationship with increased rainfall and decreased salinity. Oligochaeta had the highest density during the months with the highest rainfall and lowest salinity. The highest total Nematoda richness value, as well as richness of brackish water genera value, was registered in September and gradually decreased until March, a month with the highest rainfall. Enoplolaimus, a typical predator, had the highest densities in the months with the highest rainfall, probably due to the food availability (Oligochaeta). The highest Nematoda density at the station closest to the high-tide mark occurred due to detritus accumulation. On the other hand, the lower densities at the middle stations are due to the higher desiccation that occurs in these regions compared to the station closer to the low-tide mark. Meiofauna was strongly affected by seasonal changes in rain and salinity, both at the level of large groups and at the level of Nematoda genera. In addition, seasonal changes in salinity clearly affected organisms indirectly through their prey-predator interactions.

Assessment of satellite products for filling rainfall data gaps in the Amazon region

AbstractRainfall data series with adequate quality and length are often incomplete or nonexistent. Thus, filling in rainfall gaps becomes necessary to complete databases. This article proposes the use of satellite products (TRMM—Tropical Rainfall Measuring Mission, CHIRPS—Climate Hazards Group InfraRed Precipitation with Stations and CMORPH—CPC Morphing Technique) to fill gaps in the rainfall historical series. The simple regression method, using satellite rainfall estimates, was tested to fill the missing data from 164 rainfall gauge stations in the Amazon region. Large dispersions were observed between rainfall data, with R2 ranging from 0.383 to 0.844, the best results were found in areas with less rainfall. As well, the greatest performance of the products was verified in the dry period, with r and d higher than 0.899 and 0.950, respectively. The product with the best representation in the region was CHIRPS, which had the lowest monthly values of mean absolute error (0.979 mm) and root mean square error (3.656 mm). The results confirm that the satellite estimates satisfactorily represent the seasonal variation of rainfall in the region, despite presenting cases of overestimation and underestimation of data. The higher performance of CHIRPS can be explained by the higher spatial resolution (0.05°), allowing for more accurate weather forecasts. In fact, CHIRPS has the CHPclim model, which adds other factors to the good product performance. These characteristics justify the better performance of the CHIRPS product for filling gaps in daily rainfall data in the Amazon region, favoring the best monthly rainfall estimates for each region state analyzed.Recommendations for Resource Managers Satellite products have been increasingly used for estimating rainfall data in regions with a low number of installed rainfall gauge stations. Thus, the assessment and selection of these products needs to be elaborated for the best decision making of water resource managers. Rainfall data are important to recognize the occurrence patterns for prediction of the climatic behavior of a region. Sectors such as agriculture and disaster prevention (droughts, floods, erosion of watersheds, and river silting) need knowledge of rainfall for planning, management, and mitigation. Knowledge of rainfall behavior is very important in the Amazon region. In this case, the dry season and temperatures have been increasing due to global climate change. These changes establish conditions for more intense fires, which increases the deforestation of the region.

Spatial‐Temporal Simulations of Soil Moisture Content in a Large Basin of the Loess Plateau, China

Soil moisture is a crucial factor limiting the growth and survival of plants on the Loess Plateau. Its level has a severe impact on plants’ growth and development and the type and distribution characteristics of communities. This study area is the Jihe Basin in the Loess Plateau, China. Multiple linear regression models with different environmental variables (land use, topographic and meteorological factors, etc.) were developed to simulate soil moisture’s spatial and temporal changes by integrating field experiments, indoor analysis, and GIS spatial analysis. The model performances were evaluated in the Jihe Basin, with soil moisture content measurements. The result shows that soil moisture content is positively correlated with soil bulk density, monthly rainfall, topographic wetness index, land use coefficient, and slope aspect coefficient but negatively correlated with the monthly‐averaged temperature and the relative elevation coefficient. The selected variables are all related to the soil moisture content and can account for 75% of the variations of soil moisture content, and the remaining 25% of the variations are related to other factors. Comparing the simulated and measured values at all sampling points shows that the average error of all the simulated values is 0.09, indicating that the simulation has high accuracy. The spatial distribution of soil moisture content is significantly affected by land use and topographic factors, and seasonal variation is remarkable in the year. Seasonal variation of soil moisture content is determined by the seasonal variation of rainfall and the air temperature (determining evaporation) and vegetation growth cycle. Therefore, the proposed model can simulate the spatial and temporal variation of soil moisture content and support developing the soil and water loss model on a basin scale.

Effects of Climate Change on Pastoral Households in Harshin District of Somali Region, Ethiopia

This study was conducted in Harshin district of Somali region, Ethiopia to understand the climate change trends, their consistency with pastoralists' perceptions, and their effects on pastoral households. The study used both qualitative and quantitative data collected from 143 households through household survey. Also, FGDs and KII were employed to triangulate and substantiate the reports from household survey. Data were analyzed using SPSS and Chi-square tests to test a degree of significance between the pastoral and agro pastoral household to the impact of climate change. Mann–Kendall’s trend test and Sen’s slope estimator was employed to determine climate change trends of the study area. This study showed that pastoral households perceived an increasing trend in annual temperature and a decreasing trend in annual and seasonal rainfall. Mann- Kendall's trend analysis confirmed pastoral communities' perceptions of higher temperatures and rainfall variability, with the exception of a long-term decline in rainfall. The findings further indicated that six droughts (one severe and five moderate droughts) were observed for the period 1983 to 2017. The result indicated that the significant increment of temperature along with high inter annual and seasonal rainfall variability have been causing adverse impacts on crop and livestock production. Therefore, there is a need to provide drought tolerant and early maturing crops and improved livestock breeds for pastoral households. Water related intervention such as small-scale irrigation farming and water harvesting during good rainy seasons is also paramount to enhance climate resilience of the local people.

黄河源区不同类型冻土土壤水分入渗特性

冻土土壤水分运动由于受到冻融过程的影响而显示其独特性，而目前对于不同类型冻土土壤水分入渗特性尚缺乏足够的认识。为此，以黄河源区康穷小盆地多下坡年冻土和上坡季节冻土区为例，结合季节降雨变化，基于大气降水、冻土土壤水分、冻结层上水等野外监测数据分析，采用HYDRUS-1D软件冻融模块进行土壤水分入渗模拟，对比分析了融化期多年冻土和季节冻土土壤水分运移过程的差异性，研究结果表明：①在快速融化阶段，降雨以地表径流为主，表层土壤水分含量增加，土壤下渗有限，冻结层上水位上升幅度较小；在稳定融化阶段，土壤水分含量增加，土壤水分下渗增强，受冻土层阻隔影响，多年冻土区冻结层上水水位上升幅度较大，季节冻土区土壤水分则以深层渗漏或侧向流动为主。②受到降雨强度、土壤质地、蒸散发、植被覆盖等因素的影响，降雨损失主要以地表径流为主，下坡各层土壤水分随冻结土壤融化自上而下逐渐增加并达到饱和状态，但上坡表层土壤不易达到饱水状态。③区域河流贯穿融区地下水发育，导致上坡冻结层上水位小幅度上升，下坡冻结层上水位的变化除受到降雨入渗的影响外，还受到融区地下水的影响，引起下坡冻结层上水位的快速上升。研究结果有助于深入了解全球气候变化背景下的冻土退化及其水文效应，进而为定量评估流域水资源脆弱性与区域生态敏感性提供科学依据。;The water movement of frozen soil shows its uniqueness due to the influence of freeze-thaw process. However, there is a lack of in-depth study on soil moisture infiltration characteristics of different types of frozen soil at present. Therefore, a slope cross-section at the small basin of Kangqiong in Source Area of the Yellow River is studied. This zone on the upper slope is covered by seasonally frozen ground and that on the down slope is covered by the permafrost. Based on the field observation, the data has been collected from various sources including atmospheric precipitation, soil moisture, and suprapermafrost water from May 2017 to October 2017. In addition, the soil moisture infiltration is numerically simulated by the freeze-thaw module of HYDRUS-1D software package. Based on seasonal rainfall variation, the difference of soil moisture infiltration in permafrost regions and seasonally frozen regions is statistically investigated during thawing period. The results are summarized in the following: 1) in the rapid thawing stage, the rainfall infiltration is dominated by surface runoff. The water content of surface soil increases, but the infiltration of soil is limited. So, the suprapermafrost water flow rises slightly. During the stable thawing stage, soil water content increases and soil moisture infiltration increases. Influenced by the barrier of permafrost layer, the suprapermafrost water flow in permafrost regions rises greatly. While in seasonally frozen regions, the soil moisture infiltration is dominated by deep leakage or lateral flow. 2) Influenced by rainfall intensity, soil texture, evapotranspiration and vegetation coverage, the rainfall loss is mainly caused by surface runoff. Soil moisture content of each layer on the down slope increases gradually from top to bottom with the thawing of frozen soil and reaches the saturation state. Soil moisture content in surface layer on the upper slope has been rising with time, but it can not reach a stable state. 3) The existence of regional talik groundwater leads to a small increase of the suprapermafrost water level on the upper slope. In addition to rainfall infiltration, the change of the suprapermafrost water on the down slope is also affected by talik groundwater, which causes the rapid rise of the suprapermafrost water level on the down slope. The results are helpful to understand the degradation of frozen soil and its hydrological effects under the background of global climate change. At the same time, it will provide scientific basis for quantitative assessment of water resources vulnerability and regional ecological sensitivity.