Groundwater Drought Analysis under Data Scarcity: The Case of the Salento Aquifer (Italy)

Salento is a regional coastal karst aquifer located in Southern Italy with a highly complex geological, geomorphological, and hydrogeological structure. High and unruly exploitation of groundwater from licensed and unlicensed wells for irrigation and drinking purposes affects groundwater, with consequent degradation of its qualitative and quantitative status. The increased frequency of meteorological droughts and rising temperatures may only worsen the already compromised situation. The absence of complete and enduring monitoring of groundwater levels prevents the application of some methodologies, which require long time series. The analysis of climate indexes to describe the groundwater level variation is a possible approach under data scarcity. However, this approach may not be obvious for complex aquifers (in terms of scale, intrinsic properties, and boundary conditions) where the response of the groundwater to precipitation is not necessarily linear. Thus, the proposed research deals with the assessment of the response of the Salento aquifer to precipitation variability based on correlations between the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) and groundwater levels for nine monitoring wells from July 2007 to December 2011. The study aims at evaluating the ability of the above indicators to explain the behavior of groundwater on complex aquifers. Moreover, it has the general aim to verify their more general reliable application. Results of three different correlation factors outline direct and statistically significant correlations between the time series. They describe the Salento aquifer as a slow filter, with a notable inertial behavior in response to meteorological events. The SPI 18-months demonstrates to be a viable candidate to predict the groundwater response to precipitation variability for the Salento aquifer.

Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate

Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation–runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000–43,000 km2 size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability.

Analysis of Precipitation Variability using Memory Based Artificial Neural Networks

This article analyzes the variability in precipitation of the Barak river basin using memory-based ANN models called Gamma Memory Neural Network(GMNN) and genetically optimized GMNN called GMNN-GA for precipitation downscaling precipitation. GMNN having adaptive memory depth is capable techniques in modeling time varying inputs with unknown input characteristics, while an integration of the model with GA can further improve its performances. NCEP reanalysis and HadCM3A2 (a) scenario data are used for downscaling and forecasting precipitation series for Barak river basin. Model performances are analyzed by using statistical criteria, RMSE and mean error and are compared with the standard SDSM model. Results obtained by using 24 years of daily data sets show that GMNN-GA is efficient in downscaling daily precipitation series with maximum daily annual mean error of 6.78%. The outcomes of the study demonstrate that execution of the GMNN-GA model is superior to the GMNN and similar with that of the standard SDSM.

Evaluation of GPROF V05 Precipitation Retrievals under Different Cloud Regimes

Precipitation retrievals from passive microwave satellite observations form the basis of many widely used precipitation products, but the performance of the retrievals depends on numerous factors such as surface type and precipitation variability. Previous evaluation efforts have identified bias dependence on precipitation regime, which may reflect the influence on retrievals of recurring factors. In this study, the concept of a regime-based evaluation of precipitation from the Goddard Profiling (GPROF) algorithm is extended to cloud regimes. Specifically, GPROF V05 precipitation retrievals under four different cloud regimes are evaluated against ground radars over the United States. GPROF is generally able to accurately retrieve the precipitation associated with both organized convection and less organized storms, which collectively produce a substantial fraction of global precipitation. However, precipitation from stratocumulus systems is underestimated over land and overestimated over water. Similarly, precipitation associated with trade cumulus environments is underestimated over land, while biases over water depend on the sensor’s channel configuration. By extending the evaluation to more sensors and suppressed environments, these results complement insights previously obtained from precipitation regimes, thus demonstrating the potential of cloud regimes in categorizing the global atmosphere into discrete systems.

Dominant Controls of Cold-Season Precipitation Variability Over the High Mountains of Asia

A robust understanding of the sub-seasonal cold season (November–March) precipitation variability over the High Mountains of Asia (HMA) is currently lacking. Here, we identify dynamic and thermodynamic pathways through which natural modes of climate variability establish their teleconnections over the HMA. First, we identify evaporative sources that contribute to the cold season precipitation over the HMA and surroundings areas. The predominant moisture contribution comes from the mid-latitude regions including Mediterranean/Caspian Seas and Mediterranean land. Second, we establish that several tropical and extratropical forcing display a sub-seasonally fluctuating influence on the cold season precipitation distribution over the region, and given that many of them varyingly interact with each other, their impacts cannot be explained exclusively or at seasonal timescales. Lastly, a single set of evaporative sources cannot always be identified as the only determinant in propagating a remote teleconnection, because nature of moisture anomalies and its sources depend on the pattern of sub-seasonally varying dynamical forcing in the atmosphere.

Moisture Sources for Precipitation Variability over the Arabian Peninsula

We apply a Lagrangian-based moisture back trajectory method on two reanalysis datasets to determine the moisture sources for wet season precipitation over the Arabian Peninsula, defined as land on the Asian Continent to the south of the Turkish border and west of Iran. For this purpose, we make use of evaporative source region between 65°W–120°E and 30°S–60°N which is divided into twelve sub-regions. Our results indicate a north to south spatiotemporal heterogeneity in the characteristics of dominant moisture sources. In the north, moisture for precipitation is mostly sourced from European land and major water bodies, such as Mediterranean and Caspian Seas. Areas further south dependent on moisture transport from the Western Indian Ocean and parts of the African continent. El Nino Southern Oscillation cycle (ENSO) oscillation exhibits an overall positive but sub-seasonally varying influence on the precipitation variability over the region with mostly positive moisture anomalies form all major source regions. A significant drying trend exists over parts of the Peninsula, which is partly attributed to anomalies in the moisture advection from the Congo Basin and South Atlantic Ocean. However, precipitation trends over the terrestrial part of evaporative source region vary across observations and reanalysis datasets, which warrants the need for additional modeling studies to further our understanding in the identification of key processes contributing to the negative trends.

Assessment and quantification of meteorological data for implementation of weather radar in mountainous regions

The scope of this paper is to improve observation and detection of hydro-meteorological hazard over the Grenoble region which is characterised by significant changes of terrain in altitude and geomorphology. The city of Grenoble is located at a height between 200 up to 500 m, installing the weather radar in this range of elevation leads to better quality measurements, but visibility and as well coverage capability will be reduced at the other sites of the affected region. Two locations are shortlisted for the implementation of the future weather radar in Grenoble; (i) Moucherotte (1920 m a.s.l.) and (ii) Saint Eynard (1365 m a.s.l.). Several simulation and data analysis are performed to get the clear picture about precipitation variability by considering meteorological data from individual ground stations and radio sounding data as well. Compared to previous work, in this study is considered climatology of the vertical structure of the rainfall. In this context, several statistical computations are done regarding 0°C isotherm altitude. Concerning rainfall error estimation, ground clutter and screening effect, statistical calculations by using VISHYDRO code, are performed by for different quintiles for several elevation angles in both shortlisted sites. The results obtained from calculations carried out on two locations are almost similar. Also, significant under and over-estimation of rainfall error due to screening and ground clutter effect are detected. To achieve more accurate results, other sites need to be tested for further simulation. On the other hand since ground clutter, and screening effect at the Moucherotte is not too high compare with Saint Eynard, this site may be considered for implementing the future weather radar for observation of the meteorological processes over the Grenoble region.