Development of the Agricultural Water Demand and Supply Drought Index (AWDSDI) in Evaluating Daily Agricultural Drought in Small Administrative Districts

Drought is caused by a long period of lack of rainfall and water resources, and has a great impact on the life and ecosystem of both humans and animals. It is particularly important for the agricultural sector, which is closely related to food security. Global warming, urbanization, and industrialization have led to a gradually increasing demand for agricultural water. In response, Korea has steadily developed its agricultural water sources to reduce rising damage to the agricultural sector due to climate change. The severity of drought is evaluated by using meteorological, agricultural, hydrological, and socioeconomic drought indexes. The agricultural drought index is evaluated using soil moisture and crop dryness, and the hydrological drought index is evaluated based on water shortage by comparing demands with water resources available for supply, such as rivers and groundwater, reservoirs and dams. However, these methods were found to over- or under-estimate the relatively low sensitivity of agricultural water as they assess the shortage of water for life, industry, and agriculture. Therefore, in this study, we developed the Agricultural Water Demand and Supply Drought Index (AWDSDI), which evaluates agricultural drought by analyzing water supply systems such as agricultural reservoirs, pumping stations and drainage, groundwater systems, and demands for agricultural water. In order to review the applicability of the developed drought index, AWDSDI was applied to 32 Eps and Myuns in three cities, including Jincheongun, Changnyeonggun, and Jangseonggun in the period June-August 2017, when drought damage was significant. It was found that AWDSDI reproduced the daily agricultural drought well in small administrative districts such as Eps and Myuns. In addition, in order to verify the developed drought index, the evaluation results of AWDSDI, the previously developed agricultural drought index and the hydrological drought index were compared together. The comparison found that the AWDSDI reproduced the drought period and drought depth in 32 Eps and Myuns in three cities better than previously developed drought indices.

Identifikasi Potensi Kekeringan Agro-Hidrologi di Lahan Pertanian dan Non-Pertanian Kabupaten Pidie

Abstrak. Kekeringan agro-hidrologi adalah suatu fenomena dimana kurangnya pasokan air di dalam tanah yang mengakibatkan ketidakmampuan air dalam memenuhi kebutuhan air bagi tanaman dan masyarakat pada periode waktu tertentu. Penelitian ini bertujuan untuk mengetahui luas sebaran indeks kekeringan agro-hidrologi dan memetakan peta sebaran indeks kekeringan agro-hidrologi pada lahan pertanian dan non-pertanian di Kecamatan Simpang Tiga, Kecamatan Kembang Tanjong dan Kecamatan Peukan Baro Kabupaten Pidie. Penelitian ini menggunakan metode deskriptif kualitatif menggunakan teknik skoring dan overlay berbasis Sistem Informasi Geografis (SIG). Untuk mendapatkan hasil indeks bahaya kekeringan agro-hidrolorgi ada lima parameter (peta) yang dioverlay yaitu curah hujan, kedalaman air tanah, buffer sungai (penyangga sungai), tekstur tanah dan indeks ketersediaan air tanaman (WSVI) yang sebelumnya diberikan skor dan bobot. Hasil dari perhitungan didapatatkan empat kelas bahaya kekeringan yaitu kelas aman dengan luas 247,2 ha, kelas rendah dengan luas 2446,9 ha, kelas sedang dengan luas 3804,7 ha dan kelas tinggi dengan luas 534,4 ha. Pada penggunaan lahan pertanian yang paling luas yaitu sawah dengan kelas sedang seluas 1941,7 ha dan yang paling kecil yaitu industri garam dengan kelas aman seluas 0,0152 ha.Identification of Agro-hydrological Drought Potential in Pidie District's for Agricultural and Non-Agricultural LandAbstract. Agro-hydrological drought is a phenomenon when a lack of water supply in the soil results in the inability of water to fulfill the water needs of plants and the community at a certain time period. This study aims to determine the distribution area of the agro-hydrological drought index and map the distribution of agro-hydrological drought index on agricultural land and non-agricultural land in Simpang Tiga Sub-district, Kembang Tanjong Sub-district and Peukan Baro Sub-district in Pidie District. This research uses descriptive qualitative method using GIS utilize be attired scoring and overlay techniques. To get the results of the agro-hydrolorical drought hazard index there are five parameters (maps) overlaid, namely rainfall, ground water depth, river buffer, soil texture and water supplying vegetation index (WSVI) which were previously given scores and weights. The results of the calculations showed four drought hazard classes, namely safe class with an area of 247.2 ha, a low class with an area of 2446.9 ha, a medium class with an area of 3804.7 ha and a high class with an area of 534.4 ha. The most extensive land use of agricultural land is rice fields an area 1941.7 ha with medium class and the smallest is the salt industry with a safe class of 0.0152 ha.

Probabilistic hydrological drought index forecasting based on meteorological drought index using Archimedean copulas

Hydrological drought forecasting is considered a key component in water resources risk management. As sustained meteorological drought may lead to hydrological drought over time, it is conceptually feasible to capitalize on the dependency between the meteorological and hydrological droughts while trying to forecast the latter. As such, copula functions are powerful tools to study the propagation of meteorological droughts into hydrological droughts. In this research, monthly precipitation and discharge time series were used to determine Standardized Precipitation Index (SPI) and Standardized Hydrological Drought Index (SHDI) at different time scales which quantify the state of meteorological and hydrological droughts, respectively. Five Archimedean copula functions were adopted to model the dependence structure between meteorological/hydrological drought indices. The Clayton copula was identified for further investigation based on the p-value. Next, the conditional probability and the matrix of forecasted class transitions were calculated. Results indicated that the next month's SHDI class forecasting is promising with less than 10% error. Moreover, extreme and severe meteorological drought classes lead to hydrological drought condition with a more than 70% probability. Other classes of meteorological drought/wet conditions lead to normal hydrological (drought) condition with less than 50% probability and to wet hydrological condition with over 20% probability.

Hydrological drought index based on reservoir capacity – Case study of Batujai dam in Lombok Island, West Nusa Tenggara, Indonesia

Hydrological drought index analysis has been widely developed and applied for the development of water resources. The island of Lombok, which is largely a dry land, requires a significant hydrological drought index to be sourced from measurable data analysis. This research focused aims to obtain hydrological drought index in Lombok Island using the capacity change of reservoir. The analysis includes reservoir data especially in the event of El-Nino. The main parameters analysed in this work are data homogeneity, decrease line of reservoir volume, increase in the line of reservoir volume, reservoir volume deficit, and hydrological drought index (RDI). The basic equation uses the water balance in the reservoir, which is the inflow–outflow and change of reservoir. The results of the analysis show that in the event of El-Nino, the drought hydrological index indicates different levels depending upon the water level of the reservoir. The criteria for the drought level are as follows: weak RDI = from −0.46 to −0.01 at an reservoir elevation of 90.88 to 92.33 m a.s.l, moderate RDI: from −0.59 to −0.46 at water level of reservoir from 90.27 to 90.88 m a.s.l, sever RDI: from −0.80 to −0.59 at water level of reservoir from 88.83 to 90.27 m a.s.l. and very severe RDI: from −0.89 to −0.80 at water level of water reservoir 87.78–88.83 m a.s.l. The duration of drought was 9 months, i.e., from February to November.

Identifying a Transition Climate Zone in an Arid River Basin using a Hydrological Drought Index

Drought indices have been widely used in climate classification. However, there is not enough evidence for their ability in identifying the multiple climate types in areas with complex topography and landscape, especially in those areas with a transition climate. This study compares a meteorological drought index, the aridity index (AI) defined as the ratio of precipitation (P) to potential evapotranspiration (PET), with a hydrological drought index, the evaporative stress index (ESI) defined as the ratio of actual evapotranspiration (AET) to PET. We conducted this study using modeled high resolution climate data for period of 1980–2010 in the Heihe River Basin (HRB) in the arid northwestern China. PET was estimated using the Penman–Monteith and Hamon methods. The climate classified by AI shows two distinct climate types for the upper and the middle and lower basin reaches, while three types were found if ESI was used. This difference indicates that only ESI is able to identify a transition climate zone in the middle basin. This contrast between the two indices is also seen in the inter-annual variability and extreme dry/wet events. The magnitude of variability in the middle basin is close to that in the lower basin for AI, but different for ESI. AI has larger magnitude of the relative inter-annual variability and greater decreasing rate from 1980–2010 than ESI, suggesting the role of local hydrological processes in moderating extreme climate events. Thus, the hydrological drought index is better than the meteorological drought index for climate classification in the arid Heihe River Basin where local climate is largely determined by topography and landscape. We conclude that the land–surface processes and human disturbances play an important role in altering hydrological drought conditions and their spatial and temporal variability.