Quantifying Groundwater Resources for Municipal Water Use in a Data-Scarce Region

Groundwater is a major source of drinking water worldwide, often considered more reliable than surface water and more accessible. Nowadays, there is wide recognition by the scientific community that groundwater resources are under threat from overexploitation and pollution. Furthermore, frequent and prolonged drought periods due to climate change can seriously affect groundwater recharge. For an appropriate and sustainable management of water systems supplied by springs and/or groundwater withdrawn from aquifers through drilling wells or drainage galleries, the need arises to properly quantify groundwater resources availability, mainly at the monthly scale, as groundwater recharge is influenced by seasonality, especially in the Mediterranean areas. Such evaluation is particularly important for ungauged groundwater bodies. This is the case of the aquifer supplying the Santissima Aqueduct, the oldest water supply infrastructure of the city of Messina in Sicily (Southern Italy), whose groundwater flows are measured only occasionally through spring water sampling at the water abstraction plants. Moreover, these plants are barely maintained because they are difficult to reach. In this study, groundwater recharge assessment for the Santissima Aqueduct is carried out through a GIS-based inverse hydrogeological balance methodology. Although this approach was originally designed to assess aquifer recharge at the annual scale, wherever a model conceptualization of the groundwater system was hindered by the lack of data, in the present study some changes are proposed to adjust the model to the monthly scale. In particular, the procedure for evapotranspiration assessment is based on the Global Aridity Index within the Budyko framework. The application of the proposed methodology shows satisfactory results, suggesting that it can be successfully applied for groundwater resources estimation in a context where monthly information is relevant for water resources planning and management.

Estimation des réserves en eau souterraine régulatrices dans la partie moyen-atlasique du fleuve Sebou (Maroc)

L'estimation de la ressource en eau souterraine dans le Moyen-Atlas est investiguée à partir des débits mesurés dans l'oued Sebou à la station d'Azzaba, sur la période 1959–2015. Pour cela presqu'une centaine de phases de tarissement a été individualisé pour construire des courbes maîtresses de tarissement à partir desquelles le calcul des volumes des nappes est possible. Il est calculé mensuellement à partir du débit modal dont nous posons l'hypothèse qu'il représente la limite entre ruissellement et alimentation uniquement par les nappes. Les calculs donnent un volume de la réserve régulatrice moyen de 54 hm3, mais extrêmement variable à l'échelle interannuelle et intra-annuelle. Ces résultats démontrent la faible inertie des aquifères du Moyen Atlas et soulignent la vulnérabilité du secteur agricole vis-à-vis de cette ressource même lors de courte période de sécheresse. The estimate of the groundwater resource in the Middle Atlas is investigated from the runoff measured in the Sebou wadi at the Azzaba station, over the period 1959–2015. Almost a hundred recession curves have been individualized to build the master recession curves from which the calculation of the volumes of groundwater is possible. It was calculated at a monthly scale from the modal flow, which we assume is the limit between runoff and flow supply only by the aquifers. The results indicated a volume of the average regulatory reserve of 54 hm3, but extremely variable on an inter-annual and intra-annual scale. These results demonstrate the low inertia of the Middle Atlas aquifers and underline the vulnerability of the agricultural sector to this resource even during short periods of drought.

Evaluation of Satellite-Derived Products for the Daily Average and Extreme Rainfall in the Mearim River Drainage Basin (Maranhão, Brazil)

Satellite precipitation estimates are used as an alternative or as a supplement to the records of the in situ stations. Although some satellite precipitation products have reasonably consistent time series, they are often limited to specific geographic areas. The main objective of this study was to evaluate CHIRPS version 2, MSWEP version 2, and PERSIANN-CDR, compared to gridBR, as daily mean and extreme inputs represented on a monthly scale and their respective seasonal trends of rainfall in the Mearim River Drainage Basin (MDB), Maranhão state, Brazil. Estimates of errors were calculated (relative error, pbias; root mean square error, RMSE, and Willmott concordance index, d), and the chances of precipitation were estimated by remote sensing (RES). In addition, trends in precipitation were estimated by the two-sample Mann–Kendall test. Given the overall performance, the best products for estimating monthly mean daily rainfall in the MDB are CHIRPS and PERSIANN-CDR, especially for rainy months (December to May). For daily extremes on the monthly scale, the best RES is PERSIANN-CDR. There is no general agreement between gridBR and RES methods for the trend signal, even a nonsignificant one, much less a significant one. The use of MSWEP for the MDB region is discouraged by this study because it overestimates monthly averages and extremes. Finally, studies of this kind in drainage basins are essential to improve the information generated for managing territories and developing regionalized climate and hydrological models.

Effects of Water-Saving Irrigation on Hydrological Cycle in an Irrigation District of Northern China

In an arid and semi-arid irrigation district, water-saving practices are essential for the sustainable use of water resources. The Soil and Water Assessment Tool (SWAT) was used to simulate hydrological processes under three water-saving scenarios for the Jinghui Canal irrigation district (JCID) in Northwest China. Due to the lack of available hydrometric stations in the study area, the model was calibrated by Moderate Resolution Imaging Spectroradiometer Global Evaporation (MOD16) from 2001 to 2010 on monthly scale. The simulation results showed that using MOD16 to calibrate the SWAT model was an alternative approach when hydro-meteorological data were lacking. It also revealed that the annual average surface runoff (SURQ) decreased by 4.13%, 8.37% and 12.08% and the percolation (PERC) increased by 3.67%, 7.59% and 11.19%, with the improvement of the water-saving degree (the effective utilization coefficient of irrigation water (EUCIW) increased by 0.1, 0.2 and 0.3). Compared with the above two components, the change in actual evapotranspiration (ET) was not obvious. From the perspective of the spatial scale, the changes in every component in the east regions were generally greater than those in the west regions. On a monthly scale, the change in every component was mainly during these two periods. The analysis results of water balance in the study area showed that the proportion of SURQ in water balance decreased (from 14.02% to 12.33%), while that of PERC increased (from 10.99% to 12.22%) after the application of the water-saving irrigation. The decrease in the variation in soil water content indicates that the improvement of the water-saving degree plays a positive role in maintaining the sustainable development of water resources in irrigated areas. This study demonstrates the potential to use remotely sensed evapotranspiration data for hydrological model calibration and validation in a sparsely gauged region with reasonable accuracy. The results of this study also provide a reference for the effect of water-saving irrigation in the irrigated area.

Spatial and Temporal Characteristics of Rainfall Anomalies in 1961–2010 in the Yangtze River Basin, China

Understanding rainfall anomalies and their relationship with floods in the Yangtze River Basin (YRB) is essential for evaluating flood disasters, which have a great impact on the development of agriculture and the economy. On the basis of daily rainfall data from 1961 to 2010 from 178 meteorological stations, the temporal and spatial characteristics of rainfall anomalies in the YRB were studied on an annual scale, seasonal scale, and monthly scale. The annual rainfall of the YRB showed a generally increasing trend from 1961 to 2010 (14.22 mm/10 a). By means of the Bernaola–Galvan abrupt change test and Redfit spectrum analysis, it was found that the annual average rainfall increased abruptly after 1979 and had a cycle of 2–3 years. On the seasonal scale, the rainfall in spring and autumn showed a gradually decreasing trend, especially in September, while it showed a significant increasing trend in summer and winter in the YRB. As for the monthly scale, the rainfall in the rainy season from June to July presented a clear increasing trend during the study period, which greatly enhanced the probability of floods in the YRB. Additionally, through the analysis of the spatial distribution characteristics of rainfall in the entire YRB from 1961 to 2010, it was observed that the annual rainfall amount in the YRB presented an “increase–decrease–increase” tendency from east to west, accompanied by a rain belt that continuously moved from west to east. Moreover, the rainfall characteristics in flood years were summarized, and the results revealed that the years with rainfall anomalies were more likely to have flood disasters. However, anomalies alone would not result in big floods; the spatially and temporally inhomogeneous rainfall distribution might be the primary reason for flood disasters in the entire YRB.

Evaluation of high-resolution meteorological global data products using flux tower observations across Brazil

<p>In the past decade, the scientific community has seen an increase in the number of global hydrometeorological products. This has been possible with efforts to push global hydrological and land surface modelling to hyper-resolution applications. As the resolution of these datasets increase, so does the need to compare their estimates against local in-situ measurements. This is particularly important for Brazil, whose large continental scale domain results in a wide range of climate and biomes. In this study, high-resolution (0.1-0.25 deg) global and regional meteorological datasets are compared against flux tower observations at 11 sites across Brazil (for periods between 1999-2010), covering Brazil’s main land cover types (tropical rainforest, woodland savanna, various croplands, and tropical dry forests) to assess the quality of four global reanalysis products [ERA5-Land, GLDAS2.0, GLDAS2.1, and MSWEPv2.2] and one regional gridded dataset developed from local interpolation of meteorological variables across the country [Brazilian National  Meteorological Database (referred here as Xavier)]. Whilst the only measured variable for MSWEP was precipitation, all other gridded datasets also included surface meteorological variables such as air temperature, wind speed, pressure, downward shortwave and longwave radiation, and specific humidity. Data products were evaluated for their ability to reproduce the daily and monthly meteorological observations at flux towers. A ranking system for data products was developed based on the mean squared error. To identify the possible causes for these errors further analysis was undertaken to determine the contributions of correlation, bias, and variation to the MSE. Results show that, for precipitation, MSWEP outperforms the other datasets at daily scales but at a monthly scale XAVIER performs best. For all other variables, ERA5-Land achieved the best ranking (smallest) errors at the daily scale and averaged the best rank for all variables at the monthly scale. GLDAS2.0 performed least well at both temporal scales, however the newer version (GLDAS2.1) was an improvement of its older version for almost every variable. Xavier wind speed and GLDAS2.0 solar radiation outperformed the other datasets at a monthly scale. The largest contribution to the MSE at the daily scale for all datasets and variables was the correlation contribution whilst at the monthly scale it was the bias contribution. ERA5-Land is recommended when using multiple hydro-meteorological variables to force land-surface models within Brazil.</p>

Impact of Urbanization on Sunshine Duration from 1987 to 2016 in Hangzhou City, China

Worldwide solar dimming from the 1960s to the 1980s has been widely recognized, but the occurrence of solar brightening since the late 1980s is still under debate—particularly in China. This study aims to properly examine the biases of urbanization in the observed sunshine duration series from 1987 to 2016 and explore the related driving factors based on five meteorological stations around Hangzhou City, China. The results inferred a weak and insignificant decreasing trend in annual mean sunshine duration (−0.09 h/d decade−1) from 1987 to 2016 in the Hangzhou region, indicating a solar dimming tendency. However, large differences in sunshine duration changes between rural, suburban, and urban stations were observed on the annual, seasonal, and monthly scales, which can be attributed to the varied urbanization effects. Using rural stations as a baseline, we found evident urbanization effects on the annual mean sunshine duration series at urban and suburban stations—particularly in the period of 2002–2016. The effects of urbanization on the annual mean sunshine duration trends during 1987−2016 were estimated to be −0.16 and −0.35 h/d decade−1 at suburban and urban stations, respectively. For urban stations, the strongest urbanization effect was observed in summer (−0.46 h/d decade−1) on the seasonal scale and in June (−0.63 h/d decade−1) on the monthly scale. The notable negative impact of urbanization on local solar radiation changes was closely related to the changes in anthropogenic pollutions, which largely reduced the estimations of solar radiation trends in the Hangzhou region. This result highlights the necessity to carefully consider urbanization impacts when analyzing the trend in regional solar radiation and designing cities for sustainable development.

At which timescale does the complementary principle perform best in evaporation estimation?

The complementary principle has been widely used to estimate evaporation under different conditions. However, it remains unclear at which timescale the complementary principle performs best. In this study, evaporation estimations were conducted at 88 eddy covariance (EC) monitoring sites at multiple timescales (daily, weekly, monthly, and yearly) by using sigmoid and polynomial generalized complementary functions. The results indicate that the generalized complementary functions exhibit the highest skill in estimating evaporation at the monthly scale. The uncertainty analysis shows that this conclusion is not affected by ecosystem type or energy balance closure method. Through comparisons at multiple timescales, we found that the slight difference between the two generalized complementary functions only exists when the independent variable (x) in the functions approaches 1. The results differ for the two models at daily and weekly scales. However, such differences vanish at monthly and annual timescales, with few high x values occurring. This study demonstrates the applicability of generalized complementary functions across multiple timescales and provides a reference for choosing a suitable time step for evaporation estimations in relevant studies.

The Temporal and Spatial Variations of Stable Isotopes in Precipitation on Different Scales in Yangtze River Basin

Analysis is conducted on the temporal and spatial variations of stable isotopic composition in precipitation, using the data of IAEA/WMO sampling stations, in Yangtze river basin. The correlations between δ18O in precipitation and air temperature/amount of precipitation are analyzed at individual stations and in the whole valley on monthly or annual scales. There is a notable amount effect, the distinct inverse relationship between δ18O and precipitation, on monthly scale in the Yangtze valley and at the selected stations except Chongqing. Compared with monthly scale, the positive correlation between δ18O and air temperature is more marked on annual scale. The interannual variations of δ18O reflect the change of large-scale climate and environment, and are mainly controlled by large-scale weather conditions.