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Drought analysis is important for early warning of drought events in arid and semiarid regions. Reliance on a single variable or index is not adequate to conduct a comprehensive assessment of drought risk. Therefore, the use of multivariate drought indices can provide reliable information to assess drought characteristics. This study proposes a new multivariate drought index based on the combination of effective precipitation and runoff variables. The copula function was used to derive the joint distribution of effective precipitation and runoff. The new index, named effective precipitation runoff index (EPRI), is developed by using observed hydro-meteorological time series data collected from the Northeastern region of Iraq. This procedure can be considered as an approach that enables a comprehensive assessment of different types of drought, which can be seamlessly compared with the output of standardized precipitation index (SPI), standardized runoff index (SRI) and standardized effective precipitation index (SEPI). Based on the results of goodness-of-fit test, Clayton copula was selected as the best suitable copula for constructing the EPRI. It was found that the EPRI is approximately high correlated with SEPI and SRI indices, which indicates the reliability of the modeled EPRI index for monitoring drought.

The main goal of this study is to introduce the Archimedean copulas, which overcome the low accuracy and subjective nature of the traditional double mass curve method, to investigate the precipitation–runoff relationship (PRR) and detect change points in the Weihe River Basin (WRB). With the construction of a joint distribution between precipitation and runoff by the Archimedean copulas, a statistical variable considering the distribution parameter was estimated to judge the change point of the PRR. The results show that: (1) annual precipitation and runoff present decreasing trends that are significant and insignificant, respectively, at the 95% significance level, while annual potential evapotranspiration (PET) increases slightly; (2) change points of the PRR occurred in 1971 and 1994; (3) the annual runoff changed more dramatically than precipitation during the periods from 1972 to 1994 and 1995 to 2010 compared with 1960–1971, which indicates that in addition to precipitation, there are some other non-precipitation factors that are responsible for the change in the PRR; and (4) the contributions to runoff from human activities declined from 1972 to 1994 (84.15%) and 1995 to 2010 (57.16%). These results suggest that human activities (e.g., irrigation, reservoirs, water-and-soil conservation) were the primary driving forces leading to changes in the PRR in the WRB.

To study the impact of land use/cover change (LUCC), the relationship between precipitation and runoff was investigated. Our main objective was to ensure reasonable development, management, and sustainable utilization of water resources at a watershed scale. To investigate the relationship between precipitation and runoff, a SWAT (Soil and Water Assessment Tool) model was developed by analyzing LUCC in Naoli River basin. Then, runoff response was analyzed under different LUCC conditions. The contribution coefficient of different land use types to runoff was calculated. The results of this research study are as follows. From 1986 to 2014, dry land, forest land, paddy fields, and unused land were the major land use types, accounting for more than 93% of the total catchment. On the other hand, grass land, building land, and water bodies accounted for a small proportion. Among the four main land use types, the contribution coefficient of forest land was 3.10 mm·km−2. This indicates that forest land was suitable for runoff generation. The contribution coefficient of dry land, unused land (fluvial wetland in Naoli River basin), and paddy field are −0.11, −0.37, and −0.83 mm·km−2, respectively. This implies that these three land use types were adverse factors for runoff generation.

Rivers originating in the Tibetan Plateau provide freshwater to downstream populations, yet runoff projections from warming are unclear due to precipitation uncertainties. Here, we use a historical atmospheric circulation–precipitation relationship to constrain future modelled wet-season precipitation over the Tibetan Plateau. Our constraint reduces precipitation increases to half of those from the unconstrained ensemble and reduces spread by around a factor of three. This constrained precipitation is used with estimated glacier melt contributions to constrain future runoff for seven rivers. We estimate runoff increases of 1.0–7.2% at the end of the twenty-first century for global mean warming of 1.5–4 °C above pre-industrial levels. Because population projections diverge across basins, this runoff increase will reduce the population fraction living under water scarcity conditions in the Yangtze and Yellow basins but not in the Indus and Ganges basins, necessitating improved water security through climate change adaptation policies in these regions at higher risk.Tibetan Plateau runoff projections are uncertain due to precipitation change uncertainty in climate models. Historical precipitation–circulation relationships constrain future wet-season precipitation and runoff change, suggesting worsening water scarcity for the Indus and Ganges river basins.

Global hydroclimatic changes from 1950 to 2018 are analyzed using updated data of land precipitation, streamflow, and an improved form of the Palmer Drought Severity Index. The historical changes are then compared with climate model-simulated response to external forcing to determine how much of the recent change is forced response. It is found that precipitation has increased from 1950 to 2018 over mid-high latitude Eurasia, most North America, Southeast South America, and Northwest Australia, while it has decreased over most Africa, eastern Australia, the Mediterranean region, the Middle East, and parts of East Asia, central South America, and the Pacific coasts of Canada. Streamflow records largely confirm these precipitation changes. The wetting trend over Northwest Australia and Southeast South America is most pronounced in austral summer while the drying over Africa and wetting trend over mid-high latitude Eurasia are seen in all seasons. Coupled with the drying caused by rising surface temperatures, these precipitation changes have greatly increased the risk of drought over Africa, southern Europe, East Asia, eastern Australia, Northwest Canada, and southern Brazil. Global land precipitation and continental freshwater discharge show large interannual and inter-decadal variations, with negative anomalies during El Niño and following major volcanic eruptions in 1963, 1982, and 1991; whereas their decadal variations are correlated with the Interdecadal Pacific Oscillation (IPO) with IPO’s warm phase associated with low land precipitation and continental discharge. The IPO and Atlantic multidecadal variability also dominate multidecadal variations in land aridity, accounting for 90 % of the multidecadal variance. CMIP5 multi-model ensemble mean shows decreased precipitation and runoff and increased risk of drought during 1950–2018 over Southwest North America, Central America, northern and central South America (including the Amazon), southern and West Africa, the Mediterranean region, and Southeast Asia; while the northern mid-high latitudes, Southeast South America, and Northwest Australia see increased precipitation and runoff. The consistent spatial patterns between the observed changes and the model-simulated response suggest that many of the observed drying and wetting trends since 1950 may have resulted at least partly from historical external forcing. However, the drying over Southeast Asia and wetting over Northwest Australia are absent in the 21st century projections.

Under global warming, according to results obtained from offline drought indices driven by projections of general circulation models (GCMs), future droughts in China will worsen but the results are not consistent. We analyzed changes in droughts covering the entire hydrologic cycle using outputs of GCMs of the 6th Coupled Model Intercomparison Project (CMIP6) for SSP2-4.5 and SSP5-8.5 climate scenarios, and compared the results with that of popular, offline drought indices [the self-calibrating Palmer Drought Severity Index (scPDSI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Actual Evapotranspiration Index (SPAEI)]. Among meteorological, agricultural, and hydrological drought indices tested under both SSP scenarios, the results obtained from SPAEI and scPDSI agree better with univariate drought indices than SPEI. scPDSI generally agrees well with agricultural droughts (Standardized Soil Moisture Index with the surface soil moisture content; SSIS). Future droughts estimated using soil moisture analysis are more widespread than that from precipitation and runoff analysis in humid regions of South China by the end of the twenty-first century. In arid northwestern China and Inner Mongolia, drought areas and severity based on scPDSI and SSIS forced with the SSP scenarios show obvious decreasing trends, in contrast to increasing trends projected in South China. Trends projected using SPEI contradict those projected by other drought indices in non-humid regions. Therefore, selecting appropriate drought indices is crucial in project representative future droughts and provides meaningful information needed to achieve effective regional drought mitigation strategies under climate warming impact.

Many observed time series of precipitation and streamflow show heavy-tail behaviour. For heavy-tailed distributions, the occurrence of extreme events has a higher probability than for distributions with an exponentially receding tail. If we neglect heavy-tail behaviour we might underestimate the magnitude of rarely observed, high-impact events. Robust estimation of upper-tail behaviour is often hindered by the limited length of observational records. Using long time series and a better understanding of the relevant process controls can help with achieving more robust tail estimations. Here, a simulation-based approach is used to analyse the effect of precipitation and runoff generation characteristics on the upper tail of flood peak distributions. Long, synthetic precipitation time series with different tail behaviour are produced by a stochastic weather generator. These are used to force a conceptual rainfall–runoff model. In addition, catchment characteristics linked to a threshold process in the runoff generation are varied between model runs. We characterize the upper-tail behaviour of the simulated precipitation and discharge time series with the shape parameter of the generalized extreme value (GEV) distribution. Our analysis shows that runoff generation can strongly modulate the tail behaviour of flood peak distributions. In particular, threshold processes in the runoff generation lead to heavier tails. Beyond a certain return period, the influence of catchment processes decreases and the tail of the rainfall distribution asymptotically governs the tail of the flood peak distribution. Beyond which return period this is the case depends on the catchment storage in relation to the mean annual rainfall amount.

Temporal and spatial precipitation information is key to conducting effective hydrological-process simulation and forecasting. Herein, we implemented a comprehensive evaluation of three selected precipitation products in the Jialing River watershed (JRW) located in southwestern China. A number of indices were used to statistically analyze the differences between two open-access precipitation products (OPPs), i.e., Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Climate Prediction Center Gauge-Based Analysis of Global Daily Precipitation (CPC), and the rain gauge (Gauge). The three products were then categorized into subbasins to drive SWAT simulations. The results show the following. (1) The three products are highly consistent in temporal variation on a monthly scale yet distinct on a daily scale. CHIRPS is characterized by an overestimation of light rain, underestimation of heavy rain, and high probability of false alarm. CPC generally underestimates rainfall of all magnitudes. (2) Both OPPs satisfactorily reproduce the stream discharges at the JRW outlet with slightly worse performance than the Gauge model. Model with CHIRPS as inputs performed slightly better in both model simulation and fairly better in uncertainty analysis than that of CPC. On a temporal scale, the OPPs are inferior with respect to capturing flood peak yet superior at describing other hydrograph features, e.g., rising and falling processes and baseflow. On a spatial scale, CHIRPS offers the advantage of deriving smooth, distributed precipitation and runoff due to its high resolution. (3) The water balance components derived from SWAT models with equal simulated streamflow discharges are remarkably different between the three precipitation inputs. The precipitation spatial pattern results in an increasing surface flow trend from upstream to downstream. The results of this study demonstrate that with similar performance in simulating watershed runoff, the three precipitation datasets tend to conceal the identified dissimilarities through hydrological-model parameter calibration, which leads to different directions of hydrologic processes. As such, multiple-objective calibration is recommended for large and spatially resolved watersheds in future work. The main findings of this research suggest that the features of OPPs facilitate the widespread use of CHIRPS in extreme flood events and CPC in extreme drought analyses in future climate.

Ongoing and potential future changes in precipitation will affect water management infrastructure. Urban drainage systems are particularly vulnerable. Design standards for many stormwater practices rely on design storms based on precipitation intensity-duration-frequency (IDF) curves. In many locations, climate projections suggest relatively small changes in total precipitation volume, but increased magnitude of extreme events. We develop an approach for estimating future IDF curves that is efficient, can use widely available downscaled GCM output, and is consistent with published IDF curves for the USA that are used in local stormwater regulations and design guides. The method is GCM-agnostic and provides a relatively simple way to develop scenarios in a format directly useful to assessing risk to stormwater management infrastructure. Model biases are addressed through equidistant quantile mapping, in which the modeled change in both the location and scale of the cumulative distribution of storm events from historical to future conditions is used to adjust the extreme value fit used for IDF curve development. The approach requires only precipitation annual maxima, is readily automated, and hits a mid-point between theoretical rigor and ease of application that will be of practical use for the rapid screening of vulnerabilities across projections. We demonstrate estimation of future IDF curves at locations throughout the USA and link IDF-derived design storms to a rainfall-runoff model to evaluate the potential change in storage volume requirements for capture-based stormwater management practices by 2065.

An inconsistent relationship between precipitation and runoff has been observed between drought and non-drought periods, with less runoff usually observed during droughts than would be expected based solely on precipitation deficit. Predictability of these shifts in the precipitation–runoff relationship is still challenging, largely because the underlying hydrologic mechanisms are poorly constrained. Using 30 years of data for 14 basins in California, we show how the Budyko framework can be leveraged to decompose shifts in precipitation versus runoff during droughts into “regime” shifts, which result from changes in the aridity index along the same Budyko curve, and “partitioning shifts”, which imply a change in the Budyko parameter ω and thus in the relationship among water balance components that governs partitioning of available water. Regime shifts are primarily due to measurable interannual changes in precipitation or temperature, making them predictable based on drought conditions. Partitioning shifts involve further nonlinear and indirect catchment feedbacks to drought conditions and are thus harder to predict a priori. We show that regime shifts dominate changes in absolute runoff during droughts but that gains or losses due to partitioning shifts are still significant. Low aridity, high baseflow, a shift from snow to rain, and resilience of high-elevation runoff correlate with higher annual runoff during droughts than would be predicted by the precipitation–runoff ratio during non-drought years. Differentiating between these shifts in the precipitation–runoff relationship using a Budyko approach will help water resource managers, particularly in arid, drought-prone regions, to better project runoff magnitudes during droughts based on available climate data and, furthermore, understand under what circumstances and to what extent their forecasts may be less reliable due to nonlinear basin–climate feedbacks.